Efficient Policy Research Articles

O PRÉ NATAL FRENTE A MORBIMORTALIDADE MATERNO INFANTIL

This study addresses the critical importance of prenatal care in reducing maternal and infant morbidity and mortality, highlighting the significance of early adherence by pregnant women to these services. Through a literature review, it was possible to identify complications associated with non-adherence to prenatal care, such as premature births, low birth weight, and the prevalence of gestational complications that can be mitigated with adequate and timely care. Among the effective strategies for the recruitment and retention of pregnant women, the role of nurses was emphasized as fundamental, utilizing techniques that include health education, the use of information technology, and interdisciplinary practices. Programs such as Rede Cegonha were also mentioned, highlighting their contribution to strengthening prenatal care, childbirth, and postpartum support. The study underscores the need for efficient public policies and ongoing research to improve prenatal services, ensuring that all pregnant women have access to effective and humane care, guaranteeing a healthy pregnancy and a safe childbirth. This work emphasizes the urgency of integrated and evidence-based approaches in prenatal care, aiming for significant improvements in maternal and infant health.

Pengaruh Perceived Overqualification dan Work Stress Dengan Moderasi Transformational Leadership Terhadap Innovative Work Behavior dan Turnover Intention

This research aims to analyze and explore the influence of two key factors, namely Perceived Overqualification and Work Stress, on employee Innovative Work Behavior and Turnover Intention in the context of logistics companies. In addition, this research considers the moderating role of Transformational Leadership in the relationship between Perceived Overqualification and Work Stress with Innovative Work Behavior and Turnover Intention. This research involved a survey of employees at a logistics company using a questionnaire, then the data collected was analyzed using quantitative methods with the population in this study involving 180 employees at a multinational logistics company that provides warehousing and distribution services in the Bekasi Regency area. The research results show that Perceived Overqualification and Work Stress have an influence on Turnover Intention. Apart from that, Transformational Leadership has been proven to moderate the relationship between Perceived Overqualification and Innovative Work Behavior. These results indicate that human resource management in logistics companies must consider Perceived Overqualification and Work Stress as important factors in promoting innovative work behavior that can reduce employee turnover intentions. This research has practical implications for logistics companies in developing more effective and efficient human resource management policies. Apart from that, this research also provides theoretical contributions in understanding the relationship between Perceived Overqualification, Work Stress, Transformational Leadership, Innovative Work Behavior and Turnover Intention in the context of logistics companies.

Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning.

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.

Disclosure and Dynamic Risk Sharing with a Large Shareholder

ABSTRACT We study the effects of disclosure in a dynamic market with imperfect competition. The supply of an asset is determined by a large shareholder with price impact, who trades slowly to diversify away from concentrated ownership. Small investors provide capital and thus risk-bearing capacity to the market. Although it is well known that disclosure impedes risk sharing by shifting risk before future trading opportunities, we show that disclosure, at the same time, can enhance risk sharing by promoting more trades. Resolving this tradeoff, an interior level of disclosure quality maximizes the small investors’ surplus as well as the total surplus, but minimizes the large shareholder’s surplus. Further, efficient disclosure policies feature increasing quality over time. JEL Classifications: D80; E21; G12; L13; M41.

Does structural change matter for sustainable development in newly industrialized countries? Fresh evidence from a new sustainability indicator

The economic gap between developed and developing economies has been diminishing due to the recent rapid economic growth performance of developing economies. While achieving this swift growth level, developing economies have structurally transformed their economies. However, the impact of this structural change on environmental sustainability remains unclear. Therefore, this study focuses on the impacts of structural change, energy structure, and economic growth on the load capacity factor, a comprehensive sustainability indicator, in newly industrialized countries during 2000–2020. To this end, second-generation panel data techniques, which account for cross-sectional dependence and heterogeneity, are used to provide more robust and reliable estimates. The results show that economic growth, structural change, and fossil energy utilization decrease the load capacity factor while renewable energy utilization increases it. These findings underscore the need for energy efficiency and resource-conscious policies that align with environmental sustainability while promoting economic growth, highlighting their crucial role in the future of sustainable development.

