Adjustment Policies Research Articles

Is the business cycle getting hit by climate policy uncertainty in China?

This study apply an asymmetric time-varying test to assess the impact of China's climate policy uncertainty (CCPU) on its business cycle (2000–2022) using currency supply indices (M2, M1, and M0) as economic proxies. Linear Granger tests indicate no causal relationship between CCPU and the currency supply in general. However, the time-varying analysis reveals a significant impact of CCPU on M2 after December 2020. In addition, M2 mainly responds to negative CCPU shocks whereas M1 and M0 respond to both positive and negative shocks. Therefore, policymakers should adopt stabilisation measures to mitigate M2 contraction during CCPU surges while monitoring M1 and M0 for comprehensive currency policy adjustments.

A Preliminary Study on Current Status of Employment Guidance Work of International Students and Improving Strategies in S Province China

With the continuous opening-up to the world and the continuous improvement of the higher education quality, a growing number of international students are choosing Chinese universities for pursuing their further study. At present, China has become the largest destination in Asia and the third largest destination in the world for studying abroad (Miao & Qu, 2022). In addition to the high quality of the educational environment, the ever-growing economic situation and the vast job market have also become one of the most important factors that influence international students to choose to study in China. Around 2017, with the adjustment of relevant national and local policies for the employment of international students, the employment environment for international students in China has become more relaxed and transparent. Meanwhile, more Chinese enterprises have gradually become internationalized and the demand for international talents has also been increasing, thus providing more opportunities for international students to work in China. The gradually favorable employment environment for international students has prompted universities to continuously strengthen, prioritize and improve the employment guidance work for international students. This paper selected one provincial university in S Province, aimed to explore the current situation of international students’ employment management, the challenges they face, the necessity of employment guidance for international students in universities and the suggestions for employment guidance management. Through questionnaire with the international students and the semi-structured interviews with the international students and administrators of international student management to summarize and analyze the challenges faced by the current employment guidance work for international students. Hope the findings of this paper can provide feasible strategic suggestions for the employment guidance management of international students in S Province.

A compliance and audit model for tackling tax evasion in Nigeria

Tax evasion remains a significant challenge for Nigeria, undermining economic stability and reducing public revenue. This paper proposes a comprehensive compliance and audit model designed to address tax evasion in Nigeria. The model integrates enhanced compliance strategies, advanced audit procedures, and the strategic use of technology to create a robust framework for tackling tax evasion. The proposed model emphasizes proactive measures to improve taxpayer compliance through targeted education and outreach programs, streamlined reporting processes, and incentivization. On the audit front, it advocates for the adoption of sophisticated audit techniques and technologies, including data analytics and automated systems, to detect and address evasion more effectively. The implementation framework outlines a phased approach involving key stakeholders such as the Federal Inland Revenue Service (FIRS), local tax authorities, and the private sector. By examining domestic and international case studies, the paper highlights successful practices and potential challenges, providing actionable insights for adapting these strategies to the Nigerian context. Evaluation metrics are proposed to assess the model’s effectiveness, with expected outcomes including improved tax revenue, enhanced compliance rates, and strengthened public trust in the tax system. The study concludes with recommendations for further research and policy adjustments to refine and expand the model's application. This approach aims to not only mitigate tax evasion but also contribute to a more transparent and equitable tax system in Nigeria.

Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

A Sustainable Model for Forecasting Carbon Emission Trading Prices

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Impact of Deep Preferential Trade Agreements on Bilateral Value-Added Exports

ABSTRACT This study investigates the impact of deep preferential trade agreements (PTAs) on bilateral value-added exports. The results indicate that deeper PTAs increase bilateral exports through reducing the trade costs and the improvement in the partner country’s governance could also promote it. The heterogeneity analysis shows that the impact of PTAs on higher-income countries is mostly driven by WTO-X clauses. Deep PTAs are more effective for countries with lower policy adjustment costs or better rules of law. This study is important for understanding the effect of PTA provisions on trade and provides a theoretical reference for countries to improve trade policies.

