Search Results (5,044)

Driven by rapid global urbanization and expanding urban footprints, air pollution, particularly from industrial emissions and vehicular exhaust, has intensified, with rising concentrations of inhalable particulate matter (PM) posing direct threats to public health. To address this challenge, we conducted field measurements of ambient PM concentrations across diverse urban plant communities and quantitatively compared their capacity to mitigate four key size-fractionated pollutants: total suspended particles (TSPs), PM₁₀, PM_2.5, and PM₁. Our objective was to identify the most effective plant community type for PM abatement in urban settings. Results demonstrate that: (1) evergreen broad-leaved forests exhibit the highest overall PM removal efficiency among all studied communities; (2) removal efficacy declines markedly with decreasing particle size, indicating limited capacity to capture ultrafine particles (e.g., PM₁); and (3) seasonal performance peaks in summer, especially for deciduous broad-leaved forests attributable to maximal leaf area index, enhanced stomatal activity, and favorable meteorological conditions. By rigorously evaluating species composition, canopy structure, and seasonal dynamics, this study provides empirically grounded guidance for evidence-based urban greening strategies aimed at optimizing airborne particulate mitigation worldwide. Full article

Long-term operation of drip emitters under sediment-laden water conditions readily induces particle deposition and clogging, leading to discharge reduction and deterioration of irrigation uniformity. To clarify the temporal evolution and spatial distribution of clogging and to support structure-oriented anti-clogging improvement, three integrated drip tape emitters with different labyrinth-channel geometries were tested at sediment concentrations of 1, 2, and 3 g·L⁻¹ under a constant pressure of 100 kPa. The average relative discharge ratio (Dra) and Christiansen’s uniformity coefficient (CU) were continuously monitored, and cross-sectional observation and numerical simulation were combined to identify dominant deposition hotspot regions within the labyrinth channel. The results showed that increasing sediment concentration significantly accelerated clogging development and shortened operating lifetime. At 1 g·L⁻¹, the times required for the three emitter types to reach the clogging criterion of Dra < 75% were 120, 81, and 107 h, respectively, whereas at 3 g·L⁻¹ these values decreased to 39, 42, and 39 h. CU continuously declined with operating time and, in some treatments, responded earlier than Dra to system deterioration. Sediment deposition was mainly concentrated in the inlet section and bend regions, indicating that these locations were the dominant hotspots for clogging initiation and propagation. These findings demonstrate that clogging in drip emitters is jointly regulated by sediment load and labyrinth-channel geometry, and that hotspot-based structural optimization provides an effective basis for improving anti-clogging performance under sediment-laden water conditions. Full article

Mechanical pretreatment techniques are essential for overcoming lignocellulosic biomass recalcitrance in emerging biorefineries. This review critically synthesizes advances from 2020 to 2025 across fundamental mechanisms, hybrid technologies, energy efficiency, Life Cycle Assessment, and industrial scalability. The analysis reveals that effective pretreatment targets supramolecular modification—defect generation in cellulose crystallites and the creation of reactive sites—beyond simple particle size reduction. Impact–shear regimes prove most effective for fibrous materials. Hybrid approaches are examined: mechanocatalysis enables solvent-free depolymerization, while mechanoenzymatic technologies achieve hydrolysis without bulk water, though enzyme denaturation under mechanical stress remains unresolved. Energy consumption is the primary upscaling barrier, with Life Cycle Assessment identifying electricity use as the dominant environmental hotspot and emphasizing burden per unit of final product as the critical metric. Technology Readiness Level assessment provides a strategic framework: continuous extruders and mills are industrially mature for bulk applications, while high-intensity batch devices are suited for high-value coproducts. A research agenda prioritizing mechanistic understanding, hybrid process engineering, feedstock diversification, and embedded sustainability assessment is proposed. Full article

