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Advancing wind energy potential estimation through multidistribution wind speed analysis in coastal Pakistan

Scientific reports 2025年第1期15卷 18297页

作者： Ghulam Abbas Arshad Ali Zohaib Mushtaq Ateeq Ur Rehman Seada Hussen Habib Hamam Department of Electrical Engineering The University of Lahore Lahore 54000 Pakistan. Faculty of Computer and Information Systems Islamic University of Madinah 42351 Madinah Saudi Arabia. Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha 40100 Pakistan. School of Computing Gachon University Seongnam-si 13120 Republic of Korea. 202411144@gachon.ac.kr. Department of Electrical Power Adama Science and Technology University 1888 Adama Ethiopia. seada.hussen@aastu.edu.et. Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada. School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa. International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP 1989 Libreville Gabon. College of Computer Science and Engineering (Invited Prof.) University of Ha'il 55476 Ha'il Saudi Arabia.

Wind energy is becoming one of the most important elements toward the advancement of sustainable energy systems globally. The assessment of wind energy potential is critical to the optimization of resource application and improvement of technologies. This study focuses on fitting fourteen probability density functions (PDFs) to hourly wind speed data collected from six coastal cities of Pakistan: Gwadar, Jiwani, Karachi, Keti Bandar, Ormara, and Pasni, for the year 2023, measured at 10 m and 50 m heights. These selected distributions are Weibull, Rayleigh, Lognormal, Gamma, Normal, Generalized Extreme Value, Logistic, Nakagami, t Location-Scale, Extreme Value, Inverse Gaussian, Chi-Square, Pearson Type III, and Rician. Four goodness-of-fit (GoF) indices are employed to evaluate the performance of these distributions: root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R), and chi-squared (χ). These metrics give a clear report on each distribution's aptness to emulate the wind speed information. Observed and computed wind power density (WPD) values are also determined to investigate the application of fitted distribution functions for practical purposes. The inspection of the simulation results shows that GEV, Weibull, Nakagami, and Gamma PDFs proved to be the most promising PDFs for describing wind speed data at 10 m, whereas GEV (predominantly), Weibull, Normal, and Logistic PDFs for wind speed data at 50 m. Further investigation revealed that the GEV distribution consistently exhibited better fitting characteristics, followed closely by Weibull, Nakagami, and Gamma distributions, making them highly suitable for characterizing the wind speed and determining wind energy potential. The extensively used Weibull distribution is not always the first choice. Consequently, the results presented in the paper provide fundamental information about the usage of the resource and energy production for Pakistani coastal wind sites.

关键词： Coastal cities Energy efficiency GEV distribution Optimization PDFs Wind power density Wind speed fitting

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Input Device to Assist Occupational Therapists Treat Children with Difficulty Crossing the Midline

Input Device to Assist Occupational Therapists Treat Childre...

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AFRICON Conference

作者： Jonathan Jacobs Japie Greeff Reolyn Heymann University of Johannesburg South Africa North-West University School of Computer Science and Information Systems Faculty of Natural and Agricultural Sciences South Africa

ISBN: (数字)9781728132891

ISBN: (纸本)9781728132907

Bilateral integration, the ability to utilize both sides of one's body simultaneously and particularly crossing the midline are skills that are crucial for a child to develop by the age of 5 years old. When children have difficulty with crossing the midline, they will be unable to perform effectively. Crossing the midline allows for the advancement of various motor and cognitive skills as pathways in the brain are developed. This paper focuses on an assistive input device used with an interactive game aiming to target those children that have difficulty with crossing the midline. Previously a device was designed to accompany the game, however many complexities were encountered. Guidance from occupational therapists was sought during the development of the interactive game and the device. Using a linear potentiometer provided an innovative approach to simulate the movement of the child solving for many of the issues experienced in the design of the assistive device. Playing of the game stimulates the required pathways to create movements that were difficult or previously uncomfortable and avoided by the child. The challenges faced in the design of the assistive design will be thoroughly investigated and the ultimate working solution shall be presented.

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Robust beamforming for IRS-assisted wireless communications under channel uncertainty

arXiv

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arXiv 2020年

作者： Deng, Yongchang Zou, Yuze Gong, Shimin Lyu, Bin Hoang, Dinh Thai Niyato, Dusit School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China Faculty of Engineering and Information Technology University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP’s signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP’s transmit power, subject to the user’s worst-case data rate requirement and the IRS’s worst-case power budget constraint. Instead of using the alternating optimization method, we firstly propose a heuristic scheme to decompose the IRS’s phase shift optimization and the AP’s active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP’s transmit beamforming and the magnitude of the IRS’s reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

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Corrigendum to “A decision modeling approach for smart e-tourism data management applications based on spherical fuzzy rough environment” [applied Soft Computing 143(2023) 110297]

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applied Soft Computing 2023年 149卷

作者： R.T. Mohammed A.H. Alamoodi O.S. Albahri A.A. Zaidan H.A. AlSattar Uwe Aickelin A.S. Albahri B.B. Zaidan Amelia Ritahani Ismail R.Q. Malik Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq SP Jain School of Global Management Sydney Australia Department of Business Administration College of Administrative Science The University of Mashreq 10021 Baghdad Iraq School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Department of Computer Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Kulliyyah of Information and Communication Technology (KICT) International Islamic University Malaysia Kuala Lumpur Malaysia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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Population of tetraneutron continuum in reactions of 8He on deuterium

arXiv

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arXiv 2023年

作者： Muzalevskii, I.A. Shulgina, N.B. Bezbakh, A.A. Chudoba, V. Krupko, S.A. Belogurov, S.G. Biare, D. Egorova, I.A. Fomichev, A.S. Gazeeva, E.M. Gorshkov, A.V. Grigorenko, L.V. Kaminski, G. Khirk, M. Kiselev, O. Kostyleva, D.A. Kozlov, M.Yu. Mauyey, B. Mukha, I. Nikolskii, E.Yu. Parfenova, Yu.L. Piatek, W. Quynh, A.M. Serikov, A. Sidorchuk, S.I. Sharov, P.G. Slepnev, R.S. Stepantsov, S.V. Swiercz, A. Szymkiewicz, P. Ter-Akopian, G.M. Zalewski, B. Flerov Laboratory of Nuclear Reactions JINR Dubna141980 Russia National Research Centre "Kurchatov Institute" Kurchatov Sq. 1 Moscow123182 Russia Bogoliubov Laboratory of Theoretical Physics JINR Dubna141980 Russia Institute of Physics Silesian University in Opava Opava74601 Czech Republic National Research Nuclear University "MEPhI" Moscow115409 Russia Dubna State University Dubna141982 Russia Heavy Ion Laboratory University of Warsaw Warsaw02-093 Poland Skobeltsyn Institute of Nuclear Physics Moscow State University Moscow119991 Russia GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt64291 Germany II. Physikalisches Institut Justus-Liebig-Universität Giessen35392 Germany Laboratory of Information Technologies JINR Dubna141980 Russia Institute of Nuclear Physics Almaty050032 Kazakhstan Nuclear Research Institute Dalat670000 Viet Nam AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow30-059 Poland

