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.

Hydroponics is a modern agricultural system that enables year-round plant growth. Biochar, derived from apple tree waste, and humic acid were investigated as a replacement for the Hoagland nutrient solution to grow strawberries in a greenhouse with three replications. Growth parameters, such as leaf area, the average number of fruits per plant, maximum fruit weight, and the weight of fresh and dry fruits, were measured. A 50% increase in fresh and dry fruit weight was observed in plants grown using biochar compared to the control. Additionally, the use of Hoagland chemical fertilizer led to a 25% increase in both fresh and dry weight. There was a 65% increase in the number of fruits per plant in the biochar-grown sample compared to the control. Moreover, biochar fertilizer caused a 100% increase in maximum fruit weight compared to the control and a 27% increase compared to the Hoagland chemical fertilizer. Biochar had a higher pH compared to the Hoagland solution, and such pH levels were conducive to strawberry plant growth. The results indicate that biochar has the potential to enhance the size and weight of fruits. The findings of the study demonstrate that biochar, when combined with humic acid, is a successful organic hydroponic fertilizer that improves the quality and quantity of strawberries. Moreover, this approach enables the more efficient utilization of garden waste.