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This paper aims to investigate the corrosion resistance of two types of coatings – one is ceria sol coating and the other is ceria sol coating modified by ZnO nanoparticles on 7075 aluminum alloy in 3.5% NaCl solution.

Aluminum alloys were dipped into ceria sol and ceria sol modified by ZnO nanoparticles separately and removed after 10 min from the solutions and dried at 110°C for 30 min and heated at 500 °C for 30 min to form the coatings. The coatings have been characterized by using field emission scanning electron microscopy (FE-SEM), electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS). The EIS tests were performed in a corrosive solution of 3.5% NaCl.

The results showed that the coating of ceria sol modified by ZnO nanoparticles has higher corrosion resistance than the ceria sol coating and the bare sample. Also, the best efficiency is related to aluminum sample immersion after 1 h in NaCl corrosive solution for coating modified by ZnO nanoparticles.

In this research, the modification of ceria sol coating by ZnO nanoparticles had an effect on improving the corrosion behavior of aluminum alloy. It is also understood that modification of coatings is an effective parameter on corrosion resistance.

We present configurational theorising as a novel approach to developing middle-range theory in two steps: (1) we illustrate configurational theorising as a new form of supply chain inquiry by connecting its philosophical assumptions with a methodological execution, and (2) we generate new insights underpinning a middle-range theory for supply chain resilience.

We synthesise information from a range of sources and invoke ‘critical realism” to suggest a five-phase configurational theorising roadmap to develop middle-range theory. We demonstrate this roadmap to explain supply chain resilience by analysing qualitative data from 22 organisations within the Australian food supply chain.

Coopetition and supply chain collaboration are necessary causal conditions, but they need to combine with either supply chain agility or multi-sourcing strategy to build supply chain resilience. Asymmetrical analyses showed that the simultaneous absence of supply chain collaboration, supply chain agility and multi-sourcing results in low supply chain resilience, but coopetition was indifferent to low supply chain resilience. Similarly, high supply chain resilience is possible with the non-presence of supply chain agility and multi-sourcing.

The configurational middle-range theorising roadmap presented and empirically tested in this paper constitutes a substantial advancement to both theory and the methodological domain.

This is the first attempt at developing a middle-range theory for supply chains by explicitly drawing on configurational theorising.

This paper aims to examine the influence of supply chain management by assessing the relationship between internal and external integration and small and medium enterprises (SMEs) owners’ Islamic practices in enhancing halal supply chain integrity (HSCI) and SMEs’ performance.

A total of 176 SMEs were surveyed using a self-administered questionnaire. The sample was selected using convenience sampling from two major halal exhibition events in Malaysia. Structural equation modeling (SEM) was used to analyze the data and test the hypotheses.

The findings showed that supply chain integration (SCI), Islamic human capital and HSCI have a significant relationship with SMEs’ performance. It was also found that HSCI mediated the relationship between both SCI and Islamic human capital and SMEs’ performance.

SME owners or managers should be committed to developing the internal processes within the organization and strategizing to link these processes with the external processes to obtain the full benefits of integration. Furthermore, as the upper management, owners and managers must understand the supply chain challenges, priorities and practices thoroughly, as they are responsible for Islamic business ethics. They should work to provide support to increase religious orientation in the SMEs, as this would likely enhance all other factors.

This is one of the few types of research to use HSCI as a mediator in halal food studies in addition to improving SMEs’ performance.

In today’s complex and globalised business setting, Australian clothing retailers are ever more vulnerable to supply chain disruptions. Supply chain resilience reduces the effect of a disruption, which permits the members of a supply chain to respond aptly to disruptive events. This study, hence, aims to uncover the details of how the small and medium-sized enterprises (SMEs) of clothing retailers in Australia build supply chain resilience and what are the major issues experienced by the SMEs while building resilience.

This study is carried out using a descriptive qualitative research design, and data are collected from semi-structured interviews with key informants from managerial levels within the Australian clothing retailers’ businesses.

This study identifies five enablers, namely, collaboration, multi-sourcing, visibility, flexibility and information systems, that the SMEs of clothing retailers mostly consider to achieve resilience in the supply chain. This study also finds that SMEs’ capabilities, cost and financing, lack of managerial autonomy and the inability to create redundancy are the key impediments hindering SMEs from attaining the expected level of resilience.

To the best of the author’s knowledge, this study contributes to the body of knowledge by being one of the first empirical studies to explore the SMEs of clothing retailers’ supply chain resilience in the Australian business context, which can add valuable insights for academics and practitioners in guiding supply chain design decisions for the SMEs in other sectors.

