Search Results (16,286)

Human skin and hair act as multifunctional barriers but are highly sensitive to environmental pollutants originating from air, water, and cosmetic products. Epidemiological studies report that exposure to particulate matter (PM_2.5–PM₁₀), nitrogen oxides (NO_x), and volatile organic compounds increases the risk of skin and hair disorders. For instance, women in high-traffic areas (N = 211) show significantly more pigment spots and nasolabial wrinkles compared to those in rural areas (N = 189), indicating accelerated skin ageing. Children aged 9–11 exposed to PM₁₀, benzene, and NO_x exhibit increased incidence of atopic dermatitis. Systemic exposure to dioxins causes chloracne, while co-exposure to polycyclic aromatic hydrocarbons (PAHs) and UVA radiation elevates skin cancer risk. Psoriasis flares are associated with mean pollutant concentrations over the 60 days preceding flare events in 957 patients, and hyperpigmentation prevalence increases in populations exposed to traffic-related PM and ROS-inducing pollutants. Hair loss is linked to oxidative stress from PM and PAHs absorbed on hair fibers, with in vitro studies showing keratinocyte apoptosis in scalp hair follicles. This review evaluates natural adsorbents such as zeolites, clays, activated carbon, and polyphenol-rich plant extracts for anti-pollution cosmetic formulations. Adsorption capacities range from 60 to 150 mg·g⁻¹ depending on the pollutant, with removal efficiencies of 30–55% in model topical systems. Mechanisms include ion exchange, surface adsorption, hydrophobic interactions, and radical scavenging. Incorporating 2–5% w/w of these adsorbents in cosmetic formulations significantly reduces pollutant deposition on skin and hair. These findings support the development of evidence-based, sustainable anti-pollution cosmetic strategies that quantitatively mitigate environmental stressor effects. Full article

At the current stage, water resource shortages and significant regional disparities in resource distribution severely restrict China’s food security. Existing research primarily focuses on resource use efficiency, while lacking a systematic framework to distinguish between equality and equity in the coupled distribution of irrigation water, grain production, and nitrogen pollution across major river basins. The core objective of this study is to utilize the Concentration Index (CI) to construct a unified equity assessment framework, quantify the evolution of equality and equity in irrigation water use, grain production, and nitrogen loss to surface water in different river basins in China from 1992 to 2017, and determine the key influencing factors. For positive production resources, a distribution that benefits low-income groups is equity, while for pollution burdens, this distribution pattern is inequity. The results show that water shortages in Northern China have intensified, and higher income groups have obtained excessive benefits. The distribution of grain production has shifted from favoring higher income groups to favoring low-income groups, with the Concentration Index changing from 0.214 to −0.052, indicating an enhancement in equity. Irrigation water use has shown a certain degree of improvement, with the CI dropping from 0.023 to 0.017. However, nitrogen loss to surface water has exacerbated environmental inequality, with the CI dropping from 0.10 to 0.03, indicating that pollution burdens have shifted to low-income groups. Changes in equity across the country are driven by a small number of high-intensity grain production areas, and the key influencing factors include food security policies, urbanization, population size, and nitrogen fertilizer application. An asymmetric coupling relationship exists between water resource shortages and equity, and the regional economic foundation determines the formation of synergy or trade-offs. The findings underscore the necessity of transitioning from efficiency-focused to equity-focused agricultural governance in China. Targeted policies should include cross-basin ecological compensation mechanisms, differentiated technology promotion strategies, and integrated water–food-pollution management systems to balance food security, environmental protection, and social justice. Full article

The global demand for high-quality food is rising due to the increasing population, necessitating intensive farming practices that often involve the extensive use of pesticides, which can accumulate in soils and enter the food chain. This study explores the use of synthesized and commercial graphenes for the removal of kresoxim-methyl (KM), a common strobilurin fungicide, from soil. Adding only 1 wt% of graphene to soil enhanced its partitioning capacity from about 4.77 mg/g for unamended soil to 9.57 mg/g, indicating effective immobilization and reduced environmental risk. The adsorption efficacy was notably higher in materials rich in oxygen-containing functional groups and with a large surface area, highlighting the significance of surface characteristics and porosity. The adsorption followed pseudo-second-order kinetics, underscoring the importance of surface heterogeneity in KM adsorption. Full article

