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Features of the cadmium chalcogenide substrates with surface nanostructure

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Materials Today: Proceedings 2021年 35卷 626-629页

作者： Tetiana Mazur Mykhailo Slyotov Myroslav Mazur Volodymyr Prokopiv Oksana Kinzerska Oleksii Slyotov Department of Solid State Physics and Chemistry/Faculty of Physics and Technology Vasyl Stefanyk Precarpathian National University 57 Shevchenko Str. Ivano-Frankivsk 76019 Ukraine Department of General and Applied Physics/Institute of Architecture Construction and Power Engineering Ivano-Frankisvsk National Technical University of Oil and Gas 15 Karpatska Str. Ivano-Frankivsk 76019 Ukraine Department of Optics/Physical Technical and Computer Science Institute Publishing and Printing Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. 58012 Chernivtsi Ukraine Department of Physics of Semiconductors and Nanostructures/Physical Technical and Computer Science Institute Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. 58012 Chernivtsi Ukraine

The effect of single crystalline CdTe, CdSe, CdS substrates normalizing annealing on their optical properties was investigated, and the surface nanostructure formation was determined by the study of optical transmission spectra, photoluminescence and atomic force microscopy (AFM) topograms. Possibility obtaining of intense luminescence in the border region, formed by interband recombination and transitions due to size quantization of the charge carrier energy, is determined.

关键词： Cadmium chalcogenides Normalizing annealing Optical transmission Photoluminescence Size quantization of energy

Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives

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arXiv 2024年

作者： Chun, Kwok P. Octavianti, Thanti Dogulu, Nilay Tyralis, Hristos Papacharalampous, Georgia Rowberry, Ryan Fan, Pingyu Everard, Mark Francesch-Huidobro, Maria Migliari, Wellington Hannah, David M. Marshall, John Travis Calasanz, Rafael Tolosana Staddon, Chad Ansharyani, Ida Dieppois, Bastien Lewis, Todd R. Ponce, Juli Ibrean, Silvia Ferreira, Tiago Miguel Peliño-Golle, Chinkie Mu, Ye Delgado, Manuel Espinoza, Elizabeth Silvestre Keulertz, Martin Gopinath, Deepak Li, Cheng School of Architecture and Environment University of the West of England United Kingdom World Meteorological Organization Genève Switzerland Construction Agency Hellenic Air Force Mesogion Avenue 227-231 Cholargos15561 Greece Department of Topography School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Greece Georgia State University College of Law AtlantaGA United States Department of Geography Hong Kong Baptist University Hong Kong Department of Geography The University of Hong Kong Hong Kong Law School University of Barcelona Catalonia Spain School of Geography Earth & Environmental Sciences University of Birmingham United Kingdom Georgia State University College of Law United States Department of Computer Science and Systems Engineering Universidad de Zaragoza Spain Department of Agriculture Universitas Samawa Indonesia Coventry University Coventry United Kingdom University of the West of England United Kingdom UN Volunteer United Nations Volunteers Hermann-Ehlers-Strasse 10 Bonn Germany College of Arts Technology and Environment School of Engineering University of the West of England United Kingdom EcoWaste Coalition Philippines Philippines Department of Geography University of California Santa Barbara United States University of the West of England Faculty of Business and Law United Kingdom Inclima Department of Ecology Yangzhou University China

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. This enables adaptive systems for the rapid evolution of AI technology, which has significantly impacted the intersection of law and natural environments. Exploring how AI influences legal frameworks and environmental management, while also examining how legal and environmental considerations can confine AI within the socioeconomic domain, is essential. From a co-production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. Discrepancies in the use of language between environmental scientists and decision-makers in terms of usefulness and accuracy hamper how AI can be used based on the principles of legal considerations for a safe, trustworthy, and contestable disaster management framework. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasise environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execu

关键词： Decision making

Big mobility data reveals hyperlocal air pollution exposure disparities in the Bronx, New York

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Nature Cities

Nature Cities 2024年第8期1卷 512-521页

作者： Iacopo Testi An Wang Sanjana Paul Simone Mora Fábio Duarte Paolo Santi Carlo Ratti Erica Walker Marguerite Nyhan Senseable City Lab Department of Urban Studies and Planning Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Istituto di Informatica e Telematica Consiglio Nazionale delle Ricerche Pisa Italy ABC Department Politecnico di Milano Milan Italy School of Public Health Brown University Providence RI USA School of Engineering and Architecture University College Cork Cork Ireland

Air pollution disproportionately affects socially disadvantaged populations. Our study bridges the existing gap in quantifying mobility-based exposure and its associated disparity issues. We combined the granular mobility of over 500,000 unique anonymized users daily and hyperlocal air pollution data in 100 × 100-m grid cells to quantify disparities in particulate matter exposure in a racially diverse and dense urban area of New York City. Our approach advances the study of exposure and its disparity from individualized exposure tracking to a population-representative scale. We observed apparently different spatial patterns between personal exposure and exposure disparities, noting that people from Hispanic-majority and low-income neighborhoods were those most severely and disproportionately exposed to fine particulate matter (PM2.5) pollution. We reveal that race and ethnicity are much stronger indicators of exposure disparity than income. Our study further demonstrates that within-group variation contributes a major portion to exposure disparities, suggesting more granular mitigation plans are needed to target high-exposure individuals from socially disadvantaged groups in addition to generic air quality improvement.

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Rockburst prediction using artificial intelligence techniques:A review

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岩石力学通报（英文）

岩石力学通报（英文） 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

P-127 Spiking network models of developing mouse prefrontal cortex

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Clinical Neurophysiology 2023年 148卷 e65-e66页

作者： Lorenz, M. Bitzenhofer, S. Chini, M. Martínez-Cañada, P. Hanganu-Opatz, I. Panzeri, S. Department of Excellence for Neural Information Processing Center for Molecular Neurobiology (ZMNH) University Hospital Eppendorf Germany Center for Neuroscience and Cognitive Systems Istituto Italiano di Tecnologia Italy Department of Pharmacy and Biotechnology University of Bologna Italy Institute of Developmental Neurophysiology Center for Molecular Neurobiology (ZMNH) University Hospital Eppendorf Germany Department of Computer Architecture and Technology University of Granada Spain

An evolutionary deep learning method for short-term wind speed prediction: A case study of the lillgrund offshore wind farm

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arXiv 2020年

作者： Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan Tjernberg, Lina Bertling Garcia, Davide Astiaso Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Sapienza University of Rome Italy Australian Institute for Machine Learning The University of Adelaide Australia School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two long-term-short-term (LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

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arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Xiao, Liang Wu, Qingqing School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Information and Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Hybrid neuro-evolutionary method for predicting wind turbine power output

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arXiv 2020年

作者： Neshat, Mehdi Abbasnejad, Ehsan Heydari, Azim Garcia, Davide Astiaso Nezhad, Meysam Majidi Groppi, Daniele Tjernberg, Lina Bertling Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Australian Institute for Machine Learning University of Adelaide Australia Sapienza University of Rome Italy Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach-namely a hybrid neuro-evolutionary algorithm-for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine