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Corrosion engineering boosting bulk Fe_(50)Mn_(30)Co_(10)Cr_(10)high-entropy alloy as high-efficient alkaline oxygen evolution reaction electrocatalyst

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Journal of Materials science & technology 2022年第14期109卷 267-275页

作者： Pengfei Zhou Dong Liu Yuyun Chen Mingpeng Chen Yunxiao Liu Shi Chen Chi Tat Kwok Yuxin Tang Shuangpeng Wang Hui Pan Institute of Applied Physics and Materials Engineering University of Macao999078Macao Department of Physics and Chemistry Faculty of Science and TechnologyUniversity of Macao999078Macao Department of Electromechanical Engineering Faculty of Science and TechnologyUniversity of Macao999078Macao College of Chemical Engineering Fuzhou UniversityFuzhou 350116China

Oxygen evolution reaction(OER)is a critical process in electrocatalytic water ***,the development of low-cost,highly efficient OER electrocatalysts by a simple method that can be used for industrial application on a large scale is still a huge ***,high entropy alloy(HEA)has acquired extensive attention,which may provide answers to the current ***,we report bulk Fe_(50)Mn_(30)Co_(10)Cr_(10),which is prepared by 3D printing on a large scale,as electrocatalyst for OER with high catalytic ***,an easy approach,corrosion engineering,is adopted for the first time to build an active layer of honeycomb nanostructures on its surface,leading to ultrahigh OER performance with an overpotential of 247 mV to achieve a current density of 10 mA cm^(-2),a low Tafel slope of 63 mV dec^(-1),and excellent stability up to 60 h at 100 mA cm^(-2)in 1 M *** excellent catalytic activity mainly originates from:(1)the binder-free self-supported honeycomb nanostructures and multi-component hydroxides,which improve intrinsic catalytic activity,provide rich active sites,and reduce interfacial resistance;and(2)the diverse valence states for multiple active sites to enhance the OER *** findings show that corrosion engineering is a novel strategy to improve the bulk HEA catalytic *** expect that this work would open up a new avenue to fabricate large-scale HEA electrocatalysts by 3D printing and corrosion engineering for industrial applications.

关键词： Electrocatalysis High entropy alloy Corrosion engineering Self-supporting Oxygen evolution reaction

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Findings of the Third Shared Task on Multilingual Coreference Resolution 7

Findings of the Third Shared Task on Multilingual Coreferenc...

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7th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2024

作者： Novák, Michal Dohnalová, Barbora Konopík, Miloslav Nedoluzhko, Anna Popel, Martin Pražák, Ondřej Sido, Jakub Straka, Milan Žabokrtský, Zdeněk Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798891761711

The paper presents an overview of the third edition of the shared task on multilingual coreference resolution, held as part of the CRAC 2024 workshop. Similarly to the previous two editions, the participants were challenged to develop systems capable of identifying mentions and clustering them based on identity coreference. This year’s edition took another step towards real-world application by not providing participants with gold slots for zero anaphora, increasing the task’s complexity and realism. In addition, the shared task was expanded to include a more diverse set of languages, with a particular focus on historical languages. The training and evaluation data were drawn from version 1.2 of the multilingual collection of harmonized coreference resources CorefUD, encompassing 21 datasets across 15 languages. 6 systems competed in this shared task. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Recommender System to Identify Drug-Target Associations Using Matrix Factorization 9

Recommender System to Identify Drug-Target Associations Usin...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Phuangmalai, Sornsawan Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand Center Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9798350318241

Recommender systems, widely used in information filtering systems, demonstrate efficacy in suggesting novel items to users. In this study, we propose the application of a recommender system to predict new drug-target associations. Here, drugs are represented as users, while protein targets are treated as items within the recommender system. However, due to sparse data in a matrix of drugs and protein targets, matrix factorization (MF) techniques are employed to decompose the extensive matrix into smaller matrices. We identify non-negative matrix factorization (NMF) and singular value decomposition (SVD) as top-performing models for this task. Subsequently, we integrated them into the traditional recommender system, encompassing both drug-based collaborative filtering and target-based collaborative filtering, to identify new drug-target associations aligned with observed interactions between drugs and target proteins. Finally, we evaluated the performance of our developed recommender system with matrix factorization for drug-target associations and compared the results with those obtained from a recommender system without matrix factorization. © 2024 IEEE.

