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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India

Arabian Journal of Geosciences 2022年第23期15卷 1-26页

作者： Rawat, Deepa Mishra, Pradeep Ray, Soumik Warnakulasooriya, Himasha Harshani Fernando Sati, Sarswati Prakash Mishra, Gaurav Alkattan, Hussein Abotaleb, Mostafa College of Forestry VCSG Uttarakhand University of Horticulture and Forestry Tehri Garhwal India College of Agriculture Rewa India Centurion University of Technology and Management Paralakhemundi India Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka Centre of Excellence on Sustainable Land Management Indian Council of Forestry Research and Education (MoEF & CC Govt. of India) Dehradun India Department of System Programming South Ural State University Chelyabinsk Russia

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained.

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Improving health awareness with real-time monitoring through a three-dimensional visualized digital health avatar

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Smart Health 2024年 34卷

作者： Chokphukhiao, Chaturapron Pattaranit, Pattrawan Tun, Wonn Shweyi Thet Masa, Sakaowrat Leemananil, Rattikorn Natteerapong, Nuttaphorn Phetcharaburanin, Jutarop Boonlue, Sophon Sunat, Khamron Patramanon, Rina Information Technology International Program College of Computing Khon Kaen University Khon Kaen 40002 Thailand Center of Excellence in Digital Innovation Faculty of Education Khon Kaen University Khon Kaen 40002 Thailand Integrative Biomedical Informatics and Computational Imaging Center I-BIC Faculty of Associated Medical Science Khon Kaen University Khon Kaen 40002 Thailand Department of System Biosciences and Computational Medicine Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Khon Kaen University National Phenome Institute Khon Kaen University Khon Kaen 40002 Thailand Department of Biochemistry Faculty of Science. Khon Kaen University Khon Kaen 40002 Thailand Department of Microbiology Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Computer Science College of Computing Khon Kaen University 40002 Thailand

Introduction: The use of modern technologies has become crucial for enhancing people's awareness of health problems. By early health risk detection, there will be better treatment outcomes, the severity of illness will decrease, and costs for treatment will also reduce. Performing routine checkups could help patients feel less anxious about their health in the future. In this study, the innovation of “Digital Health Avatar” enables users to monitor physical changes, raise awareness of medical conditions, and encourage healthy behaviors by visualizing health status as a 3D figure. Methods: Health data were collected using medical devices like blood test strips, body composition meters, and automatic blood pressure monitors. API technology and cloud computing systems were used to process these collected data and produced a three-dimensional avatar that indicated the user's health status. User satisfaction survey on using the health avatar system was assessed through a survey of 61 participants. Results: Health avatar system successfully generate the data and display the measurement results on the health report page of the system. Users can easily check and record their physiological conditions through the avatar. Moreover, users showed satisfaction on the use of the health avatar system and its effectiveness in health check-ups in the survey. Conclusions: The ‘Digital Health Avatar’ has the potential to significantly promote individual well-being and health awareness. Its development will be improved by user feedback and continued study, ensuring it becomes an efficient tool for promoting healthier lifestyles. © 2024 The Authors

关键词： Digital health avatar Health sensors Healthcare Three-dimensional model

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Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

arXiv

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arXiv 2020年

作者： Henzel, Joanna Sikora, Marek Department of Computer Networks and System Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland

—In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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An Empirical Study of Security Practices for Microservices systems

arXiv

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arXiv 2021年

作者： Ali, Rezaei Nasab Mojtaba, Shahin Ali, Hoseyni Raviz Seyed Peng, Liang Amir, Mashmool Valentina, Lenarduzzi School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia Department of Computer Science Bioengineering Robotics and System Engineering University of Genoa Genoa16126 Italy Faculty of Information Technology and Electrical Engineering University of Oulu OuluFI-90014 Finland

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study. We first manually analyzed 861 microservices security points, including 567 issues, 9 documents, and 3 wiki pages from 10 GitHub open-source microservices systems and 306 Stack Overflow posts concerning security in microservices systems. In this study, a microservices security point is referred to as "a GitHub issue, a Stack Overflow post, a document, or a wiki page that entails 5 or more microservices security paragraphs". Our analysis led to a catalog of 28 microservices security practices. We then ran a survey with 74 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools. © 2021, CC BY.

