As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that...
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Diabetes is a long-term illness that results in a variety of chronic body damage, such as kidney failure, heart problems, eye damage, depression, and nerve damage. This disease is caused by several risk factors, ...
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Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven mode...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.
Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar...
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Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.
The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
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The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various di...
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Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated me...
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Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadli...
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Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.
Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much atten...
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Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational ***,crossover studies of the two fields seem to be missing in the *** paper initiates the study of quantum algorithms for matroid property *** is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits(bases,flats,hyperplanes)of a matroid,and for the decision problem of deciding whether a matroid is uniform or Eulerian,by giving a uniform lower boundΩ■on the query complexity of all these *** the other hand,for the uniform matroid decision problem,an asymptotically optimal quantum algorithm is proposed which achieves the lower bound,and for the girth problem,an almost optimal quantum algorithm is given with query complexityO■.In addition,for the paving matroid decision problem,a lower boundΩ■on the query complexity is obtained,and an O■ quantum algorithm is presented.
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