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Evaluating the Transferability of a Bistatic Radiative Transfer Model to Monostatic Scattering for Wheat Crop with Sentinel-1 SAR Dual Polarimetric Data

Evaluating the Transferability of a Bistatic Radiative Trans...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Sumana Khamrai Rajendra Prasad Suraj A Yadav Shubham Kumar Singh Gulab Singh Prashant K. Srivastava Muskan Dua Dept. of Physics Indian Institute of Technology (BHU) Varanasi India Electrical and Computer Engineering Department Mississippi State University USA Dept. of Urban and Environmental Policy and Planning Tufts University USA Centre of Studies in Resources Engineering Indian Institute of Technology Bombay India Dept. of Earth and Atmospheric Sciences IESD Banaras Hindu University India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The study presents a first-order Radiative Transfer model for monostatic scattering by integrating a modified Oh model for soil surface scattering and radiative transfer model with higher order interaction terms for volumetric scattering. The approach aims to improve crop signature analysis for monitoring and retrieval purposes of biophysical parameters, particularly soil moisture (SM) and leaf area index (LAI) in our case, with the use of Sentinel-1 satellite dual-polarized SAR data. The SAR data (GRD and SLC), along with the in situ LAI and SM data from February to April 2024, has been used for training and testing purposes of the model. The Oh model shows the surface scattering part, and it has been modified by the incorporation of attenuation factor and one soil-scaling factor fsoil. Similarly, the volumetric scattering part has been described for the vegetation components, along with the 1st order interaction terms from the soil-veg, veg-soil double bounce scattering has been used in this model. Least-square optimization technique has been used to calibrate the scattering albedo and the loss factor present in the attenuation term. Finally, the inversion algorithm to retrieve the soil moisture data, has been done again with the Look-up Table (LUT) approach and the least square technique again for the sake of simplicity. The backscattering coefficient retrieval performance metric for VV polarization ($\mathrm{R}=0.8922$; $\mathrm{RMSE}=$ 0.6126) shows a better correlation with RMSE slightly higher when compared with VH polarization ($\mathrm{R}=0.8745$; RMSE = 0.4150). However, for soil moisture, the VH ($\mathrm{R}=\mathbf{0. 6 0 7 4}$; RMSE =0.1065) polarization shows better results than $\mathrm{VV}(\mathrm{R}=0.4169$; RMSE = 0.1576).

关键词： Biological system modeling Soil moisture Scattering Sentinel-1 Vegetation mapping Crops Data models Table lookup Optimization Backscatter

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Prioritizing Strategies for Building the Resilience of public Health Systems to Disasters Across Multiple Communities and Countries

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International Journal of Disaster Risk Science 2024年第1期15卷 1-17页

作者： Benjamin J.Ryan Mayumi Kako Shelby Garner Rok Fink Ismail Tayfur Jonathan Abrahams Sanjaya Bhatia Adriana Campelo Matthew Fendt Alicia Fontenot Nahuel Arenas Garcia Tim Hatch Ryoma Kayano LaShonda Malrey‑Horne Makiko MacDermot Md Moshiur Rahman Chaverle Noel Shuhei Nomura Jeremy P.Novak Maria Opazo Kendell Oliver Luciana Peters Sohel Rahman PerihanŞimşek Andrew Stricklin Raymond Swienton Bryan W.Brooks Frist College of Medicine Belmont UniversityNashvilleTN 37212USA Department of Environmental Science Environmental Health Science ProgramCollege of Arts and SciencesBaylor UniversityWacoTX 76798USA Division of Nursing Science School of Biomedical and Health SciencesHiroshima UniversityHiroshima 734-8553Japan Gordon E.Inman College of Health Sciences and Nursing Belmont UniversityNashvilleTN 37212USA Faculty of Health Sciences University of Ljubljana1000 LjubljanaSlovenia Department of Emergency Medicine University of Health Sciences34668 IstanbulTurkey Monash University Accident Research Center MelbourneVIC 3800Australia Global Education and Training Institute United Nations Ofce for Disaster Risk ReductionIncheon 22004Republic of Korea Regional Ofce for the Americas and Caribbean United Nations Ofce for Disaster Risk ReductionPanama City 0843-03441Panama Engineering and Computer Science Baylor UniversityWacoTX 76798USA Department of Public Health Robbins College of Health and Human SciencesBaylor UniversityWacoTX 76798USA Alabama Department of Public Health MontgomeryAL 36092USA World Health Organization Centre for Health Development(WHO Kobe Centre) Kobe 651-0073Japan Waco-McLennan County Public Health District WacoTX 76707USA Department of Health Policy and Management School of MedicineKeio UniversityTokyo 160-8582Japan Department of Global Health Policy Graduate School of MedicineThe University of TokyoTokyo 113-0033Japan Tokyo Foundation for Policy Research Tokyo 106-6234Japan Global Development College BrisbaneQLD 4000Australia Municipality of Pudahuel 13124 PudahuelChile City Hall of Porto Alegre Porto Alegre 90010-170Brazil Ministry of Health and Family Welfare Government of the People’s Republic of BangladeshDhaka 1000Bangladesh Department of Emergency Aid and Disaster Management Trabzon University61335 TrabzonTurkey Department of Emergency Medicine UT SouthwesternDallasTX 75390USA

