Action Policies Research Articles

Green policies and financial development in G7 economies: An in‐depth analysis of environmental regulations and green economic growth

AbstractThis comprehensive study explores the nuanced relationship between financial development and its determinants within the G7 nations, spanning the years 1990 to 2020. Motivated by the need to understand long‐term financial trends, we meticulously analyze key variables including total natural resource rent, Environmental Policy Stringency Index, total energy consumption, green gross domestic product (GDP), and foreign direct investment inflow. Employing rigorous diagnostic tests to ensure the robustness of our findings, advanced methodologies such as “Method of Moment Quantile Regression,” along with simulations of “Bootstrap Quantile Regression," “Panel Corrected Standard Errors,” and “Feasible Generalized Least Squares” regressions uncover the statistical significance and practical implications of our results. Our pivotal findings carry substantial implications for both individual G7 member states and the collective group. Highlighting a positive correlation between stringent environmental policies, measured by the Environmental Policy Stringency Index, and financial development, our study emphasizes the imperative for these nations to align economic and environmental policies. Striking a harmonious balance between resource management and sustainable environmental regulations not only fosters financial growth but also addresses global environmental concerns. Furthermore, the adverse impact of total energy consumption on financial development underscores the urgent need for G7 nations to prioritize energy efficiency and transition to sustainable sources, aligning with the global trend towards eco‐friendly practices. In response to these critical findings, we propose actionable policy measures. To align with the growing climate crisis and standardize green finance practices, we advocate for the establishment of a jointly funded Climate Resilience and Adaptation Fund and a unified Green Bond Framework within the G7. These measures not only enhance climate resilience and streamline sustainable investments but also demonstrate the G7's collective commitment to a greener and more prosperous future.

Doing wellbeing policy: A discussion on public policy making for integrative prosperity

AbstractFocusing on holistic wellbeing rather than solely economic prosperity is becoming ever more popular among policy makers, both in Australia and New Zealand, and elsewhere. And yet, turning a complex set of system‐level indicators of wellbeing into actionable policy requires us to rethink how we develop, implement, and evaluate policy. In this article, I review the current trends in wellbeing, including developments in the measurement and tracking of wellbeing, and offer practical steps for integrating actionable wellbeing outcomes into future policymaking processes.Points for practitioners Focusing on wellbeing as part of the policy making process is becoming more popular among governments, including in Australia and New Zealand. The New Zealand Government has been doing wellbeing budgets since 2019 while the Australian Government released a new wellbeing framework in 2023. Wellbeing policy represents an approach to policy making that aims to maximize the general health and happiness of a target population on both subjective and objective measures of wellbeing. This includes both economic and non‐economic measures of prosperity and wellbeing. There are many ways of tracking the wellbeing effect of policy and so choosing the right framework is important for effective wellbeing policy making. This starts with a wellbeing purpose for the policy and a clear and concise definition of wellbeing. Doing wellbeing policy requires a good understanding of what wellbeing represents and how it is measured. You need relevant and measurable indicators of wellbeing, an evaluation strategy, and the ability to reflect and innovate as part of an iterative policy making process.

Strengthening Cooperative Consensus in Multi-Robot Confrontation

Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently and being defeated individually by their opponents. To tackle this challenge, we propose a novel joint action policy, referred to as the consensus action policy (CAP). Specifically, CAP records the number of times each joint action has caused our side to fail in the past and computes a cooperation tendency, which is integrated with each agent’s Q -value and Nash bargaining solution to determine a joint action. The cooperation tendency promotes team cooperation by selecting joint actions that have a high tendency of cooperation and avoiding actions that may lead to team failure. Moreover, the proposed CAP policy can be extended to partially observable scenarios by combining it with Deep Q network or actor-critic–based methods. We conducted extensive experiments to compare the proposed method with seven existing joint action policies, including four commonly used methods and three state-of-the-art methods, in terms of episode rewards, winning rates, and other metrics. Our results demonstrate that this approach holds great promise for multi-robot confrontation scenarios.

