Magnetic field sensors are an integral part of many industrial and biomedical applications, and their utilization continues to grow at a high rate. The development is driven both by new use cases and demand like internet of things as well as by new technologies and capabilities like flexible and stretchable devices. Magnetic field sensors exploit different physical principles for their operation, resulting in different specifications with respect to sensitivity, linearity, field range, power consumption, costs etc. In this review, we will focus on solid state magnetic field sensors that enable miniaturization and are suitable for integrated approaches to satisfy the needs of growing application areas like biosensors, ubiquitous sensor networks, wearables, smart things etc. Such applications require a high sensitivity, low power consumption, flexible substrates and miniaturization. Hence, the sensor types covered in this review are Hall Effect, Giant Magnetoresistance, Tunnel Magnetoresistance, Anisotropic Magnetoresistance and Giant Magnetoimpedance.
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- The following article is Open accessMagnetic sensors-A review and recent technologies
Mohammed Asadullah Khan et al 2021 Eng. Res. Express 3 022005
- The following article is Open accessPolyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporation
Nikolaos Chousidis 2024 Eng. Res. Express 6 025010
View article, Polyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporationPDF, Polyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporationThe properties of polyvinyl alcohol (PVA) films are intricately influenced by factors such as polymer structure, fabrication method, the addition of plasticizers and the molecular weight of monomers. This research, investigates the implication of PVA films using a solution casting method for crosslinking with boric acid (H3BO4), glycerol (C3H8O3) and citric acid (C6H8O7). This approach is compared with pure PVA films, establishing a valuable benchmark. For the experiments, tensile strength tests, physicochemical property measurements, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were conducted to gain insights into the microstructure, surface characteristics and mineral composition of the films. This comprehensive approach aims to enhance our understanding of the intricate relationship between PVA, plasticizers and crosslinking agents, providing valuable insights for applications across diverse industries, including, construction and biomedical fields. The overarching objective of this research is to revolutionize the construction industry by developing polymer films that serve as the foundation for self-healing materials, fostering durability and innovation. The experiments revealed a significant influence of crosslinking agents on the properties of PVA films as measured.
- The following article is Open accessExplainable AI in agriculture: review of applications, methodologies, and future directions
Deepthi G Pai et al 2025 Eng. Res. Express 7 032202
View article, Explainable AI in agriculture: review of applications, methodologies, and future directionsPDF, Explainable AI in agriculture: review of applications, methodologies, and future directionsAgriculture forms the backbone of the global economy, facing mounting pressure from population growth and resource constraints. The sector increasingly relies on data-driven technologies to enhance productivity while reducing environmental impact. Agriculture is being revolutionized by Artificial Intelligence (AI), which is enhancing pesticide application, weed control, and irrigation management. Deep Learning techniques that have demonstrated predictive power include Generative Adversarial Networks, Recurrent Neural Networks, and Convolutional Neural Networks. Their opacity and intricacy, however, make practical use difficult. In agricultural settings, Explainable AI (XAI) enables informed decisions by providing transparency without compromising performance. This comprehensive review analyzes peer- reviewed publications from 2020 onwards, categorizing XAI techniques and their applications in agriculture. The starting point of 2020 was deliberately chosen to capture the most recent advancements, as this period marks a phase of rapid growth and wider adoption of XAI within agricultural AI applications, making it particularly relevant for reflecting state-of-the-art developments. This review identifies significant challenges, current research trends, methodological approaches, and evaluate the efficacy of various explainability methods, including LIME, SHAP, Grad-CAM, and rule-based models. The analysis examines key domains including crop-weed discrimination, plant disease detection, precision farming techniques, yield forecasting, and soil quality assessment. The integration of XAI methodologies in precision agriculture presents promising opportunities to address pressing challenges related to resource optimization, climate adaptation, and global food security. This review also provides a structured framework for future research directions and practical implementation guidelines to enhance the interpretability, trustworthiness, and adoption of AI-powered agricultural systems among farmers, agronomists, and policymakers.
- The following article is Open accessSolvent-based revival of carbon-based perovskite solar cells
Elena S Akulenko et al 2026 Eng. Res. Express 8 125322
View article, Solvent-based revival of carbon-based perovskite solar cellsPDF, Solvent-based revival of carbon-based perovskite solar cellsPerovskite solar cells (PSCs) combine high efficiency with low-cost processing, but their rapid progress toward commercialization is challenged by insufficient end-of-life strategies. Here we present the proof-of-concept of solvent-based revival of carbon-based PSCs using the green solvent γ-valerolactone (GVL) reaching high revival rates, which requires significantly less energy compared to the production of a new device. With this revival approach, we not only recover the materials but also the structure of the whole devices, including the energy and costs invested in them in the original manufacturing, thus advancing the highest level of resource-efficient recycling within the eco-design concept. Devices subjected to the optimized protocol revived up to 95% of their initial power conversion efficiency, with a stable fill factor and only modest reductions in photocurrent and open-circuit voltage (Voc). Impedance spectroscopy revealed only a small increase in series and charge-transfer resistances, attributable to interfacial changes of the carbon/perovskite boundary, while scanning electron microscopy confirmed the preservation of the carbon scaffold and successful reinfiltration of the perovskite precursor. Energy return on investment analysis further highlighted the sustainability advantage of revival compared to single-use devices. These results establish GVL-assisted revival as a practical first-level recycling strategy for PSCs.
- The following article is Open accessAdditive manufacturing of high-performance adsorbents for environmental use
Alvin Lim Teik Zheng et al 2025 Eng. Res. Express 7 032001
View article, Additive manufacturing of high-performance adsorbents for environmental usePDF, Additive manufacturing of high-performance adsorbents for environmental useThe emergence of 3D printing (additive manufacturing) has revolutionized the fabrication of advanced adsorbents. This review provides a comprehensive, analytical assessment of 3D-printed adsorbents, critically evaluating their materials, fabrication methodologies, and performance metrics. The materials discussed include polymers, metal–organic frameworks (MOFs), zeolites, biopolymers, carbon-based materials, and hybrid composites, all of which are evaluated in terms of their adsorption efficiency, selectivity, and functionalization strategies. This review also highlights future research directions, including computational modeling-driven design, multi-material printing strategies, and sustainable material innovations. As additive manufacturing technologies progress, their integration with adsorption science presents significant potential to transform environmental and industrial purification systems.
- The following article is Open accessA beginner’s guide to thermophotovoltaic-based thermal energy storage using high-temperature phase change materials
Norbert Edomah et al 2026 Eng. Res. Express 8 053001
View article, A beginner’s guide to thermophotovoltaic-based thermal energy storage using high-temperature phase change materialsPDF, A beginner’s guide to thermophotovoltaic-based thermal energy storage using high-temperature phase change materialsThe rising global demand for reliable energy systems and the increased deployment of renewable energy infrastructure has increased the demand for efficient energy storage technologies capable of addressing the challenge of intermittency of renewable energy sources. Thermophotovoltaic (TPV)-based energy storage represents an emerging and potentially transformative approach that converts stored thermal energy into electricity through radiative photon emission and photovoltaic conversion. This tutorial paper provides an overview of the working principles, core systems components and operation of the TPV-based energy storage system, with a specific focus on latent heat storage using high-temperature Phase Change Materials (PCMs). This paper further explores the performance metrics of TPV energy conversion, and examines how PCM-based latent heat storage enables stable operation at temperatures above 1000 K. We further explored the key challenges related to efficiency losses, material degradation, costs and possible pathways for improvement, alongside recent experimental benchmarks and practical applications of TPV-based storage in long-duration energy storage, cogeneration and industrial waste heat recovery. By consolidating the current state of knowledge, technological progress, and research challenges, we aimed at providing a foundational understanding of TPV thermal storage systems and highlight their potential as a cost-effective and scalable pathway toward a sustainable energy future.
