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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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Enhanced geometric properties prediction for carrots in motion using a Multi-task YOLO-based Linked Network (YL-Net)

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computers and Electronics in Agriculture 2025年 237卷

作者： Yi-Liang Wu Sze-Teng Liong Gen-Bing Liong Jun-Hui Liang Y.S. Gan Department of Electronic Engineering Feng Chia University Taichung 40724 Taiwan ROC Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Architecture Feng Chia University Taichung 40724 Taiwan ROC

With a growing shortage of manual labor in agriculture, there is an urgent need for efficient and automated solutions to address the complex challenges of inspecting and classifying agricultural products, such as carrots, which exhibit irregular shapes, occlusions, and high variability in geometric properties. This study introduces a novel Multi-task YOLO-based Linked Network (YL-Net) designed to estimate the geometric properties of carrots in motion, including width, length, volume, and mass. The proposed network integrates RGB-D input with decoupled multi-task learning to simultaneously perform instance segmentation and regression. Building upon our previous work, the enhanced framework presented herein achieves outstanding performance, with MAPE values below 2.5% for all estimated properties. When aggregating multi-view data from a rolling conveyor system, the accuracy further improves, yielding MAPE values below 2%. In terms of detection, the model demonstrates excellent performance, achieving a mean F1-score of 98.78% and an instance segmentation IoU of 89.25%. To evaluate its scalability, the system was deployed on an NVIDIA Jetson Orin Nano, where it achieved a real-time processing speed of 80 FPS. Beyond carrots, the proposed approach can be extended to inspect other agricultural products, such as potatoes and sweet potatoes, where geometric properties are essential for sorting and grading. This work provides a scalable and transferable solution for automated agricultural inspection, laying a robust foundation for broader applications in smart farming, industrial automation, and food quality control.

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Handcrafted local feature descriptor-based point cloud registration and its applications: A review

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IEEE Transactions on Visualization and computer Graphics 2025年

作者： Tao, Wuyong Wang, Ruisheng Hua, Xianghong Liu, Jingbin Chen, Xijiang Zang, Yufu Chen, Dong Xu, Dong Zhao, Bufan Nanchang University School of Mathematics and Computer Sciences Nanchang330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen518060 China University of Calgary Department of Geomatics Engineering CalgaryT2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing210000 China Nanjing Forestry University College of Civil Engineering Nanjing210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou51100 China

Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain. © 1995-2012 IEEE.

关键词： Three dimensional computer graphics

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Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression 23rd

Species-Shared and -Specific Structural Connections Revealed...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Tuo He, Zhibin Jiang, Xi Guo, Lei Hu, Xiaoping Liu, Tianming Du, Lei School of Automation Northwestern Polytechnical University Xian China School of Life Science and Technology MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China Department of Bioengineering UC Riverside RiversideCA United States Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States

ISBN: (纸本)9783030597276

Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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A Critical Analysis on Neural Networks and Deep Learning based Techniques for the Cloud Computing System and its Impact on Industrial Management

A Critical Analysis on Neural Networks and Deep Learning bas...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Akhilesh Latoria S. K. Joshi A. Priya M. Kalyan Chakravarthi Radha Raman Chandan Bhasker Pant School of Information Technology Auro University Surat Gujarat Uttaranchal University Dehradun Uttarakhand India School of Architecture KoneruLakshmaiah Educational Foundation Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Science School of Management Sciences (SMS) Khushipur Bachhaon Varanasi UP Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different *** often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production *** businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users’ credibility and help them use cloud storage to achieve their *** order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation.

关键词： Deep learning Cloud computing Correlation Statistical analysis Supervised learning Production Syntactics

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Robotic additive construction of bar structures: Unified sequence and motion planning

arXiv

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arXiv 2021年

作者： Huang, Yijiang Garrett, Caelan R. Ting, Ian Parascho, Stefana Mueller, Caitlin T. Building Technology Program Department of Architecture Massachusetts Institute of Technology CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Princeton University CREATE Laboratory School of Architecture PrincetonNJ08544 United States

Additive robotic construction of building-scale discrete bar structures, such as trusses and space frames, is increasingly attractive due to the potential improvements in efficiency, safety, and design possibilities. However, programming complex robots, such as manipulators with seven degrees of freedom, to successfully complete construction tasks can be tedious, challenging, or impossible for a human to do manually. Namely, the structure must be constructed in a sequence that preserves structural properties, such as stiffness, at each step. At the same time, this sequence must allow for the robot to precisely manipulate elements within the in-progress structure while respecting geometric constraints that, for example, ensure the robot does not collide with what it has built. In this work, we present an automated and newly generalized planning approach for jointly finding a construction sequence and robot motion plan for additive construction that satisfies these requirements. Our approach can be applied in a variety of additive construction processes, and we demonstrate it specifically on spatial extrusion and discrete bar assembly in this paper. We demonstrate the effectiveness of our approach on several simulated and real-world extrusion and assembly tasks, including a human-scale physical prototype, for which our algorithm is deployed for the first time to plan the assembly of a complicated double tangent bar system design. © 2021, CC BY.