Efficient ortho–para hydrogen conversion is essential to suppress spontaneous heat release and boil-off losses during cryogenic liquid hydrogen storage and pre-liquefaction processes. In this study, a novel catalyst-filled wavy plate-fin heat exchanger (CFHE) is proposed to simultaneously enhance heat transfer and ortho–para hydrogen conversion under cryogenic conditions. Compared with conventional straight-fin configurations, the wavy-fin structure introduces controlled flow perturbations and increased specific surface area, thereby intensifying transport processes. Three-dimensional computational fluid dynamics (CFD) simulations, using the SST k–ω turbulence model, coupled with an ortho–para hydrogen conversion kinetic model were performed to quantitatively investigate the effects of key geometric parameters and catalyst loading on hydrogen conversion, heat transfer, and pressure drop within a Reynolds number range of 941–1577 and a temperature range of 35–20 K. Within the same CFHE configuration, the para-hydrogen fraction remains nearly unchanged without catalyst but increases significantly with catalyst loading. However, the catalyst reduces the global average Colburn j-factor by about 25%. Despite higher friction losses, the outlet–inlet temperature difference decreases to about 0.866 times that of the non-catalyst case, indicating improved temperature uniformity. A comprehensive performance index e, integrating heat transfer enhancement, flow resistance, and conversion efficiency, was introduced and optimized using a genetic algorithm. The optimized CFHE achieves an outlet para-hydrogen fraction exceeding 95% of the thermodynamic equilibrium value while maintaining hydrogen entirely in the gaseous phase to avoid catalyst deactivation. Overall, the catalyst-packed wavy channel configuration demonstrates superior conversion efficiency, enhanced thermal uniformity, and improved overall performance compared with straight-fin structures, providing quantitative design guidance for high-performance heat exchangers in cryogenic hydrogen liquefaction systems. Full article

The manipulation of the electrocatalyst nanoarchitecture, particularly transition metal compounds, regarding size, shape, facets, and composition, significantly enhances the electrocatalytic activity in energy transformations. This study introduces a novel methodology for the precipitation of mesoporous nanoparticles of nickel tungstate (meso-NiWO₄) using direct chemical deposition from a template of Brij^®78 surfactant liquid crystal. Physicochemical analyses revealed the formation of amorphous meso-NiWO₄ nanoparticles with dual sizes of 10 ± 3 and 120 ± 8 nm and a specific surface area of 34.2 m²/g, exceeding that of nickel tungstate deposited in the absence of surfactant (bare-NiWO₄, 4.0 m²/g). The meso-NiWO₄ nanoparticles exhibit improved electrocatalytic stability, reduced charge-transfer resistance (R_ct = 1.11 ohm), and a current mass activity of ~365 mA/cm² mg at 1.6 V vs. RHE during the electrolysis of urea in alkaline solution. Furthermore, by employing meso-NiWO₄ in a two-electrode urea electrolyzer, a remarkable 4.8-fold increase in the cathodic hydrogen concurrent production rate was achieved (373.40 µmol/h at a bias potential of 2.0 V), compared to that of the bare-NiWO₄ catalyst. The exceptional urea oxidation electroactivity and the enhanced hydrogen evolution rate arise from substantial specific surface area and mesoporous structure, facilitating effective charge transfer and mass transport through the meso-NiWO₄ catalyst. Using the surfactant liquid crystal template for electrocatalyst synthesis enables a one-pot deposition of diverse nanoarchitectures and compositions with high surface area at ambient conditions for an improved electrocatalytic and hydrogen green production process. Full article

