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SSRN 2025年

作者： Chen, Yuan Li, Changcheng Wang, Can Xiao, Yansong Liu, Tianbo Li, Jiaying Teng, Kai Cai, Hailin Xiao, Zhipeng Zhou, Hong Zhou, Xiangping Zeng, Weiai Du, Yongjun Yuan, Zheming Tang, Qianjun Wu, Shaolong Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making Hunan Agricultural University Hunan Changsha410125 China Yuelu Mountain Laboratory of Hunan Province Hunan Changsha410125 China Hunan Tobacco Company Changsha410004 China Zhejiang University Zhejiang Hangzhou310058 China

Tobacco is a crucial economic crop that is highly susceptible to various diseases during its growth cycle. Developing accurate predictive models is essential for devising effective disease management strategies and reducing pesticide use. This study investigated the occurrence of four major tobacco diseases—tobacco mosaic virus, black shank, bacterial wilt, and brown spot—in seven key tobacco-producing regions of Hunan Province from 2009 to 2015. An ensemble deep learning method, DCNN-LSTM, was developed by integrating bidirectional convolutional networks with Long Short-Term Memory (LSTM) networks. This approach accounts for the personalized temporal effects of meteorological factors and their interactive relationships. The predictive performance of the DCNN-LSTM model was evaluated across three temporal prediction scales including one-step prediction, short-term prediction, and long-term prediction. The results demonstrate that the DCNN-LSTM model significantly outperformed reference models across all three prediction scales. The average R2 values for the four diseases at one-step, short-term, and long-term levels were 0.941, 0.822, and 0.838, respectively. Correspondingly, the average root mean square error (RMSE) values were 3.880, 9.974, and 10.610. These findings highlight the robustness of the DCNN-LSTM model in accurately predicting tobacco disease dynamics. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Addressing Positivity Violations in Continuous Interventions through data-Adaptive Strategies

arXiv

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arXiv 2025年

作者： Bao, Han Schomaker, Michael Department of Statistics Ludwig-Maximilians-Universität München Munich Germany Centre for Integrated Data and Epidemiological Research Cape Town South Africa Institute of Public Health Medical Decision Making and Health Technology Assessment UMIT – University for Health Sciences Medical Informatics and Technology Hall in Tirol Austria Ludwig-Maximilians-Universität München Munich Germany

Positivity violations pose a key challenge in the estimation of causal effects, particularly for continuous interventions. Current approaches for addressing this issue include the use of projection functions or modified treatment policies. While effective in many contexts, these methods can result in estimands that potentially do not align well with the original research question, thereby leading to compromises in interpretability. In this paper, we introduce a novel diagnostic tool—the non-overlap ratio—to detect positivity violations. To address these violations while maintaining interpretability, we propose a data-adaptive solution, specially a "most feasible" intervention strategy. Our strategy operates on a unit-specific basis. For a given intervention of interest, we first assess whether the intervention value is feasible for each unit. For units with sufficient support –conditional on confounders–, we adhere to the intervention of interest. However, for units lacking sufficient support –as identified through the assessment of the non-overlap ratio– we do not assign the actual intervention value of interest. Instead, we assign the closest feasible value within the support region. We propose an estimator using g-computation coupled with flexible conditional density estimation to estimate high- and low support regions to estimate this new estimand. Through simulations, we demonstrate that our method effectively reduces bias across various scenarios by addressing positivity violations. Moreover, when positivity violations are absent, the method successfully recovers the standard estimand. We further validate its practical utility using real-world data from the CHAPAS-3 trial, which enrolled HIV-positive children in Zambia and Uganda. © 2025, CC BY.

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A motion-based compression algorithm for resource-constrained video camera traps

arXiv

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arXiv 2024年

作者： Ratnayake, Malika Nisal Gallon, Lex Toosi, Adel N. Dorin, Alan Agents and Decision-Making Group Dept. of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia Monash e-Research Centre Monash University ClaytonVIC3800 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3052 Australia Dept. of Software Systems and Cybersecurity Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm’s capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Corrigendum to 'Patient-related characteristics considered to affect patient involvement in shared decision making about treatment: A scoping review of the qualitative literature' Patient Education and Counseling 111 (2023) 107677

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Patient education and counseling 2024年 124卷 108257页

作者： Sascha M Keij Hanne C Lie Rebekah Laidsaar-Powell Marleen Kunneman Joyce E de Boer Saïda Moaddine Anne M Stiggelbout Arwen H Pieterse Medical Decision Making Department of Biomedical Data Sciences Leiden University Medical Center the Netherlands. Electronic address: s.m.keij@lumc.nl. Department of Behavioural Medicine Institute of Basic Medical Sciences Faculty of Medicine University of Oslo Norway. Centre for Medical Psychology and Evidence-Based Decision-Making (CeMPED) School of Psychology The University of Sydney Sydney NSW Australia. Medical Decision Making Department of Biomedical Data Sciences Leiden University Medical Center the Netherlands Knowledge and Evaluation Research Unit Mayo Clinic Rochester MN USA. Medical Decision Making Department of Biomedical Data Sciences Leiden University Medical Center the Netherlands. Erasmus School of Health Policy and Management Erasmus University Rotterdam the Netherlands.

