This research, with a qualitative approach, is based on a literature review and a press analysis related to mergers, acquisitions and dissolutions of Higher Education Institutions in South America. Our findings evidence a gap in the academic literature for analyzing and understanding these processes. The literature on the subject is scarce; however, the press has recorded them in a constant way. While in the past this phenomenon was mainly among public universities, currently it is a fundamentally private trend. The main reasons to carry out this process by Higher Education Institutions are those related to geographic expansion or positioning (for merger processes), absorption and concentration of institutions by groups of interest (for merger processes, acquisition) and, the crisis resulting from the financial-administrative management of the institutions, as well as the non-compliance with national and international quality standards designed by accreditation agencies and institutions (for dissolution processes). On the contrary of some literature results, in any of the processes the search for prestige or reputation by the institutions was detected as a reason.

The challenge of developing cadastral infrastructure in Africa is inextricably linked to the global issues of sustainable development. Indeed, in light of the constraints inherent to conventional cadastral systems, alternative systems developed through land regulation programmes (LRPs) are compelled to align with the tenets of sustainable development. A discursive study, conducted through a semisystematic literature review, enabled the selection of 53 documents on cadastral systems deployed in multiple countries across the African continent. A number of systems were identified and grouped into four categories: urban, rural, participatory and hybrid cadastral systems. These systems are developed on the basis of standards and sociotechnical approaches, including the LADM, STDM, and FFP, as well as innovative technologies such as blockchain. However, their sustainability is limited by the fact that they are not multipurpose cadastral systems. Consequently, there is an urgent need for studies to develop a global framework that will produce truly significant and sustainable results for all sections of society.

Color visually communicates the product’s flavors to consumers and further influences their taste perception. This study explores the perceived taste of tea beverages caused by the logo’s principal colors, using hand-shaken tea beverages in Taiwan as an example. To identify the linkage between the logo color and tea tastes, this study divides the taste of tea beverages into four categories: sweetness, freshness, bitterness, and astringency. Then, the 69 tea beverage logos are allocated into the 14 color sections in the CIELAB color space according to their primary colors. The Correspondence Analysis method is employed to visualize the relationships between the logos and the perceived tastes. The tea tastes are then mapped into the color sections in the CIELAB color space. The analysis results reveal that the sweetness links to logos in the Warm Scheme colors (hue angle from 0 to 59 degrees). The fresh taste is bound with the logo with the Cool White Scheme colors (hue angle from 90 to 149 degrees and brightness >80). Finally, the bitter and astringent tastes link to the logo colors in the Cold Black Scheme colors (hue angle from 60 to 89 degrees, 150 to 329 degrees, and brightness <25). This study expands the color and taste association literature from general food to tea beverages. Our obtained empirical results can be applied to hand-shaken beverage companies to select principal colors for designing logos and packages that align with tea beverages’ perceived tastes to convey brand recognition accurately.

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.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.