Engineering Science and Technology-An International Journal-Jestech最新文献

{"title":"Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues)","authors":"","doi":"10.1016/S2215-0986(26)00009-1","DOIUrl":"10.1016/S2215-0986(26)00009-1","url":null,"abstract":"","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102283"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of modification parameters of face gear pair based on TCA-NRBPNN-HYPE hybrid drive model","authors":"Kun He ,&nbsp;Ronghao Li ,&nbsp;Yongquan Chen ,&nbsp;Yachao Jia ,&nbsp;Guolong Li","doi":"10.1016/j.jestch.2025.102274","DOIUrl":"10.1016/j.jestch.2025.102274","url":null,"abstract":"<div><div>To enhance the meshing characteristics of the face gear pair and determine the optimal modification parameters, an optimization model of modification parameters, based on the TCA-NRBPNN-HYPE (Tooth Contact Analysis-Newton Raphson Back Propagation Neural Network-Hyperparameter Optimization) hybrid drive model is proposed. Firstly, a dual weight modification curve is introduced to modify the tooth surface of face gears, and the TCA model is employed to accurately obtain the meshing characteristics parameters of the modified gear pair, including contact position, transmission error, and contact stress. based on the modification parameters and TCA results, an NRBPNN prediction model is established to achieve mapping from modification parameters to meshing characteristics. Finally, the HYPE optimization model is applied to globally optimize the prediction results and obtain the optimal modification parameter combination. The results show that the optimal design reduces the contact position parameter from 4.15 to 1.50, the transmission error from 2.98″ to 0.314″, and the contact stress from 566.30 MPa to 292.33 MPa. These results indicate that the proposed method effectively improves the meshing characteristics and reliability of face gear pair.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102274"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A FEM-ANN framework to estimate the on-diagonal elements of the impedance matrix in a Cochlear Implant","authors":"M.J. Hernández-Gil ,&nbsp;A. Ramos-de-Miguel ,&nbsp;D. Greiner ,&nbsp;D. Benítez ,&nbsp;G. Montero ,&nbsp;J.M. Escobar","doi":"10.1016/j.jestch.2025.102273","DOIUrl":"10.1016/j.jestch.2025.102273","url":null,"abstract":"<div><div>Accurate estimation of the impedance matrix is essential for optimizing cochlear implant (CI) performance, yet the on-diagonal terms, that represent the contact impedances of electrodes, remain poorly characterized in existing models. In this work, we first analyze these on-diagonal terms and highlight their impact on electric field distribution. We then revisit the classic linear extrapolation approach and introduce two novel extrapolation methods to enhance prediction accuracy. To capture patient-specific variability, real impedance measurements are incorporated into a resistive–conductive finite-element method (FEM) model, whose matrices serve as the basis for a supervised neural network. The network is trained and validated on a diverse dataset of FEM-derived impedance matrices, enabling robust generalization across electrode configurations. Benchmarking against state-of-the-art techniques shows that our hybrid FEM-ANN framework reduces prediction error for diagonal terms. Moreover, when used in multipolar stimulation strategies, the ANN-based impedance matrices yield comparable focalization while requiring lower electrical power. Our results demonstrate that combining physical modeling with data-driven methods produces more reliable and efficient impedance estimates, paving the way for improved CI fitting and patient outcomes.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102273"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of PIλDμ controller for ROVs: Thruster modeling, controller parameter optimization, and FPGA realization","authors":"Hakan Ersoy,&nbsp;Berke Akgül,&nbsp;Emin Akpınar,&nbsp;Aslihan Kartci,&nbsp;Umut Engin Ayten","doi":"10.1016/j.jestch.2025.102261","DOIUrl":"10.1016/j.jestch.2025.102261","url":null,"abstract":"<div><div>Remotely operated and autonomous underwater vehicles (ROVs/AUVs) operate in a harsh environment dominated by nonlinear hydrodynamics, strong coupling, and wave–current disturbances. In most of the existing literature, surge-axis motion is still regulated by integer-order PID controllers that are tuned either heuristically or via single-scenario optimization. Such designs often exhibit limited robustness: their performance degrades significantly under severe noise, targeted wave excitation, or time-varying operational profiles. These limitations motivate the use of fractional-order control and more systematic tuning procedures. This paper investigates fractional-order <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> (FOPID) controllers for surge control and compares two popular meta-heuristics, Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA), in comparison with classical PID. A fourth-order surge plant model is first obtained via system identification of experimental data from a BlueRobotics T200 thruster. Then, PSO and DEA are used to tune both PID and <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> parameters over a multi-scenario cost function that combines step-response quality, disturbance rejection, and control effort. The resulting controllers are evaluated under four increasingly demanding tests: noiseless step tracking, severe white-noise excitation, sinusoidal “storm” disturbance, and a final scenario with time-varying set-points under the same storm condition. Across all 16 scalar performance metrics (IAE, ISE, and, ITAE over four tests), the DEA-tuned <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> achieves the best value in 12 cases, consistently outperforming both PID designs and the PSO-based <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span>. In the most demanding final test (multi-level reference + storm), it reduces the integral time-weighted absolute error ITAE from 0.1065 (best PID) to 0.0893, i.e., by approximately 16%, while preserving competitive control effort. These results provide quantitative evidence that DEA-tuned <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> offers a more robust and energy-aware solution for single-axis surge control in ROV/AUV applications.