International Journal of Advanced Engineering Application.

Fuzzy Logic Based Liquid Thermal Management of a Lithium-Ion Battery Pack for Electric Vehicles

The temperature of a lithium-ion battery decides how much power it can deliver, how safe it is and how long it lasts, and uneven heating inside the pack shortens its life further. This paper presents the design of a liquid-cooled thermal management system for a 24 V, 10 Ah lithium-ion pack built as seven series groups of four parallel cells (7S4P), together with the prototype assembled from it. An aluminum panel carries heat from the pack to a copper tube, and a 12 V pump moves an inhibited propylene-glycol/water coolant from the tube to a fan-cooled radiator. Six DS18B20 sensors read the pack temperature and an ACS758 sensor reads the battery current. An ESP32 runs a PID controller whose gains are scaled by a fuzzy supervisor using the highest temperature, its rate of rise and the load current, and it drives the pump and fan with PWM signals. A separate check sets full cooling and raises an alert on overtemperature or a sensor fault. Design calculations put the pack heat at about 5 W at 10 A and 47 W at 30 A, which needs only 0.04–0.4 L/min of coolant. In a lumped thermal-model run with the load falling from 10 A to 5 A at 30 °C ambient, fuzzy–PID control peaked at 32.77 °C, 0.77 °C above the 32 °C set-point, and ended at 31.98 °C, while the uncooled pack reached 36.51 °C. In the same model ON/OFF control held the temperature closer to the set-point but used about 42 % more auxiliary energy, and at 30 A the cooling saturated with the pack settling at 38.0 °C. These values come from a model and not from measurements. The prototype has been assembled and its display reports a normal-temperature status, but systematic measurements on it are still to be done.

Artificial Intelligence in Polymer Composites: A Review of Machine Learning Applications

Polymer matrix composites (PMCs) have become important structural and functional materials because their matrix chemistry, reinforcement type, architecture, interface, and processing history can be tailored to meet application-specific requirements. Nevertheless, the development of reliable composite systems remains difficult because material performance emerges from coupled phenomena spanning molecular, microstructural, laminate, component, and environmental scales. Conventional experimental campaigns are consequently expensive and time-consuming, while high-fidelity numerical simulations can become computationally demanding when many formulation and process variables must be explored simultaneously. Machine learning (ML) and broader artificial intelligence (AI) methods provide a complementary approach by learning relationships from experimental, computational, imaging, and process-monitoring data and using those relationships for prediction, optimization, classification, and inverse design. This review synthesizes recent developments in AI-assisted polymer-composite research, with emphasis on literature published from 2018–2024. The review covers material design and selection, mechanical and functional-property prediction, multiscale modelling, manufacturing-process optimization, additive manufacturing, defect detection, damage prognosis, fire performance, tribology, explainable AI, physics-informed learning, and digital-twin-oriented workflows. The literature indicates that ML is most mature for tabular prediction problems and increasingly capable in image-based inspection and surrogate modelling of expensive simulations. However, apparently high prediction accuracy does not necessarily establish generalizability: many studies are limited by small datasets, correlated observations, inconsistent testing protocols, leakage between training and test sets, narrow material families, and insufficient external validation. Accordingly, AI should be viewed as a decision-support and acceleration layer rather than a replacement for materials science, mechanics, or experimental validation.

Design and Development of a Solar-Assisted Water Surface Garbage Collection Boat with Two-Way Telemetry and GPS Return-to-Home

Floating litter such as plastic bottles, carry bags, thermocol and aquatic weeds gathers on lakes, ponds, temple tanks and slow-moving rivers. It is usually cleared by hand from a manned boat, which is slow and exposes workers to polluted water, while commercial skimmer vessels and aquatic drones cost far more than small water bodies can justify. This paper presents a low-cost, solar-assisted, radio-controlled boat that an operator drives from the bank to sweep floating waste into a bow-mounted bin. A handheld Arduino Nano remote and an ESP32-based boat share one two-way nRF24L01+ PA/LNA link at 2.4 GHz. The remote sends a 4-byte control packet 20 times per second, and the boat returns an 18-byte telemetry packet inside every acknowledgement, carrying hull roll and pitch (MPU6050 with a complementary filter), battery voltage, GPS position, speed and distance to home for display on an OLED. The boat steers by differential thrust of two DC motors through an L293D driver, so it needs no rudder. On a button press, or when the link is silent for more than 1 s, it returns to a stored home point using haversine distance, initial bearing and proportional heading correction. A deck-mounted 12 V panel charges the battery through a charge controller. Design calculations predict 3.4–4.3 h of full-throttle running from a 58 Wh pack, solar recovery of 50–60 % of the pack per sunny day with a 10 W panel, a working radio range of about 275–490 m over water, and a component cost of about Rs. 8,420.

Digital Transformation in Industrial Metrology: Smart Sensors, Measurement Traceability, and Data Interoperability in Industry 4.0

The digital transformation of industrial metrology is becoming increasingly important in Industry 4.0, where smart sensors and connected technologies support industrial measurement, quality assurance, and data-driven decision-making. This review examines the role of smart sensors in digital metrology, focusing on measurement traceability, measurement uncertainty, data interoperability, and data integrity. A narrative literature review approach is used to discuss peer-reviewed research and technical publications on smart manufacturing and measurement science. The reviewed literature indicates that digital connectivity alone does not guarantee reliable measurement results. Calibration, uncertainty assessment, standardized data exchange, and cybersecurity remain essential for trustworthy industrial measurement systems. The paper synthesizes recurring themes in the literature and proposes a conceptual framework integrating sensing, local data validation, communication, metrological assurance, and decision-making. It also discusses implementation challenges involving sensor reliability, heterogeneous industrial systems, and preservation of measurement context throughout digital workflows. The review does not report new laboratory experiments or quantitative performance measurements; rather, it identifies technical considerations and research directions for connected measurement systems. It concludes that effective digital transformation requires the coordinated integration of digital technologies with established principles of measurement science.

