Volume 12 - Issue 9

Vol 12, Issue 9 (September 2026) Life Science Botany

Ethnobotanical Survey Of Medicinal Plants Used By Local Healers In Jehanabad District (Bihar)

Traditional Phytotherapeutic Wisdom Constitutes An Invaluable Legacy Of Agrarian Societies In Bihar. This Investigation Was Carried Out To Systematically Record The Plant-based Medicinal Knowledge Of Indigenous Practitioners In Jehanabad, A District... Traditional Phytotherapeutic Wisdom Constitutes An Invaluable Legacy Of Agrarian Societies In Bihar. This Investigation Was Carried Out To Systematically Record The Plant-based Medicinal Knowledge Of Indigenous Practitioners In Jehanabad, A District Of The Historic Magadh Zone. The District, Situated At 25°0'N To 25°28'N And 84°31'E To 85°08'E, Possesses Diverse Vegetation And Deeply Rooted Folk Healing Traditions. Extensive Fieldwork Was Performed Between January And October 2024 Covering 12 Villages Under Four Administrative Blocks: Jehanabad, Kako, Ghoshi, And Hulasganj. Data Acquisition Was Done Through Semi-structured Interviews, Focused Group Discussions, And Transect Walks With 28 Experienced Local Healers Comprising 21 Men And 7 Women In The Age Group Of 42-78 Years. The Survey Recorded 38 Therapeutic Species Distributed Across 26 Botanical Families. Fabaceae Emerged As The Most Represented Family With 5 Species, While Lamiaceae Was Second With 4 Species. Foliar Parts Accounted For Maximum Utilization (48.5%), Followed By Roots (18.2%) And Stem Bark (12.5%). Decoction And Topical Paste Were The Predominant Modes Of Drug Preparation. High Informant Consensus Factor Was Noted For Dermatological Ailments (0.86) And Digestive Disorders (0.82). The Highest Use Value Was Calculated For Azadirachta Indica (0.92) And Ocimum Sanctum (0.89). The Findings Confirm That Vaidyas Of Jehanabad Retain Profound Knowledge Of Herbal Medicine, Yet Intergenerational Transfer Is Weakening. Therefore, Immediate Documentation, Laboratory Screening, And Community-based Conservation Are Essential For Sustainable Use And New Drug Development. Read more Read less

Authors: Pawan Prakash | Manorma Kumari | V K Prabhat

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

DESIGN AND EXPERIMENTAL EVALUATION OF A HYBRID LIFI– WIFI MULTIPLEXED DATA COMMUNICATION SYSTEM USING PACKET-LEVEL LINK AGGREGATION AND FAILOVER

LiFi Is A Wireless Communication Method That Uses Light For Sending Data, While Wi-Fi Uses Radio Signals. LiFi Can Give Good Performance Over A Short Distance, But The Connection Can Be Affected When The Light Path Is Blocked Or The Receiver Is Moved... LiFi Is A Wireless Communication Method That Uses Light For Sending Data, While Wi-Fi Uses Radio Signals. LiFi Can Give Good Performance Over A Short Distance, But The Connection Can Be Affected When The Light Path Is Blocked Or The Receiver Is Moved Away. Wi-Fi Does Not Have The Same Line-of-sight Problem, So It Can Be Used As A Backup. In This Work, A Hybrid LiFi–Wi-Fi Communication System Is Developed Using ESP32. The Data Is Divided Into Small Packets And Sent Through LiFi And Wi-Fi. Each Packet Contains A Sequence Number And CRC So That The Receiver Can Check The Packet And Arrange The Packets In The Correct Order. The Experimental Results Show That LiFi Gives Better Packet Delivery At Shorter Distances, While Packet Delivery And Throughput Decrease As The Distance Increases. Wi-Fi Is Used When The LiFi Signal Becomes Weak. The System Shows That Combining The Two Communication Methods Can Help Maintain Data Communication When The LiFi Link Is Not Reliable. Read more Read less

Authors: VINOTH KUMAR R | Dr. KULANTHAIVEL G | Dr. SIVASANKAR P

Vol 12, Issue 9 (September 2026) Civil

EFFECT OF SOIL-STRUCTURE INTERACTION ON TORSIONAL IRREGULARITY IN A MULTISTOREY BUILDING: AN ETABS RESPONSE-SPECTRUM STUDY

Soil-structure Interaction (SSI) Modifies The Effective Boundary Conditions Of Multistorey Buildings Through Foundation Translation, Rocking And Torsional Flexibility. These Effects Are Especially Important In Plan-irregular Reinforced-concrete Build... Soil-structure Interaction (SSI) Modifies The Effective Boundary Conditions Of Multistorey Buildings Through Foundation Translation, Rocking And Torsional Flexibility. These Effects Are Especially Important In Plan-irregular Reinforced-concrete Buildings, Where Eccentricity Between The Centres Of Mass And Rigidity Produces Coupled Lateral-torsional Response. This Paper Evaluates The Seismic And Torsional Behaviour Of An Irregular Multistorey RC Building Using Three-dimensional ETABS Modal And Response-spectrum Analysis In Accordance With IS 1893 (Part 1): 2016. Three Result Sets Identified In The Source Report As Hard-, Medium- And Soft-soil Spectrum Cases Are Compared Without Altering The Reported Values. Maximum Storey Displacement Increases From 36.065 Mm To 53.793 Mm And 70.296 Mm, While Maximum Storey Drift Increases From 0.000417 To 0.000621 And 0.000811. Maximum Storey Shear Rises From 3393.381 KN To 4475.290 KN And 5495.392 KN. Governing Torsional Irregularity Ratios Remain Nearly Unity In The X Direction But Increase From 1.235 To 1.267 In The Y Direction, Indicating Directional Torsional Irregularity And A Governing Soft-soil Case. The Paper Also Identifies An Inconsistency Between The Stated Seismic Weight And Reported Base Shear. Results Are Interpreted As Soil-spectrum-dependent Outputs Because Verified Foundation-spring Assignments And Matched Fixed-base/SSI Models Are Unavailable. The Study Provides A Transparent Framework For Future SSI Verification And Torsional Assessment. Read more Read less

Authors: Sakunde Adesh Sominath | Prof. N.M. Nikam

Vol 12, Issue 9 (September 2026) Civil

PERFORMANCE EVALUATION OF M60 CONCRETE INCORPORATING GGBS AND ALCCOFINE AS PARTIAL CEMENT REPLACEMENTS

This Paper Evaluates The Mechanical Performance Of M60 Concrete In Which Ordinary Portland Cement Is Partially Replaced By Ground Granulated Blast-furnace Slag (GGBS) And Alccofine 1203. A Constant Total Binder Content Of 450 Kg/m³, Water Content Of... This Paper Evaluates The Mechanical Performance Of M60 Concrete In Which Ordinary Portland Cement Is Partially Replaced By Ground Granulated Blast-furnace Slag (GGBS) And Alccofine 1203. A Constant Total Binder Content Of 450 Kg/m³, Water Content Of 135 Kg/m³ And Water-to-binder Ratio Of 0.30 Were Used. The Experimental Matrix Comprised A Control, Binary GGBS Mixtures At 20%, 30% And 40%, And Ternary Mixtures Containing 30% GGBS With 5%, 10%, 15% And 20% Alccofine. Absolute-volume Mix Calculations, Batching, Mould Preparation, Mixing, Compaction, Water Curing And Strength Testing Were Organized In Accordance With Relevant Indian Standards. Three Specimens Were Considered For Cube Compression, Cylinder Compression, Split Tension And Flexure At 7, 14 And 28 Days. The Final Report Dataset Showed That M60-G30A10 Achieved The Highest 28-day Cube Compressive Strength Of 79.40 MPa, Cylinder Compressive Strength Of 65.20 MPa, Split-tensile Strength Of 5.07 MPa And Flexural Strength Of 7.31 MPa. These Values Exceeded Control Concrete By 15.6%, 15.8%, 13.2% And 15.1%, Respectively. The Performance Gain Is Attributed To Improved Packing, Hydration Nucleation, Additional C-S-H Formation And Interfacial-transition-zone Densification. Increasing Alccofine Beyond 10% Reduced Marginal Efficiency Because Of Higher Surface Demand And Clinker Dilution. The Preferred Binder Is Therefore 60% OPC, 30% GGBS And 10% Alccofine, Subject To Independent Durability And Field-scale Validation. Read more Read less

Authors: Vaidya Sanket Satish | Prof. R.A. Thote

Vol 12, Issue 9 (September 2026) Life SBotany

Biochemical Changes In Deteriorated Seeds Due To Storage Fungi And Their Recovery By Invigoration.

