Search Results (7,603)

The built environment constitutes a significant factor influencing the physical and mental health of the elderly and has garnered sustained interdisciplinary attention in recent years. Based on 425 publications from the Web of Science database spanning 2001 to 2025, this study employed Citespace to conduct a quantitative analysis and synthesis of the relevant literature, aiming to explore the evolutionary trends, hotspot distributions, and pathways of influence regarding the impact of the built environment on elderly health. The results indicate a close positive correlation between the population ageing trend and annual publication growth. The total publication volume exhibited a shift from gradual to rapid growth, demonstrating a distinct phased evolutionary pattern. The research hotspots displayed a gradient structure of descending research intensity: “physical activity—quality of life—mental health.” The impact of the built environment (e.g., green space, street quality) on elderly health can be primarily categorised into three pathways: direct effects, physical activity, and mental health. Macro-level allocation of elderly care facilities and micro-level construction of age-friendly living circles represent the principal optimisation strategies currently employed to address elderly health needs. Finally, potential future research directions are discussed, encompassing aspects such as spatial scales, health representations, and mechanism expansion, with the aim of providing reference and insights for advancing the initiative of “healthy ageing.” Full article

In the context of green energy, the use of lake reeds is becoming an increasingly important factor. Therefore, research into the availability of reeds, determining their area in lakes, predicting the potential biomass volume and calculating the carbon footprint are important. Currently, there have been no significant research results on the availability of reeds and the assessment of the sustainability of reed products in Latvia. However, these aspects are crucial for the development of reed products, as they help to assess their market potential and environmental impact. The main goal of this work is to develop a method for modeling the distribution of lake reeds in order to predict their availability in the future, which would allow assessment of the volume of biomass and its impact on the environment. This research develops an integrated GIS–LCA framework that combines Sentinel-2 satellite data, machine learning-based classification, biomass estimation, and carbon footprint modeling. Using Lake Cirma as a case study, the classification results show that reed stands occupy 2.18–3.51 percent of the lake area in certain years, corresponding to approximately 1158–1861 tons of biomass. The framework enables quantification of harvesting potential while considering ecological constraints that limit annual extraction to approximately 50% of total biomass. The proposed GIS–LCA framework provides a replicable methodology for assessing reed biomass availability and environmental performance across lake ecosystems. It supports evidence-based decision-making for sustainable reed resource management and contributes to the development of low-carbon bioeconomy pathways in line with EU climate and bioeconomy strategies. Full article

The Mahu Sag, a hydrocarbon-rich depression within the Junggar Basin, hosts significant petroleum resources. Here, the Permian Fengcheng Formation shale oil reservoirs have emerged as a primary exploration target. This study investigates fracture development within these alkaline lacustrine shales, a critical factor governing hydrocarbon migration and accumulation. Through integrated petrographic and geochemical analyses, we elucidate a multifactorial fracture formation mechanism driven by the interplay of alkaline minerals, stress, and fluids. Two distinct fracture types were identified: bedding-complex fracture veins (BCFVs) and Y-shaped high-angle fracture veins (Y-HFVs). Both fracture types result from alkaline fluid–rock interactions, which induce fracture opening along specific orientations, alter fracture angles, and control aperture width and final morphology. Alkaline mineral assemblages further influence fracture evolution via dissolution–precipitation cycles. Concurrently, these assemblages preserve hydrocarbons by inhibiting the thermal maturation of organic matter, as evidenced by variations in fluid inclusion fluorescence. The fracture networks act as crucial migration pathways, with the BCFV containing higher-maturity hydrocarbons (indicated by blue-green fluorescence) and the Y-HFV retaining less mature fluids (indicated by yellow-green fluorescence). This study presents the first systematic characterization of the multifactorial controls on fractures in alkaline lake environments, proposing a cooperative “alkaline minerals–stress–fluids” mechanism. These findings provide a new framework for understanding fracture development in alkaline lacustrine shales and offer valuable insights for shale oil exploration in analogous depositional settings. Full article

