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Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

The main objective of the present investigation is to develop a novel efficient stochastic model for availability optimization of reverse osmosis machine system (ROMS) for water purification under the concepts of exponentially distributed decision variables and various redundancy strategies at the component level.

ROMS is a complex framework configured in a series structure using six subsystems. Initially, a state transition diagram is developed and Chapman–Kolmogorov differential-difference equations are derived using Markov birth death process. The steady-state availability of the ROMS is derived for a particular case. The impact of variation in failure and repair rates measured on availability. Furthermore, an effort is made to predict the optimal availability of the ROMS system using the metaheuristic algorithms, namely, dragonfly algorithm (DA), grasshopper optimization algorithm (GOA) and whale optimization algorithm (WOA).

It is observed that the ROMS system predicts optimal availability of 0.999926 after five iterations with a population size of 300 by the WOA. The findings of this study are significant for reliability engineers as well as for maintenance engineers to ensure the availability of ROMS for water purification.

In the present investigation, a novel stochastic model is developed for ROMS, and metaheuristics algorithms are applied to predict the optimal availability.

The demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.

This paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).

Nature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.

This paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.

Availability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.

Employee engagement has been identified as a prevalent issue affecting higher education institutions, particularly since the emergence of COVID-19. Therefore, this study aims to investigate the factors contributing to employee engagement in Islamic higher education institutions in the context of Malaysia.

A quantitative research design was used for this study, and a survey questionnaire was used to collect data from 340 staff members of Islamic higher education institutions in Malaysia. The proposed hypotheses underwent testing through the statistical technique of structural equation modelling, using statistical package for the social sciences (SPSS) and analysis of moment structures (AMOS).

The results indicate that training and development, trustworthiness, workplace spirituality, reward and recognition, management support and job autonomy significantly contribute to employee engagement in Islamic higher education institutions in Malaysia.

This study is limited to the staff of Islamic higher education institutions in Malaysia. A comparative cross-cultural research approach may be preferred for a more comprehensive understanding. Therefore, future researchers are encouraged to consider this limitation when investigating the factors contributing to employee engagement in Islamic higher education institutions, particularly to confirm the cogency of our findings.

The findings provide valuable insights into the workforce factors that play key roles in developing a highly engaged workforce in Islamic higher education institutions. This study contributes to the enrichment of the literature in this specific area of study.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

This article attempts to explore the theoretical model and structural dimensions of sustainable consumer behaviour to develop a “sustainable consumer behaviour scale” for sustainable electronic products. Further, this study has tried to elaborate sustainable consumer behaviour by considering the complete consumption cycle which includes purchase, usage and disposal of the sustainable electronic products.

The theory of planned behaviour (TPB) has been employed to understand the multidimensional nature of sustainable consumer behaviour with the help of qualitative and quantitative methods. With the help of a pilot study followed by a main study, a sustainable consumer behaviour scale for sustainable electronic products has been tested and validated for its factor study, reliability, validity and model fit, etc. Moreover, the influence of demographic variables has also been examined with the help of multi-group analysis.

This study highlights that the perceived control behaviour and subjective norms are the major factors that influence sustainable consumer behaviour. Moreover, the results also indicate that female consumers, mid income consumers, young consumers (age below 30) and consumers who have studied up to senior secondary level are more sustainable.

The results can be used by policymakers and managers to identify and target particular subjective norms and factors impacting perceived control behaviour along with a specific set of demographics to increase sustainability amongst consumers and businesses. The results of the current study can help in increasing the focus of the academic research towards sustainable consumer behaviour. It will also encourage firms to include sustainable electronic products in their product line.

To the best of authors' knowledge, the current article is the first empirical study to develop a sustainable consumer behaviour scale by including all the different stages of the consumption cycle using TPB for sustainable electronic products. Although multiple efforts have been made by researchers to analyse sustainable consumer behaviour, there is a scarcity in literature in which research has been done to analyse sustainable consumer behaviour by considering the whole consumption cycle (purchase, usage and disposal).

The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users.

From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate.

The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent.

The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city.

This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.