Search Results (14,020)

The evaluation of potentially toxic element concentrations (PTEs) in soils and plants is essential for understanding environmental quality and potential human exposure in areas affected by intense anthropogenic activity. This study addresses a research gap in the Valencian Region, focusing on soil–plant interactions of PTEs in urban and industrial environments. We assess the status of the soil–plant system in a region of the Valencian Community (eastern Spain) subjected to strong urban, industrial and agricultural pressure. A total of 55 soil samples and 47 plant samples were collected from agricultural, urban and industrial sites and analysed for soil properties, major elements (Al, Mg, Fe) and PTEs (As, Cd, Co, Cr, Cu, Li, Mn, Ni, Sr, V and Zn). Land use significantly influenced soil physicochemical characteristics, with clear differentiation among environments. Soil texture and organic matter were the main factors controlling element retention, while Al, Fe and Mg dominated the geochemical composition, consistent with Mediterranean calcareous soils. Correlation analyses revealed strong co-occurrence patterns among lithogenic elements (e.g., Fe-Al, r = 0.917 p < 0.01), soil texture and chemical properties, indicating a shared origin and preferential retention in the fine fraction and soil organic matter. Contamination indices identified potential environmental risk mainly associated with Cu, Pb, Sr and Zn, particularly in densely populated areas. Mean concentrations of Cd, Cr, Cu, Pb and Zn were, respectively, 0.63 mg kg⁻¹, 42.25 mg kg⁻¹, 31.49 mg kg⁻¹, 56.91 mg kg⁻¹ and 76.08 mg kg⁻¹. These elements exceeded Spanish regulatory reference values in several soils. Bioaccumulation indices indicated notable plant uptake of As, Sr and Zn, highlighting their potential for trophic transfer. Full article

Phenolic-rich extracts from Satureja montana were evaluated before and after diazomethane treatment to determine how chemical derivatization influences their antioxidant capacity. Native and modified extracts were compared experimentally by measuring total phenolic content, ferric reducing antioxidant power (FRAP), and Fe²⁺-chelating ability. EN1 exhibited the highest concentration of phenolic compounds, reaching 1278.54 mmol/g, whereas EM2 retained only 1.99 mmol/g. In the FRAP assay, reducing power followed the order EN1 (9.36) > EN2 (3.72) > EM2 (2.08), with EM2 still exceeding caffeic, chlorogenic, and ferulic acids. In contrast, the modified extracts showed superior metal chelating capacity, with EM1 and EM2 displaying IC₅₀ values of 0.70 and 0.82 mg/mL, respectively, both markedly lower than those of the native extracts and the pure standards. To rationalize these differences, a DFT study was performed at the B3LYP/6-311++G(d,p) level, examining 18 proposed phenolic acids and their methylated derivatives associated with the extracts. All methylation reactions were thermodynamically favorable, particularly for compounds 18 (−57.10 kcal/mol), 16 (−53.96), 6 (−53.34), and 3, 9, and 11 (−52.71). Solvent effects were found to be structure-dependent: caffeic acid showed BDE values of 72.29, 73.59, and 74.43 kcal/mol in the gas phase, water, and benzene, respectively, whereas syringic acid displayed values of 80.44, 77.09, and 80.65 kcal/mol under the same conditions. Likewise, the ionization potential of caffeic acid decreased from 180.09 kcal/mol in the gas phase to 133.26 kcal/mol in water and 154.22 kcal/mol in benzene. Among all analyzed species, methyl 3,4-dihydroxycinnamate exhibited the lowest BDE (71.60 kcal/mol) as well as the most favorable ΔG°r toward HOO• (−11.06 kcal/mol). Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

