Search Results (627)

Thanks to both endogenous and exogenous antioxidants (AOs), the antioxidant defense system ensures redox homeostasis, which is crucial for protecting the body from oxidative stress and maintaining overall health. The food industry also exploits the antioxidant properties to prevent or delay the oxidation of other molecules during processing and storage. There are many classical methods for assessing antioxidant capacity/activity, which are based on mechanisms such as hydrogen atom transfer (HAT), single electron transfer (SET), electron transfer with proton conjugation (HAT/SET mixed mode assays) or the chelation of selected transition metal ions (e.g., Fe²⁺ or Cu¹⁺). The antioxidant capacity (AOxC) index value can be expressed in terms of standard AOs (e.g., Trolox or ascorbic acid) equivalents, enabling different products to be compared. However, there is currently no standardized method for measuring AOxC. Nanoparticle sensors offer a new approach to assessing antioxidant status and can be used to analyze environmental samples, plant extracts, foodstuffs, dietary supplements and clinical samples. This review summarizes the available information on nanoparticle sensors as tools for assessing antioxidant status. Particular attention has been paid to nanoparticles (with a size of less than 100 nm), including silver (AgNPs), gold (AuNPs), cerium oxide (CeONPs) and other metal oxide nanoparticles, as well as nanozymes. Nanozymes belong to an advanced class of nanomaterials that mimic natural enzymes due to their catalytic properties and constitute a novel signal transduction strategy in colorimetric and absorption sensors based on the localized surface plasmon resonance (LSPR) band. Other potential AOxC sensors include quantum dots (QDs, <10 nm), which are particularly useful for the sensitive detection of specific antioxidants (e.g., GSH, AA and baicalein) and can achieve very good limits of detection (LOD). QDs and metallic nanoparticles (MNPs) operate on different principles to evaluate AOxC. MNPs rely on optical changes resulting from LSPR, which are monitored as changes in color or absorbance during synthesis, growth or aggregation. QDs, on the other hand, primarily utilize changes in fluorescence. This review aims to demonstrate that, thanks to its simplicity, speed, small sample volumes and relatively inexpensive instrumentation, nanoparticle-based AOxC assessment is a useful alternative to classical approaches and can be tailored to the desired aim and analytes. Full article

Manufacturing agglomeration (MA) is an important driving force for both sustained economic expansion and structural upgrading. Understanding whether and how MA contributes to energy conservation and pollutant mitigation is essential for promoting China’s green transition and offers valuable insight for emerging economies pursuing sustainable growth. The paper first theoretically examines the mechanisms linking MA, energy intensity (EI), and pollutant emission (PE). To overcome the regression bias caused by the heterogeneity of pollutant types among cities, the comprehensive index of PE is constructed. The empirical analysis yields two principal findings. First, MA significantly reduces PE, and this relationship remains robust after a series of tests. Second, EI plays a significant mediating role between MA and PE, that is, MA can achieve the reduction targets of PE by reducing EI. Therefore, in addition to its established role in fostering economic growth, MA should be utilized for its environmental advantages. Policymakers should give greater weight to the capacity of MA to enhance energy conservation and emission reduction, so as to stimulate the positive interaction among MA, EI, and PE, and thereby formulate more differentiated policies. Full article

Improving rye (Secale cereale) yield under increasing climatic stress remains a major challenge for sustainable cereal production. We examined whether early-vegetative physiological, biochemical, and molecular traits can predict final grain yield in hybrid-breeding components. Across three consecutive seasons, 14 genotypes were evaluated under controlled cold-greenhouse conditions for chlorophyll fluorescence, pigment content, hydrogen peroxide (H₂O₂), salicylic acid (SA) levels, and the expression of selected antioxidant and defence-related genes, and these traits were related to yield components. Across years, photosynthetic efficiency (F_v/F_m, Rfd), chlorophyll content, and foliar H₂O₂ emerged as the most consistent predictors of kernel mass, spike number, and kernel number. In contrast, non-photochemical quenching, SA, and carotenoid contents showed weak or inconsistent relationships with yield. These findings indicate that light-harvesting capacity, PSII performance, and oxidative balance are central to reproductive success in rye. The stability of these trait–yield correlations across three seasons provides the basis for a physiological robustness index for hybrid rye, with predictive models achieving accuracies up to R = 0.51. This work demonstrates the potential of using a compact set of early-stage, high-throughput physiological traits to accelerate selection for stress-resilient, high-yielding rye cultivars. Full article

