Engineering

Abstract: The social dimension of sustainability in building construction has long occupied an uncomfortable position in the research literature; acknowledged in theory, yet sidelined in practice. Environmental performance dominates assessment frameworks globally, while economic viability commands institutional attention. What is left, all too often, is a thin layer of social indicators that lack contextual grounding, statistical validation, or practical operationalizability, particularly in rapidly urbanizing settings across the developing world. This study confronts that gap directly. Drawing on an original mixed-methods investigation conducted across the twin cities of Rawalpindi and Islamabad, Pakistan, we develop and validate a comprehensive social sustainability assessment framework specifically tailored to building construction projects in South Asian urban environments. Employing a three-round Delphi technique with industry experts, a structured Likert-scale survey administered to 50 experienced construction professionals, and confirmatory factor analysis (CFA) using AMOS, the study identifies and statistically validates eighteen social sustainability indicators organized under five latent constructs: (i) Social Responsibility and Human Well-being, (ii) Institutional Governance and Knowledge, (iii) Stakeholder Engagement and Community Trust, (iv) Workforce Development and Labor Equity, and (v) Inclusive Design and Service Accessibility. Reliability analysis returned a Cronbach's alpha of 0.984, while the Relative Importance Index (RII) ranked project experience (SCL8, RII = 0.856), health, safety and environment at the site (SCL7, RII = 0.848), and project manager awareness (SCL12, RII = 0.828) as the most influential indicators. Kruskal-Wallis tests confirmed cross-group consensus. Crucially, the study finds that social sustainability is not merely a welfare afterthought, it is deeply interwoven with economic performance and environmental stewardship through measurable cross-pillar correlations. The resulting framework, the first of its kind validated through expert consensus and inferential statistics within the Pakistan context, offers a practical decision-support tool for project managers, urban planners, and regulatory bodies including the Pakistan Engineering Council (PEC) and the Capital Development Authority (CDA). Broader implications for South Asian and developing-country construction governance are discussed.

Abstract: Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k=5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Abstract: Institutional shuttle fleets with fixed routes and predictable terminal parking are well suited to dedicated photovoltaic–battery (PV–BESS) charging infrastructure, yet siting and sizing are usually solved numerically without clear interpretation of the governing constraints. This study develops a closed-form active-constraint sizing rule, derived via Karush–Kuhn–Tucker (KKT) analysis under verified monotonicity of the net-present-value (NPV) objective over the feasible design region, for a 10-van electric academic shuttle fleet operating between the Huay Kaew and Doi Saket campuses of Rajamangala University of Technology Lanna, Chiang Mai, Thailand. One centralized station is compared with two distributed stations under reliability, cost, solar-fraction, autonomy, charger, budget, and rooftop-area constraints. The two-station configuration eliminates 47,600 km/year of dead-run travel and increases system NPV from USD 36,980 to USD 86,293 after the year-10 BESS replacement cost. The KKT analysis identifies two binding constraints—BESS one-day autonomy and PV rooftop area—giving 30 kWp PV and 94.85 kWh BESS per station, rounded to 100 kWh. The full transition achieves IRR = 12.9%, simple payback = 6.1 years, and 95.9% annual CO₂ reduction. Monte Carlo simulation with 5,000 scenarios yields P(NPV > 0) = 100% within the simulated scenario set, VaR_5% = USD 28,959, and CVaR_5% = USD 21,248, confirming financial robustness under the adopted uncertainty ranges.

Abstract: Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability.

Abstract: This paper addresses the robust trajectory tracking problem of an Unmanned Aerial Vehicle (UAV) equipped with a 2-DOF manipulator, designed for fast aerial manipulation of varying payloads. To overcome the high computational cost and adaptability limitations of traditional model-based controllers, this work introduces a novel hybrid gain-scheduling framework that shifts the computational complexity to the pre-flight phase. The approach utilizes an approximate inverse dynamics linearization, based on fixed nominal models, which transforms the complex nonlinear system into a simple linear plant with bounded, structured uncertainties. The entire configuration space, including manipulator states and a range of payload properties, is partitioned into dynamically similar regions using K-Means clustering. For each local region, a dedicated robust PD controller is designed using a multi-objective Genetic Algorithm (GA). This framework also successfully implements a gain interpolation technique to mitigate the potential for abrupt control actions. Simulation results validate the controller’s ability to maintain high-precision tracking during fast maneuvers and payload switching, confirming the robustness and adaptability of the offline-tuned design.

