Action Policies Research Articles

Indonesian Legal Compliance with International Human Rights Law on Female Genital Mutilation

The absence of a law that strictly prohibits female genital mutilation in Indonesia has increased the number of women and girls victimized by this practice. While some communities may justify female genital mutilation as a religious practice, there is no religious justification for this, and neither is Islam, the dominant religion in Indonesia. The United Nations has defined female genital mutilation as a harmful practice that can cause physical and psychological harm to women and girls. Moreover, it is also closely linked with violations of the right to freedom of religion or belief. This qualitative research uses a normative approach by analyzing Indonesian laws related to female genital mutilation and its compatibility with international human rights legal obligations. This research describes Indonesia's role in preventing, addressing, and protecting women and girls who have undergone female genital mutilation. The results show that Indonesian laws on female genital mutilation do not comply with international human rights laws, which prohibit all types of female genital mutilation practices. This research suggests that the Indonesian government should take all measures to eliminate the practice of female genital mutilation through legal action, creating binding and gender-perspective regulations and policies. Eliminating female genital mutilation will contribute to achieving gender equality and women's empowerment in Indonesia, as the country's commitment to the Sustainable Development Goals.

Nanobiotics and the One Health Approach: Boosting the Fight against Antimicrobial Resistance at the Nanoscale.

Antimicrobial resistance (AMR) is a growing public health concern worldwide, and it poses a significant threat to human, animal, and environmental health. The overuse and misuse of antibiotics have contributed significantly and others factors including gene mutation, bacteria living in biofilms, and enzymatic degradation/hydrolyses help in the emergence and spread of AMR, which may lead to significant economic consequences such as reduced productivity and increased health care costs. Nanotechnology offers a promising platform for addressing this challenge. Nanoparticles have unique properties that make them highly effective in combating bacterial infections by inhibiting the growth and survival of multi-drug-resistant bacteria in three areas of health: human, animal, and environmental. To conduct an economic evaluation of surveillance in this context, it is crucial to obtain an understanding of the connections to be addressed by several nations by implementing national action policies based on the One Health strategy. This review provides an overview of the progress made thus far and presents potential future directions to optimize the impact of nanobiotics on AMR.

Municipalities as change agents? Reconsidering roles and policies in local energy sector-coupling

Municipalities play an important role in the energy transition process by implementing and advancing policy measures for climate protection at the local level. However, available options and technologies for the integration and defossilization of electricity, heating, industry and mobility are diverse and complex. Considering the interplay of societal action, technical options, individual values, decision-making processes, and available policy measures to defossilize the energy system, the municipality takes on a critical role in coordination and creating transparency. Addressing these challenges in the theoretical light of the Multi-Level Perspective (MLP) and the Policy Mix Approach (PMA), the authors develop a classification system of policy instruments and measures for municipal action in fostering sector coupling activities. The main argument is that municipalities dispose of considerable scope of action with several roles they can play in order to orchestra the transition. The study relies empirically on desk research as well as workshops with local actors in three municipalities in the field of sector coupling in Germany. The aim was to identify multidimensional potentials for municipal action in order to develop effective and efficient policy packages by developing a classification system. The results contribute to a structured basis for the discourse on energy transition in municipalities and are a central building block for a horizontal and vertical coordination in the development of a sector coupling strategy.

Imposter phenomenon among health professionals and students: A protocol for systematic review and meta analysis.

Health professionals and medical students are at increased risk of the imposter phenomenon (IP) in other words, the imposter syndrome, due to the challenging nature of their professions. It is particularly concerning since it is linked to a higher incidence of burnout and suicidal ideation. We must first thoroughly grasp its prevalence and associated factors to address this issue. However, no published review of the data includes a meta-analysis to help understand the character and associated factors of IP among medical workers and medical students. This study aims to investigate IP prevalence and risk factors among healthcare personnel. Five online databases will be searched for papers published in English between January 2000 and December 2022, and 2 independent reviewers will filter, select studies, extract data, and evaluate the risk of bias in each piece. The retrieved articles will be included based on strict inclusion and exclusion criteria. A third reviewer will decide on any disagreements between the 2 reviewers. Where appropriate, a meta-analysis will be conducted using the random-effects model. The heterogeneity of the studies will be examined, and a sensitivity analysis will be done depending on the study quality. The purpose of this review is to determine the prevalence and risk factors for IP among healthcare personnel. The review's findings will emphasize the severity and contributing factors of the problem, therefore guiding policy for future actions.

