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Visualizing environmental justice issues in urban areas with a community-based approach

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arXiv 2021年

作者： Flax-Hatch, Joel Srabanti, Sanjana Miranda, Fabio Sambanis, Apostolis Cailas, Michael D. Environmental and Occupational Health Sciences School of Public Health University of Illinois Chicago United States Department of Computer Science College of Engineering University of Illinois Chicago United States Health Policy and Administration School of Public Health University of Illinois Chicago United States

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this study, we adopt a proximity burden metric caused by adjacent hazardous sources, allowing us to evaluate the environmental burden distribution and vulnerability to pollution sources. In close collaboration with a predominantly Latinx community in Chicago, we highlight the usefulness of our approach through a case study that shows how certain community areas in the city are likely to bear a disproportionate burden of environmental pollution caused by industrial roads. Copyright © 2021, The Authors. All rights reserved.

关键词： Risk assessment

Remote Blood Oxygen Estimation From Videos Using Neural Networks

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arXiv 2021年

作者： Mathew, Joshua Tian, Xin Wong, Chau-Wai Ho, Simon Milton, Donald K. Wu, Min Department of Electrical and Computer Engineering NC State University RaleighNC27695 United States Department of Electrical and Computer Engineering Forensic Sciences Cluster Secure Computing Institute NC State University RaleighNC27695 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Physical Therapy and Rehabilitation Science University of Maryland School of Medicine BaltimoreMD21201 United States Maryland Institute for Applied Environmental Health School of Public Health University of Maryland College ParkMD20742 United States

Peripheral blood oxygen saturation (SpO2) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO2 before any obvious symptoms. Measuring an individual’s SpO2 without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO2 using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone’s camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO2 estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual’s hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO2 measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO2 measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO2 estimation performance. © 2021, CC BY.

关键词： COVID-19

Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

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SSRN

SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants

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arXiv 2025年

作者： Gu, Haogao Li, Jifan Sun, Wanying Li, Mengting Leung, Kathy Wu, Joseph T. Yuan, Hsiang-Yu Wang, Maggie H. Yang, Bingyi McKay, Matthew R. Ning, Ning Poon, Leo L.M. School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Statistics Texas A&M University United States Hong Kong Science and Technology Park Hong Kong HKJC Global Health Institute LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Biomedical Sciences Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong Centre for Applied One Health Research and Policy Advice Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong JC School of Public Health and Primary Care Faculty of Medicine the Chinese University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Microbiology and Immunology The Peter Doherty Institute for Infection and Immunity University of Melbourne MelbourneVIC3000 Australia Victorian Infectious Diseases Reference Laboratory Royal Melbourne Hospital the Peter Doherty Institute for Infection and Immunity Victoria Australia Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Centre for Immunology & Infection Hong Kong Science and Technology Park Hong Kong HKU-Pasteur Research Pole School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong

The global spread of viruses underscores the critical need for effective genomic surveillance to detect emerging variants and guide interventions. However, high costs and uneven resource distribution hinder its global implementation. This study demonstrates that optimizing genomic surveillance by targeting resources on international travelers in travel hubs, significantly improves the early detection of SARS-CoV-2 variants. Using a metapopulation multiple-strain model calibrated with extensive data, we show that targeting travelers effectively reduces detection delays without additional costs in retrospective analyses of the Omicron variant outbreak. Further, simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers (Reff) and vaccine effectiveness. These findings provide a sustainable framework for strengthening health security, offering actionable insights for optimizing genomic surveillance and improving preparedness for emerging infectious threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Coronavirus

Design and Characterization of a Simple and Wideband Antenna for 5G mm-wave Applications

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Design and Characterization of a Simple and Wideband Antenna...