Abstract: This paper makes a review for the studies of Space Energy Internet. Based on introducing the background of related networks, this paper discusses several key components of the Space Energy Internet (mainly including Space Solar Power Station, Energy Internet, and Artificial Intelligence Data Center), focusing on their corresponding system architectures, main research directions, and related technical challenges. Subsequently, supporting technologies such as discrete signal compression and coding, communication technology, energy transmission, power electronic devices, and artificial intelligence are discussed and analyzed. Furthermore, a highly integrated “data-computing-energy-networks” framework is established based on star computing networks and multi-orbital star link systems, and adopting the technologies like plug-and-play and modular design, which can support many innovative applications further.

Abstract: Both Noise Radar (NR) and Quantum Radar (QR), with alleged common features, aim to use the randomness of the transmitted signal to enhance radar covertness and to reduce mutual interference. While NR has been prototypically developed and successfully tested in many environments by different organizations, research and development investments on QR did not bring to practically operating prototypes. Starting from the well-known fact that radar detection depends on the energy transmitted on the target, the detailed evaluations in this work show that the detection performance of all the QR types proposed in the literature are well below the ones of a much simpler and cheaper equivalent “classical” radar set, for example of the NR type. Moreover, the absence of a “Quantum radar cross section” different from the well-known radar cross section is explained. From these facts it results that, in spite of alleged advantages in some literature, Quantum Radar proposals cannot lead to useful results, including, of course, the detection of stealth targets.

Abstract: Over the past decade, acoustically-actuated magnetoelectric (ME) antennas have been proposed as chip scale radiofrequency (RF) antennas compatible with post Complementary Metal Oxide Semiconductor (CMOS) fabrication processes. These devices have been reported to exhibit antenna gains far exceeding those of conventional electromagnetic (EM) antennas with comparable footprint. However, recent studies have challenged whether this enhanced gain originates from magnetoelastic coupling or from stray radiation sources, like the electric dipole moment in the piezoelectric film or currents in the probing pads. We resolve this controversy through a combined analytical, numerical, and experimental investigation. We model and quantify the radiated power and corresponding gain contributions from (I) magnetoelastic coupling; (II) the strain driven, time-varying electric dipole moment in the piezoelectric layer; and (III) the currents in the probing pads. Our results confirm that the radiation from magnetoelastic coupling exceeds that of the other sources by several orders of magnitude. In addition, we explain how to optimize the return loss and the radiated power of ME antennas when connected to a 50 Ω source, showing that the optimal operating point is the anti-resonance frequency. Based on this finding, we investigate the impact of the electromechanical coupling (k_t²) on gain and-10 dB fractional bandwidth. To corroborate our simulation results, we design, fabricate, and characterize the first two Aluminum Scandium Nitride (AlScN) magnetoelectric Bulk Acoustic Wave (BAW) antennas operating beyond 1.1 GHz. The two prototypes integrate different magnetostrictive materials (FeGaB and FeCoSiB) and exhibit measured realized gains of-31.8 dB and-29.7 dB, with-10 dB fractional bandwidths of 1.28% and 1.27% at 2.62 and 3.08 GHz, respectively. The achieved bandwidths are the highest reported for radiofrequency (RF) ME antennas, owing primarily to the enhanced piezoelectric coefficients of the AlScN. Benchmarking against control structures (unreleased FeGaB and FeCoSiB devices) confirms substantially degraded radiation performance in the absence of a strong magnetoelastic coupling. These results elucidate the working principle of ME antennas and provide RF designers with a rigorous framework for the design and modeling of acoustically actuated ME antennas for wireless communication and sensing.