Seed Deterioration During Storage Remains A Critical Factor Affecting Seed Quality And This Deterioration Is Significantly Accelerated By The Activity Of Storage Fungi. The Present Investigation Was Carried Out To Understand The Biochemical Alteratio... Seed Deterioration During Storage Remains A Critical Factor Affecting Seed Quality And This Deterioration Is Significantly Accelerated By The Activity Of Storage Fungi. The Present Investigation Was Carried Out To Understand The Biochemical Alterations Occurring In Seeds Deteriorated By Storage Fungi And To Assess The Possibility Of Recovery Through Seed Invigoration Approaches. Deteriorated Samples Of Wheat, Paddy And Chickpea Were Collected From Traditional Storage Structures In The Gaya Region Of Bihar Where Moisture Control Is Inadequate. Laboratory Analysis Revealed The Predominance Of Aspergillus Flavus Followed By Aspergillus Niger, Penicillium Citrinum And Fusarium Species. Biochemical Evaluation Indicated A Marked Reduction In Total Soluble Proteins, Soluble Sugars, Dehydrogenase Activity And Alpha Amylase Activity In Infected Seeds When Compared To Healthy Seeds. At The Same Time A Substantial Increase Was Observed In Malondialdehyde Content, Electrolyte Leakage And Free Amino Acids Which Reflects Oxidative Stress And Loss Of Membrane Integrity. The Fungal Load Was Found To Be Positively Associated With Lipid Peroxidation And Membrane Damage. To Recover The Deteriorated Seeds Different Invigoration Treatments Such As Hydropriming For Twelve Hours, Potassium Dihydrogen Phosphate At Two Percent, Zinc Sulphate At Zero Point Five Percent And Seed Coating With Trichoderma Viride Were Applied. Among All Treatments The Combination Of Hydropriming With Trichoderma Viride Showed Maximum Recovery In Terms Of Improved Germination, Restored Enzyme Activity And Reduced Membrane Damage Followed By Potassium Dihydrogen Phosphate Treatment. The Findings Indicate That Storage Fungi Induce Biochemical Deterioration Through Reserve Depletion And Oxidative Damage And That Simple And Low Cost Invigoration Methods Can Partially Reverse This Deterioration. Read more Read less

Authors: Manoj Kumar | V K Prabhat

Vol 12, Issue 9 (September 2026) Computer Science And Engineering

An Interpretable Deep Learning Framework For MRI-Based Alzheimer’s Disease Detection And Automated Clinical Reporting

Alzheimer’s Disease (AD) Is A Progressive Neurodegenerative Disorder That Affects Memory, Cognitive Abilities, And Overall Brain Function, Particularly In The Aging Population. Early And Reliable Diagnosis Is Essential For Timely Intervention, Diseas... Alzheimer’s Disease (AD) Is A Progressive Neurodegenerative Disorder That Affects Memory, Cognitive Abilities, And Overall Brain Function, Particularly In The Aging Population. Early And Reliable Diagnosis Is Essential For Timely Intervention, Disease Management, And Improved Patient Care. Magnetic Resonance Imaging (MRI) Provides Non-invasive Structural Information About The Brain And Has Become An Important Source Of Data For Computer-aided Alzheimer’s Disease Assessment. Although Deep Learning Techniques Have Demonstrated Considerable Potential For MRI-based Disease Classification, Many Existing Approaches Primarily Emphasize Predictive Performance While Providing Limited Insight Into The Reasoning Behind Their Decisions. Furthermore, Conventional Diagnostic Models Generally Do Not Provide An Integrated Mechanism For Transforming Model Predictions Into Understandable Clinical Information. To Address These Limitations, This Paper Proposes An Interpretable Hybrid Deep Learning And Generative Artificial Intelligence (AI) Framework For MRI-based Multi-stage Alzheimer’s Disease Diagnosis With Automated Clinical Report Generation. The Proposed Framework Combines A Convolutional Neural Network (CNN) And A Swin Transformer To Learn Complementary Local Structural And Global Contextual Representations From MRI Brain Images. The Extracted Features Are Fused For Multi-stage Classification Of Normal, Mild, And Moderate Conditions. Grad-CAM Is Incorporated To Identify And Visualize The Brain Regions That Contribute To The Classification Decision, Thereby Improving Model Transparency. Subsequently, A Generative AI-based Large Language Model (LLM) Converts The Verified Classification Result, Confidence Information, And Visual Explanation Into A Human-readable Clinical-style Report. Unlike Prediction-only Systems, The Proposed Framework Establishes A Unified Pipeline Connecting Diagnosis, Visual Explanation, And Evidence-guided Report Generation. The Framework Is Intended To Improve Diagnostic Interpretability, Communication, And Usability While Providing A Foundation For Trustworthy AI-assisted Decision Support In Alzheimer’s Disease Assessment. Read more Read less

Authors: KEERTHIKA V | Vanitha J

Vol 12, Issue 9 (September 2026) Embedded Systems

ML-Based Anomaly Detection In Healthcare Systems Using ESP32 Microcontroller

Continuous Monitoring Of Patient Vital Signs Through Wireless Body Area Networks (WBANs) Is Increasingly Central To Modern Healthcare, Yet Validating Anomaly-detection Pipelines On Real Hospital Networks Is Costly, Ethically Constrained, And Difficul... Continuous Monitoring Of Patient Vital Signs Through Wireless Body Area Networks (WBANs) Is Increasingly Central To Modern Healthcare, Yet Validating Anomaly-detection Pipelines On Real Hospital Networks Is Costly, Ethically Constrained, And Difficult To Reproduce. This Project Presents A Simulation-first Framework For ML-based Anomaly Detection In Healthcare Systems, Built Entirely In OMNeT++ With The INET Framework. The System Models Sensor Nodes (ECG, SpO2, Blood Pressure, Temperature, And Glucose), A Gateway, And A Central Server As A Discrete-event WBAN, In Which A VitalSignGenerator Module Produces Realistic Vital-sign Traffic And An AnomalyInjector Module Introduces Controlled Anomalies — Physiologically Abnormal Readings, Sensor Faults, Spoofed Packets, And DoS-style Flooding — At A Known, Configurable Probability. Because The Anomaly-injection Rate Is A Simulation Parameter, Every Generated Reading Carries An Automatic Ground-truth Label, Removing The Need For Manual Annotation. Simulation Results Are Exported Via Scavetool Into A CSV Pipeline And Used To Train And Compare Three Unsupervised Machine-learning Models — Isolation Forest, One-Class SVM, And An LSTM Autoencoder — Evaluated Against The Known Ground Truth Using Precision, Recall, F1-score, And ROC-AUC. The Framework Further Defines A Physical Sensor-hardware Layer (AD8232, MAX30100/MAX30102, MLX90614, And MPU6050 Interfaced To An ESP32 Microcontroller) As The Real-world Counterpart Of The Simulated WirelessHost Nodes, And Outlines Two Patterns — A Socket Bridge And An Embedded ONNX Runtime — For Optionally Closing The Loop With Real-time, In-simulation Detection. The Proposed Approach Offers A Reproducible, Extensible, And Ground-truth-evaluated Path From A Blank Simulation Workspace To A Deployable Anomaly Detector, Addressing Key Gaps In Existing WBAN And IoT Anomaly-detection Literature. Read more Read less

Authors: MOHAMED ABUBAKKAR N | DR.P.SIVASANKAR | DR.G.KULANTHAIVEL

Vol 12, Issue 9 (September 2026) Computer Science And Engineering

AN EXPLAINABLE GENERATIVE AI-DRIVEN MULTIMODAL DEEP LEARNING FRAMEWORK FOR KIDNEY TUMOR LOCALIZATION AND CLASSIFICATION USING CT AND MRI