IEEE MTT-S Latin America Microwave Conference (LAMC)

作者： Syed Aftab Naqvi Wahaj Abbas Awan Mohammad Alibakhshikenari Francisco Falcone Ernesto Limiti Department of Electrical and Computer Eningeering COMSATS University Islamabad Sahiwal Pakistan Department of Integrated IT Eningeering Seoul National University of Science and Technology Seoul South Korea Electronic Engineering Department University of Rome &#x201C Tor Vergata&#x201D Rome Italy Electric Electronic and Communication Engineering Department Public University of Navarre Pamplona Spain

ISBN: (数字)9781728193588

ISBN: (纸本)9781728193595

An 8.5 GHz wideband and high Gain cross elliptical shape antenna is presented in this paper for the 5G mm-wave applications. A short-pin opposite to the feeding arm is deployed for the maximum current flow and consequently high bandwidth ranging from 26.7 to 35.2 GHz is achieved. The gain of the antenna varies from 6.1 dBi to 8.47 dBi in the entire frequency range with more than 76% efficiency. The compact electrical size of the antenna is 0.89λ0 × 0.89λ × 0.14λ0, where λ0 corresponds to the free space wavelength at the lowest resonance frequency. The results are compared with the state-of-the-artwork for the 5G mm-wave applications.

关键词： Microwave antennas 5G mobile communication Shape Resonant frequency Microwave theory and techniques Pins Broadband antennas

GSLB: the graph structure learning benchmark 23

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GSLB: the graph structure learning benchmark

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Two-stage robust edge service placement and sizing under demand uncertainty

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arXiv 2020年

作者： Nguyen, Duong Tung Nguyen, Hieu Trung Trieu, Ni Bhargava, Vijay K. Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada Edward J. Bloustein School of Planning and Public Policy Rutgers University New BrunswickNJ United States Department of Electrical Engineering and Computer Sciences at Uc Berkeley CA United States

Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable various Internet of Things applications. From the perspective of a service provider (SP), how to jointly optimize the service place-ment, sizing, and workload allocation decisions is an important and challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing their service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The service placement and sizing solution of the proposed model can hedge against any possible realization within the uncertainty set of traffic demand. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second-stage after the uncertainty is revealed. To solve the two-stage model, in this paper, we present an iterative solution by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max-min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the effectiveness of the proposed two-stage robust optimization model. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

Cross-sector energy system resilience and interdependence in a changing climate

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Joule 2025年

作者： Luo Xu Ning Lin A.T.D. Perera H. Vincent Poor Qinglai Guo Hongbin Sun Michael Oppenheimer Department of Civil and Environmental Engineering Princeton University Princeton NJ USA Center for Policy Research on Energy and the Environment Princeton University Princeton NJ USA Andlinger Center for Energy and the Environment Princeton University Princeton NJ USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA Department of Electrical Engineering Tsinghua University Beijing China School of Public and International Affairs Princeton University Princeton NJ USA Department of Geosciences Princeton University Princeton NJ USA High Meadows Environmental Institute Princeton University Princeton NJ USA

Rapid global electrification is deepening cross-sector interdependence, fundamentally reshaping the resilience of energy systems in the face of intensifying climate extremes. While increased integration across energy generation, transmission, and consumption sectors can significantly enhance operational flexibility, it can also amplify the risk of cross-sector cascading failures under extreme weather events, giving rise to an emerging resilience paradox that remains insufficiently understood. This study examines evolving cross-sector interactions and their implications for climate resilience by analyzing global electrification trends and regional cases in Texas, integrated with global and downscaled projections of climate extremes. By identifying critical vulnerabilities and flexibility associated with increasing sectoral interdependence, this study highlights the necessity of adopting resilience-oriented, system-level strategies for system operators and policymakers to mitigate cross-sector cascading risks and maximize the benefits of electrification in a changing climate.

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RCMNet: A deep learning model assists CAR-T therapy for leukemia

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arXiv 2022年

作者： Zhang, Ruitao Han, Xueying Gul, Ijaz Zhai, Shiyao Liu, Ying Zhang, Yongbing Dong, Yuhan Ma, Lan Yu, Dongmei Zhou, Jin Qin, Peiwu Institute of Biomedical and Health Engineering Tsinghua Shenzhen International Graduate School China Precision Medicine and Public Health Tsinghua-Berkeley Shenzhen Institute United States The First Affiliated Hospital of Harbin Medical University China Food Inspection & Quarantine Center Shenzhen Custom China Department of Computer Science Harbin Insitute of Technology Shenzhen China School of Mc chanical Electrical & Information Engineering Shandong University China

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. Copyright © 2022, The Authors. All rights reserved.

关键词： T-cells