Exploring the Use of Invalid Action Masking in Reinforcement Learning: A Comparative Study of On-Policy and Off-Policy Algorithms in Real-Time Strategy Games

Invalid action masking is a practical technique in deep reinforcement learning to prevent agents from taking invalid actions. Existing approaches rely on action masking during policy training and utilization. This study focuses on developing reinforcement learning algorithms that incorporate action masking during training but can be used without action masking during policy execution. The study begins by conducting a theoretical analysis to elucidate the distinction between naive policy gradient and invalid action policy gradient. Based on this analysis, we demonstrate that the naive policy gradient is a valid gradient and is equivalent to the proposed composite objective algorithm, which optimizes both the masked policy and the original policy in parallel. Moreover, we propose an off-policy algorithm for invalid action masking that employs the masked policy for sampling while optimizing the original policy. To compare the effectiveness of these algorithms, experiments are conducted using a simplified real-time strategy (RTS) game simulator called Gym-μRTS. Based on empirical findings, we recommend utilizing the off-policy algorithm for addressing most tasks while employing the composite objective algorithm for handling more complex tasks.

Off-policy inverse Q-learning for discrete-time antagonistic unknown systems

This paper proposes a data-driven model-free inverse reinforcement learning (RL) algorithm to reconstruct the unknown cost function of the demonstrated discrete-time (DT) dynamical systems with antagonistic disturbances. We propose an inverse RL policy iteration scheme that uses system dynamics and the input policies, for deriving our main result of a data-driven off-policy inverse Q-learning algorithm using only demonstrated trajectories of the antagonistic system without knowing system dynamics and the control policy gain. This data-driven algorithm consists of Q-function evaluation, state-penalty weight improvement, and action policies update. We guarantee unbiased estimates in the data-driven algorithm when exploration noises exist for the persistence of the excitation. An example verifies the proposed algorithm.

Deep Dive Into Gaps and Barriers to Implementation of Antimicrobial Stewardship Programs in Hospitals in Latin America.

Antimicrobial resistance has worsened in Latin America. There is an urgent need to understand the development of antimicrobial stewardship programs (ASPs) and the barriers to implementing effective ASPs in light of limited national action plans or policies to promote ASPs in the region. We performed a descriptive mixed-methods study of ASPs in 5 Latin American countries in March-July 2022. An electronic questionnaire with an associated scoring system (hospital ASP self-assessment) was used, and ASP development was classified based on the scores (inadequate, 0-25; basic, 26-50; intermediate, 51-75; or advanced, 76-100). Interviews among healthcare workers (HCWs) involved in antimicrobial stewardship (AS) inquired about behavioral and organizational factors that influence AS activities. Interview data were coded into themes. Results from the ASP self-assessment and interviews were integrated to create an explanatory framework. Twenty hospitals completed the self-assessment, and 46 AS stakeholders from these hospitals were interviewed. ASP development was inadequate/basic in 35% of hospitals, intermediate in 50%, and advanced in 15%. For-profit hospitals had higher scores than not-for-profit hospitals. Interview data validated the self-assessment findings and provided further insight into ASP implementation challenges, which included limited formal hospital leadership support, inadequate staffing and tools to perform AS work more efficiently, limited awareness of AS principles by HCWs, and limited training opportunities. We identified several barriers to ASP development in Latin America, suggesting the need to create accurate business cases for ASPs to obtain the necessary funding for their effective implementation and sustainability.

