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A fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows in cloud computing

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Soft Computing 2025年第2期29卷 755-781页

作者： Farhood, Suha Mubdir Khorsand, Reihaneh Hussein, Nashwan Jasim Ramezanpour, Mohammadreza Branch Islamic Azad University Isfahan Iran Department of Programming Collage of Information Technology University of Babylon Hillah Iraq Department of Computer Engineering Mobarakeh Branch Islamic Azad University Mobarakeh Isfahan Iran

Cloud computing offers solutions for various scientific and business applications. Large-scale scientific applications, which are organized as scientific workflows, are carried out using cloud computing. However, the higher failure rates in cloud computing can be attributed to the numerous servers and components dealing with intense workloads. This study presents a fault-tolerant scheduling approach using proactive and clustering methods for scientific workflows in cloud computing. Initially, the task clustering issue is addressed by consolidating multiple short-duration tasks into a single job to improve the runtime performance of workflow executions. Subsequently, an automated workflow scheduling strategy is outlined with four key stages: monitoring, analysis, planning, and execution. During monitoring, clustered jobs and the capacities of available cloud resources are observed. In the analysis phase, the accuracy of failure prediction is enhanced by employing the Group Method of Data Handling (GMDH) neural network prior to any faults or failures. The planning stage introduces a novel hybrid multi-objective algorithm, MOPSO-aSA, based on MOPSO and adaptive simulated annealing (SA), to streamline workflow scheduling in error-prone execution environments. Moreover, the reliability of application execution is maintained through re-clustering and migration techniques following any faults or failures. Finally, based on the experimental findings, it is evident that the proposed strategy surpasses other methods in terms of makespan, total cost, energy consumption, and failure rate. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Simulated annealing

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Fostering Attachment to the Community by Discovering of Local Resources using Location-Based System 19

Fostering Attachment to the Community by Discovering of Loca...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Honda, Ken Itoga, Chiharu Hattori, Akira Faculty of Technology International Professional University of Technology Tokyo Japan Department of Sound Programming SITE LLC Osaka Japan Faculty of Global Media Studies Komazawa University Tokyo Japan

ISBN: (纸本)9798331507817

In Japan, population decline and the depopulation of local regions have become social issues. Various municipalities are working to discover and promote the unique attractions of their regions. Identifying the local resources can help foster attachment to the region, potentially reducing population outflow and increasing the number of new residents. In this paper, we propose a location-based system to discover local resources and their associated stories to foster attachment to the local community. To evaluate whether this system effectively identifies local attractions and cultivates attachment to the community, we organized a town walking event in collaboration with local municipalities and members of local community groups in the event area. © 2025 IEEE.

关键词： Depopulation Local Resources Location-based System Tourism Informatics Town Walking Event

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Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches

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Discover Oncology 2025年第1期16卷 1-13页

作者： Annadurai, Priyadharshini Isaac, Arnold Emerson Bioinformatics Programming Laboratory Department of Bioscience School of Bio Science and Technology Vellore Institute of Technology Katpadi Vellore - 632014 India

Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellular carcinoma (HCC). This study aims to identify the genes implicated in the prognosis of HCC among individuals suffering from metabolic fatty liver disease. We analysed protein–protein interaction (PPI) networks and constructed a weighted gene co-expression network analysis (WGCNA) using high-throughput gene expression profiling datasets. Our meta-analysis uncovered 442 differentially expressed genes (DEGs), comprising 30 upregulated and 412 downregulated genes. We constructed a PPI network from the DEGs and identified significant hub genes based on their degree centrality scores. Additionally, WGCNA highlighted impactful genes and tightly correlated modules, leading to the creation of a gene interaction network specific to metabolism-associated fatty liver disease (MAFLD). Pathway analysis revealed the candidate regulatory gene interleukin-7 receptor (IL7R), which is involved in cytokine-mediated signalling across both interaction networks. Pro-inflammatory cytokines interact with IL7R, activating the JAK/STAT pathway that influences gene expression throughout progression to HCC. IL7R activates STAT3, affecting the behaviour of activated hepatic stellate cells following initial liver damage. Furthermore, the expression of the IL7R gene was validated as a predictor of HCC malignancy through a logistic regression model, resulting in an accuracy of 92%. Findings suggest that IL7R could be the target gene associated with metabolism-linked HCC. It could significantly impact the management of metabolic-associated fatty liver disease (MAFLD) and may help enhance HCC diagnostics to improve patient outcomes.

