In this paper, we discuss the existence and uniqueness of solutions for a coupled system of nonlinear generalized Liouville–Caputo fractional Volterra–Fredholm integro-differential equations with nonlocal nonseparat...
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Schistosomiasis is a parasitic disease from the family of Schistosomatidae and genus Schistosoma,which is caused by blood *** disease is endemic in many countries and still a serious threat to global public health and...
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Schistosomiasis is a parasitic disease from the family of Schistosomatidae and genus Schistosoma,which is caused by blood *** disease is endemic in many countries and still a serious threat to global public health and *** this paper,a new deterministic model is designed and analyzed qualitatively to explore the dynamics of schistosomiasis transmission in human,cattle and snail *** from our mathematical analysis show that the model has a disease-free equilibrium(DFE)which is locally asymptotically stable(LAS)whenever a particular epidemiological threshold quantity,also known as basic reproduction number(R0)is less than *** analysis shows that the model has a unique endemic equilibrium(EE)which is globally asymptotically stable whenever R0>1 and unstable when R0<***,we adopt partial rank correlation coefficient for sensitivity analysis to reveal the most important parameters for effective control and mitigation of schistosomiasis disease in a ***,we obtain some numerical results by simulating the entire dynamics of the model,which show that the infections in the compartments of each population decrease with respect to *** further indicates that avoiding contact with infected human,cattle or infested water is vital to prevent the spread of schistosomiasis disease infection.
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
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In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)
In this article, we propose a normalized time-fractional Black–Scholes (TFBS) equation. The proposed model uses a normalized time-fractional derivative which has a distinctive feature wherein a weight function posses...
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Time predictability is a central requirement for real-time systems. The correct behavior of such a system can only be achieved if the results of programs are ready in time to affect the environment. Execution times of...
Time predictability is a central requirement for real-time systems. The correct behavior of such a system can only be achieved if the results of programs are ready in time to affect the environment. Execution times of modern systems can vary for many reasons, meaning complex analyses must be performed to ensure that the execution time is bounded and that a task always finishes before its deadline. Care must also be taken to ensure that nefarious actors do not exploit the varying execution time to compromise the system’s integrity. Avoiding variable execution times can greatly simplify systems, is inherently more secure, and eliminates the need for complex analyses. In this paper, we first argue for the value of having programs with constant execution times. We then show how the memory system around a processing core can affect execution times even on systems without intermediate storage like caches or scratch-pads. We present automatic compiler techniques for generating constant execution time programs and evaluate their implementation on the Patmos architecture. We show that combining our two compensation techniques is generally superior to either on their own. We compare the performance of our implementation to the estimates produced by the Platin worst-case execution time analyzer. While our implementation significantly impacts performance, it is generally manageable and has the potential for comparable execution times.
In this note we discuss Gauss maps for Möbius surfaces in the n-sphere, and their applications in the study of Willmore surfaces. One such "Gauss map", naturally associated to a Willmore surface that ha...
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We propose a generalization of Zhandry's compressed oracle method to random permutations, where an algorithm can query both the permutation and its inverse. We show how to use the resulting oracle simulation to bo...
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Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimiza...
Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of ‘nodes’ on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods.
Various physical systems relax mechanical frustration through configurational rearrangements. We examine such rearrangements via Hamiltonian dynamics of simple internally-stressed harmonic 4-mass systems. We demonstra...
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