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Virtual Target-Based Longitudinal Motion Planning of Autonomous Vehicles at Urban Intersections: Determining Control Inputs of Acceleration With Human Driving Characteristic-Based Constraints

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2021年第3期16卷 38-46页

作者： Yoo, Jinsoo Michael Jeong, Yonghwan Yi, Kyongsu Seoul Natl Univ Mech Engn Seoul 08826 South Korea Seoul Natl Univ Sci & Technol Dept Mech & Automot Engn Seoul 01811 South Korea Seoul Natl Univ Sch Mech Engn Seoul 08826 South Korea

This article describes the development and implementation of virtual target-based longitudinal motion planning of autonomous vehicles at urban intersections ensuring safety and ride comfort. In this study, virtual targets are designed to cope with oncoming vehicles in the blind zone at the intersection for safety. The true field of view (FOV) of cognitive sensors and the virtual target states are constructed based on the sensor specification and intersection road information from a high-definition (HD) map. The future states and intention of sensor-detected targets are inferred and predicted with an interacting multiple model (IMM) filter. The local IMM filters are employed with an intelligent driver model (IDM). Based on predicted target states, two driving modes-"cross" and "stop"-under three different intersection stages-"approach," "intersection in," and "intersection out"-are determined. The model predictive control (MPC) is formulated to determine the control inputs of acceleration with human driving characteristics-based constraints. The proposed algorithm is evaluated through simulation to indicate the effectiveness of the virtual target. The suggested motion planning has been implemented on an autonomous driving vehicle and tested on urban roads.

关键词： Road traffic Intelligent sensors Autonomous vehicles Vehicle safety Hidden Markov models predictive models Laser radar predictive control predictive algorithms Information filters

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Automated Quantification of Hydroxyl Reactivities: Prediction of Glycosylation Reactions

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ANGEWANDTE CHEMIE-INTERNATIONAL EDITION 2021年第22期60卷 12413-12423页

作者： Chang, Chun-Wei Lin, Mei-Huei Chan, Chieh-Kai Su, Kuan-Yu Wu, Chia-Hui Lo, Wei-Chih Lam, Sarah Cheng, Yu-Ting Liao, Pin-Hsuan Wong, Chi-Huey Wang, Cheng-Chung Acad Sinica Inst Chem Taipei 115 Taiwan Acad Sinica Genom Res Ctr Taipei 115 Taiwan Scripps Res Inst Dept Chem 10550 N Torrey Pines Rd La Jolla CA 92037 USA Acad Sinica Chem Biol & Mol Biophys Program Taiwan Int Grad Program TIGP Taipei 115 Taiwan

The stereoselectivity and yield in glycosylation reactions are paramount but unpredictable. We have developed a database of acceptor nucleophilic constants (Aka) to quantify the nucleophilicity of hydroxyl groups in glycosylation influenced by the steric, electronic and structural effects, providing a connection between experiments and computer algorithms. The subtle reactivity differences among the hydroxyl groups on various carbohydrate molecules can be defined by Aka, which is easily accessible by a simple and convenient automation system to assure high reproducibility and accuracy. A diverse range of glycosylation donors and acceptors with well-defined reactivity and promoters were organized and processed by the designed software program "GlycoComputer" for prediction of glycosylation reactions without involving sophisticated computational processing. The importance of Aka was further verified by random forest algorithm, and the applicability was tested by the synthesis of a Lewis A skeleton to show that the stereoselectivity and yield can be accurately estimated.

关键词： carbohydrates diastereoselectivity hydroxyl glycosylation predictive algorithms

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Exponential Functions Permit Estimation of Anaerobic Work Capacity and Critical Power from Less than 2 Min All-Out Test

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algorithms 2024年第11期17卷

作者： Tsai, Ming-Chang Thomas, Scott Klimstra, Marc Canadian Sport Inst Pacific Victoria BC V9E 2C5 Canada Univ Toronto Fac Kinesiol & Phys Educ Grad Dept Exercise Sci Toronto ON M5S 1A1 Canada Univ Victoria Sch Exercise Sci Phys & Hlth Educ Victoria BC V8P 5C2 Canada

The Critical Power Model (CPM) is key for assessing athletes' aerobic and anaerobic energy systems but typically involves lengthy, exhausting protocols. The 3 min all-out test (3MT) simplifies CPM assessment, yet its duration remains demanding. Exponential decay models, specifically mono- and bi-exponential functions, offer a more efficient alternative by accurately capturing the nonlinear energy dynamics in high-intensity efforts. This study explores shortening the 3MT using these functions to reduce athlete strain while preserving the accuracy of critical power (CP) and work capacity (W ') estimates. Seventy-six competitive cyclists and triathletes completed a 3MT on a cycle ergometer, with CP and W ' calculated at shorter intervals. Results showed that a 90 s test using the bi-exponential model yielded CP and W ' values similar to those of the full 3MT. Meanwhile, the mono-exponential model required at least 135 s. Bland-Altman and linear regression analyses confirmed that a 120 s test with the mono-exponential model reliably estimated CP and W ' with minimal physical strain. These findings support a shortened, less-demanding 3MT as a valid alternative for CPM assessment.