With the continuous promotion of the dual carbon target, effective control of high-concentration dust pollutants in industrial sites is of great value for the healthy creation of healthy industrial environments and efficient energy utilization. In this study, we used the spraying method to improve and prepare the dust removal material, polyphenylene sulfide (PPS) fiber filter material, and test the filtration performance, resistance characteristics, and dust-cleaning effect of the improved rGO-PPS material. The results showed that, compared with PPS filter material, rGO-PPS material significantly improved particle filtration efficiency, with a filtration efficiency 0.058–19.417% higher in the particle size range of 0.265–5.75 μm. The higher the spraying concentration of the composite filter material, the higher the filtration efficiency at the same particle size. The comprehensive filtration performance of rGO-PPS composite filter material with a concentration of 3 g/L was better, as it better met the requirements of “high efficiency and low resistance”. With an increase in dust load, the filtration resistance of the filter material showed a continuous upward trend. The dust peeling rate increased with an increase in blowback wind speed. When the blowback wind speed reached 0.3 m/s, the dust-cleaning effect of the filter material tended to stabilize. Under this condition, the dust peeling rate of PPS filter material was 61.58%, and the dust peeling rate of 3 g/L rGO-PPS composite filter material reached 74.52%. These research results provide an experimental basis and technical support for the development and engineering application of high-efficiency purification filter materials for industrial multi-source pollutants. Full article

Biochar amendment holds promise for improving saline soils, yet its efficacy is often constrained by the uncertainty of application rates. In this study, a large field trial and associated statistical modeling were conducted to explore the mechanisms by which biochar affects wheat yield in coastal saline soils of northern China. Results showed that biochar application significantly increased soil organic carbon (SOC) content (R²= 0.615, p < 0.001) but induced marked spatial heterogeneity across the field, with the coefficient of variation (CV) reaching 30.2%. Given the difficulty of uniformly applying biochar in the field, subplot-level SOC was used as a proxy for effective biochar distribution. Stepwise regression identified soil electrical conductivity (EC) as the dominant yield constraint (standardized coefficient = −0.69), rather than water and nutrients, and a quadratic relationship was observed between SOC and EC. Structural equation modeling (SEM) further suggested a trade-off: SOC was associated with higher yield through reduced bulk density (BD) (path coefficient = −0.603), whereas high SOC levels were also associated with increased EC under this coastal saline field setting (path coefficient = 0.243), thereby indirectly constraining growth. Consequently, the agronomic response showed a threshold-like transition: the peak wheat yield occurred at an SOC threshold of 13.87 g kg⁻¹ (equivalent to 44.41 t ha⁻¹), which exceeded the point of minimum salinity (11.71 g kg⁻¹, equivalent to ~29.90 t ha⁻¹ biochar). These results suggest that the agronomic benefit of biochar in saline soils depends on maintaining application within an estimated beneficial buffering zone. Full article

Bovine rotavirus (BRV) poses a major threat to the global cattle industry, driving significant morbidity and mortality in young calves. In Yunnan Province, China, BRV is the primary cause of neonatal calf diarrhea (NCD), yet the molecular epidemiology of circulating strains remains poorly understood. This study aimed to investigate the molecular characteristics of bovine rotavirus strains associated with a severe outbreak of the NCD on a local farm. Fecal samples were collected from 396 calves and screened for BRV by RT-PCR targeting the VP6 gene. Positive samples were subjected to virus isolation in MA104 cells, followed by whole-genome sequencing, phylogenetic analysis, and pathogenicity assessment in suckling mice. Of 396 samples, 85 tested positive for BRV, corresponding to an animal-level prevalence of 21.5% (95% CI: 17.5–25.8%), with four fatalities recorded. A strain designated as BRV-YN1-2021 was successfully isolated, exhibiting characteristic cytopathic effects, specific immunofluorescence, and typical rotavirus morphology by electron microscopy. Genomic analysis revealed the constellation G8-P[¹]-I2-R2-C2-M2-A3-N2-T6-E2-H3, identified as genotype G8P[¹]. BLAST analysis showed that four genomic segments shared the highest identity with deer rotavirus strains, five with human rotavirus strains, and two with bovine rotavirus strains. Phylogenetic analysis demonstrated close relationships with US deer strains, Japanese bovine strains, and human strains circulating in China. Experimental infection in suckling mice induced diarrhea and significant intestinal histopathology, degeneration of villous epithelial cells, goblet cell hyperplasia, and inflammatory infiltration. This study reports the first isolation of a G8P[¹] bovine rotavirus from a diarrhea outbreak in Chinese cattle. The multi-host genetic composition provides evidence of interspecies reassortment events, highlighting the zoonotic potential of BRV and emphasizing the need for continuous molecular surveillance to inform effective control strategies. Full article

