Search Results (7,542)

The concept of sustainable development has emerged as a global consensus, forged in response to environmental constraints and critical reflection on conventional growth-oriented paradigms. It now serves as the overarching framework for addressing climate, ecological, and socio-economic crises. In the period after the adoption of the Sustainable Development Goals (SDGs) in 2016, there was an observable trend of increased integration of these objectives into the strategic frameworks of national and subnational entities. However, global assessments have indicated a divergence between the progress achieved and the trajectory delineated by the SDGs. The Earth system is demonstrating signs of decreased resilience, with widening inequalities and the emergence of multiple crises, thereby hindering the implementation of the 2030 Agenda for Sustainable Development. As the 2030 deadline approaches, a fundamental question arises for global development governance: what should be the future of the SDGs beyond 2030? While insufficient progress has prompted debates over the adequacy of the SDG framework, fundamentally revising or replacing the SDGs would risk undermining a hard-won international consensus forged through decades of negotiation and institutional investment. Based on a comprehensive review of the historical evolution of the sustainable development concept, this study argues that the SDGs represent a rare and fragile achievement in global governance. While insufficient progress has sparked debates about their effectiveness, fundamentally revising or replacing the SDGs would jeopardize the hard-won international consensus forged through decades of negotiations and institutional investments. This study further analyzes the latest progress on the SDGs and identifies emerging risks, aiming to explore how to accelerate and optimize sustainable development pathways within the existing SDG framework rather than propose a new global goal system. Based on both global experience and practice in China, four interconnected strategic priorities—namely, economic reform, social equity, environmental justice, and technology sharing—are proposed as a comprehensive framework to accelerate SDG implementation and guide the transformation of development pathways towards a more just, low-carbon, and resilient future. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

The present conceptual paper proposes a purpose-driven experiential marketing framework for film-induced destinations, integrating sustainability and emotional engagement into destination management. The model under discussion comprises five interconnected dimensions, namely integrated experience, branding, people, emotional touchpoints and processes. These are articulated through purpose-driven marketing principles and aligned with selected Global Reporting Initiative (GRI) indicators. This approach positions sustainability as an inherent component of value creation rather than an external policy layer. The framework under discussion was developed through an interdisciplinary literature review and is illustrated through insights from an exploratory case study of Monsanto, a rural Portuguese village recently featured in HBO’s House of the Dragon. Semi-structured interviews were conducted with a purposive sample of local stakeholders, including tourists, residents, entrepreneurs and institutional representatives. These interviews were analysed thematically to provide indicative evidence of the framework’s relevance and potential applicability. The findings suggest that emotional engagement, co-creation and territorial authenticity play a central role in shaping memorable film-related tourism experiences that are consistent with destination purpose and stakeholder well-being. The study also emphasises the strategic importance of storytelling, audiovisual narratives and collaborative governance in the strengthening of place identity and the support of sustainable differentiation. Despite its exploratory nature, the framework provides practical guidance for destination management organisations (DMOs), cultural programmers and creative industry actors. The article concludes by identifying avenues for future research, including cross-regional application, digital experimentation and the quantitative assessment of experiential dimensions. Full article

The Amazon Deforestation Arc remains a critical region for environmental governance, where land-use strategies must consider distinct legal and institutional frameworks across the Amazon and Cerrado biomes. This study applies the Potential Productivity Model (M3P), a theoretical radiation-based framework, to estimate the upper physiological limits of sugarcane (Saccharum officinarum L.) productivity on degraded pastures within the Arc of Deforestation. The model integrates satellite-derived solar radiation with climatic variables to quantify potential productivity under optimal biophysical conditions, providing an objective benchmark for planning-oriented bioenergy assessments. Estimated potential yields range from 153 to 178 t·ha⁻¹·yr⁻¹, consistent with global reference values reported for sugarcane in high-radiation environments and relevant for informing public policies such as Brazil’s Agroecological Zoning of Sugarcane. The results demonstrate that agroclimatic potential alone is insufficient to guide land-use decisions. While degraded pastures associated with the Cerrado biome may accommodate sugarcane cultivation as part of productive land recovery strategies, areas belonging to the Amazon biome require priority actions focused on ecological restoration through agroforestry and integrated crop–livestock–forest systems. Overall, the M3P model offers a scalable and scientifically grounded decision-support framework for strategic planning in environmentally sensitive tropical regions. Full article

In this study, a laboratory-scale slag–steel reaction experiment was conducted to systematically evaluate the influence of the initial MgO content (3–7 wt.%) in LF refining slag on the cleanliness of GCr15 bearing steel. The assessment was performed from multiple perspectives by comparing the total oxygen content (T[O]) in molten steel, the inclusion area fraction, and the inclusion number density after 30 min of slag–steel interaction. To further elucidate the thermodynamic driving forces and kinetic mechanisms governing inclusion capture by slag, a predictive slag adsorption model was developed using an in-house computational code coupled with FactSage 8.1. Under conditions of slag basicity R (CaO/SiO₂) ranging from 4.0 to 8.0, MgO content varying from 0 to 7 wt.%, and a constant Al₂O₃ content of 32 wt.%, the chemical driving force ΔC (the mass-fraction difference between slag components and inclusions), the slag viscosity η, and the combined parameter ΔC/η were calculated at 1600 °C for three representative inclusion types: Al₂O₃, MgO·Al₂O₃, and MgO. In addition, the model was employed to quantitatively characterize the adsorption capacity of slag toward Mg–Al binary inclusions under varying MgO levels. Both experimental observations and model calculations demonstrate that the slag–steel reaction markedly enhances inclusion removal, as evidenced by pronounced decreases in T[O], inclusion number density, and inclusion area fraction after reaction. With increasing MgO content in slag, T[O] and inclusion-related indices exhibit a consistent trend of first decreasing and then increasing, reaching minimum values at an MgO level of 5 wt.%. Further analysis reveals a positive correlation between the apparent inclusion-removal rate constant ko and ΔC/η corresponding to MgO·Al₂O₃ inclusions. Moreover, the slag’s adsorption capacity toward Mg–Al binary inclusions decreases overall as the MgO fraction in inclusions increases. Notably, when the MgO content in inclusions exceeds 29 wt.%, the adsorption capacity undergoes an abrupt drop, indicating a pronounced cliff-like attenuation behavior. Full article

The integration of generative AI tools, such as ChatGPT, Gemini, and DeepSeek, into higher education offers transformative opportunities for personalised learning and academic productivity. However, their unregulated use raises concerns about academic integrity, critical thinking, and educational equity. This systematic review synthesises insights from 96 peer-reviewed articles, identifying six key intervention themes, namely, curriculum integration, policy and governance, faculty development, student-centred strategies, assessment adaptation, and technological infrastructure. Together, these themes form a comprehensive intervention framework designed to guide students’ ethical and effective engagement with AI. This review highlights the need for institutions to move beyond fragmented policies, fostering systemic cultural and pedagogical change to align AI use with authentic learning outcomes. By bridging theoretical gaps and providing actionable strategies, this framework equips educators and policymakers to scaffold responsible AI integration across diverse higher education contexts. Full article

Background: Large language models (LLMs) are increasingly integrated into clinical documentation, decision support, and patient-facing applications across healthcare, including plastic and reconstructive surgery. Yet, their evaluation remains bottlenecked by costly, time-consuming human review. This has given rise to LLM-as-a-judge, in which LLMs are used to evaluate the outputs of other AI systems. Methods: This review examines LLM-as-a-judge in healthcare with particular attention to judging architectures, validation strategies, and emerging applications. A narrative review of the literature was conducted, synthesizing LLM judge methodologies as well as judging paradigms, including those applied to clinical documentation, medical question-answering systems, and clinical conversation assessment. Results: Across tasks, LLM judges align most closely with clinicians on objective criteria (e.g., factuality, grammaticality, internal consistency), benefit from structured evaluation and chain-of-thought prompting, and can approach or exceed inter-clinician agreement, but remain limited for subjective or affective judgments and by dataset quality and task specificity. Conclusions: The literature indicates that LLM judges can enable efficient, standardized evaluation in controlled settings; however, their appropriate role remains supportive rather than substitutive, and their performance may not generalize to complex plastic surgery environments. Their safe use depends on rigorous human oversight and explicit governance structures. Full article

