Munger University

This study investigates the reliability of a machine dual-repair system (MDRS) with limited capacity and multiple operational vacations (OV). Multiple operational vacation is a mechanism that involves the process of the two types of vacation, one as operational vacation (OV) and another one is non-operational vacation (NOV). In this machining system, there are M operating machines, to prevent redundant breakdowns of machines and S spare machines are used to ensure smooth functioning. The server can be in a busy state, an operational vacation state, or on Non-operational vacation state. State-transition equations are formulated based on the Chapman-Kolmogorov differential-difference equations, and with the help of these equations stationary probability distribution is obtained. The Runge-Kutta IV order numerical method is used to evaluate system performance measures. The cost has been optimized using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques by testing convergence as well as comparison of the outcomes of both PSO and GWO. The exploration of the essential performance measures and graphical representations has been conducted.

Two-dimensional (2D) transition metal dichalcogenide (TMD) nanomaterials (NMs) are graphene-like single- or few-layered NMs where each layer is bonded by weak Van der Waals forces. Each layer consists of covalently bonded X-M-X moiety, where M is the transition metal belonging to group 4–7 (Ti, V, Cr, Tc, Hf, Ta, W, Mn, Zr, Nb, Mo, and Re) and X is the chalcogen (S, Se, and Te) in a trigonal prismatic orientation having unique physical, chemical, optical and electronic properties. Moreover, single layer can be exfoliated from its corresponding bulk materials by suitable processing technology e.g. ultrasonication, peeling off using scotch tape. The 2D layered TMD materials show large surface area to volume ratio (aspect ratio), corresponding to its bulk, for surface modification and treatment. So, these 2D TMD materials have emerged as promising candidates for biosensing and imaging applications ranging from clinical diagnosis to disease therapy owing to its tuneable bandgap, good cytocompatibility, wafer scale synthesis capabilities, and compatibility with standard fabrication processes. In this chapter, we have summarized the recent progress and outlook in biosensing and imaging applications of 2D TMD NMs, covering various methods of detection of nucleic acid, proteins, whole cells, and small biomarker molecules of bacteria, and viruses.

This article discusses data security issues in cloud computing. It is the study of data in the cloud and its associated security issues. This article will introduce data protection procedures and global practices to insure that data are protected as much as possible by reducing risks and threats. Keeping data in the cloud has been beneficial for many applications, but it creates risks by exposing data to applications that are already weakly secured. Similarly, if the reliability of the guest process (which may have a vulnerability) is unknown when running on a hypervisor, using cloud computing virtualization can lead to the exposure of dangerous information. The white paper will also provide insights into security information in transit and at rest. Learn all levels based on SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service)

In this study, the portfolio selection problem is tackled using coherent trapezoidal fuzzy numbers, which is an extension of traditional trapezoidal fuzzy numbers. The work outlined in this paper commences by presenting analytical expressions for the credibilistic mean and credibilistic semivariance of coherent trapezoidal fuzzy numbers. These expressions are then utilized to formulate a pioneering bi-objective Mean-Semivariance (MSv) portfolio selection model, incorporating various practical constraints. The proposed bi-objective MSv Model is solved using a modified Multi-Objective Evolutionary Algorithm (MOEA) with an integrated repair mechanism. To demonstrate the model's performance stability, an empirical analysis is conducted using monthly stock return data from 18 distinct stocks selected from the Bombay Stock Exchange (BSE), India. The obtained results are juxtaposed with the benchmark BSE Sensex for comparison.