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31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 University of Split, FESB.

关键词： Augmented reality

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Anomalous properties of spark plasma sintered boron nitride solids

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Materials Today 2025年 86卷 42-51页

作者： Biswas, Abhijit Serles, Peter Alvarez, Gustavo A. Schimpf, Jesse Hache, Michel Kong, Jonathan Demingos, Pedro Guerra Yuan, Bo Pieshkov, Tymofii S. Li, Chenxi Puthirath, Anand B. Gao, Bin Gray, Tia Zhang, Xiang Murukeshan, Jishnu Vajtai, Robert Dai, Pengcheng Singh, Chandra Veer Howe, Jane Zou, Yu Martin, Lane W. Clancy, James Patrick Tian, Zhiting Filleter, Tobin Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Department of Mechanical & Industrial Engineering University of Toronto 5 King's College Road TorontoM5S 3G8 Canada Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Department of Materials Science and Engineering University of California Berkeley94720 United States Department of Materials Science and Engineering University of Toronto 184 College St TorontoM5S 3E4 Canada Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley94720 United States

Hexagonal boron nitride (h-BN) is a brittle ceramic with a layered structure, however, recent experiments have suggested that inter-layer structural engineering could be key to new structural and functional properties. Here we report the scalable bulk synthesis of high-density crystalline h-BN solids, by using high-temperature spark plasma sintering (SPS) of h-BN powders, which show high values of mechanical strength, ductility, dielectric constant, thermal conductivity, and exceptional neutron radiation shielding capability. Through exhaustive characterizations we reveal that SPS induces non-basal plane crystallinity, twisting of layers, and facilitates inter-grain fusion with a high degree of in-plane alignment across macroscale dimensions, resulting in near-theoretical density and improved properties. Our findings highlight the importance of material design, via new approaches such as layer twisting and interlayer interconnections, to create novel ceramics with properties that could go beyond their intrinsic limits. © 2025

关键词： Ductility

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Cr-modified CuAlBe shape memory alloy: Role of microstructure, thermodynamic parameters, superelasticity, dissipated energy and corrosion resistance

Journal of Alloys and Metallurgical Systems

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Journal of Alloys and Metallurgical Systems 2024年 7卷

作者： Silva, David D.S. Candido, Gemierson V.M. Alves, Ricardo F. Dias, Elisabeth A.M.G.S. Oliveira, Danniel F. Federal University of São Carlos Graduate Program in Materials Science and Engineering SP São Carlos13565-905 Brazil Colorado School of Mines George S. Ansell Department of Metallurgical and Materials Engineering GoldenCO80401 United States Federal University of Paraíba Graduate Program in Mechanical Engineering PB João Pessoa58051-900 Brazil Federal University of Rio Grande do Norte Graduate Program in Materials Science and Engineering RN Natal59078-970 Brazil Federal University of Paraíba Graduate Program in Materials Science and Engineering PB João Pessoa58051-900 Brazil

This study reports the effects of Cr addition on the microstructure, superelasticity and corrosion resistance improvement of CuAlBe shape memory alloy. Analysis via structural, morphological, thermal, mechanical and electrochemical characterizations were performed. The analyses revealed austenite as the stable phase at room temperature for all samples. The increase in the Cr content caused a slight increase in the recovery rate and dissipated energy. Electrochemical results demonstrate that a small addition of Cr (0.1–0.3 wt%) increase the corrosion resistance due the decreases of the corrosion current density and increase of the corrosion potential. © 2024 The Authors

关键词： Corrosion resistance

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Advanced TiO2/Al2O3 Bilayer ALD Coatings for Improved Lithium-Rich Layered Oxide Electrodes

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ACS Applied Materials and Interfaces 2024年第10期16卷 13029-13040页

作者： Chen, Wei-Ming Hsieh, Hsin-Yu Wu, Dong-Ze Tang, Horng-Yi Chang-Liao, Kuei-Shu Chi, Po-Wei Wu, Phillip M. Wu, Maw-Kuen Institute of Physics Academia Sinica 128 Section 2 Academia Road Taipei11529 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Tsing Hua University 128 Section 2 Academia Road Taipei11529 Taiwan Department of Engineering and System Science National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu300044 Taiwan Graduate Institute of Energy and Sustainability Technology National Taiwan University of Science and Technology 43 Keelung Road Sec 4 Taipei10607 Taiwan Department of Applied Chemistry National Chi Nan University 1 University Road Puli Nantou545301 Taiwan College of Science National Chung Hsing University 145 Xingda Rd. South Dist. Taichung City402 Taiwan

Surface modification is a highly effective strategy for addressing issues in lithium-rich layered oxide (LLO) cathodes, including phase transformation, particle cracking, oxygen gas release, and transition-metal ion dissolution. Existing single-/double-layer coating strategies face drawbacks such as poor component contact and complexity. Herein, we present the results of a low-temperature atomic layer deposition (ALD) process for creating a TiO2/Al2O3 bilayer on composite cathodes made of AS200 (Li1.08Ni0.34Co0.08Mn0.5O2). Electrochemical analysis demonstrates that TiO2/Al2O3-coated LLO electrodes exhibit improved discharge capacities and enhanced capacity retention compared with uncoated samples. The TAA-5/AS200 bilayer-coated electrode, in particular, demonstrates exceptional capacity retention (∼90.4%) and a specific discharge capacity of 146 mAh g-1 after 100 cycles at 1C within the voltage range of 2.2 to 4.6 V. The coated electrodes also show reduced voltage decay, lower surface film resistance, and improved interfacial charge transfer resistances, contributing to enhanced stability. The ALD-deposited TiO2/Al2O3 bilayer coatings exhibit promising potential for advancing the electrochemical performance of lithium-rich layered oxide cathodes in lithium-ion batteries. © 2024 The Authors. Published by American Chemical Society.

