Search Results (22,663)

Understanding carbon dioxide (CO₂) emissions from buildings is critical for shaping effective policies toward sustainable urban development. Previous studies mainly applied bottom-up methods for small areas or top-down downscaling at national, provincial or grid scales. However, limited research has explored the relationship between building functions and CO₂ emissions at a larger scale. To bridge this gap, this study employed ridge regression to disaggregate monthly CO₂ emissions to the level of different functional buildings across England in 2022 and investigated the relationship between building functions and CO₂ emissions. Results show that commercial buildings rank highest in CO₂ intensity, reaching 1.49 kg per volume in February, while residential buildings rank lowest, reaching 0.25 kg per volume in July at the national scale, and industrial buildings have the largest total emissions. In addition, regional disparities in economic development and industrial structure contribute to emission differences among buildings of the same function. Temporally, all functional buildings exhibited lower emissions during summer compared to winter. Overall, this study offers a scalable and interpretable framework for understanding urban carbon emissions at high spatial and functional granularity. The findings may offer valuable insights to support government decision-making in urban planning and spatial policy design, thereby contributing to low-carbon development goals. Full article

Plants in agricultural systems rarely face single stressors; instead, they encounter concurrent biotic (pathogen, pests) and abiotic (drought, salinity, heavy metals) stresses that causes severely reduce crop yields and endanger food security. The traditional methods of breeding, genetic engineering, and agrochemicals tend to target individual stresses and still do not suffice in the complex field conditions. Compared to these approaches, nanotechnology offers distinct advantages: nanoparticles (NPs) can be applied as foliar sprays or seed treatments without lengthy breeding cycles or regulatory hurdles associated with genetically modified organisms. However, nanotechnology is not inherently “better” but rather complementary to crop engineering; each approach has specific strengths. Breeding and genetic engineering provide heritable, long-term solutions, while nanotechnology offers immediate, season-specific, and reversible interventions. Cross-tolerance, the phenomenon whereby exposure to one stress enhances tolerance to another, offers a promising alternative. This review critically examines how NPs act as stress-priming agents that induce cross-tolerance by activating overlapping defense networks, including antioxidant systems (SOD, CAT, APX), phytohormonal crosstalk (ABA, SA, JA), osmolyte homeostasis, and stress-responsive gene expression. We synthesize current evidence on NP uptake, translocation, and cellular interactions, and evaluate their dual role in directly suppressing pathogens while simultaneously enhancing plant immune responses and physiological resilience. However, efficacy is highly dose-dependent: low, subtoxic doses prime defense through hermetic ROS signaling, whereas supraoptimal doses cause phytotoxicity. The current challenges in nano-mediated stress alleviation include: (i) a persistent laboratory-to-field translation gap, with field outcomes averaging only 60–70% of greenhouse efficacy; (ii) dose-dependent phytotoxicity; (iii) poor reproducibility across studies; (iv) scalability and formulation stability issues; and (v) insufficient understanding of long-term environmental fate, including soil accumulation, non-target organism effects, and food chain safety. Future research should consider field-validated formulations (e.g., SiNPs, ZnONPs, Fe₃O₄NPs) across major staple crops); integrating nanotechnology with precision agriculture through nanosensors, remote sensing, and artificial intelligence for site-specific, dose-optimized applications;developing smart, biodegradable nanoparticles with stimuli-responsive release; and establishing harmonized regulatory frameworks for nano-agrochemical approval. When deployed responsibly, nanoparticle-induced cross-tolerance represents a sustainable approach to improve crop resistance against multifactorial stress, with significant implications for climate-resilient agriculture and global food security. Full article

