This financial modelling case study describes the development of the 3-statement financial model for a large-scale transportation infrastructure business dealing with truck (and some rail) modalities. The financial modelling challenges in this area, especially for large-scale transport infrastructure operators, lie in automatically linking the operating activity volumes with the investment volumes. The aim of the paper is to address these challenges: The proposed model has an innovative retirement/reinvestment schedule that automates the estimation of the investment needs for the Business based on the designated age-cohort matrix analysis and controlling for the maximum service ceiling for trucks as well as the possibility of truck retirements due to the reduced scope of tracking operations in the future. The investment schedule thus automated has a few calibrating parameters that help match it to the current stock of trucks/rolling stock in the fleet, making it to be a flexible tool in financial modelling for diverse transport infrastructure enterprises employing truck, bus and/or rail fleets for the carriage of bulk cargo quantifiable by weight (or fare-paying passengers) on a network of set, but modifiable, routes.

This research focuses on the construction of the competency of “Double-qualified” teachers in higher vocational colleges. Through comprehensive literature analysis, in-depth interviews and questionnaire surveys, a competency model covering three dimensions, namely personality charm, teaching literacy and practical skills, has been successfully established. This model provides a scientific basis for higher vocational colleges in teacher selection, performance evaluation and professional training, and particularly emphasizes the importance of teachers’ cultivation of students’ practical abilities and professional qualities in the context of vocational education. The research reveals that these three competency dimensions are interdependent and jointly influence teachers’ educational and teaching achievements as well as students’ career development.

This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.

Forest is the main carbon sink of terrestrial ecosystem. Due to the unique growth characteristics of plants, the response of their growth status and physiological activities to climate change will affect the carbon cycle process of forest ecosystem. Based on the local scale CO₂ flux and temperature observation data recorded by the FLUXNET registration site and Harvard Forest FLUX observation tower from 2000 to 2012, combined with the phenological model, this paper analyzes the impact of temperature changes on CO₂ flux in temperate forest ecosystems. The results show that: (1) the maximum NEE in 2000–2012 was 298.13 g·m^-2·a^-1, which occurred in 2010. Except in the 2010 and 2011, the annual NEE in other years was negative. (2) NEE, GPP, temperature and phenology models have good fitting effects (R² > 0.8), which shows that the stable period of photosynthesis in temperate mixed forest ecosystem is mainly concentrated in summer, and vegetation growth is the dominant factor of carbon cycle in temperate mixed forest ecosystem. (3) The linear fitting results of the change time points of air temperature (maximum point, minimum point and 0 point date) and the change time points of NEE and GPP (maximum point, minimum point and 0 point date) show that there is a significant positive correlation between air temperature and CO₂ flux (P < 0.01), and the change of air temperature affects the carbon cycle process of temperate mixed forest ecosystem.

Low levels of financial literacy cause people to have lower savings rates, higher transaction costs, larger debts and the loans acquisition with higher interest rates, therefore it becomes relevant to analyze the determinants of financial literacy. The aim of this research is to identify whether there is an association between the financial literacy level and sociodemographic characteristics. The Mexican Petroleum Company (Pemex) employees is the population analyzed. Pemex is the state-owned oil and natural gas producer, transporter, refiner and marketer in Mexico. A non-probabilistic convenience sampling was performed and 404 responses were obtained. The analysis of data was carried out with the Bayesian method. The results show that there is an association between Pemex employees’ level of financial literacy and their level of education, income, age and type of retirement saving. No association was found between their level of financial literacy and gender, marital status and whether or not they have children.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.