The enormous biological potential of herbal products is one of the main reasons for their frequent use in the production of dietary supplements and functional foods, which, in addition to their nutritional properties, have pharmacological and physiological effects. New scientific knowledge on the isolation of pharmacologically active compounds from complex matrices has led to significant advances in this field. Today, the process of extraction plays a significant scientific role, with “green” technologies occupying a special place in today’s science. Herbal medicine is one of the oldest human skills, which has worn off with its centuries-old application in the path of modern medicine. Microwave-assisted extraction, or more simply, microwave extraction, is a new extraction technique that combines traditional extraction solvents and microwaves. The mentioned method takes less time, consumes less energy, and has strong penetration power into the plant matrix to obtain more oils, but it can also reduce production costs. This can eventually increase the quality of the final product and reduce the product price at the consumer level. Microwave-assisted extraction could be useful to the herbal industry for oil extraction as well as other pharmaceutically important plant components. Based on a comparison and study of published literature, this research examines the present state of extraction procedures. This review includes a detailed discussion of the most important extraction techniques.

As urbanisation increases, questions arise about the desirability of further urban growth, as it was not accompanied by corresponding economic growth, and social and environmental problems began to grow in the largest cities in the world. The objective of the article is to substantiate the limits of urbanization growth in Kazakhstan based on the study of theoretical views on this process, analysis of the dependence of social and economic parameters of 134 countries on the urbanisation level and calculation of the urbanisation level that contributes most to economic growth and social well-being. To achieve the goal, the following tasks have been set and solved: theoretical views on the process of urbanization have been generalized; a hypothesis has been put forward about the emergence of an “urbanization trap” in which the growth of large cities is not accompanied by economic growth and improvement of social well-being; an analysis of the dependence of socio-economic indicators on the level of urbanization has been carried out on the example of 134 countries of the world; the level of urbanization that maximizes economic growth and social well-being is calculated; the necessity of the development of small towns in Kazakhstan is substantiated. To solve the problems, the methods of logical analysis, analogies and generalizations, economic statistics, index, graphical, Pearson correlation analysis, Spearman and Kendall rank regression based on models in SPSS were used. As a result, the following conclusions are made: the hypothesis of a possible deterioration of socio-economic indicators in large cities is confirmed; the best positive result is demonstrated by the level of urbanization of 50%–59%. The recommendations are justified: in Kazakhstan, it is necessary to adhere to the level of urbanization no higher than 59%; the growth of urbanization should be ensured through the development of small towns; it is necessary to improve the methods of managing the process of urbanization and develop individual city plans.

This study addresses the crucial question of the macroeconomic impact of investing in railroad infrastructure in Portugal. The aim is to shed light on the immediate and long-term effects of such investments on economic output, employment, and private investment, specifically focusing on interindustry variations. We employ a Vector Autoregressive (VAR) model and utilize industry-level data to estimate elasticities and marginal products on these three economic indicators. Our findings reveal a compelling positive long-term spillover effect of these investments. Specifically, every €1 million in capital spending results in a €20.84 million increase in GDP, a €17.78 million boost in private investment, and 72 new net permanent jobs. However, these gains are not immediate, as only 14.5% of the output increase and 38.8% of the investment surge occur in the first year. In contrast, job creation is nearly instantaneous, with 93% of new jobs materializing within the first year. A short-term negative impact on the trade balance is expected as new capital goods are imported. Upon industry-level analysis, the most pronounced output increases are witnessed in the real estate, construction, and wholesale and retail trade industries. The most substantial net job creation occurs in the construction, professional services, and hospitality industries. This study enriches the empirical literature by uncovering industry-specific impacts and temporal macroeconomic effects of railroad infrastructure investments. This underscores their dual advantage in bolstering long-term economic performance and counteracting job losses during downturns, thus offering valuable public policy implications. Notably, these benefits are not evenly distributed across all industries, necessitating strategic sectoral planning and awareness of employment agencies to optimize spending programs and adapt to industry shifts.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.

How to improve enterprise performance has been a research topic widely studied by scholars for a long time. As economic globalization deepens, the business competition becomes increasingly harsh. Technology-based small and medium-sized enterprises (SMEs) play an important role in the rapid development of the country’s economy, especially in China. This study aims to investigate the mediating effect of knowledge integration capability in the relationship between corporate social capital and enterprise performance. The sample group used in this study were 300 technology-based SMEs in China. The research tool was a questionnaire adapted from previous scholars, which passed assessment in terms of content validity and reliability. Data were analyzed using structural equation modelling. The results show that: 1) corporate social capital has a positive impact on enterprise performance, but the impact differs between well-performing and poor-performing enterprises; and 2) knowledge integration ability plays a mediating role in the relationship between corporate social capital and enterprise performance, and the mediating role is the same for both well-performing and poor-performing enterprises. But it played a partial mediating role in the good-performance comparison group and a complete mediating role in the poor-performance comparison group. This study is useful for enterprise management in cultivating and developing the abundant social capital of enterprises and expanding channels for knowledge integration ability to increase enterprise performance.