Search Results (4,617)

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

The interaction between airborne allergens and environmental microplastics is an emerging concern in the context of increasing plastic pollution and allergic disease prevalence. In this study, we investigated the molecular interaction between Cry j 1, the major allergen of Japanese cedar (Cryptomeria japonica) pollen, and polyethylene terephthalate (PET) microplastic surfaces using all-atom molecular dynamics simulations integrated with computational epitope selection analyses. The simulations showed that Cry j 1 adsorbs onto PET primarily through hydrophobic and van der Waals interactions, with residues Pro165, Ala227, Tyr228, and Val163 contributing prominently to surface association. Mapping of selected epitope regions indicated that several linear B-cell epitopes remained solvent exposed following adsorption, whereas two CD4⁺ T-cell epitope regions (T5 and T6) contributed more directly to PET interaction. PET adsorption was accompanied by moderate changes in conformational dynamics, including reduced residue-level flexibility and localized secondary-structure adjustments, while the overall protein fold remained structurally stable throughout the simulation. Small decreases in radius of gyration and solvent-accessible surface area suggested mild adsorption-associated compaction rather than major unfolding. These findings indicate that PET association can influence the structural dynamics and interfacial behavior of Cry j 1 without extensive disruption of its global architecture. Because the study is entirely computational, the immunological implications remain hypothetical and require experimental validation. Nevertheless, this work provides a molecular-level framework for understanding how airborne microplastics may influence allergen behavior and protein-surface interactions in polluted atmospheric environments. Full article

To improve the data quality of underwater point clouds acquired by UAV-borne LiDAR bathymetry, a scanline-based sliding window filtering method is proposed based on an analysis of scanline data characteristics. Scanline data of underwater point clouds are first extracted from raw point clouds, and the radius outlier removal algorithm is employed to eliminate outliers. Taking the acquisition time of scanline points as the X-axis and elevation as the Y-axis, a 3D problem is simplified into a 2D representation, and a sliding window is constructed along the scanline. Robust least-squares fitting is applied within the window. The median absolute deviation of the fitting residuals is adopted to calculate the terrain feature values for quantifying the terrain complexity, followed by an adaptive filtering threshold determination according to terrain feature values. Fine filtering of the individual scanlines is performed using a point-by-point sliding window. Experimental results demonstrate that the proposed method is adaptable to various terrain conditions, achieving a noise recall rate ≥ 96%, an overall filtering accuracy ≥99%, and an F1-score ≥ 0.9. Particularly, the precision rate in flat-water areas reached 97.37%. Overall, the proposed filtering method effectively separates noise points while preserving detailed terrain features and supports UAV-borne LiDAR bathymetry for mapping complex shallow-water regions. Full article

Urban flood modelling in heavily engineered catchments requires model structures that capture not only surface runoff processes but also hydraulic infrastructure and operational controls. This study applies a TELEMAC-2D rain-on-grid framework to two urban sub-catchments of the Emscher River (North Rhine-Westphalia, Germany) to quantify the relative effects of surface calibration, explicit infrastructure representation, and operational rules on the simulated flood response. A stepwise model development workflow was implemented, including land use-based calibration of Manning’s n and SCS Curve Numbers, explicit integration of culverts and bridges, and rule-based representation of retention basins and pumping stations. Model performance was evaluated using hydrograph shape, peak discharge, peak timing, event volume, and inundation behaviour across different antecedent moisture conditions (AMC). The results show that surface calibration alone was insufficient to consistently reproduce observed hydrographs. In the Rossbach sub-catchment area, integrating retention basins, pumping stations, and operational rules improved model performance from NSE = −0.129 under AMC I to NSE = 0.773 under AMC III. RMSE decreased from 3.380 to 1.515 m³ s⁻¹, peak discharge error from −6.198 to −0.492 m³ s⁻¹, and volume bias from −0.664 to +0.038. A targeted, routing-focused calibration further improved timing behaviour but increased volume bias, indicating residual deficiencies in the representation of rapid urban conveyance pathways. The findings show that reliable urban flood simulation in infrastructure-rich catchments depends not only on calibrating surface parameters but also on explicitly representing hydraulic structures, operational controls, and antecedent wetness conditions. Full article

