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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Neural computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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EMI Performance of Multilayered Al-CoTaZr Films in Shielded Power Inductors

EMI Performance of Multilayered Al-CoTaZr Films in Shielded ...

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Electrical Design of Advanced Packaging and Systems Symposium, EDAPS

作者： Ghaleb Saleh Ghaleb Al-Duhni Mudit Khasgiwala John L. Volakis Pulugurtha Markondeya Raj College of Engineering & Computing Florida International University Miami FL USA RF/Analog System Architecture and AI system Solutions Applied Materials Santa Clara CA USA

This paper examines the performance of multilayered Al-CoTaZr films as electromagnetic interference (EMI) shields. The first part of the paper simulates the shielding effectiveness (SE) of several combinations of multilayered stacks of a conductor and magnetic layer of 10 μm thickness. Using the NSA 65-6 standard in the frequency range of 1–100 MHz, multilayered stacks of [Al (2 layers)-CoTaZr (2 layers) and Al (2 layers)-CoTaZr (3 layers)] are shown to provide SE of 10–62 dB. By comparison, commercial EMI shielding such as copper (Cu), shows only 6–41 dB in the same frequency range. Alternatively, using the IEEE 299 standard, the same thickness of Al and CoTaZr samples gives 40–70 dB SE as compared to that of Cu which ranges from 12–46 dB. The second part of the paper studies the impact of various multilayered stacks of Al and CoTaZr to mitigate undesired magnetic radiation from power inductors. The multilayered EMI shielding helps to increase the inductance from 105 to 135 nH in the case of air-core inductors. In the case of magnetic-core inductors, a substantial increment ranging from 390–750 nH was noted.

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Investigation of Structural Defects and Magnetic Ordering in Co-Doped Magnesium Oxide Nanoparticles

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SSRN 2024年

作者： Anwar, Muhammad Shahbaz Arif, Shafaq Tahir, Jaweria Ayub, Rana Department of Physics Lahore College for Women University Lahore54000 Pakistan Centre for Advanced Studies in Physics Government College University Lahore54000 Pakistan Department of Engineering & Construction School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom

Mg1-xCoxO nanoparticles (0 ≤ x ≤ 0.1) are synthesized by utilizing co-precipitation method. The structural, chemical, morphological and magnetic properties of the nanoparticles are examined by using X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) and Magnetic Properties Measurement System (MPMS: quantum design), respectively. Cubic structure of the nanoparticles is revealed by the XRD investigations. The crystallite size (23-12 nm) and microstrain (3.26´10-3 - 8.28´10-3) are calculated using Scherrer relation and Williamson Hall (W-H) method. It is found that the results of both methods are in good agreement. The bandgap of Mg1-xCoxO (x = 0.1) decreased from 5.66 to 4.93 eV due to agglomeration of irregular shaped nanoparticles. Magnetization loops are measured of Co doped MgO nanoparticles at 5 and 100 K temperatures. Superparamagnetic behavior is observed at low temperatures (5 K) in doped MgO nanoparticles. Interestingly, undoped MgO nanoparticles show diamagnetic behavior that switched to paramagnetic with Co doping. Our findings may develop fundamental understanding of the relationship between structural and magnetic properties of nanoparticles and pave the way for the utilization of Co-doped MgO nanoparticles in magnetic and spintronic devices. © 2024, The Authors. All rights reserved.

关键词： X ray diffraction

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Scientometric review on multiple climate-related hazards indices

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Science of the Total Environment 2024年 945卷 174004页

作者： Laino, Emilio Paranunzio, Roberta Iglesias, Gregorio School of Engineering and Architecture & Environmental Research Institute MaREI University College Cork Cork Ireland National Research Council of Italy Institute of Atmospheric Sciences and Climate Corso Fiume 4 Torino10133 Italy University of Plymouth School of Engineering Computing and Mathematics Marine Building Drake Circus United Kingdom

