To achieve the electrification of private vehicles, it is urgent to develop public charging infrastructure. However, choosing the most beneficial type of public charging infrastructure for the development of a country or region remains challenging. The municipal decision’s implementation requires considering various perspectives. An important aspect of energy development involves effectively integrating and evaluating public charging infrastructure. While car charging facilities have been thoroughly studied, motorcycle charging facilities have been neglected despite motorcycles being a vital mode of transportation in many countries. The study created a hybrid decision-making model to evaluate electric motorcycle charging infrastructure. Firstly, a framework for evaluating electric motorcycle charging infrastructure was effectively constructed through a literature survey and expert experience. Secondly, decision-makers’ opinions were gathered and integrated using Bayesian BWM to reach a group consensus. Thirdly, the performance of the alternative solutions was evaluated by exploring the gaps between them and the aspiration level through modified VIKOR. An empirical analysis was conducted using examples of regions/countries with very high rates of motorcycle ownership worldwide. Finally, comparative and sensitivity analyses were conducted to demonstrate the practicality of the proposed model. The study’s findings will aid in addressing municipal issues and achieving low-carbon development objectives in the area.

Short-form content has the potential for virality and broad sharing, allowing businesses to reach large audiences in a short period of time. This type of content has transformed traditional marketing approaches, capturing the attention and curiosity of Generation Z, thereby leading to the rise of digital marketing. As Generation Z is the next generation of consumers and their purchasing power increases as they enter the workforce, marketers need to understand the factors influencing their attitudes and purchase intentions. This study aims to explore the relationship between the growing presence of short-form advertising content in corporate marketing strategies and consumer behavioral intentions. To achieve this, the sub-characteristics of short-form content were categorized into expertise, ease of use, and entertainment value, while information reliability was set as a mediating variable. Data was collected through a survey of 256 adults residing in Busan and Gyeongnam, and analyzed using SPSS 28.0. The findings of the study revealed that most sub-characteristics of short-form content advertisements positively influenced both recommendation and purchase intentions. Additionally, information reliability was identified as a significant mediating factor between short-form content and consumer behavioral intentions. These results provide important insights for corporate marketers and advertising professionals, as they offer valuable guidance on how to influence consumer purchase intentions effectively.

The creation of points where law, politics and education policies take intersection is a very complex and dynamic environment determined by philosophical shifts, economic problems, and social dynamics. This study dissects various complicated challenges facing the process of the framing of educational policies and their implementation which have become rampant due to the rapid political transformations. The researched evaluation is applied via both qualitative and quantitative methods, including juridical research, case and best practices studies and surveys, with the descriptive nature of the research as the main tool. The heart of the essay is three main themes - the contention between the rigidity of the academic standards and the holistic growth of students, its possible effects when students are too identified with a test-centric approach as their knowledge is sacrificed for their test scores, and the inclusion of rights and protections for underrepresented populations even when faced with a government’s resistance. Similarly, the research examines the perils of creating legislation too quickly, especially, because of unexpected side effects and interpretation conflicts. Findings show profound demographic differentials over districts which implies the designing and implementation of policies need to be modified accordingly. Unless a certain policy brings the best outcomes in the learning process, then nobody should choose it even if it means disrupting student well-being and decreasing their involvement. It is also emblematic of how cross-party cooperation and stakeholders’ understanding are important aspects of fairly dealing with complicated policy environments.

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.