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102261"},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-of-the-art soft robotic systems for unstructured and real-world environments: A systematic review","authors":"Arameh Eyvazian ,&nbsp;Yooseob Song ,&nbsp;Chibukhchyan Hovhannes ,&nbsp;Ardeshir Savari ,&nbsp;Narinderjit Singh Sawaran Singh","doi":"10.1016/j.jestch.2025.102264","DOIUrl":"10.1016/j.jestch.2025.102264","url":null,"abstract":"<div><div>Soft robotics has emerged as a transformative paradigm in automation, offering unprecedented compliance, adaptability, and safety for operation in unstructured and dynamic environments. This study systematically reviews the latest advances in soft robotic systems, spanning novel material innovations, intelligent hybrid architectures, and cutting-edge actuation and control strategies. Key developments in integration with artificial intelligence, computer vision, and machine learning are highlighted, enabling enhanced perception, autonomy, and adaptive behavior. Application-driven case studies in healthcare, exploration, and search-and-rescue showcase the evolving capabilities of soft robots in challenging real-world settings. Persistent challenges, such as untethered operation, robust sensorimotor integration, scalable fabrication, and interpretable AI, are discussed alongside emerging multidisciplinary solutions. The review concludes by outlining future research directions, emphasizing the need for unified codesign approaches and collaboration across robotics, materials science, AI, and biology. This synthesis provides a roadmap for advancing next-generation soft robotic systems, aiming to bridge the gap between laboratory innovations and impactful deployment in complex, unpredictable environments, in support of industrial and innovation progress.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102264"},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid fragile image watermarking for tamper detection, localization and dual self-recovery","authors":"Aditya Kumar Sahu ,&nbsp;Monalisa Sahu","doi":"10.1016/j.jestch.2025.102266","DOIUrl":"10.1016/j.jestch.2025.102266","url":null,"abstract":"<div><div>This paper presents a novel image watermarking framework that effectively addresses the issue of random block mapping. This phenomenon compromises tampered regions and their corresponding recovery blocks, resulting in irretrievable image data. To mitigate the random block mapping issue, a crisscross block mapping strategy (CrCsBMS) is proposed to enhance the robustness of block mapping by ensuring non-randomised reference allocation. The authentication bit generation leverages Gram-Schmidt Orthonormalization (GSO), extracting pivotal image characteristics, such as mean intensity, variance, and edge strength, thereby fortifying the integrity verification mechanism. The hybrid embedding strategy integrates discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) to maintain an optimal balance between imperceptibility and embedding capacity, while distortion compensated quantization index modulation (DC-QIM) is employed for recovery bit encoding. A dual self-recovery mechanism incorporating bilinear interpolation-based inpainting and an 8-neighborhood method with a 255-color range scaling (255-CRS) is introduced, significantly augmenting recovery efficiency and ensuring precise restoration of tampered pixels. Experimental analysis demonstrates superior imperceptibility, robustness against image processing attacks, and reduced computational complexity compared to contemporary techniques. The proposed scheme achieves an average PSNR of 52.22 dB, an SSIM of 0.9983, and a payload capacity of 1 bit per pixel, surpassing existing self-recovery watermarking frameworks in both accuracy and resilience.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102266"},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of pareto fronts for pump impeller design using sobol sequence sampling with water and methanol","authors":"Muhammed Donmez ,&nbsp;Onur Yemenici","doi":"10.1016/j.jestch.2025.102265","DOIUrl":"10.1016/j.jestch.2025.102265","url":null,"abstract":"<div><div>This study focuses on the optimization and performance evaluation of pump impellers for water and methanol using Sobol sequence sampling, Artificial Neural Network (ANN)-based metamodeling, and Multi-Objective Genetic Algorithm (MOGA) optimization. Initially, 40 design points generated via Sobol sequences facilitate the exploration of a multidimensional design space, enabling the design of impellers with varied geometrical parameters. The resulting head and efficiency values are used to train an ANN model, achieving high accuracy, with overall R-values above 0.99 for both fluids. Optimized impellers for water and methanol show improved flow uniformity and energy efficiency, as evidenced by smoother velocity distributions. For water, the optimized impeller achieved a head of 10.01 m and an efficiency of 72.41 %, while for methanol, it reached a head of 10.01 m and an efficiency of 73.62 %, as obtained by CFD. Pareto analysis reveals that water designs are constrained around a 10 m head, whereas methanol allows flexibility, achieving optimal efficiency across a 10–15 m head range. These findings confirm the efficacy of the optimization framework, offering an adaptable approach for enhancing pump impeller performance across different fluid applications.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102265"},"PeriodicalIF":5.