关键词： Cathodes

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Assessment of Dar es Salaam's resilience to climate change disasters using the Climate Disaster Resilience Index (CDRI)

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Singapore Journal of Tropical Geography 2024年第3期45卷 475-497页

作者： Mkasimongwa, Simon William Fakour, Hoda Hassani, Hussein Juma Sultan, Basma Abdulla Lai, Hsin-Chih Master Degree Program in Safety and Health Science Chang Jung Christian University Tainan City Taiwan International Program for Sustainable Development International College of Practice and Education for the Environment Chang Jung Christian University Tainan Taiwan Graduate Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan Taiwan

Climate change is becoming an increasingly significant issue in Africa, and the need for climate resilience assessment has intensified. Dar es Salam is one of Africa's emerging megacities. With a population of over seven million, which continues to grow, there is an urgent need to understand the city's ability to deal with natural disasters. The Climate Disaster Resilience Index (CDRI) was used in this study to assess the city's ability to withstand and cope with climatic hazards. The Index was quantified using sets of dimensions (social, physical, economic, natural, and institutional), with various parameters indicating the city's abilities, strengths, and vulnerabilities to potential climate-related disasters. Despite being moderately resilient to climate change disasters, the results of our study indicate that the city's economic and institutional features obtained the lowest scores and the least resilience level. The study's findings provide a perspective on aspects of the city management sectors in terms of resilience and which should be given greater consideration in order to strengthen the city's current and future resilience. © 2024 department of Geography, National University of Singapore and John Wiley & Sons Australia, Ltd.

关键词： climate change flooding resilience weather trends

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multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer

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PeerJ Computer science 2023年 9卷

作者： Kim, Gihyeon Park, Young Mi Yoon, Hyun Jung Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Molecular Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Department of Radiology Veterans Health Service Medical Center Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer timeto-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multiscale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2DCNNmodels of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5×5 and 6×6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence. © 2023 Kim et al.

关键词： Neural network models

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Abby Rasya Angkasa Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta Indonesia Engineering Department Cardiff Metropolitan University - Cardiff School of Technologies Cardiff United Kingdom

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences.

关键词： Emotion recognition TV Statistical analysis Text categorization Speech recognition Linguistics Feature extraction Data augmentation Market research User experience

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Enhanced fast charging capabilities in natural graphite/iron cross-linked pectin electrodes for lithium-ion batteries

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Materials Advances 2024年第17期5卷 6820-6829页

作者： Chung, Chin-Yi Chen, Wei-Ming Chen, Yan-Ruei Chen, Liang-Yu Su, Yu-Hsuan Chi, Po-Wei Wu, Phillip M. Chang-Liao, Kuei-Shu Tang, Hong-Yi Wu, Maw-Kuen Institute of Physics Academia Sinica 128 Section 2 Academia Road Taipei11529 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Tsing Hua University 128 Section 2 Academia Road Taipei11529 Taiwan Department of Engineering and System Science National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu300044 Taiwan College of Science National Chung Hsing University 145 Xingda Road South District Taichung402 Taiwan Department of Applied Chemistry National Chi-Nan University 1 University Road Puli Nantou545301 Taiwan

Rapid charging capabilities are required for the broader adoption of lithium-ion batteries in emerging technologies such as electric vehicles. Although commercial graphite anodes provide a high energy density, their limitations in quick charging warrant the exploration of novel materials. This study extends prior research on the use of pectin, a natural polysaccharide, as an organic binder for graphite anodes. By introducing iron into pectin, we propose a unique ion-crosslinking mechanism that causes the graphite anode to exhibit pseudocapacitive Li storage. Our findings, corroborated by 3D Bode analysis and AC impedance spectra, revealed a positive correlation between the iron ion and the pseudocapacitive vertex in the graphite reaction. Compared with conventional binders, graphite electrodes utilizing iron-doped pectin demonstrated superior initial and stable capacities, reaching 450 mA h g−1 after 100 cycles at 1C. This study highlights the synergistic effect of pectin and iron ions, offering a new avenue for high-performance, rapid-charging, and environmentally sustainable lithium-ion batteries. © 2024 RSC.

关键词： Lithium-ion batteries

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN)

Development of Detection Lung Disease Stand Alone System Bas...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Edgar Caesar Liewarnata Yosica Mariana Sofyan Tan Suryadiputra Liawatimena Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.

关键词： Training Accuracy Tuberculosis Pulmonary diseases Lungs Stethoscope Recording Convolutional neural networks Mel frequency cepstral coefficient Testing

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IoT-based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor 8

IoT-based tire pressure monitoring system for air and temper...

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Pangestu, Agung Sodikin, Iing Yusro, Muhammad Sapundani, Rum Al Hakim, Rosyid Ridlo Wilyanti, Sinka Jakarta Global University Faculty of Engineering and Computer Science Electrical Engineering Department Depok Indonesia State University of Jakarta Faculty of Engineering Department of Electronic Engineering Jakarta Indonesia University of Muhammadiyah Purwokerto Electromedical Engineering Technology Faculty of Health Science Purwokerto Indonesia Jakarta Global University Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Depok Indonesia

ISBN: (纸本)9781665453899

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1-the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2-the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-Time via a smartphone using the IoT-based. © 2022 IEEE.

关键词： Thermometers

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