Abstract: Aerodynamic drag is one of the two principal external sources of energy loss in on-road vehicles – the other being rolling resistance – and it critically affects the range of battery-electric and fuel cell-electric vehicles. To ensure accurate early-stage analysis such as vehicle range prediction and sizing of energy storage and powertrain components, it is essential to incorporate realistic representations of air resistance. Despite its importance, due to limited data availability air resistance is often simplified using zero crosswind and "nominal air conditions", which tend to underestimate the actual energy required to overcome aerodynamic drag. This approach also fails to capture the variability introduced by changing environmental conditions, leading to significant discrepancies in energy consumption and, consequently, vehicle range. As a result, evaluating system robustness and conducting meaningful trade-off analyses between different vehicles or vehicles configurations becomes challenging. This study demonstrates how publicly available meteorological data can be utilized to quantify long-term variations in aerodynamic drag. By analyzing multiple years of weather observations, we derive realistic distributions of aerodynamic energy losses – capturing not only mean values but also the full range of variability. These distributions enable probabilistic modeling of vehicle performance, thereby supporting robust system design and informed trade-off decisions across various levels of vehicle architecture. To demonstrate this, we compare two different tractor/semitrailer configurations.

Abstract: The rapid advancement of generative large language models (LLMs) has sparked significant interest in their poten- tial to transform higher education, particularly in fostering student engagement. While these models offer novelopportunities for personalized learning and interactive experiences, their integration into academic settings remains underexplored, with varying implications for pedagogy, ethics, and institutional policy. This systematic literaturereview examines the role of generative LLMs in enhancing student engagement across multiple dimensions, includ- ing their impact on learning outcomes, academic writing, subject-specific applications, and ethical considerations.We synthesize existing research to identify key trends, challenges, and gaps in the current understanding of how these technologies are reshaping educational practices. A rigorous methodological approach was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of the field. The findings reveal that generative LLMs can significantly influence student engagement by facilitating adaptive learning environments and supporting creative problem-solving; however, concerns about academic integrity, equitable access, and pedagogical alignment persist. The review also highlights emerging tools and systems designed to integrate LLMs into education, alongside institutional and student perspectives on adoption. Based on the synthesized evidence, we discuss future directions for research and policy, emphasizing the need for balanced frameworks that harness the benefits of generative AI while addressing its risks A. Chowdhury et al., 2025. This study contributes a structured overview of the current landscape, offering insights for educators, researchers, and policymakers navigating the evolving intersection of AI and higher education.

Abstract: The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems.

Abstract: A serious problem facing power systems today is the deterioration of power quality (PQ), driven mainly by the widespread use of non-linear loads, such as power electronic converters and adjustable-speed drives. These loads inject harmonic currents into the network, resulting in voltage distortion, increased losses, overheating, and reduced system efficiency. Therefore, harmonic mitigation and reactive power compensation are necessary to ensure stable and reliable operation. This paper presents the development of a Shunt Active Power Filter (SAPF) to become an intelligent Shunt Active Power Filter Conditioner (SAPFC) that operates under different loading conditions (linear, non-linear, or mixed). As a result, a conventional PI controller is upgraded with a Fuzzy Gain Scheduling (FGS) technique optimized by the Whale Optimization Algorithm (WOA) in order to keep the DC-link capacitor voltage stable. In addition, an adaptive instantaneous dq theory is used to produce accurate reference currents. The proposed intelligent SAPFC is implemented and validated in MATLAB-Simulink, where simulation results show maximizing the SAPFC's effectiveness in minimizing harmonic distortion to less than 0.7% and achieving a near-unity power factor under different operating conditions. The integrated SAPFC, with its intelligent control, offers a robust and adaptive solution for improving power quality in modern electrical systems, thereby increasing efficiency and reducing power losses.