Microplastics (MPs) are emerging pollutants of global concern, posing significant ecological and human health risks. They are frequently detected in stormwater systems, with urban runoff serving as a major transport pathway into the environment. Green stormwater infrastructure, particularly bioretention systems (BRSs), offers a promising approach to mitigate these risks by filtering and retaining various contaminants. However, the occurrence of MPs in BRSs and their capacity to retain these pollutants remain largely unexplored in the literature, despite being critical for stormwater management and water quality protection. Therefore, this study attempted to examine the occurrence, vertical distribution, and trapping of MPs within a field-installed BRS, potentially emphasizing their role in reducing microplastic (MP) transport. Therefore, field samples were collected at depths of 2, 12, and 24 inches below the surface and processed in the laboratory for MP detection and quantification. The results revealed an average concentration of 1095 particles per kg of dried sediment, with fragments (microplastics shape) accounting for 78.54% of the total MPs. Although no clear vertical distribution pattern was observed, the initial findings showed that MPs were mostly retained at 24 inches, potentially indicating their transport through the media and the retention capacity of a BRS (surface and middle layer) in capturing microplastics from stormwater environments. However, there is no direct evidence to explain the mechanisms driving the observed concentrations at greater depths. The preliminary findings of this study highlight that the concentrations of different sizes of MPs can vary with soil depth in bioretention media. Integrating a BRS into urban stormwater infrastructure likely provides the dual benefits of improved stormwater management and reduced plastic pollution. This study underscores the importance of optimizing bioretention design and media composition to enhance MP trapping from stormwater. Full article

The objective of this study is to develop and assess the feasibility of utilizing lotus (Nelumbo nucifera Gaertn.) leaf extract as a green corrosion inhibitor for copper in a sulfuric acid environment. The inhibitory efficacy was comprehensively evaluated using a multi-technique approach, incorporating electrochemical measurements, weight loss analysis, theoretical analysis, and surface morphological characterization. The experimental results demonstrate that the lotus leaf extract functions as an efficient corrosion inhibitor for copper, achieving an inhibition efficiency of 88.07% at 700 mg/L by effectively suppressing both cathodic and anodic corrosion processes. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) confirmed the protective effect, whereas X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) identified functional groups and surface interaction between metal and inhibitor. Theoretical calculations further confirmed the involvement of nitrogen (N) and oxygen (O) as the key active sites. Adsorption behavior adheres to the Langmuir isotherm model, involving both physical and chemical adsorption processes that inhibit the Cu⁺→Cu²⁺ oxidation reaction. This study demonstrates acid-resistant protection of copper using lotus leaf extract. Full article

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

Background: The reliable co-loading of paclitaxel (PTX) and indocyanine green (ICG) into a single lipid nanoparticle (LNP) enables synergistic antitumor delivery but remains challenging due to their distinct physicochemical properties. Methods: This study integrated COSMO-RS calculations, molecular dynamics simulations, and in vitro assays to systematically investigate the effects of lipid composition, drug modification, particle size, and solvent environment on dual-drug loading. Results: This work indicate that DMPS lipid membranes featuring highly polar headgroups and ordered bilayer structures stably bind both ICG and PTX, achieving drug-loading efficiencies (DLEs) of 7.2% and 5.6%, respectively. Carboxylation of PTX enhanced hydrogen bonding with DMPS, while alkyl chain modifications improved membrane insertion, though excessive chain length (e.g., C12) reduced stability due to increased flexibility. Increasing the LNP size from 50 nm to 250 nm raised the DLE of PTX from 4.7% to 8.1%, while sizes beyond 500 nm led to membrane destabilization. The use of 20 vol% ethanol increased total drug loading by 51% by disrupting the hydration shell of ICG and suppressing PTX aggregation; however, ethanol concentrations exceeding 40 vol% intensified drug–solvent competition and weakened membrane binding. Conclusions: This study provides a comprehensive elucidation of the multifactorial regulatory mechanisms underlying dual-drug loading in LNPs, offering a theoretical basis for the rational design of efficient co-delivery systems. Full article

