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Negligible Ion Migration in Tin-Based and Tin-Doped Perovskites

Angewandte Chemie 2022年第5期135卷

作者： Kester O. Ighodalo Wenjing Chen Zheng Liang Dr. Yongliang Shi Shenglong Chu Yihan Zhang Rashid Khan Hongmin Zhou Xu Pan Jiajiu Ye Prof. Zhengguo Xiao CAS Key Laboratory of Strongly-coupled Quantum Matter Physics Department of Physics University of Science and Technology of China Hefei Anhui 230026 China Contribution: Data curation (lead) Formal analysis (lead) Methodology (lead) Writing - original draft (lead) Writing - review & editing (equal) Contribution: Data curation (supporting) Formal analysis (equal) Methodology (supporting) Writing - review & editing (equal) Key Laboratory of Photovoltaic and Energy Conservation Materials Institute of Solid-State Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei 230031 China Contribution: Data curation (equal) Methodology (equal) Center for Spintronics and Quantum Systems State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an Shaanxi 710049 China Contribution: Data curation (lead) Contribution: Data curation (supporting) Contribution: Writing - review & editing (supporting) Instruments Center for Physical Science University of Science and Technology of China Hefei Anhui 230026 China Contribution: Supervision (supporting) Contribution: Funding acquisition (lead) Investigation (lead) Project administration (lead) Supervision (lead) Writing - review & editing (equal)

Ion migration is a notorious phenomenon observed in ionic perovskite materials. It causes several severe issues in perovskite optoelectronic devices such as instability, current hysteresis, and phase segregation. Here, we report that, in contrast to lead halide perovskites (LHPs), no ion migration or phase segregation was observed in tin halide perovskites (THPs) under illumination or an electric field. The origin is attributed to a much stronger Sn-halide bond and higher ion migration activation energy ( E a ) in THPs, which remain nearly constant under illumination. We further figured out the threshold E a for the absence of ion migration to be around 0.65 eV using the CsSn y Pb 1-y (I 0.6 Br 0.4 ) 3 system whose E a varies with Sn ratios. Our work shows that ion migration does not necessarily exist in all perovskites and suggests metallic doping to be a promising way of stopping ion migration and improving the intrinsic stability of perovskites.

关键词： Ion Migration Light-Emitting Diodes Perovskites Solar Cells Stability

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A network monitoring game with heterogeneous component criticality levels

arXiv

引用

arXiv 2019年

作者： Milošević, Jezdimir Dahan, Mathieu Amin, Saurabh Sandberg, Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Center for Computational Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Civil and Environmental Engineering Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge United States

We consider an attacker-operator game for monitoring a large-scale network that is comprised on components that differ in their criticality levels. In this zero-sum game, the operator seeks to position a limited number of sensors to monitor the network against an attacker who strategically targets a network component. The operator (resp. attacker) seeks to minimize (resp. maximize) the network loss. To study the properties of mixed-strategy Nash Equilibria of this game, we first study two simple instances: (i) When component sets monitored by individual sensor locations are mutually disjoint;(ii) When only a single sensor is positioned, but with possibly overlapping monitoring component sets. Our analysis reveals new insights on how criticality levels impact the players equilibrium strategies. Next, we extend a previously known approach to obtain an approximate Nash equilibrium for the general case of the game. This approach uses solutions to minimum set cover and maximum set packing problems to construct an approximate Nash equilibrium. Finally, we implement a column generation procedure to improve this solution and numerically evaluate the performance of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Criticality (nuclear fission)

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Identification and Prioritization of urban issues from Smart City data

Identification and Prioritization of urban issues from Smart...

