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11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Nigussie, Ethiopia Olwal, Thomas Musumba, George Tegegne, Tesfa Lemma, Atli Mekuria, Fisseha Department of Future Technologies Turku Finland Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa School of Computer Science and Information Technology Dedan Kimathi University of Technology Nyeri Kenya Center for ICT for Development Bahir Dar University Bahir Dar Ethiopia School of Electircal Engineering Haramaya University Dire Dawa Ethiopia Council for Scientific and Industrial Research Pretoria South Africa

Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management, irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. It is expected that an intelligent data analysis will help to discover new knowledge that support further development of agricultural expertise. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands. © 2020 The Authors. Published by Elsevier B.V.

关键词： Internet of things

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Theoretical study of molar entropy and gibbs free energy for HCl and DCl

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Physica Scripta 2025年第7期100卷 075402-075402页

作者： Jiawei Yao Changlong Chen Qunchao Fan Zhixiang Fan Hongrui Tian Huidong Li Jia Fu Feng Xie School of Science Key Laboratory of High Performance Scientific Computation Xihua University Chengdu 610039 People’s Republic of China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy Technology Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education Tsinghua University Beijing 100084 People’s Republic of China

Molecular molar entropy and Gibbs free energy are pivotal for understanding the thermodynamic and kinetic properties of molecular systems, facilitating the optimization of chemical reactions and phase transitions. In this work, we integrated experimental spectral constants (D_e, r_e, ω_e, ω_eχ_e, B_e, α_e) and Rydberg-Klein-Rees (RKR) data, and constructed the potential energy curves (PECs) for the ground electronic states of HCl and DCl molecules using the variationally improved Hulburt-Hirschfelder (VIHH) potential model. The complete set of rovibrational energy levels for the ground electronic states of HCl and DCl is obtained by solving the one-dimensional Schrödinger equation. Finally, the molar entropy and the reduced Gibbs free energy of these two molecules in the temperature range of 100 to 6000 K are calculated within the framework of quantum statistical theory. Our results demonstrate that the PECs and vibrational energy levels derived from the VIHH model align closely with experimental values, and the calculated thermodynamic properties are consistent with data from the National Institute of Standards and Technology.

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The ideal wavelength for daylight free-space quantum key distribution

arXiv

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arXiv 2023年

作者： Abasifard, Mostafa Cholsuk, Chanaprom Pousa, Roberto G. Kumar, Anand Zand, Ashkan Riel, Thomas Oi, Daniel K.L. Vogl, Tobias Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Computational Nonlinear and Quantum Optics SUPA Department of Physics University of Strathclyde GlasgowG4 0NG United Kingdom University of Applied Sciences Wiener Neustadt Wiener Neustadt2700 Austria Walton Institute for Information and Communication Systems Science South East Technological University WaterfordX91 P20H Ireland Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany

Quantum key distribution (QKD) has matured in recent years from laboratory proof-of-principle demonstrations to commercially available systems. One of the major bottlenecks is the limited communication distance in fiber networks due to the exponential signal damping. To bridge intercontinental distances, low Earth orbit satellites transmitting the quantum signals over the atmosphere can be used. These free-space links, however, can only operate during the night, as the sunlight otherwise saturates the detectors used to measure the quantum states. For applying QKD in a global quantum internet with continuous availability and high data rates, operation during daylight is required. In this work, we model a satellite-to-ground quantum channel for different quantum light sources to identify the optimal wavelength for free-space QKD in ambient conditions. Daylight quantum communication is possible within the Fraunhofer lines or in the near-infrared spectrum, where the intrinsic background from the sun is comparably low. The highest annual secret key length considering the finite key effect is achievable at the Hα Fraunhofer line. More importantly, we provide the full model that can be adapted in general to any other specific link scenario. We also propose a true single-photon source based on a color center in hexagonal boron nitride coupled to a microresonator that can implement such a scheme. Our results can also be applied in roof-to-roof scenarios and are therefore relevant for near-future quantum networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Particle beams

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Sure: Screening Unlabeled Samples for Reliable Negative Samples Based on Reinforcement Learning

SSRN

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SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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Corrigendum to “Enhancement of Power Quality based on Dynamic Voltage Restorer Matrix Inverter - Sliding Mode Control Scheme” [Electric Power Systems Research volume 241 (2025) 111408]

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Electric Power Systems Research 2025年 242卷

作者： Abdul Hameed Soomro Abdul Sattar Larik Mukhtiar Ahmed Mahar Anwer Ali Sahito Mohsin Ali Koondhar Yun-Su Kim Zuhair Muhammed Alaas Ezzeddine Touti M.M.R. Ahmed Department of Electrical Engineering Quaid-e-Awam University of Engineering Science and Technology Sindh 67480 Pakistan Department of Electrical Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Graduate School of Energy Convergence Gwangju Institute of Science and Technology (GIST) Gwangju 61005 South Korea Department of Electrical and Electronics Engineering Faculty of Engineering and Computer Science Jazan University Jizan 45142 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Faculty of Technology and Education Helwan University Cairo Egypt