To mitigate environmental impact, specifically the CO${}_2$ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include $LSTM$, $GRU$, $DNN$, $CNN1D-LSTM$, $BiLSTM$, attention mechanisms, $DT$, $SVM$, and $XGBoost$. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), and short-term flicker severity ($P_{st}$). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error ($RMSE$) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article

Excessive nitrate accumulation in leafy vegetables presents significant health risks, requiring sustainable strategies to optimize yield while minimizing nitrogen-related anti-nutritional factors in controlled environments. This study investigated the effects of varying LED light intensities 236.9 µmol·m⁻²·s⁻¹ (high), 189.8 µmol·m⁻²·s⁻¹ (medium), and 117.6 µmol·m⁻²·s⁻¹ (low) on nitrates (NO₃⁻) dynamics, growth, and biochemical composition in two Lollo Rossa lettuce cultivars, Carmesi and Carnelian, grown in NFT hydroponic systems. Conducted under constant temperature (20/18 °C day/night) and CO₂ (625 µmol·mol⁻¹) to isolate light’s influence, the experiment used a replicated design with three replicates per treatment, each including two cultivars. Morphological traits (plant height, rosette diameter, leaf number, biomass, root development) and biochemical parameters (nitrate and sugar contents) were assessed via mean comparisons, trends, and correlations. Results demonstrated that higher light intensity significantly suppressed nitrate accumulation in lettuce through enhanced assimilation and dilution effects linked to increased growth. Nitrate levels dropped to 2091.67 mg kg⁻¹ from 2443.33 mg kg⁻¹ in Carmesi and 2013.33 mg kg⁻¹ from 2515.00 mg kg⁻¹ in Carnelian. Negative correlations were observed between nitrate content and growth parameters: nitrates vs. fresh biomass (r = −0.89); nitrates vs. plant height (r = −0.79). Concurrently, it boosted carbohydrate content (Carmesi: 3.03 °Brix; Carnelian: 3.08 °Brix) and promoted vigorous growth, with Carmesi achieving superior metrics under high light (height: 22.12 cm, rosette diameter: 29.87 cm, fresh biomass: 206.88 g, root biomass: 19.58 g) compared to low light (17.45 cm height, 183.42 g biomass). Carnelian exhibited similar trends but prioritized root elongation. These findings underscore light’s role in regulating nitrate transporters and assimilation enzymes (e.g., nitrate reductase), offering a low-cost approach to reduce nitrate risks, enhance nutritional quality, and improve yield in controlled horticultural systems (CHS). Full article

Background/Objectives: This exploratory, cross-sectional study examined the relationships between personal factors and the nutrition intentions of participants in Healthy Choices for Your Recovering Body (HCYRB), a nutrition education program for limited-resource adults in substance use recovery (SUR). Methods: The study used a single-sample survey design where HCYRB participants (n = 2163) completed a post-participation survey. Linear regression models were tested to assess the effects of personal factors such as nutrition knowledge, cooking skills, self-efficacy beliefs and current nutrition and physical activity practices on participants’ nutrition intentions. Variables were measured with a self-reported survey that participants completed after participation in HCRYB. Results: The final model (R² = 0.39) showed statistically significant effects of self-efficacy beliefs, specifically, food resource management confidence and confidence to choose nutritious foods; current levels of water, soda, and energy drink consumption; physical activity level; and gender on nutrition intentions. Conclusions: Overall, the findings suggest that nutrition-related self-efficacy and current practices influence nutrition intentions of HCYRB participants. Future programs may focus on building participants’ nutrition-related confidence during SUR to enhance intentions and eventual behavior change. Such strategies may include programming activities that promote and affirm participants’ current positive nutrition-related behaviors (e.g., adequate consumption of water and involvement in physical activity). As participants master these healthy practices throughout the nutrition education experience, they will be more likely to gain confidence and motivation toward continuing the behavior throughout their recovery journey. Full article