Kidney Tumors Are A Major Clinical Concern In Which Early Detection, Accurate Localization, And Reliable Classification Are Essential For Effective Treatment Planning And Improved Patient Outcomes. Computed Tomography (CT) And Magnetic Resonance Imag... Kidney Tumors Are A Major Clinical Concern In Which Early Detection, Accurate Localization, And Reliable Classification Are Essential For Effective Treatment Planning And Improved Patient Outcomes. Computed Tomography (CT) And Magnetic Resonance Imaging (MRI) Provide Complementary Anatomical And Tissue Information; However, Their Interpretation Is Time-consuming And Highly Dependent On Expert Radiologists, Which May Lead To Inter-observer Variability And Diagnostic Errors. To Address These Challenges, This Research Proposes An Explainable Generative-AI-driven Multimodal Deep Learning Framework For Automated Kidney Tumor Analysis. The Proposed System Integrates CT And MRI Images Through Preprocessing And Multimodal Registration, Followed By Generative Adversarial Network (GAN)-based Synthetic Data Augmentation To Overcome Limited Medical Imaging Data And Improve Model Generalization. An Attention-based Segmentation Network Is Employed To Localize Kidney Tumor Regions, While A Hybrid CNN–Transformer Architecture Extracts Complementary Local And Global Representations From The Multimodal Images. CNNs Capture Fine-grained Spatial And Textural Characteristics, Whereas Transformer-based Learning Models Long-range Contextual Dependencies. A Cross-Modal Attention Mechanism Is Further Proposed To Learn Interactions Between CT And MRI Representations, Followed By An Adaptive Multimodal Attention Feature Fusion (AM-AFF) Mechanism That Dynamically Weights And Integrates The Learned Features For Robust Tumor Classification. To Enhance Clinical Transparency, Explainable Artificial Intelligence (XAI) Techniques Are Incorporated To Visualize The Regions Contributing To The Model's Predictions. The Proposed Framework Is Designed To Provide Automated Tumor Localization And Classification While Improving Robustness, Multimodal Feature Representation, Interpretability, And Diagnostic Consistency. The Resulting System Aims To Serve As An Intelligent Clinical Decision-support Framework For Assisting Healthcare Professionals In Accurate And Efficient Kidney Tumor Assessment. Read more Read less

Authors: VANITHA J | KEERTHIKA V

Vol 12, Issue 9 (September 2026) MECHANICAL ENGINEERING MECHATRONICS

SOLAR PV PERFORMACE MAPPING UNSER URBAN ENVIRONMENTAL CONDITIONS

Solar Photovoltaic (PV) Systems Deployed In Indian Urban Environments Are Significantly Affected By Environmental Factors Such As High Temperature, Humidity, Dust Accumulation, And Air Pollution, Leading To Gradual Performance Degradation. This Proje... Solar Photovoltaic (PV) Systems Deployed In Indian Urban Environments Are Significantly Affected By Environmental Factors Such As High Temperature, Humidity, Dust Accumulation, And Air Pollution, Leading To Gradual Performance Degradation. This Project Presents A Solar PV Performance Degradation Mapping System Designed To Analyze And Monitor The Impact Of Urban Environmental Conditions On Solar Panel Efficiency. The System Is Built Using An Arduino Microcontroller Interfaced With A 9V Solar Panel To Observe Real-time Power Generation Behaviour. Environmental Parameters Including Temperature And Humidity (DHT11 Sensor), Light Intensity (LDR Sensor), Air Quality (MQ135 Sensor), Soil Moisture, And PH Levels Are Continuously Monitored To Evaluate Surrounding Environmental Stress Factors Contributing To Panel Degradation. An LCD Display Provides Live Sensor Readings And System Status, While LED Indicators Signal Normal Operation And Degradation Alerts. The Collected Data Enables Mapping Of Performance Variation Trends Under Typical Indian Urban Conditions Such As Pollution Exposure, Heat Stress, And Moisture Variation. The Prototype Offers A Cost-effective And Practical Solution For Studying PV Degradation Patterns, Supporting Preventive Maintenance Strategies, Improving Energy Yield, And Promoting Sustainable Solar Energy Implementation In Urban Regions. Read more Read less

Authors: KILLI MANOJ KUMAR | Dr. M. Senthil Kumar

Vol 12, Issue 9 (September 2026) Library And Information Science

Research Data Management In Higher Education: The Role Of Research Support Services

The Fast Growth Of Digital Research Has Made Research Data Management (RDM) Important In Higher Education Institutions. As More Data Is Created Through Research, Managing This Data Has Become A Major Concern. In The Research Process, Things Like Data... The Fast Growth Of Digital Research Has Made Research Data Management (RDM) Important In Higher Education Institutions. As More Data Is Created Through Research, Managing This Data Has Become A Major Concern. In The Research Process, Things Like Data Quality, Data Access And Long-term Reuse All Depend On Good Data Management. Because Of This, There Is A Growing Need For Strong Research Support Services. This Review Article Looks At The Role Of Research Support Services In Managing Research Data In Higher Education Institutions.The Study Reviews The Concept And The Importance Of Research Data Management (RDM), The Essential Supports And Services That Researchers Need To Have At Their Disposal, And The Changing Nature Of The Academic Library's Role In Supporting Effective Data Stewardship. The Review Identifies The Major Functions Of Research Data Management (RDM) Support Such As Data Management Planning, Data Storage And Preservation, Metadata Activities, Repository Services, Data Sharing, And Researcher Training. It Also Explores New Trends In Research Data Management Including Open Science, FAIR (Findable, Accessible, Interoperable And Reusable) Data Principles, Cloud-based Technologies, Artificial Intelligence And Research Analytics. The Results Show That Research Support Services Are Crucial To Improve Data Quality And For Long-term Preservation And Re-use Of Research Products. There Are Certain Issues Like Infrastructure, Funding, Staff, Awareness And Data Governance That Remain To Be Addressed Within The Institutions. The Study Suggests That Institutional Policies Need To Be Further Strengthened, Capital Investment In Professional Development Is Necessary And The Use Of New Technologies Must Be Strengthened To Progress Research Data Management (RDM) Knowledge And Practices. Read more Read less

Authors: Dip Ranjan | Dr. Ashutosh Agrahari

Vol 12, Issue 9 (September 2026) Library And Information Science

Digital Resources, Digital Services, And Information Literacy In Academic Libraries: Emerging Trends, Opportunities And Challenges

Academic Librarianship Is Constantly Changing Because Of The Swift Flip Of Digital Technologies, Which Are Affecting The Development And Use Of Information Resources And Services. The Rapid Development Of Digital Resources Such As E-books, Electronic... Academic Librarianship Is Constantly Changing Because Of The Swift Flip Of Digital Technologies, Which Are Affecting The Development And Use Of Information Resources And Services. The Rapid Development Of Digital Resources Such As E-books, Electronic Journals, Online Databases And Institutional Repositories Have Increased Access To Scholarly Information And Improved The Opportunities For Teaching, Learning And Research. Meanwhile, Networked Services Like Virtual Reference, Distant Access Systems, Research Assistance And Online Information Literacy Programmes Have Enhanced The Ability Of Academic Libraries To Adequately Serve The Changing Needs Of Users Of A Digital Environment. In This Sense, Information Literacy Becomes One Of The Most Important Skills Which People Should Learn To Find, Use And Share Information In An Effective And Ethical Way. The Purpose Of This Article Is To Discuss The Relationship Among Information Literacy, Digital Resources And Digital Services In Academic Libraries. The Study Discusses The Role Of Digital Resources To Support Academic Activities, Research Productivity And The Importance Of Information Literacy For Effective Approach To Use Information. The Review Also Covers New Developments Like Artificial Intelligence, Digital Scholarship, And Customized Library Services And Problems Like Information Overload, The Digital Divide, Cyber Security, And Limited Resources. From The Results It Is Seen That Digital Resources, Digital Services And Digital Information Literacy Are Well Connected And Complement Each Other To Deliver Better Learning And Provide Research Productivity. The Authors Come To A Conclusion That Investments In Technological Innovations And Service And Information Literacy Programs Based On Users Are Needed Continuously To Ensure Optimal Effectiveness Of Academic Libraries In A Context Of The Knowledge Society Read more Read less

