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IEEE International Conference on Big Data

作者： Sara A. Alsalamah Shada Alsalamah Walaa N. Ismail Hessah Alsalamah Chang-Tien Lu Department of Computer Science Virginia Tech Falls Church VA USA College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Riyadh KSA College of Computer and Information Sciences King Saud University Riyadh KSA Department of Digital Health and Innovation Science Division World Health Organization Geneva Switzerland Department of Management Information Systems Al Yamamah University Riyadh KSA College of Engineering and Architecture Al Yamamah University KSA

ISBN: (纸本)9781665480468

Virtual hospitals empower traditional hospitals to deliver more accessible, affordable, and comprehensive patient-centered (PC) care services. However, the legacy information systems of traditional hospitals are ill-equipped to support the needs of virtual hospitals. We propose a holistic virtual hospital ecosystem design that addresses these issues. We have developed two models. The first is a VHealth-CNN model that extracts PC knowledge from multi-sourced biomedical big data by (1) extracting disease health-related features; (2) structuring the relevant health-related features as per the pre-identified factors; (3) training a convolutional neural network (CNN) double-layer structure, where we select significant health-related features in the first layer, and classify the positively and negatively correlated features in the second one; and (4) generating disease class outputs representing the PC knowledge. The second model is a granular VHealth-AC model that seamlessly grants healthcare practitioners at a hub hospital remote access to PC knowledge at the right point of care. We have deployed a granular 5-tier PC information classification scheme to enforce information security rules across hospitals. In addition, we examined the feasibility of the proposed design through a tele-monitoring service experimental case study for predicting obesity, hypertension, and diabetes. The experimental results show that the proposed model predicts obesity, hypertension, and diabetes diagnoses with 91.3%, 93.5%, and 95% accuracy, respectively. Finally, our ecosystem design should encourage the adoption of virtual hospitals and the adoption of virtual healthcare services as a new norm.

关键词： Knowledge engineering Hypertension Obesity Hospitals Biological system modeling Ecosystems Big Data

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Optimizing Solar-Thermal Collector's Angles to Enhance Useful Energy Gain

Optimizing Solar-Thermal Collector's Angles to Enhance Usefu...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Mohamed Elnaggar Hala J. El-Khozondar Ezzaldeen Edwan Rawan K Abu Mutlaq Ahmed Harb Amir Ali Awad Engineering Program Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Electrical Engineering and smart systems Department Islamic University of Gaza Gaza Palestine Engineering Department Al-Azhar University Gaza Palestine Al-Azhar University Gaza Palestinian Authority

ISBN: (数字)9780738111391

ISBN: (纸本)9781665403818

To enhance useful gained energy by solar thermal collector, two software programs are used for optimizing tilt and incidence angle of the collector. TRNSYS program is used to predict solar useful energy gained by the collector. The Design-Expert (DOE) tool with response surface methodology (RSM) program is used to optimize the tilt angle. Results show that tilt angle and the incidence angle have important effect on the gain of the useful energy. In addition, it is shown that the optimum tilt angle varies monthly or seasonally. Moreover, to get maximum useful energy, the incidence angle has small angle around 10° and almost stable throughout the seasons. The optimal tilt angle has lowest value in summer and the highest value in winter. The calculated values are of 39.57°, 26.72°, 20.15°, and 32.2° and the corresponding maximum monthly averaged useful energy gained by the collector in January, April, July, and October are 223.52 kWh, 482.61 kWh, 596.56 kWh, 386.51 kWh respectively.

关键词： Response surface methodology Software Three-dimensional displays Water heating Solar radiation Sea surface Optimization

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Analysis of real-time particulate matter (PM2.5) concentrations in Washington, DC, using generalized additive models (GAMs)

Analysis of real-time particulate matter (PM2.5) concentrati...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Jordan Frengut Anwesha Tomar Andrew Burwell Royce Francis Department of Political Science George Washington University Washington DC USA Data Science Department George Washington University Washington DC USA Engineering Management and Systems Engineering George Washington University Washington DC USA

ISBN: (数字)9781728171456

ISBN: (纸本)9781728171463

The objective of this paper is to report the results of a generalized additive model used to predict local particulate matter concentrations at a Washington, DC department of Energy and Environment (DOEE) federal regulatory monitoring station. While the DOEE uses state-of-the-art federal equivalent method (FEM) equipment to demonstrate compliance with the clean air act for regulatory purposes, these measurements reflect regional, not neighborhood air quality. A GW student-led living lab project-Fresh Air DC-has been testing uRAD INDUSTRIAL low-cost air quality sensors that can be used to collect air quality data at the neighborhood level using LoRaWAN based smart city technology. Because low-cost sensors often lack the accuracy and sensitivity of FEM equipment, research indicates that low-cost sensor (LCS) monitoring networks require post- processing and data modelling in order to apply findings to educational and policy goals. Although LCS data processing has been conducted using linear and nonlinear models, nonlinear models tend to have a greater ability to capture the nuanced relationships between air pollutants and meteorological influences. In this paper, we post-process uRAD PM 2.5 sensor data using DOEE FEM equipment as a reference instrument in the development of three models to adjust uRAD data to the DOEE FEM data-ordinary least squares linear regression, generalized linear models (GLMs), and generalized additive models (GAMs). Our model includes meteorological variables such as temperature, humidity, and wind speed. Our statistical models for post-processing are evaluated on the basis of deviance and Akaike Information Criterion (AIC). We expect that the GLM and GAM will be useful for capturing nonlinear relationships between the PM 2.5 measurements and meteorological variables.

