Engineering reports : open access最新文献

{"title":"Industry 4.0 Challenges and Implementation Strategies: Benchmarked Results From Food and Beverage Manufacturing Industries in Tanzania","authors":"Ismail W. R. Taifa,&nbsp;Ikupa Mwakagamba","doi":"10.1002/eng2.70168","DOIUrl":"https://doi.org/10.1002/eng2.70168","url":null,"abstract":"<p>The study assessed the challenges and strategies of Industry 4.0 (I4.0) in Tanzania's food and beverage manufacturing industries (FBMIs). Pertinent data were collected through a closed-ended questionnaire from 103 medium and large FBMIs. Data validity and reliability were tested using SPSS 23, where <i>p</i>-values &lt; 0.05 for validity testing and Cronbach Alpha value of &gt; 0.7 were determined for the reliability of the collected questionnaires. The normality of the data was tested using Kolmogorov–Smirnov and Shapiro–Wilk tests. The study found that employees were more informed about the benefits of adopting I4.0, with a mean score of 4.78. The prospect of I4.0 improving industry competitiveness on the international stage was ranked second in terms of awareness. Also, there was no significant difference in awareness levels for nine I4.0 technologies among the FBMIs. Most FBMIs had not fully established a roadmap for using I4.0 technologies. Findings showed that challenges have no significant differences, for example, for financial challenges (χ² = 1.121, <i>p</i> = 0.571 &gt; 0.05), return on investment and cost–benefit analysis for implementing I4.0 technologies with (χ² = 0.027, <i>p</i> = 0.987 &gt; 0.05), identifying and securing funds for implementing I4.0 technologies (with χ² = 1.918, <i>p</i> = 0.383 &gt; 0.05), among others. Implementing I4.0-related technologies is high; the findings showed that the overall mean score was 3.75, corresponding to “level 4” on the five-point Likert awareness scale. Also, some challenges should be tackled to implement I4.0-related technologies smoothly. Consequently, such challenges can be addressed by implementing the proposed strategies. The study suggests that stakeholders must implement relevant technologies fully despite FBMIs' high I4.0 awareness. Lastly, the study proposed strategies for implementing I4.0 in the FBMIs, including embracing data-driven decision-making and training leaders and top management regarding the benefits of I4.0 implementation. Strategies should be supported by top management commitment and adequate budget allocation.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Scale Time Adaptive Fusion Network for Transformer Fault Diagnosis","authors":"XuMing Liu,&nbsp;XiaoKun He,&nbsp;YongLin Li","doi":"10.1002/eng2.70152","DOIUrl":"https://doi.org/10.1002/eng2.70152","url":null,"abstract":"<p>Transformer fault diagnosis is crucial for the safe operation of power systems, enabling quick and accurate fault type identification. However, traditional methods struggle with extracting multi-scale temporal features and high-order feature representations, limiting their ability to handle complex dynamic data patterns. To address this, this paper proposes a multi-scale temporal adaptive fusion network (MSTAFN). The MSTAFN model first generates a time position vector through a temporal information encoding (TIE) module, capturing multi-scale temporal features. The adaptive high-order hybrid network (AHOHN) module then fuses multi-scale temporal data with transformer features using a hybrid attention mechanism, extracting temporal variation patterns. To enhance high-order feature representation, the high-order feature extraction (HOFE) module introduces nonlinear activation and higher-order operations to capture complex relationships between features. The adaptive feature reconstruction (AFR) module dynamically adjusts the feature fusion ratio, optimizing information integration. Finally, the multi-scale temporal fusion (MSTF) module balances the fusion of multi-scale temporal features and global dependencies, adapting to different tasks and data distributions. Extensive experiments on publicly available datasets demonstrate that the MSTAFN model outperforms comparison models across multiple evaluation metrics, proving its effectiveness and superiority in transformer fault diagnosis.