Corrosion continues to be a major concern in industrial systems, causing material degradation and raising maintenance costs. In recent years, plant-derived corrosion inhibitors have gained interest as environmentally friendly alternatives to conventional chemical treatments. In this work, ethanolic extracts from the leaves of Citrus limon (L.) Osbeck and Eucalyptus globulus Labill. were evaluated as green corrosion inhibitors for C45 carbon steel in 1 M HCl solution. The extracts were prepared by continuous Soxhlet extraction and characterized through antioxidant activity measurements using the 2,2-diphenyl-1-picrylhydrazyl DPPH radical scavenging method, gravimetric (weight loss) tests, and electrochemical techniques including potentiodynamic polarization. In addition, the extraction parameters were optimized using a face-centered central composite design (CCD) within a response surface methodology (RSM) framework, and the resulting models were analyzed by analysis of variance (ANOVA). The effects of inhibitor concentration and temperature on corrosion inhibition performance were systematically examined. The antioxidant assay indicated that E. globulus extract reached a scavenging activity above 95% at 1000 mg/L, while C. limon extract showed moderate activity around 71%. Gravimetric tests revealed that both extracts reduced the corrosion rate, with optimal inhibition efficiencies of approximately 67% for C. limon (at 0.3 g/100 mL) and 82% for E. globulus (at 1.0 g/100 mL). Beyond these optimal concentrations, a decline in performance was observed, suggesting surface saturation. The statistical optimization showed that the C. limon response model was solvent-driven (R² = 92.05%), whereas the E. globulus model was curvature-driven (R² = 95.45%), with contrasting response surface topographies. Electrochemical measurements confirmed that both extracts acted as mixed-type inhibitors, shifting the corrosion potential toward less negative values and reducing the corrosion current density. Overall, E. globulus extract demonstrated superior performance across all methods, and both extracts represent promising candidates for sustainable corrosion protection in acidic industrial environments. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. Full article

Phosphorus deficiency in the environment induces phosphate (Pi) starvation responses of plants, in which the phosphate transporter is one of the most critical functional genes in this response mechanism. As a prevalent green manure crop in China, Vicia villosa plays a critical role in sustainable agricultural systems, and the expression of its phosphate transporter gene (VvPHT1) is modulated by soil phosphorus availability, highlighting its key adaptive function in nutrient acquisition and utilization under low-Pi conditions. Functional studies of this gene and its promoter contribute to exploring the molecular mechanisms of the tolerance of green manure crops to low phosphorus stress and to improving phosphorus-efficient V. villosa varieties. In this study, analysis of the VvPHT1 promoter sequence revealed a 1524 bp region containing multiple root-specific cis-regulatory elements, including five NODCON2GM, one NODCON1GM, six OSE2ROOTNODULE, one OSE1ROOTNODULE, and fifteen ROOTMOTIFTAPOX1 motifs. Histochemical GUS staining of transgenic Arabidopsis (Arabidopsis thaliana (L.) Heynh.) showed that the VvPHT1 promoter directed root-specific expression of the GUS reporter gene. A fusion expression vector pCAMBIA1300-VvPHT1--GFP was constructed and transformed into tobacco (Nicotiana tabacum L.) cells for subcellular localization analysis, indicating that the protein encoded by VvPHT1 was localized to the plasma membrane. To quantify its expression, VvPHT1 transcript levels in VvPHT1-overexpressing Arabidopsis (OEPHT1) lines were analyzed by quantitative real-time PCR (qRT-PCR) under different phosphorus supply conditions. The results demonstrated that under low-Pi conditions, the expression of VvPHT1 was significantly upregulated in the OEPHT1 lines compared to those of normal-Pi conditions. Furthermore, under low-Pi treatment, the OEPHT1 lines showed significantly increased fresh weight, primary root length, phosphorus content, and chlorophyll content compared to the wild-type Arabidopsis (WT), while no such differences were observed under normal-Pi conditions. In conclusion, the VvPHT1 promoter exhibits root-specific activity, and the VvPHT1 gene encodes a plasma-membrane-localized phosphate transporter that is strongly induced by phosphorus deficiency. Its overexpression enhances phosphorus uptake and plant growth under low-Pi conditions, suggesting that VvPHT1 likely functions as a high-affinity phosphate transporter involved in the adaptation to phosphorus starvation. Full article

