Volume 12 - Issue 9

A STUDY ON EMERGING ONLINE GROCERY PLATFORMS: AN OVERVIEW OF OPPORTUNITIES AND CHALLENGES IN COIMBATORE CITY

Modern Technology Has Played A Vital Role In Transforming The Retail Industry, Resulting In The Emergence Of Online Grocery Platforms. These Platforms Provide Businesses And Consumers With Convenience, Market Access, Efficient Transactions, And Oppor... Modern Technology Has Played A Vital Role In Transforming The Retail Industry, Resulting In The Emergence Of Online Grocery Platforms. These Platforms Provide Businesses And Consumers With Convenience, Market Access, Efficient Transactions, And Opportunities For Business Expansion. Coimbatore City Has Witnessed The Adoption Of Online Grocery Platforms By Businesses And Consumers. The Present Study Examines The Emerging Online Grocery Platforms In Coimbatore City With A Focus On The Opportunities And Challenges. Read more Read less

The Chemical Footprint Of Industry: Water Quality In Jeopardy

Water Quality Assessment Is A Critical Component Of Environmental Monitoring, Particularly In Regions Subjected To Industrial Activity. This Review Paper Synthesizes The Literature On Physicochemical Parameters Of Water, Drawing From Studies Conducte... Water Quality Assessment Is A Critical Component Of Environmental Monitoring, Particularly In Regions Subjected To Industrial Activity. This Review Paper Synthesizes The Literature On Physicochemical Parameters Of Water, Drawing From Studies Conducted In And Around Various Industrial Areas In India And Internationally. The Review Focuses On Key Parameters Including PH, Electrical Conductivity, Total Hardness, Total Alkalinity, Bicarbonates, Phosphates, Chlorides, Sulphates, Ammonia, Sodium, Potassium, Calcium, Magnesium, And Heavy Metals. The Findings From These Studies Reveal Significant Variations In Water Quality Parameters, With Many Industrial Areas Exhibiting Elevated Levels Of Pollutants That Exceed Permissible Limits Established By Regulatory Bodies Such As The Bureau Of Indian Standards (BIS) And The World Health Organization (WHO). This Review Highlights The Need For Continuous Monitoring And Effective Pollution Control Measures To Protect Water Resources And Public Health. Read more Read less

Industrial Effluents And Heavy Metal Pollution: A Global Water Crisis

Heavy Metal Contamination Of Water Is One Of The Most Pressing Environmental And Public Health Crises Of The Industrial Era, Particularly In Rapidly Developing Nations Where Regulatory Frameworks Lag Behind Industrial Expansion. This Review Synthesiz... Heavy Metal Contamination Of Water Is One Of The Most Pressing Environmental And Public Health Crises Of The Industrial Era, Particularly In Rapidly Developing Nations Where Regulatory Frameworks Lag Behind Industrial Expansion. This Review Synthesizes Global Literature On Heavy Metal Pollution In Water, Focusing On Industrial Sources And Their Consequences For Aquatic Ecosystems And Human Health. It Examines Major Metals Of Concern—iron, Copper, Zinc, Manganese, Nickel, Chromium, And Cobalt—in Surface Water, Groundwater, And Industrial Effluents. Findings Reveal That Concentrations Frequently Exceed Permissible Limits Set By BIS, WHO, USEPA, And The European Union Across India, Bangladesh, China, Nigeria, And Other Industrializing Regions. The Review Highlights The Non-biodegradable And Bioaccumulative Nature Of Heavy Metals And Their Carcinogenic, Neurotoxic, Nephrotoxic, And Developmental Health Effects. Seasonal Variations Significantly Influence Metal Concentrations, With Higher Levels During Dry Seasons And Lower Levels During Monsoons. Conventional Treatment Technologies Remain Inadequate, While Emerging Approaches Such As Nanomaterial-based Adsorption, Membrane Filtration, And Bioremediation Show Promise. Ultimately, This Review Calls For Strengthened Regulatory Enforcement, Continuous Monitoring, Source Apportionment, And Green Chemistry Principles To Safeguard Water Resources For Present And Future Generations. Read more Read less

PHARMACOGNOSTICAL , PRELIMINARY, PHYTOCHEMICAL STANDARDIZATION,ANTI DIABETIC EVALUATION AND IN VIVO STUDIES IN IDENTIFICATION AND STEM OF POLYALTHIYA SUBEROSA(ROXB) THWAITES (ANNONACEAE)

ECOLOGICAL STUDY OF Sesamum Indicum L. WITH REFERENCE TO BIOMASS AND PRIMARY PRODUCTIVITY IN GAYA DISTRICT, BIHAR

An Ecological Investigation On Sesamum Indicum L. Was Undertaken To Evaluate Biomass Production And Productivity Pattern Under Gaya Agro-climate, Bihar. Field Work Was Executed In Kharif Season 2023-24 At Three Locations - Sherghati Upland, Barachatt... An Ecological Investigation On Sesamum Indicum L. Was Undertaken To Evaluate Biomass Production And Productivity Pattern Under Gaya Agro-climate, Bihar. Field Work Was Executed In Kharif Season 2023-24 At Three Locations - Sherghati Upland, Barachatti Midland And Bodh Gaya Lowland.Three Cultivars Krishna, T-78 And RT-351 Were Evaluated At 15 Days Interval From Germination To Maturity. Germination Potential Was Maximum In RT-351 (88.4%) Followed By Krishna (82.1%). Standing Dry Matter Showed Progressive Increase Till Harvest And Maximum Value 284.6 G/m2 Was Recorded In RT-351 At Site-III. Above Ground Parts Shared 82% Of Total Dry Matter And Below Ground 18%. Highest Net Primary Production 4.12 G/m2/day Was Noted During 45-60 Days After Sowing At Flowering Stage, Lowest 0.85 G/m2/day At Maturity. Seed Exhibited Highest Energy Value 6542 Kcal/g Compared To Leaf 4320 Kcal/g, Stem 4210 Kcal/g And Root 3980 Kcal/g. Accumulation Of NPK Was In Order Seed > Leaf > Stem > Root With Maximum N 3.42%, P 0.82%, K 0.91% In Seed. Present Study Revealed Suitability Of Gaya Climate For Sesame And Superiority Of RT-351 For Higher Biomass And Oil Yield In Magadh Region. Read more Read less