Urban stormwater is often viewed as a drainage problem rather than a local water resource, even in areas where runoff capture could simultaneously reduce flooding and promote the reuse of non-potable water. This study develops, installs, and field-tests a decentralized, school-scale stormwater harvesting system that relocates permeable concrete, transforming it from a passive infiltration surface into a purpose-built capture and pretreatment interface. The system integrates a 3 m × 3 m permeable concrete slab with load-bearing sections, an impermeable underlayer to ensure controlled flow, a double-compartment sump for staged sedimentation and hydraulic damping, sequential filtration with sand/gravel and activated carbon, and a 5000 L storage tank. The prototype was implemented at CETis 105 in Querétaro, Mexico, and evaluated during its commissioning and operation in the 2023 rainy season. Field operations demonstrated reduced ponding in the catchment area and a reliable flow of runoff to the pretreatment units. In the sump compartments, apparent color decreased from 221 to 59 Pt-Co, turbidity from 46.8 to 12.9 NTU, and COD from approximately 30–35 to 15–18 mg·L⁻¹, corresponding to approximate pretreatment reductions of 73.3%, 72.4%, and 40–57%, respectively, before post-filtration. Conversely, the elevated pH, electrical conductivity, and total dissolved solids indicated interaction with fresh cementitious materials and dissolved ionic residues during initial operation, highlighting the need for curing, initial washing, and post-filtration verification before declaring compliance with reuse requirements. Therefore, the results support the feasibility of the proposed configuration as a decentralized, low-infrastructure architecture for localized runoff control and pretreatment, while confirming that full reuse validation still requires microbiological and post-filtration evaluation. The study provides a field-proven system design adaptable to school campuses and similar institutional environments for distributed stormwater management and non-potable water storage. Full article

Digital terrain models (DTMs) are required in many hydrologic, geomorphic, and ecological applications, yet widely used global elevation products often retain above-ground elevation contributions, particularly from vegetation canopies. This study investigates whether useful bare-earth terrain can be reconstructed from DSM-derived information alone at inference time. Rather than regressing terrain elevation directly, the proposed framework predicts the residual $\mathrm{DH}=\mathrm{DSM}-\mathrm{DTM}$ and reconstructs the DTM by subtraction. The model uses Copernicus DEM GLO-30 as the input source and augments it with CSF-derived priors and DSM-derived terrain features, including slope, aspect encoding, curvature, and local relief. Unlike multi-source terrain correction products that rely on external auxiliary datasets, all inference-time inputs in the proposed framework are generated from the DSM itself. A residual U-Net is trained with a weighted Huber loss together with gradient-consistency and DTM-slope-consistency constraints. Experiments across multiple regions in the central and southeastern United States show that the proposed method outperforms the compared public DEM products and baseline methods under a unified evaluation protocol. Relative to FathomDEM, it reduces the mean absolute error from 1.0445 m to 0.8538 m and the root mean square error from 1.6969 m to 1.4697 m on the study region test split, while also improving NMAD, P99, and Recall@5m. Performance on the geographically separate Arkansas region is similar to that on the in-region test split. Remaining errors are concentrated mainly in extremely steep terrain, densely vegetated areas, and cases with large residual heights. Full article

Nonlinear equations arise extensively in engineering and applied sciences, where efficient and reliable iterative solvers are required. This study introduces two fractional-order iterative schemes based on a common predictor–corrector structure: a Caputo-based method, NCFS${}_1$, and an Atangana–Baleanu–Caputo (ABC)-based variant, NFS${}_1^{abc}$. The proposed schemes incorporate a fractional order and two tunable parameters to improve flexibility in the iterative process. The local convergence behavior of the Caputo-based method is analyzed by means of fractional Taylor expansions, yielding an explicit error equation and convergence order, while analogous asymptotic considerations are discussed for the ABC-based variant. A dynamical-systems analysis is also performed through basins of attraction, the Convergence Area Index, and the Wada measure. Numerical experiments on application-motivated nonlinear models indicate that the proposed methods can provide faster error reduction, smaller residuals, and lower computational cost than selected existing fractional iterative schemes. These results suggest that the proposed framework is a flexible and effective approach for nonlinear root-finding problems, combining local convergence analysis with global dynamical assessment. Full article

