Engineering

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.

Abstract: Addressing the issues of insufficient adaptability and limited energy efficiency optimization capabilities in traditional tunnel lighting control methods under complex traffic conditions, this paper proposes a dynamic dimming strategy for tunnel lighting based on the Proximal Policy Optimization (PPO) algorithm.First, the tunnel lighting system is modeled as a reinforcement learning environment. A state space integrating multi-dimensional information—including traffic flow, vehicle speed, external brightness, and tunnel section location—is constructed, and a continuous action space is designed to enable precise dimming control for each functional section. Based on this, a multi-objective reward function is established that integrates brightness tracking error, energy consumption optimization, control stability, and environmental adaptability to guide the agent in learning the optimal dimming strategy.Subsequently, model training and experimental validation were conducted using actual tunnel operation data.Experimental results indicate that, compared to traditional L20 control strategies, the proposed method achieves smoother brightness regulation and higher zone control accuracy while ensuring driving safety and visual comfort, and demonstrates significant energy-saving advantages during periods of high lighting demand. In summary, the dynamic dimming strategy based on the PPO algorithm shows promising application prospects and engineering value in intelligent tunnel lighting systems.

Abstract: To address the complex spatiotemporal dependencies and dynamically evolving spatial relationships in tunnel traffic flow prediction, a macro–micro collaborative two-stage prediction method is proposed.The Grey Wolf Optimizer (GWO) is first employed to optimize the GRU model for predicting incoming traffic flow at the tunnel entrance, providing reliable macro-level input for subsequent modeling.Based on this, a spatiotemporal graph structure is constructed, and an FSE-ST-GCN model integrating an adaptive adjacency matrix with spatial and channel attention mechanisms is developed to capture dynamic spatial dependencies and enhance key feature representation.Experiments are conducted using real-world traffic flow data collected from the Shizuizi Tunnel on the Jilin–Caoshi Expressway. The results show that the proposed method outperforms baseline models in terms of MAE, RMSE, and MAPE, achieving superior prediction accuracy and stability. This work provides effective technical support for refined tunnel traffic management and lighting control.

Abstract: We present a 3D laser-scanning method for fast, accurate dimensional inspection of large high-speed-rail precast box girders. The pipeline uses low-pass filtering plus sequential registration to suppress noise, and voxel filtering with curvature-aware enhancement to reduce point-cloud size by 3–5× while preserving key geometry. Reconstruction employs K-nearest-neighbors and PCA to detect boundaries and curvature jumps, B-spline fitting with moving least squares for surface completion, and CSS corner detection to extract key dimensions at millimeter precision. Field tests report absolute errors ≤2.0 mm versus manual measurement, validating the method for automated, digital acceptance.

Abstract: The transition toward a circular economy (CE) in the plastic recycling sector requires integrated management frameworks that align technical performance with organizational governance. This study proposes an exploratory diagnostic framework for formalized recycling SMEs, integrating Latent Dirichlet Allocation (LDA) and Random Forest (RF) algorithms. Given the specialized nature of the sector, a purposive sample of 16 ‘pioneer’ SMEs in Bogotá was analyzed. Data were standardized through a 5-point ordinal scale, and the Spearman rank correlation analysis (ρ≥0.85) revealed high internal consistency and structural synchronization. This high correlation reflects the operational homogeneity of the analyzed vanguard rather than a universal statistical generalization. The findings suggest that for these leading firms, circularity is driven by social impact, collaborative networks, and systemic process reengineering. The proposed framework serves as a methodological blueprint for analytical generalization, providing an adaptable diagnostic tool that can be iteratively refined as the sector matures and data availability increases.

Abstract: Student safety during daily school transportation remains a major concern, particularly in systems that rely mainly on GPS tracking and manual supervision. Existing approaches often lack proactive safety mechanisms for monitoring both student attendance and driver condition in real time. This paper presents MUTMA’INN derived from the Arabic word “مطمئن”, meaning being reassured, at peace, or tranquil, reflecting the system’s role in ensuring the safety and security of students during transportation. The proposed system is an AI-powered school bus safety framework designed to improve the security and reliability of daily student transportation in alignment with Saudi Vision 2030’s Quality of Life Program. The proposed system consists of two integrated components: a cross-platform Flutter mobile application for parents, drivers, and school administrators, and a Python-based edge system connected to Firebase for real-time synchronization. The framework automates student attendance through facial recognition at the bus gate, reducing manual effort and the risk of human error. In addition, it monitors the driver using contactless remote photoplethysmography and facial analysis techniques to estimate heart rate and detect signs of fatigue or emotional distress. When abnormal conditions are detected, immediate alerts are sent to administrators to support timely intervention. By combining mobile computing, edge intelligence, computer vision, and cloud services into a unified platform, MUTMA’INN provides a proactive approach to school transportation safety. The proposed framework demonstrates how AI can support safer and more intelligent student transit systems.

Abstract: Photovoltaic (PV) systems are fundamentally limited by spectral mismatch between the solar spectrum and semiconductor band gaps, resulting in thermalization and transmission losses that reduce overall efficiency. This paper presents a critical review of spectral management approaches, focusing on solar spectrum splitting as a means to improve energy conversion. Existing strategies, including multijunction solar cells, optical spectrum splitting, dispersive and diffractive systems, luminescent solar concentrators, hybrid photovoltaic–thermal systems, and photonic filtering, are analyzed and compared. While these approaches improve spectral utilization, they are often constrained by fabrication complexity, alignment sensitivity, angular dependence, or inherent energy losses. A qualitative, integrative literature review methodology is used to evaluate performance, limitations, and implementation feasibility across these technologies. The analysis shows that no current approach simultaneously achieves high efficiency, low complexity, and robust performance under diffuse illumination. Photonic spectrum splitting combined with independently operated photovoltaic channels is identified as a promising direction. However, the absence of experimental validation remains a limitation, and future work should focus on developing compact, alignment-tolerant systems for practical applications.

Abstract: This work provides an experimental comparison between classical PID, analytically compensated PID, and fuzzy control applied to the speed control of a rover actuator based on a permanent magnet DC motor. Unlike most studies, which focus on classical metrics such as transient response and steady-state error, this work incorporates kinematic indicators such as acceleration and jerk to characterize the dynamic effort applied to the actuator. The results indicate that the fuzzy controller achieves the fastest transient response and the best disturbance rejection, although at the cost of an IAJ 2.378 times higher than that of the classical PID and a peak jerk 79.36% higher under nominal conditions. The classical PID exhibits the smoothest kinematic profile under nominal operation, but under disturbances it generates jerk peaks 2.39 times higher than the fuzzy controller and an IAJ 1.67 times higher than the compensated PID, evidencing its inadequacy under variable loads. The compensated PID achieves the lowest cumulative IAJ under disturbance, outperforming the fuzzy controller by 6.7%, and provides the best overall balance between response speed, disturbance rejection, and cumulative mechanical wear.