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In recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.

Presented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm (Tω) based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.

The result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.

Existing fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.

The purpose of this paper is to analyze the fuzzy reliability of the compressor house unit (CHU) system in a coal fired thermal power plant under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process. This paper uses different fuzzy membership functions to quantify uncertainty and access the system reliability in terms of different fuzzy reliability indices having symmetric shapes.

This study analyses the fuzzy reliability of the CHU system in a coal fired thermal power plant using T_ω-based generalized fuzzy Lambda-Tau (TBGFLT) technique. This approach applies fault tree, Lambda-Tau method, different fuzzy membership functions and α-cut coupled T_ω-based approximate arithmetic operations to compute various reliability parameters (such as failure rate, repair time, mean time between failures, expected number of failures, availability and reliability) of the system. The effectiveness of TBGFLT technique has been demonstrated by comparing the results with results obtained from four different existing techniques. Moreover, this paper applies the extended Tanaka et al. (1983) approach to rank the critical components of the system when different membership functions are used.

The adopted TBGFLT technique in the present study improves the shortcomings of the existing approaches by reducing the accumulating phenomenon of fuzziness, accelerating the computation process and getting symmetric shapes for computed reliability parameters when different membership functions are used to quantify data uncertainty.

In existing fuzzy reliability techniques which are developed for repairable systems either triangular fuzzy numbers, triangle vague sets or triangle intuitionistic fuzzy sets have been used for quantifying uncertainty. These approaches do not examine the systems for components with different membership functions. The present study is an effort in this direction and evaluates the fuzzy reliability of the CHU system in a coal fired thermal power plant for components with different membership functions. This is the main contribution of the paper.

The purpose of this paper is to analyze the reliability of the washing system in a paper plant in a more promising way under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process using the T_ω (weakest t-norm) based fuzzy lambda-tau (TBFLT) technique.

This paper presents a unified approach for analyzing the fuzzy reliability of the washing system under vague environment. This approach applies the TBFLT technique which uses triangular fuzzy numbers for incorporating data uncertainty, fault tree and lambda-tau method for finding system failure rate and repair time mathematical expressions while simplified T_ω-based arithmetic operations are applied for computing various reliability parameters of the system. The effectiveness of the TBFLT technique has been demonstrated by analyzing fuzzy reliability of the system using five different techniques including TBFLT. Moreover, this paper applies extended Tanaka’s (1983) approach to rank the critical components of the system.

The TBFLT technique has the advantage of low computation complexity in comparison to other techniques and effectively reduces the accumulating phenomenon of fuzziness. This main finding verifies the conclusion made by Chen (1994).

The author has suggested a simple and more applicable technique for analyzing the fuzzy reliability of any complex process industrial system under vague environment. The effectiveness of the technique has been demonstrated by computing various reliability parameters of the washing system of a paper plant.

Prisons can be epicentres of infectious diseases. However, empirical evidence on the impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in prison is still scarce. This study aims to estimate the seroprevalence rates of anti-SARS-CoV-2 in the largest and most crowded Swiss prison and compare them with the seroprevalence rate in the general population.

A cross-sectional study was conducted in June 2020, one month after the first wave of SARS-CoV-2 in Switzerland. Groups included: people living in detention (PLDs) detained before the beginning of the pandemic (n = 116), PLDs incarcerated after the beginning of the pandemic (n = 61), prison staff and prison healthcare workers (n = 227) and a sample from the general population in the same time period (n = 3,404). The authors assessed anti-SARS-CoV-2 IgG antibodies.

PLDs who were incarcerated before the beginning of the pandemic had a significantly lower seroprevalence rate [0.9%, confidence interval (CI)95%: 0.1%–5.9%] compared to the general population (6.3%, CI 95%: 5.6–7.3%) (p = 0.041). The differences between PLDs who were incarcerated before and other groups were marginally significant (PLDs incarcerated after the beginning of the pandemic: 6.6%, CI 95%: 2.5%–16.6%, p = 0.063; prison staff CI 95%: 4.8%, 2.7%–8.6%, p = 0.093). The seroprevalence of prison staff was only slightly and non-significantly lower than that of the general population.

During the first wave, despite overcrowding and interaction with the community, the prison was not a hotspot of SARS-CoV-2 infection. Preventive measures probably helped avoiding clusters of infection. The authors suggest that preventive measures that impact social welfare could be relaxed when overall circulation in the community is low to prevent the negative impact of isolation.

Body-packing means concealing packets of illicit psychoactive substances in the digestive or genital system. The purpose of this paper is to investigate profiles of body-packers and comorbidities associated with body-packing.

A retrospective study (2005–2016) was conducted among all patients hospitalized for suspicion of body-packing in the Geneva hospital prison unit (n=287). Data were extracted from medical records and included demographics, somatic/psychiatric diseases, suicidal ideation and psychological distress.

