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Moderating effects of out-of-hospital cardiac arrest characteristics on the association between EMS response time and survival

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Resuscitation 2021年第0期169卷 31-38页

作者： Stoesser, Clara E. Boutilier, Justin J. Sun, Christopher L.F. Brooks, Steven C. Cheskes, Sheldon Dainty, Katie N. Feldman, Michael Ko, Dennis T. Lin, Steve Morrison, Laurie J. Scales, Damon C. Chan, Timothy C.Y. Departmentof Mechanical and Industrial Engineering University of Toronto Toronto ON Canada Departmentof Industrial and Systems Engineering University of Wisconsin – Madison Madison WI United States SloanSchool of Management Massachusetts Institute of Technology Cambridge MA United States LiKa Shing Knowledge Institute St. Michael's Hospital Unity Health Toronto Toronto ON Canada Departmentsof Emergency Medicine and Public Health Sciences Queen's University Kingston ON Canada Departmentof Family and Community Medicine Division of Emergency Medicine University of Toronto Toronto ON Canada SunnybrookCenter for Prehospital Medicine Toronto ON Canada Instituteof Health Policy Management and Evaluation (IHPME) University of Toronto Toronto ON Canada NorthYork General Hospital Toronto ON Canada Institutefor Clinical Evaluation Sciences Toronto ON Canada SchulichHeart Program Sunnybrook Health Sciences Centre Toronto ON Canada Departmentof Medicine University of Toronto Toronto ON Canada Interdepartmental Division of Critical Care Medicine University of Toronto Toronto ON Canada HealthcareSystems Engineering Massachusetts General Hospital Boston MA United States

Background: Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown. Objectives: To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival. Methods: This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics. Results: 102,216 patients were included. Three of the four characteristics – witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) – increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics. Conclusions: Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time. © 2021 Elsevier B.V.

关键词： Emergency medical services Out-of-hospital cardiac arrest Public health Response time

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Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Multi-step ahead Bitcoin Price Forecasting Based on VMD and ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ramon Gomes da Silva Matheus Henrique Dal Molin Ribeiro Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).

关键词： Predictive models Biological system modeling Data models Bitcoin Training Machine learning algorithms

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Analyzing the Implications of New Technologies to the Management of Operations – Protocol Proposal and Application Illustration

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Procedia Manufacturing 2019年 39卷 904-912页

作者： Paulo Henrique Brunheroto Cassiano Souza Beller Fernando Deschamps Manufacturing Engineering Graduate Program Department of Mechanical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Engineering Graduate Program Polytechnic School Pontifícia Universidade Católica do Paraná Curitiba PR Brazil

The increasing adoption of new automation and information technologies are transforming production systems into cyber-physical systems (CPS). Although consulting companies have developed management frameworks to better characterize the implications of these technologies to how operations are managed, mainly focusing on new business models, efficiency, and effectiveness, the real impacts on decision areas (e.g., quality management) and performance objectives (e.g., flexibility) are still unclear. How are management systems being transformed? Are new managerial approaches emerging that address the particularities of new technologies? What are the critical success factors for conducting this transformation? To address these questions, a more systematic approach needs to be undertaken. This paper contributes to this by presenting a protocol to be used in systematic studies of the implications of the adoption of new technologies to the management of operations, and by illustrating its application to the analysis of the adoption of a cognitive vision system in an automotive manufacturer primarily deployed for improving product quality. The analysis protocol is based on Pettigrew’s dimensions of context, content, and process for change initiatives and is further subdivided into information requirements, variables to be analyzed and its potential sources of data. The case shows that the application of the protocol was able to reveal that besides expected effects on quality and productivity, new forms of interaction among workers and between workers and supervisors emerged that need further investigation. From the results of the case, future applications of the protocol are discussed.

关键词： management of operations transformation quality management cognitive vision system

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Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source

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Fuel 2022年第PartC期324卷

作者： Said, Zafar Nguyen, Tuan Hai Sharma, Prabhakar Li, Changhe Ali, Hafiz Muhammad Nguyen, Van Nhanh Pham, Van Viet Ahmed, Shams Forruque Van, Dong Nguyen Truong, Thanh Hai Department of Sustainable and Renewable Energy Engineering University of Sharjah P. O. Box 27272 Sharjah United Arab Emirates U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E) National University of Sciences and Technology (NUST) Islamabad Pakistan Faculty of Technology Dong Nai Technology University Dong Nai 76000 Viet Nam Mechanical Engineering Department Delhi Skill and Entrepreneurship University Delhi 110089 India School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao 266520 China Mechanical Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Institute of Engineering HUTECH University Ho Chi Minh City Viet Nam Faculty of Marine Engineering Vietnam Maritime University Haiphong Viet Nam Science and Math Program Asian University for Women Chattogram 4000 Bangladesh University of Science and Technology The University of Danang Viet Nam PATET Research Group Ho Chi Minh City University of Transport Ho Chi Minh City Viet Nam

