Telematique

Severity Aware Early Diabetes Prediction Using Hybrid Machine Learning and Deep Learning Techniques

2026-01-10T07:07:56+00:00

In the recent trend and in medical domain, diabetes stands out as one of the most predominant disease and unfortunately considering as a life-threatening disease. Early prediction of diabetes plays a pivotal role in initiating prompt treatment and halting the progression of the condition. The proposed methodology not only aids in predicting the future diabetes but also finds its severity scores. By presenting this issue as a multi-class classification problem, hybrid machine learning (ML) and deep learning (DL) techniques are used to build the new hybrid model. This helps in incorporating both structural feature learning of ML and deep temporal pattern recognition of DL for better performance. The hybrid ML+DL for diabetes prediction used XGBoost, LightGBM, CatBoost ML models and Temporal Convolutional Network (TCN) as base layer, Logistic Regression (LR) as a meta-classifier. The model is evaluated and fine-tuned for effective diabetes disease prediction with its score of severity. The experimental findings underscore the effectiveness of each component in the framework and its impact on the accuracy. The proposed work proves the sufficient amount of accuracy as 99.4%, and HML+DL compared with the recent studies in prediction of early stage of diabetes.

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

2026-01-12T08:55:03+00:00

This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through cooccurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.

A study of Radiological Image-Based Bone sarcoma Detection Using Transfer Learning

2026-01-15T05:29:34+00:00

Bone sarcoma occurs primarily in children, adolescents and adults. Diagnostic assessment has traditionally involved subjective and often time-consuming assessments of imaging modalities such as X-ray, MRI and CT scans. This paper introduces a framework based on deep learning for automated classification of Bone Sarcoma from standard imaging modalities. The modernization utilizes MobileNetV2, a labeled dataset trained on ImageNet, to efficiently extract significant features while limiting computation. Preprocessing of the dataset included image normalization, resizing and augmentation using flipping, rotation, changing the zoom level and adding contrast. The dataset had a split of 80% affected and remaining 20% unaffected, respectively. During the fine-tuning phase the last layers of the model were unfrozen, and models trained at a reduced learning rate to accommodate the imaging data specific to Bone Cancer. The model trained with Adam optimizer and binary cross-entropy loss function with about 93% training accuracy and over 90% validation accuracy. Using evaluations from precision, recall, F1, and confusion matrix, the results verified the model robustness with minimal false negative rates being crucial for medical diagnostic. The results indicated that the suggested approach also provides a reliable, lightweight, and accurate diagnostic support for radiologists.

Optimization of Pumping Station Units Operation

2026-01-17T07:24:36+00:00

This article provides a complete analysis of the current operational condition of pumping stations and the operating modes of water transmission channels. The most dangerous processes of changing the operating mode at pumping stations caused by a sudden interruption of energy supply to the engine are studied. The issues of using advanced, modern systems during operation of pumping stations, as well as replacing outdated, serviceable equipment at pumping stations with significantly reduced efficiency of economical equipment with new ones are analyzed. A new effective method for determining the optimal operating mode of pumping units has been developed. The obtained results on reducing stress in transient modes of pumping units, adjusting operating modes and optimizing operating modes are analyzed.

Design of Experiment-Based Rp-Hplc Bioanalytical Method Development and Validation for the Estimation of Linagliptin and Dapagliflozin in Bulk and in Their Combined Dosage Form

2026-01-27T05:55:33+00:00

The aim of this study was to develop and optimize a simple, cost-effective, and robust Bioanalytical RP-HPLC method by Design of Experiment based Box–Behnken design for the simultaneous estimation of Linagliptin and Dapagliflozin in human plasma. The optimal chromatographic separation was achieved having C18 (Thermo) column (250 mm × 4.6 mm, 5 μ) and using mobile phase as Methanol and 0.1 % OPA (80:20) with a flow rate 0.9 ml/min and UV detection at 242 nm. A Box-Behnken design was used to test robustness of the method with describes the interrelationship of Mobile phase, Flow rate and Wavelength at three different levels. The method was found to be linear in the range of 2-10 μg/mL (R² >0.9997) and 4-20 μg/mL (R² >0.9994) for Linagliptin and Dapagliflozin, respectively. The developed bioanalytical method was validated as per recommended ICH guidelines which revealed the high degree of linear, precise, accurate, sensitive and robust method over the existing RP-HPLC method for Vildagliptin and Pioglitazone hydrochloride.

A Bibliometric Analysis for UPI Acceptance and Uses for Indian Customers and Business Community

2026-02-02T05:32:55+00:00

The Unified Payments Interface (UPI) has revolutionized India’s financial landscape, transitioning the nation toward a cashless economy. This study conducts a bibliometric analysis to examine UPI's adoption and usage patterns among Indian customers and businesses. By analyzing 2,560 articles with over 50,000 citations, this research identifies key trends, including user adoption determinants, technological advancements, and socio-economic impacts. It highlights the pivotal role of trust, perceived usefulness, and financial inclusion in driving UPI adoption, while addressing barriers like digital literacy and cybersecurity challenges. Using co-authorship, co-citation, and keyword mapping, this study uncovers thematic clusters and collaborations in global UPI research. Findings underscore UPI's transformative role in fostering financial inclusion, streamlining transactions for micro, small, and medium enterprises (MSMEs), and supporting government-led digital initiatives. This research provides valuable insights into the scholarly evolution of UPI, setting the stage for future explorations into its global scalability and integration with emerging technologies.

Enhancement Algorithms to Improve the Class of Endoscopy Images to Advance Gastrointestinal Track Disease Detection

2026-02-13T05:37:49+00:00

Gastro Intestinal (GI) track diseases are major health concern which require accurate detection for early diagnosis and treatment. One of the primary methods of GI track disease detection is to use endoscopic images of digestive track system. However, these images often are degraded by the presence of noise and poor contrast, which directly affect the diagnostic accuracy. Enhancing the quality of the degraded endoscopic images can greatly help physicians during diagnostics and treatment plan. In this work a unified approach which combines denoising and contrast variation correction is proposed. Denosing is executed using a fusion algorithm that pools discrete wavelet transformation and singular value decomposition that is combined with non-local means denoising algorithm. The contrast variation correction is performed using an adaptive gamma correction algorithm enhanced using particle swarm optimization method. Experiments proved that the proposed unified approach improves the visual quality while preserving significant image, edge and structure details. This method can therefore be used safely by GI track disease detection and classification system to improve its diagnostic accuracy.

Networks, Legacies and Continuity In Indian Family Businesses

2026-02-16T07:10:53+00:00

This study develops an extended theory of change explaining how family firms convert networks into performance. Drawing on an abductive, multiple-case qualitative design with eight Indian family businesses, and using Gioia-style coding, we distinguish what different ties accomplish, when their benefits turn negative, and how organizational scaffolds convert access into repeatable results. The analysis yields eight propositions that are consistent with the evidence: bonding/“as-if-family” density underwrites resilience but exhibits diminishing returns beyond an over-embeddedness threshold; non-kin diversity across industries and geographies predicts growth and co-creation; digital enablement amplifies the payoffs to bridging; governance and learning routines (partner ownership, review cadence, after-action notes) mediate the translation of diversity into scalable pipelines; and institutional bridging (banks, universities, government, associations) complements business networks, particularly in regulated or capital-intensive sectors. Three family-specific levers further refine the account: formalization of AIFB expedites funding decisions, reputation and community trust support price premiums and repeat business, and diaspora ties compress time-to-foreign-entry. We integrate these mechanisms into a four-stage network maturity ladder:S1 Kin-Centred Foundations, S2 Boundary Opening, S3 Structured Brokerage, S4 Orchestrated Partnerships—with observable progression triggers and leading indicators (deal velocity, referral yield, alliance revenue share, time-to-funds, price premium, time-to-foreign-entry). The contribution is a testable mid-range theory that specifies mechanisms and contingencies in family-firm networking while offering a practitioner-ready diagnostic and progression roadmap.

Advanced AI-Driven Techniques for Prostate Cancer Prediction

2026-02-23T06:16:14+00:00

Prostate Cancer (PCa) is one of the most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Prostate cancer is a growth of cells that starts in the prostate. Prostate is a small gland placed below the bladder to make semen, as a part of male reproductive system. Prostate cancer is one of the most common types of cancer. Prostate cancer is usually found early, and it often grows slowly. Most people with prostate cancer are cured. People diagnosed with early prostate cancer often have many treatment options. Treatments may include surgery, radiation therapy or carefully watching the prostate cancer to see if it grows. This study aims to review various machine-learning models, which are for the prediction and early detection of prostate cancer using clinical data. Machine-learning focuses on enabling systems to learn from data and improve their performance over time without explicit programming.

Enhanced Multi-Scale Attention 3D Deep Network (EMA-3DNet++) for Segmentation, Classification, and Severity Grading of Knee Injuries

2026-02-23T06:23:43+00:00

Sports-related knee injuries such as anterior cruciate ligament (ACL) tears, meniscal degeneration, and cartilage defects demand precise and early diagnosis to prevent long-term functional impairment. Although deep learning–based medical image analysis has significantly improved automated knee assessment, existing 3D convolutional models often suffer from limited global contextual reasoning, inadequate cross-structure modeling, and poor generalization across heterogeneous clinical datasets. To address these limitations, this research work proposes EMA-3DNet++, a next-generation Federated Multi-Scale Transformer–Graph Hybrid 3D Deep Network designed for accurate, explainable, and privacy-preserving sports knee analysis. EMA-3DNet++ integrates self-supervised pre-trained 3D encoders with multi-scale convolutional feature extraction and Swin Transformer blocks to capture both fine-grained anatomical details and long-range spatial dependencies in volumetric MRI data. A Graph Neural Network (GNN) module explicitly models structural relationships among knee components—ACL, PCL, meniscus, cartilage, tibia, and femur—enhancing multi-label injury classification and structural consistency. To further improve robustness, a hybrid temporal refinement unit incorporating LSTM and energy-efficient spiking neural layers captures motion dynamics in sequential scans. The framework adopts a multi-task learning strategy, simultaneously performing segmentation, injury classification, and severity grading.

To overcome data scarcity and privacy constraints, EMA-3DNet++ supports federated learning with secure aggregation, enabling collaborative training across institutions without data sharing. An advanced explainability layer combining 3D Grad-CAM++, attention rollout, and uncertainty estimation enhances clinical interpretability. Experimental evaluation on benchmark datasets including MRNet, SKI10, and OAI demonstrates that EMA-3DNet++ achieves superior Dice coefficient (94–96%), improved sensitivity and specificity, and enhanced cross-domain generalization compared to state-of-the-art 3D CNN baselines. The proposed framework represents a scalable, clinically deployable, and energy-efficient solution for next-generation AI-assisted sports knee diagnostics.

Study of Professional Hazards in Chefs through Ayurvedic Perspective

2026-02-23T10:12:56+00:00

Introduction:
Chefs face unique occupational hazards such as prolonged heat exposure, irregular eating habits, physical strain, and psychological stress. In Ayurveda, these factors correspond to Nidana like Atapa sevana, Ati-ushna-tikshna ahara, Vishama ahara, Vega-dharana, and Manasika nidana, leading to Vata-Pitta aggravation, Agnimandya, Aama formation, and Srotodushti.

Methods:
A narrative review was conducted using PubMed, Google Scholar, Scopus, AYUSH portals, and classical Ayurvedic texts. Literature linking chef occupational hazards with disease outcomes was identified and mapped to Ayurvedic concepts of Nidana, Dosha, and Srotas.

Results:
Occupational hazards identified in chefs included heat and steam exposure, irregular dietary intake, prolonged standing, repetitive strain, and high job stress. In Ayurvedic interpretation, these hazards aggravate Vata and Pitta doshas and vitiate Rasavaha, Annavaha, Mamsavaha, and Manovaha srotas. Modern studies report corresponding conditions such as gastritis, obesity, musculoskeletal disorders, varicose veins, and burnout.

Discussion:
Ayurveda offers preventive strategies including Pitta-pacifying diet, Abhyanga, Sheetali pranayama, Rasayana herbs (Amalaki, Guduchi, Shatavari), and ergonomic modifications. Integrating Ayurvedic measures with occupational safety practices may reduce disease burden and improve overall well-being in chefs.

Therapetic uses of Vidanga Leha in Children W.S.R. Krimi. A Case Series

2026-02-23T10:18:54+00:00

Krimi have been considered a major public health problem through out the world according to WHO, 1967. In our country this problem is equally significant. The helminths and bacteria discussed today are somewhat comparable to the Krimi described in ancient texts. According to CCRAS 1987 reports, it affects youngsters more often than adults. Krimi impairs a person's growth and development, causes malnutrition, and lowers immunity; therefore, an efficient remedy to this issue is required. Of all the herbs used in treating worm infestation, Vidanga leha (Embelia ribes) was used for the present study. 30 patients were randomly selected for the study. factors like Vivarnata (Skin Pigmentation), Udarshul (Abd pain ), Gudkandu (itching near anal region), MalaVishtambhata (constipation), Aganimandya (Anorexia) were observed in this study. These clinical conditions had been described by Acharya Charaka under the Lakshana of Krimi and it has been also described in Rasavaha Srotodusti Lakshanas as well. In 15 days of study, observations were recored 0n 0^th, 7^th, and 15^th day and doses were administrated according to body weight of the patients.

Objective: To evaluate the efficacy of vidanga leha in krimi betwenn the age group of 1-12 years.

Conclusion: This study revealed notable alterations in the Krimi patients. Relief had been observed in the various parameters chosen for the study.

Assessment of Moisture Content in the Form of an Indicator of Ahara Samskara Effect

2026-02-25T06:14:47+00:00

This study aims to scientifically validate the Ayurvedic principle of Ahara Samskara (food processing) by analyzing the changes in the moisture content of Shashtik Shali (a 60-day rice variety) following Kala Samskara (time-based storage) and Agni Samskara (heat processing). The research employs the oven-drying method, a standard technique in modern food science, to quantitatively assess the physicochemical transformations. Freshly harvested Shashtik Shali showed a moisture content of 4.4%. After being stored for eight months and subsequently dry-roasted, the grains' moisture content was reduced to 4.0%. This quantifiable reduction serves as a tangible metric that correlates with the qualitative changes described in Ayurveda. The fresh grain's higher moisture content reflects a predominance of Jala and Prithvi Mahabhutas, making it highly nourishing (Brimhana), but potentially contributing to Ama (toxins) in individuals with weak digestion. The processed grain, with its reduced moisture, reflects a shift towards Agni and Vayu Mahabhutas, acquiring qualities that are lighter (Laghu), drying (Ruksha), and more stimulating to the digestive fire (Dipana), making it therapeutically beneficial for managing conditions like Mandagni (weak digestion) and Kapha Dosha aggravation. The findings bridge ancient Ayurvedic wisdom with contemporary analytical methods, demonstrating that traditional food processing techniques are sophisticated interventions that purposefully modulate a food's properties for specific therapeutic and health-promoting effects.

