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This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

This paper aims to propose a stepwise method to improve the sustainability of manufacturing processes.

The proposed approach is based on an extensive literature review and research around the environmental, economic and social pillars of sustainability in manufacturing firms. Considering the lean approach, the manufacturing processes are mapped in a value stream and analyzed through the extensive identified sustainability criteria.

The findings reveal the consumption and waste of natural and nonrenewable resources, through going beyond the existing boundaries and focusing on relevant derived production pieces and tracing to their origins. The findings also present the effect of the time value of money on sustainability by using the cost–time profile as a sustainability criterion. This research finds out the employees’ impacts on sustainability improvement through an effective focus on technical, cultural and personal aspects.

The research outcomes provide operations managers and decision-makers in the field of sustainability with a practical platform to comprehend and assess the factors contributing to the manufacturing process sustainability and to plan relevant corrective actions accordingly.

The extended view of sustainability criteria in this research as well as its visual-analytical approach will help practitioners to assess and improve sustainability in their operations in a more holistic way.

Particles are of the controlled release delivery systems. Also, topically applied olive oil has a protective effect against ultraviolet B (UVB) exposure. Due to its sensitivity to oxidation, various studies have investigated the production of olive oil particles. The purpose of this study was to use chitosan and sodium alginate as the vehicle polymers for olive oil.

The gelation method used to prepare the sodium alginate miliparticles containing olive oil and particles were coated with chitosan. Morphology and size, zeta potential, infrared spectrum of olive oil miliparticles, encapsulation efficiency and oil release profile were investigated. Among 12 primary fabricated formulations, formulations F₅ (olive oil loaded alginate miliparticles) and F₁₁ (olive oil loaded alginate miliparticles + chitosan coat) were selected for further evaluations.

The size of the miliparticles was in the range of 1,100–1,600 µm. Particles had a spherical appearance, and chitosan coat made a smoother surface according to the scanning electron microscopy. The zeta potential of miliparticles were −30 mV for F₅ and +2.7 mV for F₁₁. Fourier transform infrared analysis showed that there was no interaction between olive oil and other excipients. Encapsulation efficiency showed the highest value of 85% in 1:4 (olive oil:alginate solution) miliparticles in F₁₁. Release study indicated a maximum release of 68.22% for F₅ and 60.68% for F₁₁ in 24 h (p-value < 0.016). Therefore, coating with chitosan had a marked effect on slowing the release of olive oil. These results indicated that olive oil in various amounts can be successfully encapsulated into the sodium-alginate capsules cross-linked with glutaraldehyde.

To the best of the authors’ knowledge, no study has used chitosan and sodium alginate as the vehicle polymers for microencapsulation of olive oil.

The purpose of this study is to investigate the factors affecting the silence of internal auditors. To this end, the impacts of the perceived climate of silence, professional commitment, independence commitment, role conflict and role ambiguity on internal auditor silence have been investigated. Furthermore, the effects of role conflict and role ambiguity through independence commitment on internal auditor silence were investigated.

The statistical population of the study consisted of Iranian internal auditors in 2021. The study used a self-administered survey of 217 internal auditors. In this research, a hierarchical component model in the partial least squares structural equation modeling analysis was used to examine the hypotheses.

The results of testing the hypotheses indicated that the perceived climate of silence and role ambiguity have positive effects, and professional commitment and independence commitment have negative effects on internal auditor silence. Furthermore, role conflict has an insignificant effect on internal auditor silence. In addition, role conflict and role ambiguity affect the internal auditor silence through the independence commitment.

This study examined the factors affecting the internal auditor silence by combining the construct of the perceived climate of silence that has been previously discussed in the field of management with the professional (professional commitment and independence commitment) and role (role conflict and role ambiguity) factors that are discussed in the internal audit profession. To the best of the author’s knowledge, this is the first study that examines the factors affecting internal auditor silence behavior. The importance of conducting this study is that it investigates a phenomenon among internal auditors that conflicts with the mission and origin of internal audit.

To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.

To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.

In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.

Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

This study explores the effect of internal branding on organisational financial performance and brand loyalty with the mediating role of psychological empowerment.

The data gathered from 200 Pasargad insurance employees in Iran were analysed. Structural equation modelling and R were used to evaluate the model. Financial performance was measured by four concepts (ROI, ROE, Sales growth, ROA) based on available data from March 2010 to March 2020.

The results revealed that internal branding and psychological empowerment have no significant effect on financial performance, but both have a significant positive effect on brand loyalty. Likewise, the mediating role of psychological empowerment on the subject of the impact of internal branding on brand loyalty was confirmed. Furthermore, psychological empowerment did not play a mediating role in the impact of internal branding on financial performance.

The findings of this study could be important for managers of organisations active in the insurance industry to highlight internal branding and enhance psychological empowerment and employee brand loyalty. Moreover, managers' perception of the effective role of psychological empowerment to enhance employee brand loyalty is another practical aspect of this research.

Considering the mediating role of psychological empowerment to the effect of internal branding on financial performance and brand loyalty is an innovative aspect of the present study. Meanwhile, the use of R software for VB-SEM was another point to surge the value of this paper.

Artificial intelligence (AI)-based systems can save the lives of many people by assessing the safety of flight paths. Unfortunately, the world witnessed a horrible event in January 2020 with the case of flight 752 of Ukrainian International Airlines from Tehran to Kiev and it has prompted us to ask how AI can prevent such events by warning to flight path planners. This paper aims to propose a framework for assessing the safety of flight paths from a shooting of an airplane by air defense systems installed on the path. Unlike the existing studies, this study takes a new look at pre-flight risk assessment by using textual information in social and news networks. To this end, the authors use existing information retrieval techniques to identify high flight risk areas by examining the news articles, comments, posts, tweets, etc., in social media and then estimate the probability of targeting a passenger aircraft by the air defense systems probably installed on high-risk areas with the help of a statistical model. This estimation can then be used by fight planners to avoid such events.

To design a framework for estimating the probability of a fatal shooting of an airplane by air defense systems installed on its flight path, the authors have used the idea of information retrieval in conjunction with statistical methods. The authors have extracted some significant variables in the shooting of flights and proposed an AI-based framework to estimate the probability of a fatal shooting of an airplane during its flight and sketched a case study for using machine learning approaches to assist with flight path planning. As a case study, the authors covered flight 752 to explain the usefulness of the proposed framework in this context.

Unlike the existing methods, this study investigates flight path safety assessment from the social media and crowdsourcing perspective. In this study, the authors proposed an AI-based framework to avoid aviation hazards by estimating the probability of a shooting of an airplane by air defense systems installed on its flight path. Moreover, this study was designed to show how estimating the safety of flight paths by using AI-based methods can help flight planners to avoid such events and gain further insights into the use of AI-based systems in pre-flight risk assessment.

The idea behind the proposed method is original and as the authors’ best knowledge, there is no similar framework using social media for flight path safety assessment.