Coal gangue (CG) and iron tailings (ITs) are major industrial solid wastes, and their high-value reuse is crucial for sustainable construction materials. This study explores the feasibility of fabricating sintered porous bricks using CG and ITs as primary constituents, with shale as an auxiliary component. To evaluate durability in cold regions, laboratory freeze–thaw (F-T) cycling experiments were conducted. A degradation assessment framework based on the Wiener stochastic process was developed to predict frost-resistance service life by integrating experimental data with regional climatic conditions. Results show that the fabricated bricks exhibit satisfactory initial properties, with a compressive strength of 10.6 MPa and water absorption of 13.3%. With increasing F-T cycles, compressive strength decreases significantly, accompanied by increased mass loss and water absorption. Stress–strain analysis reveals progressive stiffness reduction and a transition from brittle to ductile failure. Microstructural observations confirm degradation of the glassy phase, pore expansion, and enhanced interconnectivity. The Wiener process-based model effectively describes the stochastic accumulation of F-T damage. By establishing equivalence between laboratory and natural F-T cycles, the long-term service life of coal gangue–iron tailing sintered porous bricks (CG-IT SPBs) in cold regions is theoretically evaluated. This work provides an integrated understanding of F-T damage behavior and establishes a scientific foundation for durability-oriented design and application of such bricks in extremely cold environments. Full article

Formic acid (FA) has emerged as a promising liquid hydrogen storage material, yet efficient photothermal dehydrogenation catalysts with high activity and H₂ selectivity remain challenging. Herein, a polymetallic synergistic PdCu/M-ZNC (where M represents the co-doped In, Sn and Mo species) is fabricated by molten-salt-assisted pyrolysis of ZIF-8 precursors followed by metal incorporation. The unique molten salt environment effectively preserves the porous architecture of ZIF-8, enabling the secure anchoring of PdCu alloy nanoparticles onto the carbonaceous matrix enriched with M-Nₓ coordination sites. Under light irradiation, the PdCu alloy sites kinetically accelerated the overall adsorption and activation of FA molecules. Based on empirical observations and corroborated by the established literature, this alloying effect was inferred to facilitate the C-H bond cleavage and HCOO* desorption processes. Concurrently, the M-Nₓ sites act as efficient electron transfer channels, facilitating the rapid coupling of photogenerated electrons with protons (H⁺) to evolve H₂. Consequently, the optimal catalyst exhibits an enhancement in gaseous product yield (404.46 mmol/g/h) and H₂ selectivity (67.49%) at 75 °C. This work offers a catalyst design that aligns with several principles of green chemistry: it maximizes the atom utilization of precious Pd, incorporates synergistic non-precious metals within MOF-derived frameworks to enhance stability, and leverages solar energy to drive hydrogen production under mild conditions, presenting a more sustainable pathway for hydrogen release from liquid carriers. Full article

This study proposes a data-driven evaluation framework to quantify the impact of artificial intelligence (AI) on industrial process performance and enterprise value creation. The framework integrates enterprise value assessment based on the Feltham–Ohlson model with a multi-level performance evaluation framework that incorporates a hybrid Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) for indicator weighting, together with Fuzzy Comprehensive Evaluation (FCE) for multi-dimensional aggregation. This integrated approach enables systematic analysis of AI-driven effects from the perspectives of intelligent investment input, operational governance environment, and process output performance. Using panel data from 3515 Chinese A-share listed firms (20,076 firm-year observations) during 2014–2022, a Process Performance Index (PI) is constructed to measure AI-enabled operational capability across resource allocation efficiency, coordination effectiveness, and production performance dimensions. Empirical results indicate that PI is positively associated with abnormal earnings and firm profitability, demonstrating that AI-enabled process capability contributes to sustained enterprise value growth. The findings further show increased digital technology investment intensity, knowledge-based human capital accumulation, and improved data governance conditions, accompanied by enhanced production and service performance. By explicitly integrating AHP–EWM weighting and FCE aggregation within the Feltham–Ohlson valuation structure, the proposed framework provides an interpretable quantitative mechanism linking AI adoption, operational capability development, and enterprise value creation. The results offer practical insights for evaluating intelligent transformation strategies in the context of Industry 5.0 and data-driven industrial development. Full article