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Mathematical modelling and analysis of COVID-19 and tuberculosis transmission dynamics

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Informatics in Medicine Unlocked 2023年 38卷 101235页

作者： Singh, Ram Rehman, Attiq ul Ahmed, Tanveer Ahmad, Khalil Mahajan, Shubham Pandit, Amit Kant Abualigah, Laith Gandomi, Amir H. Department of Mathematical Sciences BGSB University Rajouri 185234 India Department of Mathematics Al-Falah University Faridabad India Ajeenkya D Y Patil University Pune Maharashtra 412105 India University Center for Research & Development (UCRD) Chandigarh University Mohali India School of Electronic and Communication Shri Mata Vaishno Devi University Katra 182320 India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq 25113 Jordan College of Engineering Yuan Ze University Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Faculty of Information Technology Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary

In this paper, a mathematical model for assessing the impact of COVID-19 on tuberculosis disease is proposed and analysed. There are pieces of evidence that patients with Tuberculosis (TB) have more chances of developing the SARS-CoV-2 infection. The mathematical model is qualitatively and quantitatively analysed by using the theory of stability analysis. The dynamic system shows endemic equilibrium point which is stable when R₀<1 and unstable when R₀>1. The global stability of the endemic point is analysed by constructing the Lyapunov function. The dynamic stability also exhibits bifurcation behaviour. The optimal control theory is used to find an optimal solution to the problem in the mathematical model. The sensitivity analysis is performed to clarify the effective parameters which affect the reproduction number the most. Numerical simulation is carried out to assess the effect of various biological parameters in the dynamic of both tuberculosis and COVID-19 classes. Our simulation results show that the COVID-19 and TB infections can be mitigated by controlling the transmission rate γ. © 2023 The Author(s)

关键词： Bifurcation COVID-19 Modelling Optimal solution Sensitivity analysis

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Hydrodynamic simulations of white dwarf-white dwarf mergers and the origin of R Coronae Borealis stars

arXiv

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arXiv 2024年

作者： Shiber, Sagiv De Marco, Orsola Motl, Patrick M. Munson, Bradley Marcello, Dominic C. Frank, Juhan Diehl, Patrick Clayton, Geoffrey C. Skinner, Bennett N. Kaiser, Hartmut Daiß, Gregor Pflüger, Dirk Staff, Jan E. Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astronomy Astrophysics and Astrophotonics Research Centre Macquarie University SydneyNSW2109 Australia The School of Sciences Indiana University Kokomo KokomoIN46902 United States LSU Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Los Alamos National Laboratory Los AlamosNM87545 United States NSF-supported REU Student at LSU United States IPVS University of Stuttgart Stuttgart Stuttgart70174 Germany Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow the orbital evolution of DWD systems of mass ratio q = 0.7 for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the helium WD donor star is tidally disrupted within 20-80 minutes since the beginning of the simulation onto the accretor carbon-oxygen WD, creating a high temperature shell around the accretor. We investigate the possible helium burning in this shell and the merged object’s general structure. Specifically, we are interested in the amount of oxygen-16 dredged-up from the accretor to the hot shell and the amount of oxygen-18 produced. This is critical as the discovery of very low oxygen-16 to oxygen-18 ratios in RCB stars pointed out the merger scenario as a favorable explanation for their origin. A small amount of hydrogen in the donor may help keep the oxygen-16 to oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations. © 2024, CC BY.

关键词： White dwarfs

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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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Modeling the performance and bandwidth of single-atom adiabatic quantum memories

APL Quantum

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APL Quantum 2024年第2期1卷

作者： Takla Nateeboon Chanaprom Cholsuk Tobias Vogl Sujin Suwanna Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok 10400 Thailand Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich 80333 Munich Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena 07745 Jena Germany

Quantum memories are essential for quantum repeaters, which will form the backbone of the future quantum internet. Such memory can capture a signal state for a controllable amount of time, after which this state can be retrieved. In this work, we theoretically investigated how atomic material and engineering parameters affect the performance and bandwidth of a quantum memory. We have applied a theoretical model for quantum memory operation based on the Lindblad master equation and adiabatic quantum state manipulation. The materials’ properties and their uncertainty are evaluated to determine the performance of Raman-type quantum memories by showcasing two defects in two-dimensional hexagonal boron nitride. We have derived a scheme to calculate the signal bandwidth based on the material parameters as well as the maximum efficiency that can be realized. The bandwidth depends on four factors: the signal photon frequency, the dipole transition moments in the electronic structure, the cavity volume, and the strength of the external control electric field. As our scheme is general and independent of materials, it can be applied to many other quantum materials with a suitable three-level structure. We, therefore, provided a promising route for designing and selecting materials for quantum memories. Our work is, therefore, an important step toward the realization of a large-scale quantum network.

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