Durability-informed life cycle assessment of concrete through machine learning for service life prediction

Clinical diagnosis in the real world often begins with ambiguous patient complaints that require iterative reasoning and testing. While large language models (LLMs) increasingly assist with specific medical queries, they currently lack the ability to autonomously drive this entire diagnostic workflow, limiting their potential to significantly alleviate physician workload. Here we present DxDirector-7B, an agentic LLM designed to navigate the full diagnostic process through advanced slow thinking capabilities. Unlike existing assistants, our model autonomously determines optimal diagnostic strategies, requesting physician intervention only for necessary clinical operations. In evaluations spanning rare diseases and complex real-world cases, DxDirector-7B achieves superior diagnostic accuracy compared to state-of-the-art medical and general-purpose LLMs with significantly larger parameters. Crucially, it drastically reduces physician involvement while maintaining a robust safety and accountability framework for high-risk conditions. These results demonstrate a paradigm shift where AI effectively leads clinical reasoning, offering a scalable solution to enhance diagnostic efficiency and accessibility.

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity.

Background: This study investigates the influence of green accounting practices on economic growth in middle-income economies, emphasizing the mediating role of technological innovation and the moderating effect of regulatory quality. It seeks to clarify whether environmental accounting frameworks can simultaneously support economic expansion and sustainability across diverse institutional and innovation contexts. Methods: Guided by Ecological Modernization Theory, Endogenous Growth Theory, and Institutional Theory, the study uses balanced panel data from 24 middle-income countries spanning 2010–2023. Fixed-effects regressions assess direct effects, Baron and Kenny’s (1986) mediation framework examines technological innovation’s role, and moderated regression models evaluate regulatory quality’s conditioning influence. Findings: Green accounting significantly enhances aggregate GDP growth but may constrain short-term per capita welfare, reflecting transitional distributional trade-offs. While green accounting promotes technological innovation, this channel does not mediate its effect on economic growth. Strong regulatory quality, particularly rule-of-law enforcement, amplifies the positive impact of green accounting on economic performance. Conclusion: Policymakers should integrate green accounting into broader governance and innovation strategies. Aligning environmental disclosure with fiscal incentives, R&D investment, and transparent regulatory systems can foster inclusive and sustainable growth trajectories. Findings may have limited generalizability across all middle-income economies due to institutional heterogeneity and data constraints. Future studies could expand country coverage and examine sector-specific effects. Novelty/Originality of this article: The study offers an integrated empirical framework demonstrating how institutional quality and innovation capacity jointly shape the developmental returns of green accounting, providing actionable insights for sustainable growth in emerging economies.

ABSTRACT Understanding the drivers of carbon emissions from the water–food–energy (WFE) system during urbanization is essential for achieving China’s carbon peak and neutrality goals; yet, systematic quantification in this domain remains limited. This study develops a consumption-based accounting framework to estimate WFE carbon emissions for 41 cities in China’s Yangtze River Delta (YRD) from 2000 to 2023. By integrating restricted cubic splines, boosted regression trees, and piecewise structural equation modeling, we systematically uncover the nonlinear impacts and multi-path transmission mechanisms of comprehensive urbanization on WFE emissions. The key findings are as follows: (1) WFE carbon emissions (WFEC) in the YRD exhibit a fluctuating pattern with an overall upward tendency since 2000, mainly driven by industrial and residential water use. (2) A significant nonlinear relationship exists between the composite urbanization index (UI) and both WFEC and WFE carbon intensity (WFECI), with a turning point at approximately UI = 0.2. (3) The marginal effects of population, spatial, and social urbanization on WFEC intensify over time, while that of ecological urbanization weakens; effects on WFECI are predominantly negative. (4) Population, economic, and spatial urbanization exert significant direct effects on emissions, alongside indirect effects mediated by resource use intensity and behavioral consumption structure, marking them as the most pivotal and complex dimensions. The present study provides new evidence on consumption-driven WFE emissions and offers theoretical and policy insights for low-carbon transitions in resource-intensive regions.