Sparsity-driven regularization has undergone significant development in single-image restoration, particularly with the transition from handcrafted priors to trainable deep architectures. In this work, a geometric prior-enhanced deep image prior (DIP) framework, termed DIP-MC, is proposed that integrates mean curvature (MC) regularization to promote natural smoothness and structural coherence in reconstructed images. To strengthen the representational capacity of DIP, a self-attention module is incorporated between the encoder and decoder, enabling the network to capture long-range dependencies and preserve fine-scale textures. In contrast to total variation (TV), which frequently produces piecewise-constant artifacts and staircasing, MC regularization leverages curvature information, resulting in smoother transitions while maintaining sharp structural boundaries. DIP-MC is evaluated on standard grayscale and color image denoising and deblurring tasks using benchmark datasets including BSD68, Classic5, LIVE1, Set5, Set12, Set14, and the Levin dataset. Quantitative performance is assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. Experimental results demonstrate that DIP-MC consistently outperformed the DIP-TV baseline with 26.49 PSNR and 0.9 SSIM. It achieved competitive performance relative to BM3D and EPLL models with 28.6 PSNR and 0.87 SSIM while producing visually more natural reconstructions with improved detail fidelity. Furthermore, the learning dynamics of DIP-MC are analyzed by examining update-cost behavior during optimization, visualizing the best-performing network weights, and monitoring PSNR and SSIM progression across training epochs. These evaluations indicate that DIP-MC exhibits superior stability and convergence characteristics. Overall, DIP-MC establishes itself as a robust, scalable, and geometrically informed framework for high-quality single-image restoration. Full article

Chlorophyll content is a key physiological indicator reflecting photosynthetic capacity, and the Soil–Plant Analysis Development (SPAD) meter is a commonly used tool for its rapid and non-destructive estimation. Hyperspectral imaging (HSI) is a non-destructive technique that captures fine spectral characteristics and thus holds great potential for high-throughput phenotyping and early stress detection. This study aimed to explore the potential of HSI combined with ensemble learning (EL) to estimate SPAD of rapeseed seedlings under different durations of waterlogging. Hyperspectral images and corresponding SPAD values were collected from six rapeseed cultivars at 0, 2, 4 and 6 days of waterlogging. The mutual information was employed to select the top 30 most relevant spectral and vegetation index features. The EL model was constructed using partial least squares, support vector machine, random forest, ridge regression and elastic net as the first-layer learners and a multiple linear regression as the second-layer learner. The results showed that the EL model showed superior stability and higher prediction accuracy compared to single models across various genotypes and waterlogging treatment datasets. As waterlogging duration increased, the overall model accuracy improved; notably, under 6 days of waterlogging, the EL model achieved an R² of 0.79 and an RMSE of 3.27, indicating strong predictive capability. This study demonstrated that combining EL with HSI enables stable and accurate estimation of SPAD values, therefore providing an effective approach for early stress monitoring in crops. Full article