Authors: Dip Ranjan | Dr. Ashutosh Agrahari

Vol 12, Issue 9 (September 2026) Mechanical Engineering

Energy And Exergy Performance Evaluation Of A Vapour Compression Refrigeration System Using Conventional (R134a) And TiO₂ Nano-Refrigerant

The Increasing Demand For Energy-efficient And Environmentally Sustainable Refrigeration Systems Has Led To The Exploration Of Advanced Working Fluids. This Study Presents The Energy And Exergy Analysis Of A Vapour Compression Refrigeration System (V... The Increasing Demand For Energy-efficient And Environmentally Sustainable Refrigeration Systems Has Led To The Exploration Of Advanced Working Fluids. This Study Presents The Energy And Exergy Analysis Of A Vapour Compression Refrigeration System (VCRS) Operating With A Conventional Refrigerant (R134a) And A Nano-refrigerant Formed By Dispersing 0.1% Titanium Dioxide (TiO₂) Nanoparticles Into The Compressor Lubricant. Energy Analysis, Based On The First Law Of Thermodynamics, Was Used To Determine The Refrigeration Effect, Compressor Work And Coefficient Of Performance (COP), While Exergy Analysis, Based On The Second Law, Was Used To Quantify Irreversibilities And Exergy Destruction In The Compressor, Condenser, Expansion Valve And Evaporator. Experiments Were Carried Out On A Vapour-compression Test Rig At Constant Water Temperatures Of 16°C, 18°C And 20°C. The Results Show That The Addition Of TiO₂ Nanoparticles Increases The Refrigeration Effect By 1.24–2.86% And Reduces Compressor Work By 5.78–8.71% Relative To Conventional R134a, Yielding An Improvement In Theoretical COP Of 9.14–10.45%, With The Maximum Improvement Of 10.45% Obtained At 18°C. Exergy Destruction Was Reduced In Every Major Component, With The Largest Reductions Observed In The Compressor (6.60–12.24%) And The Expansion Valve (7.14–15.79%), And Smaller Reductions In The Condenser (4.17–5.47%) And Evaporator (6.40–7.41%). The Study Concludes That A 0.1% TiO₂ Nano-refrigerant Is A Favourable And Thermodynamically Superior Alternative To Conventional R134a For Vapour Compression Refrigeration Systems. Read more Read less

Authors: Venkata Kishore Babu Polisetty | Dr.s.somasundaram

Vol 12, Issue 9 (September 2026) Electrical And Electronics Engineering

Simulation-based Comparative Analysis Of Positive Output Luo Converter Topologies For High-Gain DC–DC Applications

High-gain DC–DC Converters Play A Significant Role In Renewable Energy Systems, Electric Vehicles, Battery Charging Systems, Fuel Cells, And Other Modern Power Electronic Applications Where Efficient Voltage Conversion Is Essential. Conventional Boos... High-gain DC–DC Converters Play A Significant Role In Renewable Energy Systems, Electric Vehicles, Battery Charging Systems, Fuel Cells, And Other Modern Power Electronic Applications Where Efficient Voltage Conversion Is Essential. Conventional Boost Converters Often Experience Limitations Such As High Duty Cycle Operation, Increased Switching Losses, Poor Voltage Gain, And Reduced Efficiency Under High Conversion Ratios. To Overcome These Challenges, The Luo Converter Family, Based On The Voltage Lift (VL) Technique, Has Emerged As An Effective Solution For Achieving Higher Voltage Gain With Improved Performance. Over The Years, Several Positive Output Luo Converter Topologies, Including The Elementary, Self-Lift, Re-Lift, Super-Lift, And Multiple-Lift Converters, Have Been Developed To Enhance Voltage Conversion Capability While Maintaining Continuous Input Current And Reduced Output Ripple. This Paper Presents A Comprehensive Comparative Analysis Of Positive Output Luo Converter Topologies By Reviewing Their Operating Principles, Voltage Gain Characteristics, Structural Complexity, Component Requirements, Reported Performance, And Application Suitability. Furthermore, Recent Developments In Converter Modifications, Advanced Control Strategies, And Renewable Energy Applications Are Discussed. A Comparative Assessment Is Provided To Highlight The Advantages, Limitations, And Appropriate Application Domains Of Each Topology. Finally, Current Research Challenges And Future Research Opportunities Are Identified To Support The Development Of Next-generation High-gain DC–DC Converters For Renewable Energy And Electric Vehicle Applications. Read more Read less

Authors: INTI KRISHNATULASI | Dr. G.A. RATHY

Vol 12, Issue 9 (September 2026) Biochemistry

Green Synthesis And Medicinal Applications Of Bioactive Heterocyclic Compounds: A Study

Heterocyclic Compounds Are An Essential Class Of Molecules In Medicinal Chemistry Because Their Structural Diversity And Heteroatom Content Enable Specific Interactions With A Wide Range Of Biological Targets [1]. However, Classical Synthetic Routes... Heterocyclic Compounds Are An Essential Class Of Molecules In Medicinal Chemistry Because Their Structural Diversity And Heteroatom Content Enable Specific Interactions With A Wide Range Of Biological Targets [1]. However, Classical Synthetic Routes Frequently Involve Hazardous Reagents, Toxic Organic Solvents, High Energy Consumption, Multistep Operations, And Excessive Waste Generation. Green Chemistry Offers A Vital Framework To Refine Synthetic Efficiency While Minimising The Environmental Footprint Of Heterocycle Production. This Review Critically Evaluates Sustainable Methodologies For Constructing Bioactive Heterocyclic Cores, Focusing On Microwave-assisted Synthesis, Ultrasound-driven Routes, And Multicomponent Reactions (MCRs), Solvent-free/aqueous Media, And Eco-friendly Catalytic Systems. Furthermore, The Therapeutic Relevance Of These Frameworks Across Oncology, Infectious Diseases, Neurodegenerative Conditions, And Metabolic Disorders Is Detailed [1]. Despite Notable Progress, Translating Green-synthesized Leads Into Clinical Candidates Remains Bottlenecked By Aqueous Solubility Issues, Poor Metabolic Stability, Incomplete Pharmacokinetic Profiles, And Limited In Vivo Validation [1]. Future Research Must Harmoniously Integrate Green Synthetic Protocols With Systematic In Vivo Testing, Advanced Drug-delivery Platforms, And Computational/AI-driven Modelling To Accelerate The Discovery Of Sustainable Therapeutics Read more Read less

Authors: Vinod Prakash Shinde | Dr Kanchan Sonune | Mr Roshan Kumar Sah

Vol 12, Issue 9 (September 2026) Power Electronics And Drives

Adaptive Tabu Search-Optimized Speed Control Of A BLDC Motor Drive For Electric Vehicle Applications

High-efficiency Propulsion Drives Are Essential For Advancing Modern Electric Vehicle (EV) Powertrains. This Paper Presents A Speed-control Architecture For Brushless DC (BLDC) Motors Using An Adaptive Tabu Search (ATS) Algorithm To Optimize Proporti... High-efficiency Propulsion Drives Are Essential For Advancing Modern Electric Vehicle (EV) Powertrains. This Paper Presents A Speed-control Architecture For Brushless DC (BLDC) Motors Using An Adaptive Tabu Search (ATS) Algorithm To Optimize Proportional-integral-derivative (PID) Controller Parameters. Standard Empirical PID Tuning Methods Often Fail To Deliver Adequate Robustness Against The Nonlinear Dynamics, Abrupt Road-load Changes, And Fluctuating Operating Conditions Inherent To EV Drivetrains. To Address This, The ATS Algorithm Systematically Searches The Multi-dimensional Parameter Space (Kp, Ki, Kd) While Explicitly Enforcing Physical Control Voltage Limits To Prevent Actuator Saturation And Digital Over-modulation. MATLAB/Simulink Simulations Under Severe Dynamic Load Profiles Demonstrate That The Proposed ATS-optimized Controller Effectively Eliminates Steady-state Tracking Error, Accelerates Settling Time, And Mitigates Startup Overshoot Compared To Conventional Tuning Techniques. Read more Read less

