The way agri-food companies conduct business has changed as a result of changes in the market. These companies must start working in a more environmentally friendly manner. This study aims to examine, assess, and compare how various fuzzy methodologies are applied in green supplier selection (GSS), using an agri-food industry as an example. The company Biljana Brko, which engages in GSS, was observed in this study. The selection aids in the acquisition of raw materials and materials whose environmental impact will be minimized. Ecological and economic factors were taken into consideration when choosing green suppliers. Experts who assessed the weight of the criteria and the suppliers with linguistic values were chosen to carry out this selection. In order to do this, a fuzzy set that effectively applies these linguistic values was employed. The fuzzy SWARA (FSWARA) approach was utilized to calculate the weights of the criteria, revealing that the criterion of Environmental Management System has the highest weight. Drawing on the opinions of experts, suppliers were ranked using the fuzzy MABAC, MARCOS, and CRADIS techniques. The results show that supplier S2 receives the highest ratings. Along with this provider, supplier S3 is noteworthy because it excelled in the sensitivity analysis across a variety of scenarios. In light of this, Biljana Brko should give preference to these suppliers. Further, the results of the three adopted techniques were compared. The comparison reveals that the ranking order produced by all three techniques is remarkably similar. This supplier order differed slightly from the FMABAC method just in one scenario. Hence, this work demonstrates that the three fuzzy techniques can solve the GSS problem and other problems by ranking alternatives.

The decentralization of blockchain technology greatly improves the trust relationship in the supply chain network. In view of the lack of trust, uncertainty, and asymmetry in the supply chain network, this paper integrates the blockchain technology to build a network dynamics model of trust representation, calculation, and propagation, and explores how the blockchain influences the supply chain network. The result indicates that the network scale increased by 115.89%, the network connectivity increased by 60.31%, and the average shortest path decreased by 4.95%, after the blockchain trust framework had been deployed in the agricultural supply chain. Meanwhile, the network topology performance such as degree distribution and average clustering coefficient were optimized to varying degrees. Taking agricultural supply chain as an example, the practical significance of topological change was explained. Overall, the blockchain trust mechanism improves the topology of the supply chain network by affecting the trust relationship between nodes.

The number of fire incidences has increased as a result of Libya's rapid and continuous growth. The growing population in Misurata, the third largest city in Libya and the expansion of industrial facilities both have a hand in the rise in fire incidents. The capabilities available to handle these accidents are limited. One of the biggest challenges is probably the dispersed location of fire stations, which slows down reaction times because it can take an hour for rescue personnel to reach the scene. This study intends to offer decision-makers a model for determining the ideal location of fire stations, using a hybrid FUCOM-COCOSO approach, and apply the model to optimize the location of a fire station in Misurata. The alternatives were compared using six criteria, which were established based on previous research and expert opinions. High population density had a weight of 0.348, making it the most significant factor, and distance from current fire stations had a weight of 0.217. As these sites are dispersed throughout the city, four prospective locations were analyzed to implement a new station. The results indicate that the ideal station must be close to the city's industrial region. This is as a result of its proximity to the city's industrial complex and to densely populated areas. Similar results were obtained by the proposed method, and five other methods.

Over the past 15 years, online travel agencies (OTAs), such as Booking.com and Expedia.com, have grown significantly. This growth is correlated with their spending on R&D and marketing initiatives. Digital marketing promotes services and products by using an electronic platform. This study aims to ascertain the role of digital marketing in perceived quality and consumer confidence in hotel performance in the age of digital technology. This quantitative study employs statistical testing, namely the partial least squares (PLS) design, in order to understand the link between the two aforementioned variables. In the meantime, the authors conducted a data search by going through a Google form questionnaire on hotel visitors in Tangerang, specifically between October 2020 and October 2021. The survey questionnaires were distributed to 145 respondents, and Smart PLS 3.0 was used in the analytic procedure. The results of digital marketing, including consumer perceptions of quality and trust, play a key role in hotel success. This conclusion is a succinct summary of the research findings in the hope that it will be useful for future research on a comparable topic by academics and other hotel managers.

The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation.

• The purpose of the article is to study the peculiarities of managing the development of human capital in the conditions of digitalization. In the course of the study, the method of measuring the human potential index, developed by the UN - the Human Development Index, including taking into account socio-economic inequality, the Gender Inequality Index and the multidimensional poverty index, was applied as basic indicators that reflect the level of development of human capital, which is especially important in the conditions of digitalization of society. The article discusses the concept of human potential, its components, methods of determination. The dynamics of changes in the human development index of Ukraine and its components during 1990-2020 were analyzed. A comparative analysis of the values of the human development index, the human development index taking into account socio-economic inequality, the gender inequality index of Ukraine and other countries was carried out. The importance of the development of human potential in the context of the development of the information society and the digital economy is proven, the specifics of working conditions, requirements for the workforce are given. The factors affecting the development of human potential in the conditions of the digital economy are considered, and ways of solving the identified problems are proposed like creating conditions for development of the population, to ensure a positive balance of reproduction of the population and migration, development of social infrastructure, access of the population to quality medical, educational, and social services, etc.

For the integrated optimization of job-shop production scheduling and predictive maintenance, this paper fully considers such constraints as product delivery time and changing machine failure rate, and establishes a multi-objective optimization model aiming to minimize the processing cost and the product processing time. The model includes the changing machine failure rate into the integrated optimization of job-shop production scheduling and predictive maintenance, and enables the prediction of the machine state according to the processing time of the current job, laying the basis for the decision-making of the machine activity and the reasonable and effective production planning. In addition, the non-dominated sorting genetic algorithm (NSGA)-II was designed to solve the proposed model. The algorithm performance was improved through the operator crossover and mutation by the simulated binary crossover algorithm (SBX). The proposed strategy was verified through a case study.