Current Concepts in Engineering Research and Technology Vol. 4

Nonlinear Structural Response of Tall Irregular Building-layered Soil Interaction System under Dynamic Wind Forces

Aditya Kumar Singh — 2026-09-02

The rapid growth of urban infrastructure has led to the widespread construction of tall reinforced-concrete (RC) buildings with irregular geometries to meet architectural and functional requirements. However, structural irregularities significantly influence the dynamic behaviour of buildings, particularly when they are subjected to dynamic wind-induced forces. Furthermore, the interaction among the structure, foundation, and supporting soil plays a crucial role in determining the overall response of such an interaction system. The present study investigates the structural response of a G+8 irregular RC building–nonlinear layered soil–structure interaction system under dynamic wind-induced loading. A three-dimensional finite-element model of a G+8 irregular RC building was developed using ETABS software. The supporting soil was modelled as a multilayered nonlinear medium to capture variations in soil properties with depth and the associated nonlinear behaviour under dynamic wind forces realistically. The dynamic wind forces were evaluated in accordance with the relevant design standards. The interaction analysis incorporated the coupled effects of structural irregularity, soil nonlinearity, and layered soil conditions to assess their influence on displacement, acceleration, base shear, inter-storey drift, and foundation-pressure distribution. The findings reveal that soil nonlinearity significantly alters the dynamic characteristics of the building compared with conventional bare-frame analysis, leading to increased lateral displacements and modified natural frequencies. The layered soil strata further influence the distributions of stress and deformation and, consequently, the structural response of the interaction system.

Early Warning of Pollution-Induced Health Risk Using Multimodal Data and Interpretable Machine Learning

Apoorva Verma — 2026-09-02

Rapid industrialisation and urban expansion have positioned Bhiwadi, Rajasthan, as one of India's emerging air-pollution hotspots, with frequent surges in particulate matter and gaseous pollutants driven by traffic congestion, industrial activity, and seasonal crop-residue burning. These pollution peaks correspond to increased respiratory complaints, hospital emergency visits, and heightened public concern on social media. Traditional forecasting systems often rely solely on pollutant concentrations or meteorological factors, providing limited insight into population-level health risks or vulnerable areas. This study addresses this limitation by developing an integrated, interpretable early-warning framework using heterogeneous data sources, including air-quality sensors, meteorological APIs, mobility and traffic data, social-media complaints, and hospital emergency room (ER) records collected across four zones of Bhiwadi from 2020 to 2024. For model development, pollution-induced health risk was formulated as a binary classification problem based on predefined pollution and health-risk criteria. The XGBoost-based gradient-boosting model, enhanced by spatial clustering, was employed to predict pollution spikes and their associated health impacts up to 24 hours in advance, thereby identifying high-risk neighbourhoods and vulnerable demographic groups. Among the four zones, Zone 4 exhibited the highest predicted health risk. The model demonstrated strong predictive performance, achieving a ROC-AUC of 0.932, precision of 0.871, recall of 0.860, and an F1-score of 0.865. Explainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), provide transparency by revealing the key drivers of the predictions, thereby supporting policymakers in implementing targeted interventions. The proposed multimodal and interpretable approach demonstrates reliable predictive performance and provides a practical basis for developing early-warning systems and supporting evidence-based environmental health management in rapidly urbanising regions.

Agrivoltaics across Drylands and Water Bodies: A Critical Review of Food-Energy-Water Synergies, Trade-offs and Design Priorities

B. Kailashkumar — 2026-09-02

Agrivoltaics is increasingly proposed as a means of reconciling renewable-electricity expansion with food production and water stewardship. Yet two of its most resource-constrained frontiers, drylands and water bodies, have developed through partly separate literatures: terrestrial agrivoltaics emphasises crop microclimate and land sharing, whereas floating photovoltaics and aquavoltaics emphasise land sparing, water conservation, aquatic environmental effects and, more recently, aquaculture. This critical narrative review integrates these domains to assess when photovoltaic co-location can function as a sustainable food-energy-water system rather than merely as spatial co-use. Literature published from 2010 to 10 June 2026 was selected through live searches of accessible scholarly indexes, repositories and verified journal records, supplemented by citation chaining. Evidence was appraised for design quality, environmental realism, transferability, duration, measurement adequacy and alignment between claimed co-benefits and measured outcomes.