Artificial Intelligence in Behavioural Finance and Financial Literacy: An Empirical Study on Investor Decision-Making and Financial Awareness

2026-02-26T11:40:43+00:00

The rapid digitalisation of financial services has significantly altered the way individuals perceive, process, and act upon financial information. Behavioural finance highlights that financial decisions are often influenced by cognitive biases, emotions, and heuristics rather than rational evaluation alone. In recent years, Artificial Intelligence (AI) has emerged as a powerful enabler in addressing these behavioural inefficiencies by offering personalised insights, predictive analytics, and adaptive financial learning tools. Despite the growing adoption of AI-driven financial applications, limited empirical research exists on how AI influences behavioural biases and financial literacy simultaneously, particularly in emerging economies. The present study aims to examine the role of Artificial Intelligence in behavioural finance with specific reference to its impact on financial literacy and investor decision-making. The study identifies key behavioural biases such as overconfidence, loss aversion, herding, and present bias, and analyses how AI-based tools assist individuals in recognising and mitigating these biases. A structured questionnaire-based survey method is proposed to collect primary data from individual investors and working professionals using AI-enabled financial platforms. Descriptive statistics, factor analysis, and regression analysis are suggested to assess the relationship between AI usage, behavioural responses, and levels of financial literacy. The study is expected to contribute to behavioural finance literature by integrating technological intervention into behavioural models of financial decision-making. It offers practical implications for policymakers, fintech developers, and financial educators by highlighting how AI-driven financial tools can promote informed decision-making, enhance financial awareness, and foster long-term financial well-being. The findings may support the development of inclusive and responsible AI-based financial education frameworks.

Solving Bipolar Fuzzy Linear system of Equations: Least Squares Approximation Technique

2026-02-28T06:21:46+00:00

An bipolar fuzzy linear system of equations is solved in this article using the least squares approximation technique. Two crisp linear systems are created from the given bipolar fuzzy linear system, and the new systems are expressed in the matrix format. The least squares solutions for each of the systems are separately found out. The bipolar fuzzy linear system's solution is derived from these two solutions. The effectiveness of the suggested technique has been demonstrated with few numerical examples.

AML-BDA: A Sector-Aware Adaptive Multi-Layer Big Data Architecture for Scalable and Intelligent Cross-Industry Analytics

2026-02-28T08:42:06+00:00

The rapid proliferation of high-volume, high-velocity, and high-variety data across industries has intensified the need for scalable and adaptive Big Data architectures. Traditional distributed frameworks often suffer from static pipeline configurations, limited domain awareness, and inefficient resource utilization, thereby constraining cross-industry analytics performance. This research proposes an Adaptive Multi-Layer Big Data Architecture (AML-BDA) designed to address these limitations through sector-aware abstraction and dynamic orchestration. The proposed framework integrates ontology-driven domain mapping, workload-based auto-scaling, intelligent model selection, and continuous feedback optimization within a unified architecture. AML-BDA introduces a Domain Abstraction Layer to harmonize heterogeneous datasets and an Adaptive Processing Layer that dynamically reconfigures computational resources based on real-time workload metrics. Experimental validation across healthcare, financial fraud detection, smart manufacturing, and retail forecasting datasets demonstrates significant improvements in processing latency, throughput, scalability, and predictive accuracy compared to conventional architectures. Results show latency reduction of up to 35%, throughput enhancement exceeding 20%, and prediction accuracy gains of approximately 5–8%. The findings confirm that integrating sector-aware metadata intelligence with adaptive orchestration mechanisms enhances both operational efficiency and analytical performance. This study contributes a scalable, interoperable, and intelligent Big Data framework capable of supporting next-generation data-driven decision systems across heterogeneous industrial environments.

Obsessive-Compulsive Disorder (OCD): A Comprehensive Review

2026-03-09T07:36:57+00:00

Obsessive-compulsive disorder (OCD) represents a chronic neuropsychiatric condition characterized by recurrent intrusive thoughts (obsessions) and repetitive behaviors or mental acts (compulsions) that significantly impair daily functioning and quality of life. Despite substantial advances in understanding its neurobiological underpinnings and therapeutic interventions, OCD remains frequently underdiagnosed, undertreated, and associated with considerable individual and societal burden. This comprehensive review synthesizes current evidence regarding OCD epidemiology, pathophysiology, diagnostic challenges, comorbidity patterns, and evidence-based treatment approaches, with particular emphasis on treatment-resistant populations and emerging therapeutic modalities. A systematic literature search was conducted across PubMed/ MEDLINE, PsycINFO, Scopus, and Google Scholar databases, focusing on peer- reviewed articles published between 2015 and 2025. Search terms included combinations of MeSH headings and keywords related to OCD diagnosis, neurobiology, comorbidities, and treatment interventions. OCD affects approximately 2.3% of the global population across the lifespan, with onset typically occurring in childhood, adolescence, or early adulthood. The disorder demonstrates substantial heritability (approximately 50%) and involves dysregulation within cortico-striatal-thalamic-cortical circuits, with contributions from serotonergic, dopaminergic, and glutamatergic neurotransmitter systems. Comorbidity rates exceed 50% for major depressive disorder and anxiety disorders, while bipolar disorder, schizophrenia spectrum disorders, and attention-deficit/hyperactivity disorder demonstrate prevalence rates of 10-25% in OCD populations. First-line treatments comprising selective serotonin reuptake inhibitors (SSRIs) and cognitive-behavioral therapy with exposure and response prevention (ERP) achieve response rates of 50-70%; however, approximately 30% of patients exhibit treatment resistance, necessitating augmentation strategies, neuromodulation approaches, or neurosurgical interventions. Despite significant therapeutic advances, the substantial proportion of treatment-resistant patients and the complex comorbidity landscape underscore the necessity for continued research into novel treatment targets, personalized medicine approaches, and improved diagnostic methodologies. The integration of neurobiological findings with clinical phenotyping represents a promising avenue for advancing OCD care.

Ethical Artificial Intelligence, Blockchain Transparency and Immersive Digital Technologies for Sustainable Business and Economic Transformation

2026-03-09T07:58:06+00:00

The fast merging of Artificial Intelligence (AI), Blockchain and Immersive Digital Technologies (AR/VR) is significantly transforming the sustainable business practise and economic systems. However, the issues of algorithmic bias, a lack of transparency and disproportional access to digital access still remain to restrain their responsible and fair usage. This paper hypothesises and empirically testifies to an integrated digital sustainability model that looks at the joint impact of Ethical AI preparedness, Blockchain-powered transparency and Immersive Technology investment on sustainable business operations and economic change. Based on the World Development Indicators (WDI) data comprising measures of various countries and economic regions, proxy indicators of AI infrastructure, digital financial transparency, immersive digital innovation, ESG sustainability performance and economic growth were obtained and compared. The use of machine learning models such as Random Forest, Gradient Boosting and XGBoost was used to forecast outcomes in sustainability and economic transformation. The interpretability of the model was guaranteed with the help of SHAP-based feature importance analysis, whereas fairness analysis was done to identify ethical bias among income groups. The experiment shows that ethical infrastructure of digital type and blockchain enabled transparency can substantially enhance sustainability indicators of ESGs and growth outcomes of the economy. The investment on immersive technology also enhances sustainable economic efficiency. The results also indicate that predictive bias is measurable across income-level groupings and it is important to establish ethical governance in the assessment of sustainability based on AI. This paper can provide a solid empirical research that policymakers, ESG regulators and corporate leaders can use to embrace open, ethical and immersive digital policies as the means of achieving inclusive and sustainable economic change.

Effectiveness of Information Booklet on Knowledge Regarding Sequential Fetal Development among Antenatal Women

2026-03-09T08:03:51+00:00

The study assessed the effectiveness of a structured information booklet on sequential fetal development among 150 antenatal women in a tertiary care hospital using one-group pre-test–post-test design. The questionnaire included items to assess the knowledge of antenatal women regarding sequential fetal development on a trimester-wise and month-wise basis. Knowledge was assessed before and after 7 days of intervention. Data analyzed using paired t- test and chi- square analysis. At pre-test, 70% of participants had average knowledge while 29% poor knowledge. Post intervention, 53% had average knowledge and elimination of poor knowledge. The mean score improved from 7.72 ± 2.04 to 13.34 ± 2.13 (t=23.303, p<0.0001). The study concluded that structured information booklet was highly effective and can be incorporated into routine antenatal education.

Effectiveness of a Video-Assisted Teaching Programme on Newborn Care Practices among Postnatal Mothers

2026-03-09T08:08:04+00:00

The present study evaluated the effectiveness of a video-assisted teaching programme in improving maternal knowledge regarding newborn care. A one-group pre-test–post-test design was employed among mothers of newborns (n=60). Baseline knowledge was assessed using a validated questionnaire covering breastfeeding, hygiene practices, thermal care, identification of adequate feeding cues and recognition of common neonatal health problems. Participants then received a structured video-assisted teaching session for 2 days. Knowledge levels were reassessed on the seventh day. Pre- and post-test scores were compared to determine the impact of the intervention. After the teaching session, 63.3% of mothers demonstrated average knowledge, 26.7% exhibited good knowledge and 10% remained in the poor knowledge category. The increase in knowledge scores was statistically significant (t=10.73). The video-assisted teaching programme improved maternal knowledge of exclusive breastfeeding, proper positioning and attachment, hygienic practices, early identification of diaper rash and umbilical cord infection and signs of neonatal illness.

The Triple Threat in Practice: A Case Study of Stroke and Pulmonary Embolism in a Diabetic Patient

2026-03-09T08:15:56+00:00

Diabetes mellitus is a well-established risk factor for both arterial and venous thromboembolic events due to its adverse effects on vascular function and blood clotting mechanisms. This case report presents a 54-year-old female diabetic patient who was admitted with severe hyperglycemia and diabetic ketoacidosis. Shortly after stabilization, the patient experienced sudden left sided hemiplegia, suggestive of an ischemic stroke. Despite prompt antiplatelet treatment and supportive care, patient had acute dyspnea and increased D-dimer levels resulting in a diagnosis of saddle pulmonary embolism. Prompt identification and treatment with anticoagulation (heparin shifted to rivaroxaban) let to marked improvements in both respiratory and neurological symptoms. This case highlights the “triple threat” and the complex pathophysiological interaction between diabetes, ischemic stroke and thromboembolic complications. The metabolic abnormalities associated with diabetes are likely contributed to a hypercoagulable state, which could further exacerbate the prothrombotic state.

Neutrosophic Uncertainty Modeling Along With Machine Learning For Breast Cancer Outcomes: A Hybrid Intelligent Framework for Medical Prognosis

2026-03-09T08:43:48+00:00

Strong prognostic techniques that can handle the inherent ambiguities, uncertainties, and inconsistent data common in clinical practice are essential for managing breast cancer. This paper introduces a novel hybrid computational framework that combines the predictive power of machine learning (ML) with the mathematical formalism of Neutrosophic Sets for uncertainty quantification. We propose that clinical data is not just imprecise but is essentially defined by three separate dimensions: falsity (contradictory evidence), indeterminacy (ambiguous or absent evidence), and truth (supportive evidence). In order to clearly characterize these aspects of uncertainty, our method first converts unprocessed clinical data into a neutrosophic feature space. The processed data is used to train and evaluate eight ML models (Support Vector Machine (SVM), k-Nearest Neighbors (K-NN), XGBoost, Logistic Regression, Random Forest, Neural Networks, Naïve Bayes, and Decision Trees) across five critical prognostic tasks: Diagnosis (Benign/Malignant), Recurrence, Chemotherapy Recommendation, Mortality and Survival. Several models achieved flawless performance (100% accuracy, precision, recall, and F1-score) in deterministic tasks such as Chemotherapy Recommendation and near-perfect diagnosis (SVM Accuracy: 97.37%, F1-Score: 97.93%), demonstrating the remarkable effectiveness of the framework. With the top F1-scores at 51.85% and 26.67%, respectively, the model outputs accurately reflect the inherent difficulties for challenging tasks like recurrence and survival prediction.

Energy-Aware Adaptive Edge Intelligence Model for Resource-Constrained IoT Networks

2026-03-10T06:35:41+00:00

The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for intelligent data processing at the network edge. However, resource-constrained IoT nodes suffer from limited battery capacity, restricted memory, low computational power, and dynamic workload variations, making the deployment of conventional deep learning models inefficient and energy-intensive. To address these challenges, this paper proposes an Energy-Aware Adaptive Edge Intelligence (EA-AEI) model designed specifically for resource-constrained IoT environments. The proposed framework integrates dynamic model scaling, energy-aware inference control, adaptive pruning and quantization, and context-driven task offloading to optimize computational efficiency while maintaining predictive accuracy. Unlike traditional static TinyML and fixed edge inference frameworks, the EA-AEI model continuously monitors residual energy levels and system load conditions to adaptively select the most suitable model configuration in real time. This adaptive mechanism significantly reduces energy consumption, minimizes latency, and prolongs network lifetime without compromising performance. Experimental validation conducted on representative IoT datasets demonstrates that the proposed model outperforms existing edge intelligence approaches in terms of average energy consumption, inference latency, throughput, and system sustainability. The results confirm that integrating adaptive intelligence with energy-aware decision-making enables scalable and efficient deployment of AI models in next-generation IoT networks.

Numerical Solutions of Z-Number Initial Value Problem by Multi Step Method

2026-03-13T06:43:11+00:00

The paper is made with keen in creating a frame work for the study of numerical solution of Z-number initial value problem by multi step method. The Z- number initial value problem is expressed as the combination of a random initial value problem and a fuzzy initial value problem. A real time problem has been taken to demonstrate the advantage of considering Z-differential equations over fuzzy differential equation.

Sustainable and Green Machine Learning: An Energy-Aware Framework for Carbon-Efficient Training and Inference

2026-03-14T05:46:10+00:00

The rapid advancement of machine learning (ML), particularly deep learning, has led to unprecedented computational demands, resulting in significant energy consumption and carbon emissions. Large-scale models such as Transformer architectures and foundation models require extensive training on energy-intensive hardware platforms, contributing to environmental concerns. This paper addresses the research problem: How can ML training and inference processes be optimized to reduce carbon footprint and energy costs without compromising performance? This paper propose an Energy-Aware Sustainable ML Framework (EASMLF) that integrates model compression, adaptive precision scaling, carbon-aware scheduling, and dynamic resource allocation. The framework monitors real-time energy usage and carbon intensity of computing environments to optimize training schedules and inference deployment strategies. Techniques such as pruning, quantization, knowledge distillation, and edge-cloud workload balancing are combined with carbon-intensity-aware optimization to ensure environmentally responsible AI deployment. The proposed framework is evaluated using simulated energy consumption metrics and performance benchmarks. Experimental analysis demonstrates that the approach reduces energy usage by up to 30–40% while maintaining comparable predictive accuracy. The results indicate that sustainable ML practices can significantly lower operational costs and environmental impact. This work contributes to the emerging field of Green AI by providing a structured, scalable, and practical methodology for developing environmentally responsible machine learning systems.