Predicting environmental impacts of smallholder wheat production by coupling life cycle assessment and machine learning

Wheat grain production is a vital component of the food supply produced by smallholder farms but faces significant threats from climate change. This study evaluated eight environmental impacts of wheat production using life cycle assessment based on survey data from 274 households, then built random forest models with 21 input features to contrast the environmental responses of different farming practices across three shared socioeconomic pathways (SSPs), spanning from 2024 to 2100. The results indicate significant environmental repercussions. Compared to the baseline period of 2018–2020, a similar upward trend in environmental impacts is observed, showing an average annual growth rate of 5.88 % (ranging from 0.45 to 18.56 %) under the sustainable pathway (SSP119) scenario; 5.90 % (ranging from 1.00 to 18.15 %) for the intermediate development pathway (SSP245); and 6.22 % (ranging from 1.16 to 17.74 %) under the rapid economic development pathway (SSP585). Variation in rainfall is identified as the primary driving factor of the increased environmental impacts, whereas its relationship with rising temperatures is not significant. The results suggest adopting farming practices as a vital strategy for smallholder farms to mitigate climate change impacts. Emphasizing appropriate fertilizer application and straw recycling can significantly reduce the environmental footprint of wheat production. Standardized fertilization could reduce the environmental impact index by 11.10 to 47.83 %, while straw recycling might decrease respiratory inorganics and photochemical oxidant formation potential by over 40 %. Combined, these approaches could lower the impact index by 12.31 to 63.38 %. The findings highlight the importance of adopting enhanced farming practices within smallholder farming systems in the context of climate change. Spotlights•China's smallholder wheat farming is increasingly exposed to climate threats, risking resource depletion and pollution•Increasing eco-impacts of smallholder wheat farming could be mitigated by regulated fertilizer use and straw recycling•Life cycle assessment combined with machine learning targets future environmental impacts in dynamic climate scenarios•The study offers actionable policy advice for sustainable smallholder wheat farming practices•Future work may probe machine learning's role in forecasting smallholder production behaviors and farming decision aid

The interplay of affirmative action and entrepreneurial culture: Understanding institutional privilege and exclusion in Malaysia

The aim of this article is to examine how enterprise policies aimed at supporting ethnic groups influence entrepreneurial activity. Drawing on qualitative interviews with entrepreneurs and policymakers in the emerging economy of Malaysia, we show that policy intended to close interethnic gaps in entrepreneurial activity instead serves to increase differences. Parallel institutional environments are formed through socially negotiated accomplishments and are influenced by institutional antecedents including affirmative action policy. These conditions created advantages (institutional privilege) for the targeted population but disadvantages (institutional exclusion) for those who are not targeted in the preferential policies. Tensions between privilege and exclusion impact on entrepreneurial activity, increasing inequalities of different parties. The article provides a number of theoretical contributions and practical implications for policy.

The impact of AI in physics education: a comprehensive review from GCSE to university levels

With the rapid evolution of artificial intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in physics education and offers actionable AI policy recommendations. Using openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (GCSE), A-Level, and introductory university curricula. We employed various AI prompting techniques: Zero Shot, in context learning, and confirmatory checking, which merges chain of thought reasoning with reflection. The proficiency of gpt-3.5-turbo varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM’s accuracy on 5000 mathematical operations was found to be 45.2%. When evaluated as a marking tool, the LLM’s concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there’s no substantial threat from LLMs for non-invigilated physics questions. However, given the LLMs’ considerable proficiency in writing physics essays and coding abilities, non-invigilated examinations of these skills in physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect.

Barriers and Impacts of Female Participation in Formal Politics in Makassar City Period 2019-2024

The increase of women's representation in formal politics in Makassar is encouraging for democracy. Even though there has been an increase in numbers, it has not yet reached the 30 percent quota in accordance with the affirmative action policy. This article wants to discuss the barrier for women in Makassar making it difficult for them to get chance into formal politics. Then, this article also wants to discuss the barriers and impact of female in Makassar legislative and executive institutions to gender equality. In writing this article, we used the library research method. The conclusion in this article is that social structures and political party are barriers for female who want to advance into formal politics, and the impact of female participation in legislative institutions is also quite satisfying by issuing policies and programs that contribute to gender equality and women's empowerment.

Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

The challenge of water level control in steam generators, particularly at low power levels, has always been a critical aspect of nuclear power plant operation. To address this issue, this paper introduces an IHA controller. This controller employs a CPI controller as the primary controller for direct water level control, coupled with an agent-based controller optimized through a DRL algorithm. The agent dynamically optimizes the parameters of the CPI controller in real-time based on the system’s state, resulting in improved control performance. Firstly, a new observer information is obtained to get the accurate state of the system, and a new reward function is constructed to evaluate the status of the system and guide the agent’s learning process. Secondly, a deep ResNet with good generalization performance is used as the approximator of action value function and policy function. Then, the DDPG algorithm is used to train the agent-based controller, and an advanced controller with good performance is obtained after training. Finally, the popular UTSG model is used to verify the effectiveness of the algorithm. The results demonstrate that the proposed method achieves rise times of 73.9 s, 13.6 s, and 16.4 s at low, medium, and high power levels, respectively. Particularly, at low power levels, the IHA controller can restore the water level to its normal state within 200 s. These performances surpass those of the comparative methods, indicating that the proposed method excels not only in water level tracking but also in anti-interference capabilities. In essence, the IHA controller can autonomously learn the control strategy and reduce its reliance on the expert system, achieving true autonomous control and delivering excellent control performance.

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

This study provides an in-depth analysis of the complex relationship between the digital economy and carbon emissions, fully drawing on essential principles of environmental economics, coupled economics, and sustainable development theory. Focusing on the Qinghai region in the western province of China, the study employs highly sophisticated methods such as multiple regression analysis and system dynamics modeling to reveal the multidimensional coupling effects between digital economy development and carbon emission dynamics. The study's results clearly show that in the Qinghai region of China, the booming growth of the digital economy is related to carbon emissions. Of particular interest, the study finds that this relationship exhibits a high degree of complexity and non-linearity and evolves gradually over time. Initially, the rapid expansion of the digital economy, accompanied by high energy consumption and increased carbon emissions, posed a significant challenge to environmental protection. However, a clear inverted “U"-shaped relationship has emerged as the digital economy evolves. This key inflection point signals a shift in the landscape as the digital economy begins to deliver some ecological benefits, potentially reducing the trend of carbon emissions in the future. The findings of this study go beyond simple causality and reveal a complex and evolving dynamic relationship between the digital economy and carbon emissions. Through such insights, this study provides a solid academic foundation and carefully constructs actionable policy recommendations to drive sustainable development. These insights apply to the Qinghai region of China and provide valuable references and lessons for other areas facing similar challenges.

(Dis)honorably discharged: identifying policy gaps in military-civilian reintegration.

Despite the substantial transition assistance available for honorably separating servicemembers, 75% of US veterans report difficulties with the transition to civilian life. For the 16% of veterans who separate with less-than-honorable discharges, these difficulties are compounded by the lack of structural support from the US military. Social stigma, limited transition programming, and loss of benefits create a perfect storm of barriers for these discharged servicemembers. These barriers compound with post-service mental and physical health challenges to contribute to cycles of misconduct that can result in criminal incarceration. Further, because most of these veterans lack health benefits from the Department of Veterans Affairs due to their discharge status, this population is substantially understudied from a public health perspective. However, actionable policy paths forward and federal policy change offer opportunity to soften the landing for these veterans and meet their legitimate needs for care.

Metacognitive Feelings: A Predictive-Processing Perspective.

Metacognitive feelings are affective experiences that concern the subject's mental processes and capacities. Paradigmatic examples include the feeling of familiarity, the feeling of confidence, or the tip-of-the-tongue experience. In this article, we advance an account of metacognitive feelings based on the predictive-processing framework. The core tenet of predictive processing is that the brain is a hierarchical hypothesis-testing mechanism, predicting sensory input on the basis of prior experience and updating predictions on the basis of the incoming prediction error. According to the proposed account, metacognitive feelings arise out of a process in which visceral changes serve as cues to predict the error dynamics relating to a particular mental process. The expected rate of prediction-error reduction corresponds to the valence at the core of the emerging metacognitive feeling. Metacognitive feelings use prediction dynamics to model the agent's situation in a way that is both descriptive and directive. Thus, metacognitive feelings are not only an appraisal of ongoing cognitive performance but also a set of action policies. These action policies span predictive trajectories across bodily action, mental action, and interoceptive changes, which together transform the epistemic landscape within which metacognitive feelings unfold.