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short-Term Load Forecasting in Smart Grid","authors":"Syed Muhammad Hasanat,&nbsp;Muhammad Haris,&nbsp;Kaleem Ullah,&nbsp;Syed Zarak Shah,&nbsp;Usama Abid,&nbsp;Zahid Ullah","doi":"10.1002/eng2.70163","DOIUrl":"https://doi.org/10.1002/eng2.70163","url":null,"abstract":"<p>Integrating renewable energy in smart grids enables sustainable energy development but introduces challenges in supply–demand variability. Deep learning techniques are now imperative for Short-Term Load Forecasting (STLF), a significant enabler of energy flow management, demand-side flexibility, and grid stability. These methods optimize smart grid performance under variable conditions by leveraging the synergistic integration of multiple architectures. This paper proposes a novel hybrid CNN–LSTM parallel model with a soft attention mechanism to improve smart grids' STLF. The proposed model leverages Convolution Neural Networks (CNNs) to extract spatial patterns, LSTMs to capture temporal dependencies, and attention mechanisms to prioritize important information, enhancing predictive performance. A comprehensive comparative analysis uses two publicly available datasets, American Electric Power (AEP) and ISO New England (ISONE), to evaluate the proposed model's effectiveness. The proposed model provides outstanding performance across single-step and multistep forecasting operations by delivering the highest evaluation results. The proposed model delivered single-step forecasting results of 123.91 Root Mean Square Error (RMSE), 92.8 Mean Absolute Error (MAE), and 0.63 Mean Absolute Percentage Error (MAPE) on the AEP dataset and 126.16 RMSE, 64.28 MAE, and 0.44 MAPE on the ISONE dataset. The model delivered multistep forecasting results on AEP, which showed RMSE at 685.25, MAE of 490.37, and MAPE of 3.27, while ISONE produced RMSE of 598.26, MAE of 402.44, and MAPE of 2.73. The simulation results demonstrate that parallel CNN–LSTM with a soft attention mechanism effectively supports the development of adaptive and resilient smart grids, enabling better integration of renewable energy sources.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prioritized Multi-Step Decision-Making Gray Wolf Optimization Algorithm for Engineering Applications","authors":"Idriss Dagal,&nbsp;Alpaslan Demirci,&nbsp;Ambe Harrison,&nbsp;Wulfran Fendzi Mbasso,&nbsp;Said Mirza Tercan,&nbsp;Burak Akın,&nbsp;Kürşat Tanriöven,&nbsp;Havva Aysun Sezgin Köksal,&nbsp;Ahmet Nayir","doi":"10.1002/eng2.70154","DOIUrl":"https://doi.org/10.1002/eng2.70154","url":null,"abstract":"<p>This article introduces the Prey-Movement Strategy Gray Wolf Optimizer (PMS-GWO), an enhanced version of the Gray Wolf Optimizer (GWO) designed to improve optimization efficiency through a novel multi-step decision-making process. By integrating adaptive exploration–exploitation strategies, PMS-GWO dynamically manages leadership roles, balances local and global searches, and introduces a prey escape mechanism, significantly improving solution diversity. Comparative analysis across 23 benchmark functions demonstrates PMS-GWO's superior performance, achieving up to 28.6% faster convergence and a 55.5%–93.8% increase in solution accuracy compared to the standard GWO. Notably, PMS-GWO enhances computational efficiency by 21.7%–27.4% and shows a 168.8% improvement in solution accuracy for the complex Michalewicz function over the baseline GWO. Visual convergence speed analysis, evidenced by a rapid fitness value decline within 100 iterations, reveals PMS-GWO's quickest convergence time of 0.02 s among tested algorithms. Furthermore, a comparison of runtime for several algorithms, including PMS-GWO, MMCCS-GWO, CC-GWO, MGWO, and GWO, clearly indicates that PMS-GWO achieves the lowest runtime of 2.364 s, significantly faster than CC-GWO and MGWO, which both exceed 5 s. This visual representation highlights the computational efficiency of PMS-GWO compared to other algorithms. PMS-GWO also outperforms advanced GWO variants like MMSCC-GWO, MGWO, and CCS-GWO, particularly in complex optimization landscapes, highlighting its adaptability and effectiveness for real-world applications in energy systems and engineering design. The multi-step decision-making process implemented in PMS-GWO is critical to achieving these improved convergence and diversity metrics.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Local–Global Graph KAN for Multi-Class Prediction of PPI","authors":"Minghui Liu,&nbsp;Ying Qu","doi":"10.1002/eng2.70164","DOIUrl":"https://doi.org/10.1002/eng2.70164","url":null,"abstract":"<p>Traditional experimental methods for identifying protein–protein interactions (PPI) are expensive and time-consuming. Therefore, using machine learning to treat multiple PPI predictions as binary classifications has become an alternative, but there is a problem of data imbalance. The proposed GLGKAN-PPI method integrates features from both global graphs and local subgraphs to capture the complex structural information of PPI networks comprehensively. Specifically, the method utilizes the pre-trained model MASSA to extract multimodal features of proteins. The global graph features are extracted using the GKAN (Graph Kolmogorov-Arnold Network) algorithm. Meanwhile, the local subgraph features are extracted using the MOE-GKAN (Mixture of Experts-Graph Kolmogorov-Arnold Network) algorithm. To mitigate data imbalance, an asymmetric loss function is utilized to better handle minority classes and improve overall prediction accuracy. Experimental results demonstrate that GLGKAN-PPI outperforms a range of existing intelligent approaches across multiple datasets and partitioning strategies.