Automated Detection and Classification of Leg Bone Cancer Using MRI and Deep Learning Techniques

2026-03-17T05:27:09+00:00

Leg bone cancer is a severe musculoskeletal disorder that requires early and accurate diagnosis to improve patient survival and optimize treatment outcomes. This study presents an automated detection framework utilizing deep learning algorithms, specifically Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), for precise detection and classification of cancerous regions in MRI leg bone images. To enhance image quality, a median filter is applied during preprocessing to remove noise while preserving essential structural details. A Region of Interest (ROI) based approach focuses analysis on affected bone areas and eliminates irrelevant regions, improving detection accuracy and computational efficiency. The CNN and DNN models are comparatively evaluated using image based metrics to identify the most effective algorithm. Additionally, the study emphasizes patient awareness, exploring the role of lifestyle and dietary habits on disease occurrence and post-treatment recovery. The proposed framework facilitates early diagnosis, clinical decision support, preventive care, and treatment monitoring, contributing to improved patient outcomes.

EDCNGWONet: Enhanced Dilated Convolutional Neural Networks based Gray Wolf Optimizer for Deep Vein Thrombosis Identification and Classification

2026-03-17T09:49:31+00:00

Deep Vein Thrombosis (DVT) occurs due to the formation of blood clots within the body's veins, most commonly in the legs. A serious complication associated with DVT is pulmonary embolism (PE), which arises when a segment of the clot detaches and travels to the lungs via the bloodstream. Since diagnosing DVT through clinical methods can be time-consuming, the development of computer-aided diagnostic systems can significantly enhance efficiency. This study introduces a novel Enhanced Dilated Convolutional Neural Network designed for detecting DVT in CT and MRI scans. The process begins with denoising the CT images using adaptive Wiener filtering to enhance image clarity. These enhanced images are then processed through a fuzzy-based thresholding algorithm to segment the edges effectively. Following segmentation, the proposed EDCNGWONet deep learning model is employed to extract relevant features. An Enhanced Support Vector Machine (ESVM) is then used to classify the CT images into categories such as coronary thrombosis, venous thromboembolism, and pulmonary embolism. The performance of the proposed framework is evaluated using several metrics, including precision, specificity, accuracy, recall, and F1 score. Experimental results indicate that the model achieves an accuracy of 93.63%.

Design and DoE-Based Optimization of Hybrid Nano-in-Nanofiber Systems for Sustained Delivery of Metformin and Sitagliptin

2026-03-21T08:28:17+00:00

Conventional type 2 diabetes therapies (e.g., metformin, sitagliptin) often suffer from short half-lives, frequent dosing, and side effects. To address this, we developed a hybrid “nano-in-nanofiber” system: metformin encapsulated in solid lipid nanoparticles (SLNs) and sitagliptin in niosomes, both embedded within electrospun polymeric nanofibers. A statistical Design of Experiments (DoE) approach (factorial design and response surface methodology) was used to optimize key formulation variables (e.g. polymer ratio, nanoparticle loading) for minimal particle size and maximal drug entrapment. Optimized SLNs (~195 nm) and niosomes (~150 nm) showed narrow size distributions and high entrapment efficiencies. The hybrid nanofiber mats (fiber diameter ~500 nm) retained discrete nanoparticulate reservoirs (confirmed by SEM/TEM). In vitro release studies demonstrated sustained biphasic release: an initial moderate release followed by extended slow release (much slower than plain fibers or free drug), as shown in Fig.1. The DoE-guided optimization significantly reduced the initial burst and extended the release duration, potentially enabling multi-day glycemic control. This systematic DoE-driven development illustrates the advantages of combining polymeric fibers with nano-carriers to create multifunctional drug delivery platforms.

Moringa oleifera Lam.: Nutritional Composition, Pharmacodynamics, and Future Directions in Drug Development

2026-03-25T06:08:07+00:00

Since ancient times, natural resources like plants have been utilized to effectively treat a variety of diseases. Recently, there has been a growing interest in medicinal plants, particularly in low- and middle-income countries. Among these, Moringa oleifera has gained attention for its significant health and industrial potential due to its beneficial components. Moringa oleifera (MO), a tree native to India and found worldwide, has various parts such as bark, leaves, roots, seeds, pods, fruits, and flowers that are used as therapeutic alternatives for numerous ailments.

Feature Engineering Based Advanced Fake News Detection System Using NLP Techniques

2026-03-26T05:39:03+00:00

The rapid spread of misinformation across digital platforms has made fake news detection an urgent challenge, as deceptive content can significantly influence public opinion and decision-making. Existing approaches often struggle to capture deeper contextual meaning, perform poorly with multilingual content, and lack transparency in explaining their predictions. To address these limitations, this study proposes a novel framework for fake news detection that integrates bidirectional natural language processing, comprehensive feature engineering, and explainable artificial intelligence techniques. For practical application, the system includes a user-friendly interface capable of accepting news content in multiple languages, supported by automatic language detection and translation prior to analysis. The model outputs classification labels along with associated probability scores to improve interpretability. Additionally, it highlights key entities within the text to provide clear explanations for its predictions. Experimental results demonstrate strong performance, achieving a training accuracy of 99.19% and a testing accuracy of 97.338%, indicating the effectiveness and reliability of the approach. Overall, the proposed system offers a highly accurate, multilingual, and interpretable solution for fake news detection.

Machine Learning Approaches for Social Media Based Depression Detection: A Review

2026-03-28T05:41:46+00:00

Depression is one of the most prevalent mental health disorders that affect millions of individuals and leading to severe psychological, social and economic consequences. Traditional diagnosis of depression relies mainly on clinical interviews and psychological assessments. However, recent advances in machine learning, natural language processing (NLP) and physiological signal analysis have enabled automated detection of depression using social media data and bio-signals. This review summarizes current computational approaches for depression detection, focusing on machine learning models with textual and physiological data sources. It also discuss data sources, feature extraction techniques and algorithms used in depression detection.

Hypertension: Advances in Pathophysiology, Precision Medicine, and Digital Health Innovations

2026-03-28T12:21:51+00:00

Hypertension, commonly known as high blood pressure, remains a leading cause of cardiovascular morbidity and mortality worldwide. It is a multifactorial condition influenced by genetic predisposition, neurohormonal imbalances, endothelial dysfunction, and lifestyle factors. Despite the availability of effective pharmacological treatments, hypertension management remains a challenge due to issues related to treatment adherence, therapy resistance, and delayed diagnosis. Recent advancements in hypertension research have introduced precision medicine approaches, where genetic profiling aids in the selection of personalized antihypertensive therapies. Additionally, telemedicine and digital health innovations, including remote blood pressure monitoring and artificial intelligence (AI)-assisted decision-making, have improved early diagnosis and treatment adherence. Emerging pharmacological targets such as endothelin receptor antagonists, vasodilatory peptides, and inflammatory mediators offer promising alternatives for drug-resistant hypertension. This review provides a comprehensive analysis of hypertension pathophysiology, therapeutic advancements, and integrative management strategies, highlighting the future role of AI, genomics, and telemedicine in revolutionizing hypertension care. By combining pharmacological, lifestyle, and digital health interventions, hypertension management can become more effective, ultimately reducing the burden of cardiovascular diseases.

Platform Loyalty and Switching Behaviour in Online Food Delivery: Investigating Value Perception, Brand Ecosystem, and Drivers of Consumer Migration

2026-03-30T07:22:07+00:00

The rapid development in ecosystem of online food delivery has transformed in recent years. The cutting-edge competition dynamics has induced a critical challenge among the platforms to retain consumers. This study investigates the loyalty towards platform and rapid changing switching behaviour among the consumers. The study has identified value perception, brand ecosystem and drivers of consumer migration. The study explores the role of brand ecosystem, loyalty program and integrated wallet systems. Primary data was collected from a structured questionnaire based on Likert scale. The research contributes to growing literature on platform economics and digital consumer behaviour.

Novel Multi-Axis Cross-Correlation Algorithm for Precise Azimuth Angle Estimation in MEMS Underwater Acoustic Vector Sensors

2026-04-01T06:35:51+00:00

Underwater detection of acoustic sources, tracking movement of targets, and acquiring directional information requires an accurate estimation of the azimuth angle (i.e. angle in the horizontal plane). A new algorithm for use with cross-shaped piezoelectric MEMS acoustic vector sensors is described here. This new Multi-Axis Cross-Correlation (MACC) algorithm takes advantage of the orthogonal cantilever design to permit the simultaneous measurement of the particle velocity components along multiple axes, thereby obtaining a very accurate estimation of the direction-of-arrival (DOA) without requiring large sensor arrays. The MACC algorithm achieves this by combining two techniques: cross-correlated analysis of time series data with the use of phase differences between each component and maximum/minimum amplitude ratios. The resulting azimuth estimation is accurate to within ±2°, across the entire 360° of azimuth, over a frequency range of 20 kHz to 200 kHz. Comparison of MACC to conventional beam forming and MUSIC algorithms through extensive simulation and analysis has shown that MACC achieves a 65% reduction in estimation error and 40% increase in angular resolution compared to these methods. Additionally, because the computational complexity of MACC is low; it is well suited for near real-time implementation using a limited-resource MEMS platform.

Childhood Respiratory Diseases as Determinants of Health, Education, and Socioeconomic Outcomes: A Narrative Review”

2026-04-02T07:21:48+00:00

Childhood respiratory diseases are important public health issues worldwide, especially pneumonia and asthma which impacts the low income and middle-income countries (LMICs). Evidence shows these conditions have long term consequences beyond acute illness, impacting health but also education and socioeconomic outcomes.

A literature review was performed using PubMed, MEDLINE, and Web of Science for studies published between 2014 and January 2026. National surveys, literature from the World Health Organization (WHO) and Global Burden of Disease (GBD) studies were also included. Eligible studies were those including children aged 0–18 years reporting longterm respiratory, educational, psychosocial or economic outcomes. The study quality was ascertained with Joanna Briggs Institute (JBI) tools and findings were synthesized thematically.

A total of twenty-eight studies were included. According to GBD 2023 estimates, lower respiratory infections in 2023 account for approximately 2.5 million deaths and 98.7 million DALYs. While under-five mortality has been declining with improved outcomes, early childhood pneumonia and recurrent respiratory tract infections have been associated with persistent lung function impairment, increased risk of asthma and chronic respiratory disease, absenteeism from school, and psychosocial challenges. The economic burden and environmental factors such as household air pollution and PM2.5 exposures and multidimensional energy poverty worsened outcomes in LMICs

Childhood respiratory diseases may have long-term effects on health and socioeconomic status. A life-course, community-oriented public health approach that includes primary prevention, access to clean energy, and early interventions could significantly reduce long-term inequities.

Lifestyle-Based Management of Metabolic Syndrome: An Umbrella Review of Yoga, Nutrition, Exercise, and Nutraceuticals

2026-04-02T07:30:02+00:00

Metabolic syndrome (MetS) is a global health challenge characterised by abdominal obesity, dyslipidemia, hypertension, and insulin resistance. Multiple interventions have been tested, but a comprehensive synthesis of systematic reviews and meta-analyses across lifestyle, dietary, and nutraceutical domains has been lacking.
To conduct an umbrella review evaluating the effectiveness of nutrition, lifestyle, exercise, and nutraceutical interventions in improving metabolic syndrome outcomes.

A comprehensive search of PubMed, Scopus, Web of Science, and Google scholar was conducted from January 2005 to June 2025. Systematic reviews and meta-analyses of randomized controlled trials (RCTs) assessing dietary, lifestyle, exercise, yoga, or nutraceutical interventions in adults with MetS were included. Data extraction focused on intervention type, dosage, and outcomes. Methodological quality was assessed using AMSTAR-2 tool.

Fourteen systematic reviews and meta-analyses were included, which have interventions such as nutraceuticals (fenugreek, green tea, garlic, ginger, black cumin, hibiscus, curcumin), yoga, exercise and diet. Lifestyle and exercise interventions improved significantly. The Mediterranean diet improved lipid profile and blood pressure, whereas nutraceuticals provided modest improvements, with fenugreek and black cumin showing the most consistent benefits. AMSTAR-2 ratings indicated eight reviews rated as high quality and six as moderate quality.

Lifestyle interventions remain the cornerstone of MetS management. Herbal and nutraceutical supplements provide promising adjunctive benefits but require standardization and high-quality RCTs. These findings reinforce the primacy of lifestyle approaches while highlighting opportunities for integrated dietary and nutraceutical strategies in clinical management. Future research should emphasise long-term adherence, personalized strategy, and standardized protocols for lifestyle-based interventions.

Molecular targets of Panax ginseng in Inflammation and Oxidative stress: A Narrative Review

2026-04-02T07:34:56+00:00

A traditional herbal remedy with strong anti-inflammatory and antioxidant properties is Panax ginseng, also known as Korean ginseng. Its bioactive triterpene saponins (ginsenosides) are principally responsible for these pharmacological actions. Recent mechanistic research on ginsenoside targets in oxidative stress and inflammation is summarized in this review. Major ginsenosides (Rb1, Rg1, Rg3, Rh2, compound K) block IKK/IκBα activation and downregulate pro-inflammatory mediators (IL-1β, TNFα, COX-2) to inhibit NF-κB signaling in inflammation. (Jang et al., 2023) Additionally, they inhibit inflammasome activation (NLRP3/IL-1β) and MAPK (p38/ERK/JNK) pathways. Ginsenosides promote antioxidant enzymes (HO-1, SOD, and CAT) and lower ROS in oxidative stress by activating the Keap1/Nrf2/ARE pathway. By maintaining membrane potential and electron transport chain function, they reduce mitochondrial ROS and safeguard mitochondria. Crucially, ginsenosides regulate the NF-κB–ROS–Nrf2 crosstalk: ROS can trigger NF-κB and inflammasomes, whereas ginsenoside-induced Nrf2 activation strengthens antioxidant defenses that suppress ROS and reduce NF-κB signaling. (Huang et al., 2021)In models of cancer, diabetes, neurodegeneration, and cardiovascular disease, ginsenosides' dual actions provide protective effects. (He et al., 2022)This review emphasizes the necessity of standardized clinical trials and highlights particular molecular targets and dosage evidence.