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A charged hadronic string model within the R.P. Feynman proper time paradigm and vacuum field theory approach 14

A charged hadronic string model within the R.P. Feynman prop...

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14th Biennial Conference on Classical and Quantum Relativistic Dynamics of Particles and Fields, IARD 2024

作者： Bogolubov, Nikolai N. Prykarpatski, Anatolij K. ICTP Trieste Italy Department of Applied Mathematics The Cracov University of Technology Kraków Poland Department of Computational Mathematics and Programming The Lviv Polytechnic National University Lviv Ukraine

We study a novel charged hadronic string model within the least action principle and the vacuum field theory approach based on the classical R.P. Feynman’s proper time paradigm. It is stated that the hadronic string model allows the conformal local coordinates, with respect to which the resulting string dynamics is described by means of the linear second order elliptic equation under the Dirac type constraint, suitable for the canonical quantization and demonstrating a related gauge type invariance of the model. The related Lagrangian and Hamiltonian aspects of the string model, interacting with ambient both electrical and electromagnetic potential fields, are analyzed in detail. © 2025 Institute of Physics Publishing. All rights reserved.

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Renal Tumor Classification and Detection Based on Artificial Intelligence 3

Renal Tumor Classification and Detection Based on Artificial...

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3rd International Conference on Communications, Information System, and Data Science, CISDS 2024

作者： Al-Taie, Raflaa Hilmi Hussein, Nashwan J. Physiology and Organ Functions Department College of Medicine University of Babylon Iraq Department of Electrical Power Engineering AL-Hussain University College Department of Programming Collage of Information Technology University of Babylon Iraq

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The kidney is a crucial organ in the human body, co-operating billions of pipelines to cleanse the body's water. Kidney failure, renal tumor happens when cells divide uncontrollably and form an aberrant collection of cells surrounding or within the kidney. This cell type has the ability to disrupt regular kidney activity and destroy healthy cells. The prompt diagnosis of renal tumor is critical since they can be fatal if left untreated. Because it is dependent on the skill of the person analyzing the images, the traditional approach of manually checking the MR image may not be very accurate. The study concentrated on the diagnosis of normal and normal renal tumor. To enhance accuracy and expedite diagnosis, using publicly accessible individual records, the present research used methodologies for machine learning, involving support vector machine learning (SVM), adaptive optimization (AO), as well as gradient enhancement (GE). It uses an approach for making the dataset reduced multidimensional. Image feature extraction is a data preprocessing method that minimizes the time required to train the proposed algorithm. The accuracy rates of the algorithms for diagnosing normal and up normal are reported to be 88.8 % for GB, 83.8 % for ADA, 86.1 % for SVM and 93.3% for KNN and for up normal diagnosis tumor are reported to be 55.3 % for GB, 55.4 % for SVM, 55.3 % for ADA and 51.0% for KNN. © 2025 SPIE.

关键词： Image enhancement

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Prediction of key pathways in hepatocellular carcinoma (HCC): A machine learning approach using a sample pathway information matrix

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Computational Biology and Chemistry 2025年 118卷 108481页

Hepatocellular carcinoma (HCC) represents the most prevalent form of primary liver cancer, accounting for 75 % of all cases. Individuals with metabolic dysfunctions are at risk of developing significant symptoms, including cirrhosis. To address this, we proposed a novel method to find the signalling pathway based on the patient's gene expression. The objective includes examining the predictive biomarkers associated with cirrhosis-related HCC. The study combines gene expression and pathway enrichment data to find the biologically important pathways in disease progression. The differential gene expression analysis showed 58 upregulated and 62 downregulating differentially expressed genes. These DEGs were utilized to construct a protein-protein interaction network, and then the clustered genes were determined. Subsequently, pathway enrichment analysis was performed for the clustered genes and the gene-pathway interaction matrix was developed. The sample-pathway information matrix (SPIM) was obtained by multiplying the gene-expression and gene-pathway matrix. The key pathways were predicted from the SPIM using random forest model and we achieved 94 % of accuracy. The arachidonic acid metabolism was the most important pathway and genes involved in this pathway includes CYP2C9, CYP2C8, CYP2B6. These genes are well known for promoting metabolic disorders in the liver. Hence, our novel method proves that it could distinguish the samples and extract important pathways that are involved in differentiating the diseased samples based on the gene expression. Therefore, integrating gene expression and their enriched biological pathways may effectively help in identifying the key signalling pathways. © 2025 Elsevier Ltd

关键词： Transcription

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Educational Research Trends of the Use of Gaze Learning Data Through Topic Modeling and Scientometric Analysis 1st

Educational Research Trends of the Use of Gaze Learning D...