Explainable AI (XAI) Multimodal Artificial Intelligence For Ayurvedic Prakriti Assessment Using Questionnaire, Tongue Images, And Facial Features

Ayurveda Classifies People Into Vata, Pitta, And Kapha Types. This Is Called Prakriti. Manual Assessment Relies On Physician Judgment. It Can Be Subjective And Hard To Repeat. This Paper Proposes A Multimodal AI System. It Uses Questionnaires, Tongue... Ayurveda Classifies People Into Vata, Pitta, And Kapha Types. This Is Called Prakriti. Manual Assessment Relies On Physician Judgment. It Can Be Subjective And Hard To Repeat. This Paper Proposes A Multimodal AI System. It Uses Questionnaires, Tongue Images, And Facial Features Together. Each Modality Contributes Its Own Score. A Fusion Layer Combines These Scores. An Explainable AI, Or XAI, Module Then Justifies The Result. SHAP And LIME Provide Per-modality Reasons. We Define Fusion And Confidence Formulas. We Review Real Prakriti-AI Studies To Ground Our Design. Illustrative Results Suggest Fusion Beats Any Single Modality. We Discuss Ethical Limits And Non-diagnostic Use Clearly. Read more Read less

Data Security And User Privacy In Digital Library Environment : Emerging Issues, Challenges And Strategies

In Today's Times When The Conventional Libraries Are Being Replaced By Digitally Connected Service Environments, The Increase Of Collection, Processing, Storage, Analysis And Sharing Of User Information Has Expanded Beyond Imagination. Accessibility... In Today's Times When The Conventional Libraries Are Being Replaced By Digitally Connected Service Environments, The Increase Of Collection, Processing, Storage, Analysis And Sharing Of User Information Has Expanded Beyond Imagination. Accessibility And Resource Discovery And Personalisation Has Improved Through Integrated Library Management Systems, Institutional Repositories, Licensed Electronic Databases, Cloud Systems, Remote Authentication Services, Mobile Apps, Radio-frequency Identification, IoT (Internet Of Things), Artificial Intelligence, Machine Learning, And Third-Party Vendors. The Same Infrastructure Has Also Been Accompanied By Heightened Risks For Excessive Data Collection, Behavioural Data Profiling, Unauthorised Access, Vendor Exposure And Inadvertent Exposure Due To Ransomware And Other Types Of Data Breaches. A Fundamental Point Of This Paper Is That The Security Of Data And The Privacy Of Users Should Be Considered As Two Sides Of The Same Coin In The Context Of The Modern Digital-library Environment, Not As A Technical Information-technology Concern. It Is A Descriptive And Analytical Literature-based Design, Which Has Created A Synthesis Of Peer-reviewed Research Articles And Professional Standards/legal Instruments Published Predominantly In The Period 2014 To 2026 In Library And Information Science Fields. The Analysis Reveals That The Protection Of Privacy Has A Technological, Organisational, Policy, Legal And Ethical Dimension, Based On Intellectual Freedom, User Confidentiality And Trust. Legacy Systems, Incomplete Third Party Access Controls And Under-investment Have Been Documented And Seen To Act As The Vector That Turns A Cybersecurity Risk Into The Collapse Of A Service Or Confidentiality Harm, Such As The 2023 Attack On The British Library By The Rhysida Ransomware. An Original Conceptual Model, The Integrated Data Security And User Privacy Framework For Digital Libraries (IDSUP-DL), And Multi-layered Technical, Privacy, Organisational And Human Strategies Are Presented. The Framework Is A Proposal Based On Literature And Has Not Been Validated Through Empirical Data. Read more Read less

Digital Reference Services In Academic Libraries: Emerging Trends, Barriers And Future Directions

Reference Work Has Shifted From Academic Library Desks, To Email, Web Forms, Live Chat, Video, Text Messaging And Most Recently To AI Chatbots And Generative Systems. Those Channels Offer Assistance Not Just Outside Of Hours, But They Also Alter The... Reference Work Has Shifted From Academic Library Desks, To Email, Web Forms, Live Chat, Video, Text Messaging And Most Recently To AI Chatbots And Generative Systems. Those Channels Offer Assistance Not Just Outside Of Hours, But They Also Alter The Nature Of The Reference Interview, The Nature Of Work Undertaken By Staff, The Expectation Of Accuracy, The Confidentiality Of A Question And Distance Learning. Digital Reference Service Is Discussed In This Paper As A Library And Information Science Problem As A Nexus Of Virtual Reference, AI Chatbots, Emerging Technologies, User Information Need, And The Digital-library Ecosystem. It Was Created Using A Descriptive And Analytical Literature-based Design, Which Synthesized Peer-reviewed LIS Research From 2018 To 2026 With A Focus On 2022-2026 Research In Chatbots And Generative-AI, Along With RUSA And IFLA Professional Guidance. This Literature Indicates A Rising, Albeit Uneven, Rate Of Chatbot Adoption, High Accuracy For Basic Service-related Queries, Lower Accuracy For Complex Research-related Queries, Limited Disclosure Of Privacy, And Lingering Challenges In Infrastructure, Skills, Cost And Trust. The Paper Holds That The AI Technology, To A Certain Extent, Is Not A Replacement For The Role Of The Reference Librarian. It Introduces A New Conceptual Framework, Called The Integrated Digital Reference And Intelligent Library Service Framework (IDR-ILS). It Is Not An Empirically Valid Framework But One That Has Been Developed Through Literature. Read more Read less

Cybersecurity Challenges In The Academic Libraries: Emerging Issues, Risks, Strategies And Institutional Responses

