We analyze Thailand’s projected 2023–2030 energy needs for power generation using a constructed linear programming model and scenario analysis in an attempt to find a formulation for sustainable electricity management. The objective function is modeled to minimize management costs; model constraints include the electricity production capacity of each energy source, imports of electricity and energy sources, storage choices, and customer demand. Future electricity demands are projected based on the trend most closely related to historical data. CO₂ emissions from electricity generation are also investigated. Results show that to keep up with future electricity demands and ensure the country’s energy security, energy from all sources, excluding the use of storage systems, will be necessary under all scenario constraints.

As the population’s demand for food continues to increase, aquaculture is positioned as a productive activity that provides high-quality protein. Aquaculture activity is characterized by its socio-economic impact, the generation of jobs, its contribution to food, and constant growth worldwide. However, in the face of threats of competition, producers must quickly adapt to market needs and innovate. Given this, this research aims to analyze the impact of the knowledge absorption capacity with the adoption of innovations by aquaculture producers in the Mezquital Valley in Hidalgo, Mexico. The methodological strategy was carried out through structural equation modeling using partial least squares and correlation tests. The findings show that knowledge absorption capacities explain 77.8% of the innovations carried out in aquaculture farms. Both variables maintain a medium-high correlation; the more significant the absorption capacity, the greater the innovation.

This study aims to elucidate the impact of marketing investment dimensions (MTS, MTOE, ROMI) on profitability indicators (ROA, ROE, GPM, OPM) and sustainable growth indicators (SGR, ARG) for service companies. The study population consisted of 135 service companies listed on the Amman Stock Exchange. A purposive sample of 55 companies was selected from this population. Financial reports and statements from 2018–2022 for these companies were analyzed to achieve the study objectives, employing appropriate statistical methods like multiple regression to test hypotheses. Previous literature shows conflicting results regarding the relationship between marketing investment dimensions and profitability/sustainable growth. Some studies found positive impacts, while others did not. This study contributes to this debate by providing statistical evidence. The results show that higher MTS, MTOE, and ROMI have a positive impact on SGR, OPM and ROA but a negative impact on GPM, ARG, and ROE. This underscores that marketing investments should be viewed in conjunction with overall operating expenses. Companies that control other expenses and increase the marketing investment proportion of total operating expenses may achieve better financial performance. Marketing investment metrics can serve as useful diagnostics and measures of effectiveness for improving marketing profitability, financial performance, and growth. In summary, this study statistically demonstrates the nuanced impacts of marketing investments on service company profitability and sustainable growth indicators. The results emphasize analyzing marketing spends in context of broader expenses and overall company financial health.

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.

The purpose of this study is to provide empirical evidence about the relationship between Organizational Culture and Knowledge Management in public sector organizations in Colombia. This research is based on information obtained from a survey applied to workers in different positions and areas of four organizations in the Colombian government at the departmental level. A survey of 22 items measured Organizational Culture, and 19 items measured Knowledge Management. The results show that the strongest correlation is between a flexible organizational structure and leadership that foments the development of worker capabilities to register and use knowledge. Furthermore, to achieve efficiency the public organizations should foster adaptability to environment, a well-defined management and value-oriented human behavior and overcome barriers such as bureaucracy, inefficient administration, and make adequate knowledge management.