Search Results (1,321)

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic and may fail to capture the multifaceted nature of task performance, leading to reward hacking or stalled exploration. Recent work uses Large Language Models (LLMs) to generate reward functions from high-level task descriptions, but these specifications are typically static and may encode biases or inaccuracies from the pretrained model, resulting in a priori reward misspecification. To address this, we propose the Metacognitive Introspective Reward Architecture (MIRA), a closed-loop architecture that treats LLM-generated reward code as a dynamic object refined through empirical feedback. An LLM first produces a set of computable reward factors. A dual-loop design then decouples policy learning from reward revision: an inner loop jointly trains the agent’s policy and a reward-synthesis network to align with sparse ground-truth outcomes, while an outer loop monitors learning dynamics via diagnostic metrics and, upon detecting pathological signatures, invokes the LLM to perform targeted structural edits. Experiments on MuJoCo benchmarks show that MIRA corrects flawed initial specifications and improves asymptotic performance and sample efficiency over strong reward-design baselines. Full article

Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks in China’s unique institutional context. We employ a natural experiment design, constructing a binary logistic regression model to analyze data from Chinese A-share listed companies that issued convertible bonds, corporate bonds, seasoned equity offerings, or rights offerings between 2022 and 2023. Our results reveal a paradox: contrary to risk-transfer theory, firms with lower leverage exhibit a stronger propensity to issue convertible bonds. Instead, motives are driven by high profitability, operational inefficiencies, and robust operating cash flow generation—traits that align with signaling and backdoor equity theories. The study identifies China’s convertible bond market as a dual-track system where regulatory screening distorts classical motives while market frictions amplify the role of convertible bonds in resolving information asymmetry. We conclude with targeted policy implications for regulators and corporate treasurers to enhance market efficiency and governance. Full article

Rapid advances in digital technologies are reshaping value creation and the trade landscape of global financial services, yet the channels through which they influence the spatial evolution of financial services global value chains (GVCs) remain insufficiently identified. Using a global panel of 52 countries over 2013–2021, we estimate a dynamic Spatial Durbin Model (SDM) to identify overall effects and quantify spatial spillovers and temporal dynamics. We then combine Geographically and Temporally Weighted Regression (GTWR) with spatial mediation models to examine heterogeneity and underlying mechanisms. Our findings show that digital technology significantly drives the spatial evolution of financial services GVCs. Its influence is dominated by spatial diffusion, exhibiting a dynamic pattern of a strong short-run boost followed by long-run reallocation. This dynamic effect is not homogeneous; rather, it reflects a pronounced dual-driver structure: the momentum is more robust when human capital and R&D output reinforce each other, whereas increases in innovation level alone are unlikely to translate into sustained impetus for spatial restructuring. Crucially, digital technologies reshape GVC geography through three core channels: attenuating distance decay, strengthening spatial proximity, and amplifying spatial heterogeneity. These forces deepen the domestic diffusion of knowledge, capital, and technology and extend their spillovers to neighboring and connected economies. The results provide robust empirical evidence on financial geography in the digital era and have clear implications for policies that facilitate cross-border financial services and strengthen regional coordination in support of the 2030 Agenda for Sustainable Development, particularly SDG 8 (financial inclusion) and SDG 10 (global financial governance). Full article

The study assesses how effectively succession planning and talent management facilitate the establishment of organizational agility, as well as the moderating influence of organizational learning in the context of Saudi-based banking and finance sectors. Based on the Resource-Based View theory, the study indicates that learning culture and human capital are very important as primary sources of competitiveness in turbulent environments. A stratified sampling was used in the data gathering of 400 respondents and the partial least squares structural equation modeling (PLS-SEM). The result shows that there is a positive and statistically significant relationship between succession planning and organizational agility, and, therefore, a consistent stream of leadership makes an organization more adaptable and resilient. On the other hand, talent development was negatively correlated with agility, which implies that the existing training practices do not match agility needs. Representatives of organizational learning moderated the succession planning–agility, leadership readiness, and adaptability relationship in a positive manner, but moderated the talent development–agility relationship in a negative manner, which implies that the organization has a disconnection between learning and talent strategies. It highlights the necessity to redesign HR practices to make them agile, promote the development of adaptive leadership and a culture of learning, and introduce flexible talent policies. This knowledge adds to the theoretical discussion of the dual nature of organizational learning as a facilitator and constraint as well as providing practical ways to enhance competitiveness in dynamic financial markets. Full article

