The supply chain of many industries, including Oil and Gas, was significantly affected by the disruption caused by the Covid pandemic. This, in turn, had a knock-on effect on other industries around the globe. Sustaining the impact of the disruption posed a major challenge for the industry. This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic. Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry. Multi-criteria decision-making methodologies were used to analyze these drivers. The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were “Risk management capacity”, “Government regulation” and “Health and safety of employees”. On the other hand, the driver “Community Pressure” was found to be of the least importance. Furthermore, the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index. A case example was demonstrated to rank the industries based on such calculations. This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index. Additionally, the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption. This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably, which is essential for cooperation. Attempting to construct a framework that might foster international cooperation, and thus be helpful for coping with global emergencies, we relate a Western nomological approach to an Eastern systems approach to analyse intercultural trust in global contexts. Considering cultural impacts on intercultural trust and the nomological framework of cultural differences, we propose an intercultural trust model to interpret how cultural differences influence trust. A qualitative study of Chinese-Irish interactions was conducted to interpret this model. We organized 10 seminars on intercultural trust, and interviewed 16 people to further explore the respondents’ deeper feelings and experiences about intercultural trust in global contexts. Through this study, we have identified factors impacting on intercultural trust, and found that intercultural trust can be developed and improved in various ways. To illustrate these ways, we have provided tactics and methods for building intercultural trust in global contexts. Implications are highlighted for organizations to avoid cultural clashes and relevant political or economic risks.

Uncertainty is an important factor that needs to be considered while analyzing the performance of any engineering system. In order to quantify uncertainty, fuzzy set theory is frequently used by most of researchers, including energy system experts. According to the classical reliability theory, component lifetimes have crisp parameters, but due to uncertainty and inaccuracy in data, it is sometimes very difficult to determine the exact values of these parameters in real-world systems. To overcome this difficulty in the current research, failure and repair rates were taken as triangular fuzzy numbers to determine the fuzzy availability of a system undergoing calendar-based periodic inspection subject to multiple failure modes (FMs). It was assumed that each component in the system had an exponential failure rate and repair rate with fuzzy parameters. System FMs were explicitly taken into account when a functional state of the system was considered. Each FM had a random failure time. On the occurrence of any failure, a random time was selected for the relevant corrective repair work. The proposed research was studied for one of the major sources of green energy, namely a wind turbine system wherein all the derived propositions have been implemented on it.

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure. However, the regional power system is complicated and uncertain, affected by multiple factors including climate, population and economy. This paper incorporates structure expansion, parameter optimization and rolling mechanism into a system forecasting framework, and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption, improving the accuracy of the traditional grey models. The optimal fractional order is obtained by using the particle swarm optimization algorithm, which enhances the model adaptability. Then, the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province. Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026. Compared with other seven benchmark prediction models, the proposed grey system model performs best in terms of both simulation and prediction performance metrics, providing scientific reference for regional energy planning and electricity market operation.

The Scheduling of the Multi-EOSs Area Target Observation (SMEATO) is an EOS resource scheduling problem highly coupled with computational geometry. The advances in EOS technology and the expansion of wide-area remote sensing applications have increased the practical significance of SMEATO. In this paper, an adaptive local grid nesting-based genetic algorithm (ALGN-GA) is proposed for developing SMEATO solutions. First, a local grid nesting (LGN) strategy is designed to discretize the target area into parts, so as to avoid the explosive growth of calculations. A genetic algorithm (GA) framework is then used to share reserve information for the population during iterative evolution, which can generate high-quality solutions with low computational costs. On this basis, an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is sufficient. The effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different scales. The results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9% of instances, especially in large-scale instances. These fully demonstrate the high efficiency and stability of the ALGN-GA.