The evidence is strongest for microclimatic moderation in hot, water-limited terrestrial settings and for land-sparing electricity generation on artificial water bodies. Partial photovoltaic shade can lower crop heat and evaporative demand and, for suitable crops and configurations, improve water productivity, but crop responses vary with shade fraction, species, season and irrigation regime. Floating photovoltaic systems can gain modest thermal-performance advantages and can reduce evaporation, yet ecological responses depend on coverage, depth, mixing, nutrient status and climate; changes in dissolved oxygen, stratification and primary production prevent generalisation of water-quality benefits. Aquaculture studies indicate plausible production synergies, but the empirical base remains small and site-specific. Across both settings, aggregate metrics such as land-equivalent ratio can conceal losses in one subsystem, while economic performance depends strongly on capital cost, electricity value, agricultural value and governance.

Sustainable deployment therefore requires climate- and livelihood-specific design, coupled food-energy-water metrics, long-duration ecological monitoring and safeguards for land, water and resource access. Agrivoltaics is best treated as a family of coupled systems whose benefits are conditional rather than intrinsic.

Development of Solar Driven Buck-Boost Converter for Corrosion Protection

K. M. Priya — 2026-09-02

Corrosion of buried and submerged metallic structures creates substantial safety and economic concerns, particularly where conventional cathodic-protection power supplies are difficult to maintain. This study develops and simulates a solar-driven buck–boost converter for cathodic protection by integrating maximum power point tracking (MPPT), pulse-width modulation (PWM), and proportional–integral (PI) closed-loop control. The converter is intended to regulate the variable DC output of a photovoltaic source and provide the required electrical output under constant and changing input-voltage conditions. A Raspberry Pi-based controller provides feedback control, while the converter operates in both buck and boost modes. The proposed system was evaluated in Proteus Design Suite under the operating cases described in the manuscript. In buck operation, a 48 V DC input was reduced to 24 V DC, whereas in boost operation, a 12 V DC input was increased to approximately 24 V DC. Variable-input operation was also examined to assess the response of the PI-controlled PWM scheme. The simulated waveforms indicate stable DC regulation with low output ripple, and the reported settling-time parameters were 1.02 ms initially and 0.88 ms for the final settling response. Overall, the simulation results support the feasibility of the proposed solar-driven converter as a regulated power-conditioning approach for cathodic protection applications, while practical performance still requires hardware and field validation.

Edge-Aware Anomaly Sensing: Temporal Feature Learning for IoT Reliability

B. Premalatha — 2026-09-02

While sensor data are central to any IoT monitoring system, conventional approaches that use fixed thresholds to identify faults often fail to detect subtle faults. Readings that freeze, drift slowly, or depart from normal behavioural patterns may remain unnoticed even when they stay within valid operating ranges. In this chapter, a behaviour-based sensor fault-detection framework is proposed, primarily for deployment on resource-constrained edge devices in IoT environments, by combining temporal and spatial feature extraction with a lightweight Random Forest classifier. The system continuously gathers synchronised multi-sensor time-series data and then extracts features based on the delta (difference) between current and previous readings to reflect the temporal characteristics of the data before passing them to a supervised classification system trained to recognise normal behaviour or fault patterns. The proposed method classifies data with a very low computational burden using a dataset of approximately 12,500 sensor readings and an 80/20 train-test split, achieving near-perfect classification accuracy at the edge. Experimental results verify the effectiveness of the system in detecting subtle sensor faults that simple threshold-based approaches fail to identify.

Multilingual Digital Extension for Smallholder Farmers: Artificial Intelligence, Local Languages and the Democratisation of Agricultural Knowledge

Meghaa Sharma — 2026-09-02

Digital extension has reduced the marginal cost of reaching dispersed smallholder farmers, but access to information is not equivalent to access to usable knowledge. Language, literacy, trust, connectivity, and the institutional organisation of extension determine whether digital advice can be understood, acted upon, challenged, and adapted. This critical narrative review examines how artificial intelligence (AI), multilingual language technologies, voice interfaces, and locally grounded advisory systems are reshaping agricultural knowledge access for smallholder farmers. Literature published from 1 January 2000 to 7 August 2026 was identified through multidisciplinary, agricultural, biomedical, computing, and regional scholarly sources, supplemented by citation searching and bibliographic verification. Evidence from randomised field experiments, observational studies, qualitative research, systematic reviews, meta-analyses, human-computer interaction studies, and multilingual natural-language-processing research was appraised for causal credibility, external validity, language relevance, and equity implications. The evidence indicates that digital advisories can improve knowledge and adoption on average, but agronomic and income effects are modest and heterogeneous, and direct causal evidence for multilingual generative-AI systems remains limited. Local-language and voice delivery can reduce literacy-related barriers, yet low-resource language scarcity, dialect variation, speech-recognition error, translation loss, and agricultural terminology create new failure modes. Retrieval-augmented generation and multimodal interfaces can improve grounding and accessibility, but do not by themselves solve problems of outdated content, unsafe recommendations, unequal device access, weak feedback, or accountability. Democratisation therefore depends less on the number of languages nominally supported than on the architecture of participation: reliable local knowledge bases, transparent provenance, human escalation, farmer feedback, social learning, data rights, and public-interest governance. The strongest near-term model is a hybrid system in which AI expands reach and responsiveness while extension workers, farmer organisations, and local communities retain central roles in interpretation, validation, and knowledge co-production.