The Role of the U.S. Dollar in Global Trade Finance: A Study of Its Dominance and Future Prospects

This paper digs deep on what reason has led the U.S. dollar to be a dominant force for years mainly due to its stability, liquidity, and greater usage within international markets. It has made it the world's reserve currency did not just serve as an enabler for far-flung international trades and investments but also led to the establishment of the United States in terms of standing in economic and geopolitical standing. Historical factors concerning the dollar's increasing popularity over time are addressed in this paper-from the post- WWII establishment of the Bretton Woods system to the moments well after the death of the gold standard. It further explores shifting threats to the supremacy of the dollar and key drivers that might change the financial environment in the world. These are the economic ascension of emerging markets, particularly China, seeking to internationalize the yuan; extension of the BRICS bloc, composed of Brazil, Russia, India, China, and South Africa, and their joint thrust toward depolarization. Digital currencies and blockchain technology provide an emerging alternative that would allow countries to bypass traditional banking systems or lessen their dependency on the dollar when making cross-border transactions. The discussion explores how geopolitical stress - whether a trade war or the imposition of sanctions, as examples - may push the shift away from the dollar, based on a focus on increasing foreign reserves' diversity and insulation against the over-reach of the US. In addition, the paper went through how adjustments in global financial regulation, technological innovation, and central banks' policies may have an impact on the safe-haven status the dollar enjoys. Through a combination of both quantitative and qualitative methods, this paper examines the continued relevance of the dollar at the international level in terms of trade and finance. Indeed, it envisions possible scenarios: the continued reign of the dollar, the emergence of rival currencies, and the new politics of global economic crises over currency dynamics. But if nothing else, it provides a more refined and multi- dimensional view of what is going on with the dollar today and what may go on in the future in an increasingly multipolar world economy. These insights will be critical when policymakers, financial institutions, and global businesses negotiate their way through uncertainty in changing currency landscapes. Keywords: U.S. dollar, global trade, finance reserve, currency economic, uncertainty emerging economies, digital currencies, geopolitical shifts currency competition.

Comparative impacts of energy, climate, and economic policy uncertainties on renewable energy

The ongoing adjustment and fluctuation of energy, climate, and economic policies leads to potential policy uncertainty. The potential uncertainty arising from these policies has a wide range of effects on renewable energy technology, leading to changes in investment decisions or consumption patterns. Hence, given the worldwide shift towards sustainable technologies, it is critical to assess how these policy uncertainties will affect renewable power. Given this backdrop, this study delves into the impact of energy policy uncertainty, climate policy uncertainty, economic policy uncertainty, and government effectiveness on renewable energy in the United States. The cross-quantilogram (CQ) methodology is employed by using monthly data from 2002 to 2022. The outcomes reveal that energy policy uncertainty effectively promotes renewable energy in the short run, whereas it serves as a barrier to the adoption of renewables in the long run. Economic policy uncertainty reduces renewable energy consumption in the short run, but it increases in the long run. Moreover, climate policy uncertainty accelerates the transition to low-carbon energy sources. Finally, government effectiveness is a significant supportive element of the energy transition towards renewables.

Prevalence and factors influencing long COVID among primary healthcare workers after epidemic control policy adjustment in Jiangsu, China

