This article explores the transformative journey of universities in Kazakhstan, focusing on the results of recent research on the quality of higher education. The study delves into the significant reforms and innovations implemented in the Kazakhstani higher education system, assessing their impact on academic standards, student performance, and institutional efficiency. Through comprehensive data analysis and expert interviews, the research highlights the strides made in improving educational quality, fostering international collaborations, and integrating modern technologies in teaching and learning. The findings underscore the critical role of government policies, industry partnerships, and community participation in driving these transformations. This article provides valuable information on the challenges and successes experienced by Kazakhstani universities, providing a blueprint for further advances in the sector of higher education. The key factors contributing to the success of these reforms include strong government support, international collaboration, robust quality assurance mechanisms, a focus on research and innovation, and professional development for educators. While challenges remain, the future of higher education in Kazakhstan looks promising, provided that these efforts continue and are further refined to address existing gaps.

The relationship between aid and corruption remains ambiguous. On the one hand, aid may benefit a country if the aid management system runs efficiently and transparently. On the other hand, aid tends to create new problems, namely corruption, especially in developing countries. This research examines the aid-corruption paradox in Indonesian provinces from a spatial perspective. The data was obtained from the Indonesian Ministry of Finance, the National Development Planning Agency of Indonesia, the Corruption Eradication Commission of Indonesia, and the Electronic Procurement Service, referring to 34 Indonesian provinces between 2011 and 2019. The research applies the spatial panel method and uses Haversine distance to construct the weighted matrix. The spatial error model (SEM) is the best for Model 1 (Grants) and Model 2 (Loans) and the best corruption model in Model 3 (Gratification). The spatial autoregressive model (SAR) is the best approach for Model 4 (Public Complaints) and Model 5 (Corruption). The findings show that there is no spatial dependence between provinces in Indonesia in terms of grants or loans. However, corruption in Indonesia is widespread.

SMEs are characterized by a number of flaws that threaten their survival and counteract them from reaching high levels of growth and development. Access to finance is the primary problem facing these companies in the Moroccan context. Aware of the effective and potential impacts of SMEs on the country as a whole, the Moroccan Government through a variety of actors has mobilized its efforts in a number of ways to support this population of companies. This study assesses the extent to which actors within the Moroccan SMEs’ financing ecosystem align to support these companies and develop their ability to access external financing. Using the MACTOR model, based on an in-depth contextual analysis and expert interviews, our findings suggest that Morocco’s SMEs’ financing ecosystem is skewed, with high levels of convergence between its components.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The objective of this study is to explore the relationship between changing weather conditions and tourism demand in Thailand across five selected provinces: Chonburi (Pattaya), Surat Thani, Phuket, Chiang Mai, and Bangkok. The annual data used in this study from 2012 to 2022. The estimation method is threshold regression (TR). The results indicate that weather conditions proxied by the Temperature Humidity Index (THI) significantly affect tourism demand in these five provinces. Specifically, changes in weather conditions, such as an increase in temperature, generally result in a decrease in tourism demand. However, the impact of weather conditions varies according to each province’s unique characteristics or highlights. For example, tourism demand in Bangkok is not significantly affected by weather conditions. In contrast, provinces that rely heavily on maritime tourism, such as Chonburi (Pattaya), Phuket, and Surat Thani, are notably affected by weather conditions. When the THI in each province rises beyond a certain threshold, the demand for tourism in these provinces by foreign tourists decreases significantly. Furthermore, economic factors, particularly tourists’ income, significantly impact tourism demand. An increase in the income of foreign tourists is associated with a decrease in tourism in Pattaya. This trend possibly occurs because higher-income tourists tend to upgrade their travel destinations from Pattaya to more upscale locations such as Phuket or Surat Thani. For Thai tourists, an increase in income leads to a decrease in domestic tourism, as higher incomes enable more frequent international travel, thereby reducing tourism in the five provinces. Additionally, the study found that the availability and convenience of accommodation and food services are critical factors influencing tourism demand in all the provinces studied.

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.