Water levels in the Great Salt Lake (GSL), UT, USA, have been declining overall since 1989, leading to a 70% decrease in surface area. To understand GSL’s future, we seek to image fresh groundwater input and lithologic variation along the lake’s boundary. Determining the amount of groundwater recharge into GSL is crucial for lake management but currently unknown. During the Fall of 2024 and Spring 2025, we conducted 16 electrical resistivity tomography (ERT) and six transient electromagnetic (TEM) surveys along the southern shore of GSL between Burmester Road (to the West), Saltair, and Lee’s Creek (to the East). These measurements indicate a low-resistivity layer consistent with brine pore-water, with variable thickness ranging from 7.1 ± 0.1 m at Burmester to 9.6 ± 0.2 m at Saltair. The Saltair region shows a high-resistivity layer, consistent with a 4.4 ± 0.05 m thick layer of mirabilite. This layer contains vertical conduits that allow saline pore-water to upwell onto the surface forming evaporite deposits. Near Lee’s Creek, we find evidence of high resistivities consistent with fresher groundwater as shallow as 2.8 ± 0.03 m, where increased permeability along the paleo-Jordan River corridor may provide a path for groundwater recharge from the Wasatch Mountains. Full article

Glaciers in the Canadian Arctic Archipelago (CAA) contribute significantly to sea-level rise, yet sparse in situ data limit regional climate assessments. This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six CAA glaciers, utilising 9920 Landsat 8/9 and Sentinel-2 scenes. Glacier surface cover types (snow and bare ice) were mapped via machine learning, and SLA was extracted using elevation-binning and Snow-Elevation Histogram Analysis (SEHA). Elevation data were obtained from ArcticDEM v3; positive degree days (PDD) from Eureka, Pond Inlet, and Pangnirtung were used to characterize melt-season forcing. Satellite-derived SLA was validated against equilibrium-line altitude (ELA) observations from White Glacier. All glaciers exhibit a characteristic seasonal SCA cycle: maximum extent in June, minimum in August, and partial recovery in September, with extreme anomalies in 2020. Annual peak SLA correlates positively with summer warmth; sensitivities to PDD were 2.56, 0.67, and 0.83 m (°C d)⁻¹ for White, Highway, and Turner glaciers, respectively. Hypsometry strongly modulates climatic sensitivity: glaciers with limited high-elevation area (e.g., BylotD20s, Turner) frequently lose their accumulation zones in warm years. At White Glacier, SLA replicates interannual ELA variability with high correlation and lower error using the elevation-bin method (mean bias +53 m; RMSE 177 m) compared with SEHA (+165 m; 339 m). Meteorological records indicate significant summer and winter warming at Eureka, with increasing PDD; precipitation trends are spatially variable. A regionally calibrated, quality-assured elevation-bin method produces objective and transferable SLA time series, suitable for ELA estimation in data-sparse Arctic settings. The SLA–PDD relationship and hypsometry-dependent responses highlight increasing stress on accumulation zones under continued warming. Reporting SLA uncertainty and image quality, alongside expanded field observations, will enhance Arctic-wide glacier monitoring. Full article

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S²D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S²D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S²D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR. Full article

This study investigates orange peel valorisation through KOH pre-treatment and high-temperature pyrolysis (800 °C) to develop a highly porous activated char for the efficient removal of phenolic compounds, specifically 2,4-dinitrophenol (DNP) and aminophenol (AP), from water. The main objective of the study is to synthesise high-surface area activated char from orange peel and investigate its performance for the adsorption of DNP and AP from water. The synthesised adsorbent exhibited a Brunauer–Emmett–Teller (BET) specific surface area of 965 m²/g, contributing to its excellent phenol adsorption efficiency. Batch adsorption experiments were performed, and a maximum removal efficiency of 99% and 92% was observed at pH 4 and 7 with initial concentration 50 mg/L, contact time 60 min, and adsorbent dosage 0.6 g/L, for DNP and AP, respectively. The adsorption process was described by the Langmuir isotherm model (R² = 0.99), indicating monolayer adsorption and followed pseudo-second-order kinetics, achieving a maximum adsorption capacity of 366 mg/g for DNP and 341 mg/g for AP. Furthermore, DFT analysis revealed that DNP possesses a lower HOMO-LUMO energy gap (−0.54 eV), favouring a stronger adsorption interaction, whereas AP exhibited a relatively higher energy gap (−0.27 eV), corresponding to its comparatively lower adsorption capacity. Overall, the findings demonstrates that a single step chemical-thermal conversion of orange peel into biochar-based adsorbent offers a sustainable pathway for the removal of phenolic compounds from water. Full article