Maize lodging is a major factor limiting maize grain yield. Potassium (K) fertilization is known to reduce lodging, but the potential impact of straw return on lodging resistance remains unclear. A two-year field experiment was conducted with five K levels (0, 30, 60, 90, and 120 kg ha⁻¹) under straw return (S1) and no straw return (S0). Maize yield, stem lodging resistance index (SLRI), crushing strength (CS), stem morphological and physicochemical characteristics, and soil nutrient levels were measured. Compared to S0, increased K application with S1 significantly enhanced the SLRI (16.0%) and CS (19.8%) across two years, which was due to the improvement of stem morphological (internode dry weight, length, and plumpness) and physiological characteristics (soluble sugar, cellulose, lignin, phenylalanine ammonia-lyase (PAL), tyrosine ammonia-lyase (TAL), and cinnamyl alcohol dehydrogenase (CAD)), especially the third internode. The highest SLRI and CS of each internode of the two straw treatments were obtained in K120, while no significant difference between K90 and K120 was observed for these indicators under the same straw treatment. Grain yield and soil available K content of S1 were higher by an average of 5.0% and 18.0% than S0, respectively. Compared to K0, K120 increased the yield and soil available K content by 17.3% and 18.8%, but there was no significant difference with K90. As a result, S0 and S1 both achieved a soil K balance when the surplus rate was close to zero at a K input of 90 kg ha⁻¹. Fitting analysis indicated that, compared to S0, the K application rate of S1 was reduced by an average of 11.8% while maintaining a K surplus rate of 0, which means S1 could enhance soil potassium cycling and supply capacity but also reduce fertilizer input. In conclusion, straw return combined with K fertilizer (e.g., 90 kg ha⁻¹) is an effective strategy to enhance lodging resistance and maintain maize yield by improving stem morphological and physicochemical characteristics. Full article

This study introduces a decision-oriented framework integrating fresh-state rheology, standardized post-cracking performance, and cradle-to-gate embodied carbon for steel-fiber-reinforced concretes incorporating recycled and commercial fibers. The motivation lies in achieving mechanical efficiency while reducing the environmental burden of cementitious composites. Mixtures were produced with water-to-binder ratios between 0.40 and 0.60, fiber dosages of 15–45 kg/m³, and 50% GGBS replacement to mitigate binder-related carbon emissions. Equal-workability comparisons were conducted at 15 kg/m³ using ICAR-based static yield stress measurements, whereas higher dosages were evaluated without rheology-based adjustment. Post-cracking performance was assessed through residual flexural strengths at CMOD = 0.5 and 2.5 mm (f_R1, f_R3) and CMOD-based toughness indices. Embodied performance was quantified using the embodied-carbon-per-performance (ECP) index, normalized by f_R3. Results indicate that recycled fibers exhibit greater fresh-state resistance but slightly lower residual capacities under equal workability, while commercial fibers achieve competitive ECP at 15 kg/m³. Increasing fiber dosage improved toughness yet intensified the trade-off between ECP and mechanical gain. The framework highlights that optimized binder composition and fiber type selection can yield carbon-efficient, structurally resilient composite systems. Full article

Mining is an important industry for the achievement of sustainable development goals (SDGs), but it results in a significant amount of degraded land worldwide, thereby affecting local social and ecological sustainability. Little is known about the extent to which this degraded land adheres to the current SDGs. In this study, based on public geographic information data, the status of SDG 11 (Sustainable Cities and Communities) and SDG 15 (Life on Land) for global mine sites was comprehensively assessed. The results show that (1) the global aggregation index for SDG 11 and 15 in mining areas increased from 23.94 in 2000 to 24.48 in 2020, generally exhibiting a positive trend. (2) For SDG 11, all four indicators indicate improvement, suggesting enhancement of the sustainability of cities and communities surrounding global mined land, as well as urban development, mining activities, and economic growth. In contrast, regarding SDG 15, there were noticeable improvements in the water body area and land reclamation ratio, but the forest coverage ratio and net ecosystem productivity significantly declined, indicating continued stress on ecosystems caused by mining. (3) Less than 1% of mines globally met the green grade in SDG 11, and around 97% were categorized as red grade. For SDG 15, no mines reached the green grade, and at least 99.74% were categorized as red grade mines. (4) Globally, the status has exhibited obvious spatial clustering, and the region with a better status is in the equatorial region. There has been obvious spatial heterogeneity within countries, and mine sites near urban areas have had a better status according to these SDGs. The main influencing factors on the status of mines, according to the SDGs, include the degree of mining disturbance, ecosystem recovery capacity, and urban expansion. Overall, the global status of mines according to the SDGs is far from expectation, indicating a considerable gap from achieving sustainable mining and necessitating efforts to improve human habitats and restore ecosystems in mining areas. Future endeavors should focus on strengthening site specific assessment and long-term monitoring of the global SDGs in mining areas to provide foundational data and scientific evidence for sustainable mining and the realization of SDGs. Full article