- The following article is Open accessEngineering tetrazole-anchored azo sensitizers for enhanced charge transfer and photovoltaic performance in dye-sensitized solar cells
E B Yurdakul et al 2026 Eng. Res. Express 8 135002
View article, Engineering tetrazole-anchored azo sensitizers for enhanced charge transfer and photovoltaic performance in dye-sensitized solar cellsPDF, Engineering tetrazole-anchored azo sensitizers for enhanced charge transfer and photovoltaic performance in dye-sensitized solar cellsHerein, we report the design and comprehensive investigation of a series of newly synthesized tetrazole-anchored azo dyes (denoted as O1Y–O4Y) to elucidate the role of donor substituents in governing interfacial charge-transfer processes in dye-sensitized solar cells (DSSCs). Combined experimental characterization and density functional theory (DFT)/TD-DFT analyses reveal that tetrazole anchoring significantly strengthens electronic coupling with TiO2, enabling highly favorable electron injection (ΔGinject = − 1.34 to −1.39 eV) while effectively suppressing recombination. Among the series, the thioether-substituted O1Y dye delivers superior performance (Jsc = 4.59 mA cm−2, power conversion efficiencies (PCE) = 1.75%), whereas halogen-substituted analogs exhibit diminished efficiency due to unfavorable energetic alignment and increased reorganization losses. Notably, O4Y exhibits a 3.02-fold (≈202%) enhancement in PCE compared to its nitrile-anchored counterpart (O4). Unlike conventional studies focusing on donor or π-spacer modification, this work provides a direct and systematic evaluation of anchoring-group engineering, offering new mechanistic insight into how tetrazole anchoring modulates interfacial charge-transfer processes in DSSCs.
- The following article is Open accessA 5IR-inspired conceptual framework for AI-augmented construction estimation: review on developing conceptual BIM-integrated hybrid models for cost, time, and risk prediction
Demiss A Belachew and Walied A Elsaigh 2026 Eng. Res. Express 8 112101
View article, A 5IR-inspired conceptual framework for AI-augmented construction estimation: review on developing conceptual BIM-integrated hybrid models for cost, time, and risk predictionPDF, A 5IR-inspired conceptual framework for AI-augmented construction estimation: review on developing conceptual BIM-integrated hybrid models for cost, time, and risk predictionThis study introduces three hybrid AI conceptual models integrated with BIM for predictive analytics in construction management for future validation through simulations and ethical AI considerations by a systematic analysis of 66 studies. Despite the potential benefits of AI-BIM hybrid models for operational efficiency and accuracy, they remain underdeveloped and inadequately embody the principles of the fifth industrial revolution (5IR). The framework proposes three conceptual hybrid AI models for future validation: a dynamic cost predictor utilizing XGBoost and natural language processing for effective cost estimation; a schedule forecaster combining Long short-term memory networks with Monte Carlo simulations to enhance schedule predictions; and a risk analyzer that incorporates Bayesian networks and computer vision to evaluate multidimensional risks. These conceptual models are proposed to be interconnected with BIM to facilitate automated data extraction and provide real-time decision-making support. The proposed conceptual framework emphasizes ethical AI practices and promotes human-AI collaboration while aiming for sustainable resource management. A phased implementation roadmap was provided to guide pilot testing and industry adoption, highlighting the necessity for socio-technical integration. This proposed roadmap focuses on the seamless transition of these advanced methodologies into construction practices, ensuring robust collaboration among stakeholders. The proposed AI-BIM-5IR framework is conceptual and intended to guide future empirical validation and implementation studies. This research aims to present a foundation for future studies focused on enhancing resilience, accountability, and efficiency within the construction sector while adhering to the transformative principles of 5IR.
- The following article is Open accessTechno-economic analysis of large-scale battery energy storage system for stationary applications in South Africa
Christopher Borerwe and Omowunmi Mary Longe 2025 Eng. Res. Express 7 012301
View article, Techno-economic analysis of large-scale battery energy storage system for stationary applications in South AfricaPDF, Techno-economic analysis of large-scale battery energy storage system for stationary applications in South AfricaSouth Africa’s transition to renewable energy sources (RES), particularly solar photovoltaics (PV), requires robust energy storage solutions to counterbalance intermittency and meet low-carbon objectives. This study offers a comparative techno-economic analysis of three large-scale battery energy storage systems (BESS): lithium iron phosphate (LFP), lead-acid (Pb-acid), and vanadium redox flow batteries (VRFB). These technologies were selected for technical maturity, cost-effectiveness, and suitability in stationary applications. Using HOMER Pro software, two BESS capacity scenarios A (1.17 MWh) and B (2.34 MWh) were simulated and evaluated on key performance metrics: such as levelized cost of electricity (LCOE), efficiency, environmental impact, and cycle life. The results indicate LFP BESS as the optimal choice for both scenarios, achieving the lowest LCOE values (R4.05/kWh for 1.17 MWh and R4.25/kWh for 2.34 MWh), up to 34 percent (%) lower than Pb-acid BESS and 30% lower than VRFB. LFP also demonstrated significant advantages, including a high round-trip efficiency of up to 95%, a cycle life of 10–20 years, and a reduced environmental impact. Moreover, LFP requires 30% less installed capacity than Pb-acid, resulting in greater cost savings. The environmental emissions evaluation in this case study further demonstrated that a hybrid system with LFP achieved the lowest carbon dioxide (CO2) emissions, with reductions of 10.5% compared to Pb-acid and 23.4% compared to VRFB. While VRFB provides high durability and long-term efficiency, its higher LCOE and additional energy requirements make it less suitable for cost-sensitive, short-term applications. Consequently, the analysis identifies LFP batteries as the most techno-economically efficient option for large-scale stationary storage, underscoring their critical role of BESS in stabilizing energy supply, enhancing grid reliability, and reducing greenhouse gas emissions, pivotal for South Africa’s sustainable energy transition.
- The following article is Open accessApplication of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties
Belachew A Demiss and Walied A Elsaigh 2024 Eng. Res. Express 6 032102
View article, Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertaintiesPDF, Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertaintiesConstruction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns and delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) while considering construction delivery uncertainties. The first model introduces a Spatio-Temporal Attention CNN-RNN Hybrid Model with Probabilistic Uncertainty Modeling, which integrates attention mechanisms and probabilistic uncertainty modeling to provide accurate and probabilistic estimates of construction duration, offering insights into critical areas of uncertainty. The second model presents a Multi-Modal Graph CNN-RNN Hybrid Model with Bayesian Uncertainty Integration, which harnesses multi-modal data sources and graph representations to offer comprehensive estimates of construction duration while incorporating Bayesian uncertainty measures, facilitating informed decision-making and optimized resource allocation. Lastly, the third model introduces a Hierarchical Spatio-Temporal Transformer CNN-RNN Hybrid Model with Fuzzy Logic Uncertainty Handling, which addresses the inherent vagueness and imprecision in construction duration estimates by incorporating hierarchical spatio-temporal transformer architecture and fuzzy logic uncertainty handling, leading to more nuanced and adaptable project management practices. These advanced models represent significant advancements in addressing construction duration challenges, providing valuable insights and recommendations for future research and industry applications. Moreover, this review article critically examines the application of hybrid deep learning architectures, specifically the combination of CNNs RNNs, in predicting construction duration estimates at the preconstruction stage while considering uncertainties inherent in construction delivery systems.
- Research on the optimal design of yielding and energy-absorbing support structures for roadways under the coupling action of static and dynamic loads
Junchao Shen 2026 Eng. Res. Express 8 145102
View article, Research on the optimal design of yielding and energy-absorbing support structures for roadways under the coupling action of static and dynamic loadsPDF, Research on the optimal design of yielding and energy-absorbing support structures for roadways under the coupling action of static and dynamic loadsWith the continuous deepening of coal mining operations, the stress conditions of the roadway’s surrounding rock have grown progressively more intricate. This rock mass not only withstands static loads like the ever-fluctuating in-situ stress, but also is regularly subjected to dynamic influences such as rockburst events and blasting-induced perturbations. Consequently, the roadway support system encounters stability challenges when subjected to the combined effects of static and dynamic loading. Beginning with an investigation into the mechanical behavior of the surrounding rock and support system under the coupled static-dynamic load conditions, a digital characterization approach is developed to describe the displacement and stress fields of the surrounding rock, as well as the stress states of individual support elements. Additionally, the energy dissipation mechanisms and mechanical response pathways of the yielding and energy-absorbing support system are examined in detail. A parametric design and digital modeling framework for the support system, which can integrate static and dynamic load coupling, was created. Critical parameters of rock bolts, bolt-mesh assemblies, and surface-mounted components were digitally specified, allowing for flexible modeling and performance evaluation of the support system. The cooperative matching level of support parameters improved by 41%, the structural load-bearing coefficient increased to 85.1, the yielding precision rose by 23.79%, the operational condition adaptability rate reached 98.5%, the surrounding rock deformation control efficiency enhanced by 17.6%, the overall support effectiveness stood at 59.87, the energy dissipation stability achieved 83%, and the comprehensive stability indicator for the surrounding rock was 49.2. The adaptability of the support system to static and dynamic loading conditions, along with its energy-absorbing and yielding capabilities, was notably enhanced and optimized.
