Search Results (1,529)

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article

This study investigates the thermal performance of an oil-jet-cooled permanent magnet synchronous motor (PMSM), with a particular focus on end-winding heat dissipation. A high-fidelity numerical model that preserves the full geometric complexity of the end-winding is developed and validated against experimental temperature data, achieving average deviations below 7%. To facilitate efficient parametric analysis, a simplified equivalent model is constructed by replacing the complex geometry with a thermally equivalent annular region characterized by calibrated radial conductivity. Based on this model, the effects of key spray ring parameters—including orifice diameter, number of nozzles, inlet oil temperature, and flow rate—are systematically evaluated. The results indicate that reducing the orifice diameter from 4 mm to 2 mm lowers the maximum winding temperature from 162 °C to 153 °C but increases the pressure drop from 205 Pa to 913 Pa. An optimal nozzle number of 12 decreases the peak winding temperature to 155 °C compared with 162 °C for 8 nozzles, while increasing the oil flow rate from 2 L/min to 6 L/min reduces the peak winding temperature from 162 °C to 142 °C. Furthermore, a non-uniform spray ring configuration decreases maximum stator, winding, spray ring, and shaft temperatures by 5.6–9.2% relative to the baseline, albeit with a pressure drop increase from 907 Pa to 1410 Pa. These findings provide quantitative guidance for optimizing oil-jet cooling designs for PMSMs under engineering constraints. Full article

Water-use efficiency (WUE) plays a crucial role in sustainable crop production, particularly in water-limited environments where maximizing natural resource use is essential. This study evaluated the physiological and agronomic performance of two Piper nigrum cultivars, Clonada and Uthirankotta, grown under different soil water potential conditions. The trial was conducted in a 1930 m² field using a randomized block design and drip irrigation system, calibrated to 3.55 L h⁻¹ with a uniformity of 97%. Soil water availability was managed based on daily tensiometer readings at 20 and 30 cm depths, triggering irrigation at defined tensions (10–55 kPa). Clonada exhibited higher net CO₂ assimilation rates (A) and stomatal conductance (g_s), but these responses did not lead to higher yields. In contrast, Uthirankotta consistently maintained superior water-use efficiency and yield across all soil moisture conditions by favoring water conservation and targeted biomass allocation over maximized gas exchange. Both cultivars performed optimally at a soil water potential range of 25–35 kPa, with declines in yield and gas exchange parameters at higher tensions (45–55 kPa). Under such conditions, Uthirankotta was 51.3% more water-use efficient and 40.8% more productive than Clonada. Based on this, a Principal Component Analysis (PCA) further demonstrated distinct physiological profiles, underscoring trade-offs between yield and water-use strategies. These results highlight the significance of cultivar selection for optimizing WUE and provide valuable insights into irrigation management and breeding programs aimed at boosting black pepper performance under water-limited conditions. Full article

An environmentally friendly and highly sensitive analytical method for the determination of the dye Eosin Y (EY) was developed utilizing vortex-assisted liquid–liquid microextraction based on deep eutectic solvents (DESs), combined with fluorescence detection (LPME-FLD). The extraction efficiencies of conventional solvents and various DES systems, composed of tetrabutylammonium bromide (TBAB) and alcohols (hexanol, octanol, and decanol) in different ratios, were systematically compared. DFT calculations provided insights into the most stable forms of EY in solvents of varying polarity. Theoretical Hansen solubility parameters and the COSMO-RS solvation model were applied to assess extraction efficiency. Hansen parameters were obtained via semiempirical PM7 calculations, while BP86/def2-TZVPD DFT computations were employed within the openCOSMO-RS framework. The developed method exhibited a linear calibration range between 0.1 and 130 µg·L⁻¹, with a high correlation coefficient (R² = 0.9982). The limit of detection (LOD) was established at 0.028 µg·L⁻¹. Method precision and repeatability were confirmed over two days, with relative standard deviations (RSDs) ranging from 1.1% to 2.7% and with recoveries between 99.0% and 106.2%. The proposed analytical approach was successfully applied to the determination of EY in real water samples, demonstrating both its practical applicability and alignment with green chemistry principles. Full article

Arachidonic acid (AA) is a polyunsaturated fatty acid, an essential component of the brain membrane phospholipids. It is released into the bloodstream in response to various pathological conditions. Its potential as a biomarker for oxidative membrane damage determines the importance of its reliable quantification. Therefore, the aim of the current study was to develop a simple and robust LC-MS method that is easy to implement for routine testing of free arachidonic acid (AA) in human serum. The method included a simple two-step sample preparation procedure based on liquid–liquid extraction and protein precipitation, followed by AA analysis by UPLC coupled with PDA and QDa detectors. The ICH M10 guideline was followed for validation studies. The method demonstrated high selectivity and linearity with R² = 0.9952 for solvent-based and R² = 0.9979 for matrix-matched calibration. The LODs and LOQs were 0.046 µg/mL and 0.133 µg/mL, respectively. The accuracy and precision were between 6 and 14% RSD. No carry-over and matrix interferences were observed. The method was successfully applied to real serum samples, where AA concentrations ranged from 0.82 to 2.69 µg/mL, consistent with data of other studies. This method provides a reliable, reproducible, and rapid alternative for AA quantification in research and routine practice. Full article

