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Vasculitis is a clinico – pathological entity involving all organs. Despite being reported widely, the triggering etiology for the disease appears to be broadly unknown. Emerging evidence linking immunological, infectious, environmental and genetic causes are being increasingly studied with focus on Infectious agents including viruses, bacteria, fungi and parasites. Case series: Here in we assessed three autopsies demonstrating histopathological evidence of vasculitis seen in various organ systems. Relevant clinicopathological details such as demography, course in hospital, gross and microscopic reports were retrieved from the hospital medical records, reviewed and analysed. In two cases vasculitis was observed against a backdrop of fungal infection with the organs affected being the cerebrum in one and lungs of both cases. The third case demonstrated vasculitis of the coronary arteries in a patient with a known history of tuberculosis. Conclusion: The aim of the cases series was to highlight the importance of infections in causing vasculitis in the demographic set up of India along with the need to consider infection – related vasculitis when faced with diagnostic conundrums. Early stage vasculitis are often clinically silent. Hence, early signs of infections should make one vary of vasculitis as a potential complication, thus direct the investigation panel and therapeutic modalities accordingly to prevent fatal outcomes of the disease. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to study properties of Ξ-Hilfer fuzzy fractional differential equations which are useful in modeling a variety of phenomena in physical sciences. The existence, uniqueness and stability of solutions are obtained. The technique used for study is the fixed point theorem and Gronwall inequality. Ulam-Hyers-Rassias stability of the solution is also investigated. [ABSTRACT FROM AUTHOR]

With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models. The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance. [ABSTRACT FROM AUTHOR]

In this work, we have considered the fuzzy random differential equations involving the tempered Ξ-Hilfer fractional derivative. For the proposed problems, we have developed the existence and uniqueness (EU) by using successive approximations under generalized Lipschitz conditions. Proper examples have also been provided with detailed analysis to verify our results. [ABSTRACT FROM AUTHOR]

This paper deals with the fractional order Ambartsumian equation with integral multipoint boundary conditions. The reported results discuss the existence, uniqueness and stability for the solution of our proposed equation. We prove the main results by the aid of fixed point theorems and Ulam's approach. An example is included to illustrate the results. [ABSTRACT FROM AUTHOR]

Biomedical procedures such as surgical glue, knee replacement surgery, blood diagnostic and many others can exhibit the significance of viscoinelastic behavior with non-Newtonian characteristics is analyzed. The present article addresses the flow design is to accentuate the thermophysical characteristics of electrically conducting viscoinelastic nanofluid flow produced by a stretched surface. The impacts of Brownian motion and thermophoresis on viscoinelastic nanofluid are accounted in the presence of heat and mass transfer convective states. The modeled partial differential equations are transmuted into nonlinear ordinary differential equations employing similarity transformations to facilitate the computation procedure. The solution of the achieved boundary value problem is procured numerically by utilizing a program written with stiffness-switching methodology. The manners of controlling parameters on the fluid velocity, temperature, and concentration profiles are spotlighted via graphs. Finally, by changing the pertinent parameters, the Machine Learning technique is used to investigate critical properties of fluid flow, such as skin friction coefficient, Nusselt number, and Sherwood number. The effects of driving parameters are illustrated in a graphical and tabular format. The precision of the current methodology is validated by a comparison between the current and previous findings. [ABSTRACT FROM AUTHOR]