- Intelligent image identification model of crop diseases and pests based on multimodal deep feature fusion
Yunyi Zhang 2026 Eng. Res. Express 8 145208
View article, Intelligent image identification model of crop diseases and pests based on multimodal deep feature fusionPDF, Intelligent image identification model of crop diseases and pests based on multimodal deep feature fusionWith the development of smart agriculture, it has become a research hotspot to realize rapid and accurate identification of crop diseases and insect pests by using deep learning technology. In this study, a multimodal deep feature Fusion (MMDFF) optimization model for intelligent identification of crop diseases and insect pests is proposed. Based on the classic AlexNet, the study introduces transfer learning technology and makes full use of the prior knowledge obtained from the pre-training of large-scale general datasets such as ImageNet, which effectively alleviated the problem of insufficient labeled data in the agricultural field. On this basis, a multi-task learning framework is constructed, which combines pest species identification and severity assessment to optimize, and improves the parameter efficiency and feature sharing ability of the model. To further enhance feature representation, it proposes a MMDFF mechanism. The mechanism jointly employs visual features from multiple convolutional neural network layers and texture features extracted by local binary patterns. A cross-modal channel-attention module dynamically calibrates the importance of each feature channel. Consequently, the network focuses on the region’s most relevant to disease identification. To meet the requirements of edge computing, this study also implements a system lightweight scheme including structured pruning and mixed precision quantization. The experimental results on the published Plant Village dataset show that the proposed model achieves an identification accuracy of 95.12% and a mean square error of 0.069 in the test set, and its comprehensive performance is better than the original comparative models such as AlexNet, ResNet-50 and Mobilene ETV3. The ablation experiment further verifies the effectiveness and synergistic effect of each module. After lightweight, the parameters of the model are reduced to 9.3 M, and the single-frame inference delay is 32.7 ms on embedded equipment, which provides a feasible technical path for real-time and accurate pest monitoring in the field.
- Fractional-order PI-based MPPT control for improved power quality and dynamic stability in DFIG wind energy conversion systems
Abdelfatah El Ouadiki et al 2026 Eng. Res. Express 8 145308
View article, Fractional-order PI-based MPPT control for improved power quality and dynamic stability in DFIG wind energy conversion systemsPDF, Fractional-order PI-based MPPT control for improved power quality and dynamic stability in DFIG wind energy conversion systemsTracking performance is a key requirement for wind turbine systems based on Doubly‐Fed Induction Generators. The original contribution is the systematic design of a fractional-order PI controller in the frequency domain that includes clear criteria for robustness, such as limits on phase margin and gain variance. This method guarantees a regulation that is both reproducible and physically sound, taking into account the system’s high inertia. This is not the case with heuristic tuning methods. MATLAB/Simulink simulations using both deterministic step-wind profiles and a stochastic wind profile confirmed the method. The results show a clear link between changes in wind speed and the changing responses of aerodynamic and electromagnetic torque and power. The proposed fractional-order proportional-integral (FOPI) controller achieves a settling time of 0.0027 s, which is 89% faster than the conventional PI controller. Also, the steady-state inaccuracy goes down by 67% to 0.15 rad/s, with little overshoot of 0.036%. The method makes sure that the power coefficient and Tip Speed Ratio stay close to their best values. This makes tracking more accurate and makes the system more resistant to turbulence. This study finds that the frequency-designed FOPI controller offers a high-performance, industrially viable option for effective energy extraction in contemporary systems for converting wind energy.
- Fault diagnosis of rolling bearings based on WOA-VMD-YOLOv11-GBC
Xin Hu et al 2026 Eng. Res. Express 8 145507
View article, Fault diagnosis of rolling bearings based on WOA-VMD-YOLOv11-GBCPDF, Fault diagnosis of rolling bearings based on WOA-VMD-YOLOv11-GBCThe reliable and accurate diagnosis of rolling bearing faults is crucial for motor system safety. To address the prevalent industrial challenges of low signal-to-noise ratios and data imbalance, this paper proposes an integrated diagnostic framework named WOA-VMD-YOLOv11-GBC. The method synergistically combines optimized signal preprocessing, data augmentation, and a structurally enhanced deep learning network. It employs the whale optimization algorithm (WOA) to adaptively determine the key parameters of variational mode decomposition (VMD) for high-fidelity signal denoising. The reconstructed signals are converted into time–frequency images and augmented using a generative adversarial network. The core diagnostic model is an improved YOLOv11 network, where the SiLU activation is replaced with B-SiLU to stabilize gradients, and the upsampling module is substituted with Converse2D for precise feature reconstruction. Comprehensive experiments on the public CWRU and JNU bearing datasets demonstrate the framework’s superior performance. The model achieved precision, recall, and mAP@0.5 scores above 99.2% on both datasets, showing exceptional accuracy and strong generalizability. The proposed framework provides a robust and practical solution for intelligent rolling bearing fault diagnosis.
- LARM: a less-complex and accurate activity recognition method using passive RFID tags
Weiguang Shi et al 2026 Eng. Res. Express 8 145211
View article, LARM: a less-complex and accurate activity recognition method using passive RFID tagsPDF, LARM: a less-complex and accurate activity recognition method using passive RFID tagsRadio frequency identification (RFID) is a promising sensing solution for recognizing human activity. Existing RFID-based human activity recognition (RHAR) sensing systems either rely on actively worn tags or have poor recognition accuracy and computational complexity. In this paper, we present a novel device-free RHAR sensing method called less-complex and accurate recognition method (LARM). A novel dictionary-based learning is proposed to distinguish different activities from RFID sensory data. Rather than traditionally using several dictionaries, the proposed approach merely learns a background dictionary for the scenario with no activities performed. Then, using the background dictionary as a guide, a reconstructed offset matrix (RCOM) is created as activity fingerprints. It reduces computational complexity while remaining highly accurate. Meanwhile, a feature selection approach based on stability is developed for RCOM optimization, which improves the discriminability of sensor-derived fingerprints even more. Finally, a k-nearest neighbor-based recognition method is suggested to label the testing sample. The similarities between a testing sample and the fingerprints are precisely appraised via a feature selection matrix, pinpointing the optimal candidate fingerprints for activity classification. Experiment comparisons with the existing algorithms demonstrate the superiority of the proposed LARM.
- Advances in electric bicycle technologies: powertrain design, energy harvesting, and machine learning applications
Swaroopa S Bhosale and Datta S Chavan 2026 Eng. Res. Express 8 132303
View article, Advances in electric bicycle technologies: powertrain design, energy harvesting, and machine learning applicationsPDF, Advances in electric bicycle technologies: powertrain design, energy harvesting, and machine learning applicationsThe recent fast growth of electric bicycles (e-bikes) as an eco-friendly and energy-efficient means of transportation in the city has spawned a rich academic interest in powertrain design, power regulation, safety, and intelligent control. Most recent developments have expanded traditional plug-in e-bike designs to include self-charging and hybrid ones which include regenerative braking, pedal-generated, solar-assisted and advanced power electronics. This review provides an integrated overview of the state of the art of e-bike technologies, with specific focus on powertrain designs, energy flow principles, self-charging principles, battery management limitations, control principles, and new machine-learning (ML) applications. The paper presents a critical review of modelling, simulation, and experimental studies that measure the effect of mechanical design variables, operating conditions, and control strategies on energy consumption, performance, safety and rider comfort. The focus is specifically made on smart control and data-driven solutions, with the emphasis on the ways ML methods can be used to optimize energy consumption, monitor batteries, and implement rider-oriented assist systems. The review also questions practical barriers and barriers to adoption and gives a narrow analysis of the Indian context, factors such as cost, infrastructure, climatic and policy frameworks. The most important discoveries of the review outline the technological shortcomings, integration issues, and opportunities of next-generation intelligent and self-sustaining e-bikes. The conclusion section states the future research directions that will support scalable, resilient, and user-adaptive e-bikes to support sustainable urban mobility.
