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arXiv 2022年

作者： Eyiokur, Fevziye Irem Kantarcı, Alperen Erakın, Mustafa Ekrem Damer, Naser Ofli, Ferda Imran, Muhammad Križaj, Janez Salah, Albert Ali Waibel, Alexander Štruc, Vitomir Ekenel, Hazım Kemal Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Engineering Istanbul Technical University Istanbul Turkey Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Qatar Computing Research Institute HBKU Doha Qatar Faculty of Electrical Engineering University of Ljubljana Tržaška cesta 25 Ljubljana1000 Slovenia Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Carnegie Mellon University Pittsburgh United States

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for comp

关键词： COVID-19

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Can OpenAI o1 outperform humans in higher-order cognitive thinking?

arXiv

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arXiv 2024年

作者： Latif, Ehsan Zhou, Yifan Guo, Shuchen Gao, Yizhu Shi, Lehong Nyaaba, Matthew Bewerdorff, Arne Yang, Xiantong Zhai, Xiaoming AI4STEM Education Center University of Georgia AthensGA United States National GENIUS Center University of Georgia AthensGA United States School of Computing University of Georgia AthensGA United States School of Teacher Education Nanjing Normal University Nanjing Jiangsu China Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States Department of Workforce Education and Instructional Technology University of Georgia AthensGA United States School of Social Sciences and Technology Technical University of Munich Bavaria Munich Germany Faculty of Psychology Beijing Normal University Beijing China

This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's performance to human participants from diverse educational levels. o1-preview achieved a mean score of 24.33 on the Ennis-Weir Critical Thinking Essay Test (EWCTET), surpassing undergraduate (13.8) and postgraduate (18.39) participants (z = 1.60 and 0.90, respectively). In systematic thinking, it scored 46.1 ± 4.12 on the Lake Urmia Vignette, significantly outperforming the human mean (20.08 ± 8.13, z = 3.20). For data literacy, o1-preview scored 8.60 ± 0.70 on Merk et al.'s "Use Data" dimension, compared to the human post-test mean of 4.17 ± 2.02 (z = 2.19). On creative thinking tasks, the model achieved originality scores of 2.98 ± 0.73, higher than the human mean of 1.74 (z = 0.71). In logical reasoning (LogiQA), it outperformed humans with 90% ± 10 accuracy versus 86% ± 6.5 (z = 0.62). For scientific reasoning, it achieved near-perfect performance (0.99 ± 0.12) on the TOSLS,, exceeding the highest human scores of 0.85 ± 0.13 (z = 1.78). While o1-preview excelled in structured tasks, it showed limitations in problem-solving and adaptive reasoning. These results demonstrate the potential of AI to complement education in structured assessments but highlight the need for ethical oversight and refinement for broader applications. © 2024, CC BY-NC-ND.

关键词： Systems thinking

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Comparative evaluation of training schemes for the locally recurrent probabilistic neural network 28

Comparative evaluation of training schemes for the locally r...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Dukov, Nikolay Tinkov Ganchev, Todor Dimitrov Department of Computer Science and Engineering Faculty of Computing and Automation Technical University of Varna 1 Studentska str. Varna9000 Bulgaria

ISBN: (纸本)9781728125749

In the present study we evaluate the performance of various training schemes for the locally recurrent probabilistic neural network and seek for advantageous tradeoffs between required training time and classification accuracy. Specifically, we consider training schemes which make use of a simple incremental procedure for adjusting sigma, as well as methods based on particle swarm optimization or differential evolution in different configurations. The experimental evaluation was carried out in common experimental protocol based on the Parkinson speech dataset. The experimental results show that with a proper training configuration a high accuracy can be achieved even with limited training data. © 2019 IEEE.

关键词： Particle swarm optimization (PSO)

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Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

arXiv

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arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Stimulated-emission based model of fast radio bursts

arXiv

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arXiv 2020年

作者： Dogan, Mustafa Ekşi, Kazım Yavuz Istanbul Technical University Faculty of Electrical-Electronics Engineering Department of Control and Automation Engineering Istanbul34469 Turkey Istanbul Technical University Faculty of Science and Letters Physics Engineering Department Istanbul34469 Turkey

Fast radio bursts (FRBs) are bright, short-duration radio transients with very high brightness temperatures implying highly coherent emission. We suggest that the FRBs are caused by the self-focusing of an electron beam interacting with an ambient plasma right beyond the light cylinder radius of a neutron star. The magnetic field at the light cylinder radius is relatively high which can accommodate both young Crab-like systems and old millisecond pulsars addressing the diverse environments of FRBs. At the first stage, the intense pulsed-beam passing through the background plasma causes instabilities such that the trapped particles in local Buneman-type cavitons saturate the local field. The beam is then radially self-focused due to the circular electric field developed by the two-stream instability which leads to Weibel instability in the transverse direction. Finally, the non-linear saturation of the Weibel instability results in the self-modulational formation of solitons due to plasmoid instability. The resonant solitary waves are the breather-type solitons hosting relativistic particles with self-excited oscillations. The analytical solutions obtained for non-linear dispersion and solitons suggest that, near the current sheets, the relativistic bunches are accelerated/amplified by klystron-like structures due to self-excited oscillations by the induced local electric field. Boosted coherent radio emission propagates through a narrow cone with strong focusing due to radial electric field and magnetic pinching. The non-linear evolution of solitons and the stimulated emission are associated with the Buneman instability and the possibility of the presence of nanosecond shots in FRBs are investigated. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic fields

