In China, ideological and political education is currently the hot direction of teaching reform in various colleges and universities, yet the development of appropriate teaching evaluation methods needs to catch up. This study addresses the pressing need for a preliminary investigation into the complex relationships among ideological and political education, the students’ learning satisfaction and teaching quality. This research examines the influence of teaching and ideological and political education quality on students’ satisfactions by designing a set of scales, collecting about 3800 questionnaires. Utilizing Structural Equation Modeling (SEM) and qualitative interviews, this study reveals that the teaching quality directly affects students’ learning satisfaction and ideological and political education. Notably, ideological and political education can also affect students’ learning satisfaction. The findings underscore the importance of including ideological and political education assessments in evaluating courses. This research contributes to the ongoing dialogue on effective teaching evaluation methods in the context of evolving educational practices.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study scrutinizes the allocation of financial aid for climate change adaptation from OECD/DAC donors, focusing on its effectiveness in supporting developing countries. With growing concerns over climate risks, the emphasis on green development as a means of adaptation is increasing. The research explores whether climate adaptation finance is efficiently allocated and what factors influence OECD/DAC donor decisions. It examines bilateral official development assistance in the climate sector from 2010 to 2021, incorporating climate vulnerability and adaptation indices from the ND-GAIN Country Index and the IMF Climate Risk Index. A panel double hurdle model is used to analyze the factors influencing the financial allocations of 41,400 samples across 115 recipient countries from 30 donors, distinguishing between the decision to select a country and the determination of the aid amount. The study unveils four critical findings. Firstly, donors weigh a more comprehensive range of factors when deciding on aid amounts than when selecting recipient countries. Secondly, climate vulnerability is significantly relevant in the allocation stage, but climate aid distribution does not consistently match countries with high vulnerability. Thirdly, discerning the impact of socio-economic vulnerabilities on resource allocation, apart from climate vulnerability, is challenging. Lastly, donor countries’ economic and diplomatic interests play a significant role in climate development cooperation. As a policy implication, OECD/DAC donor countries should consider establishing differentiated allocation mechanisms in climate-oriented development cooperation to achieve the objectives of climate-resilient development.

Currently, numerous companies intend to adopt digital transformation, seeking agility in their methodologies to reinvent products and services with higher quality, reduced costs and in shorter times. In the Peruvian context, the implementation of this transformation represents a significant challenge due to scarcity of resources, lack of experience and resistance to change. The objective of this research is to propose a digital transformation model that incorporates agile methodologies in order to improve production and competitiveness in manufacturing organizations. In methodological terms, the hypothetical deductive method was used, with a non-experimental cross-sectional design and a quantitative, descriptive and correlational approach. A questionnaire was applied to 110 managers in the manufacturing sector, obtaining a Cronbach’s alpha coefficient of 0.992. The results reveal that 65% of the participants consider that the level of innovation is regular, 88% think that the competition in their companies is of a regular level, and 76% perceive that the level of change is deficient. The findings highlight the importance of digital transformation in manufacturing companies, highlighting the adoption of agile methodologies as crucial to improving processes and productivity. In addition, innovation is essential to developing high-quality products and services, reducing costs and time. Digital transformation with agile methodologies redefines the value proposition, focusing on the customer and improving their digital experience, which differentiates companies in a competitive market.

Introduction: Many detrimental effects on employees’ health and wellbeing might result from inadequate illumination in the workplace. Headaches and trouble focusing can result from eye strain brought on by inadequate illumination. The purpose of this study was to simulate and optimize workplace illumination in the ceramic industry. Materials and methods: A common Luxmeter ST-1300 was used to measure the illumination in seven workplaces at a height of 100 cm above the floor. DIALux evo version 7.1 software was used to simulate the illumination of workplaces. To optimize the illumination conditions, a numerical experiment design consisting of 16 scenarios was used for each of the workplaces. Four factors were considered for each scenario: luminaire height, number of luminaires, luminous flux, and light loss factor. The Design-Expert program version 13.0.5.0 was applied for developing the scenarios. Finally, by developing quadratic models for each workplace, the optimization process was implemented. Results: Every workplace had illumination levels that were measured to be between 250 and 300 lux. Instead of using compact fluorescent luminaires, LED technology was recommended to maximize the illumination conditions for the workers. Following optimization, 376 lux of illumination were visible at each workstation in every workspace. For the majority of the workspaces, the simulated illumination was expected to have a desirability degree greater than 0.9. The uniformity and illumination of the workplace were significantly impacted by the two factors of luminaire height and luminaire count. Conclusion: The primary outcomes of this optimization were the environmental, political, and socioeconomic ones, including reduced consumption power, high light flux, and environmental compatibility. Nonetheless, the optimization technique applied in this work can be applied to the design of similar situations, such as residential infrastructure.

Road accidents involving motorcyclists significantly threaten sustainable mobility and community safety, necessitating a comprehensive examination of contributing factors. This study investigates the behavioral aspects of motorcyclists, including riding anger, sensation-seeking, and mindfulness, which play crucial roles in road accidents. The study employed structural equation modeling to analyze the data, utilizing a cross-sectional design and self-administered questionnaires. The results indicate that riding anger and sensation-seeking tendencies have a direct impact on the likelihood of road accidents, while mindfulness mitigates these effects. Specifically, mindfulness partially mediates the relationships between riding anger and road accident proneness, as well as between sensation-seeking and road accident proneness. These findings underscore the importance of effective anger management, addressing sensation-seeking tendencies, and promoting mindfulness practices among motorcyclists to enhance road safety and sustainable mobility. The insights gained from this research are invaluable for relevant agencies and stakeholders striving to reduce motorcycle-related accidents and foster sustainable communities through targeted interventions and educational programs.