ObjectiveThe persistent symptoms arising from COVID-19 infection pose a substantial threat to patients’ health, carrying significant implications. Amidst the evolving COVID-19 control strategies in China, healthcare workers (HCWs) endure considerable stress. This study aims to evaluate the prevalence of long COVID infections and their influencing factors among primary HCWs after epidemic control policy adjustment in Jiangsu.MethodsA self-designed questionnaire was administered through on-site surveys among primary HCWs in five counties and districts within Jiangsu Province from July 4 to July 20, 2023. Logistic regression analysis was employed to identify factors associated with long COVID.ResultsThe prevalence of long COVID among primary HCWs stood at 12.61%, with a 95% confidence interval (CI) of 11.67-13.55%. Among those affected, the most common long COVID symptoms were hypomnesia (4.90%, 95%CI: 4.29-5.51%), sleep difficulties (2.73%, 95%CI: 2.27-3.19%), fatigue (2.35%, 95%CI: 1.92-2.78%), disturbances in the reproductive system (1.93%, 95%CI: 1.54-2.32%), hair loss (1.85%, 95%CI: 1.47-2.23%), and myalgia/arthralgia (1.51%, 95%CI: 1.16-1.86%). Multivariate logistic regression revealed that older age groups (30–45 years (adjusted odds ratio (aOR) = 1.93, 95%CI: 1.44–2.58), 45–60 years (aOR = 2.82, 95%CI: 2.07–3.84)), females (aOR = 1.26, 95%CI: 1.03–1.55), and higher work stress (high stress (aOR = 1.52, 95%CI: 1.24–1.86), extremely high stress (aOR = 1.37, 95%CI: 1.03–1.82)) were more prone to long COVID. Conversely, individuals with educational attainment below the bachelor’s degree (aOR = 0.67, 95%CI: 0.55–0.82) and those who received four or more doses of the COVID-19 vaccine (aOR = 0.55, 95%CI: 0.33–0.92) were at a reduced risk.ConclusionThis study investigates the prevalence of long COVID among primary HCWs and identifies key influencing factors. These findings are crucial for assisting in the early identification of COVID-19 patients at risk for long-term complications, developing targeted interventions aimed at optimizing healthcare resource allocation and enhancing the work conditions and quality of life of HCWs. To mitigate the prevalence of long COVID, healthcare providers and local authorities should implement effective measures, such as optimizing work-rest schedules and actively advocating for vaccination.

Influence of technology-related factors on zero-carbon building development in Lagos Nigeria

PurposeThe development of zero-carbon buildings (ZCBs) is beneficial to the society and biodiversity. Despite the benefits of ZCBs, there are challenges limiting its development in construction industry. The current study seeks to examine the technology-related factors affecting the development of ZCB in Lagos Nigeria.Design/methodology/approachThe study designed a questionnaire to achieve the main objective. Data were collected using non-probability and snowballing sampling methods. Questionnaires were distributed, and 272 valid responses were collected. Thereafter, data were analysed using mean value, percentage, frequency distribution, normality test, Kruskal Wallis test and Kendall’s coefficient of concordance.FindingsThe results from data analysis showed that, “less technical expertise in new technological advancements”, “research outcomes are not translated effectively into technology innovations”, “high cost of maintenance on ZCB”, “poor knowledge on renewable technologies” and “industry’s ability to embrace ZCB technologies (policy initiatives and industry practices)” were the topmost five technology-related factors hindering development of ZCBs in Lagos, Nigeria. Also, the results from the study show a statistically significant degree of agreement between various groups of construction organisations in Lagos, Nigeria concerning the technology-related factors hindering the development of ZCBs.Originality/valueThe study contributed to more effective ZCB studies by drawing attention to technology-related factors hindering the development of ZCBs in construction industry. An understanding of these challenges can help construction stakeholders, organisations, policymakers and governments in devising strategies targeted at minimising these challenges and fostering the development of ZCBs in the construction sector. The identified results on technological barriers to ZCBs development can guide targeted interventions and policy adjustments, promoting more effective implementation of ZCBs in Lagos Nigeria and serving as a model for addressing similar challenges in other developing countries. Recommendations for future research on ZCBs were also highlighted.