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2018 International Conference on Smart City and Emerging Technology, ICSCET 2018

作者： Kumar, Vinay Jenamani, Mamata Data Science and NLP Episource India Pvt. Ltd Mumbai400093 India Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur721302 India

ISBN: (纸本)9781538611852

Government should be able to publish and interact better for offering e-government services effectively to their netizens. In this context publish refers to delivering information through web pages and interact refers to allowing citizens to provide feedback and suggestions. Developing a model for understanding and prioritizing urban governance issues from these user-generated contents is an important research issue. This paper proposes a four-step solution approach to prioritizing urban governance issues from user-generated contents using Aspect Based Sentiment Analysis (ABSA). First, a novel lexicon-based approach is used for selecting trigrams as aspect phrase and pruning the list using cross-domain stop words. Second, the opinionated sentences are extracted using the identified aspects. Third, polarity and subjectivity of the aspects and corresponding comments along with the bag-of-words for the aspects are considered as the features for learning the category of the urban issue. Fourth, the sentiment score of a specific category is used to prioritize the urban issues. The proposed methodology is applied to a real user-generated content extracted from a discussion forum on smart cities from an e-governance portal in India. Our research demonstrates that Aspect Based Sentiment Analysis (ABSA) with necessary modifications can be utilized to extract issues, suggestions, and ideas from crowd-sourced citizen-generated content (Social data). Sentiment analysis can be used to prioritize them as well. Analysis of the data for the candidate city shows that while the citizens are happy about educational facilities in the city they are highly concerned about health issues. A deeper investigation revealed the health issues are due to high degree of carbon emission, lack of solid waste management system and lack of sanitation among the others. © 2018 IEEE.

关键词： Sentiment analysis

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Deep Neural Networks Carve the Brain at its Joints

arXiv

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arXiv 2020年

作者： Bertolero, Maxwell A. Moraczewski, Dustin Thomas, Adam Bassett, Danielle S. Department of Bioengineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Data Sharing and Science Team National Institute of Mental Health BethesdaMD United States Department of Psychiatry Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical & Systems Engineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics & Astronomy College of Arts & Sciences University of Pennsylvania PhiladelphiaPA19104 United States Santa Fe Institute Santa FeNM87501 United States

How an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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CyberSentry: Enhancing SCADA security through advanced deep learning and optimization strategies

引用

International Journal of Critical Infrastructure Protection 2025年 50卷

作者： Alaa O. Khadidos Adil O. Khadidos Shitharth Selvarajan Taher Al-Shehari Nasser A Alsadhan Subhav Singh Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS1 3HE Leeds UK Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of research and development Lovely Professional University Phagwara Punjab India Division of Research & innovation Uttaranchal University Dehradun India

SCADA systems form the core of infrastructural facilities, including power grids, water treatment facilities, and industrial processes. Changing cyber threats present increasingly sophisticated attacks against which traditional security models inadequately protect SCADA systems. These traditional models usually have drawbacks in the way of inadequate feature selection, inefficiency in detecting most attacks, and suboptimal parameter tuning, which cause vulnerabilities and reduce resilience in systems. This paper presents CyberSentry, a new security framework designed to overcome limitations so as to provide robust protection for SCADA systems. These three modules makeup CyberSentry: the RMIG feature selection model, tri-fusion net for attack detection, and Parrot-Levy Blend Optimization (PLBO) for parameter tuning. The Recursive Multi-Correlation-based Information Gain (RMIG) feature selection model enhances accuracy in detection by optimizing the set of fatal features through recursive multi-correlation analysis by Information Gain prioritization. The Tri-Fusion Net combines anomaly detection, signature-based detection, and machine learning classifiers to enhance the detection versatility and robustness. The PLBO module ensures efficient and dynamic tuning for the parameters through undocumented Parrot and Levy optimization techniques. The proposed CyberSentry framework integrates, within a unified architecture, anomaly detection, signature-based detection, and machine learning classifiers to enhance the security of SCADA systems against diverse cyber threats. Features extracted in this manner are analyzed using machine learning classifiers that exploit their predictive capabilities for robust threat classification. The proposed approaches are fused within the Tri-Fusion Net to complement each other in areas where the separate methods lack certain strengths. This, therefore, ensures broad threat detection, as is validated by extensive testing with various datasets

关键词： SCADA systems Security Cyber-attack Intrusion detection Deep learning Feature selection and Optimization

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Correction: The role of [68Ga]Ga-PSMA PET/CT in primary staging of newly diagnosed prostate cancer: predictive value of PET-derived parameters for risk stratification through machine learning