Background/Objectives: Magnetite nanoparticles represent promising candidates for a broad spectrum of biomedical applications, ranging from in vitro diagnostic assays to in vivo imaging, hyperthermia, and targeted drug and gene delivery, with some nanoagents already approved for clinical use. A critical determinant of their functionality is the nanoparticle coating, which facilitates beneficial interactions within biological systems. In the context of tumor-targeted therapeutic delivery, key design parameters—particularly surface coatings—can be optimized to enhance treatment efficacy by modulating blood circulation kinetics, biodistribution, and other critical properties. However, current preclinical screening methods primarily rely on cell culture models to identify potential nanocarriers, yet these systems often poorly correlate with actual in vivo performance. This discrepancy highlights the necessity of incorporating more biologically relevant testing platforms, such as high-throughput in vivo assays. Methods: In this work, we employed an original magnetic particle quantification (MPQ) technology to systematically evaluate the blood circulation kinetics and biodistribution patterns for magnetite nanoparticles with 17 different coatings across multiple organs and tissues, including the liver, spleen, lungs, kidneys, heart, tumor, brain, peripheral blood, muscle, and bone. This methodology offers high sensitivity, user-friendly operation, and provides quantitative measurements across a broad dynamic range of nanoparticle concentrations. These advantages enabled high-throughput acquisition of precise blood circulation and biodistribution data. In addition, histological analysis was conducted to evaluate nanoparticle penetration depth within tumor tissue. Results: Here we conducted a comprehensive study of the effect of 17 different polymer-, lectin-, and small molecule-based coatings on the behavior of magnetite nanoparticles in vivo. For each type of obtained nanoparticles, we implemented passive targeting as well as magnetic targeting, the latter using an external magnetic field localized in the tumor area. Conclusions: The collected dataset provides critical insights into how surface modifications influence nanoparticle performance in complex biological systems, offering valuable guidance for optimizing therapeutic nanocarrier design. Full article

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, primarily employing the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) architecture, we integrated multi-source SAR imagery and DEM data to generate SR products that reveal distinct signatures in areas containing dense archeological remains and clearly delineate shallow, buried anthropogenic features. We further developed deep learning classification models that combine SR SAR and DEM inputs and trained them on known archeological site locations. This approach enabled the detection of previously undocumented structural features distributed along the coastal margin and throughout the Supe Valley. Our findings indicate that enhancing publicly available remote sensing datasets with advanced SR techniques can provide cost-effective and practical high-resolution archeological data, compared to data mining using aerial photography and high-resolution commercial satellite imagery, in terms of both cost and obstacle penetration. Full article

Fire suppression in the upper U.S. Midwest has led to the expansion of flammable coniferous ladder fuels, necessitating precise tracking of conifer species basal area (BA) for fire risk management. This study benchmarks four subset-selection pipelines—xPLS, GA-xPLS, RF-xPLS, and SVR-xPLS—to optimize the fusion of high-dimensional, collinear data from Sentinel-2, Landsat-9, and LiDAR sensors. Using 141 field plots in Minnesota’s Kawishiwi Ranger District of the Superior National Forest, we evaluated 175 predictors against eight BA response variables. Results show that RF-xPLS provided the superior accuracy–parsimony trade-off, achieving the highest pooled R² (≈0.86) and lowest error with a compact 27-predictor block. GA-xPLS ranked second, excelling for specific species such as Pinus resinosa. The most effective predictors combined SWIR-based moisture indices, red-edge/NIR structure, and a single LiDAR-derived surface of vertical-structure (quadratic mean height). Our findings demonstrate that integrating machine learning selection engines with multi-sensor fusion substantially enhances the scalability and precision of forest inventory and fuels monitoring. This comparative framework offers practical insights for sustainable management and fire risk mitigation in northern temperate–boreal forests. Full article