Nature’s Ancient Remedy: Unveiling the Secrets of Nardostachys Jatamansi- Sesquiterpenoids, Alkaloids and Fatty Acid Esters

2026-04-04T08:46:38+00:00

Natural cures are generally considered safe, and plants are reported to be a traditional medicine for treating and managing various medical ailments. Recent research highlights the health benefits of biochemical compounds from Nardostachys jatamansi, particularly sesquiterpenoids, alkaloids, and fatty acid esters. These compounds, valued in traditional and modern medicine, exhibit diverse pharmacological effects. Key sesquiterpenoids—jatamanshic acid, patchouli alcohol, and valeranone—show neuroprotective, anti-inflammatory, anticancer, and antimicrobial activities by modulating various biological pathways. They hold promise for treating neurodegenerative diseases, stress disorders, and skin conditions. Some also exhibit strong anti-biofilm and antimicrobial effects, suggesting potential against drug-resistant infections.

Alkaloids like actinidine and fatty acid esters such as n-hexacosanyl arachidate demonstrated antimicrobial, antioxidant, and anti-inflammatory properties. These compounds are also associated with cardiovascular protection, immune modulation, and stress reduction. Furthermore, oroselol, another compound isolated from N. jatamansi, exhibits notable anticancer activity. This review analyzes literature from Google Scholar, EMBASE, PubMed, and PubMed Central to highlight how Jatamansi’s ability to modulate key molecular and cellular pathways underpins its broad biological actions, advancing pharmacokinetic understanding, reinforcing its pharmaceutical potential, and shaping future development of plant-based therapeutics.

Zero Hunger Portal: Surplus Food Collection and Distribution System

2026-04-04T08:52:32+00:00

Food waste and hunger have remained a world issue of concern with the wastage of edible foods and the globe being faced with millions of people under food insecurity with the current food donation processes being poorly coordinated and lack transparency. The given paper introduces the Zero Hunger Portal as a web-based application which will enhance the food collection and redistribution of the excess food by means of a centralized digital system. The portal allows food donors including restaurants, supermarkets, and event organizers to input the surplus food information by hand by adding the information like the quantity, type, location, and expiry time, and the information can be available to the NGOs and other volunteers to effectively plan the collection. It also includes basic record management and analytical functions to facilitate accountability, monitoring and transparency in the activities of donations. The proposed system is expected to minimize food wastage and enhance the power of hunger relief by digitizing the coordination of stakeholders and enhancing the visibility of surplus food resources, across which web-based solutions are capable of combating the issue of social and humanitarian challenges.

An AI Powered Inclusive Web Assistant for Accessing Government Scheme

2026-04-04T09:12:12+00:00

The AI-based Inclusive Web Assistant of Equal Access to Government Schemes is a platform solution that integrates into a single platform and aims at making the discovery and application of the Central and State government welfare schemes in India simpler. The system is also intended to seal the information and access divide among the citizens because of the disjointed portals, complicated eligibility and low levels of digital literacy .It is created as a React-based front and FastAPI-powered back end and powered by a structured database of over 4,000 government schemes sourced by government official portals. The site allows users to search schemes by key words, filters, categories or with a Smart Eligibility Wizard which dynamically assesses user entries to suggest an appropriate scheme. The system is inclusive as it supports many languages, and therefore, users with varying language backgrounds can use it. The recommendations provided by AI can help a user find the relevant schemes according to his profile and needs, and the document requirements guide is a clear example of what paperwork is required when a scheme is needed. The simplified navigation and decision-making process will reduce reliance on intermediaries and ensure that eligibility citizens receive the necessary welfare benefits because of poor navigation or ignorance.

Implementation of DSM-PI Controller in SEPIC-ZETA Integrated converter For Plug-in Electric Vehicle

2026-04-06T08:08:09+00:00

This research recommends an integrated converter for plug-in electric cars that can function in three modes: propulsion, charging, and regenerative brakes. Implementing the DSM-PI controller and comparing it with the PI controller reveals enhanced battery pack characteristics. MATLAB/SIMULINK is used to show the findings and simulations.

The Impact of Flipkart’s Reverse Logistics Service Quality (Ease of Returns, Speed of Processing and Communication) on E-Commerce Customer Satisfaction and Loyalty

2026-04-08T08:56:39+00:00

Reverse Logistics deals with the processes associated with the flows of products, components and materials from users/owners to re-users. The main purpose of reverse logistics is to make the final product flow from customer back to seller or manufacturer in order to reuse the product by recycling this reduces the cost of remanufacturing the product also it adds value to the customer through recycling the product. With the rapid growth of E-Commerce and Increasing environmental concerns, reverse logistics has become an important component of modern supply chain management. The aim of this research is to investigate the Impact of Flipkart’s Reverse Logistics Service Quality (Ease of returns, Speed of Processing and Communication) on E-Commerce customer satisfaction and Loyalty. This study uses a Descriptive and Quantitative research design based on survey data collected from 160 Flipkart customers. In this study we uses both primary and secondary data. Secondary data collected from Articles of Flipkart Reverse logistics and E-Commerce reports. Population of study is all Flipkart customers who have experienced the return process. The results Indicates that Flipkart’s replacement/exchange process is timely, communication during return process also an effective, It’s return service is better than other E-Commerce Platforms, also Flipkart’s customer support for return related issues is helpful, Speed of refund/replacement is most Influenced factor during the return related process and It is easy to Initiate a return on Flipkart.

Qualitative Crustal Interpretation of Central Indian Ocean Basin (CIOB) as Inferred from Gravity Data

2026-04-08T09:11:07+00:00

The objective of the present study is to make a crustal interpretation of the Central Indian Ocean Basin (CIOB) using qualitative techniques inferred from gravity data to delineate major crustal features such as fracture zones, ridges, and seamounts. This study area's free-air gravity anomaly data, which corresponds to short wavelengths (<20 km), is helpful for regional geophysical research. The Low-pass filtering method is used which is an application of the Fourier transform technique for qualitative analysis of free-air data which suppresses high-frequency noise or short-wavelength variations. Low-pass filters are implemented to the free-air gravity data of the study area. The free-air gravity anomaly data is qualitatively interpreted using various filtering techniques such as horizontal gradient, vertical gradient, upward and downward continuation, analytical signal, and tilt derivative technique to identify the nature of the anomaly corresponding to the major tectonic features of the study area. The free-air gravity anomaly having positive trend from 9 - 15 mGal and negatively varies from 76 - 82 mGal for the observation height (H= 100 m). The analytical signal map shows a high analytical signal of 0.0027 mGal/m observed at shallow depths indicates tectonic features. The upward continuation data show that the attenuation of high wave number anomalies increases with increasing altitude and the downward continuation of the free air gravity anomaly grid data shows the change in anomalies with increasing observation altitude. The tilt derivative technique is used to find linear, sharp tectonic features corresponding to free-air gravity anomalies. This approach is used qualitatively to identify anomalous features at different frequencies, aiding in the interpretation of complex geological structures and boundaries using 2-D modelling.

An AI and Blockchain-Based Secure Framework for Cyber-Physical Systems Using SMOTE-Enhanced Random Forest on SWAT and Power System Datasets

2026-04-11T05:33:31+00:00

Cyber-Physical Systems (CPS) are integral to critical infrastructures such as smart grids, water treatment systems, and industrial control environments, but their increasing connectivity exposes them to sophisticated cyber threats. Recent research highlights that integrating Artificial Intelligence (AI) with decentralized technologies such as blockchain can significantly enhance CPS security by enabling intelligent threat detection and secure, tamper-proof data management [1]. However, a major challenge in CPS intrusion detection is the presence of highly imbalanced datasets, which leads to poor detection of minority attack classes and increased false alarm rates.

This paper proposes a hybrid CPS security framework that combines AI-driven intrusion detection with blockchain-based secure logging. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is employed to generate representative samples of minority attack classes, improving model learning and detection capability [7] . A Random Forest classifier is utilized due to its robustness and effectiveness in handling high-dimensional CPS data. The proposed model is evaluated using the SWaT water treatment dataset and the MSU power system dataset, which are widely used benchmarks for CPS security research. Experimental results demonstrate that the integration of SMOTE significantly improves detection performance, achieving higher accuracy, precision, recall, and F1-score while reducing false positives.

Furthermore, blockchain technology ensures the integrity, transparency, and immutability of detected intrusion records, addressing trust and auditability challenges in CPS environments. Recent IEEE studies emphasize that such hybrid AI–blockchain architectures represent a promising direction for securing next-generation CPS and industrial IoT systems [6] . Overall, the proposed framework provides an efficient, scalable, and secure solution for CPS intrusion detection, particularly in scenarios with imbalanced data distributions.

Inclusive Marketing and Diversity-Based Branding: A Conceptual Framework for Consumer Engagement and Business Outcomes

2026-04-11T07:31:04+00:00

The increasing number of consumers with differentiated attributes based on gender, age, ethnicity, culture, disability and language among others has brought about inclusive marketing as one of the important marketing strategies. In this regard, the given paper tries to develop a comprehensive framework through integration of the concept of inclusive marketing and the concept of branding strategy based on the notion of diversity in order to know how these elements influence consumer trust, brand authenticity, purchasing intentions, brand loyalty, and business performance. This study tries to unify the disjointed literature on the same based on the tenets of Social Identity Theory, Consumer Culture Theory, Stakeholder Theory, and Signaling Theory, as it seeks to establish how inclusive marketing can influence the performance of businesses. In addition, the structure has been enlarged to incorporate how new-age AI-based personalization mechanisms and digital engagement pathways affect inclusive marketing. The study is trying to add to the literature by developing a comprehensive conceptualization on the subject matter, hypothesize future research, and offering suggestions on what the managers, researchers, and the policy-focused branding theorists can do.

On Line Dispute Resolution for Accessing Justice: The Way Forward

2026-04-13T04:34:38+00:00

As technology has changed the way the individuals interact, it has also changed the way to resolve the disputes. With technological advancements, a new notion of dispute settlement known as ‘Online Dispute Resolution’ (ODR) has emerged, changing the picture dramatically in few years especially after Covid. In India, Apex Court to lower courts are facing the problem of arrear of cases. In such a situation, ODR appeared as a ray of hope for millions of people waiting for justice. The emergence of Online dispute resolution in India can be both cost effective and less time consuming. It would further be beneficial to disputants which are left unheard because of various reasons either due to cost or time. ODR offers a practical solution to many of the challenges faced by traditional systems, particularly in the context of globalization and digital commerce. ODR, is at its nascent for the purpose of resolving disputes. Many ODR platforms have been established such as CADRE, SAMA, Centre of Online dispute resolution, AGAMI etc. NITI Aayog, in collaboration with Agami and Omidyar Network India etc.

Major Air Pollutant (PM2.5 and PM10) Hot Spot in Anand Vihar (Delhi) and Its Impact during 2020-2024

2026-04-15T09:36:33+00:00

The primary importance of the current study is to examine the trend of major particle air pollutants (PM_2.5 and PM₁₀) in Anand Vihar (Delhi) from January 2020 to December 2024. The Central Pollution Control Board (CPCB) provides daily statistics on pollutants, and each season's monthly average pollutants, including PM_2.5, PM₁₀, and PM_2.5/PM₁₀, are computed. The pollutant concentrations are considered based on climatic and topographic parameters. The current study analyses pollutant intensity from the results of various techniques such as Pearson correlation coefficient, statistical analysis, and wind-rose diagram. The PM_2.5 varies from 8 - 556 μg/m³, with the lowest level recorded in July 2024 and the highest in November 2020. The PM₁₀ varies from 16 - 838μg/m³, with the lowest level in August 2020 and the highest in November 2024. The current study recorded high PM_2.5 and PM₁₀ pollutants in Anand Vihar (Delhi) during the winter season and the results represent that the high mass concentrations in winter surpass the NAAQS limit. The Pearson correlation coefficient between PMs from 2020 to 2024 is much lower in the summer than in the winter, which may suggest that the main causes of air pollution are emissions from vehicles. The abrupt fall in pollutant (PM₁₀) concentration in August 2020 was most likely caused by the ongoing pandemic, and the subsequent fall is due to the ongoing post-pandemic scenario. According to Wind-Rose diagram it can be observed that elevated wind speeds improve dispersion and dilution, but they can also contribute dust particles and raise pollution levels, particularly during the winter season. The drastic change in PM_2.5/PM₁₀ revealed that the cumulative effect of relative humidity was greater in PM_2.5 than in PM₁₀ (winter season). It can be observed that adult mortality is varying more or less during 2020-2023 but it is very high in 2024 due to abrupt increase of PM_2.5 and PM₁₀concentration in Anand Vihar (Delhi).

A Hash-Driven Evolving Permutation Stream Cipher with Encrypted Keystream Generation

2026-04-15T10:30:57+00:00

Stream ciphers are widely used in low-latency and high-throughput settings, but practical designs must balance internal-state complexity, statistical quality of the keystream, and implementation cost. This paper presents a hash-driven stream cipher construction that maintains a 256-byte Dynamic Permutation State (DPS). In each round, SHA-512 is applied to the concatenation of the key, IV, and the prior DPS to derive a nonlinear control array that performs permutation updates via swap operations; the updated DPS is then masked with AES-128 to output an encrypted keystream block. Randomness was evaluated using the NIST SP 800-22 Statistical Test Suite, and the generated keystream achieved passing proportions consistent with the STS acceptance criteria across major tests. Sensitiv-ity experiments show near-ideal diffusion under single-bit key changes (average Hamming ratio ≈ 0.5), and an overlapping-round analysis indicates negligible correlation between successive round outputs. A timing study highlights a trade-off between keystream quality and speed: the prototype implementation is slower than AES-CTR and ChaCha20 but remains practical for software-based generation of robust keystreams. Finally, brute-force analysis indicates that while the nominal key size matches AES-128, the per-key evaluation time is increased due to repeated hashing and permutation evolution, raising the computational effort of exhaustive testing in practice.

Green Banking Initiatives as a Pathway to Sustainability: Insights of Indian Banking Sector in the Digital Era

2026-04-16T06:41:33+00:00

Banks play a key role in promoting sustainability by intermediating between users and suppliers of finance. They have increasingly begun to adopt green banking initiatives by facilitating programs related to environment and sustainable development. This review paper explores the various initiatives relating to green aspects undertaken by digital era banks in India, highlighting their endeavours to integrate sustainability with digital transformation .Green loans, paperless banking, and sustainable financing, efficient energy banking operations, and digitalization are the key initiatives by banks to reduce their carbon footprints. The paper also discusses environmental effects and economic sustainability, challenges, regulatory framework, awareness, perception and satisfaction of bank’s customers, factors for adoption associated with green banking. Even though green banking has been accelerated by advances in digital technologies, certain challenges persist like high implementation cost, lack of awareness among users and regulatory conformity issues. The study also highlights the problems faced by banks in implementing green initiatives suggesting strategies to enhance the adoption of green banking in India. By implementing technology enabled sustainability practices, banks in India can contribute significantly in achieving economic growth along with environmental goals.