Authors: P JAYANTHI | Dr. R. Suja Mani Malar

Vol 12, Issue 9 (September 2026) Basic Science

APPLICATIONS OF SMART TECHNOLOGIES IN HEALTHCARE: THE ROLE OF AI AND MACHINE LEARNING

This Paper Examines The Role Of AI And ML In Enhancing Patient Care Through Faster Decision-making, Personalized Treatments, And Proactive Healthcare Delivery. Key Applications Include Predictive Analytics, Medical Imaging Diagnostics, Natural Langua... This Paper Examines The Role Of AI And ML In Enhancing Patient Care Through Faster Decision-making, Personalized Treatments, And Proactive Healthcare Delivery. Key Applications Include Predictive Analytics, Medical Imaging Diagnostics, Natural Language Processing, Clinical Decision Support Systems, Robotic Surgery, Virtual Health Assistants And Remote Patient Monitoring. By Analysing Current Advancements And Future Trends, The Study Highlights How AI-driven Technologies Can Improve Accessibility, Reduce Costs, And Enhance Patient Outcomes, While Emphasizing The Need To Address Technical, Ethical, And Operational Challenges To Achieve Intelligent, Patient-centred Healthcare. Read more Read less

Authors: Deepshikha Hudda | Aftab Alam

Vol 12, Issue 9 (September 2026) ELECTRICAL ENGINEERING

Power Quality Improvement In Distribution Systems Using A Unified Power Quality Conditioner Based On A PSO-Tuned Fractional Order PI Controller

The Increasing Penetration Of Nonlinear Loads, Power-electronic Converters And Intermittent Renewable Energy Sources Has Intensified Power Quality (PQ) Disturbances Such As Voltage Sag, Swell And Current Harmonics In Modern Distribution Networks. Thi... The Increasing Penetration Of Nonlinear Loads, Power-electronic Converters And Intermittent Renewable Energy Sources Has Intensified Power Quality (PQ) Disturbances Such As Voltage Sag, Swell And Current Harmonics In Modern Distribution Networks. This Paper Presents The Design And MATLAB/Simulink-based Evaluation Of A Unified Power Quality Conditioner (UPQC) For Simultaneous Voltage- And Current-side Compensation, In Which The DC-link Voltage Regulation Loop Is Controlled By A Fractional Order Proportional-Integral (FOPI) Controller Whose Parameters (Kp, Ki, λ) Are Optimally Tuned Using Particle Swarm Optimization (PSO) With An Integral Of Time-weighted Absolute Error (ITAE) Fitness Function. Shunt Reference Currents Are Generated Using The Instantaneous P–q Theory In The Clarke–Park Reference Frame, While Series Reference Voltage Is Generated Using Unit-vector Templates; Both Inverters Are Switched Using Hysteresis Current Control. The Proposed Scheme Is Benchmarked Against An Uncompensated System, A UPQC With A Classical Ziegler–Nichols-tuned PI Controller, A UPQC With A PSO-tuned PI Controller, And A UPQC With An Empirically Tuned FOPI Controller, Under A Common Disturbance Profile Comprising A 0.7 P.u. Voltage Sag, A 1.3 P.u. Voltage Swell And Superimposed 5th/7th Order Harmonics Feeding A Three-phase Diode-bridge Rectifier Load. Simulation Results Show That The Source-current Total Harmonic Distortion (THD) Of 22.24% In The Uncompensated Case Is Reduced To 3.54% By The Proposed PSO-tuned FOPI-based UPQC, A Reduction Of 84.1%, Comfortably Satisfying The IEEE-519 Limit Of 5%, While Also Providing The Fastest DC-link Settling Time, The Lowest Overshoot, And The Most Effective Sag/swell Mitigation Among The Compared Strategies. Read more Read less

Authors: ASHOK KUMAR M | Dr. G. A. Rathy

Vol 12, Issue 9 (September 2026) ELECTRONICS AND COMMUNICATION ENGINEERING

DESIGN AND IMPLEMENTATION OF AN FPGA BASED SMART TRAFFIC LIGHT CONTROL SYSTEM USING CAMERA BASED VEHICLE DETECTION

Traffic Congestion Is A Major Challenge In Rapidly Growing Urban Areas, While Conventional Fixed-time Traffic Signals Cannot Respond Effectively To Changing Traffic Density. This Paper Presents A Camera-based Smart Traffic Light Control System That C... Traffic Congestion Is A Major Challenge In Rapidly Growing Urban Areas, While Conventional Fixed-time Traffic Signals Cannot Respond Effectively To Changing Traffic Density. This Paper Presents A Camera-based Smart Traffic Light Control System That Combines Vehicle Detection With Field-programmable Gate Array (FPGA) Based Real-time Signal Control. A Camera Is Used To Acquire Traffic Images Or Video, And An Image-processing Stage Determines The Approximate Vehicle Density In Each Traffic Direction. The Extracted Traffic Information Is Supplied To An FPGA Controller, Where A Finite-state Machine (FSM) Generates Adaptive Red, Yellow And Green Signal Sequences. The Proposed Architecture Is Intended To Reduce Unnecessary Waiting Time By Allocating Comparatively Longer Green Intervals To Approaches Having Higher Traffic Density. The FPGA Implementation Is Targeted Toward A Compact And Low-cost Platform Such As The Sipeed Tang Nano 9K. The Paper Describes The System Architecture, Image-processing Flow, FPGA Control Logic, Hardware Interfacing And An Experimental Evaluation Framework. The Final Measured Accuracy, Latency And Signal-timing Results Are To Be Inserted After Completion Of The Hardware Experiments Read more Read less

Authors: SURYA R | Dr. SIVASANKAR P | Dr.KULANTHAIVEL G

Vol 12, Issue 9 (September 2026) Electrical And Electronics Engineering

Discrete Sliding-Mode Velocity Estimation And Filter-Independent PID Tuning For Gimbal BLDC Drives

Velocity Loops On Low-cost Brushless Drives Are Al-most Always Closed On A Filtered Finite Difference Of A Magnetic Encoder Angle, And Are Almost Always Tuned By Watching That Same Filtered Signal. This Paper Reports A Hardware Study In Which The Pra... Velocity Loops On Low-cost Brushless Drives Are Al-most Always Closed On A Filtered Finite Difference Of A Magnetic Encoder Angle, And Are Almost Always Tuned By Watching That Same Filtered Signal. This Paper Reports A Hardware Study In Which The Practice Is Examined From Both Ends. Four Velocity Estimators — An Unfiltered Finite Difference, A First-order Low-pass Filter, A Two-state Extended Kalman Filter And A Two-state Sliding-mode Observer — Are Evaluated On One 7 Pole-pair Gim-bal Motor Driven By Field-oriented Control On An STM32G431 Microcontroller, All Four Consuming The Same Encoder Sample At The Same Instant. A Discrete Limit Cycle In The Sliding-mode Observer Is First Diagnosed And Removed: The Switching Action Had Been Applied As A Per-sample Increment Rather Than As A Rate Integrated Over The Sample Interval, Which Placed The In-layer Loop Gain Above The Discrete Stability Bound And Produced A 244 RPM Oscillation With The Rotor Held Stationary. With The Update Law Corrected, The Observer Becomes The Least Noisy Of The Four Estimators At Every Speed Tested, With 2.3–5.2 Less Noise Than The Raw Difference And 1.4–1.9 Less Thanthe Kalman Filter. The Corrected Estimator Is Then Used As An Independent Yardstick For A Replicated Velocity-PID Study Of 9 Gain Configurations Over 27 Runs. Scored On The Controller’s Own Feedback Signal, Raising The Feedback Filter Constant From 10 Ms To 50 Ms Appears To Cut Speed Ripple By 14 ; Scored On The Shaft It Cuts It By 1.8 . At A Proportional Gain Of 1.0 The Feedback Signal Reports A Standard Deviation Of 63 RPM While The Shaft Is Steady At 4.8 RPM And Simply Sits 26 RPM Below Setpoint. We Conclude That Such Drives Must Be Tuned Against An Estimate That Does Not Share The Control Filter, And Give A Procedure That Does So. Read more Read less

Authors: Mannar Mannan V | R. Suja Mani Malar

Vol 12, Issue 9 (September 2026) ELECTRICAL AND ELECTRONICS ENGINEERING

Efficiency Optimization Of Interleaved Boost Converters Via Dynamic Phase Management And PI Control