A biodegradable, lightweight substrate for facility-based stereoscopic planting was developed via a one-step polyurethane foaming process. The substrate was synthesized by incorporating a biomass mixture of bamboo charcoal and cassava flour into a polyurethane foam matrix. This study investigated the effects of varying the content ratios of polyether polyol, isocyanate, bamboo charcoal powder, and cassava flour on the structural and functional properties of the composite foam. Results indicated that the biomass blend significantly influenced the foam’s physicochemical properties, water retention capacity, hardness, and elasticity. Specifically, bamboo charcoal powder enhanced the porosity and degradation rate of the foam, whereas the swelling of cassava flour upon water absorption improved the matrix’s resilience and cohesion. A polyether polyol/isocyanate ratio of 4:1 yielded a substrate with superior physicochemical properties, water retention capacity, germination rate, seedling index, and plant dry weight. Subsequently, the optimal overall performance was achieved at a biomass/polyol–isocyanate ratio of 1:3. This optimal formulation exhibited a degradation rate of 6.24 ± 0.94%, porosity of 66.07 ± 1.10%, and water retention capacity of 86.03 ± 1.59%. Consequently, it also produced the highest seed germination rate (84 ± 5.16%), seedling index (12.49 ± 1.94), and mature plant dry weight (4.00 ± 0.51 g). Microscopic analysis confirmed that the biomass addition refined the substrate’s pore structure, leading to greater uniformity and stability of the internal pores. This enhancement reduced the foam’s susceptibility to collapse and improved its elasticity and cohesion, thereby making it more amenable to mechanized handling and planting operations. Full article

Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly been applied in crop growth monitoring. However, the small, thick, waxy leaves of olive, together with its complex canopy structure and dense arrangement, may reduce estimation accuracy. To identify sensitive features related to olive leaf chlorophyll and to evaluate the feasibility of UAV-based estimation methods for olive trees with complex canopy structures, UAV multispectral orthophotos were acquired, and leaf chlorophyll was measured using a SPAD (Soil Plant Analysis Development) meter to provide ground-truth data. A dataset including single-band reflectance, vegetation indices, and texture features was built, and sensitive variables were identified by Pearson correlation. Modeling was performed with linear regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Partial Least Squares Regression (PLSR), and Support Vector Machine (SVM). Results showed that two spectral bands (green and red), one vegetation index (TCARI/OSAVI), and twelve texture features correlated strongly with SPAD values. Among the machine learning models, XGBoost achieved the highest accuracy, demonstrating the effectiveness of integrating multi-feature UAV data for complex olive canopies. This study demonstrates that combining reflectance, vegetation indices, and texture features within the XGBoost model enables reliable chlorophyll estimation for olive canopies, highlighting the potential of UAV-based multispectral approaches for precision monitoring and providing a foundation for applications in other woody crops with complex canopy structures. Full article