High regeneration energy demand remains a critical barrier to the large-scale deployment of ethanolamine-based (MEA-based) CO₂ capture. This study adopts an Al₂O₃ ceramic-membrane heat exchanger (CMHE) to recover both sensible and latent heat from the stripped gas. Experiments confirm that heat and mass transfer within the CMHE follow a coupled mechanism in which capillary condensation governs trans-membrane water transport, while heat conduction through the ceramic membrane dominates heat transfer, which accounts for more than 80%. Guided by this mechanism, systematic structural optimization was conducted. Alumina was identified as the optimal heat exchanger material due to its combined porosity, thermal conductivity, and corrosion resistance. Among the tested pore sizes, CMHE-4 produces the strongest capillary-condensation enhancement, yielding a heat recovery flux (q value) of up to 38.8 MJ/(m² h), which is 4.3% and 304% higher than those of the stainless steel heat exchanger and plastic heat exchanger, respectively. In addition, Length-dependent analyses reveal an inherent trade-off: shorter modules achieved higher q (e.g., 14–42% greater for 200-mm vs. 300-mm CMHE-4), whereas longer modules provide greater total recovered heat (Q). Scale-up experiments demonstrated pronounced non-linear performance amplification, with a 4 times area increase boosting q by only 1.26 times under constant pressure. The techno-economic assessment indicates a simple payback period of ~2.5 months and a significant reduction in net capture cost. Overall, this work establishes key design parameters, validates the governing transport mechanism, and provides a practical, economically grounded framework for implementing high-efficiency CMHEs in MEA-based CO₂ capture. Full article

Urban Water Systems (UWS) are complex infrastructures that interact with energy, food, ecosystems and socio-political systems, and are under growing pressure from climate change and resource depletion. Planning circular interventions in this context requires system-level analysis to avoid fragmented, siloed decisions. This paper develops the Hydrosocial Resource Urban Nexus (HRUN) framework that integrates hydrosocial thinking with the Water–Energy–Food–Ecosystems (WEFE) nexus to guide UWS design. We conduct a structured literature review and analyse different configurations of circular interventions, mapping their synergies and trade-offs across socioeconomic and environmental functions of hydrosocial systems. The framework is operationalised through a typology of circular interventions based on their circularity purpose (water reuse, resource recovery and reuse, or water-cycle restoration) and management scale (from on-site to centralised), while greening degree (from grey to green infrastructure) and digitalisation (integration of sensors and control systems) are treated as transversal strategies that shape their operational profile. Building on this typology, we construct cause–effect matrices for each intervention type, linking recurring operational patterns to hydrosocial functionalities and revealing associated synergies and trade-offs. Overall, the study advances understanding of how circular interventions with different configurations can strengthen or weaken system resilience and sustainability outcomes. The framework provides a basis for integrated planning and for quantitative and participatory tools that can assess trade-offs and governance effects of different circular design choices, thereby supporting the transition to more resilient and just water systems. Full article

This study investigates the impact of Governance, Risk management, and Compliance (GRC) practices on operational efficiency and corporate reputation. Drawing on the Resource-Based View (RBV), Stakeholder Theory, and the signaling perspective, it conceptualizes GRC as a set of organizational capabilities that enhance operational efficiency and company reputation. It also examines the mediating role of operational efficiency in the GRC–reputation relationship, particularly within technologically advanced and regulated sectors. Data were collected through a structured questionnaire distributed to 126 professionals across various Saudi technology-driven organizations, and the analyses combined descriptive statistics, hierarchical regression, and bootstrapped mediation testing using PROCESS to assess direct and indirect effects. The results indicate that operational efficiency partially mediates the effects of governance and compliance on reputation, supporting the argument that strengthened internal processes enhance external stakeholder evaluations; meanwhile, no mediation was found for risk management. Although the study offers meaningful insights, its sample size and sectoral focus limit the generalizability of conclusions, suggesting the need for broader or longitudinal research. This study contributes by advancing the conceptualization of GRC as organizational capabilities and empirically demonstrating their roles in strengthening both efficiency and reputation within technology-driven firms where digital governance and compliance capabilities are increasingly central. Full article

The incorporation of Artificial Intelligence (AI) into medical services in Saudi Arabia offers a substantial opportunity. Despite the increasing integration of AI techniques such as machine learning, natural language processing, and predictive analytics, there persists an issue in the thorough comprehension of their applications, advantages, and issues within the Saudi healthcare framework. This study aims to perform a thorough systematic literature review (SLR) to assess the current status of AI in Saudi healthcare, determine its alignment with Vision 2030, and suggest practical recommendations for future research and policy. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 699 studies were initially obtained from electronic databases, with 24 studies selected after the application of established inclusion and exclusion criteria. The results indicated that AI has been effectively utilised in disease prediction, diagnosis, therapy optimisation, patient monitoring, and resource allocation, resulting in notable advancements in diagnostic accuracy, operational efficiency, and patient outcomes. Nonetheless, limitations to adoption, such as ethical issues, legislative complexities, data protection issues, and shortages in worker skills, were also recognised. This review emphasises the necessity for strong ethical frameworks, regulatory control, and capacity-building efforts to guarantee the responsible and fair implementation of AI in healthcare. Recommendations encompass the creation of national AI ethics and governance frameworks, investment in AI education and training initiatives, and the formulation of modular AI solutions to guarantee scalability and cost-effectiveness. This breakthrough enables Saudi Arabia to realise its Vision 2030 objectives, establishing the Kingdom as a global leader in AI-driven healthcare innovation. Full article