- A comprehensive overview of classification-enabled machine learning algorithms for islanding detection techniques
Md Siddikur Rahman et al 2026 Eng. Res. Express 8 132302
View article, A comprehensive overview of classification-enabled machine learning algorithms for islanding detection techniquesPDF, A comprehensive overview of classification-enabled machine learning algorithms for islanding detection techniquesIn modern distribution networks, addressing the issue of unintentional islanding—characterized as the inadvertent disconnection of distributed generation sources from the utility grid—continues to present a significant challenge. This phenomenon raises concerns that warrant further investigation due to its implications for system reliability and operational safety. The identification of islanding events is particularly complicated when local generation closely aligns with local demand, making detection difficult. The development of precise, rapid, and dependable methodologies for the detection of islanding in renewable and distributed generation systems requires compliance with rigorous standards. The current body of literature delineates an array of strategies for islanding detection, which can be systematically categorized into three primary approaches: (i) remote detection techniques, (ii) local detection methodologies, and (iii) machine learning-based classification-enabled intelligent classifiers. Recent advancements have garnered significant attention regarding the enhanced characteristics and benefits of intelligent methodologies in contrast to traditional approaches. Accurate and timely detection of islanding prevents hazardous back-feeding to the utility, protects inverter and downstream equipment from out-of-spec voltages and frequencies, preserves system stability during transition and reconnection, reduces the risk of large-scale protection miscoordination, and enables compliance with interconnection standards (e.g., IEEE 1547) that prescribe strict detection and ride-through requirements. However, achieving high detection accuracy introduces trade-offs: many high-performance techniques demand significant computational and communications resources, active methods may degrade power quality or increase nuisance trips, passive schemes suffer from large non-detection zones under near-balanced conditions, and machine-learning approaches require representative training data and careful feature engineering to avoid overfitting and poor generalization in realistic, noisy environments. This research provides a comprehensive overview of the transition from traditional techniques to intelligent islanding detection methodologies. Moreover, it elucidates the primary challenges, benefits, limitations, and prospective directions for research in intelligent detection schemes. Furthermore, this study provides a comprehensive and impartial analysis of intelligent classifier-based strategies for islanding detection that have been developed over the last decade. This research further examines various feature selection techniques and identifies the parameters most employed for efficient islanding detection. In conclusion, this comprehensive study presents a discussion of the findings obtained, along with strategic recommendations for future research initiatives within this field.
- Advances in intrusion detection systems: emerging trends, intelligent techniques, and future directions
Zanyar R Ahmed and Shavan K Askar 2026 Eng. Res. Express 8 132201
View article, Advances in intrusion detection systems: emerging trends, intelligent techniques, and future directionsPDF, Advances in intrusion detection systems: emerging trends, intelligent techniques, and future directionsIntrusion Detection Systems (IDS) have become an imperative in protecting the digital infrastructures against advancing and more threatening cyberattacks. Conventional security systems like firewalls and antivirus software cannot be relied upon to counter zero-day attacks, sophisticated persistent threats and large-scale distributed intrusions. Accordingly, the field of IDS has progressed rapidly, with methods that span from signature-based methods to anomaly-based systems modulated by Machine Learning (ML) and Deep Learning (DL) techniques. Although there has been a lot of progress, the research environment is fragmented, and it is difficult to determine trends, limitations, and possible opportunities. To overcome this, the current study examines over 93 credible research articles that were published within the years 2020–2025 and discussed. The study methodically classifies the Signature, Anomaly, and Hybrid Network-based IDS that includes additional information about ML, DL, and Reinforcement Learning (RL). The research critically reviews the advances and limitations of existing models while identifying research gaps and future directions. Furthermore, this research comprehensively analyzed the evaluation metrics, datasets, performance values, benefits, and limitations, numerically and graphically, to have a better representation of trends. This study is relevant as it summarizes the latest achievements, presents research gaps, and outlines the future directions of the research. It offers a basis for making methodological decisions by providing a systematic interpretation of the IDS developments, and directs researchers to innovate and implement successful IDS solutions to current cybersecurity issues.
- The following article is Open accessMechanochemical engineering of interfacial charge-transfer architectures for pollutant degradation, carbon capture, and hydrogen generation
Alvin Lim Teik Zheng et al 2026 Eng. Res. Express 8 132001
View article, Mechanochemical engineering of interfacial charge-transfer architectures for pollutant degradation, carbon capture, and hydrogen generationPDF, Mechanochemical engineering of interfacial charge-transfer architectures for pollutant degradation, carbon capture, and hydrogen generationMechanochemical ball milling has progressed beyond a solvent-free synthesis route into a precision strategy for programming interfacial charge-transfer architectures in photocatalytic systems. This review critically examines how ball-milling-induced solid–solid interactions regulate charge separation, redox retention, and electron utilization across heterojunctions employed in pollutant degradation, hydrogen evolution, and CO2 reduction. Rather than cataloging material combinations, this mini review centers on how mechanochemistry stabilizes distinct charge-transfer topologies, including Type II polymer heterojunctions, Z- and S-scheme systems, Schottky contacts, and ohmic interfaces, through enforced electronic coherence, defect–interface coupling, and Fermi-level equilibration. Quantitative comparisons reveal that catalytic performance scales with interface-governed charge regulation rather than compositional complexity, with ball-milled systems achieving hydrogen evolution rates exceeding 103–104μmol g−1 h−1 and near-complete pollutant removal under visible light. By integrating kinetic data, spectroscopic evidence, and theoretical insights, this review establishes mechanochemical ball milling as a charge-architecture engineering tool capable of deterministically shaping photocatalytic function. Remaining challenges related to scalability, defect stability, and real-matrix performance are discussed to delineate pathways toward industrially relevant, interface-programmed photocatalysis.
- Integrating FACTS devices and electric vehicles into distribution networks for a resilient and sustainable grid paradigm: challenges and opportunities
Ntumba Mbala Israel et al 2026 Eng. Res. Express 8 132301
View article, Integrating FACTS devices and electric vehicles into distribution networks for a resilient and sustainable grid paradigm: challenges and opportunitiesPDF, Integrating FACTS devices and electric vehicles into distribution networks for a resilient and sustainable grid paradigm: challenges and opportunitiesThe increasing penetration of electric vehicles (EVs) in distribution networks introduces significant operational challenges, including voltage instability, increased power losses, and higher reactive power demand. This study investigates the coordinated integration of flexible AC transmission system (FACTS) devices and EV-induced loading conditions to enhance voltage stability and operational efficiency in radial distribution systems. A simulation-based optimization framework is developed using the IEEE 33-bus distribution network implemented in MATPOWER. Particle swarm optimization (PSO) and genetic algorithm (GA) are employed to determine the optimal placement and sizing of a STATCOM under high EV penetration conditions. Simulation results show that an 80% EV penetration scenario reduces the minimum bus voltage from 0.913 p.u. to 0.858 p.u. and increases active power losses from 0.203 MW to 0.410 MW. Through coordinated FACTS optimization, a STATCOM installed at Bus 8 with a capacity of ±5 MVAr restores the minimum voltage to 0.933 p.u. using PSO and 0.931 p.u. using GA. PSO achieved slightly lower power losses (0.538 MW) compared with GA (0.675 MW) and demonstrated faster convergence characteristics. The results highlight the effectiveness of coordinated FACTS-EV optimization in mitigating EV-induced voltage stress and improving distribution network resilience.
- Domain-Generalized Multi-Channel Fusion for Bearing Fault Diagnosis Under Variable Operating Conditions
Liu et al
View accepted manuscript, Domain-Generalized Multi-Channel Fusion for Bearing Fault Diagnosis Under Variable Operating ConditionsPDF, Domain-Generalized Multi-Channel Fusion for Bearing Fault Diagnosis Under Variable Operating ConditionsTo address the issues of low accuracy and insufficient generalization capabilities in traditional methods for diagnosing bearing faults under variable operating conditions, we propose a vision-temporal bimodal multi-channel feature fusion method for rolling bearing fault diagnosis based on domain generalization. This approach constructs a parallel architecture for extracting bimodal features: on one hand, multiple signal processing techniques are employed to transform raw vibration signals into multi-perspective two-dimensional visual feature maps as visual modality input, while simultaneously employing variational modal decomposition to decompose vibration signals into a series of eigenmode functions constituting the temporal modality input. At the model level, a multi-channel large-kernel convolutional network and a global attention-enhanced bidirectional gated recurrent unit network are designed to extract deep features from the visual and temporal modalities, respectively. Subsequently, feature vectors from each channel are concatenated in the feature dimension, with fault classification performed via a progressive dimensionality reduction classifier. Experiments conducted using bearing datasets from Case Western Reserve University and the University of Paderborn in Germany demonstrate that this method can diagnose bearing failures under cross-conditions—even when trained solely on source-domain data and without exposure to target-domain data during training—and that its domain generalization accuracy outperforms that of existing mainstream advanced methods.