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

Detecting pharmaceuticals in environmental matrices, particularly in wastewater, is crucial due to their potential environmental occurrence and unpredictable ecological and health-related consequences. These substances, often present in trace amounts, require highly sensitive and selective analytical methods for effective monitoring. A modified version of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method was evaluated to evaluate 18 pharmaceuticals and 2 metabolites in wastewater samples using liquid chromatography with tandem mass spectrometry (LC-MS/MS). The method’s performance was assessed using linearity, recovery, precision, limits of quantification (LOQ) and detection (LOD), and the matrix effect (ME). The final method was based on acetonitrile, Na₂EDTA, citrate buffer, and graphene oxide (GO). Finally, the calibration curves prepared in acetonitrile and the matrix extract showed a correlation coefficient of 0.99. Most of the compounds had LOQ values lower than 0.5 μg⋅mL⁻¹. Recoveries were achieved in the 70–98% range, with RSD lower than 13%. GO allowed the elimination of the ME, which occurred in the range of −11% to 15%. The results indicate that a low-cost and straightforward method is suitable for routinely monitoring pharmaceuticals in wastewater, which is crucial for minimizing the impact of pollutants on aquatic ecosystems. Full article

This report presents the first method for simultaneous determination of the 2-S-lepidinium derivatives of total α-lipoic acid (LA), homocysteine (Hcy), cysteinylglycine (CysGly), and cysteine (Cys) in human saliva, using capillary electrophoresis with pH-mediated sample stacking and ultraviolet detection (CE-UV) at 355 nm. Electrophoretic separation is carried out at 20 kV and 25 °C using a standard fused silica capillary (effective length 91.5 cm, inner diameter 75 µm). The background electrolyte consists of 0.5 mol/L lithium acetate buffer, adjusted to pH 3.5 with 0.5 mol/L acetic acid. The limit of quantification was determined to be 1 µmol/L for LA and 0.17 µmol/L for Hcy, 0.11 µmol/L for CysGly, and 0.10 µmol/L for Cys in saliva samples. Calibration curves demonstrated linearity over the concentration range of 3 to 30 µmol/L for all analytes. Method precision did not exceed 4.7%, and accuracy ranged from 87.9% to 114.0%. The developed method was successfully applied to saliva samples from eleven apparently healthy volunteers to determine the content of LA, Hcy, CysGly, and Cys. The Hcy, CysGly, and Cys concentrations ranged from 0.55 to 13.76 µmol/L, 0.89 to 9.29 µmol/L, and 1.73 to 12.99 µmol/L, respectively. No LA-derived peaks were detected in the native saliva samples. Full article

Water quality should be tested to ensure it is acceptable for the healthy growth of plants and animals, and water hardness is one of the important testing indexes. Herein, a novel approach was proposed to achieve high accuracy and rapid quantitative analyses of water hardness by combining one-point calibration laser-induced breakdown spectroscopy (OPC–LIBS) and aerosolization. First, the water samples are aerosolized via the aerosol generation device and the LIBS spectra of aerosols are obtained. Then, a modified OPC–LIBS model is used to determine the elemental contents of the aerosols via LIBS spectra, in which the plasma temperature is calculated using the Multi-Element Saha–Boltzmann (ME–SB) plot. One suitable standard liquid sample (the concentrations of Ca, Mg, and Sr were 50 mg/L, 50 mg/L, and 500 mg/L, respectively) was selected to evaluate the quantitative performance of the modified OPC–LIBS. Then, the Ca and Mg concentrations in the three real water samples (from the Yangtze River, reservoir, and underground) were detected and quantified by the proposed method, and the quantitative results of three LIBS calibration methods were compared with that of inductively coupled plasma optical emission spectroscopy (ICP–OES). The average relative error of Ca and Mg found in the OPC–LIBS results was lower by 22.23% than the internal standard method and 14.50% lower than the external standard method. The method combining modified OPC–LIBS and aerosolization can achieve high-precision rapid quantification of water hardness detection, which provides a new path for rapid detection of water hardness and is expected to make online detection a reality in the water quality testing field. Full article