This Paper Presents The Design, Hardware Realization, And Experimental Validation Of A Two-phase Interleaved Boost Converter (IBC) With Dynamic (adaptive) Phase Management For Low-voltage Photovoltaic (PV) Energy Harvesting. The Converter Interfaces... This Paper Presents The Design, Hardware Realization, And Experimental Validation Of A Two-phase Interleaved Boost Converter (IBC) With Dynamic (adaptive) Phase Management For Low-voltage Photovoltaic (PV) Energy Harvesting. The Converter Interfaces An 18 V PV Source With A Regulated 48 V DC Bus And Tracks The Array’s Maximum Power Point Using A Perturb And Observe (P&O) Algorithm. A Classical Proportional-Integral (PI) Controller Closes The Output-voltage Loop, While An Adap-tive Phase Shedding (APS) Scheme Dynamically Disables The Sec-ond Phase At Reduced Input Voltage To Minimize Switching Losses Under Light-load Conditions. A 20 W Hardware Prototype Was Constructed Using An ESP32 Microcontroller, A TC427 Dual Gate Driver, INA240 Current-sense Amplifiers, And Precision Voltage-divider Feedback Networks. Experimental Results Confirm That The PI-controlled Prototype Regulates The 48 V Bus With A Peak-to-peak Output Ripple Of 2.4 V (5.34% Of Output) Under Nominal Two-phase Operation At 18 V Input, And Successfully Transitions To Single-phase Operation With A Peak-to-peak Ripple Of 9.6 V (25.49%) When The Input Drops To 12 V And The Second Phase Is Shed. The Results Demonstrate That A Two-phase Interleaved Topology, Combined With Dynamic Phase Management And A Well-tuned PI Loop, Provides An Efficient, Low-complexity And Practically Realizable Solution For Regulated Step-up Conversion In Distributed Solar Energy Harvesting Systems. Read more Read less

Authors: SHAJINA N | Dr. G. A. Rathy

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

Real-Time Speech Emotion Recognition Using Lightweight Convolutional Neural Network

Speech Emotion Recognition (SER) Allows Machines To Understand Human Emotional States From Their Speech, Enabling Applications In Human-computer Interaction, Call Center Analytics, And Mental Health Monitoring. Traditional Deep CNNs Achieve High Accu... Speech Emotion Recognition (SER) Allows Machines To Understand Human Emotional States From Their Speech, Enabling Applications In Human-computer Interaction, Call Center Analytics, And Mental Health Monitoring. Traditional Deep CNNs Achieve High Accuracy; However, They Are Costly When It Comes To Computation, Which Means They Cannot Be Used In Real Time On Edge Devices With Limited Computational Power. This Paper Proposes A Lightweight CNN-based Architecture For Real-time SER Using Log-Mel Spectrograms As Input Representations. The Proposed Model Incorporates Conv2D Layers, Batch Normalization, Max Pooling, And Global Average Pooling To Learn Discriminative Time-frequency Features While Reducing Model Complexity And Inference Overhead. Unlike The Existing Artificial Neural Network (ANN) Baseline, Which Was Trained Using Only The RAVDESS Dataset, The Proposed CNN Is Trained Using A Combined RAVDESS And TESS Dataset Covering Eight Emotion Classes: Neutral, Calm, Happy, Sad, Angry, Fearful, Disgust, And Surprised. Experimental Evaluation Shows That TheproposedLightweightCNNattained75.70%testaccuracywithaweightedF1-scoreof0.78, Compared To The ANN Baseline Model’s 54.86% Accuracyand0.55F1-score. Additionally, Compared To The ANN Baseline, The Proposed Lightweight CNN Architecture Demonstrated Faster Real-time Performance Using Microphone Input While Providing Confidences Cores For Each Emotion Detected. Overall, The Proposed Lightweight CNN Model Provides Faster Inference With Good Accuracy For SER Applications. Read more Read less

Authors: Natchathiram K | Prof. Dr. G. Kulanthaivel | Prof. Dr. P. Sivasankar

Vol 12, Issue 9 (September 2026) Electrical And Electronics Engineering

Dual-Loop PI–Hysteresis Control Of A Single-Phase Bridgeless AFE Rectifier For Power-Factor-Corrected CC–CV Battery Charging

Conventional Diode- And Thyristor-based Rectifiers Draw Non-sinusoidal Current, Producing Poor Power Factor And Harmonic Distortion Exceeding IEEE-519 Limits. This Paper Presents The Design, Modelling, And Closed-loop Simulation Of A Single-phase Tot... Conventional Diode- And Thyristor-based Rectifiers Draw Non-sinusoidal Current, Producing Poor Power Factor And Harmonic Distortion Exceeding IEEE-519 Limits. This Paper Presents The Design, Modelling, And Closed-loop Simulation Of A Single-phase Totem-pole Bridgeless Active Front-End (AFE) AC–DC Converter That Performs Power-factor Correction While Charging A Battery Through A Constant Current–Constant Voltage (CC–CV) Profile, Automatically Selected From Battery State Of Charge (SOC). A Proportional–Integral (PI) Outer Loop Regulates Current Below 80% SOC And Voltage Above It, While An Inner Hysteresis Current Controller Shapes The Source Current To A Synchronised Sinusoidal Reference. Simulated In MATLAB/Simulink For A 100 W, Single-phase Charger For 22.2 V, 5 Ah Lithium-ion Battery, The Converter Achieves A Power Factor Above 0.9 And A Source-current THD Below 5%, Confirming An Effective, Low-complexity Means Of Combining Power-factor Correction With Regulated CC–CV Battery Charging. Read more Read less

Authors: Jeyakrishnan V | Dr.G.A.Rathy

Vol 12, Issue 9 (September 2026) Engineering

Experimental Investigation On The Strength And Durability Properties Of M30 Concrete By Partially Replacing Cement With Eggshell Powder

This Study Investigates Egg Shell Powder (ESP) As A Partial Replacement For Ordinary Portland Cement In M30 Grade Concrete, With Emphasis On Fresh Behaviour, Mechanical Strength And Durability. Concrete Mixes Were Prepared With 0%, 5%, 10%, 15%, 20%,... This Study Investigates Egg Shell Powder (ESP) As A Partial Replacement For Ordinary Portland Cement In M30 Grade Concrete, With Emphasis On Fresh Behaviour, Mechanical Strength And Durability. Concrete Mixes Were Prepared With 0%, 5%, 10%, 15%, 20%, 25% And 30% ESP Replacement By Mass Of Cement. The Control Mix Was Proportioned In Accordance With IS 10262:2019 Using OPC 53 Grade Cement, A Water-cement Ratio Of 0.42, 158 Kg/m³ Of Water, 648.9 Kg/m³ Of Fine Aggregate And 1275.6 Kg/m³ Of Coarse Aggregate. Workability Was Evaluated By The Slump Test; Compressive And Flexural Strengths Were Measured At 3, 7 And 28 Days; And 28-day Durability Was Assessed By Water Absorption And Rapid Chloride Permeability Test (RCPT). The 5% ESP Mixture Showed The Best Overall Performance. Its 28-day Compressive Strength Was 36.40 N/mm², Approximately 12% Above The Control Value Of 32.50 N/mm², While Its Flexural Strength Reached 5.10 N/mm², A 13.3% Increase Over The Control. Water Absorption Decreased From 2.85% To 2.45%, And RCPT Charge Decreased From 3850 To 3420 Coulombs. At 10% ESP, Strength Remained Above The Control, Whereas Replacement Beyond 10% Caused Progressive Losses In Strength And Durability. The Results Identify 5% ESP As The Optimum Replacement Level For The Investigated M30 Concrete And Demonstrate The Potential Of Controlled Eggshell-waste Utilization In Sustainable Concrete Production. Read more Read less

Authors: Akshay Laxman Kamthe | Prof. Abhijeet Undre | Dr. Atul Pujari

Vol 12, Issue 9 (September 2026) POWER ELECTRONICS AND DRIVES

MPPT Solar Street Light With IoT-Enabled Vision-Assisted Adaptive Luminous Control