(1) Background: In the context of rapid urbanization and climate change, Chinese traditional villages are facing severe challenges such as deterioration of hydrological environment, weakened social resilience, and degradation of cultural heritage. (2) Methods: This paper took Baoyan Village in Zhenjiang City, Jiangsu Province as the research object and constructs a research framework of “assessment of historical hydrological resilience–diagnosis of current problems–construction of enhancement strategies”, aiming to explore the paths and driving mechanisms for enhancing the resilience of traditional villages. The spatio-temporal evolution of historical hydrological resilience in Baoyan Village was quantitatively evaluated by establishing a three-dimensional resilience index system of “ecological governance–social adaptation–cultural continuity”, combined with the Analytic Hierarchy Process (AHP) and GIS spatial overlay technology. (3) Results: The study found that ① The hydrological resilience zoning of Baoyan Village presented spatial differentiation characteristics of “core vulnerability-marginal resilience”, and the high-risk area was concentrated in the cultural building density area along the old Tongji River in the historical town area, indicating that this area requires key flood protection and resilience construction; ② this paper constructed a composite evaluation system of “Ecological Governance–cultural inheritance–social adaptation”, and the total score after evaluation was 0.67, indicating that the overall HHRI of Baoyan Village has declined. Specifically, the scores for Ecological Governance Resilience and Cultural Heritage Resilience were 0.48 and 0.46, respectively, reflecting a significant decrease compared to historical scenarios. Conversely, the score for Social Adaptation Resilience was recorded at 1.05, suggesting an improvement in this dimension. This enhancement can be attributed to advancements in water infrastructure and increased levels of community organizational support, which have bolstered the village’s capacity to withstand flooding events. ③ The integrity of weir fields, the transmission of traditional disaster prevention knowledge, and the stability of natural river channels are the main factors hindering the improvement of resilience systems. (4) Conclusions: Based on the assessment results, this study proposed the resilience enhancement path of “ecological space reconstruction-traditional water management wisdom activation–cultural resilience empowerment” for this case, and constructed a four-pronged driving mechanism consisting of government guidance, community participation, technology empowerment, and industrial synergy for implementation. Practice has shown that through specific strategies such as restoring the weir and field system, constructing sponge village units, and developing the rain and flood cultural experience industry, the key obstacle factors of the village can be effectively addressed, and the goals of flood safety and cultural inheritance can be achieved in a coordinated manner. This case provides an empirical reference that combines historical wisdom with modern technology for understanding the evolution of human–water relationships and the enhancement of resilience in traditional villages, and its research framework and methods are also of reference value for similar villages. Full article

The commitment to peak carbon emissions before 2030 and the goal of achieving carbon neutrality by 2060 have placed carbon emission efficiency (CEE) at the center of China’s low-carbon development strategy. Although national policies have promoted energy conservation and technological upgrading, substantial heterogeneity in CEE persists across cities of different administrative and economic tiers. To examine this heterogeneity, we construct a city-level CEE index based on a stochastic frontier analysis (SFA) framework that explicitly treats CO₂ emissions as an undesirable output. Based on a panel dataset covering 274 prefecture-level cities grouped into five categories from 2006 to 2022, we find that first-tier cities such as Shanghai and Beijing, with advanced technological capacity and strong support by the emission trading scheme (ETS), have shown an upward trend in improving their CEE, while middle-range cities remain locked in carbon-intensive trajectories. In particular, the lowest-level fifth-tier cities show a decreasing trend, implying a bipolarization of urbanization among cities. To address this bipolarization by urbanization, we examine the governance mechanisms of CEE using a second-stage Tobit model and find that governance factors related to urbanization—such as high labor quality and intensive land development in larger cities—have contributed to the widening gap in CEE. This implies that mitigating the negative consequences of urbanization is essential for achieving a carbon-neutral economy. Full article

