τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data by Zouhaier Brahmia, Fabio Grandi, Rafik Bouaziz

“…Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. …”
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Exploiting Partial Reconfiguration through PCIe for a Microphone Array Network Emulator by Bruno da Silva, An Braeken, Federico Domínguez, Abdellah Touhafi

“…The current Microelectromechanical Systems (MEMS) technology enables the deployment of relatively low-cost wireless sensor networks composed of MEMS microphone arrays for accurate sound source localization. …”
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Learning, economies of scale, and knowledge gap effects on power generation technology cost improvements by Yoga W. Pratama, Matthew J. Gidden, Jenna Greene, Andrew Zaiser, Gregory Nemet, Keywan Riahi

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OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS by Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong

“…This enables researchers to deploy and apply machine learning algorithms more effectively. …”
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Mind the perception gap: Identifying differences in views among stakeholder groups of shared mobility services through bayesian best-worst method by Ehsan Amirnazmiafshar, Marco Diana

“…Operators undervalue travel comfort and service availability, while overestimating travel distance, especially compared to users.Managerial Insights: For car-sharing, policy-makers should expand service coverage and incentivize vehicle deployment, while operators should use dynamic fleet management and offer flexible booking options. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection by Anjun Yu, Hongrui Fan, Yonghua Xiong, Longsheng Wei, Jinhua She

“…However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challenges to this research. …”
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Blockchain based adaptive federated learning in computational power network by LIU Tianrui, WANG Lianhai, WANG Qizheng, XU Shujiang, ZHANG Shuhui, WANG Yingxiaochun

“…However, federated learning in the computational power network environment faced certain difficulties due to the highly heterogeneous and widely distributed arithmetic edge servers. …”
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Network-Scale Impact of Vegetation Loss on Coverage and Exposure for 5G Networks by Jorn Schampheleer, Anke Huss, Margot Deruyck

“…This study investigates the effects of vegetation on 5G network performance, with a particular focus on coverage, user exposure, and base station deployment strategies in an urban environment (Utrecht, The Netherlands). …”
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A Survey on the Understanding and Viewpoints of Renewable Energy among South African School Students by Daniel Raphael Ejike Ewim, Sogo Mayokun Abolarin, Temiloluwa Olatunji Scott, Chidebe Stanley Anyanwu

“…Therefore, the government must take swift action to address these issues, by promoting the deployment of renewable energy sources in schools. …”
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Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud by Hang Mi, Bo Ai, Ruisi He, Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Yang Miao

“…Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. …”
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Bridging the gap between defossilization and decarbonization to achieve net-zero industry by Steven Griffiths, Benjamin Sovacool, Marfuga Iskandarova, Hans Jakob Walnum

“…Key policy recommendations include technology and policy leadership from countries pursuing a CCE framework, encouragement of public–private partnerships in the deployment of CCE technologies and ensuring social equity as net-zero industry is pursued in the near and long term. …”
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Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network by Xia-an Bi, Xiaohui Wang, Luyun Xu, Sheng Chen, Hong Liu

“…In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.…”
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SUSTAINABILITY PERFORMANCE OF INFRASTRUCTURE PROJECTS: THE CASE FOR LIFE CYCLE SUSTAINABILITY ASSESSMENT METHODOLOGY by Bankole AWUZIE, Alfred NGOWI

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Safeguarding forest ecosystems: harnessing IoT for fire detection by Iman Atighi, Zhi Zhou

“…It emphasizes the growing research interest in IoT sensor networks and their potential deployment in various domains. The insights provided aim to contribute to ongoing efforts in developing efficient forest fire detection systems, ultimately enhancing the safety and preservation of our natural environment.…”
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Exploring the psychological impact of working during COVID-19 on medical and nursing students: a qualitative study by Ruth Riley, Louise Griffin

“…Study undertaken between January and May 2020.Participants A purposive sample of eight medical (six women and two men) and seven nursing (all women) students who worked >2 weeks in a healthcare setting during the COVID-19 pandemic (from 1 March 2020 onwards).Results Four core themes with corresponding subthemes were identified: (1) COVID-19 sources of distress—working conditions, exposure to suffering, death and dying, relationships and teams, individual inexperience and student identity, (2) negative impact on mental health and well-being—psychological and emotional distress, delayed distress, exhaustion, mental ill health, (3) protective factors from distress—access to support, environment, preparation and induction, recognition and reward, time for breaks and rest and (4) positive experiences and meaningful outcomes.Conclusions Student pandemic deployment has had a significant negative impact on students’ psychological well-being, as a result of demanding working conditions, unprecedented exposure to death and suffering and lack of preparation for new job roles. …”
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Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection by Christine Bukola Asaju, Pius Adewale Owolawi, Chuling Tu, Etienne Van Wyk

“…According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. …”
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Anomaly detection of adversarial cyber attacks on electric vehicle charging stations by Sagar Babu Mitikiri, Vedantham Lakshmi Srinivas, Mayukha Pal

“…An EVCI system is simulated in the MATLAB/SIMULINK environment for various scenarios of data generation. …”
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Survey on the research progress of generative adversarial networks for 6G by Chanyuan MENG, Ke XIONG, Bo GAO, Yu ZHANG, Pingyi FAN

“…The deep integration of artificial intelligence (AI) and communication technology is the typical feature of the 6G network.On the one hand, AI injects new vitality into the development of the 6G network, which can effectively use the data generated by the historical operation of the network.It enables the network to be self-maintained and selfoptimized, and accelerates the process of network intelligence.On the other hand, the rich scenarios and IoT devices of the 6G network provide a large number of application fields and massive data for AI.These can enable the better deployment of AI, fully demonstrate the performance advantages of AI, and provide high-quality services for users.However, in practice, it is difficult to give full play to the performance advantages of AI due to the difficulty of sample collection, high cost of the collection, and lack of universality which caused by the complexity of the environment.Therefore, academia and industry introduce generative adversarial network (GAN) into the design of wireless networks.The powerful feature learning and feature expression ability of GAN can generate a large number of generated samples, which realizes the expansion of the wireless database.The introduction of GAN can effectively improve the generalization ability of AI models for wireless networks.Owing to the excellent performance of GAN, the generative model represented by GAN has attracted increased attention in the field of wireless networks, and rapidly became the new research hotspot of 6G networks.Firstly, the principle of GAN and its different versions of improved derived models were summarized.Then, the framework, advantages and disadvantages of each model were analyzed.Secondly, the research and application status of these models in wireless networks were reviewed.Finally, the research trends of GAN were proposed for the 6G network requirements, which provided some valuable exploration for future research.…”
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Construction of an Intelligent Analysis Model for Website Information Based on Big Data and Cloud Computing Technology by Ronghua Chen, Bei Yang

“…Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. …”
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