- Visualization of flow field for 3D printed transparent carburetor
Shi et al
View accepted manuscript, Visualization of flow field for 3D printed transparent carburetorPDF, Visualization of flow field for 3D printed transparent carburetorThis study addresses the challenge of visualizing internal fluid flow in conventional metal carburetors by proposing an innovative approach using 3D-printed transparent carburetors. By fabricating carburetors with transparent materials, a fluid visualization test platform was successfully established to investigate the internal flow characteristics under flow rates ranging from 15 to 75.4 mL/min, corresponding to Reynolds numbers from 177 to 889 and average flow velocities from 0.098 to 0.494 m/s in the carburetor throat (diameter 1.8 mm). Imaging analysis revealed that the liquid surface within the lower body at the observation point exhibited periodic evolution over a 990 ms cycle. Within one cycle, under peristaltic pump speed of 40 rpm, the liquid surface length ranged from 1.1 mm to 1.4 mm, while the height gradually decreased from 0.9 mm to 0.2 mm, and the area fluctuated downward from 1.0 mm² to 0.7 mm², indicating the completion of one cycle. The same experimental trends were also observed at pump speeds of 10, 20, 30, and 50 rpm. Similar transient flow behaviors were observed in the transparent main body, with a conical liquid surface forming over a 1221 ms cycle. The experiments systematically observed the geometric morphology and dynamic evolution of characteristic liquid surfaces within the carburetor, revealing key flow behaviors in critical regions. The findings provide direct experimental evidence for understanding the internal fluid dynamics of carburetors, offering valuable insights for optimizing carburetor design, improving fuel atomization efficiency, and enhancing combustion performance. The proposed transparent carburetor fabrication method overcomes the observational limitations of traditional metal carburetors, providing a new technical approach for carburetor performance research and optimization.
- The following article is Open accessLow-Complexity Frequency Selective Rasorber with Polarization-Independent Switching Capability
Li et al
View accepted manuscript, Low-Complexity Frequency Selective Rasorber with Polarization-Independent Switching CapabilityPDF, Low-Complexity Frequency Selective Rasorber with Polarization-Independent Switching CapabilityIn this paper, a novel low-complexity and cost-effective switchable frequency selective rasorber (SFSR) with polarization-independent switching capability is proposed. To our best knowledge, previously reported electronically reconfigurable and polarization-independent SFSR configurations requires at least 3 distinct layers with no less than 20 SMD lumped components for each unit-cell, causing high cost and fabrication complexity. Conversely, the herein developed switchable rasorber contains only 2 layers with 6 SMD lumped elements for each unit-cell, thus significantly simplifying fabrication and reducing overall cost. To achieve the independent control over TE and TM polarizations, we separate the structure design in perpendicular directions. Two switchable bands are accomplished, whose working mode can be changed from transmission to absorption and reflection to transmission respectively, while only one is usually possessed by previous literature results. By switching the operating state of PIN diodes, the switchable FSR (SFSR) working mode type is A-T/A-A-R/T. For A-T-A-R mode, it exhibits S11<-10 dB low reflection band of 2.89-8.74 GHz, with 1.17dB insertion loss (IL). For A-A-A-T mode, it exhibits low reflection band of 3.67-8.2 GHz. Besides, the SFSR has angle stability of . After the design process and numerical simulations evaluation, prototypes are fabricated through PCB technology and characterized in anechoic environment, confirming the overall approach. This work improves about 43% cost effectiveness under same capabilities and it is particularly suitable for all the applications requiring low-complexity and cost-effective fabrication, such as unmanned equipment and antennas radome.
- Parameter Optimization and Smooth Adaptive Control of VSG-Based Grid-Connected Converters via MO-POACS
Jia et al
View accepted manuscript, Parameter Optimization and Smooth Adaptive Control of VSG-Based Grid-Connected Converters via MO-POACSPDF, Parameter Optimization and Smooth Adaptive Control of VSG-Based Grid-Connected Converters via MO-POACSAbstract：To address the difficulty of fixed-parameter virtual synchronous generator (VSG) control in balancing frequency support, dynamic recovery, and steady-state power quality under varying operating conditions, this paper proposes a coordinated parameter optimization and smooth adaptive control strategy for VSG-based grid-connected converters. First, a small-signal active power–frequency model is established to analyze the effects of virtual inertia and damping on dynamic performance. Then, a six-dimensional dual-objective optimization model is formulated using the integral of time-weighted absolute frequency error (ITAE) and the total harmonic distortion (THD) of PCC voltage as optimization objectives. The model is solved using an improved multi-objective Pelican optimization algorithm (MO-POACS), and the optimized parameter set is incorporated into an online adaptive law. By combining sigmoid-based smooth triggering with first-order tracking, continuous adjustment of virtual inertia and damping is achieved. Simulation results demonstrate that the proposed method effectively reduces power overshoot and maximum frequency deviation while maintaining a low three-phase average PCC voltage THD. In addition, additional non-optimized validation cases under low-SCR weak-grid operation and grid phase-jump disturbance verify the adaptability and operational stability of the optimized parameter set.
- Effect of different device models on the performance of commercially available GaN HEMTs
KUMARI et al
View accepted manuscript, Effect of different device models on the performance of commercially available GaN HEMTsPDF, Effect of different device models on the performance of commercially available GaN HEMTsAnalysing compact models is crucial for power semiconductor devices. Hence, it becomes essential to understand how these models impact circuit and system performance, especially when using comprehensive analysis tools like SPICE. This article presents a comparison of two different device models for commercial gallium nitride (GaN)-based power devices. We have used two devices both from GaN systems: model number GS66508B and GS66516T. GaN systems provide a subcircuit model for use in SPICE simulation environment. Another model (commonly referred to as a device model) is available in the Qspice (simulator) component library for the device mentioned above. The Double Pulse Test (DPT) has been used to evaluate the switching performance of these devices under various operating conditions. The Qspice device model exhibits lower switching loss as it emphasizes on the intrinsic properties of the GaN device due to the model's reduced complexity. Whereas GaN Systems’ subcircuit model offers a detailed representation of device behavior in power electronic circuits resulting in higher switching losses. In the case of the device GS66508B, the energy loss was 34.83 µJ for the subcircuit model and 12.156 µJ for the device model, whereas for the GS66516T, it was 73.76 µJ for the subcircuit model and 32.72 µJ for the device model. The simulation results provide important insights into whether a less complex or a more complex model should be prioritized, depending on the use case. For basic understanding of GaN based device behavior, a less complex model (device model) can be used. However, where more realistic or practical results are the priority, a more complex model (subcircuit model) is far superior, as it yields results closer to realistic conditions.
- The following article is Open accessLow-Complexity Frequency Selective Rasorber with Polarization-Independent Switching Capability
Zongze Li et al 2026 Eng. Res. Express
View article, Low-Complexity Frequency Selective Rasorber with Polarization-Independent Switching CapabilityPDF, Low-Complexity Frequency Selective Rasorber with Polarization-Independent Switching CapabilityIn this paper, a novel low-complexity and cost-effective switchable frequency selective rasorber (SFSR) with polarization-independent switching capability is proposed. To our best knowledge, previously reported electronically reconfigurable and polarization-independent SFSR configurations requires at least 3 distinct layers with no less than 20 SMD lumped components for each unit-cell, causing high cost and fabrication complexity. Conversely, the herein developed switchable rasorber contains only 2 layers with 6 SMD lumped elements for each unit-cell, thus significantly simplifying fabrication and reducing overall cost. To achieve the independent control over TE and TM polarizations, we separate the structure design in perpendicular directions. Two switchable bands are accomplished, whose working mode can be changed from transmission to absorption and reflection to transmission respectively, while only one is usually possessed by previous literature results. By switching the operating state of PIN diodes, the switchable FSR (SFSR) working mode type is A-T/A-A-R/T. For A-T-A-R mode, it exhibits S11<-10 dB low reflection band of 2.89-8.74 GHz, with 1.17dB insertion loss (IL). For A-A-A-T mode, it exhibits low reflection band of 3.67-8.2 GHz. Besides, the SFSR has angle stability of . After the design process and numerical simulations evaluation, prototypes are fabricated through PCB technology and characterized in anechoic environment, confirming the overall approach. This work improves about 43% cost effectiveness under same capabilities and it is particularly suitable for all the applications requiring low-complexity and cost-effective fabrication, such as unmanned equipment and antennas radome.