Examining Critical Success Factors for Africa’s Sustainable Industrial Development with Special Reference to Zambian Manufacturing Sector – Challenges, Prospects & Opportunities

This study was intended to examine critical success factors for Africa’s industrial development with special reference to the Zambian manufacturing sector - challenges, prospects & opportunities. The study had three (3) research questions; what are critical success factors for Zambia’s industrial development? How efficacious are the existing policy frameworks and /or governmental undertakings to guarantee Zambia’s achievement of SDG # 9 by the year 2030? What are socio-economically viable and actionable policy imperatives to industrialize the country by the targeted year? The study used a qualitative interpretive paradigm in targeting middle and top management key informant policy makers in line government parastatals and association [(Zambia Development Agency (ZDA), the Industrial Development Corporation (IDC), the Citizens Economic Empowerment Commission (CEEC), the Lusaka South Multi-facility Zone (LSMZ), and the Zambia Association of Manufacturers (ZAM)] within the manufacturing sector in which 25 were interviewed comprehensively through convenient and purposive sampling techniques. Content analysis was used to analyse the data. The study established that technology, innovation, infrastructure, skills development, financing, R&D, and actualized favourable manufacturing policies were critical success factors to Zambia’s industrial development. These parametric variables and policies must be anchored on a well guided and pragmatic political will from the politicians with required technical know-how. Besides, the study found out that although the country has close to enough policy instruments and frameworks to guarantee meeting its SDG # 9 if implemented coherently, consistently, and timely, there is no coherent systematization, co-ordination, and prioritization in attending to critically important productive sector policies for achievement of intended industrial development outcomes as the said factors (variables) fall across sectors / ministries thereby creating disjoints, discord and fragmentation in development actions. On the other hand, the study discovered with concern that there were no properly streamlined and sectoral delineation policies for foreign investors’ investments as some of them were found to be investing in mundane activities such as running retail grocery shops, auto shops, and hardware; mending motor vehicle tires; operating casinos; moulding bricks / blocks; rearing livestock like chickens, pigs, goats; roasting maize on city streets.which economic activities are too basic to be done by an investor at the expense of local people. The study concluded that the country needed to implement and actualize its industrial development policies and programmes as contained in its national policy documents, SDGs and the constitution with a further caveat that the social, cultural and economic rights be enshrined in the republican constitution as a matter of urgency in order not to rationalize and make optionless socio-economic development by politicians who always want to venture into rent-seeking activities at the expense of sustainable national development. The study assuredly noted that if these policies could be implemented in correctly identified productive manufacturing sectors / industries, the nation would in no time overcome its alarming poverty, unemployment, debt mountain, and inequality levels, which have dogged it for generations now as Zambia has necessary raw materials such as minerals, land, forestry, wildlife, water, energetic youthful population, sunshine, forestry, etc. for cutting edge industrial development. Raw commodity trade does not guarantee sustainable development but bleaks the nation’s and its generations’ future, hence the imperative to circumvent this bliss by sustainable industrialization and value addition to the country’s products and services for increased value.

The Impact of Shareholder and Director Networks on Corporate Technological Innovation: A Multilayer Networks Analysis

We adopt a multilayer networks approach to assess how network structural embeddedness affects corporate technological innovation. Our findings indicate an annual increase in both single-layer and multilayer networks, although adoption of the latter by Chinese listed companies is comparatively low. We found that structural embeddedness of multilayer networks positively impacts corporate technological innovation. By reducing uncertainty within the internal environment, these networks bolster technological innovation. Moreover, such embeddedness notably spurs innovation in non-state-owned companies and those with greater internal transparency and robust external oversight. Our analysis reveals an intermediate effect where structural embeddedness in multilayer networks influences innovation. Our work provides new insights into enhancing innovation capacity via network embeddedness and supplies empirical data on utilizing network resources for innovation. We also offer actionable guidance and policy advice for managers, investors, and policymakers, especially relevant amidst economic transformation and pursuit of technological self-reliance of China.

