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Effect of Plastic Deformation on Microstructure and Properties of Cu-(1 wt%-6 wt%) Ag Alloy

Journal of Wuhan University of Technology(Materials Science) 2024年第3期39卷 747-753页

作者：茹亚东 ZHANG Zhongyuan 高召顺 ZHANG Ling ZUO Tingting XUE Jiangli TANG Zhixiang DA Bo LIU Yongsheng XIAO Liye Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190China Department of Physics Shanghai University of Electric PowerShanghai 201306China University of Chinese Academy of Sciences Beijing 100190China International Joint Labora-tory for Light Alloys(MOE) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukuba 305-0047Japan

In the present study,the Cu-(1 wt%-6 wt%)Ag alloys were prepared by melting,forging and wire *** effects of plastic deformation on microstructure evolution and properties of the alloys were *** results show that non-equilibrium eutectic colonies exist in the Cu-(3 wt%-6 wt%)Ag alloy and no eutectic colonies in the 1 wt%-2 wt%Ag containing *** eutectic colonies are aligned along the drawing direction and refined with the increase of draw *** to the refinement of eutectic colonies,the Cu-Ag alloy exhibits higher strength with the increase of draw *** Cu-6Ag alloy exhibits excellent comprehensive properties with a strength of 930 MPa and a conductivity of 82%IACS when the draw ratio reaches 5.7.

关键词： Cu-Ag alloy high strength and high conductivity microstructure eutectic structure strengthening mechanism

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Effect of Delignification Method on the type of Cellulose Water Hyacinth ( Eichornia crassipes) as a Material for Sustainable Sodium-Ion Battery Technology 3

Effect of Delignification Method on the type of Cellulose Wa...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Anugerah Putra Pradana, Revaldo Rafi Setopratama, Andyan Zulaiha Ahmad, Chanifa Prasetyo, Andry Nur Abti, Salsabilla Darminto Department of Physics Faculty of Science and Data Analytics Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Department of Chemistry Faculty of Science and Data Analytics Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Department of Material Engineering and Metallurgy Faculty of Industrial Technology and System Engineering Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Biopolymers developed for solid electrolyte materials of sodium-ion batteries are of great interest these days. The main precursor in the form of cellulose biopolymers has been successfully isolated from water hyacinths (Eichornia crassipes). The first stage is maceration using 2% NaOH to produce cellulose-Iα and Ethanol 60% to produce cellulose-Iβ by hydrothermal reaction process at 150 °C and continued at the bleaching stage with H2O2 solution at 50 °C until it changes color. Then the sample is washed to a neutral pH and dried in an oven at 60 °C. Cellulose-Iα yields were obtained with a yield of 33.98% and cellulose-Iβ of 39.11%. The cellulose-I that has been obtained is modified to obtain cellulose-II type by mercerization method, where cellulose-I type is reacted with 20% NaOH for 5 hours. The mixture is then washed to neutral and dried. Cellulose-II yield was obtained with a yield of 69.21%. Samples were characterized by XRD, FTIR, and SEM. The cellulose content of hyacinths before delignification was 36.69%. The cellulose content of hyacinths after delignification for cellulose-Iα and cellulose-Iβ types was 64.26% and 48.58% respectively. FTIR analysis proved the presence of hydroxyl and carboxyl functional groups in hyacinth cellulose. XRD analysis showed that all three samples were identified with cellulose-Iα, cellulose-Iβ, and cellulose-II amorphous structures with a crystallinity index of 28.62% and the largest crystallite size based on the hkl field (121) of cellulose-Iβ type samples was ∼10 nm. SEM analysis shows that cellulose-Iα, cellulose-Iβ has a slim fiber diameter size and straight, smooth surface and microfibrils around the fiber. While cellulose-II shows visualization of a diameter that looks larger and twisted, the surface is rough and there are no microfibrils around the fibers. © Published under licence by IOP Publishing Ltd.

关键词： Sodium hydroxide

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LSGraph: A Locality-centric High-performance Streaming Graph Engine 24

LSGraph: A Locality-centric High-performance Streaming Graph...