关键词： Collaborative filtering

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Electrochemical Studies of Date Palm Seed as a Green Corrosion Inhibitor for Carbon Steel in 0.5 M H2SO4 and CH3COOH Conditions 7th

Electrochemical Studies of Date Palm Seed as a Green Corrosi...

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7th International Corrosion Prevention Symposium for Research Scholars, CORSYM 2021

作者： Ahmad, M.F. Mohammed, N.J. Othman, N.K. Department of Applied Physics Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

ISBN: (纸本)9789811918506

This study investigated the function of date palm seed (Phoenix dactylifera) as a green corrosion inhibitor for carbon steel in 0.5 M H2SO4 and CH3COOH acidic solutions. The Electrochemical Impedance Spectroscopy (EIS) was used to investigate the inhibition efficiency (IE%). The EIS experimental results showed that the IE% for 2000 ppm in the H2SO4 medium has the highest percentage at 88% with the value still increasing whereas, in contrast with CH3COOH medium, the highest percentage of IE% was at 64% in 1000 ppm inhibitor concentration for EIS. In conclusion, date palm seed extract is an ideal alternative to a green corrosion inhibitor. Date palm seed extract performs differently in H2SO4 and CH3COOH due to the acid strength of both acids, and it shows that the date palm seed extract is potential to be used to protect carbon steel from corrosion attack. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Corrosion inhibitors

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A Comparative Study of Machine Learning Models for Human Activity Recognition Using Signal Feature Extraction 21st

A Comparative Study of Machine Learning Models for Human Act...

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21st International Conference on Computing and Information technology, IC2IT 2025

作者： Visutsak, Porawat Lording, Arzeezar Ngampromwong, Teerapong Maichum, Natdanai Wongsanit, Anusorn Suriyachay, Earn Sathitanon, Nontakorn Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031902949

This study explores the application of machine learning techniques to accurately classify human activities using sensor data. The research focuses on extracting meaningful features from accelerometer signals and training various machine learning models to recognize activities such as walking, sitting, lying, and standing. The process involves collecting accelerometer data from smartphones, preprocessing the data, and extracting relevant features. These features are then used to train four different machine learning templates: Naive Bayes classifier, K-Nearest Neighbors classifier (KNN), Decision Tree, and Discriminant Analysis classifier. The performance of each model is evaluated based on its accuracy in classifying human activities. The results show that the Naive Bayes model achieves an accuracy of 92.6%, KNN achieves 89.8%, Decision Tree achieves 95.9%, and Discriminant Analysis achieves 85.4%. The Decision Tree model achieved the highest accuracy (95.9%) in recognizing activities. The findings of this study demonstrate the effectiveness of machine learning in recognizing human activities using sensor data. The results have implications for various applications, including health monitoring, fitness tracking, and smart home automation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Accelerometer Classification Feature Extraction Human Activity Recognition Machine Learning

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Towards Explainable Renewable Energy Communities Operations Using Generative AI

Towards Explainable Renewable Energy Communities Operations ...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Nammouchi, Amal Chaabani, Cyrine Theocharis, Andreas Kassler, Andreas Center for Digital Technology and Innovation Munich Germany Karlstad University Engineering and Physics Department Karlstad Sweden Karlstad University Computer Science Department Karlstad Sweden Deggendorf Institute of Technology Institute of Applied Computer Science Deggendorf Germany

ISBN: (纸本)9789531842976

Renewable Energy Communities (RECs), characterized by localized energy generation and consumption, are a key enabler for enhancing renewable energy utilization, cost-efficient planning and clean energy transition. However, optimizing RECs operations is challenging due to the complex interplay of different stakeholders with conflicting requirements. The complexity of managing such systems often leads to a lack of transparent and reliable decision-making, creating barriers for actors within the community. This paper explores the integration of Generative AI into Renewable Energy Communities (RECs) to enhance the transparency, explainability and accessibility of energy management systems (EMSs) that depend on solving optimization problems. We propose a novel framework, Chat-SGP, which uses generative AI to synthesize optimization modeling code to provide actionable, explainable insights for managing the REC operations. Our approach allows us to interact with the EMS through natural language queries, enhancing the system's accessibility and user-friendliness. Our evaluation shows that using GPT-4 with in-context learning performs 96.72% accuracy on average in generating correct answers. © 2024 IEEE.