Academic Libraries Now Run As Networked Service Environments. Access, And Scholarly Communication, Has Been Enhanced By Integrated Library Systems, Discovery Layers, Institutional Repositories, Licensed Databases, Cloud-based Systems, Remote Authenti... Academic Libraries Now Run As Networked Service Environments. Access, And Scholarly Communication, Has Been Enhanced By Integrated Library Systems, Discovery Layers, Institutional Repositories, Licensed Databases, Cloud-based Systems, Remote Authentication Systems, RFID, And Mobile Apps And Research Data Stores. This Same Stack Has Expanded The Attack Surface. Phishing, Ransomware, Credential Theft, Insider Misuse, Cloud Misconfiguration, Unpatched Software, And Compromise Of Vendors Can Close Catalogues, Expose User Records And Stall Teaching Support. This Paper Explores Cybersecurity Issues As A Library And Information Science (LIS) Issue, Not As A Generic Information-technology (IT) Issue. It Uses A Descriptive And Analytical Literature-based Design And Synthesizes Peer-reviewed LIS Research Between The Year 2018-2026 Along With Official Incident Reviews And Governance Instruments Such As NIST Cybersecurity Framework 2.0, ISO/IEC 27001:2022, Digital Personal Data Protection Act, 2023, Digital Personal Data Protection Rules, 2025, CERT-In Directions, And Professional Privacy Standards. Written Security Policies Exist In The Literature But Are Often Extremely Difficult To Implement, Awareness Is Inconsistent Among Staff, And Incidents Such As The British Library's Ransomware Attack In 2023, Which Used A Technical Failure To Cause Months Of Outages In Services And Compromise Confidentiality, Have Occurred. As Per The Paper, An Original Conceptual Model, The “Feedback-driven Policy” Is Proposed. Integrated Cybersecurity Risk And Institutional Preparedness Framework For Academic Libraries (ICRIP-AL). The Framework Is A Literature-based Proposal And Has Not Been Validated By Empirical Research. Read more Read less

AUTHORSHIP PATTERN AND DEGREE OF COLLABORATION IN SARS: A SCIENTOMETRICS STUDY

This Study Examines The Authorship Pattern And Degree Of Collaboration In SARS Research Literature. The Required Data Were Collected From The ScienceDirect Database For The Period 2016 To 2020. Different Scientometric Methods Were Used, Such As Autho... This Study Examines The Authorship Pattern And Degree Of Collaboration In SARS Research Literature. The Required Data Were Collected From The ScienceDirect Database For The Period 2016 To 2020. Different Scientometric Methods Were Used, Such As Authorship Pattern, Degree Of Collaboration, Collaboration Coefficient, And Dominance Factor. The Study Also Tested The Applicability Of Lotka’s Law.The Findings Show That Co-authored Research Papers Were More Common Than Single-authored Papers. The Study Also Found That Author Productivity Generally Follows Lotka’s Law. Overall, The Results Indicate A Strong Trend Toward Research Collaboration Among Authors In SARS Research. Read more Read less

Predicting Ride Costs With Advanced Regression Models

Accurate Ride Cost Prediction Has Become Increasingly Important As Ride-hailing Businesses Continue To Grow, The Ability To Accurately Predict Rides Costs Becomes More Crucial Than Ever, As It Directly Influences Pricing Efficiency, Operational Plann... Accurate Ride Cost Prediction Has Become Increasingly Important As Ride-hailing Businesses Continue To Grow, The Ability To Accurately Predict Rides Costs Becomes More Crucial Than Ever, As It Directly Influences Pricing Efficiency, Operational Planning, And Customer Satisfaction. The Existing Fare Estimation Techniques Are Often Inadequate To Embody The Complex And Dynamic Relationships Among Various Factors Like Trip Distance, Traffic Conditions, Weather, Demand Fluctuation And Travel Time, Which Necessitate More Sophisticated Predictive Solution. This Research Suggests The Introduction Of A Ride Cost Prediction System Leveraging A Machine Learning Approach With Advanced Regression Models Such As Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, And LightGBM Regressor. These Models Can Be Used To Capture Nonlinearities In Ride-hailing Data And To Make More Accurate Fare Predictions. Data Preprocessing Methods Include Handling Missing Values, Scaling Features, Transforming Features, And Feature Engineering To Improve Model Accuracy. Other Enhancements—such As Rush-hour Flags, Weather Classifications, And Distance-based Measurements—are Added To Boost Predictive Power. The Performance Of The Models Is Assessed Based On The Standard Regression Performance Metrics Such As Mean Absolute Error (MAE), Mean Squared Error (MSE) And R-squared (R²) Score. Experimental Results Show That Ensemble Learning Methods, Especially XGBoost And Gradient Boosting, Predict With A Higher Accuracy Than Classical Regression Methods. Moreover, Feature Importance Analysis Reveals The Most Important Factors Affecting The Rides' Cost, Giving Insights About The Mechanisms Of Fare Determination. The Study Overall Demonstrates How Well Advanced Regression Models Can Provide Accurate And Reliable Ride Cost Estimates, Helping Ride-hailing Platforms Optimize Pricing Strategies, Enhance Transparency, And Boost User Satisfaction. Read more Read less

Repurposed Drugs Against Lung Cancer

Drug Repurposing Evaluates Established Medicines For Therapeutic Indications Beyond Their Original Use. In Lung Cancer, Repurposing Is Attractive Because Existing Pharmacological And Safety Information Can Accelerate Hypothesis Generation, While Seve... Drug Repurposing Evaluates Established Medicines For Therapeutic Indications Beyond Their Original Use. In Lung Cancer, Repurposing Is Attractive Because Existing Pharmacological And Safety Information Can Accelerate Hypothesis Generation, While Several Non-oncology Drugs Influence Pathways Involved In Tumor Growth, Angiogenesis, Metabolism, Inflammation And Cellular Survival. Itraconazole, An Imidazole/triazole Antifungal Drug, Has Attracted Particular Interest Because Preclinical Studies Describe Anti-angiogenic And Signaling Effects And Clinical Studies Have Explored Its Use With Chemotherapy In Advanced Non-small Cell Lung Cancer (NSCLC). Nevertheless, Anticancer Activity Demonstrated In Experimental Systems Does Not Establish Clinical Efficacy. This Review Discusses The Scientific Rationale For Repurposing Drugs Against Lung Cancer, With Emphasis On Itraconazole And Nano-vesicular Delivery. It Describes Molecular Mechanisms, In-silico Screening, Formulation Development, In-vitro Cytotoxicity, Apoptosis And Cellular Uptake Assays, In-vivo Evaluation, Pharmacokinetics, Safety, Translational Challenges And Future Directions. Read more Read less