The History and Impact of Women in the Parliament of Western Australia: From Golden Age to Disappointment

ABSTRACT Despite their significant contributions to parliament and the Western Australian community, the role of women in the WA public sphere has historically been overlooked due to cultural and structural prejudice. To remedy this erasure, this article assesses the body of scholarly work that has focused on women serving as members in the Parliament of WA. Finding that the literature identifies three phases of women’s engagement with the Parliament of WA, these phases can be periodised as follows: the first is the ‘age of individuals’ where a select few pioneering women entered the parliament due to a variety of historical specificities. The second we term the ‘age of Labor women’, as the literature widely acknowledges that the 1983 Burke government adopted affirmative action policies that saw a significant increase of women MPs hailing from the Labor Party. The third we label the ‘age of disappointment’, as the literature underscores the bipartisan deprioritising of women’s policy issues in the 1990s–2000s, alongside the existence of continuing barriers to gender equality in Australian parliamentary politics. Suggestions for further research are also identified, including the impact of neoliberalism on women’s representation, and the emergence of intersectionality as a key paradigm in women’s studies. .

Automatic Metamorphic Test Oracles for Action-Policy Testing

Testing is a promising way to gain trust in learned action policies π. Prior work on action-policy testing in AI planning formalized bugs as states t where π is sub-optimal with respect to a given testing objective. Deciding whether or not t is a bug is as hard as (optimal) planning itself. How can we design test oracles able to recognize some states t to be bugs efficiently? Recent work introduced metamorphic oracles which compare policy behavior on state pairs (s,t) where t is easier to solve; if π performs worse on t than on s, we know that t is a bug. Here, we show how to automatically design such oracles in classical planning, based on simulation relations between states. We introduce two oracle families of this kind: first, morphing query states t to obtain suitable s; second, maintaining and comparing upper bounds on h* across the states encountered during testing. Our experiments on ASNet policies show that these oracles can find bugs much more quickly than the existing alternatives, which are search-based; and that the combination of our oracles with search-based ones almost consistently dominates all other oracles.

Women Just Wanna Have Quotas: The Gender Divide on Affirmative Action in the Chilean Right

Objective/context: This article examines attitudes towards gender quotas by right-wing leadership in Chile and assesses gender gaps in supporting affirmative action. Methodology: The study uses original data from a survey of leaders of Chile Vamos, a right-wing political alliance in Chile. A series of logistic models with finite-sample corrections are estimated to examine the attitudes of right-wing leaders towards gender quotas and support for affirmative action policies. The models control for various demographic and political factors that may affect attitudes towards affirmative action. Conclusions: The findings for right-wing women in Chile are similar to those for Northern democracies. Right-wing elites show a significant gender gap on issues related to affirmative action and gender equality. Right-wing women are more likely to support affirmative action policies than their male counterparts. Originality: Quotas increase women’s autonomy and recognize that women are historically disadvantaged. The findings of this article support the importance of descriptive representation to the extent that attitudes of right-wing women differ significantly on issues close to their gender.

Efficient use of PV in a Microgrid using Reinforcement Learning

Artificial Intelligence is a new concept to optimize or schedule the energy storage system of the Microgrid. The reinforcement learning (RL) method can be used in the effective scheduling of the battery connected to the microgrid. The proposed strategy aims to reduce energy costs while prioritizing both energy balance and user comfort within the microgrid. The key innovation lies in developing an optimal policy for battery actions (charging, discharging, idle) using a model-free stochastic approach. One significant aspect that sets this work apart from others is its acknowledgment of the non-deterministic nature of the state of charge (SOC) of the battery. Unlike systems that solely rely on grid charging, our approach takes into account the unpredictability of renewable energy sources, particularly solar power, which heavily depends on varying time instances and weather conditions throughout the day. Consequently, the SOC of the battery exhibits non-deterministic behavior due to the uncertainty in the availability of excess renewable energy for charging. The RL-based policy presented in this research capitalizes on the effective utilization of photovoltaic sources, optimizing the battery’s discharge and idle states. By intelligently adapting to the dynamic energy supply from renewable sources, the proposed approach ensures that the battery is charged only when surplus energy is available beyond fulfilling the overall system load demand.

