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Comparative analysis of alkali-treated natural fibres for improved interfacial adhesion in composite materials

Interactions 2024年第1期245卷 206-206页

作者： Bhagat, Madhuri S. Jadhav, Varsha D. Kulkarni, Sumant Khanderao Satishkumar, P. Saminathan, Rajasekaran Department of Civil Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Natural fibers such as bamboo, kenaf, flax, ramie and hemp were chemically treated to improve their adherence with hydrophobic matrices. One common chemical process for altering the surface of these natural fibers is alkali treatment (AT). The experiment involves soaking bamboo fibers (BF) in solutions of 1, 10, and 30 wt % sodium hydroxide (NaOH) for one day, half an hour, and twenty minutes at room temperature. These alkali-treated and untreated bamboo fibers was compared with their physical and mechanical qualities. Using scanning electron microscopy (SEM) and the circular fiber assumption, the CSA of bamboo fibers both before and after AT were compared. Improving the AT procedures for BF at room temperature using varying NaOH concentrations is the focus of this effort. Additionally, the tensile characteristics of both untreated and variously treated AT Bamboo fibres are examined using a two-parameter Weibull distribution. Instead of just showing the average tensile strength, probabilistic tensile strength has shown to be a more effective method. This study proves without a reasonable doubt that adding 1 wt % NaOH to bamboo fiber makes natural fiber reinforced composites mechanically better. According to the SEM, the CSA of bamboo fibers decreases by about 13.62–20.24% and about 18.42–22.63%, respectively, after alkali treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Dual encoding for zero-example video retrieval

arXiv

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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Yuxin Liu Lizhong Zhang Tao Zhu Wenjian Luo Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Computer Science and Information Engineering University of South China Hengyang Hunan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm 4 4 https://***/DMiC-Lab-HFUT/SMDE-Transfer.

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Assessing Extraneous and Intrinsic Cognitive Load in Space Teleoperated Tasks: A Physiological Metrics Study with Cognitive Load Theory

SSRN

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SSRN 2024年

作者： Yan, Zhao Wang, Yahui Chen, Shanguang Yang, Xiaonan Hua, Yaoguang Fang, Haonan Wang, Chaoran School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China Astronaut Center of China National Key Laboratory of Human Factors Engineering China

This study aimed to explore the relationship between physiological performance indicators and both intrinsic and extrinsic cognitive loads during a space robot grasping task, which involves teleoperation requiring high levels of spatial and motor coordination, attention, and decision-making. The hypothesis posited that operational mode would influence extrinsic cognitive load, while task difficulty would impact intrinsic cognitive load. Additionally, a correlation was expected between physiological performance indicators and cognitive load. To test this, a 2x2 factorial experiment was conducted with independent variables being task difficulty (high vs. low) and operational mode (joystick vs. keys), and dependent variables including cognitive load, performance, and learning outcomes. Subjective scales were used to assess intrinsic and extrinsic cognitive load, and physiological data were collected to examine their correlation with cognitive load. The results indicate that operational mode and task difficulty significantly affect cognitive load in space exploration contexts. Specifically, the 'Joystick' mode was associated with higher extrinsic cognitive load, likely due to the challenges it presents in precise control during complex tasks. Performance metrics showed that attention allocation varied by operational mode, with the 'Joystick' mode requiring greater focus on the side view and end-effector, in line with cognitive load theory. Physiological metrics, particularly pupil diameter and EEG data, were crucial in understanding these effects;a decrease in pupil diameter with increased task difficulty reflected heightened intrinsic cognitive load. EEG data further revealed significant variations in brain activity across frontal, prefrontal, and occipital regions, with distinct changes in alpha, theta, and beta frequency bands, highlighting the substantial impact of cognitive load on neural processes. These findings underscore the importance of integrating cognitive

关键词： Space research

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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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Caching and Reproducibility: Making data Science experiments faster and FAIRer

arXiv

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arXiv 2022年

作者： Schubotz, Moritz Satpute, Ankit Greiner-Petter, André Aizawa, Akiko Gipp, Bela Chair for Data and Knowledge Engineering University of Wuppertal Wuppertal Germany FIZ Karlsruhe Leibniz Institute for Information Infrastructure Berlin Germany Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Japan Chair for Scientific Information Analytics University of Göttingen Göttingen Germany

Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computational expensive experiments. In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers. We suggest making caching an integral part of the research software development process, even before the first line of code is written. This article outlines caching recommendations for developing research software in data science projects. Our recommendations provide a perspective to circumvent common problems such as propriety dependence, speed, etc. At the same time, caching contributes to the reproducibility of experiments in the open science workflow. Concerning the four guiding principles, i.e., Findability, Accessibility, Interoperability, and Reusability (FAIR), we foresee that including the proposed recommendation in a research software development will make the data related to that software FAIRer for both machines and humans. We exhibit the usefulness of some of the proposed recommendations on our recently completed research software project in mathematical information retrieval. © 2022, CC BY.

关键词： Reusability

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Application of Clustering Methods to Health Insurance Fraud Detection

Application of Clustering Methods to Health Insurance Fraud ...

引用

International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Alan Sabatka Zhengxin Chen Deepak Khazanchi Yong Shi Peter Kiewit Institute of Information Science Technology & Engineering University of Nebraska Omaha Omaha NE USA Mutual of Omaha Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

Health insurance fraud detection is an important and challenging task. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the size of databases increases, the traditional approaches may miss a great portion of fraud for two main reasons. First, it is impossible to detect all health care fraud by manual inspection over large databases. Second, new types of health care fraud emerge constantly. SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. The goal of this paper is to understand and detect suspicious health care frauds from large databases using clustering technique. Specifically, this paper applies two clustering methods, SAS EM and CLUTO, to a large real-life health insurance dataset and compares the performances of these two methods

关键词： Clustering methods Insurance Medical services Inspection Synthetic aperture sonar Humans data mining Spatial databases Cities and towns Input variables

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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Introduction

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9792 LNCS卷 1-8页

作者： González-Beltrán, Alejandra Osborne, Francesco Peroni, Silvio Oxford e-Research Centre University of Oxford Oxford United Kingdom Data Science Group Knowledge Media Institute The Open University Milton Keynes United Kingdom Digital and Semantic Publishing Laboratory Department of Computer Science and Engineering University of Bologna Bologna Italy

We provide the motivation and overview of the SAVE-SD workshop series and introduce the manuscripts that were accepted as part of these proceedings of the SAVE-SD 2016 edition. © Springer International Publishing...

ISBN: (纸本)9783319536361

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