Metal–organic frameworks (MOFs) have received significant attention as multifunctional platforms for chemical sensing due to their adjustable porosity, high specific surface area, and modular chemical architecture, which allow for customized host-guest interactions and signal transduction. This work presents a critical overview of recent advances in electrochemical and optical sensors based on MOFs for the detection of emerging contaminants, including toxic metal ions, pharmaceutical residues, and industrial pollutants in environmental and biological matrices. Special emphasis is placed on the underlying sensing mechanisms, such as redox activity, charge transfer, and luminescence modulation, as well as the main challenges related to structural stability under realistic operating conditions, including variations in pH, humidity, and temperature. Furthermore, the development of hybrid and hierarchical architecture based on MOFs is discussed as an effective strategy to improve sensitivity, selectivity, and long-term robustness. Finally, the perspective highlights how to optimize sensor performance and enable more reliable and scalable applications in monitoring emerging contaminants. Full article

Balancing tourism development with ecological integrity remains a central challenge in the management of protected areas. This study proposes a spatial framework that integrates the Outdoor Recreation Suitability Index (ORSI) and the Landscape Ecological Risk Index (ERI) to identify and optimize low-impact recreation corridors within Giant Panda National Park, China. Recreation suitability and ecological risk were modeled using environmental variables and landscape metrics, respectively. The results reveal a clear spatial pattern: high-suitability zones are concentrated in the central and northeastern areas, characterized by gentle terrain and extensive forest cover, while ecological risk is elevated in fragmented, human-disturbed peripheral regions. Although ORSI and ERI exhibit an overall negative spatial correlation, bivariate analysis reveals localized mismatches—areas where high recreation potential coincides with ecological vulnerability—indicating potential conflict zones. These zones are typically located along transitional park boundaries where accessibility intersects with ecological sensitivity. To mitigate such conflicts, a least-cost path analysis was conducted based on a composite resistance surface combining ORSI and inverted ERI values. The resulting corridor network connects 40 core areas while effectively avoiding ecological hotspots. Corridor buffers are predominantly composed of forest and shrubland, suggesting high environmental compatibility, particularly in the Qinling region. By translating spatial trade-offs into practical corridor design, this study provides a replicable approach for harmonizing recreation planning with conservation objectives. The proposed framework offers actionable guidance for evidence-based zoning, visitor flow management, and adaptive tourism development in ecologically sensitive protected landscapes. Full article

This study investigates amplified hydraulic braking systems employed in high-performance motorsport applications, utilizing wedge mechanisms for self-energization. An analytical expression for the gain coefficient is derived from a simplified equilibrium analysis of the wedge-shaped pad, capturing the nonlinear dependency on both wedge angle and effective mean disc-pad friction. A previously validated coupled thermoelastic model for carbon-carbon (C/C) braking systems—developed in Dymola and Modelica using the finite volume method (FVM) and an analytical local friction formulation—is here adapted to wedge-amplified braking systems, with the aim of providing performance assessment during the design phase of new calipers at reduced computational cost compared to coupled thermoelastic finite element method (FEM) models. Several caliper configurations featuring different wedge angles are tested experimentally on a dynamometer. A reduction in the effective friction coefficient at high mean effective contact pressure—induced by pronounced wedge angles and reduced pad areas—is observed. To validate the thermoelastic model, simulated braking torque and disc surface temperature are compared against bench data. The model shows satisfactory predictive capability under various operating conditions and test cycles, with mean error indices on peak torque prediction below $5\%$ for the majority of the simulated cases. Finally, the validated model is used to virtually assess the performance of a new caliper prototype prior to its manufacturing and testing. Full article