This study investigates the relationship between apparent electrical resistivity (ER) and key material parameters governing moisture and pore-structure characteristics of concrete. An experimental program was conducted using six concrete mix designs, where ER was continuously measured under controlled wetting and drying cycles to characterize its dependence on the degree of saturation (DS). Results confirmed that ER decreases exponentially with increasing DS across all mixtures, with R² values between 0.896 and 0.997, establishing DS as the dominant factor affecting electrical conduction. To incorporate additional pore-structure parameters, eight input combinations consisting of DS, porosity (P), water–cement ratio (WCR), and compressive strength (f′c) were evaluated using five machine learning models. Gaussian Process Regression and Neural Networks achieved the highest accuracy, particularly when all parameters were included. SHAP analysis revealed that DS accounts for the majority of predictive influence, while porosity and WCR provide secondary but meaningful contributions to ER behavior. Guided by these insights, nonlinear multivariate regression models were formulated, with the exponential model yielding the strongest predictive capability (R² = 0.96). The integrated experimental–computational approach demonstrates that ER is governed by moisture dynamics and pore-structure refinement, offering a physically interpretable and statistically robust framework for nondestructive durability assessment of concrete. Full article

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

The heterogeneity of pore structure in deep coal reservoirs is a critical factor controlling the storage and transport capacity of coalbed methane (CBM). However, the fundamental control exerted by macrolithotypes remains inadequately quantified. This study systematically investigates the No. 8 coal seam of the Taiyuan Formation in the Daniudi gas field, Ordos Basin, using an integrated multi-technique approach including high-pressure mercury intrusion (HPMI), low-temperature N₂ adsorption (LTGA-N₂), and low-pressure CO₂ adsorption (LPGA-CO₂). Results reveal a consistent bimodal pore structure across all samples, dominated by well-developed micropores and macropores, whereas mesopores are relatively underdeveloped. More importantly, a clear macrolithotype control is established: as coal brightness decreases from bright to dull coal, the proportions of micropores and macropores decline significantly, leading to a substantial reduction in total pore volume and specific surface area. Fractal analysis further indicates that dull and semi-dull coals exhibit larger fractal dimensions, reflecting more complex pore structures and stronger heterogeneity compared to bright and semi-bright coals. This heterogeneity shows a positive correlation with ash and mineral contents, but a negative correlation with vitrinite and fixed carbon contents, suggesting that coal composition plays a primary governing role. These findings underscore that bright and semi-bright coals, with their superior micropore storage capacity and well-connected macropore networks, represent the most favorable targets for deep CBM exploration. This work establishes macrolithotype as a practical key indicator for reservoir quality assessment and production strategy optimization in deep CBM plays. Full article

Designing robust drinking water treatment schemes for eutrophic sources requires shifting from considering each treatment step separately to considering the full treatment process as a connected system. This study evaluated how treatment configuration and arrangement influence microbial community dynamics, organic carbon removal, and biological stability in a full-scale drinking water treatment plant. A Dutch treatment plant was monitored, operating two parallel lines: one conventional (coagulation, sedimentation, and rapid sand filtration) and one advanced (ion exchange, ceramic microfiltration, and advanced oxidation), both converging into granular activated carbon (GAC) filtration. Microbial and chemical water quality was assessed across treatment stages and seasons. This plant experiences periods of discoloration, taste, and odor issues, and an exceedance of Aeromonas counts in the distribution network. Advanced oxidation achieved a high bacterial cell inactivation (~90%); however, it significantly increased assimilable organic carbon (AOC) (300–900% increase), challenging biological stability. GAC filtration partially reduced AOC levels (from 70 μg Ac-C/L to 12 μg Ac-C/L) but also supported dense (10⁵ cells/mL) and diverse microbial communities (Shannon diversity index 5.83). Moreover, Gammaproteobacteria, which harbor opportunistic pathogens such as Aeromonas, persisted during the treatment. Archaea were highly sensitive to oxidative and physical stress, leading to reduced diversity downstream. Beta diversity analysis revealed that treatment configuration, rather than seasonality, governed the community composition. The findings highlight that treatment arrangement, oxidation, GAC operation, and organic and microbial loads critically influence biological stability. This study proposes integrated strategies to achieve resilient and biologically stable drinking water production when utilizing complex water sources such as eutrophic lakes. Full article