Sustainable management of industrial solid waste is critical for a circular economy. This study presents a novel approach for valorizing blast furnace slag (BFS) into NaA zeolite through selective acetic acid leaching followed by hydrothermal crystallization. The leaching step selectively extracts Ca²⁺ and Mg²⁺ while efficiently retaining silicon and aluminum in the solid residue, producing a reactive aluminosilicate precursor that facilitates zeolite nucleation and growth. The effects of the silicon-to-aluminum molar ratio (n(Si)/n(Al)), crystallization temperature, and duration on the phase evolution and morphology were systematically investigated. The results demonstrate that phase-pure NaA zeolite with high crystallinity and a uniform cubic morphology can be obtained from precursor gels with n(Si)/n(Al) ratios of 0.5–1.25. Optimal synthesis conditions were identified as n(Na):n(Si):n(Al):n(H₂O) = 6:1:1:240 at 373 K for 8 h. The resulting zeolites exhibit a BET specific surface area of 52.1 m²/g, a micropore volume of 0.016 cm³/g, an average adsorption pore size of 4.7 nm, and an external specific surface area of 12.8 m²/g. It achieved near-complete removal of Cu²⁺ and high adsorption efficiencies for Pb²⁺ (77.78%) and Ni²⁺ (71.79%) from 250 mg/L solutions at 298 K with a dosage of 4.0 g/L, following the affinity sequence Cu²⁺ > Pb²⁺ > Ni²⁺, with all pairwise differences statistically significant at p < 0.001, using one-way ANOVA and Tukey’s HSD tests. The adsorption of three metal ions was most accurately described by the Freundlich isotherm and pseudo-second-order kinetic models, indicating heterogeneous multilayer chemisorption. The theoretical maximum monolayer adsorption capacities (q_max) were 307.67 mg/g for Cu²⁺, 246.09 mg/g for Pb²⁺, and 173.79 mg/g for Ni²⁺, whereas the kinetic equilibrium adsorption capacities (q_e) reached 62.69, 48.85 and 41.69 mg/g, respectively. This study demonstrates a value-added strategy for valorizing BFS into a micro-mesoporous adsorbent, advancing both circular resource utilization and environmental remediation. Full article

This study investigates the tide-dominated hydrodynamic response of Pulandian Bay to shoreline changes by comparing numerical simulations under shoreline conditions in 2004 and 2020 using the FVCOM. The results indicate that shoreline changes exert significant spatially heterogeneous effects on tidal dynamics. Channel narrowing caused by aquaculture enclosures and saltpan construction increased flow velocity near Boji Island. Meanwhile, tidal prism decreased during both spring and neap tides due to the loss of intertidal areas from northern reclamation, thereby weakening water exchange capacity. The outer bay, directly connected to the open sea, exhibits stronger water exchange than the relatively enclosed inner bay. However, the removal of seawalls in the inner bay enhanced flow in the central deep trough, resulting in improved water exchange capacity in 2020 compared to 2004. Shoreline changes also intensified tidal residual currents, with high-value Eulerian residuals mainly distributed in the northern and central parts of the bay. In addition, the restoration of tidal channels in the inner bay slightly increased residual current velocity. Overall, shoreline modification plays a critical role in regulating tidal hydrodynamic processes, providing important implications for coastal engineering and aquaculture management. Full article

After many years of stagnation in the treatment of acute myeloid leukemia (AML), there is currently a rapid move towards personalized medicine. Improvements in molecular diagnostics, risk assessment tools, targeted therapies, overall patient fitness assessments, and quality-of-life assessments have significantly changed how patients are treated. Genetic and molecular analyses, risk and health assessments, and measurable residual disease (MRD) monitoring are now integral to the treatment plan for evaluating patient responses and recurrence. In this regard, lower-intensity treatments are provided to older or unfit individuals. On the other hand, younger patients are usually subjected to curative therapies such as intensive chemotherapy to induce remission. Depending on their fitness and disease risk, they can be considered for hematopoietic cell transplantation, which is done after close observation for MRD. In addition, newer therapeutic drugs and immunotherapy techniques are being applied for patient management. Tremendous strides have been made in improving the efficiency of treatment programs in the relatively new area of personalized AML therapy, with a focus on functionality. Full article