ForestGuard AI: Acoustic Detection Of Chainsaw Activity For Illegal Logging Monitoring

Illegal Logging Is A Major Environmental Problem That Affects Forest Ecosystems, Biodiversity, And Natural Resources. Conventional Forest Monitoring Techniques Based Mainly On Manual Patrols And Visual Inspection Can Be Difficult To Scale Across Larg... Illegal Logging Is A Major Environmental Problem That Affects Forest Ecosystems, Biodiversity, And Natural Resources. Conventional Forest Monitoring Techniques Based Mainly On Manual Patrols And Visual Inspection Can Be Difficult To Scale Across Large And Remote Forest Regions. This Paper Presents ForestGuard AI, A Software-based Acoustic Monitoring Prototype For Detecting Possible Chainsaw Activity From Short Environmental Audio Recordings. The Proposed System Processes Audio By Resampling It To 22,050 Hz, Converting It Into Fixed Four-second Segments, Normalizing The Waveform, And Extracting 13 Mel-Frequency Cepstral Coefficients (MFCCs). A Onedimensional Convolutional Neural Network (1D-CNN) Is Then Used To Classify The Input Into Two Categories: Chainsaw Sound And Normal Forest Sound. A Flask-based Web Application Provides An Interactive Monitoring Interface With Audio Upload, Browser Microphone Recording, Prediction Display, Detection History, Statistical Visualization, Alert Notification, CSV Export, And Daily Report Generation. The Current Implementation Is Designed As A Proofofconcept And Does Not Claim That An Acoustic Prediction Alone Proves Illegal Logging. The Small Prototype Dataset Also Limits The Ability To Make Field-level Generalization Claims. The Proposed Architecture Can Be Extended In Future Work With GPS-enabled Acoustic Sensor Nodes, Edge Computing, Wireless Communication, Solar Power, Geofencing, And Human Verification Workflows. Read more Read less

A Hybrid Machine Learning Framework For Explainable Insurance Risk Assessment

Insurance Providers Increasingly Rely On Analytical Solutions To Improve Underwriting Consistency, Reduce Processing Time, And Optimize Risk Assessment. Traditional Underwriting Approaches Often Depend On Manual Review And Expert Judgment, Resulting... Insurance Providers Increasingly Rely On Analytical Solutions To Improve Underwriting Consistency, Reduce Processing Time, And Optimize Risk Assessment. Traditional Underwriting Approaches Often Depend On Manual Review And Expert Judgment, Resulting In Variability Across Decisions. This Work Presents An Enhanced Smart Underwriting System That Combines Supervised Learning, Explainable Artificial Intelligence, And Actuarial Risk Profiling. Applicant Data Is Processed Through Machine Learning Models To Determine Insurance Eligibility Based On Demographic, Lifestyle, And Health-related Attributes. To Improve Transparency, SHAP (SHapley Additive ExPlanations) Is Integrated To Quantify The Contribution Of Individual Variables Toward Each Approval Decision. Following Approval, Accepted Customer Profiles Are Analyzed Using Unsupervised K-Means Spatial Clustering To Identify Hidden Risk Patterns And Classify Policyholders Into Tier 1, Tier 2, And Tier 3 Risk Segments. These Segments Can Be Directly Associated With Premium Pricing Strategies And Underwriting Recommendations. Experimental Evaluation Demonstrates That The Proposed Approach Delivers Accurate Approval Decisions While Providing Interpretable Explanations And Granular Actuarial Risk Categorization. Read more Read less

An Efficient Hybrid Cryptographic Approach For Secure Cloud Data Storage Using Cipher-Diversified Block Dispersal And Blinded RSA Key Encapsulation

Cloud Storage Has Become The Default Medium For Retaining Large Volumes Of Sensitive Records, Yet The Loss Of Physical Custody Over That Data Leaves Owners Dependent On Controls They Cannot Inspect. Single-algorithm Encryption Addresses Only Part Of... Cloud Storage Has Become The Default Medium For Retaining Large Volumes Of Sensitive Records, Yet The Loss Of Physical Custody Over That Data Leaves Owners Dependent On Controls They Cannot Inspect. Single-algorithm Encryption Addresses Only Part Of The Problem: Symmetric Ciphers Are Fast But Expose The Secret Key During Distribution, Whereas Asymmetric Ciphers Protect Key Exchange At A Computational Cost That Is Impractical For Bulk Payloads. This Paper Presents An Efficient Hybrid Cryptographic Approach That Couples Four Primitives—AES-256, Blowfish-448, A One-time Password (OTP) Mechanism And An Enhanced RSA Construction—inside A Three-tier User–proxy–storage Architecture. Plaintext Is Partitioned Into Fixed-length Blocks, And Each Block Is Assigned To Either AES Or Blowfish By A Pseudo-random Cipher Selector Seeded From The Session OTP, So That Cryptanalytic Success Against A Single Cipher Never Yields The Complete File. Session Keys Are Multiplicatively Blinded By An Auxiliary Composite Factor Derived From User Credentials Before RSA Encapsulation, And A Merkle Root Computed Over The Per-block Digests Is Signed With RSA-PSS To Supply Integrity And Non-repudiation. The Resulting Cipher Blocks Are Dispersed Across Independent Storage Zones Together With An Encrypted Manifest. Evaluation On A Healthcare Record Corpus Records A Sustained Throughput Of 93.5 MB/s, A 33.0% Reduction In Encryption Latency And A 34.1% Reduction In Decryption Latency Relative To An AES-OTP-RSA Baseline, A Tamper-detection Accuracy Of 99.47%, And Mean Squared Error Of 0.198. Read more Read less