Background: African swine fever virus (ASFV) causes a fatal hemorrhagic disease in domestic pigs and wild boars. While live attenuated vaccines (LAVs) provide protection, their use raises safety concerns. Therefore, the aim of the present study was to identify viral B-cell antigens associated with protection and to test their potential using highly immunogenic vaccine delivery platforms. Methods: We employed a microarray of 169 ASFV proteins expressed in a cell-free prokaryotic system to identify immunodominant antigens using sera from immune pigs. Six structural proteins were selected and formulated into AP205 virus-like particles (VLPs). Additionally, replication-defective vesicular stomatitis virus (VSV)-based vaccine candidates expressing glycosylated CD2v and EP153R proteins were generated. Three groups of specific pathogen-free pigs were immunized with either VLP- or VSV-based vaccines and challenged with the virulent ASFV Georgia 2007 strain. Control groups included pigs immunized with the attenuated ASFV Estonia 2014 strain and a naïve group. Results: Most vaccine candidates induced detectable antibody responses against target ASFV proteins. However, neither VLP- nor VSV-based vaccines provided protection, as clinical scores, hematology, cytokine responses, and viremia levels were similar to those in the negative control group. In contrast, only the ASFV Estonia 2014 strain elicited a robust T-cell response and protective immunity. Conclusions: These findings highlight the challenges in identifying protective B-cell antigens of ASFV and emphasize the pivotal role of cellular immunity in mediating protection. Full article

This study investigates the effects of the co-curing process and nanoparticle reinforcement on the mechanical performance of plain-woven glass fiber-reinforced plastic (GFRP) adhesive joints, aiming to address the limitations of traditional fastening methods and the inherent brittleness of epoxy adhesives. Specifically, spherical silica (SiO₂) and plate-like graphene nanoplatelets (GNPs) were incorporated into the epoxy matrix at varying concentrations (0.25 to 1.0 wt.%) to evaluate the influence of particle geometry on joint integrity. Experimental results demonstrated that the co-curing technique yields superior mechanical properties compared to secondary bonding, exhibiting improvements of 35% in shear strength (from 10.97 MPa to 14.83 MPa) and 12% in flexural strength (from 72.57 MPa to 81.28 MPa) due to enhanced chemical interlocking. Furthermore, the addition of nanoparticles significantly improved joint performance, with the optimal content identified at 0.75 wt.% for both particle types. Notably, GNPs outperformed SiO₂, enhancing shear and flexural strengths compared to the neat co-cured baseline. Ultimately, the 0.75 wt.% GNP-reinforced material exhibited a shear strength of 21.22 MPa and a flexural strength of 104.09 MPa. Morphological analysis revealed that while SiO₂ contributes to reinforcement primarily via crack deflection, the high-aspect-ratio GNPs provide superior energy dissipation through crack bridging and pull-out mechanisms. Consequently, this study suggests that the co-curing process combined with an optimal concentration of GNPs presents a highly effective strategy for maximizing the reliability and structural efficiency of composite joints in weight-critical applications. Full article

High-velocity fluid flow in porous media frequently exhibits non-Darcy behavior, where inertial losses lead to nonlinear pressure gradient velocity behavior. Predicting the Forchheimer coefficient β remains challenging because β varies sensitively with pore geometry and is often not constrained by porosity and permeability alone. This study develops a structure-based method to estimate β using intrinsic descriptors obtained from the Darcy regime flow characterization and image-based geometry analysis. A set of two-dimensional granular porous media was generated with controlled variations in porosity, particle size distribution, and grain size variability. Single phase simulations are simulated with a body-force multiple-relaxation-time lattice Boltzmann method. The transition from Darcy flow to non-Darcy flow is identified from the velocity and pressure gradient response, and β is determined by fitting the inertial flow regime. Two tortuosity responses were observed. In uniform media, hydraulic tortuosity remained nearly constant in the Darcy regime and then gradually decreased. In disordered media, hydraulic tortuosity first increased with the onset of recirculation and then decreased as dominant flow paths became stable. Based on these results, a dimensionless inertial factor was correlated with porosity, intrinsic hydraulic tortuosity, and a pore structure index derived from specific surface area and hydraulic pore size. The resulting model predicts β from permeability and structural descriptors. The resulting correlation provides β estimates from Darcy permeability and geometry descriptors. Validation with quasi-two-dimensional microfluidic pillar array data showed that the model captured both the magnitude and relative ordering of β for the tested geometries. The proposed framework should be regarded as a proof of concept for idealized granular porous media and quasi-two-dimensional structured systems. Full article