Abstract—As cyber-physical systems become increasingly integrated into the fabric of daily life, the ethical dimensions of computer security have shifted from peripheral concerns to central requirements. This paper explores the evolving landscape of cybersecurity ethics, proposing a novel Dynamic Ethical Response Framework (DERF). Unlike traditional static ethical guidelines, DERF utilizes real-time algorithmic auditing to ensure that defensive measures—specifically automated threat responses—do not inadvertently infringe on privacy rights or perpetuate bias. Through a multi-disciplinary analysis, this paper argues that the future of resilient security lies not just in technical robustness, but in the verifiable ethical integrity of the systems themselves. Keywords—Cybersecurity Ethics, Algorithmic Accountability, DERF, Privacy, Autonomous Defense, Machine Learning Bias.

This paper aims to investigate the influence of young entrepreneurship on the evolution of sustainable corporate governance in Pokhara Metropolitan City, Nepal. The study employed a mixed-methods approach and analyzed the data from 36 young entrepreneurs through a survey and semi-structured interviews with six people to evaluate the awareness, practices, and difficulties related to the modern corporate governance principles. The findings of the study show that young entrepreneurs in Pokhara Metropolitan City exhibit moderate levels of awareness of formal corporate governance principles (mean familiarity score: 3.47), with strong adherence to core operational practices. In particular, this study shows a predominant emphasis to specific area of tax compliance (mean: 4.58) and systematic financial record-keeping (mean: 4.42) and relatively less focus to the area of engagement with advanced methods, where regular stakeholder meetings (mean: 3.58) are presented. Interestingly, modernization occurs via digital tools such as significant barriers include administrative e-complications (mean: 3.75), lack of knowledge (mean: 3.25), and resource shortages (mean: 3.14). The qualitative data through interviews reveal a tension between traditional informal practices and professional models. The findings suggest a need for specific training, policy changes, and support. This study fills empirical gaps and offers implications for policymakers, educators, and business associations in creating accountable and innovative governance frameworks to enhance economic resilience and attract investors.

Short-term surgical missions have expanded access to total joint arthroplasty (TJA) in regions where degenerative joint disease remains undertreated. Reports from these initiatives frequently highlight procedural volume and low early complication rates, reinforcing the perception of success. However, these metrics capture only the earliest phase of outcome assessment following TJA. Durable arthroplasty quality is defined by implant survivorship, complication surveillance, revision capacity, and longitudinal follow-up. In many short-term mission models, long-term tracking, implant traceability, and local capacity for complication management are described incompletely. Without standardized benchmarks, the orthopaedic community risks equating surgical throughput with sustained impact. This article examines the limitations of the current reporting practices in mission-based arthroplasty and proposes an accountability framework that is centered on safety surveillance, follow-up infrastructure, implant traceability, revision capability, capacity development, and financial transparency. As global TJA efforts expand, defining meaningful quality metrics is essential to ensure that episodic interventions translate into durable patient benefit and resilient local systems.

Purpose This research investigates the phenomenon of AI complacency – The employee's tendency to intentionally neglect validating AI-generated output even in the presence of systematic errors. It identifies the lack of monitoring accountability as the underlying driver of this phenomenon, assesses its consequences, and offers strategies for mitigation. Design/methodology/approach Grounded in contingency theory, this research employs six experimental studies (N = 1,370 participants), including one study with service employees across industries (N = 160 participants), to examine how insufficient monitoring accountability facilitates the emergence of AI complacency. The research explores both the causal mechanisms and the boundary conditions that modulate this effect. Findings The results show that the primary driver of AI complacency is the lack of accountability in monitoring AI-generated outputs. This complacency leads to detrimental work-related outcomes, such as increased commission errors and a diminished willingness to evaluate AI-generated outputs critically. The research also identifies situational factors that exacerbate and buffer these effects. Practical implications The findings highlight the critical need for organizations to implement systemic accountability frameworks that ensure employees actively engage with and oversee AI-generated output. Originality/value This research is among the first to examine AI complacency in the context of service provision empirically. It provides a theoretical framework, robust empirical evidence, and practical recommendations for improving Employee-AI collaboration in service provision, contributing to both academic discourse and managerial practice.