Ethical Imperatives of CRISPR-Modified Crops in Global Food Systems

The emergence of CRISPR-Cas9 and related gene-editing technologies as commercially viable tools for crop improvement has generated unprecedented tension between the promises of agricultural biotechnology and the ethical imperatives of equitable food governance. Unlike classical genetic modification, CRISPR's site-specificity, relative affordability, and potential for gene-drive deployment across wild and cultivated plant populations introduce a qualitatively distinct set of moral hazards — including irreversible ecological modification, accelerated corporate enclosure of the seed commons, and systematic marginalisation of smallholder subsistence farming communities who collectively steward over 70% of global crop genetic diversity. This study combines applied bioethics, ecological risk modelling, and comparative regulatory analysis to construct a balanced normative framework capable of guiding international biotechnology governance beyond the current regulatory vacuum. Deploying a multi-stakeholder ethical risk matrix calibrated against eight risk dimensions — gene-drive persistence, cross-species gene flow, monopoly concentration, allergenicity unknowns, soil microbiome disruption, farmer intellectual property vulnerability, pollinator impacts, and seed sovereignty erosion — the study identifies gene-drive persistence and seed sovereignty as the highest-magnitude threats requiring immediate policy intervention. Simulation modelling of gene-drive allele propagation across twenty crop generations demonstrates that population-level irreversibility is achievable within ten to fourteen generations under realistic field diffusion assumptions, establishing a narrow pre-deployment window for precautionary governance. Stakeholder cost-benefit analysis reveals a deeply asymmetric distribution of CRISPR benefits and harms: large agrichemical corporations and consumers accrue net positive scores, while smallholder farmers, indigenous seed-keeping communities, and ecosystem stakeholders register significant net negative exposures. A six-phase international regulatory blueprint spanning 2024 to 2035 is proposed, incorporating mandatory ecological impact assessments with fifty-year gene-flow modelling, compulsory open-access licensing for food-security crops, farmer royalty exemptions codified in national seed laws, and binding benefit-sharing mechanisms analogous to the Nagoya Protocol.

Wearable PPG-Based Atrial Fibrillation Detection Using a Hybrid CNN-LSTM Model: Performance Evaluation Across Signal Quality and Noise Conditions

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a leading independent risk factor for ischaemic stroke, yet its frequently paroxysmal and asymptomatic presentation makes opportunistic, continuous screening through low-cost wearable photoplethysmography (PPG) sensors an attractive complement to intermittent clinical electrocardiography. The principal barrier to reliable wearable-based AF detection is motion-artefact and ambient-noise corruption of the PPG signal, which degrades the rhythm-irregularity features that distinguish AF from normal sinus rhythm and necessitates explicit signal-quality assessment ahead of classification. This study develops and evaluates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture for AF detection from single-channel wrist-worn PPG, incorporating a signal quality index (SQI)-based segment rejection stage trained and validated on a dataset of 41,600 thirty-second PPG segments (8,360 unique recording sessions) collected from 214 subjects, of which 3,680 segments were retained as AF-positive following cardiologist-verified ECG-synchronised labelling. The proposed CNN-LSTM model is benchmarked against a Random Forest baseline using handcrafted heart-rate-variability features, a standalone CNN, and a standalone LSTM. The CNN-LSTM model achieves the highest test-set performance with an accuracy of 94.5%, sensitivity of 91.5%, specificity of 95.7%, F1-score of 90.3%, and area under the receiver operating characteristic curve (AUC) of 0.978, outperforming the Random Forest baseline (AUC 0.912) by a statistically significant margin. Stratified performance analysis across signal-to-noise ratio (SNR) bins reveals that sensitivity falls from 97.8% at SNR greater than 15 dB to 58.2% at SNR below 0 dB, underscoring the necessity of the SQI-based rejection stage, which excludes 480 of 4,160 (11.5%) candidate segments per session-equivalent batch as unsuitable for reliable classification. Power spectral density and RR-interval Poincaré analysis confirm that the model's discriminative capacity derives from the characteristic beat-to-beat irregularity and dominant-frequency dispersion that distinguish AF from sinus rhythm. The results support the feasibility of CNN-LSTM-based PPG screening as a pre-clinical triage tool for opportunistic AF detection in continuous wearable monitoring contexts, contingent on robust signal-quality gating to maintain diagnostic reliability under real-world motion and noise conditions.

Fine-Tuning Multilingual BERT for Hindi Text Classification: Sentiment Analysis and Topic Categorisation Using the HindiSentiment-6 Corpus

Formulation, Characterisation, and In Vitro Anticancer Evaluation of Curcumin-Loaded PLGA and PLGA-PEG Nanoparticles Against MCF-7 Breast Cancer Cells

Optimisation of Turning Parameters for Inconel 718 Superalloy Using Taguchi–Grey Relational Analysis and Response Surface Methodology Under Dry, Flood, and Minimum Quantity Lubrication Conditions