关键词： Open systems

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Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

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Energy and AI 2021年第3期5卷 53-66页

作者： Meisam Booshehri Lukas Emele Simon Flügel Hannah Förster Johannes Frey Ulrich Frey Martin Glauer Janna Hastings Christian Hofmann Carsten Hoyer-Klick Ludwig Hülk Anna Kleinau Kevin Knosala Leander Kotzur Patrick Kuckertz Till Mossakowski Christoph Muschner Fabian Neuhaus Michaja Pehl Martin Robinius Vera Sehn Mirjam Stappel Institute for Applied Ecology BerlinGermany Institute for Applied Informatics(InfAI)/Leipzig University Germany Department of Energy Systems Analysis Institute of Networked Energy SystemsGerman Aerospace Center(DLR)StuttgartGermany Faculty of Computer Science Otto-von-Guericke-Universität MagdeburgGermany Reiner Lemoine Institut BerlinGermany Institute of Techno-economic Systems Analysis(IEK-3) Forschungszentrum JülichJülichGermany Institute of Energy Economics and Rational Energy Use University of StuttgartGermany Potsdam Institute for Climate Impact Research PotsdamGermany Fraunhofer Institute for Energy Economics and Energy System Technology KasselGermany

Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems ***,it is hard to re-use results,compare model results or couple models at *** provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy ***,we present the Open Energy Ontology(OEO)developed for the domain of energy systems *** the OEO provides several benefits for the ***,it enables consistent annotation of large amounts of data from various research *** example is the Open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and ***,computational model coupling becomes much *** advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface *** also describe how the ontology can be used for linked open data(LOD).

关键词： Collaborative ontology development Linked open data Metadata annotation Energy systems analysis

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On Maximal Degree of Domination for Decision Making 2nd

On Maximal Degree of Domination for Decision Making

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2nd International Conference on Advanced Data and Information Engineering, DaEng 2015

作者： Suhirman Zain, Jasni Mohamad University Technology of Yogyakarta Yogyakarta Indonesia Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Gambang KuantanPahang Malaysia

ISBN: (纸本)9789811317972

This paper presents an extended decision making of soft set to multi-valued information systems. By the notion of multi-soft sets, the concept of domination between two soft sets is proposed. Then, the highest value of domination is used for the best selection as a decision attribute. The technique gives an alternative solution for attribute-based decision making, instead of the best choice of object as a standard soft set in Boolean-valued information systems. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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A Simple Scheme to Amplify Inter-Class Discrepancy for Improving Few-Shot Fine-Grained Image Classification

SSRN

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SSRN 2023年

作者： Li, Xiaoxu Guo, Zijie Zhu, Rui Ma, Zhanyu Guo, Jun Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China Faculty of Actuarial Science and Insurance Bayes Business School City University of London LondonEC1Y 8TZ United Kingdom Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Statistical Science University College London LondonWC1E 6BT United Kingdom

Few-shot image classification is a challenging topic in pattern recognition and computer vision. Few-shot fine-grained image classification is even more challenging, due to not only the few shots of labelled samples but also the subtle differences to distinguish subcategories in fine-grained images. A recent method called task discrepancy maximisation (TDM) can be embedded into the feature map reconstruction network (FRN) to generate discriminative features, by preserving the appearance details through reconstructing the query image and then assigning higher weights to more discriminative channels, producing the state-of-the-art performance for few-shot fine-grained image classification. However, due to the small inter-class discrepancy in finegrained images and the small training set in few-shot learning, the training of FRN+TDM can result in excessively flexible boundaries between subcategories and hence overfitting. To resolve this problem, we propose a simple scheme to amplify inter-class discrepancy and thus improve FRN+TDM. To achieve this aim, instead of developing new modules, our scheme only involves two simple amendments to FRN+TDM: relaxing the inter-class score in TDM, and adding a centre loss to FRN. Extensive experiments on five benchmark datasets showcase that, although embarrassingly simple, our scheme is quite effective to improve the performance of few-shot fine-grained image classification. The code is available at https://***/Airgods/*** © 2023, The Authors. All rights reserved.

关键词： Image classification

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Evidence-Based Quality-Aware Agile Software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Learning support system using chatbot in "Kejar C Package" homeschooling program 2

Learning support system using chatbot in "Kejar C Package" h...

引用

2nd International Conference on Information and Communications technology, ICOIACT 2019

作者： Murad, Dina Fitria Fernando, Erick Irsan, Muhamad Murad, Silvia Ayunda Akhirianto, Pas Mahyu Wijaya, Michael Hendra Information Systems Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Tangerang15117 Indonesia Faculty of Computer Technology University of Bina Sarana Informatika PSDKU Tegal Indonesia

ISBN: (纸本)9781728116556

The research objective was to develop a learning media support system using chatbot technology that was integrated with the LINE messaging mobile application and content management system (CMS) with homeschooling students and teachers as target users. The renewal of this research is to add access to subject matter features and discussion forum features. These features do not yet exist in the chatbot application system in other studies. Authentication on the chatbot is needed to find out who the user is accessing the chatbot for the material, discussion forum, and work on the problem training and National Examination exercises. E-learning innovation from chatbots is integrated with the LINE and CMS messaging mobile application that contains subject matter, exam practice, discussion forums, and evaluation of student evaluations. This system supports students who have limited time to attend traditional face-to-face classes and have limited access to laptops. The results of this study prove that Chatbot on smartphones can maximize student learning functions, increase student interest and achievement (value). © 2019 IEEE

关键词： Students

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