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SSRN 2022年

作者： Mei, Tianhua Liu, Jie Guo, Jianming Siano, Pierluigi Jin, Xuanxuan School of Electrical Engineering Zhejiang University Zhejiang Hangzhou310027 China School of Public Administration Zhejiang University of Finance and Economics Zhejiang Hangzhou310018 China China Research Institute of Regulation and Public Policy Zhejiang Hangzhou310018 China Department of Management & Innovation Systems University of Salerno Salerno Italy Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg2006 South Africa China Institute of Regulation Research Zhejiang University of Finance and Economics Zhejiang Hangzhou310018 China

The initial allocation of carbon emission rights among different generating units is an important element in the initial stage of the construction of the carbon emission market for the power system, and is also the focus of attention in the construction of the current carbon market for the power system in *** practical situations, a variety of equity principles have been utilized in different allocation problems. However, conflicts always exist among these principles. It is necessary to justify and coordinate different allocation *** paper tries to apply the axiomatic method and social choice theory to propose an axiomatic and fair allocation method based on iterative election and establish a mathematical model for the fair allocation of initial carbon emission rights in power systems. This paper studies the game equilibrium problem under the iterative election allocation mechanism, and describes the finite rationality of participants in a complex environment by means of the "local optimal search" and "greedy algorithm".By introducing the method of strict intention table, the general equilibrium under specific conditions and the game equilibrium problem below four strategies are solved analytically in detail. Finally, some real-world case studies are carried out to show that the iterative election allocation algorithm can satisfy the main principle of fair allocation and the given allocation results are easily accepted by each unit *** study not only provides a reasonable and feasible path for China's power system to realize the scientific research on the fair distribution of initial carbon emissions and the practicability of distribution methods, but also provides profound policy enlightenment for the construction of carbon emission market in China's power industry. © 2022, The Authors. All rights reserved.

关键词： Game theory

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False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning

False Data Injection Attack on Atmospheric Electric Field in...

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2022 International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2022

作者： Li, Xiang Hayawi, Kadhim Chen, Yi Chang, Shih Yu Wen, Hong Ho, Pin-Han Yang, Ling Yin, Qiyuan College of Electronic Engineering Chengdu University of Information Technology Chengdu610225 China College of Technological Innovation Zayed University Abu Dhabi144534 United Arab Emirates School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Cma Key Laboratory of Atmospheric Sounding Chengdu610225 China San Jose State University Department of Applied Data Science San JoseCA95192 United States University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada Guangdong Meteorological Public Safety Technical Support Center Guangzhou510640 China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

Thunderstorm warning plays an important role in lightning prevention and disaster mitigation. In practical applications, thunderstorm warning system is also vulnerable to attacks, such as False Data Injection Attack (FDIA). However, there is a lack of research on False Data Injection Attack for thunderstorm warning. Therefore, this paper put forwards a FDIA method based on principal component analysis (PCA) for atmospheric electric field (AEF), which is usually used for thunderstorm warning. In the FDIA scenario, the AEF-based thunderstorm warning algorithm is also introduced with electric field differential index (EFDI). Finally, experiments are conducted based on AEF data collected by an atmospheric electric field meter (AEFM) about the real thunderstorm. The experimental results show that FDIA seriously interferes with the results of the AEF-based thunderstorm warning. © 2022 IEEE.

关键词： Principal component analysis

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Fine Particulate Matter Emissions from Electric Vehicle Fast Charging Stations

SSRN

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SSRN 2025年

作者： Li, Liqiao Yao, Yuan Niu, Muchuan Chen, Haoxuan Yu, Qiao Wu, Qingyang Li, Yuhang Zhang, Yijie Ozcan, Aydogan Jerrett, Michael Zhu, Yifang Department of Environmental Health Sciences Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Institute of Environment and Sustainability University of California Los Angeles Los AngelesCA United States Electrical and Computer Engineering Department University of California Los Angeles Los AngelesCA United States RAND Health Sciences Program United States

The global shift to electric vehicles necessitates the expansion of Direct Current Fast Charging (DCFC) stations, yet the related environmental and public health impacts remain unclear. Here, we report that the power cabinet at DCFC stations emit fine particulate matter (PM2.5). We collected integrated filter samples from 50 DCFC stations across 47 cities in Los Angeles County, California. The daily PM2.5 concentrations were between 7.3 and 39.0 µg m-3, significantly higher than urban background sites (p = 0.02) and the nearest U.S. EPA monitoring stations (p © 2025, The Authors. All rights reserved.