GLOBAL CLIMATIC TRANSFORMATION: IMPLICATIONS FOR PAKISTAN

Global climate change has tremendous economic, environmental and social implications for Pakistan. Recently, there has been increased awareness about inevitability of climate change and the realization that though everyone is affected, yet not equally. The central argument of this paper is that Pakistan is the victim of global climate change and facing consequences of the action it has not committed. We will analyze the global perspective of climate change, how Pakistan is being affected by climate change and what is policy response of Pakistan for adapting and responding. We will apply qualitative approach for our analysis. Developing world is facing challenges of poor governance, resource scarcity and violence, thus suffer greater because of natural disasters. Pakistan is among those countries which have experienced increasing frequency of flooding and droughts, erratic weather and agricultural pattern, loss of biodiversity and decline in freshwater availability. Social theory forms the basis of understanding of climate related threats to developing countries. It gives rise to conceptual framework of societal vulnerability, which can be used to explain why Pakistan is the major victim of global climate change. Pakistan has experienced serious impacts of climate change in various sectors like agriculture, water scarcity, inter provincial conflicts over resources, public heath, floods and other extreme weather events. This calls for rapid climate action and concrete policy for addressing this rapidly deteriorating situation. The country should enhance is disaster management and coping capacity and work on building the resilience of people and institutions. Availability and distribution of fresh water must be guided by laws formed by coordination of central ad provincial government. For efficient administration, interaction, and implementation, it is crucial to combine the relevant ministries under the aegis of provincial and federal disaster response organizations. Keywords: climate change, Pakistan, social theory, floods, food security, water scarcity.

Reducing Gender Gaps in Pakistan's Labor Market: Policy Options for Enhancing Women's Economic Empowerment

This policy paper delves into the persistent gender disparities within Pakistan's labour sector, highlighting the challenges faced by women in accessing and maintaining decent employment opportunities. Despite some progress, women encounter lower labour force participation, unequal wages, and limited access to social benefits. The concentration of women in low-paying, informal sectors hampers their economic advancement and social mobility. This paper critically assesses the gender imbalance in Pakistan's labour market and proposes actionable policy alternatives to enhance women's economic empowerment. By prioritizing legislative measures, decision-makers can address the root causes of these disparities, fostering gender equality and supporting women's participation in the workforce. The implementation of effective policies is vital to not only empower women but also contribute to Pakistan's overall economic growth and development.

Bank risk exposures and bank stability in Africa: the role of regulations in a non-linear model

PurposeThis paper aims to examine the interaction effect of regulations (monetary and macro-prudential) in explaining the possible non-linear effect of bank risk exposures (credit risk and insolvency risk) on banking stability in Africa.Design/methodology/approachThe study uses a two-step system generalized method of moments (GMM) estimator for a data set of banks across 54 African countries over the period 2006–2020.FindingsThe authors find that the relationships between bank credit risk–bank stability and bank insolvency risk–bank stability are non-linear and characterized by the presence of optimal thresholds, which are 5.3456 for credit risk and 2.3643 for insolvency. Contrary to their positive effects below these optimal thresholds, credit risk and insolvency risk become negatively linked to bank stability in Africa. The authors find that macro-prudential action and monetary policy both have a positive and significant relationship with bank stability. The authors provide evidence to support that the marginal effect of excessive credit risk and insolvency risk on bank stability is reduced when interacted with monetary and macro-prudential regulations, and the impact is significant in strong institutional environment.Research limitations/implicationsFuture research should extend data to include developing and emerging economies in the world. Also, policymakers, researchers and practitioners should consider different regulatory and institutional frameworks in explaining the relationship between the thresholds of bank risk exposures and bank stability in the world.Practical implicationsRegulatory authorities should have to deeply reform their financial systems, develop risk-based regulatory framework and effective supervision mechanism relating to appropriate techniques that maintain an optimal and desired level of bank risks and risk-taking behaviours required to ensure a stable banking system.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine how different regulatory frameworks shape the non-linear impact of bank risk exposures on bank stability in Africa.

