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

{"title":"A low cost compact network TAP device with Raspberry Pi 4","authors":"Murat Varol ,&nbsp;Murat İskefiyeli","doi":"10.1016/j.jestch.2025.102118","DOIUrl":"10.1016/j.jestch.2025.102118","url":null,"abstract":"<div><div>The communication of multiple networks in different locations is an open problem since the networks include various devices and applications. There is a necessarity to monitor the network to detect any kind of attacks or problems. Determining which device is the source of the problem and vulnerability is also a problem. Network TAP devices are positioned between network devices, allowing for device-based monitoring of communications across the entire network. With this feature Network TAP devices play a main role in network security, network visibility, network monitoring, forensics, etc. Typically, Network TAP devices copy all packets transmitted between two network devices and allow the network authority to monitor the packets through the monitoring port. However, TAP devices available in the market are high cost devices. At this point, we propose a low-cost network TAP device using Raspberry Pi 4. This article provides theoretical and practical contributions into the network TAP literature and includes a method that can be easily implemented for end users. The proposed TAP device has a cost between 1/8 to 1/30 compared to the prices in the market. The device, with its flexible software-based solutions, extensible storage options with any portable disk, can be adapted into various test cases. In addition, the proposed TAP device offers the opportunity to monitor the captured packets via a monitor or remote connection without a computer. Unlike traditional TAP devices, it captures and records packets independently without the necessary of an external computer connection, while enabling real-time monitoring through connected monitor. Experimental studies are carried out at CENTER SAU testbed center with our proposed TAP device and ET2000 device from Beckhoff. When the pcap files obtained from the experimental studies were examined, it was observed that the same packets were captured, therefore the proposed TAP device captured all packets lossless. The results show that the device is a cost-effective alternative for corporate and individual users.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102118"},"PeriodicalIF":5.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750584","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 efficient multi-legal document summarization: An ensemble approach for Turkish law","authors":"Maha Ahmed Abdullah Albayati,&nbsp;Kürşat Mustafa Karaoğlan,&nbsp;Oğuz Findik","doi":"10.1016/j.jestch.2025.102138","DOIUrl":"10.1016/j.jestch.2025.102138","url":null,"abstract":"<div><div>Legal document summarization is a critical yet challenging task due to the structural complexity, terminological density, and contextual depth of legal texts, especially in low-resource languages like Turkish. Building on our previous research in hybrid summarization (Albayati and Findik, 2025), this study proposes an advanced ensemble-based framework tailored to Turkish legal documents. The framework integrates four transformer models, LED, Long-T5, BART-Large, and GPT-3.5 Turbo, and employs three complementary techniques: the Consecutive-Aware Semantic Voting Mechanism (CASVM), the Weighted Hybrid Sentence Ranking Framework (WHSRF), and a T5-Based Meta-Model (T5-Meta). Together, these components leverage both extractive precision and abstractive fluency. Experimental results over a dataset of 2,000 Turkish court decisions show significant improvements in summary quality and consistency. The proposed ensemble achieved ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Sum scores of 0.90, 0.78, 0.88, and 0.85 respectively, reflecting relative gains of 63.64%, 122.86%, 109.52%, and 93.18% compared to the best-performing individual models. In parallel, BERTScore evaluations confirmed a high level of semantic fidelity, validating the model’s ability to preserve legal meaning even in paraphrased output. This research sets a new benchmark in Turkish legal summarization, showcasing the power of ensemble learning for multilingual legal NLP and paving the way for its integration into intelligent legal assistants that support faster, more accurate information access for legal professionals.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102138"},"PeriodicalIF":5.