Detecting Fraudulent Job Advertisements Using DistilBERT-Based Natural Language Processing

2026-04-16T06:47:13+00:00

The emergence of online job portals has taken over as the leading job recruitment environment though the problem of fraudulent job opportunities is a serious threat to job seekers. False job ads usually take advantage of the job seekers by asking them to provide personal details or procuring them money. It is hard to flag such fraud posts manually as there are a lot of job posts on the Internet. In this paper, a system that employs machine learning to identify fake job postings is suggested through DistilBERT which is a transformer-based model of natural language processing. The proposed method will process job description involving text preprocessing and tokenization and then pass it to a fine-tuned DistilBERT model to do the classification. This model describes the contextual relationship in textual information and predicting whether a job vacancy is authentic or fake. Streamlit is used to provide a web-based application in which users can input job descriptions and obtain predictions in real-time. As proved by the results of the experiment, the given system has an accuracy of 98.88, which proves the efficiency of transformer-based models in fraudulent job post identifications.

A Multi-Model Artificial Intelligence Framework for Automated Blood Clot Detection in Medical Images

2026-04-16T06:53:58+00:00

Pathological clot formation in blood vessels (thrombosis) is a major cause of cardiovascular death throughout the world, accounting for many ischemic strokes, pulmonary embolisms and deep vein thrombosis [9][10]. Timely detection of these lesions in volumetric imaging has a significant impact on a patient's outcome but the massive size of scan acquisitions makes manual radiology assessment an increasingly unrealistic endeavour. We present an AI multi-model framework that integrates a volumetric deep learning segmentation platform with an AI interpreting engine hosted in the cloud to automatically identify, quantify and report clots from CT and MRI scans.

The segmentation component is a 3D U-Net [2][7] model trained with a composite of Dice and Focal losses to address the inherent class imbalances of clot segmentation. A parallel AI vision engine generates narrative clinical reports, including the anatomical location, likely diagnosis, and potential neurological symptoms, which are provided in conjunction with segmentation results for integrated clinical decision making [4][12]. The system is integrated into a Flask web interface that supports drag-and-drop scan upload, real-time inference and visualisation, and report generation in PDF, CSV, and XML. Benchmarking against 2D approaches demonstrate higher Dice Similarity Coefficient and better anatomical coverage of the proposed approach, thus demonstrating its clinical value [1][14].

Hybrid Edge Based Deep Model for Accurate Mammogram Segmentation and Classification

2026-04-17T05:41:45+00:00

The mammogram images are used to identify breast cancer in the earlier stage. This will minimize the mortality rate in women. Now a days cancer is a rapid spreading disease, and it is hard to diagnosis accurately. It is hard for radiologist to find the correct region of spreading of disease. For this deep learning play a vital role in segmentation, extracting the feature and classify the image. In this paper four stages of findings were discussed to get the best result from the mammogram images. The pre - processing by Histogram Equalization is used to enhance the images of mammogram for better segmentation a & feature extraction. The second stage is to segment the images by ROI using Edge based segmentation. The third stage is to extract the feature using wavelet transformation to get the exact feature from the mammogram images. The fourth stage is to classify the image using Deep Resolute Neural Net. The accuracy of our proposed model’s performance was compared to that of currently used detection methods. The experimental result findings of our suggested method DR-NN model is superiority over the presently followed state-of – the art methods.

Engineering Nanostructured Lipid Carriers for Effective Topical and Dermal Drug Delivery

2026-04-17T05:49:52+00:00

Topical drug delivery has emerged as an attractive strategy for treating skin disorders and for improving patient comfort when systemic therapy is needed. Unlike oral or injectable routes, topical administration is painless and generally leads to better patient compliance. However, effective delivery through the skin remains challenging because the outermost layer of the skin, the stratum corneum, acts as a strong protective barrier. This barrier particularly restricts the passage of hydrophilic drugs, thereby limiting their therapeutic effectiveness. To overcome this limitation, several physical and formulation-based approaches have been explored to enhance drug penetration into and across the skin. The success of topical therapy largely depends on the ability of the formulation to transport the drug safely and efficiently through the skin barrier. Initial efforts in this area focused on liposomes, which opened the door to the development of more advanced Nano carrier systems. Today, Nano carriers are widely recognized as promising vehicles for drug delivery. Among them, nanostructured lipid carriers (NLCs) have attracted particular interest. Their unique structure offers several advantages, including excellent biocompatibility, low toxicity, improved drug loading capacity, and enhanced stability of incorporated drugs. In addition to topical use, NLCs have shown potential in a variety of administration routes such as oral, intravenous, pulmonary, ocular, dermal, and transdermal delivery. This review discusses the key characteristics of NLCs, their methods of preparation, and the techniques used to evaluate their quality and safety. Overall, available evidence suggests that NLCs represent a promising and versatile platform for effective dermal and transdermal drug delivery.

A Comprehensive and Forward-Looking Approach to Diagnosing Breast Cancer: Methods Based on Deep Learning through PRISMA

2026-04-18T09:27:45+00:00

Background: Breast Cancer (BC) is the predominant form of cancer among women globally. We conducted a comprehensive investigation and meta-analysis to address the information gaps and enhance the Balance of life (BoL) for individuals with breast cancer. Consequently, researchers and clinicians will have a deeper understanding of the issue.

Methods: This research aims to look at the current computer and digital pathology methods used to find breast cancer, focusing on deep learning. The first step is to look at public sources that have information about breast cancer diagnoses. In addition, the study looks at the newest developments in using deep learning to find breast cancer.

Results: The study's results indicate that deep learning-based testing methods have pros and cons. This study thoroughly analyses the present condition of machine learning-based classifiers and image modalities in computer-aided design (CAD) systems. Research is advised to develop CAD systems that are both objective and efficient.

Conclusion: Deep learning-based methods for detecting breast cancer in computational pathology show promise but have advantages and disadvantages.

Biodiversity Conservation in the Context of Climate Change: Facing Challenges and Management Strategies

2026-04-20T05:38:06+00:00

As the effects of climate change and human activities become more severe, protecting biodiversity becomes an ever more daunting task. This research takes a close look at how ecosystems are changing due to climate change, how successful current conservation efforts are, and how the dynamics of biodiversity loss are changing over time. Using quantitative data from a primary survey of 250 respondents, the study uses a technique to investigate biodiversity and climate science hotspots, emerging topics, and research trends worldwide. The results show that community involvement, public knowledge, successful policy implementation, and climate mitigation activities greatly affect the results of biodiversity conservation efforts. Deforestation, habitat degradation, and climate-induced disruptions like changed precipitation and temperature patterns are the primary causes of the worrying increase in biodiversity loss, which is highlighted in the study's opening section. Biodiversity conservation initiatives are investigated in relation to international policy frameworks like the CBD and national legal instruments like the Biological Diversity Act, 2002 (India). The critical need for conservation efforts to prioritise adaptation-centric planning is highlighted by the fact that species losses, range shifts, and phonological changes are all caused by climate change. This study looks at how conservation results, community participation, awareness of biodiversity, policy actions, and data analysis with SPSS are related, and it uses one-sample t-tests to evaluate how the community views and reacts to conservation efforts. A high level of public awareness and support for biodiversity protection is supported by the findings, which demonstrate statistical significance across all factors and high mean scores. Two interventions that stand out as particularly helpful are habitat restoration efforts and the use of traditional ecological knowledge. The paper looks at the global policy REDD+ (Reducing Emissions from Deforestation and Forest Degradation) and discusses its challenges in being put into action and checked, along with its benefits for protecting biodiversity. In addition, the study highlights adaptive ecosystem management, local stakeholder involvement, and scenario planning as important approaches to strengthen resilience to climate variability. Although there have been some improvements, the research highlights some serious issues with financing, localised estimates, cooperation between agencies, and community inclusion. The absence of resources for real-time ecological monitoring and databases relevant to regions' biodiversity hampered conservation efforts. Thus, we must change our thinking and transition from a reactive to a proactive conservation approach by integrating climate and biodiversity, increasing capacity, and implementing participatory governance.

Sustainable Geographical Indications as an Important Intellectual Property: A Holistic and Integrated Framework

2026-04-20T05:47:25+00:00

When properly implemented and overseen, Geographical Indications (GIs) have the potential to be powerful instruments for promoting sustainability (The Food and Agriculture Organisation of the United Nations—FAO, 2009; 2017). GI producer’s contributions to many aspects of sustainable development may be maximized by including them in a sustainability plan. There may not always be consensus in the research about the benefits of GIs across all sustainability aspects, including social, economic, and environmental. In order to assist GI producers and their organisations in using a place-based and participatory approach to yield tangible outcomes, FAO and oriGIn created the sustainability strategy for GI (SSGI). Original research for a database and methodology to identify and apply appropriate sustainability indicators for GIs is presented in this study. Several SSGI principles have served as a roadmap for the work as we've reviewed, selected, and improved pertinent indicators creatively; the Sustainability Assessment of Food and Agriculture (SAFA) has given us the framework to align with the SDGs and other well-known sustainability frameworks. This study characterised 372 strong sustainability indicators pertinent to GIs and created a database to make their usage easier for practitioners. The place-based approach and its participatory, inclusive process are emphasised as crucial to empowerment and the capacity to form coalitions in the conversation. Action is also emphasised, along with the requirement of enhancing communication inside and beyond the organisation.

Formulation, Optimization and Evaluation of Dexrabeprazole Mucoadhesive Buccal Patch to Overcome First-Pass Metabolism

2026-04-20T05:53:27+00:00

Objectives: The study aimed to develop, optimize, and evaluate dexrabeprazole buccal patches to overcome limitations of conventional oral administration by enhancing bioavailability and providing sustained drug release.

Methods: Dexrabeprazole buccal patches were formulated using Hydroxypropyl Methylcellulose (HPMC K4M) and Carbopol 934P as polymers. A 3² factorial design was employed to optimize key formulation variables. Preformulation compatibility was confirmed by FTIR and DSC. Prepared patches were evaluated for physicochemical properties, mucoadhesion, swelling index, in-vitro drug release, ex-vivo permeation, and stability as per ICH guidelines. Statistical models (ANOVA and polynomial equations) and response surface methodology were applied to identify the optimized batch.

Results: All formulations showed acceptable physicochemical characteristics (thickness 0.22 ± 0.03 mm, surface pH 6.7 ± 0.1, folding endurance >250). Among them, batch F9 emerged as optimized, exhibiting cumulative drug release of 93.5% at 8 h, mucoadhesive strength of 6.1 ± 0.4 N, and swelling index of 58.79 ± 1.5%. Statistical validation revealed close agreement between predicted and experimental values, with relative error <2%. Stability testing confirmed no significant changes over 3 months, with drug content maintained at 98.61% under long-term and 97.92% under accelerated conditions.

Conclusion: The optimized dexrabeprazole buccal patch (F9) demonstrated favorable mechanical strength, sustained release, and stability, highlighting its potential to bypass first-pass metabolism, enhance bioavailability, and reduce dosing frequency in clinical use. Future in vivo studies are warranted to establish pharmacokinetic advantages and therapeutic efficacy for translation into clinical application.

Privacy-Preserving Federated Learning to Improve AI Cybersecurity

2026-04-20T06:01:28+00:00

Federated Learning (FL) has become a transformative paradigm for distributed and privacy-preserving machine learning, enabling multiple participants to collaboratively train models without centralizing their raw data. Despite its inherent privacy advantages, recent studies have exposed several critical security vulnerabilities in FL ecosystems, including model poisoning, backdoor insertion, data reconstruction, and membership inference attacks [1]–[4]. These threats can compromise both model integrity and data confidentiality, making the protection of federated environments an urgent research priority. In this paper, we present Secure-ML-FL, a comprehensive machine-learning-driven defense framework that strengthens federated learning security through three integrated mechanisms: (1) client-level anomaly detection, using meta-feature-based outlier identification to detect poisoned updates; (2) trust-weighted robust aggregation, which dynamically reduces the influence of low-trust or adversarial clients; and (3) meta-learning adaptation, enabling the system to evolve against new attack patterns and data distribution shifts [5]–[8]. To validate our approach, we evaluate Secure-ML-FL on benchmark network intrusion and cybersecurity datasets, namely CICIDS2017 and UNSW-NB15, under varying non-IID and adversarial settings. The results demonstrate that Secure-ML-FL achieves an average reduction of 85% in poisoning success rate, while maintaining model accuracy within 2% of the baseline FedAvg model and reducing communication overhead by approximately 7%. Comparative analysis against state-of-the-art defenses such as Krum, Trimmed Mean, and FedProx confirms the superior robustness and adaptability of our framework in both cross-silo and cross-device federated environments. The findings underscore that integrating intelligent ML-based detection mechanisms with robust aggregation offers a viable path toward trustworthy and secure federated intelligence systems for next-generation privacy-sensitive applications [9]–[12].

To Investigate the Potential Diuretic Effect of Securinega Leucopyrus Leaf Extract in Vivo Using Experimental Animal Models and Determine its Comparative Efficacy with Standard Diuretics

2026-04-20T06:09:34+00:00

Diuretics are agents that promote urine production by increasing the excretion of water and electrolytes from the body and are widely used to manage conditions such as hypertension, edema, and heart failure. The present study evaluated the diuretic activity of the aqueous leaf extract of Securinega leucopyrus using an in vivo experimental model. Preliminary phytochemical screening and spectroscopic analyses (UV–Visible and FTIR) confirmed the presence of bioactive compounds such as alkaloids, flavonoids, tannins, saponins, terpenoids, and polyphenols, which are known to possess therapeutic properties. The diuretic activity was assessed in Wistar rats at doses of 100, 200, and 400 mg/kg and compared with the standard diuretic drug furosemide (10 mg/kg). The extract produced a dose-dependent increase in urine output and significantly enhanced the excretion of sodium, potassium, and chloride ions. The highest dose showed diuretic activity comparable to the standard drug. These findings suggest that Securinega leucopyrus possesses significant diuretic potential and may serve as a promising natural therapeutic agent.

Next-Generation Order Management with Generative AI for Edge Computing and Hybrid Cloud Data Centers

2026-04-20T09:44:08+00:00

Next-generation order management with generative AI is a timely concept aligned with ongoing developments in edge computing and hybrid cloud data centers. Most applications contemplate online inference of generative neural network models without strict requirements for low latency. Workloads that demand fast, real-time responses and support distributed execution of edge services, however, are encountering challenges with respective latency-sensitive applications. Reliable, low-delay order management for such settings can greatly benefit from generative AI. The active exploration of Generative AI methods for operational decision automation, particularly in areas such as order scheduling and work order forecasting, may enhance responsiveness and shorten time-to-decision.

Implementation or use-case analyses often remain sketchy or even absent. Performance considerations are commonly limited to standard generative approaches, namely the ability of generative methods to provide accurate outputs. Generic Latency metrics, however, are equally important. Although generative methods are frequently described as costly relative to their discriminative counterparts, this argument merits careful scrutiny in decision-automation scenarios, especially when local deployment at the edge is considered.