Spatial Accessibility Analysis of Emergency Shelters with a Consideration of Sea Level Rise in Northwest Florida

Hurricane-induced storm surge and flooding often lead to the closures of evacuation routes, which can be disruptive for the victims trying to leave the impacted region. This problem becomes even more challenging when we consider the impact of sea level rise that happens due to global warming and other climate-related factors. As such, hurricane-induced storm surge elevations would increase nonlinearly when sea level rise lifts, flooding access to highways and bridge entrances, thereby reducing accessibility for affected census block groups to evacuate to hurricane shelters during hurricane landfall. This happened with the Category 5 Hurricane Michael which swept the east coast of Northwest Florida with long-lasting damage and impact on local communities and infrastructure. In this paper, we propose an integrated methodology that utilizes both sea level rise (SLR) scenario-informed storm surge simulations and floating catchment area models built in Geographical Information Systems (GIS). First, we set up sea level rise scenarios of 0, 0.5, 1, and 1.5 m with a focus on Hurricane Michael’s impact that led to the development of storm surge models. Second, these storm surge simulation outputs are fed into ArcGIS and floating catchment area-based scenarios are created to study the accessibility of shelters. Findings indicate that rural areas lost accessibility faster than urban areas due to a variety of factors including shelter distributions, and roadway closures as spatial accessibility to shelters for offshore populations was rapidly diminishing. We also observed that as inundation level increases, urban census block groups that are closer to the shelters get extremely high accessibility scores through FCA calculations compared to the other block groups. Results of this study could guide and help revise existing strategies for designing emergency response plans and update resilience action policies.

Multi-Level Compositional Reasoning for Interactive Instruction Following

Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks, e.g., bring a cup of coffee. To address the challenge, we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction. We call it Multi-level Compositional Reasoning Agent (MCR-Agent). Specifically, we learn a three-level action policy. At the highest level, we infer a sequence of human-interpretable subgoals to be executed based on language instructions by a high-level policy composition controller. At the middle level, we discriminatively control the agent’s navigation by a master policy by alternating between a navigation policy and various independent interaction policies. Finally, at the lowest level, we infer manipulation actions with the corresponding object masks using the appropriate interaction policy. Our approach not only generates human interpretable subgoals but also achieves 2.03% absolute gain to comparable state of the arts in the efficiency metric (PLWSR in unseen set) without using rule-based planning or a semantic spatial memory. The code is available at https://github.com/yonseivnl/mcr-agent.

Neural Policy Safety Verification via Predicate Abstraction: CEGAR

Neural networks (NN) are an increasingly important representation of action policies pi. Recent work has extended predicate abstraction to prove safety of such pi, through policy predicate abstraction (PPA) which over-approximates the state space subgraph induced by pi. The advantage of PPA is that reasoning about the NN – calls to SMT solvers – is required only locally, at individual abstract state transitions, in contrast to bounded model checking (BMC) where SMT must reason globally about sequences of NN decisions. Indeed, it has been shown that PPA can outperform a simple BMC implementation. However, the abstractions underlying these results (i.e., the abstraction predicates) were supplied manually. Here we automate this step. We extend counterexample guided abstraction refinement (CEGAR) to PPA. This involves dealing with a new source of spuriousness in abstract unsafe paths, pertaining not to transition behavior but to the decisions of the neural network pi. We introduce two methods tackling this issue based on the states involved, and we show that global SMT calls deciding spuriousness exactly can be avoided. We devise algorithmic enhancements leveraging incremental computation and heuristic search. We show empirically that the resulting verification tool has significant advantages over an encoding into the state-of-the-art model checker nuXmv. In particular, ours is the only approach in our experiments that succeeds in proving policies safe.