Abstract: For long-term and continuous monitoring of ECG signals, textile electrodes may be an option. In this study, woven stainless steel and silver copper-plated polyester fabrics were used to create electroconductive textile-based electrodes that were simultaneously tested against commercial Ag/AgCl electrodes. The ECG signals were recorded using the static and dynamic BIOPAC MP360 ECG data capture module (BIOPAC Systems, Inc., Goleta, CA, USA). Using the algorithmic features retrieved for each electrode, sensor characterization involved ECG monitoring, surveys on the comfortability of human participants, and wash ability effect evaluation. Under both static and dynamic conditions, the obtained ECG signal waveform was observable for each electrode. Based on the signal shape, HR, and R-R interval, the ECG signals recorded while the participants were running on a treadmill machine were evaluated and compared. The findings showed that signals acquired using all electrodes had visible P, QRS, and T waves but that under both static and dynamic conditions, silver copper-plated polyester textile electrodes had a greater R-peak amplitude (1.28 mV) than did standard Ag/AgCl electrodes and stainless-steel textile electrodes. The signals were distorted slightly during running, which could have been caused by shaky skin-electrode contact.

Abstract: This paper presents a comprehensive comparative analysis of recent advances in smart bone prosthetics. The emphasis is made on the integration of embedded sensors, adaptive control systems, and wireless monitoring into metallic, carbon-based and bioceramic materials. The evaluation of essential characteristics of mechanical strength, durability, and biocompatibility is combined with its integration of smart functionality. The key mechanical properties, such as tensile strength, Young’s modulus, and fatigue life, are reviewed to assess how each material supports long-term prosthetic performance. Concurrently, biocompatibility factors, tissue integration and inflammatory response are examined to ensure safe and effective clinical application. The integrative approach can help clinicians and biomedical engineers to fine-tune the selection of the optimal material-smart system and provide individually tailored combinations to specific patient needs and surgical-operative contexts.

Abstract: The experimental determination of the relationships between the stress distribution zone in the soil layer and the parameters of tillage working bodies is a labor-intensive process. Therefore, preliminary mathematical modeling of this process is recommended to mi-nimize the total number of experiments. The research was conducted using the principles of classical mechanics and soil mechanics. Using an equation proposed by J. Boussinesq,a graphical-analytical method was developed to evaluate the stress state in the soil layer induced by a dihedral wedge. This method incorporates both the geometric parameters of the dihedral wedge and the physico-mechanical properties of the soil. A direct pro-portional relationshipwas established between the length of the dihedral wedge and the total area of the deformed soil mass. Specifically, increasing the length of the dihedral wedge by 83% (from 0.05 to 0.30 m) resulted in an 80% increase in the area of the de-formed soil mass (from 0.02 to 0.10 m²). The proposed graphical–analytical method can be employed in the design of tillage implements.

Abstract: Early identification of corrosion-prone conditions remains a major maintenance challenge in closed, hard-to-access structural zones. This paper presents a multi-sensor data fusion approach for early warning of corrosion-prone conditions in selected closed zones of a medical rescue aircraft, as part of a structural health monitoring framework. The study combines sensor selection, installation in restricted-access compartments, and analysis of in-service data collected during helicopter operation. The workflow includes data acquisition, preprocessing, feature extraction, fused interpretation of multi-channel data, and assignment of warning levels linked to maintenance actions. Environmental, conductance, and electrochemical channels provide a first-stage early-warning layer that indicates persistent conditions favorable to long-term corrosion development, rather than direct proof of existing damage. Persistent warning states are intended to trigger staged follow-up diagnostics: PZT sensing localizes suspect subregions, while eddy-current sensing verifies and monitors the growth of local metallic degradation. Field inspection evidence of corrosion in hidden zones supports the practical relevance of this approach. Although demonstrated on an aircraft, the methodology is transferable to other closed or poorly accessible structural zones, including civil engineering applications.

Abstract: This project checks methods in wind power forecasting by comparing Gregorian calendar based on seasonal alignments with the vedic lunisolar calendar parallely. Rather than using timestamps like most forecasting methods, this project seeks to determine whether periodic cycles based on nature’s cosmos could reveal correlational patterns of wind activity surges and enhance accuracy. This study exploits the SOLETE dataset from SYSLAB, Denmark, which consists of 15 months of power generation alongside weather data. The dataset underwent processing with the CleanTS tool (an R package) and it was transformed into Gregorian and Vedic time frameworks. Within both time frameworks, the forecast approaches a hybrid forecasting model integrating “Variational Mode Decomposition (VMD) with Gaussian Process Regression (GPR)” was designed and assessed [11][12 ]. The Vedic forecasting approach is slightly better as it gives RMSE of 2.5519 and MAE of 2.0763, while the Gregorian forecasting approach gives RMSE of 2.6123 and MAE of 2.1424. The MAE correlation analysis over months revealed differing patterns within the two forecasting approaches with vedic giving better correlation than gregorian. This suggests that the Vedic calendar forecasting approach is better than the gregorian calendar system, which is based on natural cycles and is lunisolar, it is more accurate in capturing the chaotic signal of wind patterns than the arbitrary gregorian forecasting approach. This project helps in research, questioning the standard time representation in forecasting models which uses the gregorian timestamps and gives idea that if we put natural cycles through alternative calendar systems will it enhance the accuracy of energy predictions, potentially updating grid integration and operational planning.