A Method for Predicting Insulator Lifespan and Aging Based on Infrared Thermography under Various Environmental Factors

2026-04-20T09:58:23+00:00

Presently, a typical use of insulators is external insulation for transmission and distribution lines. However, silicone rubber and other materials of various insulators gradually lose their age and electrical insulation characteristics over time due to the long-term effects of pollution, UV radiation, discharge, temperature, humidity, altitude, and other complex and natural environmental and service factors. Aging of various insulators in a mountainous environment is very important for its longevity. To determine the aging of insulators the microscopic structure, physical characteristics, group functioning, and chemical conditions of several samples with varying exposure durations are to be considered for accuracy. In this paper, infrared thermography algorithm is proposed to find the aging and lifespan of various insulators consisting of porcelain, glass and ceramic insulators.Infrared thermography algorithm of image processing is used for detecting and analysing temperature anomalies that develop over time due to aging and degradation. Simulation is performed for various samples of insulators for identifying the key factors such as aging, lifespan, expose time, accelerated time and the tensile strength parameters etc.

Credit Card Fraud Detection Using Quantum Machine Learning

2026-04-20T10:05:47+00:00

The rise of digital payment systems has increased the incidence of financial fraud. Conventional fraud detection systems, relying upon preset rules and parameters, may not be equipped to respond in the face of changing fraud patterns. We propose a hybrid fraud detection mechanism that utilizes various machine learning algorithms alongside other behavioral and geo-velocity checks. This model uses both SVM and QSVM hybrid to evaluate transaction risk real time. Depending on the score generated towards the chance of a transaction being fraudulent, they will either be approved, flagged for additional verification or blocked. Tools used for the implementation of this system is based on various Python frameworks and quantum computing. The experimental results prove that the proposed method achieves better performance in fraud detection with less false alarms than traditional methods.

The Impact of Spacer Shape on the Performance of Three Phase Green Gas Insulated Busduct

2026-04-20T11:31:59+00:00

Currently, reduction of electric field stress in Gas insulated substations (GIS) is majorily responsible for maintenance of dielectric strength In this paper, focused on conducted on different spacer types like Bulb, Rectangular, Circular Vase, Rectangular Vase, and Delta type spacers with and without metal inserts (MI). To study electric-field distribution, stress, and insulation risk, Finite Element Method (FEM) analysis was performed. The findings show geometric characteristics plays a role in field uniformity such that sharp edges lead to localized field enhancement and increased electric field stress. Among all the considered designs, the Delta spacer yielded the optimal performance in terms of less field stress, more voltage distribution, and improved dielectric reliability, rendering it an ideal choice in GIS applications when compared to bulb type spacer available in the existing studies.

Analysis of Troposcatter Link Performance in Remote Terrains:A Comparative Study

2026-04-20T11:38:50+00:00

Establishing resilient communication in geographically isolated and high-altitude regions like the Himalayas remains a formidable challenge. Traditional line-of-sight (LOS) microwave links are often rendered ineffective by massive mountain peaks, while satellite infrastructure is frequently hampered by high operational costs and significant latency. Troposcatter communication offers a robust beyond-line-of-sight (BLOS) alternative by utilizing the scattering of radio waves in the lower atmosphere. This study investigates the technical feasibility and performance of a 266 km troposcatter link between Dehradun and Dharamshala, India. By integrating high-resolution Digital Elevation Models (DEM) and ERA5 atmospheric reanalysis data, we provide a site-specific analysis of path loss, Signal-to-Noise Ratio (SNR), and climatic influence across a 1–60 GHz frequency spectrum. We perform a rigorous comparison of three internationally recognized ITU-R propagation standards: P.617, P.452, and P.2001. Our findings identify the critical influence of local refractivity gradients and terrain diffraction on link reliability, providing a technical blueprint for implementing high-performance BLOS systems in remote rugged environments.

Partition Trauma: A Depiction through Visual Art

2026-04-23T06:08:30+00:00

The partition of India and Pakistan in 1947 remains a pivotal event in the history which has affected the lives of many and it still is. It resulted in mass migration, violence, deaths, murders, rapes and trauma that continue to haunt people and communities even today. The research explores how this trauma is represented in visual arts, particularly paintings focusing on the cultural and psychological effects of a historical incident of partition as a disaster rather than a mere relocation that is based on religion. The research studies the traumatic scars of partition through paintings by artists like Pran Nath Mago, Jimmy Engineer and Satish Gujral.

The paintings explore the themes of loss, identity, trauma, separation and grief. These paintings reflect the agony, pain, mourning of the refugees and mass migration with a feeling of dislocation and fears of death and offers emotional turmoil of the mass migration. The paintings not only serve as a reflection of personal grief but are powerful social commentaries on lasting impacts of partition.

The research analyzes the paintings to understand the role of art in depicting traumatic histories. Painters translate the emotions into visual form to understand the impact of partition in contemporary society. The research explores the trauma that revolves around partition and how the trauma is depicted through the medium of visual art.

Theoretical Study of Electron Impact Ionization Cross Sections of Molecular Co2 and Co2 Ice in Martian Dust

2026-04-23T08:47:54+00:00

The electron impact ionization on the Martian dust is the physical mechanism occurring on Mars1. In this paper, we have calculated the electron impact ionization cross-sections of CO2 and CO2 Ice molecules in the energy range of 10–2000 eV. Inelastic collisions caused by (e-CO2) ionizations at energies above 14 eV justify the electron-molecule interactions, which in turn define the cross-section values. A group of CO2 vibration modes that can be triggered by inelastic electron collisions make up the majority of the cross-section at energies around 14 eV 2. Three main processes influence the electron avalanche process in the Martian atmosphere. 1) electron impact ionization, which is an electron source in the gas; 2) e-CO2 dissociative attachment, which results in the loss of electrons when an electron of a certain energy interacts with the CO2 molecules and splits the molecules into CO and O-; and 3) electron recombination, which is also an electron loss process3,4. Here, spherical complex optical potential (SCOP) is used to derive the total cross section and determine the inelastic cross section. We use the variant Complex Scattering Potential-ionization Contribution (vCSP-ic) method to calculate ionization cross-section. Our results are compared with the available experimental and theoretical data.

Development of Beetroot–Carrot Nutraceutical Capsules

2026-04-24T05:32:50+00:00

The term nutraceutical is a hybrid of “nutrition” and “pharmaceutical”. Reportedly, it was coined in 1989 by DeFelice, Chairperson of the Foundation for Innovation in Medicine. It is applied to products that are isolated from herbal sources and serve as dietary supplements (nutrients). The idea of using phytocompounds in combination results in a dramatic synergism even at low concentrations. The beetroot is the taproot portion of the beet plant, usually known in North America as beets while the vegetable is referred to as beetroot in British English, and also known as the table beet, garden beet, red beet, dinner beet or golden beet. It is one of several cultivated varieties of beet vulgaris grown for their edible taproots and leaves called beet greens, They have been classified as the B.vulgaris Conditiva group.

Carrots (Daucus carota Linn) are a multi-nutritional and multifunctional root vegetable, rich in natural phytochemicals (bioactive compounds), which are recognized for their nutraceutical effects and health benefits in the human body. The main aim of our research was to create a sustainable nutraceutical capsule containing a combination of beetroot and carrot in dry powder form for daily use as a health supplement. Also to identify and quantify the contents available in the capsule by various analytical techniques.

PERSONALIZED FEDERATED LEARNING WITH ADAPTIVE CLIENT SELECTION AND CONCEPT DRIFT HANDLING FOR DYNAMIC DISTRIBUTED ENVIRONMENTS

2026-04-24T06:34:41+00:00

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed clients while preserving data privacy. However, traditional FL frameworks assume homogeneous data distribution and stable environments, which rarely hold in real-world applications. Clients often exhibit heterogeneous data distributions, varying computational capabilities, and dynamic data patterns over time, leading to degraded model performance. Additionally, concept drift—where data distributions evolve—poses a significant challenge for maintaining model accuracy in non-stationary environments. To address these limitations, this paper proposes a Personalized Federated Learning framework with Adaptive Client Selection and Drift Handling (PFL-ACSD). The proposed approach integrates dynamic client selection based on resource availability, data quality, and contribution significance, along with a drift detection mechanism to identify changes in local data distributions. Personalized local models are maintained for each client while a global model is adaptively updated using weighted aggregation. A drift-aware retraining strategy ensures continuous model adaptation in evolving environments. Experimental evaluation demonstrates that the proposed framework improves model accuracy, reduces communication overhead, and enhances robustness against data heterogeneity compared to conventional FL approaches. The results highlight the effectiveness of combining personalization, adaptive participation, and drift awareness in federated learning systems. The proposed framework is suitable for applications such as healthcare, mobile intelligence, and IoT systems where privacy, adaptability, and scalability are critical.

Sensor-Driven Intelligent Irrigation Using Machine Learning for Precision Farming Applications

2026-04-24T09:47:46+00:00

Managing irrigation well is not merely a technical concern. It is tied closely to how carefully we use water, especially in regions where every drop matters during a dry season. The system discussed here explores a fairly practical idea: combining Internet of Things technology with machine learning to help farmers decide when irrigation should actually occur. A small network of low-cost sensors records soil moisture, temperature, and humidity, while an ESP32 microcontroller gathers and transmits the readings. These data points are then interpreted using a K-Nearest Neighbors classifier that predicts a simple outcome, either irrigation is required or it is not. Before training the model, the dataset was cleaned through feature selection, label encoding, and normalization. Using an 80:20 train test split, the model reached about 65.9 percent accuracy. Interestingly, it was better at identifying when irrigation should be activated, which may help prevent under watering. Future improvements could involve adaptive learning and cloud monitoring.

Hierarchical Emotional Intelligence in the Indian Workplace: Psychometric Validation of the Drigas–Papoutsi Pyramid Model

2026-04-24T09:59:22+00:00

Purpose: Hierarchical models of emotional intelligence (EI) offer theoretically rich frameworks for understanding the developmental architecture of emotional competencies. Despite the growing prominence of the 9-layered pyramid model advanced by Drigas and Papoutsi (2018), its psychometric properties and factorial validity remain untested within non-Western professional populations. This study addresses that gap by providing the first quantitative validation of this model among working professionals in India.

Design/Methodology/Approach: A cross-sectional survey design was employed. A purpose-built 45-item instrument operationalising all nine EI dimensions was administered to 380 working professionals across three sectors in Gujarat, India, using a five-point Likert scale. Data were analysed using Cronbach’s alpha reliability analysis, Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests, exploratory factor analysis (EFA) with varimax rotation, one-way ANOVA, Pearson correlation, and multiple linear regression.

Findings: The instrument demonstrated excellent overall reliability (α = 0.92) with all nine subscales meeting or exceeding the 0.70 threshold. EFA yielded a four-factor structure—Intrapersonal EI, Interpersonal EI, Transcendent EI, and Foundational EI—explaining 46.29% of total variance. Self-awareness was the strongest predictor of overall EI (β = 0.31, p < 0.001). Statistically significant gender differences were observed for self-awareness and social awareness, with female respondents scoring higher on both dimensions.

Research Limitations/Implications: The cross-sectional design and regional sampling from Gujarat limit temporal inference and national generalisability. Future research should employ confirmatory factor analysis, structural equation modelling, and longitudinal designs to extend these findings.

Originality/Value: This study provides the first empirical psychometric evidence for the 9-layered pyramid model in an Indian professional context. The validated four-factor structure constitutes a parsimonious and actionable framework for EI assessment and training in organisational and educational settings.

Phytochemical Characterization, Antibacterial Potential and Molecular Docking Insights of Beta Vulgaris

2026-04-25T08:39:04+00:00

Natural products have been a rich source of bioactive compounds with potential medicinal applications. Among them, beetroot (Beta vulgaris) has gained attention due to its diverse phytochemical composition and associated health benefits. This study focuses on the phytochemical and antibacterial properties of beetroot, along with molecular docking analysis, to understand its potential interactions with bacterial targets. Beetroot is known to contain bioactive compounds such as betalains, flavonoids, phenolics, and saponins, which contribute to its antioxidant, anti-inflammatory, and antimicrobial properties. Investigating its antibacterial activity can provide insights into its potential use in natural therapies against pathogenic bacteria. To further explore the mechanism of action, molecular docking studies are conducted to predict the interaction of beetroot-derived phytochemicals with bacterial proteins. As a standard reference, we compare both the antibacterial activity and molecular docking results with ciprofloxacin. This comparison will help assess the effectiveness of beetroot phytochemicals in relation to conventional antibiotics.

Micro vs. Macro Influencers: Comparative Impact on Purchase Intention among Gen Z in Gujarat

2026-04-30T08:26:14+00:00

Introduction of numerous social media sites has revolutionized marketing by creating new ways for marketers to connect with their consumers through the practice of influencer marketing. This study attempts to compare the influence of micro-influencers (followers range: 1K-100K) vs. macro-influencers (followers range: 100K-1M) in the purchasing intention of GenZ consumers in Gujarat, India. Underpinned by Source Credibility Theory and Elaboration Likelihood Model (ELM), this study attempts to investigate the mediating effects of influence credibility, authenticity, and engagement rate among other variables. A survey-based methodology was utilized for gathering data, using a set questionnaire with the aid of which data were collected from 312 GenZ individuals aged between 18 and 26 years old from major cities such as Ahmedabad, Vadodara, Surat, and Rajkot. Data analysis was performed using descriptive statistics, Pearson correlation, multiple regression, and independent samples t-test. Results have shown a positive influence of both micro- and macro-influencers on the buying intention of Gen Z (micro: β = 0.318, p < .001; macro: β = 0.224, p < .001); yet, micro-influencers prove to be more effective than macro-influencers. Additionally, the elements of authenticity and engagement became crucial distinguishing factors. The findings will be useful for marketers and digital marketing professionals who aim to develop efficient influencer marketing strategies for Generation Z consumers in the state of Gujarat.

An Explainable Federated Deep Learning Framework for HRV-Based Early Detection of Diabetic Autonomic Dysfunction: Experimental Evaluation Using ROC–AUC Optimization and Computational Efficiency Analysis

2026-05-02T09:37:12+00:00

Diabetic autonomic dysfunction is a progressive and frequently underdiagnosed complication of diabetes mellitus that significantly increases cardiovascular morbidity and mortality. Early identification through non-invasive physiological biomarkers such as heart rate variability (HRV) provides a critical opportunity for preventive intervention. This study proposes a structured federated analytical framework for HRV-based early detection of diabetic autonomic dysfunction, emphasizing predictive discrimination and computational stability. Experimental findings demonstrate progressive improvement in classification performance across training epochs, reflected by increasing accuracy and area under the curve (AUC) metrics. Receiver operating characteristic (ROC) analysis confirms reliable discriminative capability between affected and non-affected individuals. Loss trends indicate stable optimization without significant overfitting, while time complexity analysis shows consistent computational efficiency per epoch. The framework further enables interpretation of HRV biomarker contributions, supporting clinical transparency and translational relevance. Collectively, the results validate the feasibility of HRV-driven computational modelling as a scalable and reliable approach for early cardiovascular risk stratification in diabetic populations. The proposed system demonstrates potential integration into modern digital health infrastructures to enhance preventive cardiometabolic care.