Gingival recession is commonly linked to alveolar bone dehiscence, inflammatory burden, traumatic brushing, or excessive orthodontic forces. However, recession is also observed in some patients despite apparently mild or “biologically acceptable” loading, particularly in thin periodontal phenotypes. Here, we propose the Gingival Creep Failure Theory, a hypothesis-driven conceptual framework in which gingival soft tissues undergo time-dependent viscoelastic deformation (creep) under sustained or repetitive tensile microstrain. Over time, accumulated deformation and microstructural fatigue may reduce recoil capacity and shift the gingival margin apically once tissue-level tolerance is exceeded. Gingival connective tissue is modeled as a fiber-reinforced, fluid-rich viscoelastic composite whose response depends on collagen architecture, cross-linking, proteoglycan-mediated hydration, and vascular support. In thin phenotypes characterized by reduced connective tissue volume and altered extracellular matrix (ECM) organization, creep progression is hypothesized to accelerate, lowering the threshold at which fatigue-related microdamage translates into clinically detectable marginal migration. Evidence from collagenous connective tissue biomechanics supports the plausibility that sub-failure sustained or cyclic loading can produce cumulative deformation and incomplete recovery; however, direct creep–fatigue data for human gingiva remain limited, underscoring the need for targeted validation studies. This hypothesis integrates soft tissue mechanics with periodontal phenotype biology and orthodontic loading patterns and proposes creep and microstructural fatigue as plausible time-dependent contributors to gingival recession in susceptible phenotypes. Because direct in vivo gingival strain and creep–fatigue measurements remain limited, the model should be interpreted as hypothesis-generating and in need of targeted clinical and experimental validation. Full article

Background/Objectives: Human immunodeficiency virus remains a significant public health challenge, with breastfeeding contributing to the risk of mother-to-child transmission. Although antiretroviral therapy significantly reduces this risk, obstetric nurses face complex challenges in translating evolving guidelines into clinical practice. This scoping review aims to map existing scientific evidence on obstetric nurses’ approaches to evidence-based practice regarding breastfeeding in the context of HIV. Methods: Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, a search was conducted across PubMed, Scopus and EBSCOhost (MEDLINE Complete, CINAHL Complete, Cochrane Central Register of Controlled Trials, and Nursing & Allied Health Collection: Comprehensive) for studies published in Englissh and Portuguese between 2015 and 2025. Studies were included if they focused on the role of obstetric nurses, nurse-midwives, or midwives in infant-feeding practices for women living with HIV. Results: Eight studies were included, predominantly from sub-Saharan Africa, with additional evidence from Europe and Canada. Findings reveal that infant-feeding counseling is shaped by a complex interplay of clinical protocols and personal beliefs. Significant gaps in knowledge translation were identified. While nurses demonstrate high technical confidence in lactation support, their distinct professional contribution is often obscured by research that aggregates all healthcare providers. Conclusions: The challenge of supporting breastfeeding in the context of HIV extends beyond technical protocol adherence. It points to persistent gaps in knowledge translation, variability in counselling practices, and the influence of contextual and professional factors on guideline implementation. Strengthening care requires sustained investment in profession-specific education, institutional support, and evidence-informed practice frameworks that enable obstetric nurses to exercise informed clinical judgement. Full article

To enhance the engineering performance of fine-grained composite soils with unbalanced particle gradation, high plasticity, and poor water stability, a synergistic stabilization strategy combining particle structure regulation and microbially induced calcium carbonate precipitation (MICP) was proposed. The particle size distribution and fundamental engineering properties of a titanium gypsum–clay (TSC) composite soil were first optimized through systematic single-factor blending tests. The results indicate that a TS:C ratio of 60:40 significantly improved gradation characteristics, reduced plasticity, and enhanced both compaction behavior and load-bearing capacity. Based on the optimized gradation framework, MICP treatment was subsequently introduced to further enhance water stability. The effects of key parameters, particularly the type of calcium source, on the evolution of water stability were systematically investigated. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to elucidate the underlying reinforcement mechanisms. The results demonstrate that the water stability coefficient increased markedly from 0.35 to 0.83 following MICP treatment, while strength degradation under water immersion was effectively mitigated. Microscopic observations reveal that microbially precipitated calcite fills pore spaces and forms a continuous cementation network via particle bridging and interfacial bonding, leading to an approximately 32% reduction in porosity. Overall, the proposed synergistic strategy offers an effective and sustainable approach for improving the water stability and structural integrity of complex fine-grained composite soils. Full article