This study examines the gap between formal and substantive accountability in the financial management of the Bandung City Government during 2022–2024. Using a Comprehensive Accountability Framework (CAF), the study analyzes how improved audit opinions may coexist with persistent risks of irregularities. Data were collected through document analysis of audit reports issued by the Supreme Audit Agency, local government financial statements, inspectorate reports, and relevant regulations. The findings show that improved audit opinions reflect strengthened administrative accountability but have not been accompanied by enhanced substantive accountability, particularly in public procurement and asset management. This study concludes that favorable audit opinions do not necessarily indicate the actual quality of financial governance, reinforcing the audit opinion paradox in local government.

Algorithmic governance has become a defining feature of modern public service delivery, offering enhanced efficiency, scalability, and data-driven decision-making. However, its adoption raises critical concerns regarding accountability, transparency, and fairness. This paper examines the role of accountability frameworks and bias audits in addressing these challenges within public-sector algorithmic systems. It explores the conceptual foundations of algorithmic governance, emphasizing the interplay between data stewardship, legitimacy, and accountability across the data, model, and decision layers. The study highlights how bias can emerge at multiple stages of the algorithmic lifecycle, from data collection to deployment, and underscores the importance of systematic bias auditing as a mechanism for detecting and mitigating discrimination. Furthermore, it analyzes institutional responsibilities, legal and ethical considerations, and the need for transparent governance structures that enable effective oversight and redress. The paper argues that robust accountability architectures supported by standardized audit practices, stakeholder engagement, and processual transparency are essential for fostering public trust. Ultimately, integrating bias audits into governance frameworks strengthens the legitimacy of algorithmic decision-making and ensures that public services remain equitable, accountable, and aligned with democratic values. Keywords: Algorithmic governance; Accountability; Bias audits; Public services; and Transparency.

Guidance and counseling program accountability faces growing demands about the extent to which the work of counselors in guidance and counseling services programs makes significant, measurable changes in students' lives, their contribution to student success and the improvement of school quality. Counselors are encouraged to work within an accountability framework together with other educators to prove that the counseling program is accountable and effective in the overall students learning process. This paper addresses the issues and challenges of results-based accountability by covering the discussion of the level of accountability level, ways of measuring key data, critical tools of accountability, analyzing critical data elements, and measurement for systemic change.

Beyond literacy to clinical competency: A framework for integrating generative AI into nursing education.

Relative value units (RVUs) were developed to standardize reimbursement within a fee-for-service payment system by quantifying discrete clinical services. Although effective for encounter-based compensation, RVUs were not designed to measure the longitudinal, coordination-intensive, and complexity-adjusted work required in contemporary value-based care environments. As health systems assume greater accountability for population outcomes and financial risk, reliance on RVU-dominant productivity models may create misalignment between workforce expectations and organizational goals. This policy analysis synthesizes historical literature on the development of RVUs, empirical studies examining clinician workload and panel complexity, and contemporary workforce research to evaluate structural limitations of encounter-based productivity metrics. Evidence demonstrates that indirect care activities, multimodal clinical engagement, interdisciplinary coordination, and management of complex patient panels substantially influence workload but are inconsistently represented in RVU calculations. Research within nurse practitioner practice provides illustrative examples of these measurement gaps. A multidimensional clinician workload and panel accountability framework that incorporates panel metrics, multimodal clinical activity, and indirect work provides a more accurate and equitable approach to workforce evaluation in value-based systems. Modernizing productivity measurement may enhance workforce sustainability, improve operational planning, and strengthen performance in accountable care environments.