Measuring ecological effects and policy mechanisms of urban land use transition – a case study of Xi'an, China

ABSTRACT Measuring ecological effects and policy mechanisms of urban Land Use Transition (LUT) is the key to understanding regional environmental changes and achieving sustainable urban developments. This paper illustrates and demonstrates a formal method for measuring the ecological effect of LUT characterized by Ecological Security Index (ESI) and Ecosystem Value (ESV), and enhanced with a correlational analysis between the ecological effect model and urban policy mechanism. Using Xi'an, a major capital city of western China as a case study, based on multi-temporal Thematic Mapper (TM) remote sensing images, Digital Elevation Model (DEM) and relevant statistical data, the results show that the method using ESI and ESV, together with the Land Use Transition Probability Matrix (LUTPM) can comprehensively measure the ecological effects of LUT process in Xi'an from 2000 to 2020. The results show that due to the impact of urban expansion, regional construction land increased significantly (by 67.07%), while farmland decreased considerably (by 13.12%). The farmland and forestland around the main urban area of Xi'an were centrally converted into construction land, yet the mutual transition of other types of land was relatively scattered. The total amount of ESV has slightly increased from 40.1 billion yuan to 42.8 billion yuan, and the proportion of ecological service function value presents a decreasing trend in regulation function (65%), support function (22%), supply function (9%), and cultural function (4%). The ESI value slightly decreased from 0.517 to 0.512, and overall it is still in a state of ecological security. The trend of ESV and ESI decreasing from mountainous areas to plains indicates that high-intensity human activities in plain areas have had a significant impact on the ecological environment. The correlation analysis between policy mechanisms and ecological effects indicates that ecological protection policies have improved the ecological environment and maintain ecological security (high ESV-high ESI mode), and may also have limited economic activities and been detrimental to the security of urban composite ecosystems (high ESV-low ESI mode). Spatial expansion policies have driven the rapid growth of construction land and threatened ecological security (low ESV-low ESI mode). Zoning adjustment policies have led to the transformation of ecological land to construction land, threatening ecological security and reducing ESV (low ESV-low ESI mode), and may also have promoted industrial upgrading and economic development, providing economic assurance for improving ecological security (low ESV-high ESI mode). These comprehensive findings provide valuable insights for understanding the ecological effects and policy mechanisms of LUT in Xi'an for achieving sustainable urban development, and demonstrate and explore a novel method to more appropriately and effectively measure LUT by combining ESV and ESI.

Comparative Analysis Between Japan's Real Estate Economy and China's Real Estate Status

Japan's real estate bubble economy in the 1990s caused a major blow to the Japanese economy, Japan entered the "disappearing 30 years". The causes of Japan's real estate bubble economy and the reasons why the bubble burst are worthy of the world's countries to learn from. 21st century since the rapid development of China's real estate economy, housing prices are growing rapidly. The real estate economy has been overheated in some areas, although the Chinese government has taken relevant measures to cool down, still can't relax the vigilance, people should always pay attention to the overheated areas of real estate prices, land prices. Through the comparison between China and Japan, although the economic structure and development trend are similar when the property overheating phenomenon occurs, the economic development background and international background between the two countries are different, and the governments of the two countries have adopted different strategies to deal with the property overheating phenomenon. So in conclusion, although there are some areas in China where real estate is overheating, China's real estate market will not burst the bubble. In order to prevent China's real estate bubble from bursting and its impact on economic development, China should pay attention to the reasons behind it and make targeted policy adjustments, while also drawing on the lessons learned from other countries to promote the healthy and stable development of the real estate economy.

The Beautiful City of Jakarta, Indonesia

This research aims to analyze environmental conditions in Jakarta and identify practical solutions to air pollution, water pollution, and waste management. The research methods include air and water quality monitoring with modern sensors, analysis of emissions data, and interviews with stakeholders. Simulation models evaluate the impact of various interventions, such as public transportation improvements and pollution control technologies. The research results show that Jakarta faces significant challenges, with pollutant concentrations often exceeding safe limits. Simulation models estimate that reducing vehicle emissions and using renewable energy could reduce air pollution by up to 30% within five years. Pilot projects such as sensor-based air quality monitoring systems and household waste separation programs are progressing positively. However, budget constraints and resistance to change are still obstacles. The conclusions of this research emphasize the importance of integrating modern technology, strengthening environmental policies, and active community involvement. This research also offers a model that can be implemented by other cities in developing countries. Suggestions include policy adjustments, increased financial support, and community education programs to increase awareness and participation in environmental improvement.

