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This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Reliability study plays a significant role in supporting the operation of any machinery working in a dynamic and harsh environment. This quality is inherently uncertain and a stochastic variable of any system. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.

This study focused on the effects of operating conditions (OCs) on the operational reliability and remaining useful life (RUL) of machinery. A probabilistic graphical method called Bayesian network (BN) was used for studying the effect of OCs on the system performance. The developed methodology has been demonstrated by analyzing the operational reliability and predicting the RUL of electrical motors operated in a heavy mining machinery.

The failure probabilities estimated from the historical data of the motor system are failure likelihood, and OCs are the evidence in the developed BN model. It has been observed that the performance and RUL of the motor are significantly influenced by OCs and maintenance. A threshold value of reliability at which the motor system requires maintenance or replacement has been proposed to guide management in decision making.

The Bayesian approach for studying the covariate of motor reliability and RUL estimation is a novel approach. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.

The purpose of this study is to revisit the measures of internal consistency for multi-item scales in hospitality research and compare the performance of Cronbach’s α, omega total (ω_Total), omega hierarchical (ω_H), Revelle’s omega total (ω_RT), Minimum Rank Factor Analysis (GLB_fa) and GLB algebraic (GLB_a).

A Monte Carlo simulation was conducted to compare the performance of the six reliability estimators under different conditions common in hospitality research. Second, this study analyzed a data set to complement the simulation study.

Overall, ω_Total was the best-performing estimator across all conditions, whereas ω_H performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ω_RT, GLB_fa and GLB_a performed consistently well when loadings were high and less variable as well as the sample size and the number of scale items increased. Of the two GLB estimators, GLB_a consistently outperformed GLB_fa.

This study provides hospitality managers with a better understanding of what reliability is and the various reliability estimators. Using reliable instruments ensures that organizations draw accurate conclusions that help them move closer to realizing their visions.

Though popular in other fields, reliability discussions have not yet received substantial attention in hospitality. This study raises these discussions in the context of hospitality research to promote better practices for assessing the reliability of scales used within the hospitality domain.

The primary objective of this study is to investigate the ethical implications of deploying AI-enabled chatbots in the healthcare sector. In addition, the research underscores trust and reliability as critical factors in addressing the ethical challenges associated with these chatbots.

This study takes a qualitative approach, conducting 13 semi-structured interviews with a diverse range of participants, including patients, healthcare professionals, academic researchers, ethicists, and legal experts. This broad spectrum of perspectives ensures a comprehensive understanding of the ethical implications of AI-enabled chatbots in healthcare. The rich exploratory data gathered from these interviews is then analysed using thematic analysis.

The findings of this study are highly significant in the context of AI-enabled healthcare chatbots. They highlight four major themes: developing trust, ensuring reliability, ethical considerations, and potential ethical implications. The interconnectedness of these themes forms a coherent narrative, highlighting the pivotal role of trust and reliability in mitigating ethical issues.

This study contributes to the existing literature on AI-enabled healthcare chatbots. It not only reveals potential ethical concerns associated with these technologies, such as data security, patient privacy, bias, and accountability, but it also places a significant emphasis on trust and reliability as critical elements that can boost user confidence and engagement in using AI-enabled chatbots for healthcare advice.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

The purpose of this paper is to illustrate the new technical demands and reliability challenges to printed circuit board (PCB) designs, materials and processes when the transmission frequency increases from Sub-6 GHz in previous generations to millimeter (mm) wave in fifth-generation (5G) communication technology.

The approach involves theoretical analysis and actual case study by various characterization techniques, such as a stereo microscope, metallographic microscope, scanning electron microscope, energy dispersive spectroscopy, focused ion beam, high-frequency structure simulator, stripline resonator and mechanical test.

To meet PCB signal integrity demands in mm-wave frequency bands, the improving proposals on copper profile, resin system, reinforcement fabric, filler, electromagnetic interference-reducing design, transmission line as well as via layout, surface treatment, drilling, desmear, laminating and electroplating were discussed. And the failure causes and effects of typical reliability issues, including complex permittivity fluctuation at different frequencies or environments, weakening of peel strength, conductive anodic filament, crack on microvias, the effect of solder joint void on signal transmission performance and soldering anomalies at ball grid array location on high-speed PCBs, were demonstrated.

The PCB reliability problem is the leading factor to cause failures of PCB assemblies concluded from statistical results on the failure cases sent to our laboratory. The PCB reliability level is very essential to guarantee the reliability of the entire equipment. In this paper, the summarized technical demands and reliability issues that are rarely reported in existing articles were discussed systematically with new perspectives, which will be very critical to identify potential reliability risks for PCB in 5G mm-wave applications and implement targeted improvements.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

As an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.

First of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.

The feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.

This paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

This study aims to comprehensively identify supply chain risks and their causes, the factors influencing supply chain management and techniques to successfully mitigate and control supply chain risks in construction projects. This study developed a comprehensive framework showing various supply chain risks and how these risks that influence project execution are systematically identified and managed for the overall construction project success.

The research conducted was characterised by its descriptive, exploratory and quantitative nature. The collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly. This group included individuals with various roles in the construction industry, such as project managers, civil/structural engineers mechanical engineers, risk managers, architects, quantity surveyors, electrical engineers, construction managers, health, safety and environment managers, estate managers and other professionals. All participants were actively involved in construction projects located in the Gauteng province of South Africa. The data was analysed, using descriptive statistical methods, including factor analysis, reliability assessment and calculations of frequencies and percentages.

The result showed that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, construction supply chain management coordination, delivery reliability, the proximity of suppliers, identification of supply chain risks in the conceptualisation stage of a project, identification of supply chain risks in the planning stage of a project, identification of supply chain risks in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the construction supply chain management the most. The result also showed that subcontractor’s negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery reliability, disorganised construction supply chain management approach, delayed funding, low delivery reliability, poor inventories, poor construction supply chain co-ordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the factors that have the greatest impacts on construction supply chain risks management.

For future research, it is recommended to incorporate fourth industrial revolution) such as machine learning prediction models and algorithms, Artificial intelligence and blockchain to identify and manage supply chain, supply chain risks and project stakeholders involved in supply chain in construction projects. Green construction or sustainable construction was not fully covered in this study. The findings will be beneficial for sustainable construction projects in developing countries for sustainability, although it did not extensively cover green buildings and related risks.

Supply chain risk is one of the major challenges facing the construction industry because construction projects are complex by nature involving a lot of activities and participants with different responsibilities and tasks therefore it is highly recommended to implement the proposed frameworks in this paper from the conceptualisation stage to the execution stage, carefully identifying parties involved in supply chain, supply chain management, stakeholders, tasks, activities, responsibilities and supply chain risks generated as a result of the interactions between stakeholders involved in supply chain management and coordination to realise project objectives. The findings will be a foundation for identifying and managing supply risks in sustainable buildings in developing countries.

Supply chain management is crucial in every enterprise. Managing supply chain risks is a major aspect of risk and disaster management and this implies that supply chain excellence is achievable by building communication, trust and mutual objectives, no blame culture, performance measurement, constant improvement and partnering.

The implementation of construction supply chain risk management framework involves assessing the impacts of these supply chain risks on the objectives of construction projects with respect to time, cost, safety, health, environment, stakeholders, financial performance, client satisfaction and quality.