I like therefore I can, and I can therefore I like: the role of self-efficacy and affect in active inference of allostasis.

Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input. The discrepancy between a prediction and the actual input results in prediction error. GMs then execute action policies predicted to minimize the prediction error. The free-energy principle provides a rationale for AIF by stipulating that information-processing open systems must constantly minimize their free energy (through suppressing the cumulative prediction error) to avoid decay. The theory of homeostasis and allostasis has a similar logic. Homeostatic set points are expectations of living organisms. Discrepancies between set points and actual states generate stress. For optimal functioning, organisms avoid stress by preserving homeostasis. Theories of AIF and homeostasis have recently converged, with AIF providing a formal account for homeo- and allostasis. In this paper, we present bacterial chemotaxis as molecular AIF, where mutual constraints by extero- and interoception play an essential role in controlling bacterial behavior supporting homeostasis. Extending this insight to the brain, we propose a conceptual model of the brain homeostatic GM, in which we suggest partition of the brain GM into cognitive and physiological homeostatic GMs. We outline their mutual regulation as well as their integration based on the free-energy principle. From this analysis, affect and self-efficacy emerge as the main regulators of the cognitive homeostatic GM. We suggest fatigue and depression as target neurocognitive phenomena for studying the neural mechanisms of such regulation.

Digital financial services adoption: a retrospective time-to-event analysis approach

Expanding digital financial services (DFS) such as mobile money has become a key policy intervention for many developing and emerging countries as they seek to fast-track financial and economic inclusion. To date, adults in these markets have had more mobile money accounts than traditional bank accounts. Numerous studies have used binary approaches to understand the factors explaining DFS adoption. However, there is a dearth of studies, that investigate the time to DFS adoption and factors that predict time to adoption. To close this gap, this study used a time-to-event analysis approach to estimate the time to DFS adoption and investigate the factors that explain the variation in time to adoption. Using a sample of 1800 survey respondents from Zimbabwe, the study found that it took 4.4 and 8.5 years, respectively, for urban and rural residents to adopt DFS. Overall, the findings show that individuals who are significantly more likely to adopt DFS faster are those residing in urban areas, near mobile money agents, banked, with high levels of education and financial literacy, middle-aged, belonging to social groups, and self-employed. In addition, an expansionary macroeconomic environment was associated with greater DFS adoption intensities. The findings also show that gender and income level do not predict the time to DFS adoption in the studied market. This study provides policy and practitioner recommendations for possible actions to accelerate the adoption of DFS.

Ghana's Public Health Act, AI algorithms and Vaccine Distribution in Ghana

Objective: This paper examines the adequacy of Ghana’s Public Health Act 2012 for governing rising use of algorithmic systems like AI in automating vaccine distribution. Method: Employing the structured CREAC legal reasoning framework, it systematically analyses current statutory flexibility, rights safeguards and accountability provisions in the Act to balance technological innovation against risks of automated opacity, bias and exclusion errors. Results: While the legislation provides ample principles-based scope for administrative pilots and controlled deployment of AI coordination tools to improve immunization equity, reliance on 20th century assumptions of technological neutrality means significant gaps in addressing unique socio-ethical hazards of autonomous predictive analytics. Contributions: First structured application of legal study methodology to contemporize public health law assessments for coming healthcare automation advances, yielding actionable policy upgrades. Advances interdisciplinary discourse on ethical technological transformation of vital services. Significance: Anchored in legal realities and public access imperatives of a developing country, declines facile overhaul recommendations, favoring participative, evidence-led amendments upholding innovation incentives within updated rights regimes. Socially-grounded contribution bridging theory with practice in governance discourse. Sets agenda for anticipatory, democratic legal regimes upholding reliable and unbiased AI assistance in equitable healthcare access.