关键词： Additives

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Exocentric to Egocentric Image Generation Via Parallel Generative Adversarial Network

Exocentric to Egocentric Image Generation Via Parallel Gener...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gaowen Liu Hao Tang Hugo Latapie Yan Yan Department of Computer Science Texas State University USA Chief Technology & Architecture Office Cisco USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a nontrivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [1] show that our model outperforms the state-of-the-art approaches.

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A new computational approach to the fractional-order Liouville equation arising from mechanics of water waves and meteorological forecasts

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Journal of Ocean Engineering and science 2022年

作者： Yue, Xiao-Guang Zhang, Zeying Akbulut, Arzu Kaabar, Mohammed K.A. Kaplan, Melike Wuchang Institute of Technology Wuhan China Department of Computer Science and Engineering European University Cyprus Nicosia 1516 Cyprus CIICESI-ESTG Politécnico do Porto Felgueiras 4610-156 Portugal Mathematics-Computer Department Art-Science Faculty Eskişehir Osmangazi University Eskişehir Turkey Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics and Statistics Washington State University Pullman 99163 WA United States Jabalia Camp United Nations Relief and Works Agency (UNRWA) Palestinian Refugee Camp Gaza Strip Jabalya Palestine Department of Computer Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Turkey

The current analysis employs the improved F-expansion, modified extended tanh, exponential rational function, and (g′)−expansion procedures to find a divergent collection of the fractional Liouville equation's exact solutions in the context of beta-derivative. Also, we have given the graphical representations of the obtained results. These plots are useful in describing the dynamic characteristics of the solutions. The investigated equation is very essential in studying the mechanics of water waves and atmospheric predictability. Therefore, our techniques provide a great help in investigating various nonlinear models formulated in the contexts of fractional derivatives arising from oceanography and mathematical physics. © 2022

关键词： Beta-derivative Exact solutions Fractional differential equations Symbolic computation

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Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset

Egok360: A 360 Egocentric Kinetic Human Activity Video Datas...

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IEEE International Conference on Image Processing

作者： Keshav Bhandari Mario A. DeLaGarza Ziliang Zong Hugo Latapie Yan Yan Department of Computer Science Texas State University USA Chief Technology & Architecture Office Cisco USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://***/.

关键词： Convolution Activity recognition Cameras Three-dimensional displays Kinetic theory Optical distortion Distortion

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Wind Comfort Analysis Using an Immersed Boundary Method for Early-Stage Building Design

SSRN

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SSRN 2024年

作者： Mitkov, Radostin Hunger, Franziska Pantusheva, Mariya Naserentin, Vasilis Gösta, Alexander Mark, Andreas Petrova-Antonova, Dessislava Logg, Anders GATE Institute Sofia University "St. Kliment Ohridski" Sofia1113 Bulgaria Fraunhofer-Chalmers Centre for Industrial Mathematics Computational Engineering and Design Chalmers Science Park GothenburgSE-412 88 Sweden Department of Mathematical Sciences Chalmers University of Technology GothenburgSE-412 969 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki ThessalonikiGR- 54124 Greece RISE - Research Institutes of Sweden Architecture and spatial planning Drottning Kristinas Väg 61 StockholmSE-114 28 Sweden Liljewall Arkitekter Odinsplatsen 1 GothenburgSE-411 02 Sweden

This paper presents a thorough exploration of wind comfort dynamics in urban environments through the application of computational fluid dynamics simulations, with a specific emphasis on the Immersed Boundary Method. The study validates the Immersed Boundary Octree Flow solver, IBOFlow, against experimental data and then simulates the flow in a real-world case study of an area in Mölndal, Sweden. The research examines the impact of varying the number of simulated wind directions on the wind comfort maps, revealing that employing 16 wind directions is a reliable balance between computational efficiency and precision. Further, the investigation delves into the implications of using a simplified building geometry as available in early-stage design, demonstrating that computational simulations can provide reliable information about the pedestrian wind comfort early in the design process. While underscoring the significance of early-stage CFD simulations in informing design decisions, the paper concludes by advocating for a holistic approach. It emphasizes the relevance of simulation testing in later design iterations, particularly as architectural details continue to be refined. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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