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Emerging and Changing Patterns in Prevalence of Anti-leptospiral Antibodies Against Different Serogroups in Livestock in Andaman-Islands Ecosystem

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Proceedings of the National Academy of sciences India Section B - Biological sciences 2024年第5期94卷 977-983页

作者： Kumar, K. Vinod Swathi, M. Bokade, Prajakta P. Bharath, V. SowjanyaKumari, S. Sunder, Jai Hemadri, D. Shome, B.R. Balamurugan, V. Indian Council of Agricultural Research -National Institute of Veterinary Epidemiology and Disease Informatics (ICAR-NIVEDI) Karnataka Yelahanka Bengaluru 560 064 India Animal Science Division ICAR- Central Island Agricultural Research Institute Andaman & Nicobar Islands Port Blair 744101 India National Post-Doctoral Fellowship Science and Engineering Research Board Department of Science and Technology Government of India New Delhi India

This study investigated the relative distribution of Leptospira serovars and changing patterns of anti-leptospiral antibodies in the endemic Andaman Islands of India. A total of 490 purposive random serum samples from cattle (n = 386) and goats (n = 104) were collected from December 2019 to January 2020 and were tested in a microscopic agglutination test (MAT) using a panel of 17 reference Leptospira serovars comprising 16 serogroups including two serovars of intermediate Leptospira species. The total overall seroprevalence of 17.6% (86/490) was observed, with 17.6% attributed to cattle and 17.3% to goats. The major reactive serogroup-specific anti-leptospiral antibodies observed were Icterohaemorrhagiae (32.6%), Hardjo (15.1%), Hebdomadis (11.6%), Grippotyphosa (10.5%), Pomona (9.3%), Tarassovi (9.3%), amongst others. This investigation provides evidence for the circulation and emergence of pathogenic Tarassovi and Djasiman serovars and intermediate Hurstbridge serovar in the livestock of South Andaman. This study’s findings would help further strengthen the one-health strategy and mitigate the leptospirosis burden in the Andaman Islands ecosystem of India. © The Author(s), under exclusive licence to The National Academy of sciences, India 2024.

关键词： Andaman Islands India Leptospirosis Livestock MAT Predominant Leptospira serovars

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HomoMGC: Homophily-Enhanced Adaptive Graph Refinement for Multi-View Graph Clustering 24

HomoMGC: Homophily-Enhanced Adaptive Graph Refinement for Mu...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Chen, Man-Sheng Cai, Xiao-Sha Wang, Chang-Dong Huang, Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Sun Yat-sen University Zhuhai China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

Due to the emergency of multi-view graph data, considerable attention is focused on the multi-view graph clustering. Although great efforts have been made in developing the multi-view graph clustering methods, most of them implicitly follow the homophily assumption, where the connected nodes with edges tend to be in the same category. As a matter of fact, such an ideal assumption is hard to be satisfied in the real-world graph data, and there are some heterogeneous edges connecting dissimilar nodes in graph. How to well consider the homophily and refine the noisy/heterogeneous edges in multi-view graph clustering still remains an under-explored challenge. Therefore, in this paper, we propose a Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering (HomoMGC) method, where an adaptive graph refinement strategy is seamlessly designed. Specifically, a feature-oriented graph is constructed based on the shared feature, and an integrated graph is computed by averagely fusing all the input adjacent graphs. Then, the feature-oriented graph and integrated graph are stacked into a graph tensor with a low-rank tensor constraint, where a refined affinity probability matrix can be adaptively recovered from the integrated graph by considering multiple graph information as well as the semantics features. Extensive experiments on several benchmark datasets demonstrate the superiority of HomoMGC compared with the state-of-the-art graph clustering methods. For the code reproducibility, the source code of HomoMGC is public available at https://***/ManshengChen/Code-for-HomoMGc-master. © 2024 IEEE.

关键词： Tensors

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Optimizing the Resource’s Mobility, Transportation, and Networking in the Project Construction Industry, Using a Fractal Method 9th

Optimizing the Resource’s Mobility, Transportation, and Net...

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9th International Conference on Mobility, IoT and Smart Cities, EAI Mobility IoT 2022

作者： Cheraghi, Esmaeil Zohrehvandi, Shakib Knapčíková, Lucia Shadrokh, Shahram Soltani, Roya Khalilzadeh, Mohammad Department of Industrial Engineering Science and Research Branch Islamic Azad University Tehran Iran Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics The Technical University of Košice Prešov Slovakia New Technologies Department Center for European Studies Kharazmi University Tehran Iran Department of Industrial Engineering Sharif University of Technology Tehran Iran Department of Industrial Engineering Faculty of Engineering Khatam University Tehran Iran

ISBN: (纸本)9783031282249

When the number of projects in a company increases, it becomes very important to choose the right strategy to supply resources and raw materials in profit and loss and finish the work according to the schedule. This study presents a new mathematical model using the fractal method to optimize the resource planning system in project-oriented construction and industrial companies, and the results are analyzed. This article aims to introduce a fractal resource requirement planning (FRRP) model in project-oriented construction and industrial companies by providing a fractal framework to minimize resource costs and the smooth flow of materials between projects to meet the existing demand. Assuming three levels of headquarters, regions, and projects, each of them in the collection is a self-similar structure defined as a fractal. The study was carried out in one of the contracting companies in the industrial and construction fields, which operates as a project-oriented project in different regions of the country. The finding shows the proposed model could be used and implemented for resource management in project-oriented construction companies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Construction industry

