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Structural insights, synthesis, and electrocatalysis of high entropy nanoparticles for fuel cell, metal-air battery, and water-splitting applications

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Progress in Natural science:Materials International 2024年第3期34卷 512-523页

作者： Xin Tong Hao Ye Yunrou Wu Xinxing Zhan Manqi Gu Shixia Luo Jiangning Gong Juan Tiana Yadian Xie School of Chemistry and Material Science Guizhou Normal University Center for the Physics of Low-Dimensional Materials School of Physics and Electronics School of Future Technology Henan University Key Laboratory of Low-Dimensional Materials and Big Data Guizhou Minzu University

High-entropy alloy nanoparticles(HEA-NPs) have recently sparked great interest in materials science. Their solidsolution states, derived from distinct HEA configurations, make them promising candidates for catalysts with exceptional activity, stability, and tunable performance. However, a comprehensive understanding of the underlying mechanisms governing their electrocatalytic behavior is still lacking, hindering the rational design of HEA electrocatalysts. This review summarizes the fundamental knowledge of HEA-NPs, including the structureactivity correlations of HEA-NPs, diverse synthesis strategies, and applications in electrochemical catalysis. The design strategies for guiding improvements in tunable performance were highlighted. The article concludes with insights, perspectives, and future directions, encapsulating the state-of-the-art knowledge and paving the way for further exploration in this dynamic field.

关键词： High-entropy alloy nanoparticles Structure-activity correlations Synthesis strategies Electrochemical catalysis

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Expressive Access Control Encryption With Bilateral Policy Privacy for Consumer Electronics in the Metaverse

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 90-101页

作者： Peng, Zongfeng Peng, Changgen Xu, Dequan Ding, Hongfa Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University of Finance and Economics College of Information Guiyang550025 China

The metaverse offers an unprecedented immersive experience and a fresh connection paradigm for consumers, which brings security threats related to unauthorized data access, potential misuse, and increasing attacks in the bilateral information flow. Therefore, it is necessary to provide an efficient and secure access control scheme. However, current efforts struggle to address these more complicated security issues. To do this, in this paper, we present an expressive access control encryption (EACE) scheme for bilateral control of information flow in metaverse-enabled consumer electronics. Our EACE supports fine-grained and bilateral control of senders and receivers by carefully taking advantage of access control encryption, dual-policy attribute-based encryption, and matchmaking encryption. Furthermore, we propose a novel sanitization algorithm with sender anonymity and bilateral policy privacy against the malicious sanitizer. We formalize the corresponding security definitions and give rigorous proofs. Performance analysis demonstrates that our EACE can simultaneously achieve bilateral control with policy privacy, sender anonymity against sanitizer, and lightweight decryption. The experimental results show that our proposal costs less computational and storage overhead in the symmetric bilinear group. The impressive performance indicates that our EACE is an excellent choice to balance security and efficiency when consumer electronics meet the metaverse. © 1975-2011 IEEE.

关键词： Differential privacy

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Keypoint-based Progressive Chain-of-Thought Distillation for LLMs 41

Keypoint-based Progressive Chain-of-Thought Distillation for...

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41st International Conference on Machine Learning, ICML 2024

作者： Feng, Kaituo Li, Changsheng Zhang, Xiaolu Zhou, Jun Yuan, Ye Wang, Guoren Beijing Institute of Technology China Ant Group China Hebei Province Key Laboratory of Big Data Science and Intelligent Technology China

Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically require the student to mimic the step-by-step rationale produced by LLMs, often facing the following challenges: (i) Tokens within a rationale vary in significance, and treating them equally may fail to accurately mimic keypoint tokens, leading to reasoning errors. (ii) They usually distill knowledge by consistently predicting all the steps in a rationale, which falls short in distinguishing the learning order of step generation. This diverges from the human cognitive progression of starting with easy tasks and advancing to harder ones, resulting in sub-optimal outcomes. To this end, we propose a unified framework, called KPOD, to address these issues. Specifically, we propose a token weighting module utilizing mask learning to encourage accurate mimicry of keypoint tokens by the student during distillation. Besides, we develop an in-rationale progressive distillation strategy, starting with training the student to generate the final reasoning steps and gradually extending to cover the entire rationale. To accomplish this, a weighted token generation loss is proposed to assess step reasoning difficulty, and a value function is devised to schedule the progressive distillation by considering both step difficulty and question diversity. Extensive experiments on four reasoning benchmarks illustrate our KPOD outperforms previous methods by a large margin. Copyright 2024 by the author(s)

关键词： Students

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RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pedestrian Detection 5

RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pe...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wang, Juanmin Huang, Kun Pi, Jianyong College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guizhou Guiyang China

