Over the past decade, the study of stability theory in integro-differential systems has grown significantly owing to their relevance in solving physical and engineering problems, such as viscoelasticity and thermo-vis...
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Over the past decade, the study of stability theory in integro-differential systems has grown significantly owing to their relevance in solving physical and engineering problems, such as viscoelasticity and thermo-viscoelasticity in materials with memory properties. This paper concentrates on a class of infinite-dimensional stochastic integro-differential systems. We establish the well-posedness of the system and identify mild solutions to the system and an abstract stochastic Cauchy problem. This identification is identified by employing a semigroup approach combined with Yosida approximation. We derive sufficient conditions that ensure the mean-square exponential stability of mild solutions to the system boils down to the boundedness of a certain function and a norm estimate for the stochastic part. These conditions are implemented through the semigroup approach and the composition operator method. Illustrative examples are provided and the obtained theoretical results are validated by numerical simulations.
Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial pr...
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Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar *** a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity *** experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.
This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease data...
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The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes t...
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The aim of the research is to utilize deep learning techniques to support radiologists in enhancing the effectiveness and precision of breast cancer diagnosis. This paper employs a deep neural network (DNN) classifier...
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Plastic waste in Polyethylene Terephthalate (PET) bottles is creating new challenges, which in today’s scenario is a major concern in every industry. The defect in PET bottles mainly occurs due to improper selection ...
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Plastic waste in Polyethylene Terephthalate (PET) bottles is creating new challenges, which in today’s scenario is a major concern in every industry. The defect in PET bottles mainly occurs due to improper selection of levels of process parameters during the production process. Therefore, optimizations of process parameters are very important to minimize the defects produced due to improper stretch blow molding process parameter selections. The study is carried out on 2 L bottle since they have the highest demand and the highest defect rate in the PET bottling sector. The PET bottles have been developed from the preform at various blowing temperature, blowing speed and blowing angle on commercially available SBM setup. The design and plan of experiments have been done according to L9 orthogonal array of Taguchi method. The optimization of process parameters has been done through the hybrid Taguchi grey relational analysis optimization technique on four factors namely: blowing temperature, first blow angle, second blow angle and blowing speed. Each value of parameter was taken at three levels in combination. The testing of all samples such as tensile test, wall thickness measurement and hoop stretch ratio measurement have been carried out as per ASTM standards. The morphology of the samples has been carried out through scanning electron microscope. A maximum tensile strength of 65 MPa, minimum hoop stretch ratio of 5.45 and a maximum wall thickness 0.24 mm was found at varying process parameter combinations. It has been observed that among the process parameters: blowing temperature was the most significant factor for minimizing defects while second blow angle was the least significant parameter. It was found that optimized values of the process parameters yielded 15% reduction in defects on PET bottles. The present study is expected to contribute significantly for the industries working in this field for carrying out production at optimum levels of process parameter
Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence perio...
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Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
The usage of online social networks (OSNs) has been significantly swelling over the years. Data from SNs has been shared publicly for the purpose of deeper understanding user behaviour and data mining tasks. However, ...
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Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually...
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Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.
In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precisi...
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