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Erosion-corrosion resistance of Mo-Ti- and Ni-Cr-Mo-alloyed medium-carbon martensitic steels: a critical analysis of synergistic effect of erosion and corrosion

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Journal of Iron and Steel Research International 2022年第8期29卷 1299-1311页

作者： De-fa Li Hang-yu Dong Cheng-yang Hu Kai-ming Wu Serhii Yershov Oleg Isayev School of Mechanical and Vehicle Engineering West Anhui UniversityLuan 237012AnhuiChina The State Key Laboratory of Refractories and Metallurgy Hubei Province Key Laboratory of Systems Science on Metallurgical ProcessingInternational Research Institute for Steel TechnologyWuhan University of Science and TechnologyWuhan 430081HubeiChina Laboratory for Excellence in Advanced Steel Research Materials Science and Engineering ProgramDepartment of MetallurgicalMaterials and Biomedical EngineeringUniversity of Texas at El PasoEl PasoTX 79968USA

Grain refinement renders Mo-Ti-alloyed medium-carbon martensitic steel to exhibit high hardness,high strength,and good toughness,but the erosion-corrosion resistance of steel in a corrosive slurry environment is not ***-Ti-alloyed medium-carbon martensitic steel is compared with Ni-Cr-Mo-alloyed medium-carbon martensitic steel,and the erosion-corrosion resistance of those two steels under impingement by NaCl solution with gravels has been *** components,pure-corrosion rate,pure-erosion rate,and synergistic effect of erosion and corrosion(SEEC)of erosion-corrosion rate,were *** pure-corrosion and pure-erosion rates of Mo-Ti-alloyed steel were higher than those of Ni-Cr-Mo-alloyed one;however,its erosion-corrosion rate was relatively lower because of a weak *** plastic deformation and work hardening due to gravel impingement were the essential reason for SEEC,which could be reduced by grain refinement,and consequently,Mo-Ti-alloyed steel with finer grains had better erosion-corrosion *** refinement could be an effective way to improve the erosion-corrosion resistance of martensitic steels.

关键词： Grain refinement Mo-Ti alloying Ni-Cr-Mo alloying Martensite Erosion-corrosion

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Portfolio Selection Using Mean Variance Theory Based on the 30-year Historical Returns of Selected Subsectors of the Philippine Industrial Sector

Portfolio Selection Using Mean Variance Theory Based on the ...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Yamanaka, A.D. Young, M.N. Chuahay, T.N. Pratseyo, Y. Persada, S. Nadlifatin, R. School of Graduate Studies Mapua University Manila Philippines School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Finance Chung Yuan University Taoyuan City Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan City Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan City Taiwan Entrepreneurship Business Creation Business School Binus University West Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350386097

Portfolio selection is an important part of investment management that aims to select the right combination of assets to achieve an optimal risk-return ratio. This study integrates the Mean-Variance Theory utilizing the 30-year historical returns of the Philippine Stock Exchange. Three subsectors of the Industrial Sector were used as the investment pool. The back-test results showed that the Mean-Variance portfolios of selected subsectors can outperform the industrial sector and not the market. Stakeholders must be cautious when investing in these three subsectors, considering the values obtained from the simulations. There are recommended portfolio selections wherein companies have a specific allocation that investors and financial managers can use whenever they decide to invest in these subsectors. Overall, this study offers a framework for investors to use as a guide in diversifying their stakes in the Philippine Stock Exchange. © 2024 IEEE.

关键词： Decentralized finance

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Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in engineering 1

Towards Scalable Automated Grading: Leveraging Large Languag...

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1st Workshop on Large Foundation Models for Educational Assessment

作者： Gao, Rujun Guo, Xiaosu Li, Xiaodi Narayanan, Arun Balajiee Lekshmi Thomas, Naveen Srinivasa, Arun R. J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University United States Computer Science Department University of Texas Dallas United States Intelligent Systems Program University of Pittsburgh United States Department of Artificial Intelligence and Informatics Mayo Clinic United States

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M University, each answered by approximately 225 students. Both the LLM and TAs followed the same instructor-provided rubric to ensure grading consistency. We evaluated performance using Spearman’s rank correlation coefficient and Root Mean Square Error (RMSE) to assess the alignment between rankings and the accuracy of scores assigned by GPT-4o and TAs under zero- and few-shot grading settings. In the zero-shot setting, GPT-4o demonstrated a strong correlation with TA grading, with Spearman’s rank correlation coefficient exceeding 0.6 in seven out of ten datasets and reaching a high of 0.9387. Our analysis reveals that GPT-4o performs well when grading criteria are straightforward but struggles with nuanced answers, particularly those involving synonyms not present in the rubric. The model also tends to grade more stringently in ambiguous cases compared to human TAs. Overall, ChatGPT shows promise as a tool for grading conceptual questions, offering scalability and consistency. © 2024 R. Gao1, X. Guo2, X. Li4, A.B.L. Narayanan3, N. Thomas1 & A.R. Srinivasa1.