• A spatial DSS supports ecological sustainability across land systems • The framework translates ecological metrics into actionable landuse strategies • Context-sensitive accounting weights ecological footprints by social-ecological vulnerability • Spatial optimization exposes efficiency-equity-equality trade-offs • Spatial targeting improves ecological efficiency by 32% over uniform land treatment Cities disproportionately concentrate environmental burdens, yet existing environmental accounting frameworks often treat urban systems as spatially homogeneous and overlook variation in land quality, management practices, and social vulnerability. This oversimplification limits the capacity of sustainability assessments to translate ecological indicators into spatially actionable strategies that indicate where to intervene. The fundamental gap lies in the disconnect between environmental assessment, spatial planning, and trade-off management. This research presents an integrated decision support system addressing this gap through multi-objective spatial optimization and context-sensitive ecological footprint accounting. The framework incorporates fine-resolution analysis at planning-relevant scales; context-sensitive weighting integrating environmental degradation and socioeconomic vulnerability indicators; spatially explicit multi-objective optimization balancing environmental effectiveness, social equity, and economic viability through Pareto analysis; and graph-theoretic spatial coherence ensuring feasible interventions. Applied to the Netherlands, the framework demonstrates substantial improvements in sustainability planning. By incorporating local environmental and social conditions into footprint accounting, it distinguishes unsustainable from sustainable land uses, enabling 32% greater targeting efficiency than approaches treating all land as equivalent. When allocating interventions across land types, the framework achieves 57% reduction in ecological deficit within realistic budget constraints, though Pareto analysis shows that each 10% environmental gain requires approximately 9% trade-off in social equity. This approach demonstrates that environmental footprint accounting, traditionally limited to performance monitoring, can guide spatially explicit decision-making when weighted by local conditions and embedded within multi-objective optimization frameworks. Requiring widely available spatial data (land cover, environmental quality, socioeconomic conditions), the methodology enables cities worldwide to translate sustainability commitments into spatially targeted interventions.

The article explores the role of accounting as a foundation for the information system used to assess the investment attractiveness of enterprises. The purpose of the study is to substantiate approaches for forming a high-quality accounting and analytical framework that facilitates informed investment decisions. The research identifies key directions for improving the quality of accounting information, including its completeness, reliability, neutrality, usefulness, and timeliness. The scientific novelty lies in the development of a model of qualitative characteristics of accounting information and the proposal of weighting coefficients that reflect their importance to users. The methodology involves the method of scientific generalization, as well as the analysis of regulatory sources, statistical data, and expert assessments. The main findings confirm the necessity of integrating all types of accounting—financial, managerial, tax, and statistical—into a unified information system. The paper emphasizes the strategic importance of accounting information in conditions of an unstable external environment. The practical significance of the study lies in the applicability of its conclusions for improving managerial decision-making, developing business strategies, and evaluating the investment attractiveness of both existing and newly established enterprises.

In recent years, Turkey has emerged as a global hub for plastic waste processing, primarily fueled by imports from countries such as the UK and members of the European Union. This study investigates the environmental impacts of illegal wastewater discharges from plastic recycling facilities (PRFs) in Adana, a majorplastic waste import hub in Turkey. The region's canal networks, used extensively for agricultural irrigation and linked to the Seyhan River and the Mediterranean Sea, are increasingly exposed to unregulated PRF effluent. Water samples collected upstream and downstream of canals showed MP concentrations ranging from 16.5 to 2174.5 MPs/L, with a fold increase of up to 132× downstream. Estimated MP fluxes exceeded 5.3 billion MPs/h. Polymeric analysis identified a predominance of polyethylene, polypropylene, and polyethylene terephthalate. Notably, pellets and industrial copolymers were found exclusively downstream, confirming a direct recycling origin. This research provides evidence supporting calls for stricter import bans, mandatory MP filtration, and global accountability frameworks to address the toxic legacy of plastic waste colonialism.