This study aims to investigate the efficacy of Ag@GO/Na₂SiO₃ nanocomposite in eliminating As from aqueous solutions. Employing response surface methodology, the research systematically examines the adsorption process.

Various experimental parameters including sample pH, contact time, As concentration and adsorbent dosage are optimized to enhance the As removal process.

Under optimized conditions, the initial As concentration, contact time, pH and adsorbent dosage are determined to be 32 ppm, 50 mins, 6.5 and 0.4 grams, respectively. While the projected removal of As stands at 97.6% under these conditions, practical application achieves a 93% removal rate. Pareto analysis identifies the order of significance among factors as follows: adsorbent dosage > contact time > pH > As concentration.

This study highlights the potential Ag@GO/Na₂SiO₃ as a promising adsorbent for efficiently removing industrial As from aqueous solutions, and it is likely to have a good sufficiency in the filtration of water and wastewater treatment plans to remove some chemical pollution, including paints and heavy metals.

The simplicity of the nanocomposite preparation method without the need for advanced equipment and the cheapness of the raw materials and its potential ability to remove As are the prominent advantages of this research.

With the emergence of the different Industry 4.0 technologies and the interconnectedness between the physical and cyber components within manufacturing systems, the manufacturing environment is becoming more susceptible to unexpected disruptions, and manufacturing systems need to be even more resilient than before. Hence, the purpose of this work is to explore how does incorporating Industry 4.0 into current manufacturing systems affects (positively or negatively) its resiliency.

A Systematic Literature Review (SLR) was performed with a focus on studying the manufacturing system’s resilience when applying Industry 4.0 technologies. The SLR is composed of four phases, which are (1) questions formulation, (2) determining an adequate search strategy, (3) publications filtering and (4) analysis and interpretation.

From the SLR results’ analysis, four potential research opportunities are proposed related to conducting additional research within the research themes in this field, considering less studied Industry 4.0 technologies or more than one technology, investigating the impact of some technologies on manufacturing system’s resilience, exploring more avenues to incorporate resiliency to preserve the state of the system, and suggesting metrics to quantify the resilience of manufacturing systems.

Although there are a number of publications discussing the resiliency of manufacturing systems, none fully investigated this topic when different Industry 4.0 technologies have been considered. In addition to determining the current research state-of-art in this relatively new research area and identifying potential future research opportunities, the main value of this work is in providing insights about this research area across three different perspectives/streams: (1) Industry 4.0 technologies, (2) resiliency and (3) manufacturing systems and their intersections.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

This paper explores the direct and indirect effects of family emotional, social and organizational support on Innovative Work Behavior (IWB) through psychological capital (Psy.Cap).

Selected by conducting stratified random sampling techniques, 397 employees completed a questionnaire. We used structural equation modeling and multi-group testing by Smart-PLS3 to analyze the data.

Findings reveal that all sources of social-emotional support, including family, supervisor and co-worker support, positively affect Psy.Cap. Moreover, Psy.Cap mediates the effect of family, co-workers and supervisors' emotional support on IWB. The multi-group analysis indicates that all relationships in the model are significant for both groups of males and females; however, there are no significant differences in the link between organizational support and psychological capital, as well as family and co-worker support and innovative work behavior between males and females. The study's results demonstrate the significantly higher impact of family emotional support – Psy.Cap and supervisor support on IWB amongst females compared to their male counterparts.

The implications of this research highlight the importance of considering affective factors on employees’ IWB, as well as the differences between genders in this regard.

Architecture, engineering and construction (AEC) is an important industry worldwide and one of the largest economic sectors in several developing countries, particularly in Iran. The Iranian AEC sector suffers from low productivity and needs to adopt building information modeling (BIM) to reduce inefficiencies. Therefore, this paper was conducted to identify the BIM barriers and propose practical solutions to overcome them in Iran.

A comprehensive literature review, two rounds of the Delphi technique and semi-structured interviews with 12 Iranian experts in the AEC sector were conducted. The data were analyzed using the mean score, standard deviation and nonparametric tests.

The present study identified 26 BIM barriers in the Iranian AEC community and provided practical strategies for improving BIM adoption. The identified barriers were categorized into six main groups including source barriers, financial barriers, unawareness barriers, organizational barriers, regulatory barriers and market-demand barriers. The main three BIM barriers in Iran were the lack of government intervention, change-resistant and the gap between industry and academia. Kruskal–Wallis tests revealed that there are no statistically significant differences in perceptions of BIM barriers between respondents. The Mann–Whitney test indicated that there is no statistically significant difference in perceptions between engineers and architects except for one.

There are few studies on BIM adoption across developing countries, particularly in Iran. Moreover, the results can also be used in other developing nations with similar conditions.

Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.