A Privacy-Preserving Federated Learning Framework With Dual Explainability For Hybrid CNN–LSTM Based Anomaly Detection In IoT Network Traffic

Intrusion Detection Systems (IDS) For The Internet Of Things Face A Three-way Tension: Deep Models Detect Attacks Accurately But Behave As Opaque Black Boxes, Centralised Training Pipelines Require Raw Traffic To Be Exported From Every Device In Viol... Intrusion Detection Systems (IDS) For The Internet Of Things Face A Three-way Tension: Deep Models Detect Attacks Accurately But Behave As Opaque Black Boxes, Centralised Training Pipelines Require Raw Traffic To Be Exported From Every Device In Violation Of Data-protection Expectations, And Lightweight Edge Hardware Cannot Host Heavyweight Detectors. This Paper Proposes A Framework That Resolves All Three Constraints Simultaneously. A Hybrid One-dimensional Convolutional And Long Short-term Memory (CNN–LSTM) Network Captures Both The Spatial Structure Of Individual Flow Records And The Temporal Signature Of Multi-stage Attacks. The Model Is Trained Under A Federated Learning Protocol In Which Distributed IoT Clients Perform Local Optimisation And Exchange Only Weight Updates, Aggregated At A Central Server With The FedAvg Rule, So Raw Traffic Never Leaves Its Point Of Capture. On Top Of The Converged Global Model, A Dual Explainability Layer Couples SHAP, Which Supplies Global Feature-attribution Rankings, With LIME, Which Produces An Instance-level Rationale For Every Alert Raised. The Framework Is Evaluated On The CIC-IoT2023 Benchmark, Whose 46.7 Million Flow Records From 105 Real Devices Are Grouped Into Eight Classes Spanning DDoS, DoS, Reconnaissance, Web-based, Brute-force, Spoofing And Mirai Activity Alongside Benign Traffic. Under A Ten-client Federated Deployment The Proposed Detector Attains 99.21% Binary And 97.86% Eight-class Accuracy With A Macro F1-score Of 97.42%, Remaining Within Half A Percentage Point Of Its Centralised Counterpart While Eliminating Raw-data Centralisation, And Inter-arrival Time, Transmission Rate And TCP-flag Statistics Emerge As The Dominant Explanatory Features. The Results Indicate That Privacy, Accuracy And Interpretability Need Not Be Traded Against One Another In IoT Intrusion Detection. Read more Read less

“Emotional Intelligence And Educational Attitude Among Tribal And Non-Tribal College Students: The Mediating Role Of Self-Efficacy”

The Present Study Examined Emotional Intelligence, Educational Attitude, And Self-efficacy Among Tribal And Non-tribal College Students And Investigated Whether Self-efficacy Mediated The Relationship Between Emotional Intelligence And Educational At... The Present Study Examined Emotional Intelligence, Educational Attitude, And Self-efficacy Among Tribal And Non-tribal College Students And Investigated Whether Self-efficacy Mediated The Relationship Between Emotional Intelligence And Educational Attitude. A Quantitative Comparative-correlational Design Was Employed With 220 College Students Aged 18–24 Years From Chhatrapati Sambhajinagar, Maharashtra, Comprising 110 Scheduled Tribe And 110 Non-tribal Students. Emotional Intelligence Was Measured Using The Mangal Emotional Intelligence Inventory, Self-efficacy Through The Self-Efficacy Scale (SES-SANS), And Educational Attitude Using C. Bhasin’s Educational Attitude Scale. Tribal Students Reported Significantly Higher Emotional Intelligence Than Non-tribal Students, Whereas Non-tribal Students Demonstrated Higher Self-efficacy And Educational Attitude. Emotional Intelligence Showed Significant Positive Correlations With Self-efficacy And Educational Attitude, While Self-efficacy Was Also Positively Associated With Educational Attitude. Mediation Analysis, Controlling For Community Group And Gender, Indicated That Emotional Intelligence Significantly Predicted Self-efficacy, Which In Turn Significantly Predicted Educational Attitude. The Bootstrapped Indirect Effect Was Significant, While The Direct Effect Of Emotional Intelligence Remained Significant, Indicating Partial Mediation. The Findings Suggest That Self-efficacy Represents An Important Psychological Mechanism Through Which Emotional Competence May Influence Students’ Educational Attitudes, While Community Differences Should Be Understood Within Broader Social, Cultural, And Educational Contexts. These Findings May Inform Culturally Responsive Student Support And Counselling. Read more Read less

“Reasoning Ability And Creative Thinking Among Generation Z College Students In The AI Era: The Mediating Role Of Self-Efficacy”

Generative Artificial Intelligence Has Rapidly Entered Higher Education, Creating New Opportunities For Idea Generation And Problem-solving While Also Raising Concerns About Cognitive Offloading And Dependence On Automated Support. The Present Study... Generative Artificial Intelligence Has Rapidly Entered Higher Education, Creating New Opportunities For Idea Generation And Problem-solving While Also Raising Concerns About Cognitive Offloading And Dependence On Automated Support. The Present Study Examined The Relationships Among Reasoning Ability, Creative Thinking, And Self-efficacy In Generation Z College Students And Tested Whether Self-efficacy Mediated The Association Between Reasoning Ability And Creative Thinking. A Quantitative Cross-sectional Model Was Specified For 180 Undergraduate Students Aged 19–24 Years From Colleges In Chhatrapati Sambhajinagar, Maharashtra. Reasoning Ability Was Assessed With The Shailaja Bhagwat Reasoning Ability Test (RAT-BS), Creative Thinking With Baqer Mehdi’s Verbal Test Of Creative Thinking (VTCT-M), And Self-efficacy With The Self-Efficacy Scale (SES-SANS) Of Arun Kumar Singh And Shruti Narain. Reasoning Ability Correlated Positively With Self-efficacy (r = .51) And Creative Thinking (r = .51), While Self-efficacy Showed A Stronger Positive Relationship With Creative Thinking (r = .63), All P < .01. Mediation Analysis Indicated That Reasoning Ability Significantly Predicted Self-efficacy (b = 0.971, P < .01), Which In Turn Predicted Creative Thinking (b = 0.935, P < .01). The Bootstrapped Indirect Effect Was Significant (b = 0.908, 95% CI [0.594, 1.260]), With The Direct Pathway Remaining Significant, Indicating Partial Mediation. The Model Highlights Self-efficacy As A Psychological Resource That Links Analytical Competence To Creative Performance In An AI-saturated Academic Environment. Read more Read less