Emerging Technologies and Smart Strategies for Advanced Energy Conservation in Modern Power Systems

2026-05-02T10:07:24+00:00

Energy conservation has become a critical global priority due to increasing energy demand, rapid urbanization, and environmental concerns. Modern power systems face challenges such as transmission losses, inefficient load management, and integration of renewable resources. This paper presents emerging technologies and smart strategies for advanced energy conservation in modern power systems. The study integrates smart grid technologies, IoT-based monitoring, AI-driven predictive analytics, demand-side management, and optimization algorithms to enhance system efficiency. A hybrid optimization framework combining Particle Swarm Optimization and Machine Learning-based load forecasting is proposed to minimize energy losses and improve demand response performance. Mathematical modelling of power flow and energy efficiency metrics is developed to quantify system improvements. Simulation results demonstrate a reduction in transmission losses by 18–25% and overall energy savings of 20% compared to conventional systems. Comparative performance analysis shows significant improvement over traditional energy management techniques. The proposed framework offers a scalable, sustainable, and intelligent solution for next-generation power networks. Future work includes real-time deployment in smart cities and integration with blockchain-based energy trading platforms.

The Impact of Blue Economy Issues and Challenges: A Way Forward to Achieve SDG 14: Life below Water

2026-05-04T06:03:11+00:00

The Blue Economy has emerged as a forward-looking development framework that seeks to balance economic progress with the sustainable management of ocean resources. As global dependence on marine ecosystems continues to increase, there is a growing need to shift from exploitative practices toward strategies that ensure long-term ecological stability and economic resilience. This study presents a comprehensive analysis of the Blue Economy by synthesizing insights from a wide range of existing research, focusing on its conceptual foundations, sectoral contributions, environmental significance, and implementation challenges.

At its core, the Blue Economy promotes the responsible utilization of ocean-based resources to support industries such as fisheries, aquaculture, maritime transport, tourism, renewable energy, and marine biotechnology. These sectors collectively contribute to global GDP, employment generation, and food security, particularly in coastal and island regions. However, the expansion of these activities has also intensified pressures on marine ecosystems, leading to issues such as overfishing, habitat destruction, pollution, and climate-induced changes like ocean acidification and rising sea levels. This paper emphasizes that the sustainability of ocean resources is not only an environmental concern but also a critical factor for maintaining economic productivity and social well-being.

The analysis further highlights the role of technological innovation and policy frameworks in advancing the Blue Economy. Emerging technologies, including satellite-based monitoring systems, artificial intelligence, and autonomous marine vehicles, are enhancing the ability to manage ocean resources efficiently and reduce environmental risks. At the same time, effective governance mechanisms, international cooperation, and financial instruments such as blue bonds are essential for ensuring equitable and sustainable development. The study also underscores the importance of inclusivity, noting that coastal communities must be actively involved in decision-making processes to ensure that the benefits of the Blue Economy are widely shared.

Despite its potential, the transition to a sustainable Blue Economy faces several obstacles, including limited institutional capacity, inadequate funding, regulatory gaps, and conflicting interests among stakeholders. Addressing these challenges requires integrated approaches that combine scientific research, policy innovation, and community engagement. The findings of this paper suggest that a well-implemented Blue Economy can serve as a powerful tool for achieving global sustainability goals by fostering economic growth while preserving the health of marine ecosystems.

In conclusion, the Blue Economy represents a paradigm shift in ocean governance and resource utilization. By aligning economic objectives with environmental stewardship and social equity, it offers a viable pathway for sustainable development in the 21st century.

Modeling Generalized Ghost Pilgrim Dark Energy within Self-Creation Gravitational Theory

2026-05-04T06:49:21+00:00

This work is based on the Bianchi type-II space-time and the generalized ghost pilgrim dark energy in the self-creation theory of gravitation. To determine the exact solutions to the field equations, we employed the average scale factor offered by Mishra and Dua [12], the relation between metric potentials proposed by Collins et al.,[40], and the trace of the energy momentum tensor is zero proposed by Reddy et al., [10]. We have investigated the GGPDE and dark matter (DM), both in the presence and absence of interaction. The Hubble parameter, the equation of state (EoS) parameter, the deceleration parameter, and other important and well-known quantities are generated for both theories. It is found that the EoS parameter is constant for both models, whereas the deceleration parameters indicate an accelerated phase. The stability analysis and the energy conditions are analyzed for both the interacting and non-interacting models.

AI-Driven Smart Energy Management and Fault Detection in Grid-Tied Hybrid PV/Wind/Battery Power Generation System

2026-05-06T09:22:31+00:00

This paper presents an AI-driven Smart Energy Management and Fault Detection System for a grid-tied hybrid photovoltaic (PV), wind, and battery power generation system. The proposed model employs a Fuzzy Logic Controller (FLC) for intelligent power flow decisions, optimizing charging, discharging, and grid interaction based on real-time conditions. An ESP32 microcontroller enables data acquisition, control execution, and IoT-based monitoring through platforms like Blynk or ThingSpeak. The integrated AI-based fault detection identifies abnormal PV, wind, or battery behavior, ensuring fault-tolerant operation and improved system reliability. Experimental results confirm enhanced energy efficiency, faster fault response, and stable grid performance. The system offers a scalable and sustainable solution for smart homes, microgrids, and distributed renewable networks.

Development of an AI-Driven Pulse Charging System with Real-Time Battery Health Monitoring for EVS

2026-05-06T09:29:36+00:00

Electric Vehicles (EVs) have emerged as a sustainable alternative to conventional transportation, necessitating advanced energy management strategies for efficient and reliable battery utilization. This paper presents the modeling, simulation, and performance evaluation of an intelligent Battery Management System (BMS) integrated with a multilevel power converter, controlled using an Artificial Neural Network (ANN). The proposed ANN-based controller is trained to estimate the State of Charge (SoC) of individual battery modules and to make real-time intelligent switching decisions for optimal energy management. The multilevel converter enables bidirectional power flow, supporting both charging and discharging operations between the battery pack, load, and grid. A modular battery bank consisting of multiple parallel-connected batteries is managed using ANN-driven logic to achieve SoC balancing, over-discharge prevention, and improved overall system efficiency. The complete system is developed and simulated in MATLAB/Simulink and validated under dynamic operating conditions. Simulation results demonstrate accurate SoC estimation, stable voltage regulation, effective battery selection, and robust system performance. The proposed approach contributes toward the development of intelligent and reliable EV energy management and charging infrastructure, with enhanced battery longevity and improved operational efficiency.

Modeling and Analysis of Solar Power Generation Forecasting using Machine Learning Technique

2026-05-06T09:34:56+00:00

Accurate forecasting of solar power generation is critical for efficient grid operation, energy management, and large-scale integration of renewable energy sources. The intermittent and nonlinear nature of solar energy, influenced by meteorological and environmental factors, makes prediction a challenging task. This research paper presents a comprehensive modeling and analysis of solar power generation forecasting using machine learning (ML) techniques. Various ML models, including Linear Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Long Short-Term Memory (LSTM) networks, are investigated and compared. Historical solar power output and meteorological data such as solar irradiance, temperature, humidity, and wind speed are utilized for model training and validation. Performance evaluation is conducted using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The results demonstrate that advanced ML and deep learning models significantly outperform conventional statistical approaches, offering improved accuracy and robustness. The study highlights the potential of machine learning-based forecasting systems in enhancing the reliability and efficiency of solar power integration into modern power grids.

Marder Type Universe with Barrow Holographic Dark Energy

2026-05-06T10:11:08+00:00

We analyse the Marder Universe in the existence of new holographic dark energy (HDE), such as the Barrow Holographic Dark Energy (BHDE), and matter accompanied by Einstein’s general relativity by the Hubble horizon cutoff, regarded as the IR cutoff. The time-dependent deceleration parameter of modified field equations is taken into account along with the metric potential relation, leading to an exponential solution and accelerated expansion (Mishra et al. Int. J. Theor. Phys. 52 2546, (2013)). We study important cosmological parameters like the EoS parameter (ωde), Om(z) diagnostics, density parameter (Ωde), cosmographic parameters, stability analysis and cosmological planes like (r − s), (r − q), and ωde − ω′ . In this work, we plot these parameters against redshift z and consider the consistency with contemporary findings. The EoS parameter projects quintom-like behaviour, and the ωde − ω′ plane varies in the freezing region, and the derived model has a stable behaviour. The results of our work indicate that the universe is an accelerating model with a rapid expansion rate.

Design of a Convolutional Neural Network-Based Power System Stabilizer for Improved Small-Signal Stability

2026-05-06T10:21:54+00:00

The rapid integration of renewable energy sources, advanced power electronic interfaces, and distributed generation has significantly increased the nonlinear dynamics and structural complexity of modern power systems. These developments have intensified small-signal stability challenges, particularly low-frequency electromechanical oscillations that degrade power quality and system reliability. Conventional Power System Stabilizers (PSS), typically based on fixed-parameter lead–lag compensators, often lack adaptability and exhibit reduced damping performance under varying operating conditions. This paper proposes an Efficient Power System Stabilizer (EPSS) based on a Convolutional Neural Network (CNN) architecture to enhance oscillation damping in interconnected power networks. The CNN-based controller extracts spatiotemporal features from dynamic signals, including rotor speed deviation, terminal voltage, and power angle variations. Unlike conventional neural networks that treat inputs independently, the CNN effectively captures localized correlations and nonlinear interdependencies inherent in oscillatory power system behavior. A hybrid training strategy combining supervised learning with adaptive optimization algorithms is employed to ensure fast convergence and robust generalization across diverse disturbance scenarios. The model is trained using simulation data from a Single-Machine Infinite Bus (SMIB) system and validated on a two-area, four-machine benchmark system to assess scalability and robustness. Simulation results under symmetrical faults, load perturbations, and topology variations demonstrate superior damping performance compared to conventional lead–lag PSS and ANFIS-based controllers, achieving reduced overshoot, faster settling time, and improved stability margins. The proposed CNN-PSS also shows resilience to parameter uncertainties and measurement noise while maintaining real-time feasibility on embedded control platforms. This work demonstrates the effectiveness of deep learning–based stabilizers for enhancing small-signal stability in modern smart grids.

Engineering Elasticity: Spanlastics as Smart Vesicular Systems for Enhanced Therapeutic Delivery- A Review

2026-05-06T10:55:18+00:00

Spanlastics are innovative, elastic Nano-vesicular carriers designed to improve medication delivery efficiency and overcome the constraints of traditional dosage forms. These systems are made up of non-ionic surfactants and edge activators, which exhibit high deformability and can penetrate biological barriers. Spanlastics may encapsulate both hydrophilic and lipophilic pharmaceuticals, which improves medication stability, bioavailability, and therapeutic efficacy. Their unique structure allows for regulated and targeted medication release, which reduces systemic side effects and dose frequency. In terms of toxicity, biocompatibility, and chemical stability, sapanlastics perform better than conventional vesicular systems. Ophthalmic, transdermal, oral, and nasal delivery methods have all shown encouraging outcomes. The improved permeability and retention properties of spanlastics make them an ideal smart vesicular system for site-specific drug delivery. All things considered, spanlastics offer better treatment results and patient compliance, marking a substantial leap in drug delivery based on nanotechnology. This review summarises composition, preparation methods, advantages, evaluation and applications of spanlastics systems.

Development and Characterization of Nabumetone Solid Dispersions for Enhanced Solubility and Dissolution Performance

2026-05-07T08:19:19+00:00

Objective of the present investigation was to improve the solubility of poorly water soluble drug Nabumetone by solid dispersion (SD) techniques using HPβCD and PVP as a hydrophilic carriers. Effect of polymer concentration and methods of preparation on solubility and dissolution rate enhancement were studied. The result of solubility study showed increase in solubility of Nabumetone with increase in concentration of HPβCD and PVP. It was found that the dissolution rate of Nabumetone from its Solid dispersion was dependent on the method of preparation of solid dispersions. Dissolution study revealed that the kneading technique was most convenient and effective method for solubility enhancement of poorly water soluble drug Nabumetone than solvent evaporation method. The prepared solid dispersion was characterized by differential scanning calorimetry, Infrared spectroscopy and X-ray diffraction study. The results of the in vitro drug release studies clearly demonstrated potential of hydrophilic HPβCD in enhancing solubility and dissolution rate of Nabumetone.

From Profit to Purpose: Transformational Leadership in the C-Suite for Sustainable Value Creation

2026-05-09T08:52:19+00:00

Purpose: This study aims to examine the role of transformational leadership among C-suite executives in managing technological disruption, climate risks and geopolitical uncertainties while transitioning from profit-driven to purpose-driven organizational strategies. The research seeks to understand how leadership influences sustainable value creation in a dynamic and uncertain business environment. Design/Methodology/Approach: The study adopts a quantitative research design based on primary data collected from 20 senior managers and executives from BFSI sector. A structured questionnaire using a five-point Likert scale was employed. Statistical tools such as correlation, multiple regression and ANOVA were used to analyze the data and examine relationships among technological disruption, climate risk, geopolitical risk, transformational leadership, purpose orientation and organizational performance. Findings: The findings reveal that transformational leadership has the strongest positive impact on organizational performance, followed by purpose orientation. Technological disruption, climate risk and geopolitical uncertainty also significantly influence performance. The results highlight that transformational leadership acts as a critical factor enabling organizations to effectively respond to external disruptions while aligning with purpose-driven strategies. Research Limitations/Implications: The study is limited by its reliance on a relatively small sample size, which affects generalizability. Future research can employ longitudinal designs and a larger datasets to enhance validity and explore sector-specific dynamics. Practical Implications: The study provides valuable insights for C-suite leaders, emphasizing the need to integrate ESG principles, digital transformation, and purpose-driven strategies into organizational decision-making. It highlights the importance of leadership development focused on adaptability, ethical governance and strategic foresight, particularly in emerging economies such as India. Originality/Value: This study contributes to the literature by integrating transformational leadership theory and disruption management. It offers a perspective on how purpose-driven leadership enables organizations to achieve resilience and sustainable performance in a VUCA environment.