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750695","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":"Retrofitting of seismically deficient RC bridge pier using HSP concrete jacket and FRP rebars","authors":"Muhammad Waleed Ahmad,&nbsp;Muhammad Rizwan,&nbsp;Muhammad Fiaz Tahir","doi":"10.1016/j.jestch.2025.102146","DOIUrl":"10.1016/j.jestch.2025.102146","url":null,"abstract":"<div><div>Bridges in high seismic zones are prone to failure due to inadequate seismic design or cumulative damage from repeated earthquakes, compromising structural integrity and functionality This study investigates the effectiveness of retrofitting seismically deficient reinforced concrete (RC) bridge piers using high-performance strength concrete (HPSC) jackets and Fiber-reinforced polymer (FRP) rebars applied at the plastic hinge zone. Two 1:4 scaled RC pier specimens with different transverse reinforcement arrangements were tested under constant axial load and quasi-static cyclic lateral loading to induce damage. The damaged piers were then retrofitted and retested to assess seismic performance improvements. Key parameters such as load-carrying capacity, hysteresis behaviour, energy dissipation, and stiffness degradation were evaluated. The retrofitted specimens showed substantial restoration of strength and stiffness and exhibited enhanced energy dissipation compared to both the original and CFRP-wrapped specimens. Additionally, a finite element model was developed in SeismoStruct (2024), showing good agreement with experimental results and validating the proposed retrofitting approach. The study concludes that the HPSC-FRP retrofit method significantly enhances the seismic resilience of damaged bridge piers and presents a viable solution for extending the service life of aging bridge infrastructure.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102146"},"PeriodicalIF":5.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723025","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":"Transparent and reliable construction cost prediction using advanced machine learning and explainable AI","authors":"Lifei Chen ,&nbsp;Changyong Xu ,&nbsp;Wei Hong Lim ,&nbsp;Abhishek Sharma ,&nbsp;Sew Sun Tiang ,&nbsp;Kim Soon Chong ,&nbsp;El-Sayed M. El-kenawy ,&nbsp;Amel Ali Alhussan ,&nbsp;Marwa M. Eid ,&nbsp;Doaa Sami Khafaga","doi":"10.1016/j.jestch.2025.102159","DOIUrl":"10.1016/j.jestch.2025.102159","url":null,"abstract":"<div><div>Accurate construction cost prediction is vital for project management, influencing budgeting, resource allocation, and overall success. This study proposes a comprehensive framework that combines machine learning models, uncertainty quantification through Confidence Intervals, and explainable AI techniques using SHAP (SHapley Additive exPlanations) to enhance transparency and decision-making. Ten machine learning models, including Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbor Regression, and advanced ensemble methods such as XGBoost, CatBoost, and HistGradient Boosting, were evaluated on the RSMeans dataset. Among these, HistGradient Boosting achieved the best performance on the testing dataset. Beyond traditional metrics, Confidence Intervals quantified prediction reliability, and SHAP identified critical cost drivers like “Formwork” and “Tributary Area,” enabling interpretable and robust prediction. This study highlights the potential of machine learning models to revolutionize construction cost estimation by integrating predictive accuracy, uncertainty analysis, and explainability. The proposed framework supports resource efficiency and enables process innovation in cost management. It also contributes to the advancement of sustainable building practices, offering a strong foundation for future research and promoting the adoption of machine learning-based solutions with enhanced transparency and confidence.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102159"},"PeriodicalIF":5.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738636","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":"Enhancing multi-class skin lesion diagnosis through ensemble learning of CNN and transformer architectures","authors":"Hasan Erbay,&nbsp;Yassen Mohamed Abulgasim,&nbsp;Doğan Eren Özer,&nbsp;Fatih Ertürk","doi":"10.1016/j.jestch.2025.102145","DOIUrl":"10.1016/j.jestch.2025.102145","url":null,"abstract":"<div><div>Skin cancer remains one of the most prevalent forms of cancer worldwide, highlighting the critical need for accurate and automated diagnostic systems to support early detection and improve patient outcomes. This study presents a deep learning-based framework for multi-class classification of dermoscopic skin lesion images using the HAM10000 dataset. A range of state-of-the-art convolutional neural networks (CNNs) — including DenseNet201, InceptionResNetV2, and Xception — were evaluated under both frozen and fully fine-tuned configurations. Additionally, the performance of Vision Transformer (ViT) architectures was assessed to examine their potential in skin lesion analysis. To enhance classification performance, ensemble learning strategies — namely hard voting, soft voting, and weighted soft voting — were implemented. Experimental results indicate that fully fine-tuned models outperform their frozen counterparts, with InceptionResNetV2(full) achieving the best individual performance with accuracy of 0.88% and F1-score of 0.77%. The highest overall performance was obtained using the proposed weighted soft voting ensemble, yielding an accuracy of 0.89% and an F1-score of 0.80%. These findings demonstrate the effectiveness of ensemble methods and transfer learning based models in advancing automated skin lesion classification. Moreover, the results highlight the potential and limitations of each architecture in clinical applications and provide valuable insights for future research in computer-aided dermatological diagnosis.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102145"},"PeriodicalIF":5.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713614","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":"Advancing sustainable shipbuilding through a hybrid risk prioritization model: integrating FMEA and machine learning","authors":"Ahmet Fatih Yılmaz ,&nbsp;Ozan Köse","doi":"10.1016/j.jestch.2025.102158","DOIUrl":"10.1016/j.jestch.2025.102158","url":null,"abstract":"<div><div>Effective risk management is critical in modern shipbuilding, where production complexity continues to grow. Traditional Failure Modes and Effects Analysis (FMEA), despite its widespread use, often lacks objectivity and scalability. This study introduces a novel hybrid methodology that integrates FMEA with machine learning (ML), specifically, Random Forest (RF) regression, to enhance failure prediction and defect prioritization within a real-world shipyard context. The model was trained on 489 documented defects from the construction of an LNG-powered fishing vessel at Cemre Shipyard. Each defect was assessed using four risk factors: cost, time, frequency, and stage. Risk Priority Numbers (RPNs) were computed accordingly and used as target values in model training. The framework also incorporates Pareto analysis and feature importance evaluations to identify dominant risk contributors. The ML model achieved high predictive accuracy (Coefficient of Determination (R²) = 0.9738; Mean Absolute Error (MAE) = 1.3470) under current operational conditions. Deformation, inappropriate production, and defective part usage were identified as the most critical categories. Time loss and frequency emerged as the most significant features influencing RPNs. Improvement scenarios revealed the model’s robustness and capacity to estimate risk reduction potential for high-priority failure modes. This hybrid approach bridges expert judgment with data-driven intelligence and offers a scalable, objective framework for real-time quality control. Its potential for integration with enterprise systems suggests broader industrial applications, including automated risk monitoring and continuous improvement. The results demonstrate that combining FMEA with ML can significantly advance predictive defect management in maritime manufacturing.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102158"},"PeriodicalIF":5.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713631","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":"An investigation of innovative strategies in underwater soft robotics","authors":"Shahid A. Hasib ,&nbsp;Muhammad Majid Gulzar ,&nbsp;Sumaiya Rahman Oishy ,&nbsp;Muhammad Maaruf ,&nbsp;Salman Habib ,&nbsp;Adnan Shakoor","doi":"10.1016/j.jestch.2025.102123","DOIUrl":"10.1016/j.jestch.2025.102123","url":null,"abstract":"<div><div>Soft robotics has become a transformative technology for underwater applications, providing distinct advantages such as flexibility, adaptability, and safe interactions with fragile environments. This review offers a comprehensive analysis of the latest developments in actuation mechanisms, intelligent materials, fabrication methods, and control strategies specific to underwater soft robotics. It highlights key challenges like material degradation, energy efficiency, and control in dynamic aquatic conditions. Noteworthy advancements include bio-inspired actuators that mimic marine life and the integration of smart materials to enhance responsiveness and durability. Unlike previous reviews that focused on general aspects, this paper emphasizes cutting-edge soft actuator technologies and interdisciplinary applications that address challenges in complex aquatic environments with greater precision. Additionally, innovative fabrication techniques such as 3D printing and soft lithography are explored, revolutionizing the design and construction of underwater robots. The review also identifies critical gaps in current research and suggests future directions, stressing the importance of interdisciplinary approaches to tackle the complexities of underwater environments. It serves as a valuable resource for researchers and professionals seeking to advance the field and its applications.