- The following article is Open accessExperimental identification and numerical validation of interface parameters for layered concrete without mechanical connectors
Michaela Frantová et al 2026 Eng. Res. Express 8 145101
View article, Experimental identification and numerical validation of interface parameters for layered concrete without mechanical connectorsPDF, Experimental identification and numerical validation of interface parameters for layered concrete without mechanical connectorsThe mechanical response of layered concrete members is strongly governed by the shear-transfer capacity of interfaces between concretes cast at different times. This study investigates an old-to-new concrete interface representative of precast-monolithic wall construction without mechanical connectors crossing the interface. Direct shear tests were performed on two-stage concrete specimens under unconfined and externally compressed conditions in order to evaluate the influence of normal compression on interface resistance and failure mode. The average peak interface shear resistance increased from 0.969 MPa in the unconfined configuration to 2.975 MPa under an average imposed normal stress of 1.778 MPa. The tests also showed a change in the governing failure mechanism, from brittle tension–shear failure without confinement to compression–shear failure followed by frictional transfer under confinement. The experimental results were then used to calibrate a nonlinear interface model in ATENA. The calibrated model reproduced the measured force–deformation trends and was subsequently applied to an illustrative multilayer wall analysis. The wall simulation showed nearly monolithic behaviour before interface failure and a transition to friction-dominated behaviour after interface damage. The study therefore provides a practical workflow for converting direct shear test results into constitutive input parameters for nonlinear analysis of multilayer concrete members.
- The following article is Open accessA Modular and Scalable BIST-BISR Framework for Reliable Testing and Self-Repair of Synchronous SRAM Arrays
Aanchal Patel et al 2026 Eng. Res. Express
View article, A Modular and Scalable BIST-BISR Framework for Reliable Testing and Self-Repair of Synchronous SRAM ArraysPDF, A Modular and Scalable BIST-BISR Framework for Reliable Testing and Self-Repair of Synchronous SRAM ArraysThe state-of-the-art semiconductor devices benefitted from the large density of memory component possible due to scaled CMOS technologies. The yield and long-term reliability of System-on-Chip (SoC) platforms heavily depends on densely packed Static Random-Access Memory (SRAM) arrays that suffers from manufacturing defects, process variations, aging-induced degradation, and dynamic timing failures. This paper presents a modular and scalable Built-In Self-Test (BIST) and Built-In Self-Repair (BISR) framework for synchronous SRAM arrays focusing on comprehensive detection and correction of dominant fault classes appear during the operation. A 16×8 SRAM architecture is developed with parameterizable fault injection capability supporting Stuck-At Faults (SAFs), Neighbourhood Pattern-Sensitive Faults (NPFs), and Transition Faults (TFs). The proposed BIST architecture uses finite state machine (FSM)-based controllers that use the March C-, extended March C-, and Checkerboard algorithms to cover both static and dynamic faults. A closed-loop BISR mechanism captures the first failing address it finds and uses a spare row to remap it to a different row. After the repair, it checks again to make sure that the function has been restored. Verilog HDL is used to model the whole architecture, and ModelSim and Cadence NC-Launch are used to check it. The results of the simulation show that it is possible to accurately find faults, activate repairs in a predictable way, and restore memory functionality. The proposed framework provides a scalable solution for reliable on-chip memory testing and repair, addressing reliability challenges widely reported in modern embedded memory systems.
- The following article is Open accessA Lightweight YOLO-Based Model for Embedded Plate Detection in Smart Catering
Rongrong Chen et al 2026 Eng. Res. Express
View article, A Lightweight YOLO-Based Model for Embedded Plate Detection in Smart CateringPDF, A Lightweight YOLO-Based Model for Embedded Plate Detection in Smart CateringTo support smart catering billing scenarios, this paper proposes SC-YOLO, a lightweight plate detection component designed for embedded platforms. SC-YOLO is designed as the visual detection front end of a smart-catering billing workflow; downstream price mapping, duplicate handling, and transaction-level billing accuracy are treated as separate system-level modules. Based on YOLOv8n, SC-YOLO introduces SPD-Conv in the backbone and neck to enhance multi-scale feature representation, integrates CBAM in the neck to strengthen responses to discriminative plate regions, and adopts SIoU for bounding-box regression. A self-collected and manually labelled dataset comprising 22,750 images across nine plate categories was constructed and divided into training, validation, and test sets at a 7:1.5:1.5 ratio. Under the reported experimental protocol, SC-YOLO achieves an mAP@0.5 of 94.3% and an mAP@0.5:0.95 of 92.9%, improving over YOLOv8n by 4.1 and 15.1 percentage points, respectively, while reducing the parameter count to 1.99 M and maintaining a computational cost of 9.0 GFLOPs. These results suggest a favorable accuracy-efficiency trade-off for embedded plate detection, while transaction-level billing accuracy and cross-scene generalization remain directions for further study.
- The following article is Open accessGeneration of trajectories with optimal parameters using an L8 orthogonal Taguchi design and spectral analysis using short-time Fourier transform
Estrada-Segura José Francisco et al 2026 Eng. Res. Express 8 135351
View article, Generation of trajectories with optimal parameters using an L8 orthogonal Taguchi design and spectral analysis using short-time Fourier transformPDF, Generation of trajectories with optimal parameters using an L8 orthogonal Taguchi design and spectral analysis using short-time Fourier transformThis research proposes a methodology for studying vibration signals by generating a trajectory with controlled dynamics, specifically using a trapezoidal profile. The methodology begins with the experimental design and signal-to-noise analysis, implemented using the Taguchi method with an L8 orthogonal array at two levels and four replicates. The Taguchi analysis was further strengthened through an analysis of variance (ANOVA) to determine the statistical significance of the factors and interactions involved in the study. The research produced important findings: the ANOVA indicated that none of the factors or interactions were statistically significant. Consequently, the best run could not be determined solely through the Taguchi method. However, after performing a spectral analysis using the short-time Fourier transform, it was possible to identify the best runs for each axis (x, y, and z) by analyzing mean power values. In addition, the statistical and Taguchi analyses provided complementary information that supported the selection of the best run for the x-axis. The results showed that the best runs for each axis were run 1 for the x-axis, run 7 for the y-axis, and run 5 for the z-axis. Determining the optimal run for each axis enables addressing the specific requirements of any machine, robot, or dynamic system under analysis.
- The following article is Open accessCLIMB: a graph-theoretic framework for certificate-based lateral movement detection and hardening optimization
Ilker Kara 2026 Eng. Res. Express 8 135241
View article, CLIMB: a graph-theoretic framework for certificate-based lateral movement detection and hardening optimizationPDF, CLIMB: a graph-theoretic framework for certificate-based lateral movement detection and hardening optimizationCertificate-based authentication is a core component of modern enterprise identity infrastructures, as it strengthens trust relationships and reduces dependence on password-based access. However, misconfigurations in Active Directory Certificate Services (AD CS) may enable privilege escalation and lateral movement without conventional credential theft. Although such weaknesses are well documented in operational practice, they are often examined at the level of individual configuration flaws or abuse techniques rather than as causally connected stages of an attack chain. This paper presents CLIMB, a graph-theoretic framework for modeling certificate-based identity abuse and deriving hardening actions from the structure of feasible attack paths. The framework integrates a parametric risk model for certificate template exposure, a formal attack graph representation of certificate issuance, certificate-based authentication, and remote access transitions, an event-correlation rule for detection hypotheses, and a minimal cut-based hardening formulation. This design enables certificate abuse to be analyzed not only as a configuration problem, but also as a path-level security problem with explicit defensive intervention points. The framework was evaluated in a controlled AD CS laboratory environment using multiple abuse scenarios, including direct template abuse (ESC1), relay-based abuse (ESC8), and EKU-related template misconfiguration (ESC15). The results show that these scenarios activate different subsets of the modeled attack graph, ranging from complete attack-path realization to partial exploitability. Under the baseline configuration, certificate issuance, certificate-based authentication, and remote access were observed as a connected sequence. After the application of selected hardening controls, no complete feasible attack path remained in the evaluated environment. These findings indicate that certificate-based identity abuse can be analyzed systematically through graph-based modeling and that early-stage controls can eliminate downstream attack feasibility with limited configuration changes. The study is presented as a structural validation of the proposed framework rather than a statistical detection benchmark, and it provides a basis for future telemetry-driven evaluation and broader validation across heterogeneous enterprise environments.