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking with attention: A review of transformer-based object tracking","authors":"Seyed Alireza Khoshnevis ,&nbsp;Abdollah Amirkhani","doi":"10.1016/j.jestch.2025.102263","DOIUrl":"10.1016/j.jestch.2025.102263","url":null,"abstract":"<div><div>Traditional object tracking methods are often based on convolutional neural networks and handcrafted feature extraction techniques where they have seen remarkable success. However, these methods still face limitations in capturing global dependencies and contextual relationships in complex scenarios. Transformers, which were initially introduced to the field of natural language processing, have transfigured vision tasks by leveraging the self-attention mechanisms and global feature modeling capabilities. One of the tasks that has been most affected by the use of transformers is the object tracking task. This review explores the transformative impact of attention-based architectures in object tracking, and provides a comprehensive analysis of the current frameworks and their core principles. The ability of the attention mechanism to capture local and global dependencies and to associate queries between frames, has helped transformer-based models to achieve state-of-the-art performance. The utilization of transformers in object tracking has drastically increased over the past few years, initiating the new “tracking-by-attention” paradigm. This work focuses on different applications of transformer architecture in both single and multi-object tracking where each task is divided further by methodology. End-to-end approaches and hybrid fusion models that leverage additional data for tracking are also discussed. The models that are discussed, are categorized by their main approaches and transformer usage, and challenges such as computational cost and scalability are outlined, along with future research opportunities informed by successful methods. By examining recent advancements, this review is intended to advance understanding of transformer-based tracking capabilities and to promote continued innovation in this rapidly evolving field.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102263"},"PeriodicalIF":5.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurocomputing capability evaluation of FHN model-based MSNN architectures with different window functions through the XOR problem","authors":"Ahmet Yasin Baran ,&nbsp;Obeid Abdalla ,&nbsp;İsmail Öztürk ,&nbsp;Nimet Korkmaz ,&nbsp;Recai Kiliç","doi":"10.1016/j.jestch.2025.102259","DOIUrl":"10.1016/j.jestch.2025.102259","url":null,"abstract":"<div><div>This study focuses on utilizing Memristive Spiking Neural Network (MSNN) structures based on the Fitzhugh-Nagumo (FHN) neuron model coupled with the Voltage ThrEshold Adaptive Memristor (VTEAM), and constrained by five different window functions, to solve the Exclusive OR (XOR) problem through both numerical simulations and real-time hardware implementations. Specifically, two input and two output neurons have been constructed as the 2 <span><math><mo>×</mo></math></span> 2 MSNN architectures, and the Joglekar, Biolek, Strukov, Prodromakis, and Blackman window functions define their memristor models. The optimal memductance values for synaptic connections in these five network configurations are set using the Spike-Timing-Dependent Plasticity (STDP) learning rule combined with the Winner-Takes-All (WTA) algorithm. The performance of these networks is evaluated based on their efficiency in solving the XOR problem. In this context, the XOR problem has been conducted as an image formed by a 2-pixel array. These pixels are transmitted as noisy signals from the MSNN input layer, where input neurons convert them into spike activities. These spike activities are then integrated through the memristive synapse layer and forwarded to the output layer as excitatory current signals, enabling the output layer to classify the input correctly. The simulation and Field-Programmable Gate Array (FPGA) hardware implementation results exhibit strong consistency. This work demonstrates, for the first time, the capacity of MSNNs to solve nonlinear problems by providing both simulation-based and hardware-based solutions to the XOR problem using various MSNN architectures with different window functions.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102259"},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards automated metaphase cell detection using foundation models: A SAM and DINO-based approach","authors":"M. Cihad Arslanoglu ,&nbsp;Abdulkadir Albayrak ,&nbsp;Huseyin Acar","doi":"10.1016/j.jestch.2025.102262","DOIUrl":"10.1016/j.jestch.2025.102262","url":null,"abstract":"<div><div>Karyotyping is a widely used laboratory technique to detect abnormalities in chromosomes. Karyotyping consists of many steps, including collecting samples, cell culturing and metaphase cell detection. Cytogeneticists examine all the images manually and annotate metaphase cells that is tedious and time consuming after collecting the samples and creating cell cultures. Metaphase cells can be detected by using computer-aided techniques. In this study, Segment Anything Model (SAM) and Self-Distillation with No Labels (DINO) foundation models were employed for metaphase detection task to minimize training time and costs. Potential metaphase regions were detected using SAM foundation segmentation model and these regions were given to classification models to identify metaphase cells. ResNet-50, a convolutional neural network algorithm, Vision Transformer (ViT) and Cross-Covariance Image Transformer (XCIT) based backbones were used in metaphase cell identification step. The results show that foundation models can give promising results on metaphase cell detection as much as supervised deep learning-based models. As a result, while supervised XCIT model with small architecture exhibit 0.9966 True Positive Ratio (TPR) and self-supervised ViT base accomplish 0.9961 TPR.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102262"},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}