Spray-Dried PVP K30/Β-Cyclodextrin Ternary Polymer System as an Amorphous Composite Matrix for Enhanced Solubilization of A Hydrophobic Model Drug

2026-05-11T06:48:25+00:00

As functional composite matrices for the solubilization of hydrophobic bioactive compounds in biomedical applications, polymer-based amorphous systems are being investigated increasingly. In this study, a ternary polymer system made of polyvinyl pyrrolidone K30 (PVP K30) and β-cyclodextrin (βCD) was created to alter the solid-state behavior and apparent composite perfomanceof the model drug etoricoxib, which is weakly soluble in water. Solvent evaporation, kneading, and spray drying were used to create solid dispersions, and a central composite design was used to maximize the proportions of PVP K30 and βCD. Practical yield, drug content, apparent solubility, and dissolution in phosphate buffer pH 7.4 were assessed for the resultant polymer–drug composites, and their structural and morphological properties were examined using FTIR, DSC, PXRD, and SEM. A significant decrease in drug crystallinity and the development of an amorphous phase inside the PVP K30/βCD matrix, along with broadening and shifting of thermal transitions, were confirmed by solid-state analysis. Highly porous, irregular particles were visible in SEM images, suggesting a linked polymer network that improves surface area and wettability. Compared to the limited release from crystalline Etoricoxib, the modified spray-dried ternary system produced a quick dissolution profile and significantly improved apparent solubility, releasing around 90% of the medication after 120 minutes. These results show that PVP K30/βCD-based amorphous polymer composites provide a promising platform for engineering functional polymer matrices that enhance the solubilization and release behavior of hydrophobic molecules in pharmaceutical and biomedical polymer applications, especially when made via scalable spray drying.

A Multimodal Geospatially-Aware Image Retrieval Framework for Non-Geotagged Image Localization Using Contrastive Vision-Language Learning

2026-05-12T11:03:20+00:00

The large-scale growth of digital image collections across mobile platforms, online media, and public repositories has created significant demand for intelligent retrieval systems capable of understanding visual content together with its geographic context. Existing image retrieval approaches mainly rely on semantic feature similarity and often neglect spatial relationships, reducing their effectiveness for geospatial reasoning and location inference tasks. This work presents GeoCLIP-BLIP, a multimodal framework for retrieving and localizing non-geotagged images through combined semantic and geographic representation learning. The proposed approach integrates CLIP to extract semantic visual embeddings, a lightweight geographic encoding module to capture spatial information from coordinate data, and BLIP to generate descriptive captions that improve interpretability. Using a geo-referenced image database, the framework identifies visually related samples and estimates the probable geographic location of an input query image through similarity-based ranking. Retrieved results are further presented through an interactive map interface for intuitive spatial visualization. Experimental evaluation shows that the proposed framework achieves better retrieval relevance and geographic consistency than conventional CLIP-based retrieval methods. By combining semantic feature extraction, spatial embedding fusion, and caption-based explanation, GeoCLIP-BLIP provides an efficient solution for multimodal geospatial image retrieval and localization of non-geotagged images.

Formulation and Evaluation of Alverine Citrate-Loaded Polymeric Nanoparticles for Oral Delivery

2026-05-13T06:43:10+00:00

This study aims to formulate and evaluate polymeric nanoparticles of Alverine Citrate for oral administration. Alverine Citrate was selected as a suitable drug for polymeric nanoparticles due to its low solubility, low bioavailability, and high frequency of administration. The nanoprecipitation method was used to prepare nanoparticles to avoid both chlorinated solvents and surfactants, thereby preventing their toxic effects on the body. Polymeric nanoparticles of Alverine Citrate were prepared by using the hydrophilic polymer chitosan. The prepared formulations were then characterized for particle size, polydispersity index, zeta potential, loading efficiency, encapsulation efficiency, and drug-excipient compatibility. The prepared nanoparticulate formulations of Alverine Citrate with different polymer ratios have shown an average particle size of 114.8 nm, a polydispersity index (PDI) of 0.366, a zeta potential of -2.4 mV, a loading efficiency in the range of 16.96 to 40.15, and an entrapment efficiency in the range of 77.96 % to 85.78 %. Transmission Electron Microscopy (TEM) analysis of the polymeric nanoparticles revealed their spherical shape. The Differential Scanning Calorimetry (DSC) study confirmed that there was no intermolecular interaction between the drug and polymer. FTIR study concluded that no major interaction occurred between the drug and polymers used in the present study.

Advances in Polyherbal Emulgel Systems for Topical Antifungal Therapy: A Comprehensive Review

2026-05-13T06:46:09+00:00

Emulgel is a cutting-edge topical drug delivery method that improves the distribution of hydrophobic medications by fusing the characteristics of gels and emulsions. The formulation, assessment, and therapeutic potential of polyherbal emulgels are the main topics of this review, especially with regard to antifungal applications. Patient compliance is decreased by the limits of conventional topical formulations, such as creams and ointments, which frequently include greasiness, poor spreadability, and stability problems. By offering greater drug loading, stability, penetration, and controlled release, emulgels get around these problems.

The review highlights the significance of penetration enhancers and the physiological and physicochemical characteristics that affect topical medication absorption. Based on their properties and capabilities, several emulgel types—such as microemulgels, nanoemulgels, and macroemulsion gels—are examined. Herbal extracts with strong antifungal and anti-inflammatory properties, such as Ocimum sanctum and Nyctanthes arbor-tristis, are included with particular care. The production process and evaluation criteria, including pH, globule size, spreadability, and medication content, are also described. All things considered, polyherbal emulgels present a viable, efficient, and patient-friendly method for topical antifungal treatment with possible future uses.

Adaptive Reinforcement Learning Algorithms for Intelligent Resource Management in Software-Defined Networks

2026-05-13T09:34:32+00:00

Software-Defined Networking (SDN) has emerged as a transformative architecture that decouples the control plane from the data plane, offering unprecedented programmability, scalability, and flexibility in managing modern communication networks. However, the increasing heterogeneity of network traffic, rapid growth of latency-sensitive applications, and dynamic variations in user demands have intensified the complexity of efficient resource management in SDN environments. Traditional static or heuristic-driven resource allocation strategies often fail to adapt to rapidly changing network states, leading to congestion, sub-optimal flow scheduling, and inefficient bandwidth utilization. Reinforcement Learning (RL), with its ability to learn optimal actions through continuous interaction with the environment, presents a compelling solution to these challenges, but standard RL models still struggle with high-dimensional state spaces, delayed rewards, convergence instability, and limited generalization across diverse network conditions. This paper proposes an adaptive reinforcement learning framework tailored for intelligent resource management in SDN, integrating model-free RL, model-based RL, and deep reinforcement learning (DRL) mechanisms with adaptive feedback loops and network-aware learning strategies. The framework dynamically adjusts flow rules, bandwidth distribution, routing paths, and QoS policies by learning from real-time traffic patterns, system congestion levels, and predictive demand modeling. Comparative analysis demonstrates that adaptive RL algorithms significantly outperform static SDN controllers and classical optimization approaches in terms of throughput, latency minimization, fairness, and energy-efficient routing.

Next-Generation 6G Network Design Using AI-Enhanced Resource Allocation and Spectrum Optimization

2026-05-13T09:47:30+00:00

The rapid emergence of sixth-generation (6G) wireless systems marks a pivotal shift in global communication infrastructures, driven by unprecedented demands for ultra-high data rates, massive device connectivity, and near-zero latency. As traditional resource allocation and spectrum management frameworks become inadequate for increasingly complex network environments, artificial intelligence (AI) emerges as a foundational enabler of next-generation 6G design. This paper investigates the integration of advanced AI models including deep reinforcement learning, federated intelligence, and self-evolving neural architectures into the core mechanisms of 6G resource distribution and dynamic spectrum optimization. It examines how AI-driven algorithms enhance spectral efficiency, minimize interference, and support autonomous decision-making across heterogeneous network layers comprising terahertz bands, reconfigurable intelligent surfaces, and ultra-dense cell deployments. By exploring synergistic interactions between AI systems and emerging 6G technologies, the study highlights the transformative potential of intelligent orchestration in achieving energy-aware, latency-adaptive, and context-responsive communication ecosystems. Furthermore, the paper addresses the challenges related to computational complexity, data privacy, and algorithmic scalability, emphasizing the need for robust, trustworthy, and interoperable AI frameworks. Ultimately, this research situates AI-enhanced resource allocation as a central paradigm for realizing resilient, adaptive, and high-capacity 6G networks capable of supporting future global digital infrastructures.

Formulation and Evaluation of Tinospora Cordifolia Loaded Nanosponges

2026-05-14T05:27:04+00:00

Background: Tinospora cordifolia, a natural product with numerous uses in traditional ayurvedic medicine, has gained global interest due to its active components and biological role in illness prevention.

Methodology: This study focuses on the formulation and evaluation of nanosponges loaded with Tinospora cordifolia extract, using an emulsion solvent diffusion method.

Results and Discussion: SEM images revealed the nanosponges, which were visually inspectable through optical binocular microscopy. The F12 batch showed fine, spherical nanosponges with a maximum percentage yield of 74%. The nanosponges were porous, smooth, and spherical, with a particle size of 192.674 nm.

Conclusion: Diabetes mellitus may be treated with nanosponges loaded with Tinospora Cordifolia. This may lead to a decrease in insulin resistance and an increase in insulin receptor sensitivity on cells. Furthermore, it could lead to continuous drug delivery, which would lower dosage, frequency of administration, and adverse effects.

Natural Deep Eutectic Solvents: Mechanistic Insights, Analytical Applications and Emerging Role of Drug Solubility Enhancement

2026-05-14T05:40:24+00:00

Natural Deep Eutectic Solvents (NADES), are an emerging class of green solvent system that has a great potential in analytical and pharmaceutical sciences. Strong intermolecular interactions (hydrogen bonding) between naturally occurring hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs) including sugars, organic acids, amino acids and polyols form NADES. The interactions lead to the formation of eutectic mixtures that have distinct physicochemical characteristics including tunable polarity, high solubilization capacity, low volatility, and increased thermal stability rendering them desirable substitutes of traditional organic solvents that are characterized by toxicity and environmental challenges. Mechanistically, NADES increase the solubility of water-insoluble compounds via various mechanisms such as large hydrogen-bonding structures, interruption of crystalline drug frameworks, the modulation of polarity, and encapsulation into structured solvent networks at a molecular-scale level. Such properties help to enhance the solubility, stability, and bioavailability possibilities of the hydrophobic drugs and bioactive substances. Analytically, NADES have seen extensive use as green extraction agents to recover bioactive compounds phenolics, flavonoids, alkaloids and terpenoids as examples of complex biological matrices. The fact that they are compatible with various forms of analysis such as high-performance liquid chromatography (HPLC), gas chromatography (GC), capillary electrophoresis and spectroscopic methods further underscores the fact that they can be used as eco-friendly solvents. NADES have proven useful in pharmaceutical sciences to improve drug solubility, to stabilize labile compounds, and to develop more complex drug delivery systems such as oral and transdermal systems. Future directions involve using NADES with nanotechnology, computational modeling, and solvent design by artificial intelligence to incorporate the rational and application-specific development of formulations. It is hoped that these advances will result in more rapid translation of NADES into industrial and pharmaceutical applications. NADES are a fast-developing area that is consistent with the principles of green chemistry and provides new mechanisms on how to improve solubility and ensure sustainable processing.

AI Supported Writing Tools and Its Consequence on Students’ Written Skills: ESL Instructors’ Perceptions

2026-05-14T10:31:08+00:00

An essential objective of this research study is to discover the different types of writing apps of Artificial Intelligence (AI) that are presently existing and assess in what way ESL instructor’s assessment their impression on the writing skills of learners, accurately in respect to arrangement of ideas and sentence structure. The investigation was shaped using a strategy for case studies and a qualitative approach. The researchers used semi structured interviews to acquire information about the variety of AI devices and their impact on learners’ writing composition. In order to provide insight into the range of AI writing applications applied in the students’ language learning process. The data was collected on the practice of Chatgpt, Quillbot, Jenni, Copy.ai, and Wordtune by four instructors with different experiences and qualifications. Finally, the instructors agreed that the AI writing applications enhanced the quality of writing of their students, mainly the Organization of ideas and grammatical structure. The outcome of the study recommend, English language learners can be assisted in improving the distinction of the content writing through AI based writing applications. The present research limitations were mentioned, and also the recommendation of future study.

ESP32-Enabled Iot Framework for Diagnostic and Prognostic Health Management of Electric Vehicle Motors and Batteries

2026-05-18T05:57:23+00:00

The rapid proliferation of electric vehicles (EVs) has heightened the demand for robust diagnostic and predictive health monitoring of critical subsystems, particularly traction motors and battery packs. To address the need for enhanced reliability, safety, and operational longevity, this research presents the design and deployment of a cost-effective, IoT-enabled condition monitoring system utilizing the ESP32 microcontroller. The architecture integrates a multi-sensor suite comprising temperature, current, voltage, vibration, and smoke sensors to facilitate continuous surveillance of the motor and battery states. Through real-time data acquisition and embedded processing, the system enables precise estimation of State of Charge (SOC) and State of Health (SOH) while identifying critical anomalies such as overcurrent, overheating, excessive mechanical vibration, and incipient thermal hazards. Processed telemetry is transmitted to a cloud-based dashboard, supporting remote visualization, instantaneous alarm notifications, and strategic predictive maintenance planning. Experimental validation demonstrates that the proposed framework identifies faults with high accuracy and responsiveness. Ultimately, this study offers a scalable and economical solution for intelligent EV health management and early-stage fault detection, contributing significantly to the dependability of modern electric mobility.

Development and Validation of A Green Ultra-Performance Liquid Chromatography (Uplc) Method for the Simultaneous Estimation of Sacubitril and Valsartan in Tablet Dosage Form: Assessment of Method Greenness

2026-05-18T06:00:52+00:00

Today, analysts prioritize environmental considerations when creating new analytical methods by emphasizing energy-efficient instruments, minimization of hazardous substances, and reduction of waste. The primary aim of this study was to develop and validate a simple, rapid, and eco-friendly Ultra-Performance Liquid Chromatography (UPLC) method for the simultaneous estimation of Sacubitril and Valsartan in tablet formulation in accordance with Green Analytical Chemistry (GAC) principles. Chromatographic separation was achieved on a Kromasil C18 column (250 mm × 4.6 mm, 5 µm particle size) using an optimized mobile phase of Methanol: Acetonitrile: Buffer (pH 3.0) in a ratio of 40:20:40 v/v/v. The analysis was performed at a flow rate of 1.0 mL/min with UV detection at 250 nm, achieving a total run time of 5.0 minutes. The developed RP-HPLC method was validated in accordance with International Council for Harmonisation (ICH Q2(R1)). Linearity was observed in the range of 6–36 µg/mL for Sacubitril and 6.5–39 µg/mL for Valsartan using six calibration levels. Recovery studies showed values between 98–102%, and precision was high with %RSD values less than 2 %. The greenness of the method was evaluated using Analytical Eco-Scale and Green Analytical Procedure Index (GAPI). The developed method is suitable for routine quality control while promoting sustainable laboratory practices.