This study analyzes the profile of candidates enrolling in Alternative Certification Programs (ACP) in Bosnia and Herzegovina—specialized programs in pedagogical, psychological, didactic, and methodological education for graduates of non-teaching faculties to obtain a teaching qualification. Using the Dual Labor Market Theory (DLMT) as a framework, the research examines structural factors and systemic challenges shaping these career paths. It explores whether teaching in Bosnia and Herzegovina serves as a “safe haven” or an alternative career for highly educated individuals, and considers implications for the feminization of the profession and education quality. The study is based on demographic and educational data of ACP participants, including age, gender, previous academic background, and institution attended. Findings indicate that the typical participant is a woman under 30, often graduating from a public university in technical or social sciences. Results suggest that teaching is frequently chosen for employment stability and security rather than vocational calling, consistent with DLMT. These insights offer a better understanding of labor market dynamics and have implications for teacher recruitment, retention, and professional development policies in Bosnia and Herzegovina. Full article

Current calls to integrate science and mathematics in PK-16 education build on decades of prior initiatives, yet the United States still lacks consensus on what integration entails and consistent policies to support it. This study systematically reviews current U.S. policies to identify guidance on the preparation of teachers to integrate science and mathematics. Given that teacher preparation is inherently connected to PK-12 policy, we also review PK-12 policy guidance focused on dual or integrated teacher endorsements, school designations, and PK-12 science and mathematics learning standards. Drawing on an established framework that defines meaningful integration as authentic problem solving supported by the use of multiple STEM disciplines, we examine the degree to which current policies enable such practice. Our findings reveal recommendations for integrating science and mathematics, yet policies overwhelmingly reinforce a siloed approach. We argue that misalignment between teacher preparation policy and PK-12 policy creates a circular problem: teachers cannot be expected to implement integrated science and mathematics instruction without adequate preparation, yet preparation programs have little incentive to design coursework for an instructional approach not systematically supported in PK-12 settings. Clarifying and aligning these policies is therefore essential for advancing coherent, scalable integration across the PK-16 system. Full article

In the context of dual-carbon goals, Park-Level Integrated Energy Systems (PIES) are pivotal for enhancing renewable energy integration and promoting clean, efficient energy use. However, the inherent non-linearity from multi-energy coupling and the high dimensionality of operational data present substantial challenges for conventional scheduling optimization methods. To overcome these obstacles, this paper introduces a novel multi-objective scheduling framework for PIES leveraging deep reinforcement learning. We innovatively formulate the scheduling task as a Markov Decision Process (MDP) and employ the Trust Region Policy Optimization (TRPO) algorithm, which is adept at handling continuous action spaces. The state and action spaces are meticulously designed according to system constraints and user demands. A comprehensive reward function is then established to concurrently pursue three objectives: minimum operating cost, minimum carbon emissions, and maximum exergy efficiency. Through comparative analyses against other AI-based algorithms, our results demonstrate that the proposed method significantly lowers operating costs and carbon footprint while enhancing overall exergy efficiency. This validates the model’s effectiveness and superiority in addressing the complex multi-objective scheduling challenges inherent in modern energy systems. Full article

High-density cities face increasing pressure to curb the rapid growth of building-sector carbon emissions, yet the specific impacts of China’s dual-control policy—regulating both total emissions and emission intensity—remain insufficiently understood. To address this gap, this study aims to quantify how dual-control constraints shape urban building-emission trajectories by developing a dynamic scenario model that integrates both operational and embodied emissions while accounting for technological progress, energy-structure adjustments, and socioeconomic change. The model is applied to Guangzhou, a representative high-density metropolis in China’s low-carbon transition. Three policy scenarios are evaluated: a baseline pathway reflecting existing trends, an energy-conservation pathway emphasizing efficiency improvements, and a deep-decarbonization pathway that combines enhanced efficiency with accelerated clean-energy adoption. The results show that rising residential energy demand is the primary driver of emission growth, whereas technological advancement provides the strongest mitigation potential. Under the energy-conservation scenario, emissions are projected to peak around 2029, consistent with China’s national carbon-peaking target for 2030. Overall, the findings offer clear empirical evidence and actionable policy insights for accelerating urban decarbonization in high-density contexts under dual-control constraints. Full article