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INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

arXiv

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

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Eminent destruction of organics and pathogens concomitant with power generation in a visible light-responsive photocatalytic fuel cell with NiFe2O4/ZnO pine tree-like photoanode and CuO/Cu2O nanorod cathode

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Chemosphere 2023年 344卷

作者： Lam, Sze-Mun Sin, Jin-Chung Warren Tong, Meng Wei Zeng, Honghu Li, Haixaing Huang, Liangliang Lin, Hua Lim, Jun-Wei College of Environmental Science and Engineering Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control Guilin University of Technology Guilin541004 China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of Technology Guilin541004 China Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Jalan Universiti PerakBandar Barat Kampar31900 Malaysia HICoE-Centre for Biofuel and Biochemical Research Institute of Self-Sustainable Building Department of Fundamental and Applied Sciences Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar32610 Malaysia Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Environmental conservation and energy scarcity have become two core challenges with the ever-increasing advancement of industry, particularly chemical energy rich wastewater comprising refractory organics and pathogenic microbes. Here, a multifunctional photocatalytic fuel cell (PFC) was devised using NiFe2O4 nanoparticle-loaded on pine tree-like ZnO/Zn (NiFe2O4/ZnO/Zn) photoanode and CuO/Cu2O nanorods-loaded on Cu (CuO/Cu2O/Cu) cathode for extracting electricity upon wastewater treatment. When fed with Rhodamine B (RhB) dyestuff, the NiFe2O4/ZnO/Zn-PFC provided the maximum power density (Pmax) of 0.539 mW cm−2 upon visible light irradiation with an average RhB degradation of 85.2%, which were 2.8 and 2.7 times higher than ZnO/Zn, respectively. The remarkable enhanced NiFe2O4/ZnO/Zn-PFC performance was owing to the synergistic effect of pine tree-like structure and Z-scheme heterostructure. The pine tree-like with high surface area was not only for effective harnessing photon energies but also provided more directional routes for rapid segregation and transport of carriers and higher interface contacting areas with electrolyte. Through a series of systematic characterizations, the Z-scheme heterostructure mechanism of the system and organics degradation pathway were also speculated. Additionally, the performance of the NiFe2O4/ZnO/Zn-PFC in industry printing wastewater showed Pmax of 0.600 mW cm−2, which was considerably impressive as real wastewater was challenging to accomplish. The phytotoxicity outcome also manifested that the comprehensive toxicity of RhB was eradicated after PFC treatment. Lastly, the excellent recyclability and the pronounced bactericidal effect towards Escherichia coli and Staphylococcus aureus were other attributions which enabled the NiFe2O4/ZnO/Zn-PFC for possible practical application. © 2023 Elsevier Ltd

关键词： Zinc oxide

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Effective removal of chromium by adsorption using Delonix regia bark derived activated carbon from aqueous solution: a sustainable approach

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Environmental Geochemistry and Health 2024年第9期46卷 308-308页

作者： Ganesan, Janet Joshiba Chien, Chia-Shang Chang Kumar, P. Senthil Sundaram, Hemavathi Thangappan, Hariharan Achuthan, Aravindan Rajamanickam, Sivarethinamohan Rangasamy, Gayathri Railway Technical Centre National Kaohsiung University of Science and Technology No 1 University Road Yanchao District Kaohsiung City82445 Taiwan Department of Construction Engineering National Kaohsiung University of Science and Technology No 1 University Road Yanchao District Kaohsiung City82445 Taiwan Centre for Pollution Control and Environmental Engineering School of Engineering and Technology Pondicherry University Puducherry Kalapet605014 India Department of Civil Engineering K. Ramakrishnan College of Technology Tamilnadu Trichy621112 India Department of Chemical Engineering Mohamed Sathak Engineering College Keelakarai Tamilnadu Ramanathapuram623806 India Department of Civil Engineering Koneru Lakshmiah Education Foundation Greenfeild Andhra Pradesh Vaddeswaram Guntur522302 India Bengaluru Karnataka560100 India Department of Civil Engineering Faculty of Engineering Karpagam Academy of Higher Education Pollachi Main Road Eachanari Post Tamil Nadu Coimbatore641021 India University Centre for Research and Development and Department of Civil Engineering Chandigarh University Gharuan Punjab Mohali140413 India