引用

Clinical and Translational Imaging 2024年第1期13卷 93-93页

作者： Jafari, Esmail Dadgar, Habibollah Zarei, Amin Samimi, Rezvan Manafi-Farid, Reyhaneh Divband, GhasemAli Nikkholgh, Babak Fallahi, Babak Amini, HamidReza Ahmadzadehfar, Hojjat Keshavarz, Ahmad Assadi, Majid The Persian Gulf Nuclear Medicine Research Center Department of Nuclear Medicine Molecular Imaging and Theranostics School of Medicine Bushehr Medical University Hospital Bushehr University of Medical Sciences Bushehr Iran Cancer Research Center RAZAVI Hospital Imam Reza International University Mashhad Iran Khatam PET/CT Center Tehran Iran Research Center for Nuclear Medicine Shariati Hospital Tehran University of Medical Sciences Tehran Iran Department of Nuclear Medicine Klinikum Westfalen Dortmund Germany Department of Nuclear Medicine Institute of Radiology Neuroradiology and Nuclear Medicine University Hospital Knappschaftskrankenhaus Bochum Germany IoT and Signal Processing Research Group ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran

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Analyzing national and state opioid abuse treatment completion with multilevel modeling

Analyzing national and state opioid abuse treatment completi...

引用

2018 systems and Information engineering Design Symposium, SIEDS 2018

作者： Pan, Huitong Gao, Sally Grant, Kennan Novicoff, Wendy Kang, Hyojung Data Science Institute University of Virginia United States Department of Public Health Sciences University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

The misuse of opioids has become a national public health emergency in the United States. Successfully completing a program of treatment at a substance abuse treatment facility is associated with better long-term outcomes, but a significant portion of opioid use patients are discharged without successfully completing treatment. The objective of this study is to evaluate factors associated with treatment success. We used the Treatment Episodes datasets: Discharges (TEDS-D) dataset, a national sample of discharges from substance abuse treatment centers, and the National Survey of Substance Abuse Treatment Services (N-SSATS) dataset, an annual survey of all known substance use treatment facilities, for the years 2013 and 2014. A random slope, random intercept multilevel model was used to estimate the effects of patient- and state-level variables on treatment completion. We find that a patient is more likely to complete treatment if that patient is older, employed, has private insurance, waited less time before entering treatment, is not receiving medication-assisted treatment, and is in residential care. In addition, we find that patients living in with states with higher levels of Vivitrol adoption are more likely to complete treatment. Our findings could encourage policymakers and treatment facilities to consider prioritizing Vivitrol adoption. At the same time, our results demonstrate substantial variation of predictors across states and suggest the need for further research into the relationship between state contexts and the predictors of successful opioid use treatment. © 2018 IEEE.

关键词： Surveys

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A multi-metabolite signature robustly predicts long-term mortality in the PREDIMED trial and several US cohorts

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Metabolism: Clinical and Experimental 2025年 156195页

作者： Fernández-Duval, Gonzalo Razquin, Cristina Wang, Fenglei Yun, Huan Hu, Jie Guasch-Ferré, Marta Rexrode, Kathryn Balasubramanian, Raji García-Gavilán, Jesús Ruiz-Canela, Miguel Clish, Clary B. Corella, Dolores Gómez-Gracia, Enrique Fiol, Miquel Estruch, Ramón Lapetra, José Fitó, Montse Serra-Majem, Luis Ros, Emilio Liang, Liming Dennis, Courtney Asensio, Eva M. Castañer, Olga Planes, Francisco J. Salas-Salvadó, Jordi Hu, Frank B. Toledo, Estefanía Martínez-González, Miguel A. Department of Preventive Medicine and Public Health Navarra Health Research Institute (IdiSNA) University of Navarra Pamplona Spain Institute of Data Science and Artificial Intelligence (DATAI) University of Navarra Pamplona Spain Consorcio CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain Department of Nutrition Harvard T.H. Chan School of Public Health Boston MA United States Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Public Health Section of Epidemiology University of Copenhagen Copenhagen Denmark Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen Denmark Division of Preventive Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Division of Women's Health Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Universitat Rovira i Virgili Departament de Bioquímica i Biotecnologia Unitat de Nutrició Humana Reus Spain Institut d'Investigació Sanitària Pere Virgili (IISPV) Reus Spain Metabolomics Platform Broad Institute of MIT and Harvard Cambridge MA United States Department of Preventive Medicine and Public Health University of Valencia Valencia Spain Department of Preventive Medicine University of Malaga Instituto de Investigación Biomédica de Málaga (IBIMA) Málaga Spain Platform for Clinical Trials Instituto de Investigación Sanitaria Illes Balears (IdISBa) Hospital Universitario Son Espases Palma de Mallorca Spain Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS) Barcelona Spain Department of Family Medicine Research Unity Distrito Sanitario Atención Primaria Sevilla Sevilla Spain Hospital del Mar Research Institute Barcelona Spain Nutrition Research Group Research Institute of Biomedical and Health Sciences (IUIBS) University of Las Palmas de Gran Canari