Effects of Industrial Structure on the Green Utilization Efficiency of Urban Land: A Case Study of the Bohai Rim Region, China

The green utilization of urban land is a critical component of regional high-quality development. Enhancing the green utilization efficiency of urban land (GUEUL) is of great significance to improving the quality of ecological environment and achieving a green lifestyle and low-carbon production. With the process of urbanization, the industrial structure is constantly adjusted, which has an impact on the structure and efficiency of urban land utilization. Taking 43 cities in the Bohai Rim region as an example, the super-efficiency slack-based measure model with undesirable outputs (super-SBM-undesirable model) was employed to evaluate the GUEUL from 2006 to 2021, and the panel quantile model was used to explore the impact of industrial structure on GUEUL. The results showed that the GUEUL in the Bohai Rim region appeared a fluctuating upward trend from 0.664 in 2006 to 0.837 in 2021. High-value zones shifted from western Liaoning province and southern Shandong province to a “C”-shaped belt around the coastline and expanded continuously, while low-value zones decreased significantly. Furthermore, the influence of industrial structure on GUEUL was significantly positive, but there were significant differences among different efficiency levels. Industrial structure upgrading exerted the most significant effect on GUEUL improvement in low-value zones, while industrial structure rationalization was dominant in high-value zones, and the influence of industrial structure technologization was more significant in medium-low-value zones. Therefore, differentiated industrial structure adjustment policies should be formulated based on the actual condition of each city to improve GUEUL.

Cash Incentive on Remittance Inflow and its Impact on Economic Growth in Bangladesh

Remittances are crucial for driving economic growth in developing countries like Bangladesh, as they effectively influencenearly all macroeconomic indicators within the financial ecosystem. This study examines the impact of remittance inflow on economic growth in Bangladesh considering the cash incentive policy of the government. The ARDL Bound testing approach is employed for the analysis using a monthly dataset covering the period from January 2015 to April 2023. The evidence from the results provides a substantial and favorable influence of remittances on the economic growth of Bangladesh, both in the short term and the long term. According to the expected error correction term, it is projected that the economy will undergo a correction of its imbalances at an annual rate of 58%. Consequently, a strategic blend of short-term policy adjustments and long-term initiatives has the potential to enhance the influence of remittances in fostering sustainable economic growth and elevating the the quality of life for both expatriates and the nation as a whole.

Evolution of policy-driven ecosystem of original innovation talents

In an era of unprecedented global challenges, the cultivation, attraction, and retention of original innovation talent have become critical determinants of national competitiveness and sustainable development. This study introduces a novel multi-agent original innovation talent ecosystem model (MOITEM) to systematically analyze the effects of different talent policies on the ecosystems of original innovation talent across various regions in China. The simulations reveal that while direct talent policies drive short-term talent growth, they often fall short of addressing the long-term needs for resource allocation and environmental optimization, thereby limiting sustained competitiveness. In contrast, indirect and combined talent policies prioritize the overall quality of the ecosystem, providing more enduring support for original innovation. Furthermore, this study explores the interactions among agents within the ecosystem and their synergistic relationships with the external environment, highlighting the necessity of targeted policy formulation and multi-agent collaboration. The findings not only elucidate the strengths and limitations of various talent policies but also offer tailored recommendations based on regional characteristics, emphasizing the importance of continuous evaluation and adaptive policy adjustment. These insights hold substantial theoretical and practical implications for the development and optimization of global policies aimed at fostering original innovation talent.

Will peer-to-peer online lending affect the effectiveness of monetary policy?