- The following article is Open accessPassive Protection Limits: A parametric analysis of arcing horn efficiency under high grounding resistance
NUR AFIQAH ABDUL RAHMAN et al 2026 Eng. Res. Express
View article, Passive Protection Limits: A parametric analysis of arcing horn efficiency under high grounding resistancePDF, Passive Protection Limits: A parametric analysis of arcing horn efficiency under high grounding resistanceObjective: This study aims to determine the physical and economic limit of passive arcing-horn protection under high tower footing resistance (TFR), and to define the grounding threshold beyond which utilities should transition from passive geometric retrofitting to active transmission line arresters (TLAs) in high-isokeraunic tropical environments.
Methods: This paper presents a detailed parametric analysis of arcing horn efficiency using PSCAD/EMTDC simulations driven by a 15-year local lightning dataset. By subjecting a 275 kV double-circuit tower model to both standard and severe tropical fast-front lightning waveforms (0.25/100 µs), a definitive performance ceiling of passive protection is identified. A novel Gap Efficiency (ηgap) metric is proposed to quantify this saturation point, helping utility planners understand the physical limits of passive measures.
Results: Results show a clear saturation point: when TFR exceeds 20 Ω, extending the arcing horn beyond 2.55 m yields negligible improvements to critical current, dropping gap efficiency below 40 kA/m. Furthermore, under fast-front strikes, massive inductive voltage rises render gap adjustments entirely ineffective. This vulnerability sustains a critical Backflashover Rate of 16.51 outages/100 km/year.
Conclusion: To address this, a Grounding-Dependent Decision Framework is introduced that clearly defines the threshold at which utilities must transition from passive retrofitting to active protection devices, such as Transmission Line Arresters (TLAs). This threshold guides protection strategy decisions based on soil and lightning conditions, ensuring timely upgrades.
- The following article is Open accessImproving skin disease classification via enhanced local and global feature fusion
Bo Liu et al 2026 Eng. Res. Express 8 135237
View article, Improving skin disease classification via enhanced local and global feature fusionPDF, Improving skin disease classification via enhanced local and global feature fusionDermatological images often have high inter-class similarity and large variations in lesion sizes. Traditional methods struggle to accurately extract and fuse multi-scale features, which limits their classification performance. For this reason, this paper proposes an enhanced multi-scale feature fusion network (EM-Hifuse) to boost the accuracy and robustness of skin disease classification. First, the network introduces an enhanced local feature extraction block, which employs depthwise separable convolution to capture local features and integrates a positional convolutional block attention module. This module dynamically enhances key lesion regions while preserving original feature information through residual concatenation, thereby improving local feature extraction capability. Second, in terms of feature fusion, a multi-scale dual attention fusion block is proposed, which combines an enhanced convolutional attention module to integrate global and local features and introduces the triplet attention mechanism to realize adaptive fusion of cross-level features. This design significantly boosts the multi-scale feature fusion ability. Experiments on the ISIC2018 and ISIC2019 datasets demonstrate that EM-Hifuse achieves an accuracy of 85.72% and an F1-score of 74.13% on ISIC2018, as well as an accuracy of 82.37% and an F1-score of 71.35% on ISIC2019. The proposed method outperforms existing mainstream methods, validating its effectiveness.
- The following article is Open accessPropagation of uncertainties in Doppler effect current meters and current profilers
Marc Le Menn et al 2026 Eng. Res. Express
View article, Propagation of uncertainties in Doppler effect current meters and current profilersPDF, Propagation of uncertainties in Doppler effect current meters and current profilersDoppler current meters and current profilers are currently used in oceanography to measure and record large amounts of data on ocean currents. They can be vessel-mounted or standalone, single point or profiler. Calibration techniques have been tested and described and are regularly used for both vessel-mounted or standalone equipment's, but the uncertainties on the currents measured by these instruments are rarely estimated. This publication proposes a method based on the "Guide to the expression of uncertainty in measurement" to fill this gap. The results of simulations made by applying this method on two instruments from the best-known manufacturers, highlight a few points concerning the speed of sound, the beam slant angles from the nominal value and the uncertainty of corrections to the heading, pitch and roll angles given by the instrument when they are applied or not, to reduce the uncertainty on the measured velocities. The method proposed in this publication can be used to simulate the uncertainties that can be expected in different configurations and different practical applications.
- The following article is Open accessMultilevel Inverse Estimation of Acoustic Parameters of a Sugarcane-Waste-Based Porous Liner
hajer daoud et al 2026 Eng. Res. Express
View article, Multilevel Inverse Estimation of Acoustic Parameters of a Sugarcane-Waste-Based Porous LinerPDF, Multilevel Inverse Estimation of Acoustic Parameters of a Sugarcane-Waste-Based Porous LinerImproving acoustic comfort in industrial environments increasingly relies on the use of high-performance porous materials. This study focuses on the identification of acoustic parameters of a porous material made from sugarcane waste and intended for acoustic liner applications. A multilevel inverse identification strategy is employed to estimate the key physical parameters by minimizing the difference between measured and predicted sound absorption coefficients obtained from impedance-tube measurements. The approach is based on the successive use of three acoustic models (Miki, Hamet–Bérengier, and Johnson–Champoux–Allard) combined with three optimization algorithms (genetic algorithm, Nelder–Mead method, and interior-point algorithm). The results show good agreement between numerical predictions and experimental data, with low identification errors. The proposed method proves to be a reliable and robust tool for characterizing bio-based porous materials from limited acoustic measurements, supporting the design of sustainable acoustic solutions.
- The following article is Open accessMagnetic sensors-A review and recent technologies
Mohammed Asadullah Khan et al 2021 Eng. Res. Express 3 022005
View article, Magnetic sensors-A review and recent technologiesPDF, Magnetic sensors-A review and recent technologiesMagnetic field sensors are an integral part of many industrial and biomedical applications, and their utilization continues to grow at a high rate. The development is driven both by new use cases and demand like internet of things as well as by new technologies and capabilities like flexible and stretchable devices. Magnetic field sensors exploit different physical principles for their operation, resulting in different specifications with respect to sensitivity, linearity, field range, power consumption, costs etc. In this review, we will focus on solid state magnetic field sensors that enable miniaturization and are suitable for integrated approaches to satisfy the needs of growing application areas like biosensors, ubiquitous sensor networks, wearables, smart things etc. Such applications require a high sensitivity, low power consumption, flexible substrates and miniaturization. Hence, the sensor types covered in this review are Hall Effect, Giant Magnetoresistance, Tunnel Magnetoresistance, Anisotropic Magnetoresistance and Giant Magnetoimpedance.
- Numerical simulation of CIGS, CISSe and CZTS-based solar cells with In2S3 as buffer layer and Au as back contact using SCAPS 1D
Md Ali Ashraf and Intekhab Alam 2020 Eng. Res. Express 2 035015
View article, Numerical simulation of CIGS, CISSe and CZTS-based solar cells with In2S3 as buffer layer and Au as back contact using SCAPS 1DPDF, Numerical simulation of CIGS, CISSe and CZTS-based solar cells with In2S3 as buffer layer and Au as back contact using SCAPS 1DA solar cell capacitance simulator named SCAPS 1D was used in the prediction study of Cu(In, Ga)Se2 (CIGS), CuIn(S, Se)2 (CISSe) and Cu2ZnSnS4 (CZTS) based solar cells where indium sulphide (In2S3), fluorine-doped tin oxide/FTO (SnO2:F) and gold (Au) were used as buffer layer, window layer and back contact respectively. We investigated the effect of thickness, defect density and carrier density of the different absorber layers, thickness of the buffer layer and at 300 K temperature and standard illumination, the optimum devices revealed highest efficiencies of 18.08%, 22.50%, 16.94% for CIGS, CISSe, CZTS-based cells respectively. Effect of operating temperature, wavelength of light and electron affinity of the buffer layer on the optimized solar cell performance was also observed. Moreover, simulations were run with tin (Sn) doped In2S3 buffer layer to see the change in electrical measurements in comparison with undoped condition and also, investigation was carried out by replacing In2S3 buffer layer with traditional cadmium sulphide (CdS) buffer layer with the aim of comparing their respective output parameters. All these simulation results will provide some vital guidelines for fabricating higher efficiency solar cells.