Droplet size is a key factor in minimizing spray drift. Different types of adjuvants and sprayer operating pressures can affect the droplet size distribution in various ways. This study aimed to evaluate the effects of commercial adjuvants, namely, acids and surfactant (AS), silicone surfactant (SS), organosilicone surfactant (OS), mineral oil (MO and MO₂), and copolymer (CP) adjuvants, on the droplet spectra and physicochemical properties of aqueous solutions. Hydrogen potential (pH), volumetric mass ($VM$), electrical conductivity (EC), surface tension (ST), contact angle (CA), and droplet spectra were measured. The droplet spectrum variables, including volumetric diameters ($D_{v0.1}$, $D_{v0.5}$, and $D_{v0.9}$), the Relative Span Factor ($RSF$), and percentages of the total volume of droplets with a diameter smaller than 100 µm (V100) and larger than 500 µm (V500), were determined using a laser diffraction particle analyzer (Malvern Spraytec). Spraying tests were carried out using the AXI 11003 flat fan nozzle at pressures of (0.1, 0.2, 0.3, 0.4, and 0.5) MPa. The increase in pressure increased the V100 and the $RSF$, with greater sensitivity observed for SS. Adjuvants such as AS, MO₂ and OS showed a more balanced trend, with a smaller increase in fine droplets and a greater reduction in coarse droplets. The principal component analysis (PCA) revealed that the droplet spectrum variables were the ones that best explained the variation among the solutions. A negative correlation was identified between EC and other physicochemical properties, such as pH, ST, and CA. Therefore, these properties alone did not determine the atomization pattern. The study demonstrates that optimizing spray quality and minimizing drift require a combined consideration of adjuvant physicochemical properties and their interaction with operational pressure. Full article

This study investigates the reutilization of polylactic acid (PLA) waste generated by 3D printing through its transformation into electrospun membranes with tunable morphological, surface, thermal, and filtration properties. Polymer solutions containing 5–10 wt % recycled PLA were prepared in a dichloromethane/dimethylformamide system and characterized in terms of viscosity and electrical conductivity. Increasing PLA concentration raised solution viscosity (41.87–339.83 mPa·s) and reduced conductivity (7.63–1.63 µS·cm⁻¹), promoting the formation of bead-free fibers with larger diameters (0.221–1.213 µm) and enhanced hydrophobicity (contact angles 112.34–124.38°). FTIR confirmed preservation of the polymer chemical structure after recycling and electrospinning, while DSC revealed reduced crystallinity in the fibrous membranes. Exploratory correlation analysis indicated consistent associations between solution properties, fiber morphology, and wettability. Increasing the number of electrospun layers (1–3) generated denser networks with reduced pore size and improved particle retention. Filtration tests conducted under controlled airflow conditions (85 L min⁻¹, 1 cm s⁻¹ frontal velocity, 50 cm² effective area) showed removal efficiencies above 90% for PM2.5 and PM5, while PM1 capture improved with increasing membrane thickness. Quality factor analysis highlighted the trade-off between filtration efficiency and pressure drop, identifying intermediate multilayer configurations as providing a favorable balance. These findings demonstrate that electrospinning offers an effective strategy for converting recycled PLA into structurally tunable membranes with adjustable filtration performance, supporting sustainable valorization of additive manufacturing waste. Full article