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Risk Management in Stochastic Problems of Stock Investment 13

Risk Management in Stochastic Problems of Stock Investment

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13th International Conference Management of Large-Scale System Development, MLSD 2020

作者： Gorelik, Victor Zolotova, Tatiana Moscow State Pedagogical University Department of Simulation Systems and Operations Research Institute of Mathematics and Computer Science Dorodnicyn Computing Centre Frc Csc Ras Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis Decision Making and Financial Technologies Moscow Russia

ISBN: (数字)9781728110943

ISBN: (纸本)9781728110943

We consider a game with nature as a decision-making model in stochastic problems of stock investment. The mathematical expectation of the investor's gain is taken as an assessment of efficiency. The standard deviation is used as an assessment of risk. The emerging two-criterion problem is formalized using the method of translating one criterion into a constraint. The case of pure strategies (the choice of one investment option) and the case of mixed strategies (diversification of investment) are considered. For the case of mixed strategies, either the problem of maximizing a linear function with one linear and one quadratic constraint or the problem of minimizing a quadratic function with two linear constraints arises. Analytical solution methods based on the Karush-Kuhn-Tucker optimality conditions are obtained for both problems. Practical examples of the application of these methods using real statistical data are given. © 2020 IEEE.

关键词： Investments

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Establishment of a novel engineering breeding technology for cytoplasmic male sterility

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Crop Journal 2025年

作者： Xu, Na Yu, Dong Zhang, Longhui Wang, Tiankang Hu, Zhongxiao Lei, Quanqiang Qiu, Yingxin Yuan, Zheming Song, Shufeng Li, Li Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making College of Plant Protection Hunan Agricultural University Hunan Changsha 410128 China State Key Laboratory of Hybrid Rice Hunan Hybrid Rice Research Center Hunan Academy of Agricultural Sciences Hunan Changsha 410125 China Yuelushan Laboratory Hunan Changsha 410128 China Longping Branch College of Biology Hunan University Hunan Changsha 410125 China

The three-line hybrid rice system, which relies on cytoplasmic male sterility (CMS), faces challenges in breeding efficiency. To address these challenges, we developed CMS engineering breeding technology (CEBT). First, we constructed a complementation vector containing the fertility restoration module Double Rf4-Rf20, sorting marker gene DsRed2, and pollen lethal gene ZmAA1, then transformed this into a CMS line. We obtained reproductive lines without alterations to main agronomic traits, which can produce engineering sterile lines (ESL) and reproductive line seeds at a 1:1 ratio by self-crossing. CEBT converts the traditional three-line system into an efficient ‘new two-line’ hybrid framework: the reproductive line is improved while simultaneously improving the ESL, which significantly shortens the breeding process for three-line hybrid rice. © 2025 Crop Science Society of China and Institute of Crop Science, CAAS

关键词： CEBT CMS-WA Reproductive lines Rf gene Three-line hybrid rice

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Assessing responsible innovation training

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Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Principles of Optimality in Problems of Large-Scale Systems Management 11

Principles of Optimality in Problems of Large-Scale Systems ...

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11th International Conference "Management of Large-Scale System Development", MLSD 2018

作者： Gorelik, Victor Zolotova, Tatiana Department of Simulation Systems and Operations Research Dorodnicyn Computing Centre FRC CSC RAS Moscow Russia Department of Data Analysis Decision Making and Financial Technologies Financial University Government of the Russian Federation Moscow Russia

ISBN: (纸本)9781538649244

An approach is proposed to determine the principle of optimality in decision-making under incomplete information. It concludes in the introduction of efficiency and risk estimations, followed by their convolution or another selection procedure in the two-criteria problem. Examples of application of this approach to the tasks of project management are given. © 2018 IEEE.

关键词： Project management

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Truncated unscented particle filter

Truncated unscented particle filter

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作者： Straka, Ondřej Duník, Jindřich Šimandl, Miroslav Research Centre Data-Algorithms-Decision Making Department of Cybernetics University of West Bohemia Pilsen Czech Republic

ISBN: (纸本)9781457700804

The problem of state estimation of nonlinear stochastic dynamic systems with nonlinear inequality constraints is treated. The paper focuses on a particle filtering approach, which provides an estimate of the state in the form of a probability density function. A new computationally efficient particle filter for the constrained estimation problem is proposed. The importance function of the particle filter is generated by the unscented Kalman filter that is supplemented with a designed truncation technique to accommodate the constraint. The proposed filter is illustrated in a numerical example. © 2011 AACC American Automatic Control Council.

关键词： State estimation

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