Search Results (1,867)

Timely detection and monitoring of diseases in sweet potato crops are important for hunger alleviation and food security. This study aimed to evaluate the efficacy of the optimized field spectrometric reflectance thresholds in spatially partitioning sweet potato crops on the unmanned aerial vehicle (UAV) multispectral imagery based on infection types. A field survey was carried out to sample deformed leaves for laboratory diagnosis of possible identification of sweet potato leaf infection types. Laboratory analysis results revealed nutrient deficiency, SPVC-positive, fungi isolates (i.e., alternaria, bipolaris, fusarium, phoma), and mechanical damage as the causes of leaf deformation. Overlap analysis results revealed reflectance overlaps across all leaf deformation types, making it difficult to spatially partition sweet potato crops based on deformation types. Instead, sweet potato crops were spatially partitioned by considering the minimum and maximum thresholds acquired from the whole dataset. Area covered by deformed sweet potato leaves in blue, green, red, red edge and NIR were found to be 11.91%, 28.71%, 43.66%, 46.41% and 30.6% respectively. Coefficient of determination results revealed poor classification results, with R² value of 0.23, 0.19, 0.28, 0.17 and 0.63 for blue, green, red, red edge and NIR respectively. However, the NIR spectral band yielded R² value closer to the acceptable value of 0.7. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Green fair-faced concrete (GFFC) is characterized by low surface porosity and small pore sizes and is widely used in architectural concrete engineering. It remains challenging to meet the appearance quality requirements of GFFC with conventional mix ratios and additives. This paper introduces double-mix defoamers and air-entraining agents into GFFC slurry to further refine the internal bubble size of GFFC slurry, optimize the surface pore structure, and thereby improve the apparent morphology of cured GFFC. The effects of double-agent doping on the slump, mechanical strength, shrinkage performance and impermeability durability of GFFC were also investigated. The results show that, compared with the baseline, after binary doping of the defoamer and air-entraining agent, the slump loss over time of GFFC slurry has been significantly reduced; the average porosity of GFFC is 0.132%, and the maximum average pore diameter is only 1.01 mm, which is decreased by 57.35% and 67.68%, respectively; the 45 day shrinkage of the GFFC doped with 3‱ defoamer and 4‱ air-entraining agent is 338 × 10⁻⁶ with a decrease of 33.98%, and the resistance to 84d chlorine ionization migration coefficient is 1.3 × 10⁻¹² m²/s with a decrease of 38.09%. These outcomes can effectively contribute to the pore refinement and apparent morphology improvement of GFFC doped with a binary defoamer and air-entraining agent. Full article

This paper asks whether the development of national artificial intelligence ecosystems contributes to greener economic performance, and whether public governance shapes that relationship. The analysis covers a balanced panel of 36 advanced and emerging economies from 2017 to 2023. We capture general national artificial intelligence ecosystem development through a multidimensional index built on five pillars (innovation, economic diffusion, skills, policy, computing infrastructure) aggregated by within-pillar principal component analysis, and estimate the model by two-step System-GMM, with instrumentation anchored in Wooldridge endogeneity tests robust to heteroscedasticity. Green growth is highly path-dependent, with an autoregressive coefficient close to 0.96 that corresponds to an annual convergence speed of 4.5 percent. Government effectiveness contributes positively and significantly. The artificial intelligence ecosystem index displays no detectable independent effect once persistence and endogeneity are addressed, and its interaction with government effectiveness is similarly indistinguishable from zero, a result that calls for caution in narratives expecting artificial intelligence to deliver sustainability gains on its own. Full article

The synergistic transition toward digital transformation and green development has been widely regarded as a core pathway to achieving sustainable development in knowledge production. Using balanced panel data from 39 economies covering 2018–2024, this study employed a two-way fixed-effects model to examine the associations of the energy efficiency of digital infrastructure and the energy structure with carbon intensity (CI). The findings showed that: (1) Reductions in Power Usage Effectiveness (PUE) values were significantly associated with higher macro-level CI (coefficient = −2.1564, p < 0.05), which is consistent with the possibility of a rebound effect in the digital sector. Further, time-series discontinuity tests further suggested that the surge in AI computing power, especially in 2023–2024, may have coincided with a structural shift in this relationship (Chow test, p < 0.05). (2) A Panel Threshold Regression (PTR) identified an optimal renewable energy threshold at 59.82%. Crucially, the carbon rebound effect remained highly significant across both high and low green power regimes, demonstrating that supply-side energy transition alone cannot fully absorb the exponential carbon footprint of digital expansion. Furthermore, Instrumental Variable (IV-2SLS) and Placebo Break Tests confirmed the strict validity of these findings. (3) The emission-reduction benefits related to digital knowledge spillovers appeared to be subject to time lags and a possible energy lock in effect, while current environmental policies and carbon pricing mechanisms appear to impose insufficient constraints. This study provides a crucial quantitative framework for monitoring and evaluating the environmental sustainability of the ICT sector. By highlighting the limitations of pure supply-side greening and the necessity of absolute carbon caps, our findings offer integrated policy approaches to align the exponential growth of Generative AI with global sustainable development goals. Full article