Design And Development Of A Mechanical Water Level Based Automatic Pump Cut-Off System

Water Tank Overflow Is A Common Practical Problem When A Pump Is Left Running After The Tank Has Reached Its Required Level. Many Automatic Systems Solve This Problem By Using Electronic Sensors, Microcontrollers Or Other Control Hardware. This Paper... Water Tank Overflow Is A Common Practical Problem When A Pump Is Left Running After The Tank Has Reached Its Required Level. Many Automatic Systems Solve This Problem By Using Electronic Sensors, Microcontrollers Or Other Control Hardware. This Paper Presents Aqua Alert, A Low-cost Prototype That Uses A Simple Mechanical Principle To Stop The Pump Automatically. The System Uses A Main Water Container, An Overflow Path And A Small Receiving Container Supported By An Elastic Member. When The Main Container Reaches The Overflow Condition, Water Enters The Receiving Container. The Added Water Increases Its Weight And Causes Downward Displacement Against The Elastic Support. This Movement Operates An Electrical Contact Connected To The Pump Circuit And Opens The Circuit, Thereby Stopping The Pump. The Final Prototype Was Developed As A Simple Alternative After An Electronic Approach Was Considered During Early Development. The System Uses The Physical Weight Of Overflow Water As Its Trigger And Does Not Depend On A Microcontroller Or Electronic Level Sensor. Prototype Operation Demonstrated The Intended Sequence Of Overflow Collection, Mechanical Movement And Automatic Pump Cut-off. The Design Is Simple, Low-cost And Suitable For Further Development And Testing. Read more Read less

Design And Development Of A Low-Cost Automatic Rotary Sieving Machine For Spice Powder Processing

Sieving Is An Important Step In Spice Powder Processing Because It Helps Obtain A More Uniform Powder And Removes Relatively Coarse Particles. Manual Sieving Needs Continuous Physical Effort And Becomes Difficult When The Quantity Of Material Increas... Sieving Is An Important Step In Spice Powder Processing Because It Helps Obtain A More Uniform Powder And Removes Relatively Coarse Particles. Manual Sieving Needs Continuous Physical Effort And Becomes Difficult When The Quantity Of Material Increases. This Paper Presents The Design And Fabrication Of A Low-cost Automatic Rotary Sieving Machine Developed For Practical Spice Powder Processing. The Machine Uses A Three-phase AC Motor With A Rated Power Of 0.6 KW And A Rated Speed Of 910 Rpm. The Motor Drives A Shaft And Rotating Bracket Directly, While A Variable Frequency Drive (VFD) Is Used To Control The Operating Speed. The Fabricated Prototype Was Tested In Three Trials. During Operation At A VFD Setting Of 20.8 Hz, The Machine Processed Approximately 50 Kg Of Spice Powder In 30 Minutes, Corresponding To A Nominal Throughput Of Approximately 100 Kg/h Under The Observed Condition. The Prototype Was Built Mainly From Reused Components From Older Machines, While The VFD/controller Was Purchased At A Cost Of Approximately Rs. 9,000. The Work Focuses On A Simple Mechanical Arrangement, Reduced Manual Effort, And Practical Use For Small-scale Spice Processing. Read more Read less

SmartLife AI: An Intelligent, Inflation-Aware Personal Finance Management System — Design, Implementation, And A Roadmap Toward Predictive Financial Intelligence

Personal Financial Management (PFM) Applications Typically Report Nominal Income, Expenses, And Balances Without Indicating How Inflation Erodes Purchasing Power Or Whether A User Is On Track For Long-term Goals Such As Retirement. This Paper Present... Personal Financial Management (PFM) Applications Typically Report Nominal Income, Expenses, And Balances Without Indicating How Inflation Erodes Purchasing Power Or Whether A User Is On Track For Long-term Goals Such As Retirement. This Paper Presents SmartLife AI, A Full-stack, Inflation-aware Personal Finance Platform Built On A React/Next.js Frontend And A Python FastAPI Backend. The Current (Phase 1) Implementation — 32 REST Endpoints Across 19 Database Entities, Validated By 24 Automated Unit Tests — Provides Financialrecord Management, Bank-statement Ingestion With Rule-based Categorization, And Four Deterministic Engines: Inflation, Real Wealth, Life Value, And A Retirement Projection Engine With Accumulation-and-drawdown Simulation, Alongside An FY 2025-26 Indian Income-tax Optimization Engine And A Linear-regression Expenseforecasting Baseline. Every Worked Example In This Paper Is Computed Directly From The Deployed Engines. The Paper Also Specifies The Phase 2 Roadmap — Comparative ARIMA/LSTM Forecasting, A MyScheme-aligned Government-scheme Recommender, A Credit-card Suitability Scorer, And A Gemini-based Advisory Layer — Establishing A Reproducible Baseline For The Completed Half Of The Project And A Falsifiable Specification For The Remainder. Read more Read less

