In this study, we proposed a novel approach to improve centrifugal pump performance with regard to the pump head, pump efficiency, and power. Firstly, to establish constraints, an optimal numerical model that accounted for factors such as pump efficiency and the head was considered. The pump was designed, and an artificial intelligence algorithmic approach was applied to the pump before performing experiments. We considered a set of models by selecting the parameters of the centrifugal pump casing section area, the interference of the impeller, the volute tongue length, and the volute tongue angle. The weights of the factors of safety and displacement on the optimization indices were estimated. The matrix of the weights for the optimal process was less than 38% or greater than 62%. This approach guarantees a complicated multi-objective optimization problem. The results show that the centrifugal pump performances were improved.

Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to ensure sustainable supply chains, recover better from the pandemic and effectively respond to any future unprecedented crises. The purpose of this study is to assess and choose a possible supplier based on their capability to adapt to the COVID-19 epidemic in a sustainable manner. For this assessment, a framework based on multi-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts. In the first stage, the evaluation criteria system is identified through a literature review and experts’ opinions. The SF-AHP is then used to determine the criteria weights. Finally, the G-COPRAS method is utilized to select sustainable suppliers. A case study in the automotive industry in Vietnam is presented to demonstrate the proposed approach’s effectiveness. From the SF-AHP findings, “quality”, “use of personal protective equipment”, “cost/price”, “safety and health practices and wellbeing of suppliers”, and “economic recovery programs” have been ranked as the five most important criteria. From G-COPRAS analysis, THACO Parts (Supplier 02) is the best supplier. A sensitivity study was also conducted to verify the robustness of the proposed model, in which the priority rankings of the best suppliers are very similar. For long-term development and increased competitiveness, industrial businesses must stress the integration of response mechanisms during SSS implementation in the COVID-19 epidemic, according to the findings. This will result in significant cost and resource savings, as well as reduced environmental consequences and a long-term supply chain, independent of the crisis.

The design of cooperative applications combining several unmanned aerial and aquatic vehicles is now possible thanks to the considerable advancements in wireless communication technology and the low production costs for small, unmanned vehicles. For example, the information delivered over the air instead of inside an optical fiber causes it to be far simpler for an eavesdropper to intercept and improperly change the information. This article thoroughly analyzes the cybersecurity industry’s efficiency in addressing the rapidly expanding requirement to incorporate compelling security features into wireless communication systems. In this research, we used a combination of DEA window analysis with the Malmquist index approach to assess the efficiency of the cybersecurity industry. We used input and output factors utilizing financial data from 2017–2020 sources from a US market. It was found that U1—Synopsys and U9—Fortinet exhibited the best performances when relating Malmquist and DEA window analysis. By evaluating ten big companies in the cybersecurity industry, we indicate that U2—Palo Alto Networks and U6—BlackBerry Ltd. companies needed significant improvements and that four other companies were generally more efficient. The findings of this study provide decision-makers a clear image and it will be the first study to evaluate and predict the performance of cyber security organizations, providing a valuable reference for future research.

The traditional meat and poultry farms use a fixed quantity of supply, which creates an imbalance between demand and supply. Due to this imbalance, a huge amount is spent on balancing the requirements. There is an inequality among demand and supply since typical meat and poultry farms use a fixed amount of supply. A lot of money is spent trying to balance the requirements because of this mismatch. In addition, when connecting and building the meat and poultry farm system, the procedure ignores the impact on the environment. The owner’s primary goals are to retain massive profits and raise reliability. The classical method neglects the effect on the environment while linking and designing the meat and poultry farm system. The main aim of the owner is to increase the quality and maintain the maximum profit. This paper deals with the meat and poultry farms in two folds. In the first step, the IoT based system is implemented for the traceability and demand‐supply monitoring. The second steps include optimization of the supply network to reduce the carbon emission from the transportation. Both steps take data analytics as an input to process the final result for the farm to run and optimize. Effective inventory optimization algorithms have been shown to be able to evaluate a significant portion of previous sales data and anticipate inventory future demand by taking seasonality and lead times into account. Revenue, productivity, and customer satisfaction are just a few of the business variables that these strategies may affect. Finally, the comparison is done with the traditional farm and supply chain on the points of demand‐supply balance, cost, carbon emission, and wastage. It is found that the farms using data analytics to optimize the overall system perform better and with 37% more efficient than the traditional systems.