Curcuminoids from Curcuma longa L. (curcumin, demethoxycurcumin, bisdemethoxycurcumin) are attractive bioactives yet constrained by low water solubility and chemical instability. Herein, we introduce a Supramolecular Deep Eutectic Solvent (SupraDES) as a “Janus” green platform, combining extraction and stabilization with a subsequent solvent-to-material strategy. Eight NaDES/SupraDES formulations based on choline chloride (ChCl) or betaine with glycerol (Gly) or citric acid (CitA), with/without β-cyclodextrin (βCD), were assessed. The extinction coefficients of the most promising solvents were extrapolated at 425 nm for the UV–vis quantification of curcuminoids, to determine extraction performance. The SupraDES ChCl:Gly:βCD gave the best performance during the first solvent screening, improving at the same time the bioactive stability (after 30-day, 47.5% loss vs. 62.8% of ChCl:Gly alone). Subsequent microwave-assisted extraction (MAE) optimization identified 80 °C as the optimal process temperature, with near-equilibrium reached within 15 min (3139.4 µgCurc/gEXT). Peleg modelling (R² = 0.997) indicated a fast extraction rate and limited benefit from longer residence times. Finally, the curcuminoid-loaded SupraDES was incorporated into polyvinyl alcohol (PVA) networks crosslinked with CitA and 2,5-bis(hydroxymethyl)furan (BHMF); thermal analysis confirmed the formation of a stable crosslinked structure. To the best of our knowledge, this is the first report of a βCD-based SupraDES acting as a Janus platform that couples supramolecular extraction of lipophilic bioactives with their direct incorporation into bio-based polymeric materials, exemplifying an integrated green chemistry approach aligned with circular bioeconomy principles. Full article

This study examines the determinants of carbon dioxide (CO₂) emissions across 25 European Union countries over the period 2000–2021, with particular emphasis on the roles of environmental taxation and green innovation in shaping environmental sustainability. The analysis is grounded in ecological modernization theory, endogenous growth theory, and the Environmental Kuznets Curve hypothesis, which collectively explain the long-run and dynamic interactions between environmental policy, economic activity, structural transformation, and environmental outcomes. To ensure robust empirical inference, this study applies a comprehensive econometric framework that accounts for cross-sectional dependence, heterogeneity, non-stationarity, cointegration, and endogeneity. The empirical strategy begins with Pesaran cross-sectional dependence tests and slope heterogeneity diagnostics, followed by second-generation panel unit root tests (Pesaran CADF/CIPS) and Westerlund cointegration tests to establish the existence of long-run equilibrium relationships among the variables. Long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Panel Autoregressive Distributed Lag (ARDL) model is employed to capture both short-run dynamics and long-run adjustment processes, while the System Generalized Method of Moments (System GMM) estimator addresses potential endogeneity, reverse causality, omitted variable bias, and dynamic persistence in CO₂ emissions. The empirical results indicate that environmental taxation has a positive and statistically significant association with CO₂ emissions, suggesting that current fiscal environmental policies in EU-25 countries may not yet be sufficiently effective in discouraging pollution-intensive activities. In contrast, green innovation is found to significantly reduce CO₂ emissions, underscoring the critical role of innovation-driven environmental investment and technological progress in improving environmental quality. Economic growth, exports, and urbanization are associated with higher emissions, while imports contribute to emission reductions, reflecting differences between domestic production-based effects and trade-related structural adjustments. The System GMM results further confirm the persistence of CO₂ emissions over time and validate the robustness of the long-run relationships identified by alternative estimators. Likewise, the CCEMG and Panel ARDL results support the stability and consistency of the findings under conditions of cross-sectional dependence and heterogeneous country dynamics. Taken together, the results highlight the importance of integrating environmental taxation with green innovation policies, innovation-driven investment, and sustainable trade policies to achieve long-term emission reductions in the European Union. This study contributes to the environmental economics literature by providing robust empirical evidence using second-generation panel econometric techniques that explicitly address cross-sectional dependence, heterogeneity, and endogeneity in the analysis of environmental sustainability. Full article