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102123"},"PeriodicalIF":5.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713630","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":"Transformer differential protection with wavelet transform and difference function","authors":"Merve Oztekin ,&nbsp;Serap Karagol ,&nbsp;Okan Ozgonenel","doi":"10.1016/j.jestch.2025.102144","DOIUrl":"10.1016/j.jestch.2025.102144","url":null,"abstract":"<div><div>The Transformer Differential Protection (TDP) algorithm instantly compares the target transformer’s terminal currents for each phase. The differential current is not expected to appear during regular operation. However, nonlinear characteristics of the core material, such as the hysteresis curve, result in significant variation in the differential current, known as magnetizing inrush current. This inrush current lasts for a while before disappearing, causing significant variation in the differential current. TDP algorithm is supposed to remain silent during this transient time (selectivity). In addition, one of the most difficult tasks for protection systems is detecting inter-turn faults in their early stages. This fault type typically begins at low levels due to moisture, high temperature, and so on, and gradually spreads to other turns. It is vital to detect inter-turn faults before they expand more than 10% of total windings [RW-2-1]. This paper presents a transformer differential protection algorithm that distinguishes between inter-turn, low-level internal faults, and inrush current. Maximum Overlapped Discrete Wavelet Transform (MODWT) energy and difference function are used for feature extraction, and the traditional 87T TDP method has been updated. Performance is evaluated using data collected from a laboratory-based experimental rig. The results demonstrate that the suggested approach performs very well in a range of low-level, inter-turn fault, and transient scenarios, including internal fault, inrush current, and sympathetic inrush current. These results are confirmed by the identified indices for Accuracy (AC), Dependability (DP), and Selectivity (SE).</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"70 ","pages":"Article 102144"},"PeriodicalIF":5.1,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712896","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":"Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues)","authors":"","doi":"10.1016/S2215-0986(25)00209-5","DOIUrl":"10.1016/S2215-0986(25)00209-5","url":null,"abstract":"","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"69 ","pages":"Article 102154"},"PeriodicalIF":5.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704009","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":"Additive manufacturing of thermoset elastomers: A review of emerging technologies","authors":"Csenge Tóth ,&nbsp;Ábris Dávid Virág ,&nbsp;István Halász-Kutasi ,&nbsp;Norbert Krisztián Kovács ,&nbsp;Tamás Bárány","doi":"10.1016/j.jestch.2025.102143","DOIUrl":"10.1016/j.jestch.2025.102143","url":null,"abstract":"<div><div>Thermoset elastomers (TSEs) are widely used in industries such as automotive, household appliances, healthcare, and fashion due to their flexibility and stability. However, these same properties make TSEs challenging to process using additive manufacturing (AM) techniques. This review categorizes AM technologies for producing TSEs into three main groups: photopolymerization-based, two-phase, and material extrusion–based techniques. Photopolymerization offers high resolution and material versatility but is constrained by build size and environmental impacts. Two-phase systems enable tunable properties but suffer from rheological and bonding issues, while material extrusion is more cost-effective yet less precise. Overall, current research primarily concentrates on technology and formulation development. Mechanical characterization of 3D-printed TSEs is typically limited to tensile properties. Comprehensive mechanical testing, including application-specific properties, is still rare and remains an essential area for future qualification of technological advances. The sustainability aspects of 3D printing TSEs are also addressed, with a focus on environmentally friendly raw material selection and the general environmental considerations of 3D printing methods. Emerging trends in this field include the development of smart materials, sustainable solutions, and integrated hybrid methods that incorporate artificial intelligence and machine learning.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"69 ","pages":"Article 102143"},"PeriodicalIF":5.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656542","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}