The aviation sector has been one of the most significant contributors to greenhouse gas (GHG) emissions;there is an urgent need to transition from traditional fossil-based jet fuel to sustainable aviation fuel to meet net-zero targets by 2030. The use of renewable aviation fuel may be regarded as the most effective option for reducing GHG emissions in the aviation sector while allowing for long-term growth. In this study, the microalgae oil was transformed to develop hydrocarbons with the boiling point range of jet fuel. Following the transesterification of algal oil fatty acids to algal oil methyl ester, fractional distillation was carried out. The Box-Behnken technique was used to design experiments for efficient resource utilization by minimizing the number of tests during transesterification. The quantitative relationship function between input (molar ratio of methanol to oil, catalyst concentration, and temperature) and % yield of methyl ester as an output of the process was developed using analysis of variance (ANOVA). The ANFIS (adaptive neuro-fuzzy inference system) was utilized to develop a prognostic model with good prediction effectiveness. MORSM (multi-objective response surface methodology) was also used to build a prediction model using correlations. A set of statistical indicators and Theil's U2 were used to examine the prediction efficacy and model uncertainty of ANFIS and MORSM. On both statistical indices and Theil's U2, the ANFIS-based model outperformed. To get the best results, the desirability approach was used to improve operating parameters. According to the desirability approach, the best operating parameters were catalyst concentration at 2.09%, methanol to oil ratio at 9.17%, and temperature at 60.49 °C, which resulted in the greatest yield of 91%. © 2022 Elsevier Ltd

关键词： Alternative fuel Jet fuel Microalgae Optimization Sustainable aviation fuel

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Dynamic Multi-criteria Classifier Selection for Illegal Tapping Detection in Oil Pipelines

Dynamic Multi-criteria Classifier Selection for Illegal Tapp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Victor Henrique Alves Ribeiro Pedro Henrique Domingues Paulo Rodrigo Cavalin Gilberto Reynoso-Meza Helon Vicente Hultmann Ayala Luís Fernando Alzuguir Azevedo Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil IBM Research Rio de Janeiro Brazil Department of Mechanical Engineering Pontifícia Universidade Católica do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Illegal tapping of fuel pipelines has recently become one of the most relevant safety problems faced by the industry. Hundreds of illegal interventions have been reported around the world, causing a significant number of deaths, relevant impacts on the environment, and capital loss. Therefore, it is important to develop systems that are able to detect such scenarios at an early stage, enabling a fast counteract. To this end, machine learning algorithms can train models on available data for detecting future issues. Most recently, ensemble learning and dynamic classifier selection (DCS) techniques have been achieving promising results in supervised learning tasks. Such models are usually trained based on a single criterion. However, it is desirable to take into account both the number of false positives (FP) and false negatives (FN) for the illegal tapping detection task, since they are conflicting and both lead to financial losses and/or accidents. Therefore, this work proposes a novel DCS technique based on multiple criteria, namely overall local class-specific accuracy (OLCA), which employs multi-criteria decision making for dynamically selecting the best classifier for a new sample given the local true positive and negative ratios. A numerical experiment is conducted for assessing the generalization performance of the proposed method in an oil pipeline, with the goal of detecting illegal taping using pressure transient signals. Results show that OLCA is able to reduce the number of both FP and FN when dynamically selecting the classifiers of a baseline Random Forest ensemble.

关键词： Pipelines Leak detection Oils Decision making Feature extraction Biological system modeling Task analysis

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Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

arXiv

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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Correction to “A Sigmoid-Colon-Straightening Soft Actuator With Peristaltic Motion for Colonoscopy Insertion Assistance: Easycolon”

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IEEE Robotics and Automation Letters 2021年第3期6卷 5736-5736页

作者： Hansoul Kim Joonhwan Kim Jae Min You Seung Woo Lee Ki-Uk Kyung Dong-Soo Kwon Division of Mechanical Engineering School of Mechanical Aerospace and Systems Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea Division of Gastroenterology Department of Internal Medicine College of Medicine The Catholic University of Korea Seoul South Korea

There are two corresponding authors who have contributed to this letter, yet only one of them has been mentioned as the corresponding author in the original letter. As a result, “Ki-Uk Kyung” was added to footnote a... 详细信息