Abstract: Federated learning (FL) is an attractive learning paradigm for privacy-preserving edge intelligence because it allows distributed devices to train a shared model without moving raw data to a central server. This feature is especially relevant to 5G and emerging 6G networks, where ultra-low latency, dense connectivity, and edge-native computing are expected to support large-scale intelligent services. Nevertheless, practical FL deployment remains difficult in heterogeneous wireless environments because client devices differ in processing capability, battery budget, data volume, and channel quality. These differences create stragglers, increase round latency, and waste scarce communication resources when client participation is scheduled naively. This study develops a deployment-oriented framework for dynamic client selection and resource allocation in heterogeneous edge environments. We formulate each FL round as a latency-constrained optimization problem that jointly captures computation time, uplink transmission time, and minimum participation requirements. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that ranks clients using a weighted score combining computational capability, channel quality, and a fairness term, followed by a greedy radio-resource allocation procedure that prioritizes the largest marginal reduction in estimated completion time. Using the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces average round-completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. The results indicate that lightweight joint scheduling can substantially improve wall-clock efficiency for FL over heterogeneous 5G/6G edge networks.

Abstract: During the measurement process, load cells are susceptible to temperature variations, which can significantly degrade measurement accuracy. To address this problem, this paper presents a temperature compensation method based on an improved neural network. First, the mechanism of sensor temperature drift is analyzed from a thermodynamic perspective. Subsequently, an Improved Honey Badger Algorithm (IHBA) is developed to optimize the initial weights and biases of a Back-Propagation (BP) neural network, aiming to enhance global search capability and convergence stability. To validate the proposed method, a dedicated calibration experimental system was constructed, and temperature-dependent output data were collected over a range of 0 °C to 60 °C. Comparative experiments with conventional methods, including IMA-BP, PSO-BP, standard BP, and polynomial fitting, were conducted. In addition, an ablation study was performed to verify the effectiveness of the proposed improvements. The results demonstrate that the IHBA-BP model achieves superior compensation performance. The temperature drift coefficient and sensitivity temperature coefficient are reduced by 86.6% and 95.86%, respectively. The proposed method shows strong potential for improving measurement accuracy of load cells under varying temperature conditions and provides a practical solution for industrial sensor calibration applications.

Abstract:

Okra (Abelmoschus esculentus) stems constitute an abundant lignocellulosic biomass with significant potential for sustainable composite reinforcement. In this study, okra fibers were extracted using biological retting, alkaline treatment (1-7.5 wt% NaOH), and combined extraction processes. The influence of extraction conditions on the physicochemical, mechanical, thermal, and structural properties of the fibers was investigated. FTIR analysis revealed the progressive removal of hemicellulose and lignin after alkaline treatment, while XRD results showed an increase in cellulose crystallinity. Optical microscopy observations revealed progressive fiber separation and cleaner surface morphology after alkaline treatment. Fiber density increased with NaOH concentration, whereas water absorption and moisture regain decreased due to the reduction of hydrophilic amorphous components. Mechanical properties, particularly tensile strength and Young’s modulus, improved under moderate treatment conditions but decreased under severe alkaline conditions because of partial cellulose degradation. The optimal treatment condition (1 wt% NaOH for 60 min) provided the best balance between delignification, structural preservation, and mechanical performance. These results demonstrate that okra fibers are promising lightweight reinforcements for sustainable bio-composite and technical textile applications.