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Drivers of the Sustainability Performance of Induction Stove Conversion Program in Indonesia

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International Journal of Sustainable Energy Planning and Management 2025年 43卷 5-30页

作者： Damayanti, Retno Wulan Safira, Silvia Dhea Laksono, Pringgo Widyo Setiadi, Haryono Entifar, Nisa Aqilla Ellenahaya Industrial Engineering Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Center of Technology Development and Industrial Collaboration Institute for Research and Community Service Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Informatics and Science Data Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Smart Green Technology Engineering Department Pukyong National University 45 Yongso-ro Nam-Gu Busan Korea Republic of

In 2022, Indonesia initiated a pilot project to transition from liquid petroleum gas (LPG) stoves to induction stoves, targeting 8.2 million households by 2025. While LPG is considered a clean cooking fuel by the World Health Organization due to its low PM2.5 emissions, this program aims to further reduce CO2 emissions from cooking activities, aligning with Indonesia’s clean energy transition and 2060 Net Zero Emission goals. Throughout the program, doubts and criticisms arose among various sectors of society, particularly concerning the sustainability of this program. This research aims to determine and analyze the driving factors that support the sustainability of the induction stove transition program. Five sustainability aspects, namely, economic, social, environmental, technical, and institutional, were analyzed. A total of 315 Indonesian citizens participated as respondents in this research survey. The Partial Least Squares-Structural Equation Model (PLS–SEM) method was utilized as the analytical approach for the study. The results revealed that nine factors significantly influenced program sustainability, with the strongest impacts from technology costs (path coefficient = 0.859), contextual factors (0.803), electrical infrastructure (0.792), and community perception (0.773). Economic sustainability was driven by technology costs and business opportunities (0.643), while environmental sustainability was strongly influenced by electrical infrastructure. Post-installation support (0.307) and financial management (0.391) significantly impacted technical and institutional sustainability respectively. These empirical findings improve previous research on the sustainability of an energy transition program, which provides insights for researchers and stakeholders in the energy management field, especially in developing countries, for policy determination and decision-making. © 2025, Aalborg University Press. All rights reserved.

关键词： Clean energy

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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Optimizing hyperparameters for dual-attention network in lung segmentation

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Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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International Seminar on Application for technology of Information and Communication (ISemantic)

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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Evaluation of Three Different Operators for Object Highlighting in Medical RGB Images: Canny, Roberts, and LoG in Independent Color Spaces 25

Evaluation of Three Different Operators for Object Highlight...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Ababneh, Jafar Abu-Jassar, Amer Abuowaida, Suhaila Liubchenko, Valentyn Lyashenko, Vyacheslav Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Amman Arab University College of of Computer Sciences and Information Amman Jordan Al Al-Bayt University Faculty of Information Technology Department of Computer Science Al-Mafraq Jordan Kharkiv National University of Radio Electronics Department of Informatics Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798331540012

The research presents a comparative analysis of the selection of objects of interest in color medical cytology images. For these purposes, the individual edge detection operators studied are Canny, Roberts, and LoG. The comparison contrasts the standard method of assessing color photographs with a technique that incorporates supplementary information. Data from individual color channels, derived from the breakdown of a color image in RGB format, serve as supplementary information. The following individual cases for potential examination are emphasized: Image processing with a complete level of reuse of edge detection operators;with a partial level of reuse of edge detection operators. The results are displayed through an array of tables, graphs, and images. Metrics of visual perception of images are utilized for comparative analysis. Compared with traditional edge detection methods, the proposed approach yields better results, which can exceed the estimates of the classical approach by 20%. © 2024 IEEE.

关键词： Color photography

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IoT-Fog Computing Sustainable System for Smart Cities: A Queueing-based Approach 1

IoT-Fog Computing Sustainable System for Smart Cities: A Que...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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Effect of Self-efficacy on Open Card Sorts for Websites

Effect of Self-efficacy on Open Card Sorts for Websites

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Thematic area Human Interface and the Management of Information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Katsanos, Christos Zafeiriou, Georgia Liapis, Alexandros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Peloponnese Patras Greece School of Science and Technology Hellenic Open University Patras Greece

ISBN: (纸本)9783031064234

Card sorting is a popular way for creating website information architectures based on users’ mental models. This paper explores the effect of participants’ self-efficacy on card sorting results. A two-phase study was carried out. The first phase involved 40 participants rating their self-efficacy on a standardized scale, followed by an open card sort experiment. The median self-efficacy score was used to split the open card sort data into two groups: one for low and one for high participants’ self-efficacy. These two datasets were analyzed following state-of-the-art techniques for open card sort data analysis, which resulted in two information architectures for the eshop. In the second phase, two functional prototypes were first created for the eshop, one for each information architecture of the first phase. Subsequently, 30 participants interacted with both prototypes in a user testing study. This paper found that users interacting with the information architecture produced by open card sort participants with low self-efficacy made statistically significantly more correct first clicks, significantly less time to find content items, rated the tasks as significantly easier, and provided higher perceived usability ratings compared to when they interacted with the information architecture produced by users with high self-efficacy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Websites

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