This study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.

The study aims to examine the labor market challenges and motivational factors for employee retention through the example of a small machinery company in Hungary. In recent years, Hungary’s labor market has faced significant difficulties, particularly due to the COVID-19 pandemic, which resulted in temporary unemployment followed by labor shortages. The research aims to identify the motivational, welfare, and financial factors that contribute to employee retention. Due to the small sample size, we did not investigate the relationships concerning loyalty, commitment, and performance. The research methods included comprehensive data collection at a machinery company employing 24 people located near the Austrian-Hungarian border. During the data collection, we conducted a questionnaire survey that included questions related to benefits, performance, commitment, and loyalty. The collected data were processed by calculating weighted averages and differences. The results indicate that flexible working hours and easy accessibility to the workplace are of utmost importance to employees. There is also a significant demand for performance-based pay and diverse, flexible benefit packages. Employees require both formal and informal professional recognition, such as praise and awards. The research has practical significance for both organizational management and employee well-being. Understanding employee opinions and implementing measures based on these can have four primary effects: improvement in employee performance, reduction in turnover, increase in organizational commitment, and enhancement of the company’s positive perception.

The Method of Discretization in Time (MDT) is a hybrid numerical technique intended to alleviate upfront the computational procedure of timedependent partial differential equations of parabolic type upfront. The MDT engenders a sequence of adjoint second order ordinary differential equations, wherein the space coordinate is the independent variable and time becomes an embedded parameter. Essentially, the adjoint second order ordinary differential equations are considered of “quasistationary” nature. In this work, the MDT is used for the analysis of unsteady heat conduction in regular bodies (large wall, long cylinder and sphere) accounting for nearly constant thermophysical properties, uniform initial temperature and surface heat flux. In engineering applications, the surface heat flux is customarily provided by electrical heating, radiative heating and pool fire heating. It is demonstrated that the approximate, semianalytical temperature solutions of the first adjoint “quasistationary” heat conduction equations using the first time jump are easily obtainable for each regular body. For enhanced acccuracy, regression analysis is applied to the deviations of the dimensionless surface temperature as a function of the dimensionless time for each regular body.

This research uses both quantitative and qualitative research methodologies to examine the complex factors affecting community resilience in various settings. In this case, the research explores how social cohesion, governance effectiveness, adaptability, community involvement, and the specified difficulties influence resilience results by using the five pillars of resilience as variables. Descriptive and inferential statistics are used to test hypotheses on the relationships between social cohesion, governance effectiveness, adaptive capacity, and community resilience variables. Qualitative data provides further insights into the quantitative results by providing broader views and experiences of the community. The study shows how social capital is important in increasing community capacity, stressing the importance of social relations and trust in developing community solutions to disasters. Another major factor that stands out is the governance factor that ensures that decisions are made, and actions taken in line with the community’s best interest in improving its ability to prepare for and respond to disasters. Adaptive capacity is seen as a key component of resilience and this paper emphasizes the importance of communities to come up with measures that can be adjusted to the changing circumstances. In summary, this study enriches theoretical understanding and offers practical applications of the processes that can enhance community resilience based on the principles of social inclusion, sound governance, and context-specific solutions.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.

This study aims to develop a robust prioritization model for municipal projects in the Holy Metropolitan Municipality (Makkah) to address the challenges of aligning short-term and long-term objectives. The research explores How multi-criteria decision-making (MCDM) techniques can prioritize municipal projects effectively while ensuring alignment with strategic goals and local needs. The methodology employs the analytic hierarchy process (AHP) and exploratory factor analysis (EFA) to ensure methodological rigor and data adequacy. Data were collected from key stakeholders, including municipal planners and community representatives, to enhance transparency and reliability. The model’s validity was assessed through latent factor analysis, confirming the relevance of identified criteria and factors. Results indicate that flood prevention projects are the highest priority (0.4246), followed by road projects (0.3532), park construction (0.1026), utility projects (0.0776), and digital transformation (0.0416). The study highlights that certain factors are critical for evaluating and prioritizing municipal projects. “Capacity and Demand” emerged as the most influential factor (0.5643), followed by “Strategic Alignment” (0.2013), “Project Interdependence” (0.1088), “Increasing Investment” (0.0950), and “Risk” (0.0306). These findings are significant as they offer a structured, data-driven approach to decision-making aligned with Saudi Vision 2030. The proposed model optimizes resource allocation and project selection, representing a pioneering effort to develop the first prioritization framework specifically tailored to Makkah’s unique municipal needs. Notably, this is the first study to establish a prioritization method specifically for Makkah’s municipal projects, providing valuable contributions to the field.