ISBN: (纸本)9798350385557

Dense pedestrian detection is a key research direction in the field of computer vision, which plays a significant role in large-scale crowded public spaces. It provides strong technical support for security assurance, traffic management, and behavioral analysis in public areas. To address the issue of missed detection of small-sized occluded pedestrians in dense scenarios, this paper proposes an improved detection algorithm based on YOLOv8, named RUP2S-YOLO. For the backbone network, the RFCBAM module is used to replace the convolution module to enhance the capability of capturing subtle features and better extract the characteristics of small-scale pedestrians;for the C2f module, the UKMS module is designed as a substitution aiming to improve the model's capacity to process information of different scales;for the detection head, the P2S module is introduced for the accurate positioning of small-scale targets. Experiments on dense pedestrian datasets show that the improved RUP2S-YOLO outperforms the original algorithm in terms of performance, achieving an AP50 of 82.8%, and an AP50:95 of 55.8%, which represents an increase of 2.2% and 2.8% respectively compared to the original algorithm. Meanwhile, in comparison with other leading pedestrian detection models, the improved YOLOv8 algorithm presented in this article also demonstrates competitive advantages. © 2024 IEEE.

关键词： Object detection

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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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Wordle data Exploration and Prediction Based on BP-GA Model 4

Wordle Data Exploration and Prediction Based on BP-GA Model

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4th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Zhou, Luyao Ruan, Peiguang Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou China Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai China

ISBN: (纸本)9798350342710

Wordle, a fun puzzle game, the acquisition of Wordle by the New York Times led to a surge in the game's popularity. In this article, we aim to explore various data related to Wordle, including the number of daily reported results, words of varying difficulty, and the distribution of user attempts. We use the ARIMA model to predict the number of reported results in the world in the short term, and verify it with the LSTM model, and the results show that our interval prediction is correct. We forecast to reach 14,737 within the forecast range (13,667, 15,807) in the coming months. In addition, we built a BP-GA model to predict the distribution of future reported outcomes and found a number of word attributes, including seven numerical factors and two potential uncertainties that could affect reported outcomes. Based on the random forest model and grid search, the difficulty classification model of words is constructed, and it is found that word frequency and ASCII mean have great influence on the difficulty classification of words. The classification accuracy is 88.89%, which verifies the superiority of the model. © 2023 IEEE.

关键词： Forecasting

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Multi-Perspective data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang Yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

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Penalized M-Estimation Based on Standard Error Adjusted Adaptive Elastic-Net

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Journal of Systems science & Complexity 2023年第3期36卷 1265-1284页

作者： WU Xianjun WANG Mingqiu HU Wenting TIAN Guo-Liang LI Tao School of Statistics and Mathematics Zhongnan University of Economics and LawWuhan 430073China School of Statistics and Data Scicence Qufu Normal UniversityQufu 273165China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods.

关键词： Adaptive elastic net -estimation oracle property standard error

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Web Application Development using Flask - Faculty Directory Management 14

Web Application Development using Flask - Faculty Directory ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Tarunika, R. Rohit Ram, S.S. Sujithra, M. Sudha Devi, D. Department of Computing-Data Science Coimbatore Institute of Technology India Department of Computing-Data Science Coimbatore Institute of Technology India

The main goal of web development is to create, build and maintain websites. It is what allows the user to experience seamless performance when accessing a website. The web applications landscape has evolved tremendously over the years using various web development frameworks. A web development framework is a set of resources and tools for software developers to build and manage web applications, web services and websites. One such framework is Flask written in Python. The objective is to create a faculty directory using Flask with a goal to provide a centralized resource that can be used by students, staff, and administrators to easily find and connect with faculty members. By improving access to information about faculty members, the directory project aims to promote collaboration and facilitate the sharing of knowledge within the academic community. © Grenze Scientific Society, 2023.

关键词： Python

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A TRUST-REGION METHOD FOR NONSMOOTH NONCONVEX OPTIMIZATION

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Journal of Computational Mathematics 2023年第4期41卷 683-716页

作者： Ziang Chen Andre Milzarek Zaiwen Wen Department of Mathematics Duke UniversityUSA School of Data Science(SDS) The Chinese University of Hong KongShenzhenShenzhen Research Institute for Big Data(SRIBD)China Beijing International Center for Mathematical Research Peking UniversityBeijing 100000China

We propose a trust-region type method for a class of nonsmooth nonconvex optimization problems where the objective function is a summation of a(probably nonconvex)smooth function and a(probably nonsmooth)convex *** model function of our trust-region subproblem is always quadratic and the linear term of the model is generated using abstract descent ***,the trust-region subproblems can be easily constructed as well as efficiently solved by cheap and standard *** the accuracy of the model function at the solution of the subproblem is not sufficient,we add a safeguard on the stepsizes for improving the *** a class of functions that can be“truncated”,an additional truncation step is defined and a stepsize modification strategy is *** overall scheme converges globally and we establish fast local convergence under suitable *** particular,using a connection with a smooth Riemannian trust-region method,we prove local quadratic convergence for partly smooth functions under a strict complementary *** numerical results on a family of Ei-optimization problems are reported and demonstrate the eficiency of our approach.

关键词： Trust-region method Nonsmooth composite programs Quadratic model function Global and local convergence

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