Metabolome-based biomarkers contribute to identify mechanisms of disease and to a better understanding of overall mortality. In a long-term follow-up subsample (n = 1878) of the PREDIMED trial, among 337 candidate baseline plasma metabolites repeatedly assessed at baseline and after 1 year, 38 plasma metabolites were identified as predictors of all-cause mortality. Gamma-amino-butyric acid (GABA), homoarginine, serine, creatine, 1-methylnicotinamide and a set of sphingomyelins, plasmalogens, phosphatidylethanolamines and cholesterol esters were inversely associated with all-cause mortality, whereas plasma dimethylguanidino valeric acid (DMGV), choline, short and long-chain acylcarnitines, 4-acetamidobutanoate, pseudouridine, 7-methylguanine, N6-acetyllysine, phenylacetylglutamine and creatinine were associated with higher mortality. The multi-metabolite signature created as a linear combination of these selected metabolites, also showed a strong association with all-cause mortality using plasma samples collected at 1-year follow-up in PREDIMED. This association was subsequently confirmed in 4 independent American cohorts, validating the signature as a consistent predictor of all-cause mortality across diverse populations. © 2025 Elsevier Inc.

关键词： All-cause mortality Biomarkers Metabolomic Metabolomic signature Plasma metabolites

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Twitter and facebook posts about COVID-19 are less likely to spread false and low-credibility content compared to other health topics

arXiv

引用

arXiv 2020年

作者： Broniatowski, David A. Kerchner, Daniel Farooq, Fouzia Huang, Xiaolei Jamison, Amelia M. Dredze, Mark Quinn, Sandra Crouse Department of Engineering Management and Systems Engineering School of Engineering and Applied Science George Washington University WashingtonDC20052 United States Institute for Data Democracy and Politics The George Washington University WashingtonDC20052 United States George Washington University Libraries The George Washington University WashingtonDC20052 United States Department of Epidemiology Graduate School of Public Health University of Pittsburgh PittsburghPA15260 United States Department of Computer Science University of Memphis MemphisTN38152 United States Department of Family Science Center for Health Equity School of Public Health University of Maryland College ParkMD20742 United States Department of Computer Science Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States

On February 2, 2020, the World Health Organization declared a COVID-19 social media "infodemic", with special attention to misinformation - frequently understood as false claims. To understand the infodemic's scope and scale, we analyzed over 500 million posts from Twitter and Facebook about COVID-19 and other health topics, between March 8 and May 1, 2020. Following prior work (Lazer et al., 2019), we assumed URL source credibility is a proxy for false content, but we also tested this assumption. Contrary to expectations, we found that messages about COVID-19 were more likely to contain links to more credible sources. Additionally, messages linking to government sources, and to news with intermediate credibility, were shared more often, on average, than links to non-credible sources. These results suggest that more ambiguous forms of misinformation about COVID-19 may be more likely to be disseminated through credible sources when compared to other health topics. Furthermore, the assumption that credibility is an adequate proxy for false content may overestimate the prevalence of false content online: less than 25% of posts linking to the least credible sources contained false content. Our results emphasize the importance of distinguishing between explicit falsehoods and more ambiguous forms of misinformation due to the search for meaning in an environment of scientific uncertainty. © 2020, CC BY-NC-ND.

关键词： Social networking (online)

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Beware of "Explanations" of AI

arXiv

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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