We developed a novel and portable magnetic nanochannel electrochemical sensor for the sensitive detection of cadmium ions (Cd²⁺), which pose serious risks to food safety and human health. The sensor was fabricated by co-modifying an anodic aluminum oxide (AAO) nanochannel membrane with a composite of glutathione (GSH) and ferric oxide nanoparticles (Fe₃O₄), denoted as GSH@Fe₃O₄. This modified membrane was then integrated with a screen-printed carbon electrode (SPCE) to construct the GSH@Fe₃O₄/GSH@AAO/SPCE sensing platform. The performance of the sensor was evaluated using differential pulse voltammetry (DPV), which demonstrated a strong linear correlation between the peak current response and the concentration of Cd²⁺ in the range of 5–120 μg/L. The calibration equation was I_DPV(μA) = −0.31 + 0.98·C_{Cd²⁺(μg/L)}, with an excellent correlation coefficient (R² = 0.999, n = 3). The calculated limit of detection (LOD) was as low as 0.1 μg/L, indicating the high sensitivity of the system. These results confirm the successful construction of the GSH@Fe₃O₄/GSH@AAO/SPCE portable nanochannel sensor. This innovative sensing platform provides a rapid, sensitive, and user-friendly approach for the on-site monitoring of heavy metal contamination in agricultural products, especially grains. Full article

Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstructure evolution. This is because the process is sensitive to many parameters and has a complex thermal history. Thin-walled geometries present an added challenge because their dimensions often approach the scale of individual grains. Thus, microstructure becomes a critical factor in the overall integrity of the component. This study focuses on applying cellular automata (CA) modeling to establish robust and efficient process–structure relationships in L-PBF of 316L stainless steel. The CA framework simulates solidification-driven grain evolution and texture development across various processing conditions. Model predictions are evaluated against experimental electron backscatter diffraction (EBSD) data, with additional quantitative comparisons based on texture and morphology metrics. The results demonstrate that CA simulations calibrated with relevant process parameters can effectively reproduce key microstructural features, including grain size distributions, aspect ratios, and texture components, observed in thin-walled L-PBF structures. This work highlights the strengths and limitations of CA-based modeling and supports its role in reliably designing and optimizing complex L-PBF components. Full article

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article

Background/Objectives: Selinexor is a selective nuclear-export inhibitor approved for hematologic malignancies, characterized by extensive plasma protein binding (>95%). However, a validated analytical method to accurately measure the clinically relevant unbound fraction of selinexor in human plasma has not yet been established. This study aimed to develop a fully validated bioanalytical assay for simultaneous quantification of total and unbound selinexor concentrations in human plasma. Methods: We established and fully validated an analytical method based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) capable of quantifying total and unbound selinexor concentrations in human plasma. Unbound selinexor was separated using ultrafiltration, and selinexor was efficiently extracted from 50 μL of plasma by liquid–liquid extraction. Chromatographic separation was achieved on a C18 column using an isocratic mobile phase (0.1% formic acid:methanol, 12:88 v/v) with a relatively short runtime of 2.5 min. Results: Calibration curves showed excellent linearity over a range of 5–2000 ng/mL for total selinexor (r² ≥ 0.998) and 0.05–20 ng/mL for unbound selinexor (r² ≥ 0.995). The precision (%CV ≤ 10.35%) and accuracy (92.5–104.3%) for both analytes met the regulatory criteria. This method successfully quantified selinexor in plasma samples from renally impaired patients with multiple myeloma, demonstrating potential inter-individual differences in unbound drug concentrations. Conclusions: This validated bioanalytical assay enables precise clinical pharmacokinetic assessments in a short runtime using a small plasma volume and, thus, assists in individualized dosing of selinexor, particularly for renally impaired patients with altered protein binding. Full article

Background: An effective vaccine against Nipah virus (NiV) is crucial due to its high fatality rate and recurrent outbreaks in South and Southeast Asia. Vaccine development is challenged by the lack of validated accessible neutralization assays, as virus culture requires BSL-4 facilities, restricting implementation in resource-limited settings. To address this, we standardized and validated a pseudotyped virus neutralization assay (PNA) for assessing NiV-neutralizing antibodies in BSL-2 laboratories. Methods: The NiV-PNA was validated following international regulatory standards, using a replication-defective recombinant Vesicular stomatitis virus (rVSV) backbone dependent pseudotyped virus. Assessments included sensitivity, specificity, dilutional linearity, relative accuracy, precision, and robustness. The assay was calibrated using the WHO International Standard for anti-NiV antibodies and characterized reference sera to ensure reliable performance. Findings: Preliminary evaluation of the developed NiV-PNA showed 100% sensitivity and specificity across 10 serum samples (5 positive, 5 negative), with a positive correlation to a calibrated reference assay (R² = 0.8461). Dilutional linearity (R² = 0.9940) and accuracy (98.18%) were confirmed across the analytical titer range of 11-1728 IU/mL. The assay also exhibited high precision, with intra-assay and intermediate precision geometric coefficients of variation of 6.66% and 15.63%, respectively. Robustness testing demonstrated minimal variation across different pseudotyped virus lots, incubation times, and cell counts. Conclusions: The validated NiV-PNA is a reproducible and scalable assay platform for quantifying NiV neutralizing antibodies, offering a safer alternative to virus culture. Its validation and integration into the CEPI Centralized Laboratory Network will enhance global capacity for vaccine evaluation and outbreak preparedness. Full article