The COVID-19 pandemic highlighted the urgent need to strengthen public health *** response,the United Nations Disaster Risk Reduction(UNDRR) public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple *** aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system *** conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training Institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience *** were drawn from 70 strategies identified from 13 distinct workshops in eight *** two surveys,23 strategies were *** received ratings of "High" or Very High" from89% of *** related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of *** Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system *** leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

关键词： Health system resilience public policy Resilience scorecard Workshops

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CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Pl...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Deepanjali Chowdhury Ahana Roy Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Department of Electrical and Computer Engineering Texas A&M University College Station United States Howard County Public School System Maryland United States Department of Computer Science Bowie State University Bowie United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose.

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Diffusion Gaussian Mixture Audio Denoise

arXiv

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arXiv 2024年

作者： Wang, Pu Li, Junhui Li, Jialu Guo, Liangdong Zhang, Youshan Department of Mathematics School of Science University of Science and Technology Liaoning Anshan China School of public policy Cornell University IthacaNY United States School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China Department of Artificial Intelligence and Computer Science Yeshiva University New YorkNY United States

Recent diffusion models have achieved promising performances in audio-denoising tasks. The unique property of the reverse process could recover clean signals. However, the distribution of real-world noises does not comply with a single Gaussian distribution and is even unknown. The sampling of Gaussian noise conditions limits its application scenarios. To overcome these challenges, we propose a DiffGMM model, a denoising model based on the diffusion and Gaussian mixture models. We employ the reverse process to estimate parameters for the Gaussian mixture model. Given a noisy audio signal, we first apply a 1D-U-Net to extract features and train linear layers to estimate parameters for the Gaussian mixture model, and we approximate the real noise distributions. The noisy signal is continuously subtracted from the estimated noise to output clean audio signals. Extensive experimental results demonstrate that the proposed DiffGMM model achieves state-of-the-art performance. © 2024, CC0.

关键词： Diffusion

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Post COVID-19 Intelligent public Healthcare Management

Post COVID-19 Intelligent Public Healthcare Management

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bristy, Anika Tabassum Ferdousi, Rahatara Hossain, Arman Islam, M M Faniqul Hossain, M. Anwar El Saddik, Abdulmotaleb North South University Pharmaceutical Sciences Bangladesh Canada North South University Public Health Bangladesh Queen Mary University of London Global Public Health Policy United Kingdom King Saud University College of Computer Information Sciences Department of Software Engineering Saudi Arabia

ISBN: (纸本)9781665423908

Epidemic outbreaks are collective effects of ongoing globalization, urbanisation, population mobility, climate change, demographic change and evolution of newer strains of infectious agents that result in high morbidity, mortality and huge financial loss, such as COVID-19. Thus, the early prediction of the emergence of a disease can play a pivotal role to prevent a disease to become epidemic. The Edge AI based solution has been proposed for healthcare prediction using machine learning (ML). In this paper, our focus is to propose ML based advanced model for public healthcare to reduce and control epidemic outbreaks. Collective knowledge from interconnected disciplines, shared data repository, and diverse roles have been embedded into the proposed framework. An evaluation based on actual COVID-19 related data demonstrates that ML can be used for COVID risk prediction for public health data as well as to take preventive steps to combat epidemics in early-stage. © 2021 IEEE.

关键词： COVID-19

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Keep it Local: Comparing Domain-Specific LLMs in Native and Machine Translated Text using Parallel Corpora on Political Conflict

Keep it Local: Comparing Domain-Specific LLMs in Native and ...

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Foundation and Large Language Models (FLLM), International Conference on

作者： Javier Osorio Sultan Alsarra Amber Converse Afraa Alshammari Dagmar Heintze Latifur Khan Naif Alatrush Patrick T. Brandt Vito D’Orazio Niamat Zawad Mahrusa Billah School of Government and Public Policy University of Arizona Tucson United States Department of Software Engineering King Saud University Riyadh Saudi Arabia Department of Linguistics University of Arizona Tucson United States Department of Computer Science University of Texas – Dallas Dallas United States School of Economic Political and Policy Sciences University of Texas – Dallas Dallas United States Department of Political Science West Virginia University Morgantown United States