Conventional Solar Street Lighting Systems Generally Operate With Fixed Illumination Levels And Non-optimal Power Extraction, Resulting In Inefficient Utilization Of Harvested Solar Energy. This Paper Presents The Modelling, Simulation And Analysis O... Conventional Solar Street Lighting Systems Generally Operate With Fixed Illumination Levels And Non-optimal Power Extraction, Resulting In Inefficient Utilization Of Harvested Solar Energy. This Paper Presents The Modelling, Simulation And Analysis Of A Maximum Power Point Tracking (MPPT) Based Solar Street Lighting System Integrated With An Internet Of Things (IoT)-enabled, Vision-assisted Adaptive Luminous Control Strategy. A Perturb And Observe (P&O) MPPT Algorithm, Implemented On A DsPIC30F2010 Digital Signal Controller, Regulates The Duty Cycle Of A SEPIC DC–DC Converter To Extract Maximum Power From A 50 W Monocrystalline Photovoltaic (PV) Module During The Day. At Night, The Same Converter Is Reconfigured Through A Relay-based Changeover To Drive The LED Load With A Duty Cycle Jointly Determined By The Day's Harvested Energy, Expressed As An Energy Sufficiency Ratio, And Real-time Road Activity Sensed By An ESP32-CAM Vision Module, Ensuring That Continuous 12-hour Illumination Is Guaranteed Irrespective Of Solar Variability While Never Compromising Visibility For Detected Pedestrians Or Vehicles. The Complete System, Including The Single-diode PV Model, The Fourth-order SEPIC Converter, The P&O Control Law And The Two-layer Adaptive-brightness Controller, Is Modelled And Simulated In MATLAB/Simulink. Simulation Results Show Stable Maximum-power-point Operation With An MPPT Tracking Efficiency Of 99.83%, A Cold-start Convergence Time Of Approximately 50 Ms, An Estimated Converter Efficiency Of Approximately 90%, And An Average Night-time Energy Saving Of 58–61% Relative To Fixed Full-brightness Operation. A Day-level Energy Balance Under A 5 Peak-sun-hour Irradiance Profile And A 12 V/18 Ah Battery Confirms A Positive Surplus Of +49.1 Wh Under Full-brightness, Full-night (12 H) Operation And +121.1 Wh Under Adaptive Dimming, Providing Resilience Against Consecutive Overcast Days. The Results Confirm That The Proposed Architecture Provides An Efficient, Low-cost, Scalable And Intelligent Solution For Adaptive Solar Street Lighting Suitable For Rural And Semi-urban Deployment, And Establish A Validated Simulation Baseline For Subsequent Hardware Realization. Read more Read less

Authors: VIJAY A J | G. A. Rathy

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

IoT Based Smart Public Transport Framework For Location Tracking And Seat Availability Monitoring

Public Transportation Systems Face Significant Challenges In Providing Real-time Information About Bus Location And Seat Availability To Passengers, Resulting In Long Waiting Times, Overcrowding, And Inefficient Utilization Of Transport Resources. Th... Public Transportation Systems Face Significant Challenges In Providing Real-time Information About Bus Location And Seat Availability To Passengers, Resulting In Long Waiting Times, Overcrowding, And Inefficient Utilization Of Transport Resources. This Paper Presents An IoT-Based Smart Public Transport Framework That Integrates Embedded Systems, Wireless Communication, GPS Technology, And Cloud-based Monitoring To Address These Challenges. The Proposed System Consists Of Two Main Units: The Bus Unit And The Bus Stop Unit. The Bus Unit Employs An ESP32 Microcontroller As The Central Controller, Utilizing Ultrasonic Sensors For Passenger Counting At Entry And Exit Points, Infrared (IR) Sensors For Seat Occupancy Detection, And Servo Motors For Automatic Door Control. A 433 MHz RF Communication System With HT12E/HT12D Encoder-decoder Modules Enables Wireless Transmission Of Passenger And Seat Information From The Bus Unit To The Bus Stop Unit. The System Incorporates GPS-based Location Tracking And Wi-Fi Connectivity For Transmitting Real-time Data To An IoT Cloud Platform, Enabling Remote Monitoring Through A Web-based Dashboard. The Design Was Implemented Using The ESP32 Development Board With Sensors And RF Modules. Experimental Results Demonstrate Accurate Passenger Counting, Reliable Seat Occupancy Detection, Stable RF Communication, And Effective IoT-based Remote Monitoring. Total System Power Consumption Was Measured At Different Sparsity Conditions Representing Passenger Occupancy Levels. The Proposed Framework Provides A Cost-effective, Scalable Solution For Improving Passenger Convenience, Reducing Waiting Uncertainty, And Supporting Smart City Transportation Initiatives. Read more Read less

Authors: Kalaivanan K | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) ELECTRONICS ENGINEERING

AI-Driven Edge-Based Smart Parking System With NFC Authentication For Parking Slot Recommendation And Access Time Prediction

Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Fore... Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Forest And Neural Network Models For Parking Slot Recommendation And Parking Access-time Prediction, In Which The Neural Network Model Was Selected For Its Higher Accuracy, Consistency, And Smaller Model Size. The Selected Neural Network Models Were Independently Trained As Two TinyML Regression Models In Edge Impulse (test_slot And Test_time), Deployed In Float32 Form, And Merged Into A Single Inference Library (test7time_inferencing_v2.h) Running On An ESP32-WROOM-DA. The Implemented System Performs NFC-based User Authentication Using A PN532 Reader/writer, IR-sensor-based Parking-slot Status Detection Across A 3-lane, 9-slot Layout, Lane-wise Congestion Calculation, On-device Slot Recommendation Based On The Lowest Suitability Score, User Confirmation Through A 2.8-inch SPI TFT Touch Display, On-device Access-time Prediction, Servo-actuated Barrier Control, And RTC DS1307 With Logger.py-based Real-hardware Data Logging. Both TinyML Models Achieved Strong Test-set Performance, With 97.26% Accuracy For Slot Recommendation And 98.68% For Access-time Prediction, While Maintaining Approximately 3 Ms Inference Latency And RAM Usage Below 1.5 KB On The ESP32. Real-time Behavioural Testing Confirmed That The Deployed Models Respond Correctly To Changes In Slot Status, Congestion, And Distance, Dynamically Generating Slot Scores And Access-time Predictions, And Real-hardware Testing Confirmed Successful Integration Of NFC Authentication, IR-based Slot Sensing, TinyML Inference, User Interaction, Barrier Control, RTC Timestamping, And CSV-based Data Logging. The Results Demonstrate That The Selected Neural Network Models Can Be Deployed And Operated As A Real-time, Authenticated, Edge-based Smart Parking System Without Dependence On Cloud-based Processing. Read more Read less

Authors: NITHYA M | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) Information Technology

A Review On Machine Learning And Deep Learning Models For Forecasting Renewable Energy Production

The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumpti... The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumption, Are Finite And Contribute Significantly To Greenhouse Gas Emissions. Shifting To Renewable Energy Sources Such As Solar, Wind, Hydropower, And Biomass Offers A Sustainable Path Forward, Benefiting Both The Environment And Global Economies. Despite Its Benefits, The Migration To Renewable Energy Faces Challenges, Including The Need For Substantial Initial Investments, Technological Limitations, And Resistance From Entrenched Fossil Fuel Industries. Governments, Private Sectors, And International Organizations Must Work Together To Create Policies And Incentives That Encourage Renewable Energy Adoption. Public Awareness Campaigns, Subsidies, And Research Funding Are Critical In Overcoming These Barriers. To Develop A Sustainable Renewable Energy Infrastructure, We Need To Forecast The Amount Of Renewable Energy Production In Future And Meet Energy Demands In Future Through Renewable Sources. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Renewable Energy Along With Associated Challenges That The Sector Faces. This Review Would Enable Future Research In Forecasting The Patterns In A Particular Renewable Energy Source. Read more Read less

Authors: Rakesh Madhukar | Prof. Kamlendra Verma

Vol 12, Issue 9 (September 2026) Computer Science

A Comprehensive Review On Machine Learning And Deep Learning Models For Forecasting Stock Market Trends

Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Dee... Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Deep Learning Are Improving Predictive Models, Making Them An Essential Tool For Traders And Investors. Stock Market Prediction Extremely Challenging Due To The Dependence Of Stock Prices On Several Financial, Socio-economic And Political Parameters Etc. For Real Life Applications Utilizing Stock Market Data, It Is Necessary To Predict Stock Market Data With Low Errors And High Accuracy. This Needs Design Of Appropriate Artificial Intelligence (AI) And Machine Learning (ML) Based Techniques Which Can Analyze Large And Complex Data Sets Pertaining To Stock Markets And Forecast Future Prices And Trends In Stock Prices With Relatively High Accuracy. This Paper Presents A Comprehensive Review On The Various Techniques Used In Recent Contemporary Papers For Stock Market Forecasting. Read more Read less

Authors: Narendra Rai | Prof. Shailendra Kumar Shriwastava

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

Design And Physical Implementation Of An INT8 Output-Stationary Systolic Array CNN Accelerator For TinyML Inference Using Open-Source SKY130 PDK And OpenLane EDA Flow

This Paper Presents The Design And Complete Physical Implementation Of A 4×4 INT8 Output-stationary Systolic Array Accelerator For TinyML Convolutional Neural Network (CNN) Inference, Targeting Edge Vision Applications Such As Image Classification An... This Paper Presents The Design And Complete Physical Implementation Of A 4×4 INT8 Output-stationary Systolic Array Accelerator For TinyML Convolutional Neural Network (CNN) Inference, Targeting Edge Vision Applications Such As Image Classification And Object Detection On Resource-constrained Devices. The Proposed Architecture Addresses The Fundamental Energy Inefficiency Of General-purpose Processors Executing Fixed Computational Workloads. The Accelerator Was Designed In Verilog HDL Comprising Seven Modules Including MAC Units, Processing Elements, 4×4 Systolic Array Fabric, On-chip Input And Output Data Buffers, And A Finite State Machine Controller And Physically Implemented Using The Open-source OpenLane RTL-to-GDSII EDA Flow Targeting The SkyWater SKY130 130nm/180nm Hybrid Process Design Kit. Two Complete Implementation Runs Achieved Zero Design Rule Check (DRC) Violations. The Complete Buffered System Achieves A Post-route Critical Path Delay Of 3.07 Ns, Enabling Operation Up To 325 MHz, With 23.3 µW Total Power At The Typical Process Corner (TT, 1.8V, 25°C). Functional Correctness Was Verified Using A Sobel-X Edge Detection Conv2D Kernel With Outputs Confirmed Identical Between RTL Simulation And ESP32-WROOM-DA Hardware. Power Comparison Against ESP32 At 347.43 MW Demonstrates A 14,911× Total Power Reduction And 477,187× Better Energy Per Conv2D Operation. Read more Read less

Authors: JUSTUS A | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) Library And Information Science

User Satisfaction With Library Services In Management Institutions: A Study

User Satisfaction Is An Important Indicator For Evaluating The Effectiveness And Quality Of Library Services In Management Institutions. Academic Libraries Supporting Management Education Are Expected To Provide Relevant, Timely, Accessible, And Tech... User Satisfaction Is An Important Indicator For Evaluating The Effectiveness And Quality Of Library Services In Management Institutions. Academic Libraries Supporting Management Education Are Expected To Provide Relevant, Timely, Accessible, And Technology-enabled Information Resources To Meet The Changing Academic And Research Requirements Of Their Users. The Present Theoretical Study Examines The Concept Of User Satisfaction With Library Services In Management Institutions And Discusses The Major Factors That Influence Users' Perceptions And Experiences. The Study Focuses On The Relationship Between Library Resources, Service Quality, Accessibility, Staff Support, Technology, Infrastructure, And Overall User Satisfaction. The Paper Also Discusses Important Theoretical Perspectives Related To Service Quality And User Satisfaction And Highlights The Need For Management Institution Libraries To Continuously Evaluate And Improve Their Services. The Study Concludes That User Satisfaction Is Multidimensional And Depends Not Only On The Availability Of Information Resources But Also On The Quality, Accessibility, Responsiveness, And Effectiveness Of Library Services. Read more Read less

Authors: Rajshri Bansidhar Gadkari | Dr.Bhagyashri Yeshwantrao Keskar

Vol 12, Issue 9 (September 2026) Mechanical Engineering

Performance Study Of Diesel Engine Using Pongamia Biodiesel

Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxi... Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxic Fuel Derived From Non-edible Oil Sources, Has Emerged As One Of The Most Promising Substitutes For Conventional Diesel Fuel. In This Study, Pongamia Biodiesel Was Produced From Pongamia Oil Through The Transesterification Process. The Primary Objective Of This Research Is To Investigate The Performance Of A Single-cylinder Compression Ignition Diesel Engine Fuelled With Pongamia Biodiesel Blends, With Particular Emphasis On How The Supplied Heat Energy Is Utilized And The Associated Losses. Biodiesel Blends — B20, B40, B60, B80, And B100 (20%, 40%, 60%, 80%, And 100% Biodiesel Mixed With Diesel) — Were Prepared And Tested Under Varying Load Conditions. Mechanical Efficiency, Brake Thermal Efficiency, Indicated Thermal Efficiency, And Specific Fuel Consumption Were Analysed Together With A Detailed Heat Balance Sheet For Each Fuel. The Experimental Results Indicate That Pongamia Biodiesel Blends Deliver Engine Performance Comparable To Conventional Diesel. Brake Thermal Efficiency Increases With Engine Load For All Fuel Blends, While Specific Fuel Consumption Is Marginally Higher For Biodiesel Blends Owing To Their Lower Calorific Value. Emission-related Combustion Behaviour Also Improves, With Better Utilization Of Oxygen Content Promoting More Complete Combustion. Among The Tested Blends, B60 Offers A Favourable Overall Compromise Between Performance And Fuel Economy, While B60 Shows The Most Promising Brake Thermal Efficiency. The Study Concludes That Pongamia Biodiesel Is A Viable, Renewable, And Eco-friendly Alternative To Diesel Fuel That Can Be Used In Existing Diesel Engines Without Major Modification, Contributing To Reduced Environmental Pollution And Decreased Dependence On Fossil Fuels. Read more Read less

Authors: Vinod Harikanth¹ | Dr. M. Senthilkumar

Vol 12, Issue 9 (September 2026) ELECTRONICS ENGINEERING

Embedded Edge-AI Framework For Autonomous UAV-Based Human Detection And Remote Alerting Using ESP32-S3 Flight Control And ESP32-CAM Vision

Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper... Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper Presents The Design And Experimental Implementation Of A Compact Embedded Edge-AI UAV Platform For Autonomous Defense-oriented Human Monitoring, Built Around A Custom-developed Quadcopter Whose ESP32-S3 Microcontroller Performs Real-time Flight Control While A Separate ESP32-CAM Module Performs On-board Visual Inference. The Flight-control Subsystem Interfaces With An Inertial Measurement Unit (MPU6050) And Pulse-width-modulated Motor Drivers To Support Stabilized Takeoff, Hover, Landing And Coordinate/waypoint-based Autonomous Navigation Over A Crazyflie-compatible Wi-Fi Command Interface. For Visual Intelligence, Two Edge Impulse Models Were Trained And Evaluated On The ESP32-CAM: An Image-classification Model Distinguishing “Face” And “no_human” Classes, And An Object-detection Model Providing Spatial Localization. The Classification Model, Deployed With A 0.60 Confidence Threshold, Triggers Transmission Of The Captured JPEG Frame To A Telegram Bot For Remote Notification Whenever A Positive Detection Persists Beyond A Five-second Upload Cooldown. The Classification Model Achieved 96.0% Validation Accuracy, An F1 Score Of 0.97 For The Face Class And 0.95 For The No_human Class, And An Area Under The ROC Curve Of 0.95, While The Comparative Object-detection Model Achieved A Validation F1 Score Of 84.0% Over 60 Training Cycles. Experimental Results Confirm Functional Autonomous Navigation, Correctly Triggered Telegram Alerting And A Working End-to-end Pipeline. The Comparison Shows That Image Classification Is Best Suited To The Binary Alerting Requirement Of This Application, While Object Detection Offers Additional Spatial Information That Is Valuable For Future Localization-based Tasks. Read more Read less

Authors: KATHAR MYDEEN S | Dr. P. Sivasankar | Dr. G. Kulanthaivel