Ragweed (Ambrosia artemisiifolia) represents a major and expanding source of aeroallergen exposure in Europe, with rising sensitization rates and substantial clinical impact. However, population-level data integrating airborne pollen exposure with detailed clinical sensitization patterns remain limited. We conducted a 5-year retrospective cross-sectional observational analysis of sensitization prevalence, complemented by a parallel temporal analysis of aerobiological data and symptom-driven healthcare presentation patterns (2020–2024) in Timisoara, Romania, including all patients undergoing first-time sensitization evaluation at a tertiary referral hospital. Sensitization was assessed using standardized skin prick testing to common aeroallergens and other allergen categories, while airborne ragweed pollen concentrations were monitored through a peri-urban network of real-time bio-particle analyzers. Statistical analyses included descriptive statistics, multivariable logistic regression, χ² tests for co-sensitization patterns, and comparative analyses of clinical manifestations across sensitization profiles. Among 4404 eligible patients, 50.7% were sensitized to at least one allergen. Ragweed sensitization was identified in 24.1% of patients, with a mean age of 31.1 years at diagnosis and no significant sex-related difference. Most ragweed-sensitized patients were polysensitized (71.5%), predominantly to other aeroallergens. Increasing age was independently associated with lower odds of polysensitization to other aeroallergens (adjusted OR = 0.97 per year, 95% CI: 0.96–0.98), while sex showed no independent association. Patients with ragweed sensitization alone and those cosensitized with aeroallergens exhibited similar prevalence of respiratory manifestations, whereas individuals with additional non-aeroallergen sensitization showed lower prevalence of rhinitis, conjunctivitis, and asthma but slightly higher rates of asthma exacerbations. Weekly diagnoses of ragweed sensitization demonstrated a pronounced seasonal peak between calendar weeks 33 and 38 (mid-August to late September), coinciding with peak airborne ragweed pollen concentrations. Ragweed sensitization therefore represents a substantial and seasonally driven healthcare burden in western Romania, characterized by frequent polysensitization, distinct clinical manifestation patterns across sensitization profiles, and close temporal alignment between airborne pollen exposure and clinical presentation. Integrating aerobiological monitoring with clinical surveillance may support targeted prevention strategies and improved patient management. Full article

Accurate prediction of landslide run-out distance is fundamental to hazard mapping, emergency planning, and risk-informed engineering design. However, many data-driven studies implicitly treat landslides as a homogeneous population and provide limited, physically interpretable insights into how geomorphic factors govern run-out behavior. To address these limitations, we propose a cluster-aware and explainable modeling framework to predict run-out distance L using four source-region and slope descriptors: crown–toe relief H, source area A, source volume V, and mean source-slope inclination $\theta$. The dataset consists of 10,159 rainfall-induced landslides compiled from official inventories and peer-reviewed literature. After standardizing predictors, the optimal number of clusters is determined using information criteria (AIC/BIC), followed by k-means clustering to identify distinct landslide regimes. We first benchmark Random Forest, eXtreme Gradient Boosting, CatBoost, and LightGBM on identical data splits without hyperparameter tuning, using $R^2$, RMSE, and MAE as performance metrics. LightGBM consistently outperforms the alternatives and is therefore selected as the base learner. Within each cluster, LightGBM is further optimized using the Alpha Evolution (AE) algorithm, with Particle Swarm Optimization and Bayesian Optimization serving as benchmarks. The resulting AE-LightGBM model achieves the highest predictive accuracy across clusters. Model interpretability is achieved using TreeSHAP, which decomposes predictions into cluster-specific baselines and additive contributions from H, A, V, and $\theta$. By integrating regime-sensitive learning with robust explainability, the proposed framework improves run-out distance prediction while providing transparent, physically meaningful insights to support scenario analysis and engineering decision-making. Full article

In this study, the effect of boron carbide (B₄C), hexagonal boron nitride (hBN), holy super graphene (HSG), and hybrid (B₄C + hBN + HSG) nano-additives on the tribological performance of SAE 5W-30 gasoline engine oil was investigated on Al-Si-based samples (Al 4032) prepared by cutting from a single-cylinder gasoline engine block. The addition of nano-additives regularly increased the kinematic viscosity; the 63.80 mm²/s (BO) value rose to 68.90 mm²/s at the highest level of B₄C and to 70.50 mm²/s in the hybrid oil (≈10.5% increase). The lowest and most stable friction performance was found in the hybrid 0.025 g/25 mL nano-additive oil, which remained between 0.03 and 0.05 during the entire COF test. The EDS mapping and line scan results confirmed the formation of tribofilm by identifying the additive elements (B for B₄C, B and N for hBN, C for HSG) in the wear scar, and the presence of increased O elements showed the restricted formation of tribo-oxidation. The results show that hybrid nano-additive oils provide the most effective friction and wear improvement, especially at low concentrations, while at high additive levels, performance does not show a consistent increase due to particle accumulation and third-body effects. Full article

The rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for antivirals that are resilient to resistance. Current Food and Drug Administration (FDA)-approved therapies primarily target single viral mechanisms, leaving gaps in efficacy. Here, we developed a Deep Learning-based Activity Screening Model (DLASM), which integrates graph convolutional network with machine learning to identify SARS-CoV-2 inhibitors, using experimental 3-chymotrypsin-like (3CL) main protease assay data. The optimized DLASMs virtually screened ~170,000 compounds from diverse in-house collections and yielded novel hits, several of which not only inhibited the 3CL protease but also blocked viral entry by interfering with heparan sulfate-mediated host interactions. These activities were validated through multiple assays, including 3CL enzymatic inhibition, SARS-CoV-2 pseudotyped particle entry, α-synuclein fibril uptake as a proxy for endocytosis, live virus cytopathic effect, heparan sulfate-dependent entry assay, and a 3D human lung mucociliary tissue model. Molecular docking studies elucidated binding modes at the 3CL protease active site, while molecular dynamics simulations provided insights into compound–heparan sulfate interactions. The identified compounds represent early-stage hits with moderate potency that demonstrate dual-mechanism antiviral activity. Together, these findings establish dual-target inhibition as a promising antiviral strategy, offering not only enhanced potency but also reduced risk of resistance. Moreover, our DLASM framework provides a generalizable pipeline for identifying chemically diverse scaffolds and for broader applications beyond SARS-CoV-2. Full article

This study aimed to develop a 100 mg immediate-release (IR) tablet containing dasatinib monohydrate, a tyrosine kinase inhibitor, using a Quality by Design (QbD) framework at laboratory scale. The development strategy focused on systematic identification and control of critical process parameters (CPPs) affecting tablet quality during wet granulation. Preformulation studies were conducted to evaluate key physicochemical properties of the active pharmaceutical ingredient (API), including solubility, particle size distribution, and crystallinity, which may influence dissolution behavior. A risk assessment approach based on preliminary hazard analysis (PHA) and failure mode and effects analysis (FMEA) was applied to identify high-risk process variables. Based on the risk assessment results, chopper speed during wet granulation and compression force during tableting were identified as critical process parameters. These factors were further investigated using a Design of Experiments (DoE) approach based on Define Custom Design (DCD) and response surface methodology (RSM) to evaluate their effects on critical quality attributes (CQAs), including dissolution performance, disintegration time, and tablet friability. Response surface analysis established a design space in which chopper speed ranged from approximately 2300–2500 rpm and compression force ranged from 11 to 13 kN, ensuring consistent tablet quality within the investigated operating range. The optimized process conditions produced tablets that satisfied predefined quality targets. Comparative dissolution studies demonstrated dissolution profiles comparable to the reference product across pH 1.2, 4.0, and 6.8 media, with similarity factor (f₂) values ranging from 51.18 to 85.23. The experimentally established design space demonstrated reproducible in vitro performance and physicochemical stability under accelerated storage conditions. Overall, this study demonstrates the practical application of a QbD-based development strategy integrating risk assessment and response surface optimization to improve process understanding and manufacturing robustness in wet granulation-based tablet production. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

Extracellular vesicles (EVs) are lipid-enclosed particles secreted from a wide variety of cells, with the ability to transfer biologically active content from parent to recipient cells. Lung epithelial-derived EVs (LE-EVs) play an important role in the progression of pulmonary disease, but there is limited evidence regarding the impact of cigarette smoke (CS) and electronic cigarette aerosol (ECA) on epithelial-derived EVs. The aim of this systematic review was to evaluate the current published literature on the impact of cigarette smoke and electronic cigarette aerosol on LE-EVs. Original research studies and clinical data were included, but research involving microparticles or non-epithelial-derived EVs was excluded. A total of 29 articles were identified from three databases (EMBASE, Web of Science and PubMed), of which nine demonstrated that CS exposure leads to molecular changes in epithelial-derived EVs, whereas 21 reported that CS-induced LE-EVs can deliver their cargo to neighbouring cells. The results highlighted that LE-EVs secreted in response to cigarette or e-cigarette exposure presented altered EV cargo, associated with increased cellular damage, inflammation and disease development. The current literature suggests that conventional and electronic cigarettes can influence the secretion of EVs from lung epithelial cells, with these EVs potentially playing a role in the development of lung inflammation. Nonetheless, there is limited research studying the impact of ECA on LE-EVS. Further research examining the impact of electronic cigarettes on lung epithelial-derived EVs, using robust human in vitro models coupled with clinical studies, is required. Full article