This study comprehensively analyzed the compounded effects of climatic factors and non-climatic factors on vegetation dynamics in the northern Shaanxi Loess Plateau region in China. The objective was to provide robust scientific insights and a solid theoretical framework to support the long-term stability and sustainable development of the local ecosystem. The temperature vegetation dryness index was used to improve the water stress factor of the CASA model, so as to estimate the NPP of vegetation on the Loess Plateau of northern Shaanxi from 2000 to 2020. The temporal and spatial change characteristics of vegetation NPP and its relationship with climatic factors were analyzed using the coefficient of variation, the Mann–Kendall test of significance, and second-order partial correlation analysis. The partial derivative residual trend method was used to isolate the specific impacts of climatic factors and non-climatic factors on vegetation NPP. The results indicate the following: (1) The vegetation NPP shows a notable upward trend, with an annual growth rate of 9.4195 gC·m⁻²·a⁻¹ and a long-term average of 269.71 gC·m⁻², with the spatial distribution showing markedly high south, low north, and latitudinal zonation characteristics. (2) Vegetation NPP exhibits positive correlations with temperature, precipitation, and solar radiation. Among these factors, precipitation shows the strongest correlation with variations in vegetation NPP. (3) Non-climatic factors are the main factor affecting vegetation NPP across most parts of the study area, which is greater than the effect of selected climatic factors, and human activities may be the key component within non-climatic factors. Full article

The Qingshankou Formation (K₂qn) represents a key interval for lacustrine shale oil accumulation in the Songliao Basin. However, the spatial heterogeneity of organic-rich shales and their controlling mechanisms remain poorly constrained. Here, we investigate the Qijia–Gulong and Sanzhao sags by integrating drilling, well-log, geochemical, and mineralogical data to systematically evaluate source rock characteristics and their dominant controls. Based on well-log data from 442 wells, total organic carbon (TOC) was continuously predicted using an improved ΔlogR method. In addition, mineral compositions and lithofacies distributions were quantitatively characterized for representative wells in the eastern and western sags by combining X-ray diffraction (XRD) data with a deep residual shrinkage network (DRSN) model. The results reveal a dual depocenter pattern within K₂qn across the study area. The Qijia–Gulong Sag is characterized by thicker mudstone successions (30–600 m), higher sedimentation rates, and stronger stratigraphic continuity, whereas the Sanzhao Sag exhibits comparatively thinner deposits (30–300 m). Significant differences are also observed in organic matter type and thermal maturity: the Qijia–Gulong Sag is dominated by Type II₁ kerogen with higher maturity (R_o = 1.0%–1.5%), while the Sanzhao Sag mainly contains Type I kerogen with relatively lower maturity (R_o = 0.8%–1.3%). Despite this, TOC values in the Sanzhao Sag are markedly higher than those in the Qijia–Gulong Sag, with average values of 3.34% and 2.19%, respectively. These differences reflect the coupled control of palaeoenvironmental conditions and terrigenous input on organic matter enrichment. Elevated salinity and enhanced water-column stratification in the Sanzhao Sag promoted the development of reducing conditions favorable for organic matter preservation, resulting in higher TOC contents. In contrast, although the Qijia–Gulong Sag experienced high sedimentation rates and developed thick shale sequences, strong terrigenous input and dilution effects limited organic matter enrichment, while simultaneously leading to higher thermal maturity. Consequently, two distinct enrichment modes are identified in the study area: a “high-salinity stratification–efficient preservation” mode and a “high maturity–thick shale development” mode, which together govern the spatial heterogeneity of shale oil resources. Full article

The deposition of volcanic ash in areas affected by erupting volcanoes can contaminate the soil with heavy metals, thereby jeopardizing food security and public health. This study focused on the use of compost for the bioremediation of this type of contaminated soil and on evaluating the effectiveness of this remediation technique in a horticultural crop. To this end, composts made from organic waste generated in the areas with volcanic-ash-affected soil, such as crop residues, cow manure, and cheese whey, were used. The design and optimization of the composting process for these wastes were described using three piles with the same proportion of crop residues and cow manure but different doses of whey (pile 1: without whey, pile 2: whey diluted with water (1:2 (v:v)); and pile 3: with undiluted whey) and by monitoring the evolution of physicochemical and biological parameters throughout the compositing process. The effectiveness of the composts obtained for soil remediation was evaluated by assessing the physiological response of a lettuce crop in pots. Five treatments were used: control soil without fertilization, inorganic fertilization, and the three composts obtained. The main agronomic properties of the soil and heavy metal availability were measured, along with the physiological and chemical parameters of the lettuce, including growth and macronutrient and heavy metal content. The results obtained in the composting experiment showed that the addition of cheese whey only affected the rate of organic matter degradation and the salt content of the final composts, without negatively affecting the stability and humification of their organic matter or their plant nutrient content. In the pot experiment, all composts improved soil fertility and reduced the availability of Ni, As, Cd, and Pb, but this did not consistently reduce uptake into lettuce, except in the case of Pb. Therefore, it is advisable to adjust the compost application rate and optimize crop selection to minimize the impact of heavy metals on the food chain, thereby ensuring safe production. Full article