Search for the population of the low-energy continuum of a tetraneutron system was performed for reactions of the 8He beam on a deuterium target. These studies are based on the data [I.A. Muzalevskii et al., Phys. Rev. C 103, 044313 (2021)], previously used for the studies of 7H and 6H in the 2H(8He, 3He)7H and 2H(8He, 4He)6H reactions. Evidence for a hump in the 4n continuum at 3.5±0.7 and 3.2±0.8 MeV was observed in the 2H(8He,6Li)4n and 2H(8He,3He)7H→3H+4n reactions, respectively. The observed statistics is quite low (6 events and up to 40 events) corresponding to very low cross sections of few microbarns or tens of microbarns. The background conditions for the 2H(8He,6Li)4n reaction are shown to be good, favoring the physical nature of the observed events. The 2H(8He,3He)7H→3H+4n process transforms to the 2H(8He,6Li∗)4n reaction in the limit of the highest 7H decay energies. The population of the low-energy region in the 4n spectrum is found to be perfectly correlated with the population of the lowest 6Li state in the 3He+3H continuum with E∗ = 18 MeV. Theoretical calculations of 8He in a five-body α+4n and of 4n in a four-body hyperspherical models are presented. The 8He wave function is shown to contain strong specific correlations, which may give rise to very low-energy structures in tetraneutron continuum in extreme-peripheral reaction scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Continuum mechanics

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An investigation of interpretability techniques for deep learning in predictive process analytics

arXiv

引用

arXiv 2020年

作者： Moreira, Catarina Sindhgatta, Renuka Ouyang, Chun Bruza, Peter Wichert, Andreas School of Information Systems Science and Engineering Faculty of Queensland University of Technology 2 George St Brisbane City BrisbaneQLD4000 Australia Department of Computer Science and Engineering Instituto Superior Técnico / INESC-ID University of Lisbon Av. Prof. Dr. Cavaco Silva Porto Salvo2744-016 Portugal

This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical dataset containing information about patients with cancer, where we learn models that try to predict the type of cancer of the patient, given their set of medical activity records. We explored different algorithms based on neural network architectures using long short term deep neural networks, and random forests. Since there is a growing need to provide decision-makers understandings about the logic of predictions of black boxes, we also explored different techniques that provide interpretations for these classifiers. In one of the techniques, we intercepted some hidden layers of these neural networks and used autoencoders in order to learn what is the representation of the input in the hidden layers. In another, we investigated an interpretable model locally around the random forest’s prediction. Results show learning an interpretable model locally around the model’s prediction leads to a higher understanding of why the algorithm is making some decision. Use of local and linear model helps identify the features used in prediction of a specific instance or data point. We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well. In addition, the structured deep learning approach using autoencoders provided meaningful prediction insights, which resulted in the identification of nonlinear clusters correspondent to the patients’ different types of cancer. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Optimal locally repairable codes: An improved bound and constructions

arXiv

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arXiv 2020年

作者： Cai, Han Fan, Cuiling Miao, Ying Schwartz, Moshe Tang, Xiaohu Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva8410501 Israel School of Mathematics Southwest Jiaotong University Chengdu610031 China Faculty of Engineering Information and Systems University of Tsukuba Tennodai 1-1-1 Tsukuba305-8573 Japan School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China

We study the Singleton-type bound that provides an upper limit on the minimum distance of locally repairable codes. We present an improved bound by carefully analyzing the combinatorial structure of the repair sets. Thus, we show the previous bound is unachievable for certain parameters. We then also provide explicit constructions of optimal codes that show that for certain parameters the new bound is sharp. Additionally, as a byproduct, some previously known codes are shown to attain the new bound and are thus proved to be optimal. Copyright © 2020, The Authors. All rights reserved.

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks

arXiv

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arXiv 2020年

作者： Dev, Kapal Khowaja, Sunder Ali Jaiswal, Aman Bist, Ankur Singh Saini, Vaibhav Bhatia, Surbhi CONNECT Centre Trinity College Dublin Ireland Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department KIET Group of Institutions Ghaziabad India Signy Advanced Technologies. Gurugram Haryana India Dep Indian Institute of Technology Dehli India Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for potential alternatives to reverse transcriptionpolymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of largescale annotated data makes the clinical implementation of machine learning-based COVID detection methods difficult. Another important issue is the usage of ImageNet pretrained networks which does not guarantee to extract reliable feature representations. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from wellknown pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. © 2020, CC-BY.

关键词： Convolution

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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