Natural deep eutectic solvents (NADESs) coupled with ultrasound-assisted extraction (UAE) were evaluated as an extraction technique for phenolic compounds from Crataegus monogyna leaves and flowers. Nine well-known hydrophilic NADESs were investigated as green extraction media, and their extractability was assessed in terms of major individual compounds, total flavan-3-ols and proanthocyanidins, as well as antioxidant activity. Water and ethanol–water solutions (70% and 50%, v/v) were used as reference solvents. An HPLC method was developed and partially validated for the quantitative determination of key individual components, including chlorogenic acid, hyperoside, vitexin, vitexin-2″-O-rhamnoside, and vitexin 2″-O-(4‴-O-acetyl)-rhamnoside. The subsequent chemometric analysis of the datasets revealed that the NADES systems choline chloride:urea:water (1:1:6) and choline chloride:glucose:water (5:2:25) exhibited pronounced extraction performance for all investigated metabolites, while preserving high antioxidant activity of the extracts. Pearson correlation coefficients and corresponding p-values demonstrated strong and statistically significant relationships among the majority of the investigated parameters: solvents’ physicochemical properties, the yield of phenolic compounds, and the antioxidant activity of the hawthorn extracts. The results highlight the potential of choline chloride based NADESs containing urea or glucose as alternative solvents for the green production of hawthorn-derived ingredients for functional foods, nutraceuticals, and herbal preparations, thereby contributing to the development of scalable, application-oriented extraction technologies. Full article

The assessment of the “Do No Significant Harm” (DNSH) principle in European Union (EU)-funded projects currently relies on narrative justification, which generates subjective evaluations, inconsistent results, and high administrative effort. This study aims to develop an operational framework and project-level tool to standardize how environmental impact is measured across multiple sectors and project types. The methodology applies a stepwise, non-compensatory approach, combining typology-based filtering, financial thresholds derived from carbon intensity and sustainability coefficients, checklists, spatial analysis, and quantitative indicators such as the circular economy transition metric. Each environmental objective is evaluated independently, ensuring that compliance cannot be offset by positive performance in other areas. The framework was preliminarily validated using a dataset of 1406 projects implemented in Romania, indicating its potential to distinguish low-risk from high-risk projects, reduce evaluator subjectivity, and improve the proportionality of analytical effort. While the tool is tested on Romanian case studies, its design allows for application across various European funding programmemes. The tool supports early-stage screening, encourages green procurement, and aligns project planning with EU environmental objectives, including climate mitigation, adaptation, water resource protection, pollution prevention, circular economy, and biodiversity conservation. The proposed methodology provides a clear, reproducible, and practical approach, offering evaluators a consistent mechanism for DNSH compliance verification and integrating environmental protection into project design and implementation. Full article

Accurate radiometric calibration is essential for high-resolution optical satellite sensors with limited onboard calibration capability, such as the FORMOSAT-5 (FS-5) Remote Sensing Instrument (RSI). The Radiometric Calibration Network (RadCalNet) provides standardized nadir-equivalent surface reflectance for vicarious calibration, but its direct application to off-nadir observations can introduce systematic biases over non-Lambertian surfaces. This study presents a BRDF-corrected vicarious calibration framework for the FS-5 RSI. The framework integrates RadCalNet data with an empirical BRDF lookup table built from in situ multi-angle measurements at Railroad Valley Playa, which is then propagated through 6S radiative transfer simulation. Applied to four FS-5 overpasses, BRDF correction reduced the median relative error of the calibration coefficient $K_0$ from 13–17% to 1–4% across all five spectral bands, providing a quantitative assessment of calibration improvement. The downstream impact was evaluated over an FS-5 La Crau scene. Scene-mean top-of-atmosphere (TOA) reflectance differences across the four multispectral bands ranged from 8.62% (NIR) to 10.99% (Green). The near-infrared reflectance of vegetation (NIRv), a proxy for gross primary production, showed a scene-mean relative difference of 7.88% ± 7.32%, with localized values exceeding 20% in densely vegetated areas. These results establish quantitative calibration-accuracy requirements for sensors relying on vicarious calibration and demonstrate the operational necessity of BRDF correction for reliable TOA reflectance and vegetation product retrieval. Full article

Automated analysis of peripheral nerve ultrastructure is bottlenecked by heterogeneous electron microscopy (EM) datasets, where varying staining protocols and resolutions create domain shifts that confound deep learning. To address this, we developed a generalized segmentation pipeline. Using a custom pre-processing workflow (CLAHE and noise suppression) integrated into ZEISS Arivis Pro, we standardized inputs across three disparate domains: traditional osmium-based Palade, lanthanide-based “green” Uranyl-free method, and low-resolution Ellisman preparations. A U-Net trained on a highly constrained 15-image composite dataset achieved peak internal Intersection over Union (IoU) scores >0.95 for myelin and Schwann cells. Crucially, during open-world, zero-shot inference on an expanded independent testing cohort (N = 40), the model sustained robust Dice Similarity Coefficients of 0.854 for myelin and 0.597 for mitochondria. This demonstrates that integrating classical image standardization with deep learning effectively mitigates EM domain gaps, enabling comprehensive 3D multi-organelle reconstructions from challenging data. To ensure transparency and community utility, the pre-trained models and standardization scripts are provided in a public, open-access repository. Ultimately, this pipeline supports the transition to sustainable, non-toxic EM protocols and provides a robust pathway for unlocking historical clinical archives for automated organellomics. Full article