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Self-Compacting Concrete Reinforced With Basalt Fiber: A Comprehensive Review","authors":"M. Kiran Prabha,&nbsp;K. Vishnu Vardhan,&nbsp;A. S. Santhi,&nbsp;G. Mohan Ganesh","doi":"10.1002/eng2.70147","DOIUrl":"https://doi.org/10.1002/eng2.70147","url":null,"abstract":"<p>In recent years, the incorporation of fibers into self-compacting concrete has garnered significant attention in addressing various issues. Among the different types, basalt fiber stands out due to its excellent mechanical properties, high tensile strength, and resistance to alkali and acidic environments. This review paper addresses the impact of basalt fiber on fresh, mechanical, microstructural, and durability properties of self-compacting concrete. It's been noted that a significant amount of research is still being conducted on using basalt fiber. This work states the basalt fiber's chemical compositions, which are crucial in its applications. Based on the literature, it has been cited that the optimum dosage of the fiber is different for the compression and tensile loads on the concrete specimens. This work actively explores basalt fiber's potential in various concrete applications. The discussion emphasizes the chemical composition and optimal dosage of basalt fiber in self-compacting concrete and underscores the importance of further research in this area. Finally, this paper recommends other studies to understand the keen effect of basalt fiber in self-compacting concrete.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Walk-Based GOOSE Algorithm for Solving Engineering Structural Design Problems","authors":"Sripathi Mounika,&nbsp;Himanshu Sharma,&nbsp;Aradhala Bala Krishna,&nbsp;Krishan Arora,&nbsp;Syed Immamul Ansarullah,&nbsp;Ayodeji Olalekan Salau","doi":"10.1002/eng2.70048","DOIUrl":"https://doi.org/10.1002/eng2.70048","url":null,"abstract":"<p>The proposed Random Walk-based Improved GOOSE (IGOOSE) search algorithm is a novel population-based meta-heuristic algorithm inspired by the collective movement patterns of geese and the stochastic nature of random walks. This algorithm includes the inherent balance between exploration and exploitation by integrating random walk behavior with local search strategies. In this paper, the IGOOSE search algorithm has been rigorously tested across 23 benchmark functions where 13 benchmarks are with varying dimensions (10, 30, 50, and 100 dimensions). These benchmarks provide a diverse range of optimization landscapes, enabling comprehensive evaluation of IGOOSE algorithm performance under different problem complexities. The algorithm is tested by various parameters such as convergence speed, magnitude of solution, and robustness for different dimensions. Further, IGOOSE algorithm is applied to optimize eight distinct engineering problems, showcasing its versatility and effectiveness in real-world scenarios. The results of these evaluations highlight IGOOSE algorithm as a competitive optimization tool, offering promising performance across both standard benchmarks and complex structural engineering problems. Its ability to balance exploration and exploitation effectively, combined with its ability to deal with different problems, positions IGOOSE algorithm as a valuable tool.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical Solution for Railway Transition Zones With Abrupt Changes in Elastic Stiffness","authors":"Josiah Murray,&nbsp;Michael H. Meylan,&nbsp;Trung Ngo,&nbsp;Ngamta Thamwattana,&nbsp;Buddhima Indraratna","doi":"10.1002/eng2.70106","DOIUrl":"https://doi.org/10.1002/eng2.70106","url":null,"abstract":"<p>Transition zones in railway systems, where properties of the track foundation change abruptly, are known to increase dynamic loads, track deterioration, and passenger discomfort. As such, it is of particular importance to study railway transition zones with abrupt changes in foundation properties to minimize these railway problems. This paper presents a closed-form solution for the long-term deformation of an Euler-Bernoulli beam on an elastic foundation with multiple abrupt changes in foundation stiffness and under multiple applied stationary point loads. The solutions are obtained by dividing the beam into segments and applying the method of undetermined coefficients. This exact analytical solution constitutes an improvement upon an approximate solution, which is presented in the literature as a recent method for modeling rail infrastructure at transition zones. A limitation of the approximate solution is its inability to account for the changed behavior of the beam close to a transition zone. The closed-form solution overcomes this limitation and can be used to assess the suitability of the approximate solution.