关键词： exercise testing fitness assessment predictive algorithms endurance performance metrics physiological modeling

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The Moli-sani risk score, a new algorithm for measuring the global impact of modifiable cardiovascular risk factors

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INTERNATIONAL JOURNAL OF CARDIOLOGY 2023年第1期389卷 131228-131228页

作者： Di Castelnuovo, Augusto Bonaccio, Marialaura Costanzo, Simona De Curtis, Amalia Persichillo, Mariarosaria Panzera, Teresa Bracone, Francesca Baldassarre, Damiano Roncaglioni, Maria Carla Baviera, Marta Cerletti, Chiara Donati, Maria Benedetta de Gaetano, Giovanni Iacoviello, Licia Mediterranea Cardioctr Naples Italy IRCCS NEUROMED Dept Epidemiol & Prevent Via Elettron I-86077 Pozzilli IS Italy IRCCS Ctr Cardiol Monzino Milan Italy Univ Milan Dept Med Biotechnol & Translat Med Milan Italy Ist Ric Farmacol Mario Negri IRCCS Lab Cardiovasc Prevent Milan Italy Univ Insubria Dept Med & Surg Varese Italy

Background: Cardiovascular (CV) disease is preventable through interventions targeting modifiable factors. Most algorithms based on modifiable CV risk factors (CV-rf) have been developed in US populations and do not account for the role of diet. We aimed to assess an algorithm based on modifiable CV-rf including diet, using data from an Italian population. Methods: To derive the Moli-sani Risk Score (MRS), we used data on 16,656 men and women (age >= 35 y) from the population of the Moli-sani Study. The Risk-and-Prevention-Study, Italy (N = 8606) acted as external validation cohort and the Life's-Simple-7 score was used as benchmark. The MRS targeted at fatal or non-fatal CV events and included 9 common modifiable CV-rf. Results: After 8.1 years (median) of follow-up, 816 events occurred in the derivation cohort. The MRS was calculated as a weighted sum of its 9 components, with weights reflecting the strength of the association. In comparison with individuals in the first, those in the fourth quartile of the score had hazard ratio (HR) for CV events equal to 3.18 (95%CI: 2.54-3.97). One more point in the score was associated with 7% (6%-8%) and 4% (3%-5%) higher hazard of events in the derivation and validation cohort, respectively. The MRS performed better than the Life's Simple-7 for discrimination. Conclusion: We propose the Moli-sani Risk Score, a validated, performing algorithm able to measure the combined impact that modifiable CV-rf have on CV risk. The score can be used to design preventive interventions, quantify the effectiveness of interventions, and compare different preventive strategies.

关键词： Cardiovascular risk Modifiable risk factors Prevention predictive algorithms

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Big Data Surveillance-The Case Of Policing

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KOLNER ZEITSCHRIFT FUR SOZIOLOGIE UND SOZIALPSYCHOLOGIE 2021年第SUPPL 1期73卷 359-395页

作者： Brayne, Sarah Univ Texas Austin Dept Sociol 305 East 23rd StRLP 3-306 Austin TX 78712 USA

This article examines the intersection of two structural developments: the growth of surveillance and the rise of "big data". Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does-and does not-transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems have merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

关键词： predictive algorithms Inequality Crime Law Ethnography

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HANDBOOK OF MODEL predictive CONTROL

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IEEE CONTROL SYSTEMS MAGAZINE 2020年第5期40卷 96-99页

作者： Pannocchia, Gabriele Univ Pisa Chem Engn I-56100 Pisa Italy

This handbook contains 27 chapters that are organized into three parts. Part 1 is on theory and comprises 12 chapters, ranging from basic MPC theory to advanced studies and model predictive control (MPC) formulations. Part 2, on computation, includes eight chapters and covers numerical implementation of MPC-related optimization algorithms. Part 3 discusses applications of MPC in numerous fields, such as automotive, power and energy systems, health care, and finance. The book is designed for a wide audience. It is an excellent reference for graduate students, researchers, and practitioners in the field of control systems and numerical optimization who want to understand the potential, challenges, and benefits of MPC and its applications. Alternately, it is an up-to-date reference for MPC research experts (both in academia and industry). For this audience, the book helps experts address new MPC-related problems and research directions. The book provides a thorough and comprehensive reference of the underlying theory, implementation, and applications of MPC. The content of the book, contributed by various experts in the field, is well written and suitably organized into three parts. Furthermore, this book does an excellent job meeting several competing goals: clarity of communication to a diversified audience, formal rigor, and a self-contained presentation of the topics in each chapter. This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the art overview of new and exciting areas of application at the forefront of MPC research.