Against the backdrop of the deepening “dual carbon” strategy and the globalization of ESG investment, China’s construction industry, an important key carbon-emitting sector, faces a “triple institutional dilemma”. It includes high carbon lock-in, human capital alienation, and an ambiguous governance structure. Current research on the practical paths of ESG compliance and its localized adaptation in this industry remains limited. Drawing on the green transformation theory, this study systematically explores the theoretical logic, realistic picture, and breakthrough path of ESG compliance in the industry. Firstly, it clarifies the connotation of ESG compliance and maps out the industry’s policy framework and practical patterns. Secondly, it analyzes core dilemmas from three dimensions: environmental constraints related to technical pathways, social conflicts between labor and community arising from institutional imbalances, and governance inefficiencies caused by irregular information disclosure and imperfect structure. Finally, it proposes a “three-dimensional collaborative” mitigation mechanism. This study provides localized, practical pathways for ESG compliance in the construction industry and offers a theoretical reference for the sector’s green transformation, thereby contributing to advancing Chinese-style modernization and ecological civilization construction. Full article

This article presents a novel methodology for assessing the visibility and reachability of cultural heritage objects within urban structures, tested through a pilot study in Kaunas New Town (Naujamiestis), Lithuania. While heritage protection policies usually emphasize architectural composition, details, and external visual protection zones, interior urban views and functional spatial dynamics remain underexplored. Building upon Space Syntax theory and John Peponis’s concepts of distributive and correlative situational codes, this study integrates detailed visibility analysis with graph-based accessibility modeling. Visibility was quantified through a raster-based viewshed analysis of building footprints and street-based observation points, producing a normalized visibility index. Reachability was examined using a new graph indicator based on the ratio of reachable polygon area to perimeter (A²/P), further weighted by the area of adjacent buildings to reflect the potential for urban activity. Validation against independent datasets (population, companies, and points of interest) confirmed the superior explanatory power of the proposed indicator over traditional centralities. By combining visibility and reachability in a bivariate matrix, the model provides insights into heritage objects’ dual roles as landmarks, everyday hubs, or hidden sites, and offers predictive capacity for evaluating urban transformations and planning interventions. Full article

Background/Objectives: Clinical instruction and mentorship are essential components of nursing education and early professional development. In Greece, while nursing curricula align with EU directives mandating both theoretical and clinical training, significant gaps persist in the quality, coordination, and legislative support of mentorship. This work aims to (i) synthesise evidence on clinical instruction and mentorship in Greece and draw on selected European examples to provide contextual insight, and (ii) integrate national stakeholder perspectives to generate actionable recommendations for a Greek clinical mentorship framework. Methods: A narrative literature review was conducted, identifying 19 eligible articles examining mentorship, clinical instruction and preceptorship in European and Greek contexts. In addition, a national stakeholder focus group with 25 participants, including representatives from academia, healthcare institutions, regulatory bodies, and nursing associations, was held in Athens in 2024. Data from both sources were thematically analysed and integrated to identify gaps, best practices, and context-specific recommendations. Results: Findings revealed inconsistent collaboration between universities and clinical institutions, limited training and recognition for clinical instructors, and the absence of a unified national framework. Stakeholders highlighted structural barriers to clinical mentoring such as understaffing and lack of policy support and expressed strong interest in a mentorship reform. Comparative analysis with European models demonstrated feasible pathways for Greece, including structured training, certification, and non-financial incentives. During the national stakeholder focus group, a dual-pathway mentorship system tailored for nursing students and newly hired nurses was most recommended to ensure both continuity and quality in professional development of nurses. Conclusions: Despite alignment with EU directives, Greece lacks an integrated national mentorship framework that ensures consistent clinical learning and supports workforce development. Two priority policy actions emerge from this work: (1) establishing a legally supported national certification and training system for clinical mentorship, and (2) educational structures in the clinical setting to improve educational quality, workforce retention and patient care outcomes. Full article

The rapid development of industry has led to intensive energy and resource consumption, increasing carbon emissions. As key areas for carbon control, metropolitan regions play an essential role in China’s urbanization and regional development, yet research on predicting industrial carbon emissions remains insufficient. This study takes the Hangzhou Metropolitan Area in China as a case study and employs an extended STIRPAT model to predict industrial carbon emissions from 2024 to 2050 across different scenarios. The results show that industrial carbon emission intensity has the most significant impact on carbon emissions, followed by urbanization, population, economy, industrial structure, technology, energy intensity, and openness. The peak time of industrial carbon emissions varies significantly under different scenarios. The peak appears in 2026 under the deep emission reduction scenario, in 2028 under the green economy scenario, in 2030 under the baseline scenario, and does not occur by 2050 under the extensive development scenario. The green economy scenario achieves effective emission reductions with the least economic impact and is superior to the single-emission-reduction-oriented deep-emission-reduction scenario. This study responds to China’s “dual-carbon” strategy and provides a replicable and transferable regional pathway for industrial decarbonization and policy-making in other metropolitan areas. Full article