This study aims to explore the preparation process and properties of unburned lightweight aggregate using red mud synergistically with fly ash, granulated blast-furnace slag, and other multi-source solid wastes. Curing regimes and alkali-activated systems were controlled. Their effects on physical properties and environmental safety of lightweight aggregate were systematically evaluated. Results show that curing temperature and alkali activator exert significant synergistic effects on physical properties of lightweight aggregates. Steam curing performs better than standard curing. Performance improves with increasing steam temperature. Sodium silicate solution with a modulus of 1.0 is determined as the optimal activator. Under 90 °C steam curing, Sample D2 achieves the best overall performance. Its cylinder compressive strength reaches 6.92 MPa. 1 h water absorption is 14.8%. Softening coefficient is 0.93. Porosity is as low as 31.07%. Microscopic analysis reveals that higher curing temperature significantly accelerates the hydration reaction of the RMLWA system. It promotes the formation of abundant cementitious products such as C-S-H gel. These products fully fill internal pores and microcracks of the aggregate. A dense three-dimensional network skeleton structure is finally formed. For environmental safety, heavy metal leaching concentrations of steam-cured samples are generally lower than those of standard-cured samples. This study realizes high-value resource utilization of industrial solid wastes. It also provides a new technical route for the development of green building lightweight aggregate. Full article

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

The urban tree canopy is an important resource that spans public and private property and whose form and quantity change over short distances. Although remote sensing and deep learning approaches have been used to map urban tree canopy, the high cost of commercial imagery and the technical complexity of model development have limited their adoption by urban forestry practitioners. We developed a structured and reproducible deep learning workflow optimized for freely available USDA National Agriculture Imagery Program (NAIP) imagery. The workflow incorporates a reproducible U-Net segmentation model for canopy delineation and a YOLOv9e object detection model for individual tree identification, enabling complementary estimation of the canopy extent and individual tree locations. Across two neighborhoods in Los Angeles, the optimized U-Net achieved a Dice coefficient of 0.824 for canopy segmentation, while YOLOv9e reached an F1-score of 0.687 for individual tree detection on a held-out test set with 17,466 annotated trees. A data sufficiency experiment showed that model performance stabilizes when approximately 130 trees are annotated per 320 × 320 pixel (px) tile, corresponding to about 25,379 training and 2641 validation labels, providing a practical target for annotation effort. Additional experiments demonstrate a structured workflow for spatial sampling, training data requirements, and the use of model inferences to estimate tree canopy extent and individual tree locations. The workflow also shows encouraging evidence of transferability to previously unseen urban areas without retraining. By relying solely on NAIP-optimized approaches, this new workflow bridges the gap between complex deep learning techniques and the practical needs of urban foresters; empowers local stakeholders to create accurate, affordable, and timely urban tree inventories; and fosters data-driven decision-making for the sustainable management of urban green infrastructure. Full article

Food loss and waste (FLW) represent a significant source of resource inefficiency in water-scarce economies. This study quantifies the water footprint (WF) of FLW in Saudi Arabia using product-level blue, green, and grey WF coefficients from the Water Footprint Network database. Our analysis covers 3.997 million tons of FLW across 19 commodities grouped into cereals, fruits, vegetables, and meat. Results indicate that FLW is associated with a total blue and green WF of 7.3 billion m³, of which 2.1 billion m³ is blue water directly associated with managed water resources. The blue WF is equivalent to approximately 20% of agricultural water withdrawals and 62% of domestic water demand. Despite constituting only 13% of total FLW by mass, meat products account for 53% of the total water footprint, driven by their exceptionally high water intensity (7474 m³/ton). The consumption stage dominates water losses, contributing 56% of the total blue and green WF. Based on alternative water supply cost benchmarks, the blue WF embedded in FLW corresponds to an indicative production-cost equivalent ranging from 1.03 to 6.5 billion SAR. A 25% reduction in FLW could save over 500 million m³ of blue water annually. These findings demonstrate that FLW reduction represents an important supporting strategy for water resource management and provides a quantitative basis for prioritizing intervention across food groups and supply-chain stages. Full article