Transformer fires within indoor substations constitute severe hydrocarbon fire scenarios characterized by rapid heat release rates and extreme peak temperatures, posing a critical threat to the structural integrity of steel frameworks and power grid stability. To rigorously assess structural safety under such conditions, this study employs a sequential thermal-mechanical coupled numerical methodology combining Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). Focusing on a 110 kV indoor substation, the research simulates the transient, non-uniform temperature fields induced by transformer oil combustion and analyzes the thermo-mechanical response of key steel components. Furthermore, the protective efficacy of two non-intumescent coatings (Material A and Material B) with distinct thermal conductivities is systematically evaluated. Computational results elucidate significant thermal stratification, with upper-level structures sustaining exposure to temperatures exceeding 1500 K. Unprotected steel components subjected to direct flame impingement exhibit severe stress concentrations and plastic deformation, reaching their load-bearing limit within 4825 s. The application of fire-retardant coatings markedly enhances fire resistance; a 5 mm layer of Material A (λ = 0.20 W/(m·K)) extends the time to failure to approximately 9390 s. Notably, increasing the thickness of Material A to 20 mm, or alternatively employing a 10 mm layer of Material B (λ = 0.10 W/(m·K)), effectively mitigates thermal stress concentrations. This ensures structural deformation remains within safe limits throughout a 3 h (10,800 s) fire duration. This study provides a theoretical basis and quantitative engineering references for the optimal fire protection design of substation steel structures. Full article

Offshore industries depend solely on diesel-based power generation systems or mainland grids, which are expensive and carbon-intensive. The demand for renewable energy-based offshore DC microgrids (MGs) has significantly increased due to rising fuel prices, high costs of fuel transportation and storage, extreme operation and maintenance expenses, and associated carbon emissions. This research study optimises the size of an offshore DC MG that integrates wave, solar, energy storage, and diesel, utilising real-world data from a specific geographical location (latitude −33.525587 and longitude 114.772211), thereby accurately representing the availability of renewable energy sources. An algorithm is designed to optimise the utilisation of highly variable renewable sources via battery-based energy management, resulting in optimal energy dispatch. Utilising economic performance metrics, such as levelised cost of energy (LCoE) and net present value (NPV), this research aims to minimise the energy, operating, and greenhouse gas emission costs while maximising the economic feasibility of the system. A sensitivity analysis is performed to determine the impact of fuel prices, discount rates, and system lifespans on the feasibility of the system. The findings demonstrate that the proposed renewable-based offshore DC MG can substantially reduce fuel consumption (93%), operational expenses (77.56%), and carbon emissions (89.50%) compared with a diesel-only system for offshore platforms, while improving the sustainability and reliability of power supply for aquaculture and marine activities. In addition, the proposed renewable-energy-based offshore DC MG achieves a lower LCoE (0.5649 $/kWh) and a higher NPV (2.987 × ${10}^4$ $) than a conventional diesel-based power generation system for offshore industries. The results provide a decision-making framework for the design and implementation of renewable energy-based offshore DC MGs. Full article

Higher education institutions (HEIs) play a central role in fostering sustainability competencies to address environmental challenges. Within Education for Sustainable Development (ESD) and Sustainable Development Goal (SDG) 4 frameworks, universities must cultivate not only knowledge but also attitudes and behaviours promoting environmental responsibility. This study examines environmental attitudes as sustainability learning outcomes among undergraduate students, analysing differences by gender, age, and discipline in six Andalusian universities. Sustainable Education is defined as an approach integrating environmental, social, and economic sustainability dimensions into teaching to develop active competencies for sustainable development. A cross-sectional survey (n = 1471) used the validated CASEM questionnaire (see previous validation studies) to assess environmental knowledge, environmental education knowledge, and pro-environmental behaviour. The results show significant differences: women outperformed men across all dimensions, students aged over 25 exhibited stronger profiles, and Education Sciences students outperformed Engineering students. A persistent knowledge–behaviour gap emerged, especially in technical fields. These findings reveal curricular inequalities in sustainability integration. Mandatory, discipline-specific ESD—particularly in engineering—may help bridge these gaps and enhance uniform learning outcomes. By employing a multidimensional instrument and stratified sample, this study offers robust evidence of structural disparities, informing policy for equitable Higher Education for Sustainability. Full article