关键词： Book reviews predictive control Computational modeling Optimal control Stability analysis predictive algorithms

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Enhancing Suicide Risk Prediction With Polygenic Scores in Psychiatric Emergency Settings: Prospective Study

JMIR Bioinformatics and Biotechnology

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JMIR Bioinformatics and Biotechnology 2024年第1期5卷 e58357页

作者： Lee, Younga Heather Zhang, Yingzhe Kennedy, Chris J. Mallard, Travis T. Liu, Zhaowen Vu, Phuong Linh Feng, Yen-Chen Anne Ge, Tian Petukhova, Maria V. Kessler, Ronald C. Nock, Matthew K. Smoller, Jordan W. Psychiatric & Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Psychiatry Harvard Medical School Boston MA United States Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA United States Center for Precision Psychiatry Massachusetts General Hospital Boston MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Harvard College Harvard University Cambridge MA United States Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei Taiwan Department of Health Care Policy Harvard Medical School Boston MA United States Department of Psychology Harvard University Cambridge MA United States Mental Health Research Program Franciscan Children’s Brighton MA United States Department of Psychiatry Massachusetts General Hospital Boston MA United States

Background: Despite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. Objective: This study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. Methods: Study participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. Data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. Results: Of the 333 patients (n=178, 53.5% male;mean age 36.8, SD 13.6 years;n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination

关键词： electronic health record genomics genotypes machine learning polygenic risk score predictive algorithms suicide attempt suicide risk prediction

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Abolish! Against the Use of Risk Assessment algorithms at Sentencing in the US Criminal Justice System

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Philosophy and Technology 2021年第4期34卷 1883-1904页

作者： Schwerzmann, Katia Media Studies Department Bauhaus Universität Weimar Weimar Germany

In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk. © 2021, The Author(s).

关键词： Biopolitics Criminal justice Cybernetics predictive algorithms Social justice

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Prediction of pipe failures in water supply networks using logistic regression and support vector classification

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 196卷

作者： Robles-Velasco, Alicia Cortes, Pablo Munuzuri, Jesus Onieva, Luis Univ Seville ETSI Dept Org Ind & Gest Empresas 2 C Camino de los Descubrimientos S-N Seville 41092 Spain Univ Seville Catedra Agua EMASESA Seville Spain

Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure. This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed. The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the networks pipes per year, which is a realistic and feasible option. As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan.

关键词： Water supply networks Pipe failures Support vector classification Logistic regression predictive algorithms

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Risky business: a scoping review for communicating results of predictive models between providers and patients

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JAMIA OPEN 2021年第4期4卷 ooab092页

作者： Walsh, Colin G. McKillop, Mollie M. Lee, Patricia Harris, Joyce W. Simpson, Christopher Novak, Laurie Lovett Vanderbilt Univ Dept Biomed Informat Med Ctr 2525 West End AveSuite 1475 Nashville TN 37203 USA Vanderbilt Univ Med Ctr Dept Med Nashville TN USA Vanderbilt Univ Med Ctr Dept Psychiat & Behav Sci Nashville TN USA IBM Watson Hlth Ctr AI Res & Evaluat Cambridge MA USA Vanderbilt Univ Ctr Knowledge Management Med Ctr Nashville TN USA

Objective: Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is-or should be-communicated with patients. Materials and Methods: We conducted a scoping review informed by PRISMA-ScR guidelines to identify current knowledge and gaps in this domain. Results: Ten studies met inclusion criteria for full text review. The following topics were represented in the studies, some of which involved more than 1 topic: disease prevention (N = 5/10, 50%), treatment decisions (N = 5/10, 50%), medication harms reduction (N = 1/10, 10%), and presentation of cardiovascular risk information (N = 5/10, 50%). A single study included 6- and 12-month clinical outcome metrics. Discussion: As predictive models are increasingly published, marketed by industry, and implemented, this paucity of relevant research poses important gaps. Published studies identified the importance of (1) identifying the most effective source of information for patient communications;(2) contextualizing risk information and associated design elements based on users' needs and problem areas;and (3) understanding potential impacts on risk factor modification and behavior change dependent on risk presentation. Conclusion: An opportunity remains for researchers and practitioners to share strategies for effective selection of predictive algorithms for clinical practice, approaches for educating clinicians and patients in effectively using predictive data, and new approaches for framing patient-provider communication in the era of artificial intelligence.

关键词： predictive analytics predictive algorithms patient communication risk communication shared decision-making

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