Background/Objectives: To evaluate the association between adjunct tendon vibration and changes over 12 months in dual-energy X-ray absorptiometry (DXA)-derived bone mineral density (BMD) T-score and bone turnover markers in older adults with osteoporosis receiving standard care in a non-randomised controlled cohort study. Methods: This 12-month prospective non-randomised controlled cohort study included 100 adults aged ≥60 years with DXA-confirmed osteoporosis recruited from orthopaedic clinics in the Greater Thessaloniki area. Fifty participants received adjunct tendon vibration therapy in addition to usual care, while 50 received usual care alone. Usual care consisted of calcium and vitamin D supplementation. The primary outcome was post-intervention BMD T-score, analysed using analysis of covariance (ANCOVA) adjusted for baseline T-score. Secondary outcomes included changes in bone turnover markers and calcium/phosphate metabolism. Sensitivity analysis was conducted using a linear mixed-effects model with repeated BMD measurements. Results: Baseline characteristics were comparable between groups. Over 12 months, the intervention group showed greater improvement in BMD T-score than controls (median change 0.90 [0.70–1.00] vs. −0.10 [−0.10–0.10], p < 0.001). The adjusted between-group difference was 0.871 (95% CI 0.773–0.968; p < 0.001). Results remained consistent after adjustment for age and sex. The mixed-effects model confirmed a significant group × time interaction (β = 0.922, 95% CI 0.806–1.038; p < 0.001). Bone resorption markers decreased more in the intervention group. The magnitude of the observed BMD improvement (~0.9 T-score units) is notable for a non-pharmacological intervention and should be interpreted cautiously. Conclusions: Adjunct tendon vibration was associated with a more favourable BMD trajectory and changes in bone turnover markers in older adults with osteoporosis receiving standard care. Given the non-randomised design and potential residual confounding, these findings should be interpreted as associative rather than causal. Full article

Trauma-related autobiographical memories can manifest as involuntary, vivid, emotionally charged intrusions that perpetuate avoidance, negative emotions, and functional impairment. While these memories are central to post-traumatic stress disorder (PTSD), they also occur across diagnoses and are often reported in depressive disorders, including treatment-resistant depression (TRD). Although trauma-focused psychotherapies are effective, their routine implementation can be limited by dropout, residual symptoms, and difficulty engaging patients with severe depression, dissociation, or complex comorbidities. Intranasal esketamine is an approved rapid-acting treatment for TRD and has been hypothesized to create transient conditions that may facilitate psychotherapeutic work on traumatic memories. This narrative review synthesizes clinical and translational evidence on ketamine and esketamine for PTSD and trauma-related symptoms, with particular attention to the distinction between intravenous ketamine studies, intranasal esketamine data, and studies combining these compounds with psychotherapy. Currently, the most robust evidence in this area comes from three randomised trials of intravenous ketamine for PTSD. In contrast, data on intranasal esketamine and psychotherapy-combination approaches are mainly from pilot studies, retrospective analyses, or case reports. We additionally propose a pragmatic, feasibility-oriented protocol integrating intranasal esketamine with a structured traumatic-memory intervention for TRD patients with clinically relevant trauma-memory symptoms. The novelty of the proposal does not lie in claiming efficacy, but in specifying a standardised imagery rescripting module and predefining two timing hypotheses. The proposal targets patients with TRD with relevant trauma-memory symptoms, and it embeds the intervention within existing esketamine-care infrastructure. Overall, the available literature supports mechanistic plausibility and preliminary feasibility more than clinical efficacy. The evidence base remains small, heterogeneous, and largely uncontrolled, and controlled studies are needed before efficacy claims can be made. Full article