Cadmium is an element that is unnecessary for the functioning of plant and animal organisms, and its widespread presence in the environment poses a serious threat to human and animal health. Therefore, effective methods are being sought to remediate soils contaminated with this element, including through the enrichment of degraded soils with organic matter. To this end, the effectiveness of selected organic sorbents, including starch, fermented bark, compost and humic acids, in mitigating the transfer of cadmium and other heavy metals from soil to plants was assessed. Model studies compared the effects of 15 and 30 mg of cadmium (Cd) per kg of soil with an uncontaminated control sample. The sorbents were applied on a carbon basis at a rate of 3 g C per kg of soil. The test plant was Zea mays. Cadmium was found to significantly impair plant growth, causing reductions of 21%, 85%, and 77% in leaf greenness, aboveground biomass and root biomass, respectively. Excess cadmium increased the translocation of lead, chromium, copper, nickel, zinc, iron, and manganese from the roots to the aboveground parts of the plant, while simultaneously limiting their uptake. All of the organic sorbents tested reduced the negative impact of cadmium on leaf greenness, except starch. Compost and HumiAgra significantly improved the condition of Zea mays plants weakened by cadmium exposure. Cadmium contamination increased soil acidification. pH was positively correlated with maize yield and the SPAD leaf greenness index and negatively correlated with the cadmium translocation index and cadmium content in the aboveground parts of maize. Compost and humic acids are among the most effective and practically feasible approaches for reducing cadmium bioavailability in soil and its accumulation in Zea mays, and are therefore recommended for the remediation of cadmium-contaminated soils. Full article

Deep learning models represent one of the most advanced and effective approaches in predictive modeling. Their hierarchical architectures enable the extraction of complex, non-linear feature relationships and the identification of latent patterns within data, making them highly suitable for tasks involving high-dimensional or unstructured inputs. However, these models are computationally demanding, requiring significant processing resources and time. Furthermore, their predictive performance is largely contingent upon the availability of large-scale datasets. In this study, a Deep Green Framework is employed for the prediction of two computer vision tasks. CIFAR-10 and CIFAR-00 have been taken for image classification. Fifteen convolutional neural network (CNN) variants categorized into light-weight and heavy-weight are trained for the prediction of these two datasets. Based on energy footprint, time, memory usage, Top-1 accuracy, Top-3 accuracy, model size, and model parameters. The study highlights that MobileNetV3-Small produces the best outcomes when compared to other trained models having low task latency and higher efficiency, making it highly suitable for edge environments where resources are scarce. Full article

To mitigate fossil fuel dependency and facilitate the transition towards a green economy, utilization of hydrogen energy has emerged as a paramount objective. Nevertheless, during transportation, this goal introduces novel challenges pertaining to material integrity, notably hydrogen embrittlement. This review systematically examines contemporary research on hydrogen embrittlement in natural gas pipelines conveying hydrogen blends and elucidates the hydrogen sources, permeation pathways, and embrittlement mechanisms. By scrutinizing the intrinsic material attributes and operational environments, this study provides an in-depth analysis of the pivotal factors influencing the susceptibility of pipeline steel to hydrogen embrittlement, thereby furnishing a theoretical foundation for the enduring safety of hydrogen pipelines. Full article

Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ_s′, with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ_s′, a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses. Full article

Tissue scaffolds are one of the main components of the tissue engineering triad, playing a vital role in tissue engineering. However, their production procedures heavily rely on solvent-intensive and energy-demanding methods. This raises serious questions about industrial-scale manufacturability, residual solvent toxicity to living health, and sustainability for nature and the environment. Therefore, the main aim of this study is to identify robust scaffolds that provide a suitable microenvironment for resident cells and promote tissue regeneration, while reducing waste through environmentally friendly production methods. In this context, the scalable and ecologically friendly production methods emerge as necessary alternatives as biodegradable polymer scaffolds are used in more therapeutic settings. Clinically applicable and green synthesis-based supercritical carbon dioxide (scCO₂) technologies, melt electrowriting (MEW), and solvent-free processing techniques are the main topics of this study for a critical analysis of biodegradable polymer scaffold production techniques. Scaffold structure–property correlations, polymer selection and interactions, production procedures, the benefits and drawbacks of existing fabrication technologies, and sustainability issues are discussed in detail. It aims to contribute a novel perspective and approach to literature by presenting and comparing production-oriented approaches as sustainable and green methods. The challenges in the development of biodegradable tissue scaffolds, along with the significance of green manufacturing techniques, are also revealed. The approach is designed to connect processing factors to scaffold features in addition to evaluating current technologies. This review tries to offer a framework for producing biodegradable polymer scaffolds in a sustainable and clinically implementable context. Full article