Online lending is a product of digital transformation, which has had a profound impact on the traditional money market. This paper discusses the impact of peer-to-peer (P2P) online lending on the effectiveness of monetary policy. Through the bootstrap sub-sample rolling-window Granger causality tests show that P2P has both positive and negative impacts on the money supply (M2). The positive impact of P2P on M2 indicates that online loans increase the amount of money supply. The negative impact of P2P on M2 shows that it may cut the money supply, thus weakening the monetary policy effectiveness. The general equilibrium model is inconsistent with these results, which underlines a positive effect from P2P to M2. In turn, the negative impact points out that the adjustment of monetary policy will hinder the development of P2P. The negative impact of M2 on P2P indicates that through the regulation of money supply, the online lending market can be correctly guided to prevent financial market from getting out of control. Through the supervision of online lending industry, we can accurately grasp the development of the internet financial industry and reduce its impact on monetary policy.

Sustainability Analysis of the Revolving Fund Program of the LPDB (Revolving Fund Management Institution): MICMAC Method Approach

The importance of access to credit for micro, small, and household craft enterprises, including cooperatives, has become a major focus in improving working capital financing and capital accumulation to support industrial income. The government through the LPDB (Revolving Fund Management Institution) has launched a revolving fund program that aims to strengthen the capital base of cooperatives, increase the capacity of human resources in business and financial management, and increase the role of cooperatives in expanding employment opportunities and reducing poverty. According to data from the Cooperative Business Development Division of the Denpasar City MSME Cooperative Office, from 2021 to 2023, there were five cooperatives that received the LPDB revolving fund program, but did not consistently reapply for the funds. This raises concerns regarding the sustainability of the program and the factors that influence it. This study aims to identify the factors that influence the sustainability of the LPDB program for cooperatives in Denpasar City. Using the MICMAC (Matrix of Cross Impact Multiplications Applied to a Classification) analysis method, this study reveals that the revolving fund administration requirements and members' information and knowledge about the revolving fund have the most significant direct influence. These findings underscore the importance of simplifying the administrative process and improving member knowledge to ensure the success of the program. In addition, active member participation emerged as an important predictor of program sustainability, highlighting the importance of members' direct involvement in supporting their economic activities. Thus using this method can uncover critical factors, and provide a comprehensive understanding that can inform policy adjustments and strategic implementation to improve program sustainability.

Resistance to Antimalarial Drugs in Uganda: Current Status and Future Directions

Antimalarial drug resistance posed a significant challenge to malaria control in Uganda, where Plasmodium falciparum remains the predominant strain. This review examined the current status of antimalarial drug resistance in Uganda, focusing on the mechanisms underlying resistance, epidemiological trends, and the impact on malaria control programs. Chloroquine and sulfadoxine-pyrimethamine resistance have been well-documented, and recent concerns about reduced efficacy of artemisinin-based combination therapies (ACTs) highlight ongoing challenges. The review identified key mechanisms of resistance, including genetic mutations, drug efflux, metabolic pathway alterations, and potential biofilm formation. The implications of resistance on treatment efficacy, healthcare costs, and policy adjustments are discussed. Future directions included strengthening surveillance systems, optimizing combination therapies, investing in research and development, implementing integrated vector management strategies, and enhancing public education and community engagement. Related published literatures were reviewed and analysis of recent data on drug resistance patterns in Uganda was carried out and utilised in compiling this paper. By exploring these dimensions, the review aims to provide actionable insights for policymakers, healthcare providers, and researchers to effectively address drug resistance and advance malaria control efforts in Uganda. Keywords: Antimalarial Drug Resistance, Plasmodium falciparum, Chloroquine Resistance, Artemisinin-Based Combination Therapies (ACTs), Surveillance and Monitoring.

Curriculum Updates and Their Impact on School Management Practices

Curriculum renewal influences not only educational content, but also overall school management practices. In this study, we investigate the impact of curriculum reform on school management practices with an interdisciplinary approach. Through a literature review, we identified various forms of impact, including changes in management paradigm, organizational structure, school culture, teacher skills, education quality, community involvement, management skills, resource quality, student involvement, and service quality. Our findings suggest that school management must adapt planning, training, resource provision, and community involvement to manage change. Regular evaluations are needed to develop more effective education policies and school management practices, with policy adjustments based on evaluation findings. Good communication and involvement of all stakeholders is the key to implementing significant changes in increasing educational effectiveness.