Managing risks from water pollution is central to public health, environmental quality, and economic prosperity worldwide. While improvements in water quality have been attained in some parts of the world, much remains to be done to deliver clean rivers, lakes, and seas in line with public interest, changing regulatory landscapes, increasing awareness of risks from pollutants of emerging concern, and climate change. This Perspective explores the potential for artificial intelligence (AI) to help tackle complex water quality challenges. We take a system-oriented approach to define a general pipeline of AI-informed water quality decisions and critically assess the potential of AI to contribute to regulation and decision-making in the context of water quality management. Building on insights obtained from the literature and through a workshop with academics, environmental regulators, industry, and civil society stakeholders in England, we assess the maturity of current AI applications to meet a range of priorities and challenges. While current AI research shows maturity in responding to operational efficiency and modeling and prediction challenges, far less attention has been paid to aligning algorithmic development with user needs and organizational constraints, including the need for trustworthiness and explainability. The full potential of AI to support water quality decisions could be realized through clear institutional processes and accountability frameworks for decision-making. Looking ahead, the development of AI-ready data sets and the availability of clear, open-source examples of AI applications in the water quality domain are potential avenues for supporting wider uptake by regulators and other stakeholders.

Purpose: This study aims to develop and empirically examine an integrated ethical and assured accounting framework that enhances green economy outcomes and contributes to emission reduction. Responding to growing concerns about measurement uncertainty, credibility deficits, and greenwashing risks in sustainability reporting, the study positions accounting, ethical governance, and assurance as interde-pendent mechanisms for climate-related accountability and environmental performance improvement. Methodology, Design, and Approach: The study adopts a comparative cross-country research design, combining quantitative empirical analysis with an integrative measurement framework. Sustainability accounting indicators, ethical governance dimensions, and assurance mechanisms are jointly modeled and tested using advanced statistical techniques. Comparative evidence is drawn from Egypt and a set of advanced economies to assess contextual variation and institutional effects on green economy performance and emission reduction out-comes. Findings: The findings demonstrate that integrated ethical and assured accounting significantly improves the reliability of sustainability measurement and is positively associated with enhanced green economy performance and lower emission levels. Assurance mechanisms strengthen the credibility of sustainability disclosures, while ethical governance amplifies their effectiveness. Cross-country comparisons re-veal that institutional context moderates these relationships, with stronger effects observed where governance and assurance infrastructures are more developed. Originality and Value: This study offers an original contribution by advancing an integrative accounting framework that explicitly links eth-ics, assurance, and sustainability measurement to green economy enhancement and emission reduction. It moves beyond deterministic and fragmented approaches by addressing uncertainty and credibility challenges in sustainability accounting. Theoretical, Practical, and Social Implications The study extends sustainability accounting and accountability literature by integrating ethical governance and assurance into a unified framework. Practically, it informs regulators, standard-setters, and practitioners on designing credible sustainability reporting and assurance systems. Socially, the framework supports climate accountability, environmental transparency, and sustainable development goals by rein-forcing trust in sustainability information.

Traditional one size fits all pharmacotherapy often yields suboptimal clinical outcomes, preventable adverse drug reactions (ADRs), and significant drug waste, imposing substantial economic and ecological burdens on healthcare systems. This review evaluates the transformative potential of pharmacogenomics (PGx) testing, particularly cytochrome P450 (CYP) gene variants, as a foundation for an ecosystem-centric accountability framework for green pharmacy and links human metabolic variability to specific environmental outcomes. Personalized CYP profiling is shown to minimize the environmental release of unused drugs and potentially ecotoxic metabolites into aquatic ecosystems, in contrast to standard uniform drug use approaches. The limitations of ethnicity-based dosing models, which rely on population genetic variation, are examined in the context of increasing global genetic admixture. It is argued that individual genetic profiling, conceptualized as a PGx-Green Passport, provides a reliable safety standard that accounts for individual differences, thereby enhancing efficiency and well-being in a globalized society. By integrating clinical data, including real-world evidence on hospital utilization, with sustainability frameworks, this review demonstrates that PGx-guided therapy is not only a tool for clinical efficiency but also a fundamental requirement for systematically achieving environmentally sustainable healthcare.