- The following article is Open accessApplication of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties
Belachew A Demiss and Walied A Elsaigh 2024 Eng. Res. Express 6 032102
View article, Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertaintiesPDF, Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertaintiesConstruction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns and delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) while considering construction delivery uncertainties. The first model introduces a Spatio-Temporal Attention CNN-RNN Hybrid Model with Probabilistic Uncertainty Modeling, which integrates attention mechanisms and probabilistic uncertainty modeling to provide accurate and probabilistic estimates of construction duration, offering insights into critical areas of uncertainty. The second model presents a Multi-Modal Graph CNN-RNN Hybrid Model with Bayesian Uncertainty Integration, which harnesses multi-modal data sources and graph representations to offer comprehensive estimates of construction duration while incorporating Bayesian uncertainty measures, facilitating informed decision-making and optimized resource allocation. Lastly, the third model introduces a Hierarchical Spatio-Temporal Transformer CNN-RNN Hybrid Model with Fuzzy Logic Uncertainty Handling, which addresses the inherent vagueness and imprecision in construction duration estimates by incorporating hierarchical spatio-temporal transformer architecture and fuzzy logic uncertainty handling, leading to more nuanced and adaptable project management practices. These advanced models represent significant advancements in addressing construction duration challenges, providing valuable insights and recommendations for future research and industry applications. Moreover, this review article critically examines the application of hybrid deep learning architectures, specifically the combination of CNNs RNNs, in predicting construction duration estimates at the preconstruction stage while considering uncertainties inherent in construction delivery systems.
- The following article is Open accessPolyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporation
Nikolaos Chousidis 2024 Eng. Res. Express 6 025010
View article, Polyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporationPDF, Polyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporationThe properties of polyvinyl alcohol (PVA) films are intricately influenced by factors such as polymer structure, fabrication method, the addition of plasticizers and the molecular weight of monomers. This research, investigates the implication of PVA films using a solution casting method for crosslinking with boric acid (H3BO4), glycerol (C3H8O3) and citric acid (C6H8O7). This approach is compared with pure PVA films, establishing a valuable benchmark. For the experiments, tensile strength tests, physicochemical property measurements, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were conducted to gain insights into the microstructure, surface characteristics and mineral composition of the films. This comprehensive approach aims to enhance our understanding of the intricate relationship between PVA, plasticizers and crosslinking agents, providing valuable insights for applications across diverse industries, including, construction and biomedical fields. The overarching objective of this research is to revolutionize the construction industry by developing polymer films that serve as the foundation for self-healing materials, fostering durability and innovation. The experiments revealed a significant influence of crosslinking agents on the properties of PVA films as measured.
- The following article is Open accessConstrained robust adaptive control design for fixed wing uav under parameter uncertainties and external disturbances
Tofik Kemal Mohammed et al 2025 Eng. Res. Express 7 025254
View article, Constrained robust adaptive control design for fixed wing uav under parameter uncertainties and external disturbancesPDF, Constrained robust adaptive control design for fixed wing uav under parameter uncertainties and external disturbancesThis paper presents a Robust Model Reference Adaptive Control (RMRAC) for fixed wing UAV trajectory tracking. Trajectory tracking of Fixed Wing UAV(FWUAV) is extremely complicated due to the under actuated and coupled dynamics with unknown aerodynamic coefficients. The proposed adaptive control technique consists of two loops to address the issue of under actuation: an inner loop regulates attitude, while an outer loop generates reference trajectories for the inner loop. First, the Newton-Euler technique is used to establish FWUAV dynamic models. To simplify complexity, the dynamic models are decoupled. There are six second order single-input multiple-output (SIMO) systems in the decoupled dynamics. Second, a conventional Model Reference Adaptive Control (MRAC) is designed. Nevertheless, in the face of unparalleled unpredictability, this controller experiences instability. Third, to avoid parameter drift in off-nominal situations, a Robust Model Reference Adaptive Control (RMRAC) was proposed. To solve the robustness issue, the paper also suggests robustness modification strategies. For the stability analysis, Lyapunov’s direct technique is employed. Lastly, using extensive simulation studies, the RMRAC is tested for parametric uncertainty and external disturbance, demonstrating the efficacy of the proposed controller in tracking the intended trajectory.
- Auxetic meta-materials and their engineering applications: a review
Yangzuo Liu et al 2023 Eng. Res. Express 5 042003
View article, Auxetic meta-materials and their engineering applications: a reviewPDF, Auxetic meta-materials and their engineering applications: a reviewAuxetic or negative Poisson’s ratio (NPR) materials and structures are exemplary mechanical meta-materials, possessing greater energy absorption capacity, stronger indentation resistance, and other advantages. Due to their unique indentation resistance, auxetic meta-materials have tremendous potential for use in impact engineering applications. To unveil the categories, characteristics, and applications of auxetic meta-materials, this study expounded upon the basic principles of auxeticity at the structural level and its associated mechanical properties. Additionally, it outlined the typical applications within the fields of medicine, automotive manufacturing, protective gear, and garments. The auxetic honeycomb structures of interest were first classified into three types: re-entrant, chiral, and rotational rigid structures. The auxetic mechanism and mechanical properties of these structures were then discussed and compared. Furthermore, by examining their current applications and characteristics of these structures, development directions for auxetic meta-materials were highlighted to meet future engineering demands for multi-functionality.
- A review of primary technologies of thin-film solar cells
Erteza Tawsif Efaz et al 2021 Eng. Res. Express 3 032001
View article, A review of primary technologies of thin-film solar cellsPDF, A review of primary technologies of thin-film solar cellsThin-film solar cells are preferable for their cost-effective nature, least use of material, and an optimistic trend in the rise of efficiency. This paper presents a holistic review regarding 3 major types of thin-film solar cells including cadmium telluride (CdTe), copper indium gallium selenide (CIGS), and amorphous silicon (α-Si) from their inception to the best laboratory-developed module. The remarkable evolution, cell configuration, limitations, cell performance, and global market share of each technology are discussed. The reliability, availability of cell materials, and comparison of different properties are equally explored for the corresponding technologies. The emerging solar cell technologies holding some key factors and solutions for future development are also mentioned. The summarized part of this comparative study is targeted to help the readers to decipher possible research scopes considering proper applications and productions of solar cells.
- Data-driven prediction of residual flexural capacity in corroded RC beams using PSO and GA-optimized CatBoost ensemble models
Yuzhuo Zhang et al 2025 Eng. Res. Express 7 035129
View article, Data-driven prediction of residual flexural capacity in corroded RC beams using PSO and GA-optimized CatBoost ensemble modelsPDF, Data-driven prediction of residual flexural capacity in corroded RC beams using PSO and GA-optimized CatBoost ensemble modelsReinforced concrete (RC) beams inevitably experience steel corrosion when exposed to chloride ingress or carbonation, leading to progressive deterioration of both durability and structural capacity. This corrosion-induced degradation poses critical challenges to structural safety while substantially increasing life-cycle maintenance costs. A machine learning framework integrating CatBoost algorithm with metaheuristic optimization was developed to predict residual flexural capacity of corroded RC beams. An experimental database encompassing 543 test specimens with 12 critical parameters (including geometric dimensions, material properties, and corrosion characteristics) was established. Three hybrid models (BO-CatBoost, GA-CatBoost, PSO-CatBoost) were subsequently developed through hyperparameter optimization using Bayesian optimization (BO), genetic algorithm (GA), and particle swarm optimization (PSO). Quantitative evaluations demonstrated the superior predictive accuracy of metaheuristic-optimized models, with PSO-CatBoost emerging as the top performer (testing R2 = 0.972, RMSE = 3.4183). This represents a 35.9% reduction in RMSE compared to the baseline CatBoost. The GA-CatBoost variant also showed significant improvements (testing R2 = 0.970, RMSE = 3.6285), outperforming both baseline CatBoost and BO-CatBoost. The marked superiority of PSO and GA algorithms underscores their enhanced capability in navigating complex hyperparameter spaces, effectively capturing the nonlinear relationships between corrosion degradation and structural response. Sensitivity analysis revealed that beam height and reinforcement ratio positively correlate with load-bearing capacity, whereas rebar mass loss ratio and water-to-binder ratio exhibit significant negative impacts. The proposed framework provides a robust assessment tool for corrosion-damaged RC members while identifying critical degradation mechanisms, enabling more informed maintenance decisions for aging concrete infrastructure.
- The following article is Open accessEnhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control
Yebekal Adgo Wendemagegn et al 2024 Eng. Res. Express 6 045204
View article, Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode controlPDF, Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode controlThe rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.
- The following article is Open accessPSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehicles
Chala Abdulkadir Kedir and Chala Merga Abdissa 2024 Eng. Res. Express 6 035229
View article, PSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehiclesPDF, PSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehiclesIn this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.
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Journal information
- 2019-present
Engineering Research Express
doi: 10.1088/issn.2631-8695
Online ISSN: 2631-8695