Purpose: Solar photovoltaic (PV) is commonly used as a renewable energy source to provide electrical power to customers. This research establishes a method for testing the performance reliability of large grid-connected PV power systems. Solar PV can turn unrestricted amounts of sunlight into energy without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, large-scale solar PV generation has been increasingly incorporated into power grids to meet energy demand. The capability of the installation and the position of the PV are the most important considerations for a utility company when installing solar PV generation in their system. Because of the unpredictability of sunlight, the amount of solar penetration in a device is generally restricted by reliability constraints. PV power systems are made up of five PV modules, with three of them needing to be operational at the same time. In other words, three out of five. Then there is a charge controller and a battery bank with three batteries, two of which must be consecutively be in operation. i.e. two out of three. Inverter and two distributors, all of which were involved at the same time. i.e. two out of two. In order to evaluate real-world grid-connected PV networks, state enumeration is used. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems. Every module's test results on a realistic 10-kW PV system. To see how the model works in practice, many scenarios are considered. Tables and graphs are used to show the findings.Design/methodology/approach: The system of first-order differential equations is formulated and solved using Laplace transforms using regenerative point techniques. Several scenarios were examined to determine the impact of the model under consideration. The calculations were done with Maple 13 software.Findings: The authors get availability, reliability, mean time to failure (MTTF), MTTF sensitivity and gain feature in this research. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems.Originality/value: This is the authors' original copy of the paper. Because of the importance of the study, the references are well-cited. Nothing from any previously published articles or textbooks has been withdrawn.

A network bridge is a computer networking device that creates a single aggregate network from multiple communication network or network segments. One of the type of network bridge is transparent bridge saddle with responsibility of checking incoming network traffic to identify media access control addresses. In this present research work on series parallel computer network performance, availability and cost analysis of complex computer network was considered to focus on a network that has four subsystems A, B, C and D and all the subsystems are arranged in series-parallel, subsystem A and B are working on 1-out-of-2: G and 2-out-of-3: F policy respectively, C subsystem behaved as a bridge with one unit and D subsystem has five units and are working in 3-out-of-5: G scheme. The system has two types of failure, degraded (partial failure) or complete failed states. The system was analyzed using supplementary variables techniques and Laplace transform, general distribution and copula family were employed to restore the partial failure and complete failure states. Computed results have been highlighted by the means of tables and graphs to investigate the performance of computer network. The result has shown that computer network with transparent bridge will be more reliable to filter incoming frame and forward it to media access control (MAC).

In this study, we investigate the availability and performance analysis of reverse proxy servers within a novel series–parallel distributed architecture, which serves as an intermediary between client devices and backend servers. The model is designed to enhance load distribution, availability, and fault tolerance, incorporating round-robin load balancing at the application layer and least-connections load balancing at the database layer. This dual-layer strategy ensures optimal traffic distribution and resource utilization, reducing bottlenecks and improving system responsiveness. Our work introduces an analytical availability model based on first-order differential-difference equations, which provides a formal mathematical framework for deriving system availability and mean time to failure. Unlike conventional approaches, our method captures dynamic failure–repair interactions within the load-balanced architecture, leading to more precise availability predictions. The modeling approach is validated through numerical simulations, demonstrating its effectiveness in optimizing system performance. Furthermore, this study offers valuable insights into maintenance policy design, particularly in the context of failure detection and recovery. The failure detection mechanism swiftly identifies faults, initiating an automatic reboot and repair. This contributes to improved system resilience, reducing downtime, and enhancing long-term operational efficiency. Our approach contributes to the field in three key areas, such as: The integration of in-application load balancing with database redundancy, which enhances fault resilience and performance, and the use of differential-difference equations provides a robust analytical foundation for availability and performance assessment, emphasizing proactive failure detection and recovery strategies to improve system resilience, particularly during and after the burn-in period. Our numerical findings are highly relevant to system engineers, designers and maintenance planners, providing actionable strategies to optimize system availability and safety, especially during and after the burn-in period. By addressing both architectural design and mathematical modeling, this research advances the field of distributed computing, offering a robust framework for designing and maintaining high-availability proxy server systems.

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious ac-tors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evalua-tion, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, re-liability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment.

In rural areas around the world, agriculture is a vital source of livelihood and sustenance. However, limited access to electricity and water resources often hampers agricultural productivity, leaving farmers dependent on rainfall or manual labor-intensive irrigation methods. To address this challenge, solar irrigation systems have emerged as a sustainable and efficient solution. However, the existing solar irrigation systems often suffer from reliability issues and suboptimal performance, leading to inefficient water usage, increased maintenance costs, and reduced overall agricultural output. Addressing the reliability and performance optimization challenges of solar irrigation systems for rural farming is essential to ensure sustainable agriculture, increased crop yield, and improved livelihoods for farmers. This study aimed to enhance the performance of solar irrigation systems using reliability, maintainability, availability, and metrics like MTBF and MTTF. The system consists of four subsystems 1, 2, 3, and 4 namely, PV panel, controller, submersible pump, and storage tank sequentially arranged. Subsystem 1 consists of ten (10) units of panels arranged in parallel and connected to subsystem 2 which consists of two (2) unit controllers arranged in parallel, whereas subsystem 3 consists of two (2) units of pumps arranged in parallel and lastly, subsystem 4 comprises of four (4) units of storage tanks arranged in parallel, However, all the four subsystems combined are arranged in series-parallel. For design and prediction, the Markovian birth-death method is employed to assemble the system governing the differential difference equation from the state-to-state transition diagram. The rates of repair and failure of each subsystem are assumed to be exponentiated Weibull distributions and statistically independent. The findings of this research are thought to be valuable for analyzing performance and determining the best solar irrigation system design and feasible preventive and corrective maintenance strategies that may be used in the future to improve system performance, strength, effectiveness, food production output as well and revenue mobilization.