A scoping functionalist reflections on the implementation of affirmative action in Higher Education Institutions in South Africa

<p>The scoping review was carried out to investigate the functionalist perspective on affirmative action for women in the workplace in the Eastern Cape Province’s selected South African universities. Given that university-specific information is available on affirmative action for women in the workplace, this scoping review employed a systematic review to investigate the implementation of the legislative framework for affirmative action policy in African-selected universities and ways to make improvements. Purposive sampling was used to select four higher education institutions (HEIs), in which there were two historically black and two historically white institutions, for representativeness. Evidently, affirmative action is indeed an existing policy in the selected institutions to redress the exclusion of women in senior positions in the past. While this review revealed the heightened visibility of women in executive positions in the four universities, women tend to be represented more than men in some executive categories. This demonstrates that the implementation of affirmative action for women has largely been successful in the country. Although with some challenges, the inclusion of women, without gender discrimination and biases, is a necessary ingredient for the sustainability of higher education administration in the country from a functionalist perspective.</p>

Research on the Spatial Dynamic Evolution of Digital Agriculture—Evidence from China

Digital agriculture serves as a pivotal means of ushering in innovative agricultural practices and achieving sustainable agricultural development. Although agricultural digitalization has received increasing attention, the unbalanced development and regional disparities of digital agriculture are still key obstacles to sustainable agricultural development. Based on the data of 31 provinces in China from 2013 to 2021, this study evaluates the development level of digital agriculture in China, and further analyzes the distribution pattern, spatial characteristics, and transition probabilities of digital agriculture from a regional perspective. The index system of the digital agriculture development level is constructed from five aspects: infrastructure, talent resources, agricultural informatization, the digitization of agricultural production processes, and agricultural production efficiency. Among these, infrastructure and talent resources reflect the resources needed for the development of digital agriculture; agricultural informatization and the digitization of the agricultural production process indicate the role of digitization in the process of agricultural development; and the agricultural production efficiency is the goal of the digital agriculture development, which is a critical criteria of its evaluation. The weighted analysis method of objective sequential analysis, which combines the dynamic level of indicators and sequential relationships, is used to assign weights to the indicators. In addition, to address the regional disparities in the development level of digital agriculture, kernel density estimation, Moran’s index, and (spatial) Markov chain analysis are applied to analyze the spatial dynamic evolution of digital agriculture in China. The findings reveal substantial regional disparities in digital agriculture development within China, particularly in the Western region, where development lags behind. Moreover, this study offers actionable policy recommendations for policymakers to strengthen regional infrastructure and talent cultivation, as well as other aspects of digital agriculture development, to mitigate regional differences and provide reference for other emerging countries.

ICORPP: Interleaved commonsense reasoning and probabilistic planning on robots

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to “understand the world” and planning to “achieve the goal” respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies.

Task-Related Leadership Competencies and Physical Work Environment as Predictors of Organizational Effectiveness in The Cameroon Baptist Convention Primary Schools

Aim: The research addresses two primary objectives: examining the impact of headteachers' task-related leadership competencies (TR_competencies) and work environment (WE_index) on organizational effectiveness in terms of stakeholder involvement (SI_OE).
 Methods: This study employs ordinary least squares (OLS) regression analysis to scrutinize the predictive efficacy of leadership competence and conditions of service on organizational effectiveness in Cameroon Baptist Convention (CBC) primary schools.
 Results: Statistically significant relationships are identified. Regression coefficients underscore the robustness of CBC headteachers' task-related leadership competencies, encompassing accountability, creativity, diversity promotion, culture shaping, and strategic planning. Challenges in change management highlight areas necessitating targeted professional development. A negative coefficient for the work environment implies an inverse relationship with organizational effectiveness in terms of stakeholder involvement. The F-statistic tests the overall model significance, yielding p-values of 0.0001 and 0.0000 for Models 1 and 2, respectively.
 Conclusion: These results prompt rejection of the null hypothesis, confirming the overall model significance. Furthermore, the coefficients for TR_competencies and WE_index are statistically significant at the 0.01 level, affirming their individual impact.
 Recommendations: This study recommends that the CBC Education Department should engage comprehensive strategy to bring about organisational effectiveness in the primary schools. That strategy should involve targeted professional development for headteachers on key leadership competencies, simultaneous improvements in the physical work environment, and stronger stakeholder engagement strategies. Policymakers are equally encouraged to translate these findings into actionable policies. This should organisational effectiveness in the CBC Primary schools.