Tax Morale, Governance Quality And Tax Compliance

This study aims to investigate the relationship between tax morale, governance quality, and tax compliance in Nigeria. The study used the library research approach, which involved examining books, academic journals, and conference papers. According to the evaluation, tax compliance in Nigeria remains below standard, resulting in low tax morale, which is influenced by people's perceptions of government services, a lack of justice, and trust. The study also discovers that tax morale has a significant impact on tax noncompliance, such as a decrease in foreign portfolio equity investment inflows for tax avoidance, self-employed tax payers, and perceptions of higher tax fines and difficulty evading taxes. The study also discovers a favourable link between government quality (excellent governance, views of government accountability and provisions) and tax compliance. Studies have shown a considerable association between tax morale, governance quality, and tax compliance. The study recommends that government put in place efficient and effective tax collection policies, through providing people with accessible ways of paying their taxes, simplifying the process and reduction of taxes imposed on lower income individuals.

Efficient Queue Control Policies for Latency-Critical Traffic in Mobile Networks

Efficient Queue Control Policies for Latency-Critical Traffic in Mobile Networks

An Efficient Multi-Agent Policy Self-Play Learning Method Aiming at Seize-Control Scenarios

Aiming at the problem of multi-agent cooperative confrontation in seize-control scenarios, we design an efficient multi-agent policy self-play (EMAP-SP) learning method. First, a multi-agent centralized policy model is constructed to command the agents to perform tasks cooperatively. Considering that the policy being trained and its historical policies usually have poor exploration capability under incomplete information in self-play trainings, the intrinsic reward mechanism based on random network distillation (RND) is introduced in the self-play learning method. In addition, we propose a multi-step on-policy deep reinforcement learning (DRL) algorithm assisted by off-policy policy evaluation (MSOAO) to learn the best response policy in the self-play. Compared with DRL algorithms commonly used in complex decision problems, MSOAO has more efficient policy evaluation capability, and efficient policy evaluation further improves the policy learning capability. The effectiveness of EMAP-SP is fully verified in MiaoSuan wargame simulation system, and the evaluation results show that EMAP-SP can learn the cooperative policy of effectively defeating the Blue side’s knowledge-based policy under incomplete information. Moreover, the evaluations results in DRL benchmark environments also show that the best response policy learning algorithm MSOAO can promote the agent to learn approximately optimal policies.

Role of Energy Intensity and Investment in Reducing Emissions in Türkiye

Addressing the challenge of rising carbon dioxide (CO2) and greenhouse gas (GHG) emissions is a critical priority in global efforts to combat climate change. The primary aim is to assess the relationship between energy intensity, private investments in energy, renewable energy consumption, export-related factors, and their influence on CO2 and GHG emissions in Turkey. The study employs a multi-level approach using correlation and regression analyses to explore the impact of the selected variables. A Bayesian correlation analysis was conducted to evaluate the strength of relationships between variables, and a regression model was used to test the significance of each factor. Data were gathered from official sources on energy intensity, renewable energy consumption, private investments in energy, and export-related variables in Turkey from 2007 to 2022. The study employed the JASP statistical software. The analysis showed that energy intensity and private energy investments are the most significant predictors of CO2 and GHG emissions. Energy intensity exhibited a strong negative correlation with CO2 emissions per capita (r = -0.717, BF₁₀ = 10.456) and GHG emissions (r = -0.802, BF₁₀ = 44.224), highlighting the critical role of energy efficiency in reducing emissions. Renewable energy consumption also played a role, though its influence was less pronounced than energy efficiency and investment. Based on the findings, it is recommended that policymakers prioritize energy efficiency improvements and create incentives for private investment in renewable energy technologies. Future studies should focus on sector-specific energy efficiency improvements and policy frameworks to enhance private sector engagement in clean energy initiatives.

The Paradox of the ‘Care’ of London’s Children: Discourses of ‘Safety’ and ‘Respect’ in England’s Ministry of Justice Inspection Reports

Using English prison inspectorate reports, the article presents an Ervine Goffman-inspired sociological discourse analysis of official political accounts about the living conditions of incarcerated children held in London’s Feltham prison. Through a close reading of inspection reports, we develop a critical window into their lived experiences in an exceptionally harmful UK prison regime. The construction of this prison estate conjures its dilapidation, unhygienic conditions, and endless social danger. The stigmatizing construction of the child prisoner intimates a pervasive culture of violence and bullying, resulting in their aversion to purposive activities. While, at first blush, prison inspectorate reporting is based on the policy of efficiency to ensure a safe and rehabilitative prison experience for youth, it is argued that the nature of the reporting of incarceration obviates a critique of the wider political fabric that custodial interventions will invariably reproduce. The Inspectorate operates within the state’s dominant class-stratified political ideology. The adoption of a generic labeling discourse in the reports minimizes the communication of harms inflicted on children by criminal ‘justice’ that can only worsen their wellbeing and reproduce the harmful intensity of their pre-existing marginality.