关键词： Mean square error

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LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction 48

LE-DTA: Local Extrema Convolution for Drug Target Affinity P...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Langore, Tanoj Hsu, Te-Cheng Hsieh, Yi-Hsien Lin, Che Taipei10617 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9781728163277

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset. © 2023 IEEE.

关键词： Graph neural networks

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Performance Analysis of Building Integrated Photovoltaic (BIPV): MANOVA Analysis 1

Performance Analysis of Building Integrated Photovoltaic (BI...

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1st Middle East and North Africa Solar Conference, MENA-SC 2023

作者： Alzahmi, Wadhah Alyaarbi, Alyaa Alnaqbi, Tahani Alzaatreh, Ayman Dubai Electricity and Water Authority Dubai United Arab Emirates American University of Sharjah Sharjah Department of Industrial Engineering Engineering Systems Management Program Sharjah26666 United Arab Emirates College of Arts and Sciences American University of Sharjah Sharjah Department of Mathematics and Statistics Sharjah26666 United Arab Emirates

ISBN: (纸本)9781665491464

Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contributing to about 36% of the total global energy consumption in 2020. The global interest in reducing energy consumption allows different sectors to adopt renewable resources such as solar photovoltaics (PV). However, utilizing solar PV in buildings has limitations due to rooftop space constraints in newly designed buildings. Due to this limitation and continuous innovation in solar PV technologies, Building Integrated Photovoltaics (BIPV) have been introduced as a potential alternative for solar PV in buildings. As such, BIPV energy performance and panel temperatures are affected by many factors such as orientation setup and technology type. Therefore, this research aims to analyze the impact of technology type (indium gallium selenide (CIGS) and monocrystalline (MONO) & orientation on BIPV energy production & module temperature. The data used for this study is based on a project completed in Dubai for BIPV in building applications. The results indicate that BIPV type and system orientation during each session significantly affect BIPV energy production and BIPV module temperature. © 2023 IEEE.

关键词： Energy utilization

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Numerical Investigation of Hydrogen-Enriched Diesel Addition Effects in a Furnace Burner: Flame Temperatures and Exhaust Emissions

Numerical Investigation of Hydrogen-Enriched Diesel Addition...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Seifeldin Mohamed Abdelrahman Kamal Amr Guaily Mahmoud Shaaban Mechanical Engineering Program School of Engineering and Applied Sciences Nile University Giza Egypt Smart Engineering Systems Research Center (SESC) Nile University Giza Egypt

In this work, the effects of adding hydrogen to diesel non-premixed combustion with air in a cylindrical furnace burner are investigated. A numerical model is developed to study the effects of hydrogen addition on combustion flame temperature and exhaust emissions. Different hydrogen addition ratios of 5%, 10%, 15%, and 20% by volume in the burning mixture are considered. The turbulence-chemistry interaction is modelled through the eddy dissipation combustion model to predict the concentration of exhaust emissions and the temperature of the combustion flame. The air–fuel equivalence ratio is consistently set to 1 (stoichiometry) across all the considered cases. Results show that the hydrogen enrichment of diesel increases flame temperature and diminishes carbon emissions while enhancing the generation of NO x emissions. Hence, careful consideration should be given to NO x emission control in high temperature flame systems when hydrogen is used to enrich the fuel mixture and reduce carbon emissions.

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Aerial multi-spectral AI-based detection system for unexploded ordnance

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Defence Technology（防务技术） 2023年第9期27卷 24-37页

作者： Seungwan Cho Jungmok Ma Oleg A.Yakimenko Republic of Korea Army Republic of Korea/Defense Acquisition Program Administration47Gwanmun-roGwacheon-siGyeonggi-do13809Republic of Korea Department of Defense Science Korea National Defense University1040Republic of Korea Hwangsanbul-ro Yangchon-myeonNonsan-siChungcheongnam-do33021Republic of Korea Department of Systems Engineering Naval Postgraduate SchoolUSA 1 University Circle MontereyCA93943USA

Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this *** technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO *** new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on *** paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)***,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.

关键词： Unexploded ordnance(UXO) Multispectral imaging Small unmanned aerial systems(sUAS) Object detection Deep learning convolutional neural network(DLCNN)

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Metaverse for Public Welfare and the United Nations Sustainable Development Goals

Metaverse for Public Welfare and the United Nations Sustaina...

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Cloud Computing, Big Data and Internet of Things (3CBIT), International Conference on

作者： Amjad Umar Information Systems Engineering & Management Program Harrisburg University Harrisburg USA

ISBN: (纸本)9781665492263

Metaverse is extremely popular at present in the gaming industry but can it accelerate the advancement of public services as defined by the United Nations Sustainable Development Goals (UN SDGs)? In particular, can metaverse provide any value to developing nations and how can the challenges and risks introduced by the metaverse be addressed through govrnment regulations and policies? This paper presents a systematic analysis of metaverse and utilizes the United Nations SDGs as a metric to explore public welfare aspects of metaverse. A digital transformation Lab is presented for rapid deployment and experimentation with metaverse.

关键词： Measurement Industries Systematics Metaverse Digital transformation Urban areas Developing countries

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