CONTENT ANALYSIS OF MOLECULAR AND CELLULAR BIOLOGY

The Present Study Aims To Conduct A Content Analysis Of The Journal Of Molecular & Cellular Biology For The Period From 2016 To 2020. The Study Examines The Publication Pattern And Characteristics Of The Articles Published During This Period. It Focu... The Present Study Aims To Conduct A Content Analysis Of The Journal Of Molecular & Cellular Biology For The Period From 2016 To 2020. The Study Examines The Publication Pattern And Characteristics Of The Articles Published During This Period. It Focuses On The Year-wise Distribution Of Publications, Authorship Pattern, Author-wise Productivity, Institution-wise Distribution, Country-wise Distribution, Relative Growth Rate (RGR), Doubling Time (DT), And References Cited In The Articles. The Study Also Examines The Major Patterns And Characteristics Of The Contributions Published In The Journal. The Findings Of The Study Are Expected To Provide A Clear Understanding Of The Growth, Distribution, And Research Trends Of Publications In The Field Of Molecular And Cellular Biology During The Selected Period. Read more Read less

BEYOND MEASUREMENT: BOARD-BASED ASSESSMENT AS A MECHANISM OF SOCIAL ENGINEERING IN EDUCATION

Board-based Assessment Is Generally Regarded As A Mechanism For Measuring Academic Achievement, Maintaining Educational Standards And Ensuring Institutional Accountability. However, Its Influence Extends Beyond The Measurement Of Learning. Because Bo... Board-based Assessment Is Generally Regarded As A Mechanism For Measuring Academic Achievement, Maintaining Educational Standards And Ensuring Institutional Accountability. However, Its Influence Extends Beyond The Measurement Of Learning. Because Board Examinations Establish Performance Expectations And Attach Significant Consequences To Academic Outcomes, They Can Shape The Behaviour, Priorities And Practices Of Students, Teachers, Schools And Families. The Present Study Examines Board-based Assessment Through The Lens Of Social Engineering In Education, Defined As The Systematic Or Indirect Shaping Of Individual And Collective Behaviours, Expectations, Priorities And Practices Through Institutional Structures, Norms, Incentives And Accountability Mechanisms. A Descriptive, Exploratory And Mixed-methods Approach Was Adopted, Drawing Upon Survey Responses From Students And Academic And Administrative Stakeholders. Three Likert-scale Instruments Were Examined For Internal Consistency. Reliability Analysis Demonstrated Good To Very High Internal Consistency, With Cronbach’s Alpha Coefficients Of 0.9505 For The Academic And Administrative Survey, 0.9067 For The Analytical Opinion Survey, And 0.8476 For The Student Perception Survey. The Social Engineering And Nation Building Dimension Of The First Instrument Demonstrated Particularly High Preliminary Internal Consistency (α = 0.9410). The Findings Indicate That Board Examinations Are Associated With Constructive Behavioural Outcomes Such As Motivation, Responsibility, Self-regulation, Goal Setting And Accountability, While Simultaneously Being Associated With Stress, Competition And Examination-oriented Practices. The Study Argues That These Apparently Contrasting Outcomes Illustrate The Dual Nature Of Assessment As A Mechanism Of Behavioural Regulation. The Paper Concludes That Board-based Assessment May Function As An Indirect Mechanism Of Social Engineering, Not Necessarily Through Deliberate Manipulation, But Through The Expectations, Incentives And Institutional Practices Generated Around High-stakes Assessment. Read more Read less

ProcureAi: An AI-Driven Procurement Spend Intelligence Platform For Automated Anomaly Detection, Supplier Optimisation And Predictive Expenditure Forecasting

- Enterprises Routinely Lose Between Three And Eight Percent Of Addressable Procurement Spend To Fragmentation, Duplicate Payments, Maverick Buying, Contract Price Leakage, And Unmanaged Tail Spend. This Paper Presents ProcureAI, A Full-stack, AI-dri... - Enterprises Routinely Lose Between Three And Eight Percent Of Addressable Procurement Spend To Fragmentation, Duplicate Payments, Maverick Buying, Contract Price Leakage, And Unmanaged Tail Spend. This Paper Presents ProcureAI, A Full-stack, AI-driven Spend-intelligence Platform That Ingests Multi-source Procurement Data, Classifies It Automatically Using A Hybrid NLP Classifier Combining Multinomial Naive Bayes With TF-IDF Cosine K-NN Re-ranking, Detects Leakage Through A Six-detector Unsupervised Anomaly Ensemble, Segments The Supplier Base With K-Means++ Clustering Augmented By Herfindahl-Hirschman Index Concentration Analysis, Forecasts Future Expenditure Via A Four-model Inverse-MAPE-weighted Time-series Ensemble With Residual-quantile Prediction Intervals, And Synthesises All Signals Into A Ranked, Costed, Auditable Cost-reduction Portfolio. Applied To A 36-month Synthetic Enterprise Dataset Of ~8,000 Transactions And $85 Million In Total Spend, The Platform Achieves ~86% NLP Classification Accuracy (macro-F1 ~87%), Detects ~4,000 Anomaly Events, Achieves A Forecasting Ensemble MAPE Of ~18%, And Identifies $7.3 Million (8.4%) In Annual Run-rate Savings. All Algorithms Are Implemented From First Principles In TypeScript, Ensuring Full Auditability Without Opaque Model Binaries Or External ML Services. Read more Read less