关键词： Actuators Colonoscopy

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Design and analysis of component for non-rotor unmanned aerial vehicle

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Journal of Physics: Conference Series 2021年第1期2020卷

作者： Y R Shiau J Fang Y L Chen Department of Industrial Engineering and Systems Management Feng Chia University Taichung 407 Taiwan Department of Aerospace and Systems Engineering Feng Chia University Taichung 407 Taiwan Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Taichung 407 Taiwan

Unmanned aerial vehicles (UAVs) are widely applied in various industries and fields. Because they use a variety of rotor types, from single rotors to multi-rotors, UAVs offer a wide array of functions. However, continuously rotating rotors can be dangerous if they accidentally encounter foreign objects or bare hands. Therefore, non-rotor UAVs are the focus of discussion and modification in the present study. Non-rotor UAVs do not contain visible rotor mechanical components. Compared with the development of popularized multirotor UAVs, that of non-rotor UAVs is challenging in terms of structure and flight control. To address this challenge, wind tunnel structure models were developed in this study for different levels of aerodynamic force, and computer-aided engineering was used to conduct structural and flow field analyses to determine the key elements that reinforce non-rotor UAV structures. The research results revealed that the thickness and fillet design at the joint of the non-rotor UAV aerodynamic wind tunnel system were crucial factors influencing the system's performance. Motor rotors can be embedded inside support structures to reduce airflow turbulence in the wind tunnel.

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Explainable Machine Learning for Anaerobic Digestion: A Critical Review

SSRN

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SSRN 2022年

作者： Li, Wangliang Gupta, Rohit Zhang, Le Liu, Hongtao Zhang, Zhikai Hou, Jiayi You, Siming Ok, Yong Sik James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom Department of Resources and Environment School of Agriculture and Biology Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Water Resources and Environment Hebei GEO University Hebei Shijiazhuang050031 China Korea Biochar Research Center APRU Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of

Anaerobic digestion (AD) is a traditional process to achieve energy recovery from a variety of organic waste streams. However, the AD performance in terms of biogas production and process stability relies on the cooperation of the functional microbial communities and the operating parameters. The latest progress in machine learning for AD processes including optimization, parametric prediction, perturbation detection, and long-term monitoring were critically reviewed. The explainable data-driven models including black-box machine learning (ML) models and nature-inspired optimization techniques were compared, while summarizing their applications. The review also highlighted the challenges, perspectives, and opportunities for future research of applying ML for AD processes. © 2022, The Authors. All rights reserved.

关键词： Optimization

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A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression

Device

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Device 2025年

作者： Alona Shagan Shomron Christina Chase-Markopoulou Johannes R. Walter Johanna Sellhorn-Timm Yitian Shao Tobias Nadler Audrey Benson Isabell Wochner Ellen H. Rumley Isabel Wurster Philipp Klocke Daniel Weiss Syn Schmitt Christoph Keplinger Daniel F.B. Haeufle Robotic Materials Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart Germany Stuttgart Center for Simulation Science (SimTech) University of Stuttgart Stuttgart Germany Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Computer Engineering (ZITI) University of Heidelberg Heidelberg Germany Paul M. Rady Department of Mechanical Engineering University of Colorado Boulder CO USA Center for Neurology Department of Neurodegenerative Diseases University of Tübingen Tübingen Germany Center for Bionic Intelligence (BITS) Tübingen and Stuttgart Germany Material Science and Engineering Program University of Colorado Boulder CO USA Werner Reichardt Center for Integrative Neuroscience University of Tübingen Tübingen Germany

Nearly 80 million people in the world deal with medical conditions that cause involuntary periodic movements known as tremors. Wearable soft robotic devices offer a potential solution for actively suppressing these tremors. However, existing prototypes face limitations in actuation performance and complex testing procedures. We present a comprehensive approach for the rapid evaluation of emerging wearable tremor-suppression technologies. This method combines reproducing patient-recorded tremor episodes and measuring tremor suppression in a robotic platform, termed a “mechanical patient”, with validation of the achieved suppression performance of soft actuators via biomechanical modeling, thereby avoiding time-consuming clinical testing in the early stages of development. Using this approach, we highlight that an antagonistic pair of slim and lightweight electrohydraulic actuators can effectively suppress clinically relevant tremors between 2 and 8 Hz. The biomechanical model confirms that the pair of actuators generates adequate suppression forces for all tested tremors.

关键词： tremor suppression mechanical patient soft actuators wearable devices biomechanical simulation DTI-4: Validate

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