Capsaicin, a natural bioactive compound, has attracted wide interest for its potential health benefits. However, its rapid metabolism and strong irritancy upon oral administration have greatly limited its further application. To address these issues, this study developed a nanoparticle delivery system using corn Zein and Brassica rapa L. polysaccharide (BP) as carriers, with capsaicin (CAP) as the core. The optimized formulation (BP:Zein = 1:2, Zein:CAP = 2.5:1, mg/mg) produced stable, uniform spherical nanoparticles with an average particle size of 203.05 nm, a polydispersity index (PDI) of 0.138, a zeta potential of −44.9 mV, an encapsulation efficiency of 54.03%, and a drug loading capacity of 184.57 μg/mg. Fourier transform infrared spectroscopy (FTIR), fluorescence spectroscopy (FS), X-Ray diffraction, scanning electron microscope (SEM), and transmission electron microscopy (TEM) analyses confirmed that CAP was successfully encapsulated, forming nanoparticles through hydrogen bonding and hydrophobic interactions between CAP and Zein. The obtained nanoparticles displayed regular spherical morphology and uniform size distribution. Compared with single-layer Zein–CAP nanoparticles, BP–Zein–Capsaicin (BZC) nanoparticles exhibited markedly improved stability under different pH, ionic strength, and storage conditions. In vitro simulated digestion showed a sustained-release profile, with 36.76% of CAP released after 4 h. The anti-inflammatory experiment showed that both the nanoparticle and free capsaicin groups significantly inhibited xylene-induced acute ear edema in mice, with the medium- and high-dose nanoparticle groups exhibiting stronger anti-inflammatory effects than the free capsaicin group. These findings suggest that the nanoparticle delivery system effectively enhances the anti-inflammatory activity of capsaicin, possibly by improving its stability, achieving sustained release, and enhancing its bioavailability in vivo. Overall, capsaicin-loaded Brassica rapa L. polysaccharide–Zein nanoparticles combine small particle size, high drug loading, and excellent stability, providing a promising strategy for functional food development and targeted bioactive delivery. Full article

Accurate modeling of pavement performance is vital to maintaining safe, reliable, and sustainable transportation infrastructure. This review synthesizes current approaches to pavement deterioration modeling, with emphasis on key influencing factors, performance indicators, and methodologies employed within Pavement Management Systems (PMS). Primary deterioration drivers, including traffic loading and environmental stressors, are analyzed for their impact on degradation patterns. Performance indicators such as the Pavement Surface Evaluation and Rating (PASER), Pavement Condition Index (PCI), and International Roughness Index (IRI) are evaluated for their effectiveness in capturing pavement condition and guiding maintenance decisions. Modeling techniques are broadly categorized into deterministic, probabilistic, and intelligent (machine learning–based) frameworks to illustrate the evolution of predictive approaches. Across these approaches, the notion of symmetry can be interpreted as the balance and consistency achieved between model assumptions, input variables, and predicted pavement behavior, while asymmetry represents deviations caused by uncertainty, variability, and nonlinearity inherent in real-world conditions. Recognizing these symmetrical and asymmetrical relationships helps unify different modeling paradigms and provides insight into how each framework handles equilibrium between accuracy, complexity, and interpretability. The review also highlights persistent challenges in data availability, quality, and standardization. Notably, the increasing adoption of machine learning reflects its capacity to handle high-dimensional and spatiotemporal datasets. Recommendations are proposed to improve the robustness, scalability, and transparency of future deterioration models, thereby enhancing their role in data-driven, resilient, and cost-effective pavement management strategies. Full article

The growing demand for sustainable proteins has driven interest in Limnospira platensis (Spirulina) due to its high protein content. However, the presence of the cell wall limits the availability and recovery of proteins within it. Conventional alkaline extraction is widely applied but often results in low yields and excessive solvent consumption. This study compares the efficiency and functional properties of Spirulina proteins extracted using an alkaline method and high-pressure homogenisation (HPH) at 20, 50, 80 and 100 MPa. Following isoelectric precipitation, proteins were collected in precipitate and supernatant fractions and characterized for yield, solubility, phycobiliproteins content, emulsifying and foaming properties, water– and oil–holding capacity, thermal stability and rheological behaviour. Microscopy confirmed progressive cell disruption with increasing homogenization pressures. HPH at 50 MPa increased protein extraction by 28% compared to alkaline extraction and significantly (p < 0.05) improved solubility, oil-holding capacity, foaming and emulsion properties. Phycobiliproteins, particularly C–phycocyanin, were more efficiently recovered in HPH supernatants, achieving a higher purity index than the alkaline method. Rheological analysis showed weak gel-like network formation, whereas excessive mechanical stress reduced functionality. Overall, HPH emerges as an interesting method for obtaining Spirulina proteins with enhanced technological properties; however, pressure optimisation is required to avoid denaturation and functionality loss. Full article