The rotational evolution of pulsars is governed by torque mechanisms whose mathematical structure encodes fundamental symmetries of the underlying physics. We demonstrate that the standard spin-down equation $\dot{f}=-sf-r{f}^3-g{f}^5$ derives from a discrete antisymmetry requirement, namely invariance of the torque under reversal of rotation sense, which restricts the frequency dependence to odd integer powers. We show that physically motivated plasma processes systematically break this symmetry, introducing fractional frequency exponents: viscous Ekman pumping at the crust–superfluid boundary layer ($f^{3/2}$), magnetohydrodynamic turbulent dissipation via Kolmogorov and Sweet–Parker cascades ($f^{10/3}$, $f^{11/3}$), non-linear superfluid vortex dynamics ($f^{5/2}$), and saturated r-mode oscillations ($f^{7-2\beta }$). The central result is an exact analytical resolution of the long-standing Crab pulsar braking index puzzle: the observed $n=2.51\pm 0.01$, which has defied explanation for nearly four decades, emerges naturally from the superposition of magnetic dipole radiation ($\dot{f}\propto f^3$) and boundary layer Ekman pumping ($\dot{f}\propto f^{3/2}$), with analytically derived coefficients yielding a dipole-component surface field $B_p=6.2\times {10}^{12}$ G—higher than the standard $\sqrt{P\dot{P}}$ estimate of $3.8\times {10}^{12}$ G, because that formula conflates dipole and non-dipole torques, but lower than applying the Larmor formula to the full spin-down rate ($7.6\times {10}^{12}$ G), since $32.7\%$ of the total torque is non-radiative boundary-layer dissipation. We develop the Riemann–Liouville fractional calculus formalism for these equations, showing that fractional derivatives break time-translation symmetry through intrinsic memory effects, with solutions expressed in terms of Mittag-Leffler and Fox H-functions that interpolate continuously between exponential (fully symmetric) and power-law (scale-free symmetric) relaxation. Lambert–Tsallis $W_q$ functions with non-extensive parameter q encoding broken statistical symmetry enable equation-of-state-independent inference of neutron star compactness and tidal deformability. Our framework establishes a unified symmetry-based classification of pulsar spin-down mechanisms and predicts frequency-dependent braking indices evolving at rate $\mathrm{d}n/\mathrm{d}t\sim 2\times {10}^{-4}$ yr⁻¹, yielding $\mathrm{\Delta }n\approx 0.01$ over 50 years—testable with current pulsar timing programmes. The formalism provides a coherent theoretical foundation connecting plasma microphysics at the neutron star interior to macroscopic observables in electromagnetic and gravitational wave channels. Full article

Soot and black carbon (BC) are typically regarded as troublesome products of incomplete combustion; however, growing interest in circular economy strategies and sustainable manufacturing highlights their potential as secondary functional carbon materials, including additive manufacturing (AM). This review synthesises the recovery, upgrading, and valorization pathways for soot/BC and recovered carbon black (rCB), with a particular focus on streams captured by mandatory emission-control systems (e.g., diesel/gasoline particulate filters, electrostatic precipitators, baghouse filters, and chimney soot) and the requirements for transforming these heterogeneous residues into reproducible AM feedstocks. A two-stage approach was applied, combining (i) an analysis of the European Union regulatory context (waste classification, end-of-waste routes, and chemical safety obligations, including REACH) with (ii) a structured literature review of studies published in 2017–2026 indexed in the Web of Science and Scopus, culminating in a qualitative synthesis of 152 papers. Evidence indicates that scale-up is primarily constrained by strong compositional variability and contaminant burdens (ash, metals, and PAHs), which affect dispersion, rheology, and property reproducibility, necessitating robust standardisation and risk assessment. This review maps key preparation and upgrading strategies (e.g., classification, ash/metal reduction, and control of organic fractions) and discusses their relevance across AM routes such as FDM/FFF, SLS, DLP, and DIW. Overall, realising credible waste-to-value pathways requires aligning technical performance targets with regulatory compliance and developing consistent characterisation protocols to enable the safe and predictable use of soot/rCB-derived fillers in AM. Full article

Monascus sp. NP1 is a significant filamentous fungus with valuable properties for food industries. Initially isolated from the fermented rice product ang-kak, this strain is known for its ability to produce natural pigments. In this study, we therefore sequenced its genome together with the 26S rRNA D1/D2 domain and ITS fragment for identifying species of Monascus sp. NP1, and further conducted functional annotations of its overall genes related to metabolic capability and growth adaptation using comparative genomics. As a result, promisingly, the NP1 strain was identified as Monascus purpureus with the genome sequences, which was shown to be 23.54 Mb with a GC content of 49.01%. Genome annotation predicted 8031 protein-encoding genes. Comparative genomics between NP1 and 11 other related strains revealed 6024 core groups, 2204 accessory groups, and 5 strain-specific groups. Metabolic pathway analysis promisingly showed carbohydrate metabolism as the most enriched category, particularly central carbon metabolism involving key precursors, e.g., acetyl-CoA and pyruvate that support energy generation and the biosynthesis of pigments, fatty acids, and lipids. These findings highlighted the metabolic versatility and adaptive growth potential of M. purpureus NP1. This study provides key genetic insights into the cellular functions of M. purpureus NP1, laying the groundwork for exploring metabolic properties. It offers a comprehensive understanding for developing targeted applications of M. purpureus NP1 as an alternative fungal cell factory in food and nutrition. Full article