As the spectre of climate change looms large, there is an increasing imperative to develop comprehensive risk assessment tools. The purpose of this work is to evaluate the evolution and current state of research on multi-hazard indices associated with climate-related hazards, highlighting their crucial role in effective risk assessment amidst the growing challenges of climate change. A notable gap in cross-regional comparative studies persists, presenting an opportunity for future research to enhance global understanding and foster universal resilience strategies. However, a significant surge in research output is apparent, following key global milestones related to climate change action. The research landscape is shown to be highly responsive to international policy developments, increasingly adopting interdisciplinary approaches that integrate physical, social, and technological dimensions. Findings reveal a robust emphasis on geospatial analysis and the development of various indices that transform abstract climate risks into actionable data, underscoring a trend towards localized, context-specific vulnerability assessments. Based on dataset systematically curated under the PRISMA guidelines, the review explores how prevailing research themes are reflected in influential journals and author networks, mapping out a dynamic and expanding academic community. Moreover, this work provides critical insights into the underlying literature by conducting a thematic analysis on the typology of studies, the focus on coastal areas, the inclusion of climate change scenarios, the geographical coverage, and the types of climate-related hazards. The practical implications of this review are profound, providing policymakers and practitioners with meaningful insights to enhance climate change mitigation and adaptation efforts through the application of index-based methodologies. By charting a course for future scholarly endeavours, this article aims to strengthen the scientific fo

关键词： Climate change

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Blockchain Vulnerabilities and Recent Security Challenges: A Review Paper

Blockchain Vulnerabilities and Recent Security Challenges: A...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Aysha AlFaw Wael Elmedany Mhd Saeed Sharif College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering University of East London London England

ISBN: (纸本)9781665490597

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system’s security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security.

关键词： Industries Technological innovation Systematics Non-repudiation Blockchains Peer-to-peer computing Security

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Levelized Cost of Energy for Various Floating Offshore Wind Farm Designs in the Areas Covered by the Spanish Maritime Spatial Planning

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SSRN 2024年

作者： Thomas, Brieuc Costoya, Xurxo deCastro, M. Iglesias, Gregorio Gómez-Gesteira, M. Campus da Auga Ourense32004 Spain University College Cork School of Engineering and Architecture & MaREI Environmental Research Institute College Road Cork Ireland University of Plymouth School of Engineering Computing and Mathematics Marine Building Drake Circus PlymouthPL4 8AA United Kingdom

Spain took a significant step towards achieving its goal of installing 1 to 3 GW of floating offshore wind capacity by 2030, by implementing Maritime Spatial Planning encompassing 19 designated areas. Therefore, it is essential to calculate the Levelized Cost of Energy for strategically designed wind farms, taking into account the number and capacity of wind turbines. To find the most cost-effective offshore wind energy sites, the optimal combination of rated power and the number of turbines was determined and compared across different locations. This analysis used downscaled wind data from the 6th phase of the Coupled Model Intercomparison Project multi-model ensemble, covering 2025-2049 under the Shared Socioeconomic Pathway 2-4.5 scenario, matching the expected lifespan of wind farms. The results show that the Levelized Cost of Energy is lower for farms with a higher number of wind turbines with increased rated power. The areas located in the Canary Islands exhibit the most economically advantageous prices overall, ranging from 100 to 135 €/MWh. Regions such as Galicia and Cataluña also present a high diversity in prices (105 – 160 €/MWh), while Andalucía shows moderate values (135 – 140 €/MWh). © 2024, The Authors. All rights reserved.

关键词： Offshore wind farms

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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Adaptive Electric Vehicle Charge Controllers Using Deep Reinforcement Learning for Real-Time Optimization 1

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kavitha, N. Bdair, Mohammad Chandrasekar, P. Veerakumar, S. Kumar, M. Hema Kumar, Vinay Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of EEE Tamil Nadu Chennai India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of EEE Tamil Nadu Erode India Sona College of Technology Department of ECE Tamil Nadu Salem India B N M Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331518578

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement Learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution. © 2025 IEEE.

关键词： Deep reinforcement learning

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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