Body-packers were mostly young men (mean age=33.4). A total of 42.2 percent of the participants had at least one psychiatric or somatic comorbidity reported during incarceration (somatic: 28.2 percent, psychiatric: 18.8 percent). The most frequent somatic diseases were infectious (10.5 percent), cardiovascular (10.1 percent), and endocrinological (4.2 percent) diseases, and more precisely HIV (4.5 percent), hepatitis B (3.5 percent), hepatitis C (1.4 percent), high blood pressure (8.0 percent) and diabetes (4.2 percent). The most frequent psychiatric conditions were substance use disorders (10.5 percent) and mood disorders (8.0 percent). Depressed mood/psychological distress and suicidal ideation were frequently reported during hospitalization (27.2/6.6 percent). Comorbidities were associated with demographics: Females were more likely to have somatic and psychiatric diseases detected during hospitalization in detention and participants from Western, educated, industrialized, rich and democratic countries were more likely to report diseases known before detention.

Body-packers bear a heavy burden of disease and psychological distress. This vulnerable subgroup of incarcerated people has been overlooked in previous research and their health needs are not correctly understood. This study was a first step to improve their health care and reintegration.

Assistive technology has been developed to assist the visually impaired individuals in their social interactions. Specifically designed to enhance communication skills, facilitate social engagement and improve the overall quality of life, conversational assistive technologies include speech recognition APIs, text-to-speech APIs and various communication tools that are real. Enable real-time interaction. Using natural language processing (NLP) and machine learning algorithms, the technology analyzes spoken language and provides appropriate responses, offering an immersive experience through voice commands, audio feedback and vibration alerts.

These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social interactions. Moreover, they promise to improve social competence and foster better relationships. In short, assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals, elevating the quality of their social engagement.

The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social contexts. This technology helps them communicate effectively with acquaintances, family, co-workers and even strangers in public places. By enabling smoother and more natural communication, it works to reduce feelings of isolation and increase overall quality of life.

Research findings include successful activity recognition, aligning with activities on which the VGG-16 model was trained, such as hugging, shaking hands, talking, walking, waving and more. The originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern technology. Research adds to the body of knowledge in the area of assistive technologies, which contribute to the empowerment and social inclusion of the visually impaired individuals.

This viewpoint paper aims to explore the adoption of artificial intelligence (AI) in personalized learning for employees and its potential, challenges and ethical consideration to transform the landscape of employee’s performance improvement.

A review of relevant research papers, articles and case studies is done to highlight developments in research and practice.

Personalised learning powered by AI will transform employee education and training as AI evolves, helping individuals and organisations succeed in an ever-changing, knowledge-driven world. Concerns like data privacy, algorithmic bias and human engagement must be addressed to achieve ethical and fair implementation.

This paper discusses the overall influence of AI adoption on personalised learning for employees. It examines how AI technologies have the ability to disrupt traditional learning methodologies, modify organisational learning cultures and encourage an environment that fosters ongoing learning and innovation.

This paper aims to discuss the academic literature on the impact of audit committee effectiveness on different outcomes (accounting, auditing, governance and economics) in China.

The authors have conducted a systematic review using the PRISMA guidelines.

The key finding is that the regulatory organisations in China, such as the China Securities Regulatory Commission (CSRC) and the State-Owned Assets Supervision and Administration Commission (SASAC), need to play the active role that is expected of them to enhance the transparency and independence of an audit committee. Also, Chinese listed companies are facing institutional barriers (CEO power, concentrated ownership and government influence) to effectively implement the imported concept within China. Research relating to the audit committee's effectiveness has focused mainly on agency and resource dependence perspectives.

China’s regulatory bodies (CSRC and SASAC) should make necessary reforms to enhance the audit committee’s effectiveness. This study also provides implications for the other settings that have imported the audit committee concept from the Anglo-American countries.

This study contributes to the literature by synthesising the prior mixed findings on audit committee literature in China and providing suggestions to the regulators and future research.

The purpose of this paper is to model the key influencers or predictors for usage of technology by sales professionals in India.

This study examines the effect of individual, organizational and social factors on the use of technology. The method used for primary research is survey, and data have been collected from 556 sales professionals from across four industries in India. The empirical analysis consists of confirmatory factor analysis using structural equation modeling.

Confirmatory factor analysis has been used to validate the constructs, namely, peer usage, support service, perceived usefulness, attitude, usage and performance. Based on the results, it has been concluded that attitude and peer usage have a direct effect on the use of technology. Support service affects peer usage and perceived usefulness affects attitude. This model helps us understand the direct and indirect predictors of use of sales technology (ST), and its effect on sales performance.

The limitation of this study is that data pertain to India only.

The findings of this paper will help sales managers and organizations develop strategies to increase the acceptance and use of new technologies, and also strengthen their arguments for convincing the various stakeholders about the benefits and relevance of the ST.

The paper attempts to build and validate a parsimonious model taking into account peer usage (social factor) and support service (organizational factor) in addition to perceived usefulness and attitude.

The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.