Turbid Class II inland waters such as Taihu Lake exhibit a “spectral uplift” effect driven by suspended particulate matter (SPM) scattering and colored dissolved organic matter (CDOM) absorption, which can obscure chlorophyll-a (Chl-a) signals in the visible–red-edge region and challenge retrieval under small-sample, collinear feature settings. Using multispectral observations from the BC-1A satellite (carrying the Lightweight Hyperspectral Remote Sensing Imager, LHRSI) and synchronous satellite–ground in situ measurements acquired over Taihu Lake in late autumn, this study proposes Chl-a-oriented PCA–RF (COP-RF), a leakage-safe inversion framework integrating correlation screening, principal component analysis (PCA), and random forest (RF) regression. Candidate band-combination features are generated, and PCA is applied for orthogonal compression to mitigate collinearity before RF learning. A stratified five-fold cross-validation based on Chl-a quantile bins is adopted, with screening, standardization, and PCA fitted only on training folds. COP-RF achieves stable performance under the current dataset ($R^2=0.671$, RMSE $=1.80\mathsf{\mu }$g/L, MAE $=1.25\mathsf{\mu }$g/L). Spatial inversion shows higher Chl-a near shores and bays and lower values in the lake center, consistent with Sentinel-2 hotspot ranks. Full article

The segregation of brittle Laves phases remains a critical bottleneck limiting the performance of additive manufacturing (AM) nickel-based superalloys. While its evolution is governed by complex transient physical fields within the melt pool, a quantitative kinetic correlation between processing parameters and microstructural features is currently lacking. In this study, a high-fidelity multiphysics numerical model was developed to establish a cross-scale mapping logic of “Process-Physical Field-Microstructure” by dissecting the global distribution of temperature gradient (G) and solidification rate (R) along the quasi-steady-state melt pool boundary. It is revealed that increasing the scanning speed synergistically enhances R while compressing G. Beyond driving a transition from oriented columnar dendrites to refined mixed-dendritic structures, this shift effectively blocks the continuous enrichment channels of Nb and Mo elements by compressing the “kinetic time window” for solute redistribution. Consequently, the morphology of the Laves phase is forced to evolve from a continuous interconnected chain-like network into dispersed isolated particles. This research clarifies the kinetic essence of microstructural evolution under non-equilibrium solidification, providing critical physical criteria for the precise intervention of deleterious phases and the regulation of microstructural consistency in high-performance AM components. Full article

Entropic cosmology with the Rényi entropy of the apparent horizon $S_R=(1/\alpha )ln(1+\alpha S_{BH})$, where $S_{BH}$ is the Bekenstein–Hawking entropy, is studied. By virtue of the thermodynamics–gravity correspondence, a model of dark energy is investigated. The generalized Friedmann equations for the Friedmann–Lemaître–Robertson–Walker spatially flat universe with barotropic matter fluid are obtained. We compute the dark energy density ${\rho }_D$, pressure $p_D$, and the deceleration parameter q of the universe. At some model parameters, the normalized density parameter of the matter ${\mathsf{\Omega }}_{m0}\approx 0.315$ and the deceleration parameter $q_0\approx -0.535$ for the current epoch, which are in the agreement with the Planck data, are found. Making use of the thermodynamics–gravity correspondence, we describe the late-time acceleration of the universe. The entropic cosmology considered here is equivalent to cosmology based on the teleparallel gravity with the definite function $F\left(T\right)$. The Hubble parameters are in approximate agreement (within 5 percents) with the observational Hubble data for redshifts $0.07\le z\le 1.75$ at the entropy parameter $\alpha \approx 0.305G{H}_0^2$. Full article