关键词： Clean energy

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Dependence of thermodynamic quantities at freeze-out on pseudorapidity and collision energy in p-p collisions at LHC energies

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Chinese physics C 2024年第10期48卷 235-246页

作者： Murad Badshah Yahia A.H.Obaidat Haifa I.Alrebdi M.Waqas Muhammad Ajaz Refka Ghodhbani Department of Physics Abdul Wali Khan University MardanMardan 23200Pakistan Department of Physics College of ScienceUniversity of BishaBisha 61922Saudi Arabia Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia School of Mathematics Physics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyan 442002China Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafhaSaudi Arabia

The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies(√s)of 0.9 TeV and 2.36 TeV,as measured by the CMS detector at the Large Hadron Collider(LHC),have been analyzed within various pseudorapidity classes utilizing the thermodynamically consistent Tsallis *** fitting procedure resulted in the key parameters,namely,effective temperature(T),non-extensivity parameter(q),and kinetic freezeout volume(V).Additionally,the mean transverse momentum()and initial temperature(T_(i))of the particle source are determined through the fit function and string percolation method,*** alternative method is employed to calculate the kinetic freezeout temperature(T_(0))and transverse flow velocity(β_(T))from ***,thermodynamic quantities at the freezeout,including energy density(ε),particle density(n),entropy density(s),pressure(P),and squared speed of sound(C_(s)^(2)),are computed using the extracted T and *** is also observed that,with a decrease in pseudorapidity,all thermodynamic quantities except V and q *** trend is attributed to greater energy transfer along the mid pseudorapidity.q increases towards higher values of pseudorapidity,indicating that particles close to the beam axis are far from ***,V remains nearly independent of *** excitation function of these parameters(q)shows a direct(inverse)correlation with collision *** ε,n,s,and P show a strong dependence on collision energies at low *** verification of the thermodynamic inequality ε≥3P suggests the formation of a highly dense droplet-like Quark-Gluon Plasma(QGP).Additionally,the inequality T_(i)>T>T_(0)is explicitly confirmed,aligning with the evolution of the produced fireball.

关键词： charged hadrons tsallis statistics effective temperature non-extensivity parameter pseudorapidity kinetic freeze-out stage multiple freeze-out scenario QGP QCD matter squared speed of sound

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Vehicle Management System: Fuel, Mileage, Cost, and Maintenance Tracker

Vehicle Management System: Fuel, Mileage, Cost, and Maintena...

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2023 International Conference on Cyber Management and Engineering, CyMaEn 2023

作者： Kamolsin, Chiranun Rungkul, Woramet Limpalasuk, Chananon Pensiri, Fuangfar Ratanavilisagul, Chiabwoot Visutsak, Porawat Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Kasetsart University Faculty of Science at Sriracha Chonburi Thailand

ISBN: (纸本)9781665493291

According to the supply chain disruptions induced by the COVID-19 pandemic and the situation between NATO and Russia that led to the rising of fuel costs. Rising fuel costs are a massive problem for business and consumers. People must pay more money for their living because higher fuel costs affect costs of products, services, and transportation. This paper aims to develop the vehicle management system to track the fuel usage, mileage, cost, and maintenance of cars in an organization. The system is developed in a form of a web application for reporting a fuel usage and requesting permission to reserve a car. We used HTML, PHP, and Laravel Jetstream to develop this web application for employees and administrators. Employee can choose a car and submit a reservation form through a website. While administrator can manage cars information through the dashboard. User can use the mobile application to check in to get a car when a car is ready to use and check out a car when he/she wants to return a car to a company. © 2023 IEEE.

关键词： Supply chains

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