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19th European Conference on Computer systems, EuroSys 2024

作者： Qi, Hao Wu, Yiyang He, Ligang Zhang, Yu Luo, Kang Cai, Minzhi Jin, Hai Zhang, Zhan Zhao, Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom Zhejiang Lab China

ISBN: (纸本)9798400704376

Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high computation complexity for streaming graph analytics, or need high overhead to search and move graph data to ensure ordered neighbors during streaming graph update. This paper presents a novel locality-centric streaming graph engine, called LSGraph, to enable efficient both graph analytics and graph update. The main novelty of this engine is a differentiated hierarchical indexed streaming graph representation approach to achieve efficient data search and movement for graph update and also maintain data locality and ordered neighbors for efficient graph analytics simultaneously. Besides, a locality-aware streaming graph data update mechanism is also proposed to efficiently regulate the distance of data movement, minimizing the overhead of memory access during graph update. We have implemented LSGraph and conducted a systematic evaluation on both real-world and synthetic datasets. Compared with three cutting-edge streaming graph engines, i.e., Terrace, Aspen, and PaC-tree, LSGraph achieves 2.98×-81.08×, 1.46×-12.56×, and 1.26×-10.31× speedups during graph update, while obtaining 1.02×-4.28×, 1.58×-3.55×, and 1.20×-2.72× speedups during graph analytics, respectively. © 2024 Owner/Author.

关键词： Engines

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Dashboard for Location Segmentation in New Student Admission Promotion 10

Dashboard for Location Segmentation in New Student Admission...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Hidayati, Sri Rahmawati, Dyah Putri Arismawati, Paramaditya Bahar Johan, Ahmad Wali Satria Suharto, Rendika Nurhartanto Hidayat, Rahmat Sigit Telkom University Direktorat Kampus Surabaya Department of Information System Indonesia Kampus Surabaya Telkom University Department of Informatics Direktorat Indonesia Telkom University Direktorat Kampus Surabaya Department of Industrial Engineering Indonesia Telkom University Kampus Surabaya Department of Data Science Direktorat Indonesia

ISBN: (纸本)9798331521295

The intense competition for new student admissions among universities demands a strategic approach to attract applicants effectively. Telkom University Surabaya has traditionally promoted itself by sending employees and student representatives to various schools. However, the admissions team has not yet segmented school locations to prioritize those with the highest potential. This study addresses this gap by clustering schools based on their potential contribution to new student admissions using K-Means Clustering with Principal Component Analysis (PCA). The dataset consists of new student admissions data from 2020 to 2024, specifically focusing on students who purchased registration PIN. Variables include school category, district, province, registration path, PIN price, school type, domicile city, study program, school island, and faculty. The K-Means clustering analysis, after preprocessing, identified the optimal number of clusters as 3, confirmed through the Elbow Method, Silhouette Score, and Davies-Bouldin Score. The three clusters illustrate different levels of student interest, allowing the admissions team to target schools across various cities, primarily focusing on East Java, where the highest applicant numbers are concentrated in areas like Surabaya, Sidoarjo, and Malang. Visualization through three-dimensional PCA components provided clear distinctions among clusters, with one cluster showing particularly high variance in student interest, mainly for popular programs such as Informatics and Digital Business. This clustering outcome serves as a valuable tool for the university's admissions team to develop a targeted and efficient promotional strategy. By identifying clusters with high potential, Telkom University Surabaya can allocate resources toward schools more likely to generate applicants. Additionally, strategic geographic areas are pinpointed for promotion, enabling the university to increase new student numbers in key regions for future admissi

关键词： Students

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Empirical Assessment and Detection of Suicide Related Posts in Twitter using Artificial Intelligence enabled Classification Logic 2

Empirical Assessment and Detection of Suicide Related Posts ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Ravishankar, T. Nadana Kumar, Ata. Kishore Venkatesh, J. Prabhu, M.Ramkumar Bhargavi, V. Sharmila Muthamilselvan, S. Srm Institute of Science and Technology Department of Data Science and Business System Kattankulathur India School of Engineering Department of ECE Tirupati India Chennai Institute of Technology Department of Cse Chennai India Peri Institute of Technology Department of ECE Chennai India R.M.K. Engineering College Department of Cse Chennai India Sri Sairam Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350315905

The identification of suicidal thoughts in online social networks is an expanding field of study fraught with major challenges. Recent studies have shown that the readily available data, dispersed over many online life phases, contains useful clues for accurately identifying persons with suicidal intentions. The primary challenge in preventing suicide is learning to recognize and respond appropriately to the sometimes-confusing risk factors and warning indications that may precipitate an attempt. Indicators useful for diagnosing people with suicide thoughts can be found in publicly available material shared over social media platforms, according to recent studies. Understanding and recognizing the myriad risk factors and warning symptoms that may precede a suicide attempt is the primary difficulty in this area of public health. In this research, we developed a benchmark for multi-class categorization using machine learning models. We used a majority classifier, a frequency-based technique, and two deep learning models as our models. Both deep learning models outperformed the majority and the word frequency classifier, with results that were very comparable. These classification results are on par with the state-of-the-art on similar problems and, in most cases, with human results. © 2023 IEEE.

关键词： Support vector machines

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： data accuracy

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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