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Simulation-Based Optimization for the Integrated Control of Production and Logistics: A Performance Comparison

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IFAC-PapersOnLine 2020年第2期53卷 10639-10644页

作者： Matheus C. Pires Enzo M. Frazzon Djonathan Quadras Eike Broda Michael Freitag Department of Production Engineering and Systems (EPS) Federal University of Santa Catarina Florianópolis Brazil Graduate Program in Production Engineering Federal University of Santa Catarina Florianópolis Brazil Faculty of Production Engineering University of Bremen Bremen Germany BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen Bremen Germany

Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter.

关键词： Supply Chain Planning Control Simulation-based Optimization Integrated Planning Simulation Optimization Simulated Annealing

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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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Effects of carbon nanofiber z-threads on the longitudinal compressive strength of unidirectional CFRP laminates

Effects of carbon nanofiber z-threads on the longitudinal co...

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SAMPE 2019 Conference and Exhibition

作者： Kirmse, Sebastian Kim, Keonhyeong Ranabhat, Bikash Hsiao, Kuang-Ting College of Engineering University of South Alabama MobileAL36688 United States Systems Engineering Program University of South Alabama MobileAL36688 United States Department of Mechanical Engineering University of South Alabama MobileAL36688 United States

ISBN: (纸本)9781934551301

In this study, unidirectional carbon fiber prepregs that contain long carbon nanofiber (CNF) z-threads as a through-thickness (z-directional) reinforcement were manufactured. The CNF z-threads are long enough to thread through multiple carbon fiber (CF) arrays, which creates a multi-scale CNF/CF/resin-composite. The CNF z-threaded prepregs were manufactured using an electric-field aligned flow-transferring process. It was hypothesized that the CNF z-threads with the zig-zag threading pattern reinforces the interlaminar and intralaminar regions of the CFRP laminate thus improve the compressive strength by reducing the chance of carbon fiber buckling. Compressive testing was performed per modified version of ASTM D695 (i.e., SACMA SRM 1R-94) to evaluate the compressive strength of the CNF z-threaded CFRP (ZT-CFRP) laminates. The samples were manufactured using AS4 carbon fibers, EPON 862/Epikure-W resin and a 1wt% CNF content. ZT-CFRP testing results were compared with unaligned CNF-modified CFRP (UA-CFRP) and unmodified CFRP samples to investigate the impact of the CNF z-threads on the compressive strength. Results showed an increase of ~15% for the compressive strength of ZT-CFRPs, whereas the UA-CFRPs experienced a decrease of ~8% when compared to unmodified CFRPs. It was concluded that CNF/carbon fiber interlocking stops and delays crack growth, and helps to stabilize carbon fibers from further buckling. Copyright 2019 by Sebastian Kirmse et al. Published by Society for the Advancement of Material and Process engineering with permission.

关键词： Carbon nanofibers

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A newton tracking algorithm with exact linear convergence rate for decentralized consensus optimization

arXiv

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arXiv 2020年

作者： Zhang, Jiaojiao Ling, Qing So, Anthony Man-Cho Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong School of Data and Computer Science Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China

This paper considers the decentralized consensus optimization problem defined over a network where each node holds a second-order differentiable local objective function. Our goal is to minimize the summation of local objective functions and find the exact optimal solution using only local computation and neighboring communication. We propose a novel Newton tracking algorithm, where each node updates its local variable along a local Newton direction modified with neighboring and historical information. We investigate the connections between the proposed Newton tracking algorithm and several existing methods, including gradient tracking and second-order algorithms. Under the strong convexity assumption, we prove that it converges to the exact optimal solution at a linear rate. Numerical experiments demonstrate the efficacy of Newton tracking and validate the theoretical findings. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal systems

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Corrigendum to “Towards equitable and sustainable indoor air quality guidelines – A perspective on mandating indoor air quality for public buildings” [Indoor Environ. 2 1 (2025) 100070]

Indoor Environments

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Indoor Environments 2025年第1期2卷

作者： Ulla Haverinen-Shaughnessy Marzenna R. Dudzinska Samy Clinchard Sani Dimitroulopoulou Xiaojun Fan Piet Jacobs Henna Maula Amelia Staszowska Oluyemi Toyinbo Ju-Hyeong Park Civil Engineering Research Unit Faculty of Technology The University of Oulu Finland Indoor Air Program The Russell School of Chemical Engineering The University of Tulsa OK USA Faculty of Environmental Engineering Lublin University of Technology Poland Air Quality & Public Health Group Environmental Hazards and Emergencies Department RCCE UK Health Security Agency UK Chair of Architecture and Building Systems Institute of Technology in Architecture ETH Zurich Zurich Switzerland Department of Building & Energy Systems TNO Delft the Netherlands Built Environment Research Group Turku University of Applied Sciences Turku Finland Respiratory Health Division CDC/National Institute for Occupational Safety and Health USA

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