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1st International workshop on AI for Research, AI4Research 2024 and 4th International workshop on Democracy and AI, DemocrAI 2024, held in conjunction with 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Lee, Jungmin Shin, Huigon Yoo, YoungJoon Choi, Jongwon Kim, Mi Song Department of Advanced Imaging GSAIM Chung-Ang University Seoul Korea Republic of Computer Programming and Analysis School of Information Technology Fanshawe College London Canada Department of Artificial Intelligence Chung-Ang University Seoul Korea Republic of Curriculum Studies Western University London Canada

ISBN: (纸本)9789819795352

Multimodal learning analytics have become increasingly important in enabling a deeper understanding of teaching and learning in educational research. However, in comparison to other multimodal learning data, there is a limited understanding of the trends within gaze learning data. To address this challenge, this study aims to identify latent topics in gaze learning data through topic modeling and scientometric analysis. We analyzed the abstracts of 573 peer-reviewed and conference proceeding papers that used gaze learning data, written in English, and published between 2008 and February 2024. The findings are as follows. First, three main topics were identified through topic modeling analysis: (learning analytics, multimodal learning, and inclusive learning). Second, the scientometric analysis revealed the structure in which diverse clusters in cited references and institutions are connected around the emerging topics. Based on these findings, the study would provide insights into research directions in both educational research and applications using gaze learning data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Fostering Attachment to the Community by Discovering of Local Resources Using Location-Based System

Fostering Attachment to the Community by Discovering of Loca...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ken Honda Chiharu Itoga Akira Hattori Faculty of Technology International Professional University of Technology Tokyo Japan Department of Sound Programming SITE LLC Osaka Japan Faculty of Global Media Studies Komazawa University Tokyo Japan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

关键词： Legged locomotion Surveys Collaboration Games Aging Information management Augmented reality

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A blockchain-oriented covert communication technology with controlled security level based on addressing confusion ciphertext

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Frontiers of Computer Science 2025年第2期19卷 133-135页

作者： Lejun ZHANG Bo ZHANG Ran GUO Zhujun WANG Guopeng WANG Jing QIU Shen SU Yuan LIU Guangxia XU Zhihong TIAN Sergey GATAULLIN Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China Research and Development Center for E-Learning Ministry of EducationBeijing 100039China College of Information Engineering Yangzhou UniversityYangzhou 225009China School Math and Computer Science Quanzhou Normal UniversityQuanzhou 362000China Pazhou Lab Guangzhou 510330China School of Physics and Materials Science Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of SciencesMoscow 117418Russia Industrial Programming Department MIREA-Russian Technological UniversityMoscow 119454Russia

1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods also exist,such as peer-to-peer networks and distributed hash *** technology,which is decentralized,anonymous,non-tamperable and anti-attack,can be applied to covert communication to solve the problems of traditional methods and improve the quality of communication.

关键词： communication tables networks

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PRECEDENCES IN SPECIFICATIONS AND IMPLEMENTATIONS OF programming-LANGUAGES

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THEORETICAL COMPUTER SCIENCE 1995年第1期142卷 3-26页

作者： AASA, A Department of Computer Sciences Programming Methodology Group Chalmers University of Technology S-412 96 G?teborg Sweden

Although precedences are often used to resolve ambiguities in programming language descriptions, there has been no parser-independent definition of languages which are generated by grammars with precedence rules. This paper gives such a definition for a subclass of context-free grammars. The definition is shown to be equivalent to the implicit definition an operator precedence parser gives. A problem with a language containing infix, prefix and postfix operators of different precedences is that the well-known algorithm, which transforms a grammar with infix operator precedences to an ordinary unambiguous context-free grammar, does not work. This paper gives an algorithm that works also for prefix and postfix operators, and the correctness of it is proved. An application of the algorithm is also presented.

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