ISBN: (数字)9798350354799

ISBN: (纸本)9798350354805

The dynamics of political conflict and cooperation require powerful computerized tools capable of effectively tracking security threats and cooperation around the world. This study compares the performance of domain-specific Large Language Models (LLMs) against generically-trained LLMs in binary and multi-class classification using native text in English, Spanish, and Arabic, and their corresponding machine translations. This endeavor yields four key contributions. 1) We present and make available a novel database of annotations using a multi-lingual parallel corpus from the United Nations. 2) Using various metrics, we assess the quality of different machine translation tools. 3) Our results indicate that the ConfliBERT family of LLMs, a set of domain-specific models tailored for political conflict, outperform generically-trained LLMs in English, Spanish, and Arabic in both binary and multi-class tasks. 4) We also disentangle the heterogeneous effects of machine translation on LLM performance in different languages. Overall, results reveal the comparative advantage of native-language domain-specific LLMs specialized on political conflict to understand the dynamics of violence and cooperation worldwide using native text. Our multi-lingual ConfliBERT LLMs provide critical cyber-infrastructure enabling scholars and government agencies use their local languages and information to foster safer, more stable political environments.

关键词： Measurement Translation Databases Annotations Computational modeling Large language models Government Multilingual Machine translation Security

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A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

arXiv

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

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Preliminary Technoeconomical Evaluation of Cold Ironing

Preliminary Technoeconomical Evaluation of Cold Ironing

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Eleni P. Lamprinidi Stamatios Orfanos-Pepainas George J. Tsekouras Fotios D. Kanellos John M. Prousalidis Andreas Vlachos School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Department of Electrical & Electronics Engineering University of West Attica Aigaleo-Athens Greece School of Electrical & Computer Engineering Technical University of Crete Akrotiri Chania Greece School of Naval Architecture & Marine Engineering National Technical University of Athens Athens Greece Department of Long Term Energy Planning Renewable Energy Sources & Environmental Policy Regulatory Authority for Energy Athens Greece

In the context of reducing air pollution in ports, which are often located in the urban environment of cities, such as the port of Piraeus or Thessaloniki in Greece, the European Directive 2014/94/EU requires the application of Cold Ironing in the shipping sector until the end of 2025. That is, the berthed ships need to be supplied by the electricity system of the port, turning off the operation of their thermal electrical power units. A key question that arises is whether the application of cold ironing is economically viable for various types of ships and under what conditions, without subsidy. In this article, a method for estimating the economic viability of connecting ships to the port network by simply ignoring the network constraints (assuming the availability of an infinite bus) is presented. The proposed method is applied to cruise ships, containerships, and bulk carriers, indicative for the port of Thessaloniki, incorporating standard Medium Voltage (MV) tariffs of the Greek electricity system, determining the allowable corrected annual construction & maintenance cost of a Cold Ironing (CI) investment.

关键词： Renewable energy sources Costs Oils Urban areas Tariffs Europe Seaports

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Anticipating for Smart Healthcare System by Deep Ensemble Model Using Multi-Layer Perceptron in Early Human Facial Emotion Recognition

SSRN

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SSRN 2024年

作者： Riaz, Waqar Gong, Tao Ji, Jiancheng Zaman, Khalid Ullah, Inam Alwabli, Abdullah Almutairi, Sulaiman Sulmi Institute of Intelligent Manufacturing Shenzhen Polytechnic University 4089 Shahe West Road Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Department of Electrical Engineering College of Engineering and Computing in Al-Qunfudhah Umm al-Qura University Mecca Saudi Arabia Department of Health Informatics College of Public Health and Health Informatics Qassim University Qassim51452 Saudi Arabia

In the advancement of Artificial Intelligence, this study anticipates towards smart healthcare systems by pioneering anticipation in humans (being a patient) emotions classification. Followed by the investigation delves into the challenge of accurately classifying human emotions, a critical factor in understanding patients' recent mood and situation (if critical). Integrating Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multi-Layer Perceptron (MLP) for comprehensive facial emotion analysis. Initiated with Faster Region-Based Convolutional Neural Network (R-CNN) for face detection, the methodology extract features from three CNN models and employ advanced feature fusion techniques to combine them. These amalgamated features are then used to train an ensemble classification model. The process commences with the deployment of a Faster Region-Based Convolutional Neural Network (Faster R-CNN) for the swift and accurate identification of human’s face in real-time and recorded video feeds, including advanced feature extraction across three CNN models and innovative fusion techniques by strengthened the InceptionV3 for superior accuracy, replacing improved Faster R-CNN feature learning module. This replacement aims to enhance the accuracy and efficiency of face detection in our proposed framework. Validation on EMOTIC, CK+, FER-2013, and AffectNet confirms notable accuracy results of 98.01%, 99.53%, 99.27% and 96.81%, respectively with accuracy rates highlighting its potential to revolutionize medical environment and measures in smart healthcare systems. It is important to note that these datasets were carefully acquired in a simulated environment. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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