Central Sulawesi has been grappling with significant challenges in human development, as indicated by its Human Development Index (HDI). Despite recent improvements, the region still lags behind the national average. Key issues such as high poverty rates and malnutrition among children, particularly underweight prevalence, pose substantial barriers to enhancing the HDI. This study aims to analyze the impact of poverty, malnutrition, and household per capita income on the HDI in Central Sulawesi. By employing panel data regression analysis over the period from 2018 to 2022, the research seeks to identify significant determinants that influence HDI and provide evidence-based recommendations for policy interventions. Utilizing panel data regression analysis with a Fixed Effect Model (FEM), the study reveals that while poverty negatively influences with HDI, underweight prevalence is not statistically significant. In contrast, household per capita income significantly impacts HDI, with lower income levels leading to declines in HDI. The findings emphasize the need for comprehensive policy interventions in nutrition, healthcare, and economic support to enhance human development in the region. These interventions are crucial for addressing the root causes of underweight prevalence and poverty, ultimately leading to improved HDI and overall well-being. The originality of this research lies in its focus on a specific region of Indonesia, providing localized insights and recommendations that are critical for targeted policy making.

To fight inflation, European Central Bank (ECB) announced 10 successive interest rate hikes, starting on 27 July 2022, igniting an unprecedented widening of interest rate spreads in the euro area (ΕΑ). Greek banks, however, recorded among the highest interest rate spreads, far exceeding ΕΑ median and weighted average. Indeed, we document a strong asymmetric response of Greek banks to ECB interest rate hikes, with loan interest rates rising immediately, whilst deposit interest rates remained initially unchanged and then rose sluggishly. As a result, the interest rate spread hit one historical record after another. Greek systemic banks, probably taking advantage of the high concentration and low competition in the domestic sector benefited from key ECB interest rate hikes, recording gigantic increases in net interest income (NII), and consequently, substantial profits (almost €7.4 billion in the 2022–2023 biennium). Such excessive accumulation of profits (that deteriorates the living conditions of consumers) by the banking system could be called the inflation of “banking greed”, or bankflation. This new source of inflation created by the oligopolistic structure of the Greek banking sector counterworks the very reason for ECB interest rate increases and requires certain policy analysis recommendations in coping with it.

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.

Urban facilities and services are essential to human life. Access to them varies according to the geographical location of the population, whether urban, peri-urban or rural, and according to the modes of transport available. In view of the rapid development of peri-urban areas in developing countries, questions are being asked about the ability of the inhabitants of these areas to access these facilities and services. This study examines the ability of the inhabitants of Hêvié, Ouèdo and Togba, three peri-urban districts of Abomey-Calavi in the Republic of Benin, to access commercial, educational, school and health facilities. To this end, we have adopted a GIS-based methodology. It is a combination of isochronal method and accessibility utility measurement. The isochrones were produced according to the main modes of travel recorded on the study area and over a time t ≤ 20 min divided into intervals of 05 min. Analysis of the data enabled us to understand that the main modes of travel adopted by residents are walking, motorcycle and car. Access to educational and health facilities is conditioned by the mode of travel used. Access to commercial and entertainment facilities in t ≤ 20 min is not correlated with the modes of transport used.

In the context of digital transformation, Chinese small and medium sized enterprises (SMEs) face significant challenges and opportunities in adapting to market dynamics and technological advancements. This study investigates the impact of coopetition strategy on the core competencies of SMEs, with a particular focus on marketing, technological, and integrative competencies. Data were collected from a sample of 300 SMEs in Anhui Province through an online survey, and reliability and validity were tested using SPSS and AMOS. The results indicate that dependency and trust significantly enhance the effectiveness of coopetition strategy from an external perspective, while managerial ambidexterity and strategic intent are critical internal factors driving the successful implementation of coopetition strategies. Both external and internal factors positively impact the core competencies of SMEs. Additionally, environmental uncertainty moderates the relationship between coopetition strategy and core competencies, underscoring the need for flexibility and adaptability in dynamic market environments. The findings suggest that SMEs can better integrate internal and external resources, optimize resource allocation, and improve operational efficiency through coopetition strategy, thereby enhancing their core competencies. This study provides valuable insights and practical guidance for policymakers and business practitioners aiming to support the digital transformation of SMEs.

As the aging trend intensifies, the Chinese government prioritizes technological innovation in smart elderly care services to enhance quality and efficiency, catering to the diverse needs of the elderly. This study examines the acceptance and usage behavior of smart elderly care services among elderly individuals in Xi’an, using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that includes digital literacy as a moderating variable. Data were collected via a survey of 299 elderly individuals aged 60 and above in Xi’an. The study aims to identify factors influencing the acceptance and usage behavior of smart elderly care services and to understand how digital literacy moderates the relationship between these factors and usage behavior. Regression analysis assessed the direct effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on usage behavior. These dimensions were then integrated into a comprehensive index Service Acceptance to evaluate their overall impact on usage behavior, with behavioral intention examined as a potential mediating variable. Results indicate that EE and SI significantly impact the adoption of smart elderly care services, whereas PE and FC do not. Behavioral intention mediates the relationship between these variables and usage behavior. Additionally, gender, age, and digital literacy significantly moderate the impact of service acceptance on usage behavior. This study provides valuable theoretical and practical insights for designing and promoting smart elderly care services, emphasizing the importance of usability and social promotion to enhance the quality of life for the elderly.