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This paper aims to evaluate corrosion behavior of bare and PbO₂-coated stainless steel 316L, as prospective candidates for bipolar plates, in simulated proton exchange membrane fuel cell’s (PEMFC’s) environment under operating potentials.

A set of potentiodynamic, as well as potentiostatic, electrochemical experiments was carried out under both anodic and cathodic potentials. Gathered data were analyzed via fast Fourier transform algorithm for further investigation. X-ray diffraction analysis was also used for determining coating characteristics upon completion of electrochemical experiments.

Results revealed that bare SS316L is a better candidate for bipolar plate material under anodic potential, as it is cathodically protected. However, PbO₂-coated SS316L is favorable under cathodic potential, as bare specimen will suffer localized corrosion in the form of pitting.

It would be of interest if all the experiments are carried out in a PEMFC stack.

This research strives to promote the use of electrochemical noise measurement for practical corrosion monitoring of coated bipolar plates in fuel cells.

Improving the corrosion resistance of bipolar plates will expedite commercialization of PEMFCs, which in turn will translate into a substantial reduction in carbon footprint.

This research strives to promote the use of electrochemical noise measurement for practical corrosion monitoring of coated bipolar plates in fuel cells.

This paper aims to investigate the corrosion behavior of AZ31 alloy as a prospective biomedical implant in two different simulated biological solutions and various immersion times.

Results of electrochemical experiments indicated that corrosion resistance of specimens immersed for 24 h was superior, suggesting that the surface layer is capable of protecting alloy.

Scanning electron micrographs revealed that this layer abounds with cracks, exhibiting optimum quality in 24 h immersion time, after which it begins to develop corrosion pits. Energy dispersive spectroscopy analysis suggested that the layer is mainly composed of magnesium hydroxide with precipitates of P and Ca containing species present on its surface, which is an indication of biocompatibility.

Finally, corrosion performance of Mg alloy was found to be slightly better in Lac-simulated biological solution (SBF) solution, which is more representative of actual physiological environment as compared to conventional SBF solutions.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.

As technology becomes more widely accessible, there is a growing concern about internet gaming disorder (IGD) around the world. The aim of this study is to evaluate the prevalence of IGD and also assess the effects of depression, anxiety, stress and insomnia levels on the IGD of youths in Bangladesh.

A cross-sectional quantitative study design was used to collect data from the youths of different locations in Bangladesh between October 21, 2023 and January 15, 2024. A total of 501 samples were collected using the convenience sampling technique. The following measurement scales were Internet Gaming Disorder Scale short form, depression, anxiety and stress scales and insomnia severity index, which were used to assess scores for IGD, psychological distress and insomnia, respectively.

The study found that the prevalence of IGD was 9.8%. IGD was shown to be significantly associated with depression, anxiety and stress. Aside from that, IGD and insomnia had a significant association, as did a friendly family environment.

The generalizability of the results could be improved by conducting additional studies with a more diverse sample, such as the general population or a different age group.

The study will help the government reduce the prevalence of IGD, improving the mental and physical health of youth.

No research has been conducted on youth and different professions in Bangladesh. There has also been very little research on the prevalence of gaming addiction and mental health.

The study of supply chain resilience (SCRES) continues to gain interest in the academic and practitioner communities. The purpose of this paper is to present a focused review of the SCRES literature by investigating supply chain (SC) capabilities, their relationship to SCRES outcomes and the underpinning theoretical mechanisms of this relationship.

The paper uses the systematic literature review approach to examine 383 articles published between 2000 and 2017, ultimately down selecting to the most relevant 228 peer-reviewed studies. Context-interventions-mechanisms-outcomes (CIMO) logic is applied to organize and synthesize these peer-reviewed studies. A typological framework is developed from the CIMO-based classification of the SCRES literature.

The findings of this study outline the gaps in the SCRES literature and present an agenda for future research.

This paper presents an exploratory research; therefore, the typological model presented is just one of the possible perspectives.

The typology of SCRES literature can help practitioners to understand SCRES and to measure and assess the resilience of SCs.

The paper provides clear definitions of SCRES constructs, develops a typological framework to further understand SCRES and identifies SCRES measures and assessment techniques.

This study aims to determine how to influence the customer journey of mobile food ordering applications (MFOAs) users. It researches how available information could influence customers’ intention to use MFOAs platforms in the prepurchase stage and explores the potential of personalized information to improve customer satisfaction with these services in the postpurchase stage.

This research followed a mixed design, combining qualitative (focus groups) and quantitative (online survey) research and using both content analysis and partial least squares structural equation modeling.

Two types of available information (firm-generated information and online customer reviews) had a positive influence on the behavioral intention to use MFOAs. Additionally, findings showed that different web personalization strategies, namely, content personalization, functional personalization and system-driven personalization, were useful tools to create customer satisfaction with this type of platform.

The study discusses limitations regarding the sample and sampling process, indicator variables and measures.

The present research provides actionable insights for online food delivery providers.

This study addresses a research gap in the literature and provides a novel and richer understanding of customer behavior toward mobile food delivery platforms. Also, it adds to the personalization research by identifying and testing a range of web personalization strategies.