Low-risk prostate cancer (PCa) has historically been overtreated, exposing men to unnecessary morbidity. Emerging evidence supports conservative management of low-risk PCa without immediate radical intervention. Contemporary data show a marked decline in surgical overtreatment, with the proportion of radical prostatectomies yielding only Grade Group 1 cancers falling from 32.4% in 2010 to 7.8% in 2020 in the US SEER registry. Long-term studies confirm that deferring treatment is safe for low-risk disease, with PCa-specific survival exceeding 95% at 15–25 years for cohorts managed with surveillance. Major guidelines now endorse active surveillance (AS) as the preferred management for low-risk PCa. An alternative risk stratification system that expands the low-risk category was shown to reclassify 45–83% more men as low risk without increasing 15-year PCa mortality. Focal therapy has emerged as a potential middle-ground strategy, though evidence is still limited. The paradigm for managing low-risk PCa has shifted toward conservatism, with AS firmly established as the standard of care. Continued efforts to refine risk stratification and evaluate focal therapy are needed to further optimize individualized care, minimize harm, and maintain excellent cancer-specific outcomes for low-risk PCa. This comprehensive review aims to create a practical, risk-adapted framework for managing patients on AS. We will: (i) summarize inclusion criteria and outcomes, (i) compare AS follow-up schedules across major institutions and guidelines, (iii) provide evidence-based criteria to de-intensify surveillance in men with sustained stability and (iv) clarify the role of focal therapy as an intermediate treatment option within the AS continuum. Full article

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

Qualitative analysis of biochemical reaction systems reveals fundamental system-level properties that are independent of precise kinetic parameters, often context-dependent, or experimentally inaccessible. By focusing on structural and topological features—such as conservation relations, feedback loops, and pathway interconnections—qualitative analysis identifies invariant behaviors, robustness mechanisms, and potential failure modes inherent to the signaling network. In this study, we use Petri nets as a formal modeling framework to conduct qualitative analysis of the integrated MAPK and PI3K/Akt signaling network. By exploiting structural properties including place invariants, transition invariants, and siphons, the analysis establishes a direct correspondence between the Petri net structure and biologically meaningful conservation laws, signaling modules, and characteristic dynamic behaviors. The results demonstrate that the proposed model is structurally consistent, biologically plausible, and modular. Minimal semi-positive place invariants confirm mass conservation, indicating that proteins and enzymes circulate within closed molecular pools. Minimal semi-positive transition invariants identify canonical kinase–phosphatase cycles underlying sustained and reversible signaling. Hierarchical decomposition reveals a modular organization reducible to reusable enzymatic motifs, reflecting biological reuse across cascades and supporting scalability. Additionally, the identification of sixteen siphons that are also traps highlights persistent subsystems that ensure continuous regulator availability, confirming the robustness and dynamic sustainability of the integrated network. Full article

Sustainable development results from the harmonious integration of economic growth, social equity, and environmental sustainability. Building on available risk analysis capacities, this study employs risk perception as a diagnostic tool to evaluate the adequacy of decision-making regarding environmental sustainability in vulnerable human settlements under a changing climate in the Dominican Republic. Using the perceived risk profile approach and a specially designed questionnaire, the research explores issues related to climate change and sustainability, targeting a population composed of decision-makers and professionals engaged in risk assessment. The findings reveal a systematic underestimation of risk across most perception variables, as well as a generally low collective risk perception. The study’s methodological framework enables the identification of proactive measures to strengthen knowledge and performance among decision-makers and stakeholders involved in advancing sustainable development in Dominican human settlements. Full article