关键词： Air quality

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Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty

arXiv

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arXiv 2025年

作者： Jain, Yoshee Hollander, John He, Amber Tang, Sunny Zhang, Liang Sabatini, John Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72401 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Psychology University of Memphis MemphisTN38152 United States

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation;however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI’s GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional Systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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DESCRIBE WHERE YOU ARE: IMPROVING NOISE-ROBUSTNESS FOR SPEECH EMOTION RECOGNITION WITH TEXT DESCRIPTION OF THE ENVIRONMENT

arXiv

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arXiv 2024年

作者： Leem, Seong-Gyun Fulford, Daniel Onnela, Jukka-Pekka Gard, David Busso, Carlos Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States Occupational Therapy and Psychological and Brain Sciences Boston University MA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University MA02138 United States Psychology Department San Francisco State University CA94132 United States

Speech emotion recognition (SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER performance under noisy conditions. To address this task, we propose a text-guided, environment-aware training where an SER model is trained with contaminated speech samples and their paired noise description. We use a pre-trained text encoder to extract the text-based environment embedding and then fuse it to a transformer-based SER model during training and inference. We demonstrate the effectiveness of our approach through our experiment with the MSP-Podcast corpus and real-world additive noise samples collected from the Freesound repository. Our experiment indicates that the text-based environment descriptions processed by a large language model (LLM) produce representations that improve the noise-robustness of the SER system. In addition, our proposed approach with an LLM yields better performance than our environment-agnostic baselines, especially in low signal-to-noise ratio (SNR) conditions. When testing at -5dB SNR level, our proposed method shows better performance than our best baseline model by 31.8 % (arousal), 23.5% (dominance), and 9.5% (valence). © 2024, CC BY.

关键词： Speech recognition

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computer-Aided Lung Auscultation Screening and Radiographic Evaluation of Pediatric Pneumonia

Computer-Aided Lung Auscultation Screening and Radiographic ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Annapurna Kala Daniel Chong Abdullah H. Baqui Salahuddin Ahmed ASMD Ashraful Islam Nabid Chowdhury Arunangshu Roy Eric D. McCollum Mounya Elhilali Dpt. Electrical & Computer Engineering Lab. for Computational Audio Perception Johns Hopkins University Dpt. International Health Bloomberg School of Public Health Johns Hopkins University Projahnmo Research Foundation Dhaka Bangladesh Dpt. Pediatrics Global Program in Pediatric Respiratory Sciences Eudowood Division of Pediatric Respiratory Sciences Johns Hopkins School of Medicine Baltimore USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

While chest auscultations provide an accessible and low-cost tool for pediatric pneumonia diagnosis, its subjectivity and low reliability continues to hinder its inclusion in global pneumonia guidelines; eventhough more robust tools like chest radiography also suffer from cost and accessibility issues. Advances in computer-aided analytics is offering more robust tools for interpreting digital auscultation signals though little has been done to explore variations of lung sounds across different chest positions and the correspondence between auscultations and specific radiographic findings. The present study explores interpretation of lung auscultations across chest positions in a pediatric pneumonia population, using a deep neural network classification of normal and abnormal breathing patterns. The results reveal a strong alignment between computer-aided auscultation findings and radiographic interpretation not only in terms of presence of adventitious lung patterns, but also in terms of localizing the abnormality along the left or right lung. Though evaluated in a small clinical population, this research underscores the potential of computer-aided auscultation analysis as a cost-effective substitute for radiography in resource-limited settings.

关键词： Pneumonia Costs Lungs Artificial neural networks Reliability engineering in medicine and biology Diagnostic radiography Guidelines

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Retrieve, reason, and refine: generating accurate and faithful patient instructions 22

Retrieve, reason, and refine: generating accurate and faithf...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Fenglin Liu Bang Yang Chenyu You Xian Wu Shen Ge Zhangdaihong Liu Xu Sun Yang Yang David A. Clifton Department of Engineering Science University of Oxford School of ECE Peking University Department of Electrical Engineering Yale University Tencent JARVIS Lab China Department of Engineering Science University of Oxford and Oxford-Suzhou Centre for Advanced Research China MOE Key Lab of Computational Linguistics School of Computer Science Peking University School of Public Health Shanghai Jiao Tong University School of Medicine China

ISBN: (纸本)9781713871088

The "Patient Instruction" (PI), which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn reduce hospital readmission rates. However, writing an appropriate PI can be extremely time-consuming for physicians, and is subject to being incomplete or error-prone for (potentially overworked) physicians. Therefore, we propose a new task that can provide an objective means of avoiding incompleteness, while reducing clinical workload: the automatic generation of the PI, which is imagined as being a document that the clinician can review, modify, and approve as necessary (rather than taking the human "out of the loop"). We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the PI for previously-unseen patient according to their health records during hospitalization. Our experiments show that, using our method, the performance of five different models can be substantially boosted across all metrics, with up to 20%, 11% and 19% relative improvements in BLEU-4, ROUGE-L and METEOR, respectively. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of its usefulness for clinical practice.

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Development of Microcontroller-Based Manipulator Arm: A Trilemma Problem-Solving Framework for Robotics and STEM Education in Nigerian Schools

Development of Microcontroller-Based Manipulator Arm: A Tril...