PLANNING AND SCHEDULING OF A COMMERCIAL G+6 BUILDING PROJECT USING MANAGEMENT TOOL

Buildings Are Constructed And Managed Differently As Cities Continue To Expand, And Project Complexity Keeps Rising Dramatically. A Key Idea To Take Into Account For A Successful Construction Project's Execution Is Planning And Scheduling. An Efficie... Buildings Are Constructed And Managed Differently As Cities Continue To Expand, And Project Complexity Keeps Rising Dramatically. A Key Idea To Take Into Account For A Successful Construction Project's Execution Is Planning And Scheduling. An Efficient Technology Called Oracle's Primavera P6 Is Used To Determine The Optimal Schedule For Building Activities In Order To Minimise The Complexity. The Essential Prelude To Scheduling Is Construction Planning, Which Entails Establishing The Overall Order, Delegating Duties, And Specifying The Construction Techniques And Job Tasks. Planning Includes All Of The Actions You Take To Ensure The Success Of Your Project. Cost And Scheduling Details Are The Primary Determinants Of Any Project's Success Or Completion.The Purpose Of This Study Is To Use Primavera Software To Explain The Planning And Scheduling Of A G+6commercial Mall In Jabalpur, (MP). The Process Of Scheduling Necessitates The Use And Management Of A Lot Of Data. One Of The Most Important Components In Creating And Managing A Project's Schedule Is A Project Coding System, Like A Work Breakdown Structure. As High-rise Buildings Have Proliferated In Urban Areas, Planning And Scheduling Have Emerged As Crucial Ideas To Take Into Account For A Successful Construction Project. Project And Resource Scheduling Is Done Using The Critical Path Method (CPM) Logic In Oracle's Primavera P6 Management Software.In This Project, The Continuing Construction Of A Commercial Building Interrupts The Software To Explain The Significance Of Scheduling. Using Primavera, The Actions Were Tracked And Observed Every Day. This Report Clearly Demonstrates All Of The Crucial Phases, Including Establishing An EPS, A WBS, Linking Activities Based On Their Interdependencies And Resource Availability, Lowering Float Values, And Determining The Critical Path. Read more Read less

HIGH STRENGTH CONCRETE BY USING MANUFACTURED SAND

This Article Will Focus On The Study Of Manufactured Sand As A Volumetric Partial Substitute Of Conventional Crushed Fine Aggregate In M60, M70 And M80 Grade. Author Believes That A Better Approach For Covering Variety Of Mixes In A Modest Apartment... This Article Will Focus On The Study Of Manufactured Sand As A Volumetric Partial Substitute Of Conventional Crushed Fine Aggregate In M60, M70 And M80 Grade. Author Believes That A Better Approach For Covering Variety Of Mixes In A Modest Apartment With Limited Trials Is By Having Maximum Fresh Property Test Data And Three More Compact Compressive Tests At 7, 14 And 28 Days Apart Which Becomes Very Handy. The Water-binder Ratios For Design Mix M60, M70 And M80 Were Adopted As 0.30, 0.27 And 0.24, Respectively, While Manufactured-sand Replacement Ranged From 0% To 100% At 20% Intervals. The Source Report Presents A Full Engineering Validation Data Set Instead Of Independently Certified Field Observations; Thus, The Tabulated Replicate Data Were Considered The Controlling Record And Were Verified For Internal Consistency Before Being Processed. Initial Depression Reduced Following Replacement In All Grades, While Density Increased Slightly. The Optimal Results At 28 Days Showed A Differing Optimum Of Replacement At M60 – 80%, M70 – 60% And M80 – 40%. The Compressive Strength Represents That Of Concrete Material In A Cast Iron Sphere. Corresponding Compressive Strengths Were 71.20, 82.80 And 92.80 MPa; Splitting Tensile Strengths Were 5.22, 5.78 And 6.12 MPa; And Flexural Strengths Were 6.62, 7.28 And 7.82 MPa. The Lower Optimum At Higher Grade Reflects The Increasing Sensitivity Of Low-water, High-powder Matrices To Angularity, Fines And Paste-film Demand. Mathematical Modelling Using Mean Response, Coefficient Of Variation, Relative Gain And Quadratic Replacement Functions Supports The Rise-and-fall Behaviour Observed. The Study Concludes That In Addition To Nominal Replacement Percentage, The Specification Of Manufactured Sand Must Be Done Through Grading, Fines, Moisture, Particle Shape And Admixture Compatibility. Read more Read less

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

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

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

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

LIBRARIANS OF TOMORROW: NAVIGATING AWARENESS, ADOPTION, AND ASPIRATIONS IN THE AGE OF ARTIFICIAL INTELLIGENCE

The Fast Pace At Which Artificial Intelligence Is Being Folded Into Library Work Is Reshaping Both Information Management And The Librarian's Professional Role. This Study Looks At How Librarians Perceive AI — Their Awareness Of It, How Much They Act... The Fast Pace At Which Artificial Intelligence Is Being Folded Into Library Work Is Reshaping Both Information Management And The Librarian's Professional Role. This Study Looks At How Librarians Perceive AI — Their Awareness Of It, How Much They Actually Use It, The Institutional Support (or Lack Of It) Behind That Use, Its Perceived Effects On Their Work, The Obstacles They Face, And How Ready They Feel For What's Coming. Survey Responses From 180 Librarians Were Analyzed Using Means And Standard Deviations. Awareness Levels Were Generally High (M = 3.75), With AI-based Plagiarism-detection Tools The Most Familiar (M = 4.21). Actual Use, However, Was Low (M = 2.59) Plagiarism Detection Was The Only Application Used Heavily, While Cataloguing, Analytics, And Automation Tools Saw Minimal Uptake. Institutional Support Was The Weakest Area Overall (M = 2.40), Reflecting Scarce Training, Tight Budgets, And No Formal AI Policies. Even So, Librarians Reported A Moderate-to-high Positive View Of AI's Professional Impact, Especially Around Service Efficiency And Information-retrieval Quality (M = 3.40). The Biggest Challenges Cited Were Inadequate Training, Limited Funding, Weak Infrastructure, And Ethical Or Privacy Concerns (M = 3.75). Notably, Librarians Showed Strong Enthusiasm And Readiness For AI (M = 4.19), Voicing Clear Willingness To Train And To See AI Literacy Built Into LIS Education. Overall, The Study Surfaces A Paradox: High Awareness And Eagerness Paired With Low Actual Use, Driven Mainly By Institutional Roadblocks Rather Than Personal Hesitation. Recommendations Include Beefing Up Training, Setting Aside Dedicated Budgets, Drafting Formal AI Policies, Upgrading Infrastructure, And Weaving AI Literacy Into Professional Curricula To Help Translate Awareness Into Everyday Practice Read more Read less

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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