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Insights Into Load Current Control Using Power MOSFET","authors":"Huy Hung Nguyen,&nbsp;Quoc Minh Lam,&nbsp;Minh Nhat Huynh,&nbsp;Trong Trung Nguyen,&nbsp;Van Tu Duong,&nbsp;Tan Tien Nguyen","doi":"10.1002/eng2.70131","DOIUrl":"https://doi.org/10.1002/eng2.70131","url":null,"abstract":"<p>Power MOSFETs are critical in applications involving load current control, where system performance is significantly affected by their operating regions. This paper investigates the operational characteristics of power MOSFETs and proposes a novel approach for load current control based on their transconductance properties. By analyzing the relationship between MOSFET operating regions and load current dynamics, a control strategy that directly regulates the gate-source voltage to achieve precise current control is established. Furthermore, a gate driver circuit is designed to extend the controllable voltage range and enhance sensitivity to drain current variations. To improve current sensing accuracy, a methodology for designing a shunt resistor is introduced, leveraging the theoretical load curve of MOSFETs. Simulations using Simscape models and experimental validation confirm that the proposed method effectively extends the controllable voltage range of the drain current. Specifically, the voltage range can increase by up to 75% under load conditions compared to the absence of a shunt resistor, providing improved flexibility in current regulation. These findings offer novel insights into the use of power MOSFETs for load current control, with potential applications in digitally controlled power systems and motor drives.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor Profile: Mubashir Husain Rehmani","authors":"Mubashir Husain Rehmani","doi":"10.1002/eng2.70156","DOIUrl":"https://doi.org/10.1002/eng2.70156","url":null,"abstract":"&lt;p&gt;I received the BEng degree in computer systems engineering from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2004, the MS degree from the University of Paris XI, Paris, France, in 2008, and the PhD degree from the University Pierre and Marie Curie, Paris, in 2011. I am currently working as a Lecturer in the Department of Computer Science, Munster Technological University (MTU), Ireland, since January 2019. Prior to this, I worked as a Postdoctoral Researcher at the Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology (WIT), Waterford, Ireland, from 2017 to 2018. I also served for 5 years as an Assistant Professor at COMSATS Institute of Information Technology, Wah Cantt., Pakistan. Before joining COMSATS, I worked as a Postdoctoral Researcher at PASNET, LIGM, Université Paris Est Marne La Vallée, France in 2012.&lt;/p&gt;&lt;p&gt;Over the last two decades, my research focused on wireless networks, smart grid, Internet of Things, cognitive radio networks, energy harvesting, privacy preserving algorithms, renewable energy systems, Terahertz communication, cybersecurity—protecting critical infrastructure, biomedical image analysis, generative artificial intelligence, blockchain, non-fungible tokens, tokenization, tokenomics, decentralized finance, metaverse, and cryptocurrency.&lt;/p&gt;&lt;p&gt;Initially, I was in doubt that I didn't do anything extraordinary in teaching and learning. Thanks to my MTU colleagues, who motivated me to think and write about my leadership role and how I influenced others in teaching and learning. These few words of appreciation and motivation from colleagues boosted my confidence and now I am proud to share that I have achieved the status of Senior Fellowship from Advance HE, United Kingdom.&lt;/p&gt;&lt;p&gt;I am serving in various academic leadership positions to shape the future of teaching and learning and participate in policy making for higher education. I am a member of the Academic Council, MTU, and a member of the Research and Innovation Committee, MTU.&lt;/p&gt;&lt;p&gt;I extensively serve in international scientific community in various roles and capacities. I am currently an Area Editor of the &lt;i&gt;IEEE Communications Surveys and Tutorials&lt;/i&gt; and &lt;i&gt;IEEE Open Journal of Communications Society&lt;/i&gt;. I am also serving as an Editorial Board Member of &lt;i&gt;NATURE Scientific Reports&lt;/i&gt;. Additionally, I served for 3 years (from 2015 to 2017) as an Associate Editor of the &lt;i&gt;IEEE Communications Surveys and Tutorials&lt;/i&gt;. I served both as Column Editor for Book Reviews and Associate Editor in &lt;i&gt;IEEE Communications Magazine&lt;/i&gt;. I am currently serving as an Associate Editor for &lt;i&gt;IEEE Transactions on Green Communication and Networking&lt;/i&gt; and &lt;i&gt;IEEE Transactions on Cognitive Communications and Networking&lt;/i&gt;, Elsevier &lt;i&gt;Journal of Network and Computer Applications&lt;/i&gt; (&lt;i&gt;JNCA&lt;/i&gt;), the &lt;i&gt;Journal of Communications and Networks&lt;/i&gt; (&lt;i&gt;JCN&lt;/i&gt;), and several other journals.&lt;/p&gt;&lt;p&gt;I served as Lead Gu","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}