Economic Assessment of Small-Scale Mountain Dairy Farms by Using Accounting Data: Evidence from an Italian Case Study

Dairy mountain farms are economically disadvantaged due to small farm sizes and high production costs. This situation was exacerbated firstly by the opening of the market linked to the abolition of milk quotas; secondly by the rising prices due to the energy crisis due to covid 19 and the war in Ukraine. However, these farms are important for the preservation of traditional landscapes, the economy of these areas and the offered ecosystem services, especially when they are managed extensively. The objective of this research is to understand if mountain dairy farms are economically sustainable and competitive through the analysis of a case study. The economic analysis takes into account the production cost of milk, profitability, and some economic indicators using accounting data. The results show that the farm profitability is decreasing, and the costs are unsustainable. As a consequence, more efficient policy support is needed to overcome this crisis.

Bangladesh's national education policy for English teaching: Policy direction and gaps over the last decade

This study aims to demonstrate the perceived differences between the direction and the gaps in the execution of Bangladesh's National Education Policy (NEP) for English Teachers. It specifically examines the changing requirements of English teachers over the past decade by enhancing their proficiency and teaching methodologies. In this study, we show that communicative language teaching (CLT) approaches significantly modify the existing structure of educational policy in Bangladesh. The present study is grounded in the constructivist research paradigm and utilized a qualitative case study approach from diverse sources. The data was gathered via document analysis, classroom observations, and interviews with six secondary school English teachers from a pool of educators representing four schools in different areas of Bangladesh. The data was analyzed using theme analysis via NVivo 11 software. The findings indicate several areas where the objectives of Bangladesh's NEP could be better aligned with their practical execution. These areas include reforming elementary and secondary schools and restructuring school leadership, teacher education, teacher status, and classroom pedagogy. Furthermore, this study is a valuable contribution toward advancing a more robust and efficient education policy at the national level, which can effectively improve the standard of education for all to provide quality teaching.

Labor markets during COVID-19: gaps and challenges in Latin America.

The effects of the COVID-19 pandemic on Latin American labor markets continue to be quantified, to identify the social and economic impacts that this pandemic had, and to design more efficient public policies that would protect the most vulnerable groups. For this reason, the research question was as follows: what were the changes in the labor formality rates before and two years after the main contingency measures of the COVID-19 pandemic were implemented? Using data from Argentina, Bolivia, Chile, Colombia, Ecuador, Guatemala, Mexico, Peru, and Uruguay, the formality rate (τ) was analyzed, which was calculated using a weighted average between the formal employment rates of the number (i) of economic sectors (p) in a specific period (t). The results suggest that the weighted labor formality rate increased in the countries of the region. These changes in formality could be the result of greater capital accumulation, the integration of productive processes, the integration of commercialization processes, and differentiated fiscal stimuli (i.e., the intrasectoral aspect), but it was not due to the displacement of workers from highly informal economic sectors to more formalized sectors (i.e., the intersectoral component). The findings emphasized the precarious situation of women in the region, regardless of the country, particularly in Ecuador, Peru, Bolivia, and Argentina. These findings suggest the need to design public policies that reverse the current situation of the labor market and prevent future economic shocks, with special emphasis on the informal sector and women.

Vision-based reinforcement learning control of soft robot manipulators

PurposeThis study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.Design/methodology/approachA closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.FindingsPhysical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.Research limitations/implicationsConstraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.Practical implicationsBy eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.Originality/valueThis research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

Pengaruh Komunikasi Pemasaran, Kualitas Produk, dan Label Halal terhadap Keputusan Pembelian di TikTok Shop

TikTok is a effective medium to promote and selling product. Because Tiktok has hundred of thousand users, which of course makes the product known to various group. This application has been successfully used by various brand to promote their products to consumers, especially to cosmetic industry, both local product and international brand. One of local product that dominates market share is Wardah Kosmetik as with its trademark as halal cosmetics. This study aims to analyze and examine the factors influence consumer purchase decisions Wardah Lipstick products of TikTok aplication users. These factors include the product quality, promotion and halal label. The research used the quantitative with regression analysis method. The sampling method used purposive sampling and the number of samples used in this study amounted to 75 respondents source was distribution of questionnaires to TikTok users in Central Lombok by online via WhatsApp. The data were analysed with IBM SPSS version 21 and analysis was carried out by several tests such as validity test, realibility test, classic assumption test, linear regression analysis and coefficient of determination test. The results of this study indicate that the product quality, promotion and halal label has a significant effect on purchase decisions Wardah Lipstick products of TikTok aplication users. The findings of this study can be reference uses as a reference for future researchers who will study similar problems. This research can also be a reference for companies to always be able to maintain aspects of product quality, promotion and halal label in designing and determining effective and efficient markering policies.

Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis

The work program promised by the 2024 Presidential Candidate and Vice Presidential Candidate pair, namely, Prabowo Subianto and Gibran Rakabuming Raka, one of which is a free lunch program, this program is an effort to improve community welfare, but this has attracted public attention on social media, one the other is platform X. The public response to the free lunch program is the main focus. In this research, researchers analyzed sentiment related to the free lunch program using the Naive Bayes method using the Python programming language on Google Colab to analyze sentiment towards social media users. X. the results obtained from 920 tweets data, there are 167positive value tweets, 744tweets are negative. The evaluation results with the confusion matrix showed an accuracy of 86.95%, with precision 93%, recall 61%, and F1-Score 65%.From the results of this research it can be concluded that the majority of the public who commented on social media X gave a negative response to the free lunch program. These results can be used as material for government evaluation in determining effective and efficient policies for society.

Model-based reinforcement learning approach for federated learning resource allocation and parameter optimization

In this paper, we investigate the performance of a model-based approach for solving resource allocation and parameter adjustment problems in federated learning (FL) within a wireless network. Given the existence of models for energy, communication channels, and accuracy, such models can be leveraged to achieve improved performance. Additionally, machine learning techniques can be employed to identify known parts of the model and also exploit training data for unknown parts of the model, enabling the creation of complex policies. Model-based reinforcement learning (RL) methods have the potential to offer such solutions, particularly in resource allocation and parameter optimization settings where the model can be partially derived mathematically. Our results demonstrate that the use of such a method in FL scenarios leads to improvements in both performance and the number of iterations required to identify the desired policy. Our simulations demonstrate the significance of allocating appropriate resources for FL applications through proper consideration of inherent tradeoffs, as performance will not improve beyond a certain saturation point. Additionally, our proposed FL model takes intelligently into account the presence of slow users to propose efficient policies for users that may have access to more abundant resources.

Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap

Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called “efficiency gap”. Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions.

Application of thermal energy optimization based on improved neural network algorithm in green innovation capability evaluation of manufacturing enterprises

As the global focus on sustainable development continues to increase, manufacturing companies face the dual challenges of energy efficiency and environmental impact in the process of enhancing green innovation capabilities. The purpose of this study is to explore the application of improved neural network algorithm in thermal energy optimization, so as to improve the green innovation ability of manufacturing enterprises and promote their sustainable development. By constructing an improved neural network model and using big data analysis technology, this paper conducts in-depth analysis of thermal energy use in manufacturing enterprises, so as to identify and optimize energy consumption patterns. The study considered a variety of influencing factors, including equipment efficiency, production process and environmental policy, and evaluated the optimization effect through model training and testing. The experimental results show that the improved neural network algorithm can effectively identify thermal energy waste points, and put forward the corresponding optimization measures. After optimization, the energy use efficiency of manufacturing enterprises has been improved, carbon emissions have been significantly reduced, and the comprehensive evaluation score of green innovation ability has been improved, providing effective technical support for promoting the sustainable development of enterprises.

CBDCs, regulated stablecoins and tokenized traditional assets under the Basel Committee rules on cryptoassets

Purpose This paper examines the impact of Central Bank Digital Currencies (CBDCs), regulated stablecoins and tokenized traditional assets on the cryptocurrency market, following the guidelines set by the Basel Committee. This study aims to analyze the implications for secure storage, cross-border transfers and necessary investments. Design/methodology/approach The paper uses a policy analysis approach to assess the potential effects of the Basel Committee’s regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It explores their impact on the cryptoasset market, strategies of central and commercial banks, payment systems and risk management. Findings The adoption of CBDCs, regulated stablecoins and tokenized traditional assets is expected to grow rapidly in the coming years. It raises concerns about secure storage, cross-border transfers and required investments. Central banks are likely to introduce CBDCs and authorize stablecoin issuance, aiming for efficient monetary policies and risk management. Basel III regulations may lead to asset tokenization by banks, reducing asset size and increasing fee-based income. Originality/value This paper provides insights into the potential impact of the Basel Committee's regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It contributes to the understanding of the evolving cryptoasset market and the strategies of central and commercial banks in adopting these technologies. The findings offer valuable information for policymakers, regulators and market participants in navigating the changing landscape of digital assets.