Oil palm boom: its socioeconomic use and abuse

Oil palm cultivation has experienced explosive expansion in recent decades. Although the oil palm boom has serious environmental and socioeconomic consequences, only environmental issues have garnered attention in public discussion and synthesis studies. In this study, we synthesize the literature on the socioeconomic impacts of oil palm expansion across various observation units and spatial and temporal scales, which is of significant relevance to policymakers, researchers, and the wider public. Our review demonstrates that oil palm expansion is a double-edged sword. On the one hand, it has significantly helped both producing and non-producing countries’ economies by facilitating the (re)export of crude palm oil and its byproducts and by creating employment opportunities. This has stimulated rural economies, led to rural poverty reduction, and improved food security and diets for rural households in producer countries. On the other hand, it has increased conflicts and tension between local communities and agro-industrial companies, worsened workers’ conditions, and widened domestic economic inequality between producing and non-producing areas within a given producer country. Monetary quantification of the costs of oil palm’s negative environmental and social impacts indicates that the loss of ecosystem services and social services far outweighs the benefits derived from the increased production of oil palm. The direct income benefits through harvest sales and employment are more evident in the Southeast Asia production frontier, where oil palm cultivation and marketing are relatively advanced and well-integrated into global value chains. The negative outcomes are observed in all oil palm production regions, with more negative effects on food security in the African production frontiers. We propose multiple actionable research and policy recommendations that may jointly lead to inclusive and sustainable socioeconomic (and environmental) outcomes in oil palm cultivation. The socioeconomic consequences of oil palm expansion at the global level, in the African and American production frontiers, and the role of gender in oil palm production systems remain understudied. Hence, studies should be conducted.

CO-IMPACT OF SECURITY CHALLENGES, ENERGY PRICE SHOCKS, AND ENVIRONMENTAL POLLUTION ON NUTRITION AND FOOD SECURITY IN NIGERIA

Global food insecurity is on the increase, and factors responsible for it are dynamic and differ from country to country. The purpose of this study was to investigate the impacts of security challenges, energy price shocks, and environmental pollution (SEE) on food production, availability, affordability, stability, utilization, and on malnutrition using information from existing literature. Information relevant to the scope of this study was sourced from local Journals, African Journal Online, Science Direct etc. The sourced information deduced that the security challenges in Nigeria had affected (though still affecting) food availability, affordability, and accessibility. While energy price shocks affects negatively, financial access to food especially among the low and middle income households in Nigeria. Environmental pollution on the other hand impairs soil fertility and degradation, aquatic animal’s extinction, and food safety. The impacts of SEE on food security are enormous and would in no doubt breed more malnutrition especially among children. Also, local, regional, and national security stability and development may be affected. Therefore, it is imperative that the government and other relevant stakeholders find information in this study impactful and useful, and enable them formulate, propagate, and put to action policies and programs that could in short time thwarts the menaces associated with SEE for a better food secured nation.

Research on TD3-Based Distributed Micro-Tillage Traction Bottom Control Strategy

Due to its flexibility and versatility, the electric distributed drive micro-tillage chassis can be used more often in the future in Intelligence agriculture scenarios. However, due to the complex working conditions of the agricultural operation environment, it is a challenging task to distribute the torque demand of four wheels reasonably and effectively. In this paper, we propose a drive torque allocation strategy based on deep reinforcement learning to ensure straight-line retention and energy saving, using a distributed electric traction chassis for greenhouses as the research object. The torque assignment strategy can be represented as a Markovian decision process, and the approximate action values and policy functions are obtained through an Actor–Critic network, and the Twin Delayed Deep Deterministic Policy Gradient (TD3) is used to incorporate the vehicle straight-line retention rate into the cumulative reward to reduce energy consumption. The training results under plowing working conditions show that the proposed strategy has a better straight-line retention rate. For typical farming operation conditions, the proposed control strategy significantly improves the energy utilization and reduces the energy by 10.5% and 3.7% compared to the conventional average torque (CAT) distribution strategy and Deep Deterministic Policy Gradient (DDPG) algorithm, respectively. Finally, the real-time executability of the proposed torque distribution strategy is verified by Soil-tank experiments. The TD3 algorithm used in this study has stronger applicability than the traditional control algorithm in dealing with continuous control problems, and provides a research basis for the practical application of intelligent control algorithms in future greenhouse micro-tillage chassis drive control strategies.