Primary school students are in a period of rapid development of thinking. Primary school mathematics is particularly important for the cultivation of students' abstract thinking ability. The section of number and algebra is the most basic and important content in mathematics. This paper takes number and algebra as an example to analyze the abstract thinking ability of primary school mathematics and its training strategies, so as to provide some practical guidance for teaching.

Under the concept of independent maintenance proposed by the Meteorology, Climatology, and Geophysics Agency (BMKG) for operational equipment, a thorough analysis of its management processes is necessary. Leadership involvement at various levels can affect maintenance outcomes, impacting sustainability. This research creates a thinking model that connects responsible leadership (RL) with sustainable performance (SP) through agile organization (AO) mediation and maintenance management implementation (MMI) in the management of leading operations equipment. The method used was a survey of 366 respondents who were BMKG employees, and explanatory analysis was analyzed based on descriptive statistical analysis using SmartPLS. The research results show that the third hypothesis proposed is acceptable, and the two mediator variables are partial mediation. The discussion of the study results shows some theoretical and practical implications for achieving the goals of SP, where organizations should encourage RL behavior that can implement current practices regarding AO and MMI. The test results show that AO and MMI have a significant role as mediators in encouraging the influence of RL on SP. This study is the first step in examining the relationship of RL to SP using AO and MMI mediation. Furthermore, this model can be developed and analyzed in other sectors or fields to increase knowledge.

This study aims to evaluate the relationship between financial resilience, exchange rate, inflation, and economic growth from 1996 to 2022 using secondary data from the World Bank. The analysis method uses vector autoregressive to understand the causality dynamics between these variables. The results show that past economic growth positively impacts current economic conditions, but an increase in the exchange rate can hinder economic growth. The exchange rate also tends to be influenced by previous values, but high economic growth does not always increase the exchange rate. Previous conditions significantly affect financial resilience and can be strengthened by a strong currency. Meanwhile, inflation has an inverse relationship with economic growth, where past inflation seems to suppress current inflation, which price stabilization policies can cause. From an institutional economics perspective, this study provides an understanding of the interaction between various economic factors in the structural framework and policies that regulate economic activities. The impulse response function (IRF) shows that economic growth can react strongly to sudden changes, although this reaction may not last long. The exchange rate fluctuates with economic changes, reflecting market optimism and uncertainty. Financial resilience may be strong initially but may weaken over time, indicating the need for policies to strengthen the financial system to ensure economic stability. Furthermore, the role of social capital in economic resilience is highlighted as it can amplify the positive effects of a robust institutional framework by fostering trust and collaboration among economic actors. Inflation reacts differently to economic changes, challenging policymakers to balance growth and price stability. Overall, the IRF provides insights into how economic variables interact with each other and react to sudden changes, albeit with some uncertainty in the estimates. The forecast error decomposition variance (FEVD) analysis in this study reveals that internal factors initially influence economic growth, but over time, external factors such as the exchange rate, financial resilience, and inflation come into play. The exchange rate, which was initially volatile due to internal factors, becomes increasingly influenced by economic growth, indicating a close relationship between the economy and the foreign exchange market. From an institutional economics perspective, financial resilience, which was initially stable due to internal factors, becomes increasingly dependent on global economic conditions, suggesting the importance of a solid institutional framework for maintaining economic stability. In addition, inflation, which was initially explained by economic growth and exchange rates, has gradually become more influenced by financial resilience, indicating the importance of effective monetary policy in controlling inflation. This study highlights the importance of understanding how economic variables influence each other for effective economic governance. Integrating institutional economics and social capital perspectives provides a comprehensive framework for enhancing financial resilience and promoting sustainable economic development in Indonesia.

Islamabad’s 2019 ban on single-use plastic shopping bags aimed to reduce plastic waste, but compliance is limited. This study evaluates the effectiveness of the ban as well as other factors in curtailing plastic bag use in Islamabad. Regression modeling within a rational choice framework analyzed survey data from 406 retailers across 18 selected urban and rural markets. We found that the subjective belief that a fine was unlikely (β = −16.10; t = −3.90; p < 0.001), likely (β = −24.99; t = −4.95; p < 0.001), or very likely (β = −43.84; t = −4.07; p < 0.001) for selling bags versus very unlikely was significantly associated with lower usage. Additionally, older retailer age (β = −0.25; p < 0.001) and more education (β = −0.77; p < 0.01) were associated with lower plastic bag usage. Business registration (β = −3.94; p < 0.10) and trade membership (β = −4.04; p < 0.05) also decreased use. Rural location (zone II: β = 13.28; p < 0.001) and plastic bags stock availability (β = 16.75; p < 0.001) increased use. Awareness, viewing bags as “Good”, unlikely fines and lack of substitutes lowered use. Results provide insights to inform more effective policies for reducing plastic waste.

Introduction: In Central Europe, in Hungary, the state guarantees access to health care and basic health services partly through the Semmelweis Plan adopted in 2011. The primary objectives of the Semmelweis Plan include the optimisation and transformation of the health care system, starting with the integration of hospitals and the state control of previously municipally owned hospitals. The transformation of the health care system can have an impact on health services and thus on meeting the needs of the population. In addition to reducing health inequalities and costs, the relevant benefits include improving patients’ chances of recovery and increasing patient safety. The speciality under study is decubitus care. Our hypothesis is that integration will improve the chances of recovery for decubitus patients through access to smart dressings to promote patient safety. Objective: to investigate and demonstrate the effectiveness of integration in improving the chances of recovery for decubitus ulcer patients. Material and methods: The research compared two time periods in the municipality of Kalocsa, Bács-Kiskun County, Southern Hungary. We collected the number of decubitus patients arriving and leaving the hospital from the nursing records and compared the pre-integration period when decubitus patients were provided with conventional dressings (01.01.2006–2012.12.31) and the post-integration period, which entailed the introduction of smart dressings in decubitus care (01.01.2013–2012.12.31). The target population of the study was men and women aged 0–99 years who had developed some degree of decubitus. The sample size of the study was 4456. Independent samples t-test, Chow test and linear trend statistics were used to evaluate the results. Based on the empirical evidence, a SWOT analysis was conducted to further examine the effectiveness of integration. Results: The independent samples t-test model used was significant (for Phase I: t (166) = −16.872, p < 0.001; for Phase II: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −16.872, p < 0.001). For stage III: t (166) = −10.078, p < 0.001; for stage IV: t (166) = −10.078, p < 0.001; for stage III: t (166) = −10.078, p < 0.001). for stage III: t (166) = −14.066, p < 0.001). For the Chow test, the p-values were highly significant, indicating a structural break. Although the explanatory power of the regression models was variable (R-squared values ranged from 0.007 to 0.617), they generally supported the change in patient dynamics after integration. Both statistical analyses and SWOT analysis supported our hypothesis and showed that integration through access to smart dressings improves patients’ chances of recovery. Conclusions: Although only one segment of the evidence on the effectiveness of hospital integration was examined in this study, integration in the study area had a positive impact on the effective care of patients with decubitus ulcers, reduced inequalities in care and supported patient safety. In the context of the results obtained, these trends may reflect different systemic changes in patient management strategies in addition to efficient allocation of resources and quality of care.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.