The precise identification of energy poor households is a critical step towards achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 1 (No Poverty), while also intersecting with climate action (SDG 13). As the world’s largest developing country, China faces unique energy poverty challenges characterized by significant regional disparities and uneven access to modern energy services. To support targeted interventions and equitable policy-making, this study proposes an explainable artificial intelligence (XAI) framework for predicting and interpreting energy poverty. Utilizing nationally representative data from the China Family Panel Studies (CFPS) from 2014 to 2020, we developed a predictive model that integrates a Convolutional Neural Network with SHapley Additive exPlanations (SHAP). Our model, EPPE-FCS, demonstrated exceptional predictive performance, achieving an average accuracy of 98.23%, outperforming several mainstream benchmarks. Crucially, the SHAP interpretability analysis revealed that annual per capita household expenditure is the most influential driver, while the contribution of energy burden indicators (electricity and gas expenses) exhibited a significant decreasing trend. This trend likely reflects the positive impact of China’s national policies, such as the “Clean Heating Initiative” and “Targeted Poverty Alleviation,” on improving energy infrastructure and affordability. The findings underscore the necessity of a dual-track policy that combines immediate energy cost subsidies with long-term strategies for income enhancement and clean energy transition. This research provides policymakers with a robust tool to alleviate energy poverty, thereby advancing a just, sustainable, and climate-resilient energy future in China and other developing regions. Full article

The urban–rural relationships in China are experiencing a dual structure period, balancing an urban–rural development period and coordinated urban–rural development period, and urban–rural integrated development has become the current strategy. Urban–rural integrated development has become an important measure to address the unbalanced development between urban and rural areas. Despite proactive explorations by governments at various levels to promote integrated urban–rural development, the anticipated outcomes remain difficult to achieve due to multiple constraints, such as inefficient flow of production factors and unequal provision of basic public services between urban and rural areas. There is an urgent need to re-examine how to advance deeper urban–rural integration from the perspective of collaborative governance. Taking the Yangtze River Delta region as a case study, this research reviews related policy documents, official texts, and development plans regarding urban–rural integrated development, social (urban–rural community) collaborative governance, and urban development at the central and regional levels in recent years. Meanwhile, this study interviews experts in the field of public administration and government officials, and visits the experimental area and demonstration area of integrated development in the Yangtze River Delta region. Through grounded theory method and multi-level coding, concepts, initial categories, main categories are clear, and six core categories in total are identified: policy planning capability, public participation, participation of non-governmental organization, openness of government information, supervision and evaluation, and implementation capacity. This bottom-up construction of the theoretical framework serves as an extension and enrichment of collaborative governance theory. Based on the six core elements identified through the research, the Yangtze River Delta region may implement targeted policy adjustments across these dimensions to enhance the effectiveness of collaborative governance, and it may provide referential insights for urban–rural development practices in other regions. Full article

Educational construction projects face the dual challenge of achieving sustainability targets while remaining cost-effective, yet existing studies often analyze supply chain drivers in isolation. This research addresses this gap by developing and validating a comprehensive model that examines five critical drivers—material selection, stakeholder engagement, waste management, energy efficiency, and digital technologies—within the context of educational infrastructure. Using a mixed-methods approach, data were collected through surveys with 100 industry professionals (35% project managers, 30% architects/designers, and 35% policymakers/consultants), 20 semi-structured interviews, and comparative analysis of three international case studies (Egypt, Singapore, and the United States). Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test hypothesized relationships, supported by thematic analysis for qualitative depth. Results show that stakeholder engagement (β = 0.31), material selection (β = 0.28), and digital technologies (β = 0.23) exert the strongest influence on sustainability performance, while energy efficiency (β = 0.19) and waste management (β = 0.16) demonstrate weaker but still significant effects. Regional variations highlight the role of contextual factors such as governance, policy support, and infrastructure readiness. Unlike prior studies that focus on single aspects, this research offers an integrated framework for evaluating and implementing sustainable supply chain practices in educational construction. The findings provide both theoretical advancement and actionable insights for policymakers and practitioners seeking to accelerate sustainable transformation in the education sector. Full article