This study introduces a new biosorbent derived from Delonix regia bark-activated carbon to efficiently remove Chromium Cr(VI) metal ions from aqueous systems. The biosorbent was synthesized from the bark powder of the plant species and chemically activated with phosphoric acid. The biosorbent was characterized using FTIR, SEM, and BET to determine its functional properties and structural morphology. The batch adsorption experiments examined the optimal conditions for Cr(VI) metal ion adsorption, identifying that the highest removal efficiency occurred at pH levels of 2. The ideal adsorbent dosage was determined to be 2.5 g/L, with equilibrium achieved at a contact time of 60 min at the optimal temperature of about 303 K for a Cr(VI) metal ion concentration of 20 mg/L. Various isotherm models were applied to the adsorption equilibrium values, revealing that the adsorbent had a maximum removal capacity of approximately 224.8 mg/g for Cr(VI) metal ions. The adsorption process of Cr(VI) on the DAC biosorbent was best described by the Freundlich isotherm, indicating multilayer adsorption. The kinetic data fit well with the pseudo-second-order model. Thermodynamic parameters suggested that the adsorption process was spontaneous, exothermic, and feasible across different temperatures. Furthermore, the desorption studies showed that the DAC biosorbent can easily be rejuvenated and utilized several cycles with high adsorption capacity. These findings indicate that the developed adsorbent is environmentally friendly and effective for removing Cr(VI) from water systems. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Activated carbon

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Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms

Detection of Polycystic Ovary Syndrome (PCOS) Using Machine ...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Dana Hdaib Noor Almajali Hiam Alquran Wan Azani Mustafa Waleed Al-Azzawi Ahmed Alkhayyat Department of Biomedical Engineering Jordan University Of Science And Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

One of the most common diseases in women of reproductive age is Polycystic Ovary Syndrome (PCOS). PCOS diagnosis can be tricky, because not everyone with PCOS has polycystic ovaries (PCO), nor does everyone with ovarian cysts have PCOS, hence the pelvic ultrasound as a stand-alone diagnosis is not sufficient. The full diagnostic plan is mainly a combination of a pelvic ultrasound besides blood tests of specific parameters that indicate the presence of PCOS. Since PCOS is a hard-to-diagnose widespread hormonal disorder, blood tests, symptoms, and other parameters with the help of a computer can form a new and easy method to diagnose it. Therefore, we had successfully built a high performing diagnostic model using MATLAB. The data was obtained from the website Kaggle, and the dataset is called Polycystic Ovary Syndrome. In this paper various machine algorithms were employed by utilizing seven classifiers. Results demonstrated that Linear Discriminant classifier exhibits the best performance in terms of accuracy, while in terms of sensitivity, the KNN classifier had the best result. Also, a comparison with four other research papers that exploited the same PCOS dataset was done in terms of implementation platforms, evaluation methods, classifiers, classes, accuracy, and precision of each classifier. Our research excelled among all in terms of accuracy and varied in precedence with precision. MATLAB had shown substantial results and a great model fitting embedded approaches, scoring a high accuracy and precision outcome compared to other studies. Other improvements on the overall PCOS prediction can involve employing preprocessed ultrasound images with the features presented in the dataset.

关键词： Solid modeling Ultrasonic imaging Sensitivity Fitting Predictive models Feature extraction Prediction algorithms

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Enhancing electrolysis performance: Nanoparticles and micro-arc oxidation in green hydrogen generation

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Mohammad Hamdan Randa Abulail Osama Ayadi Khadeejeh Afaneh Ubiedullah Al-Qawabeha Ahmad Sakhrieh Department of Renewable Energy Engineering Faculty of Engineering and Technology Applied Science Private University Amman Jordan Department of Mechanical Engineering The University of Jordan Amman Jordan Faculty of Engineering Tafila Technical University Tafila Jordan Department of Mechanical Engineering School of Engineering and Computing American University of Ras Al Khaimah P.O. Box 10021 Ras Al Khaimah UAE

Green hydrogen is vital for the global energy transition, offering a sustainable and emissions-free alternative to fossil fuels. This research aims to enhance the performance of alkaline electrolysis systems and reduce their cost by exploring two key strategies: improving aluminum electrodes through surface treatment and integrating nanomaterials into the process. To address material degradation and efficiency challenges, aluminum electrodes were treated using micro-arc oxidation (MAO), which created a corrosion-resistant ceramic-like surface layer. This treatment increased electrode durability and enhanced interaction with the electrolyte, significantly boosting hydrogen production. Additionally, incorporating nanomaterials such as copper oxide and zinc oxide nanoparticles optimizes electrochemical activity, reduces energy requirements, and improves system performance. Experimental results confirmed the efficiency of these enhancements. MAO-treated electrodes improved hydrogen output by up to 30 % at lower potassium hydroxide (KOH) concentrations and maintained a 5 % boost even at higher concentrations. Including nanoparticles provided remarkable results: copper oxide increased hydrogen production by 107 %, while zinc oxide achieved an astounding 745 % improvement. These findings underscore the transformative potential of these techniques in advancing clean hydrogen production technologies.

关键词： Green hydrogen production Water electrolysis Nanoparticles Micro arc oxidation Surface treatment Corrosion resistance Aluminum electrodes

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