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Technologies Applied to Electronics Teaching (TAEE)

作者： Mbadiwe S. Benyeogor Tobore L. Igbigbi Olusegun I. Lawal Udoh T. Akpan Andrew O. Benyeogor Nnaemeka M. Mbachu Kosisochukwu P. Nnoli Muhammad S. Aliero Adeboye Olatunbosun Institute of Physics University of Münster Münster Germany Department of Public Utilities City of Cleveland Ohio State USA Advanced Aerospace Engines Lab. Nat. Space Research & Dev. Agency Oka Akoko Ondo State Nigeria Dept. Mechatronics Engineering Lagos State Univ. of Sc. & Tech. Ikorodu Lagos State Nigeria CTE Computer Science & Robotics International Leadership of Texas Aggieland Texas USA Dept. Comp. Sc. & Electrical Engineering Constructor University Bremen Germany ICT Department Kebbi State Univ. of Sc. & Tech Aliero Nigeria Dept. Electrical & Electronics Engineering Ajayi Crowther University Oyo Town Oyo State Nigeria

ISBN: (数字)9798350348675

ISBN: (纸本)9798350348682

Advances in microprocessor technology and software engineering have resulted in the development of sophisticated robots that are used in modern society as assistive automata for production, security, and farming, among other things. To sustain the development of robots, robotics education should be included in school curricula (i.e., teaching and learning process). However, schools in many developing nations, such as Nigeria, are unable to adapt to this trend due to personnel, technological, infrastructural, and fiscal limitations. To address this problem, a low-cost robotic manipulator arm with real-time control algorithms and a human-robot interface that allows it to be controlled by a microcomputer is developed. This includes its functional model, geometry of link movement, mechanical design, control circuitries, and a Teraterm-based human-robot interface (HRI). Furthermore, an encryption method was developed that maps selected control keys to actuation functions in manipulator chains. We also explored how this robotic manipulator may be used as a teaching tool, particularly for teaching three major industrial technologies: machine-making, electronics, and computing. Our manipulator arm has undergone both unit and system tests and has been used for classroom teaching. Results of its applicability assessment show that our system could aid the teaching of robotics and STEM subjects in schools, as a way to solve the trilemma of harmonizing the concept of machines, electronic control, and computation in multidisciplinary domains.

关键词： Service robots Educational robots Education Manipulators Real-time systems Production facilities Security

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Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

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Yearbook of Medical Informatics 2023年第1期32卷 253-263页

作者： He, Zhe Pfaff, Emily Guo, Serena Jingchuan Guo, Yi Wu, Yonghui Tao, Cui Stiglic, Gregor Bian, Jiang School of Information Florida State University United States Department of Behavioral Sciences and Social Medicine College of Medicine Florida State University United States Department of Medicine University of North Carolina at Chapel Hill School of Medicine United States Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida United States School of Biomedical Informatics University of Texas Health Science Center at Houston United States Faculty of Health Science University of Maribor Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Usher Institute University of Edinburgh United Kingdom

OBJECTIVE: To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. METHODS: In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. RESULTS: Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. CONCLUSIONS: To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long

关键词： electronic health records exposome public health informatics Social determinants of health

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A Framework for Smart City Implementation using IoT-Cloud Based System Architecture

A Framework for Smart City Implementation using IoT-Cloud Ba...

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Distributed Computing and electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： V Dankan Gowda KDV Prasad Tanmoy De Ved Srinivas N Anil Kumar Tejashree Tejpal Moharekar Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India Symbiosis Institute of Business Management (SIBM) Symbiosis International (Deemed University) (SIU) Hyderabad India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Public Policy Thiagarajar School of Management Madurai India Department of Electronics and Communication Engneering School of Engineering and Technology Mohan Babu University (Erst While Sree Vidyanikethan Engineering College) Tirupati India Computer Department Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India

The Internet, a key participant (world wide web) in worldwide information exchange and media sharing, has experienced cumulative upgrades, developments, and given up consequent product IOT, making technology affordable and practical for all uses. Research development includes smart sensors, a communication channel and protocols for exchanging data, and peripherals like an Arduino, a Texas Instruments MSP430G2553, a Weather-proof Tx. Rx. Kit (for exchanging data), a Data Receptor and Decoder Shield for Lab View Kit (for exchanging data), an addressing and de-addressing data module, a communication shield, etc. It is now possible to communicate from a desktop computer or an Android smartphone to a portable device that has an embedded device, such as a microcontroller, a microprocessor, or a sensor. In other words, IOT combines always-on networking, always-on computation, and always-on intelligence. The Internet of Things facilitates communication between the central hub (fitted with a Lab-View interface) and the distributed modules (found in places like parks, subways, and highways) that control the lights. The Internet of Things is also useful for keeping the overall cost of setup and maintenance within reasonable economic bounds. The planned development takes into account IoT on two main scales: city-wide automation (also known as smart city) and home-level automation (also known as covering up).

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