Monitoring and Control of Induction Motor in Electric Vehicle Using IoT

G. Mahalakshmi — 2026-10-13

In electric vehicles, IoT technology is crucial for monitoring and controlling the induction motors that power the vehicle. As in industrial applications, the motor's parameters are continuously monitored using various sensors to ensure optimal performance and prevent failures. This study presents an Internet of Things (IoT)-based prototype for monitoring and controlling an induction motor intended for electric-vehicle-related operation. The system integrates voltage, current, temperature, vibration, and speed sensing with an Arduino Uno, an ESP32-WROOM module, GSM communication, a PWM triac drive, and automatic motor disconnection. Sensor data are transmitted for remote observation, while abnormal operating conditions trigger warning functions and disconnection of the motor from the supply. The prototype also enables motor-speed control and graphical presentation of monitored parameters. Performance was evaluated under three operating conditions: Speed 1 (1800–2500 rpm), Speed 2 (2600–3500 rpm), and the motor-off state. At Speed 1, reported measurements included 2141 rpm with 314 mA, 216 V, temperature 32, and vibration 0, and 2215 rpm with 364 mA, 216 V, temperature 36, and vibration 0. At Speed 2, reported values included 2633 rpm with 235 mA and 214 V, and 3059 rpm with 230 mA and 215 V, with temperature 32 and vibration 0 in both cases. When the motor was switched off, its speed decreased to zero. Within the tested conditions, the prototype demonstrates integrated sensing, remote monitoring, speed control, fault notification, and automatic disconnection in a single induction-motor monitoring arrangement.

Governing Farmer Data in the Digital Extension Era: A Critical Review of Ownership, Privacy, Consent and Data Sovereignty

Meghaa Sharma — 2026-09-02

Digital agricultural extension is shifting from episodic information delivery to continuous, data-intensive interaction among farmers, advisers, public agencies, platform firms and other agri-food actors. This transition can improve the timeliness and personalisation of advice, yet it also creates governance problems because agronomic recommendations increasingly depend on the collection, linkage, inference and reuse of data about farms, households, locations, transactions and production decisions. This critical narrative review examines how agricultural data governance should be understood in the digital extension era, with particular attention to ownership, privacy, consent and farmer data sovereignty. Literature published from 1 January 2010 to 7 August 2026 was identified through multidisciplinary and agriculture-focused scholarly sources, complemented by authoritative policy and legal materials and citation searching. The evidence indicates that ownership is an intuitively attractive but legally and practically incomplete organising principle. Control often arises through contracts, technical architecture, confidentiality rules, intellectual-property claims, platform design and unequal bargaining power rather than a single property right in data. Privacy protection is similarly uneven because farm data can move across personal, household, business and non-personal categories, while aggregation and inference can create risks not visible at collection. Consent remains necessary in many settings but is frequently weakened by bundled terms, low comprehensibility, dependency on essential services and poorly specified secondary uses. The strongest emerging conception of farmer data sovereignty therefore concerns sustained practical capacity to understand, authorise, monitor, contest, port and revoke data uses, supported by enforceable rights and interoperable infrastructure. Empirical studies consistently connect willingness to share data with trust, transparency, perceived benefit, data type and recipient, although evidence remains geographically concentrated and institutionally heterogeneous. The review proposes a layered governance architecture combining rights baselines, purpose-bound authorisation, security, portability, accountable intermediaries, participatory oversight and accessible redress. Future research should test these mechanisms in real extension systems, especially among smallholders and under-represented regions, using longitudinal and comparative designs that measure both service benefits and distributional consequences.