Search Results (212)

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

This study introduces a framework for comparing and integrating revealed and potential accessibility maps, using the Dempster–Shafer theory to identify regions with varying spatial accessibility while accounting for uncertainty. It presents a method for determining revealed accessibility from individuals’ trajectory data, weighting accessibility inversely to the square of uncertainty. This dual approach aids urban planners in making more reliable decisions. The methodology is applied to supply centers, including shops, restaurants, and sports centers, using data from the Mobile Data Challenge (MDC) in Vaud, Switzerland. The results show good access to shops in the northwestern and southeastern regions and good access to restaurants in the eastern regions. The final maps indicate that areas with low access to sports centers form the highest proportion (62.7%) of regions with low access, while those with low access to shopping centers form the lowest (9.3%). The findings suggest the need for more sports centers in Nyon and Jura-Nord Vaudois and more accessible restaurants in Nyon and southern Aigle. Additionally, the analysis reveals that lower station densities correlate with smaller discrepancies between real and expected accessibilities, while higher population densities are linked to lower uncertainty, underscoring the importance of considering density in spatial accessibility assessments. Full article

This study investigates steady-state heat conduction in materials with stepwise discontinuities in thermal conductivity, a phenomenon frequently encountered in layered composites, thermal barrier coatings, and electronic packaging. The problem is formulated for a 2D two-domain region, where each subdomain has a distinct constant conductivity. Both the direct problem—determining the temperature field from known conductivities—and the inverse problem—identifying conductivities and the internal heat source from limited temperature measurements—are addressed. To this end, three deterministic finite-difference-type models are developed: two for the standard formulation and one for a meshless formulation based on Moving Least Squares (MLS), all derived within a local framework that efficiently enforces interface conditions. In addition, two Monte Carlo models are proposed—one for the standard and one for the meshless setting—providing pointwise estimates of the solution without requiring computation over the entire domain. Finally, an algorithm for solving inverse problems is introduced, enabling the reconstruction of material parameters and internal sources. The performance of the proposed approaches is assessed through 2D benchmark problems of varying geometric complexity, including both structured grids and irregular node clouds. The numerical experiments cover convergence studies, sensitivity of inverse reconstructions to measurement noise and input parameters, and evaluations of robustness across different conductivity contrasts. The results confirm that the hybrid difference-meshless Monte Carlo framework delivers accurate temperature predictions and reliable inverse identification, highlighting its potential for engineering applications in thermal design optimization, material characterization, and failure analysis. Full article

Purpose: Preclinical studies suggest that interleukin-1β (IL-1β) influences tumor behavior in non-small cell lung cancer (NSCLC). While the CANTOS trial demonstrated reduced lung cancer incidence with IL-1β inhibition, the CANOPY trials failed to show survival benefit when combined with chemoimmunotherapy. The role of IL-1β in NSCLC with oncogenic mutations remains unclear. We evaluated the prognostic and predictive significance of IL-1β expression across NSCLC subtypes. Methods: We analyzed 21,698 NSCLC tumors profiled by Caris Life Sciences using DNA and RNA next-generation sequencing. IL-1β expression was stratified into quartiles (Q1: lowest 25%, Q4: highest 25%). Real-world overall survival (OS) and time on treatment (TOT) were obtained from insurance claims. Statistical comparisons used Chi-square, Fisher’s exact, or Mann–Whitney U tests. Survival outcomes were assessed with Cox models. Results: Across unselected NSCLC patients, low IL-1β expression (Q1) was associated with modestly longer OS versus high expression (Q4) (median OS 19.5 vs. 17.4 months; HR 0.94; p < 0.0001). This effect was more pronounced in EGFR-mutant adenocarcinoma (36.7 vs. 27.2 months; HR 0.76; p < 0.001) and ALK fusion-positive NSCLC (53.0 vs. 35.2 months; HR 0.62; p = 0.002). In NSCLC without targetable mutations, IL-1β expression was not prognostic. In KRAS-mutant adenocarcinoma, high IL-1β expression was associated with modestly longer TOT on immunotherapy (7.4 vs. 6.4 months; HR 1.15; p = 0.041), but not OS. High IL-1β expression correlated positively with TP53 mutation, TMB-high, and PD-L1 expression and inversely with EGFR, KRAS, BRAF, ERBB2, KEAP1, and STK11 mutations. Conclusions: IL-1β expression is a potential prognostic and predictive biomarker in NSCLC, associated with survival outcomes in defined molecular subsets. These findings suggest that IL-1β-targeted strategies may be particularly relevant in EGFR- or ALK-altered tumors. Full article

Despite its potential danger, site amplification effects are often neglected in seismic hazard analysis. Appropriate amplification factors can be determined from shear wave velocity, but impracticality in in situ measurements leads to reliance on regional correlation with geotechnical parameters such as SPT N-value. Modified power law and logarithmic equations were derived from past correlation studies to determine V_s30 values for each borehole location in the City of Manila. V_s30 profiles were spatially interpolated using the inverse-distance weighted and thin-spline methods to approximate the variation in shear wave velocities and add more detail to the existing contour map for soil profile classification across Metro Manila. Statistical analysis of the interpolated models indicates percentage differences ranging from 0 to 10% with a normalized root mean square error of nearly 5%. Generated equations and geospatial models in the study may be used as a basis for a seismic microzonation model for Metro Manila, considering other geological and geophysical layers. Full article

The Unit Inverse Maxwell–Boltzmann (UIMB) distribution is introduced as a novel single-parameter model for data constrained within the unit interval ($0,1$), derived through an exponential transformation of the Inverse Maxwell–Boltzmann distribution. Designed to address the limitations of traditional unit-interval distributions, the UIMB model exhibits flexible density shapes and hazard rate behaviors, including right-skewed, left-skewed, unimodal, and bathtub-shaped patterns, making it suitable for applications in reliability engineering, environmental science, and health studies. This study derives the statistical properties of the UIMB distribution, including moments, quantiles, survival, and hazard functions, as well as stochastic ordering, entropy measures, and the moment-generating function, and evaluates its performance through simulation studies and real-data applications. Various estimation methods, including maximum likelihood, Anderson–Darling, maximum product spacing, least-squares, and Cramér–von Mises, are assessed, with maximum likelihood demonstrating superior accuracy. Simulation studies confirm the model’s robustness under normal and outlier-contaminated scenarios, with MLE showing resilience across varying skewness levels. Applications to manufacturing and environmental datasets reveal the UIMB distribution’s exceptional fit compared to competing models, as evidenced by lower information criteria and goodness-of-fit statistics. The UIMB distribution’s computational efficiency and adaptability position it as a robust tool for modeling complex unit-interval data, with potential for further extensions in diverse domains. Full article

Water masses are continuously redistributing across the Earth, so accurately estimating their availability is essential. Global Navigation Satellite Systems (GNSSs) have demonstrated potential for observing vertical deformations, which is partly driven by terrestrial water storage (TWS) variations. This capability has been used in hydrogeodesy to estimate TWS variations. However, GNSS data inversions are often ill-posed, requiring regularization for stable solutions. This study considers the Least Squares Collocation (LSC) statistical method as an alternative. LSC uses covariance functions to characterize observations, parameters, and their interdependence. By incorporating additional physical information into inverse models, LSC allows ill-posed problems stabilization. To assess LSC effectiveness, we apply it to observed and simulated GNSS vertical displacement on Haiti island. Hydrological signals are modeled using Global Land Data Assimilation (GLDAS) data. In sparse GNSS data regions, findings indicate poor agreement between TWS and hydrological input, with a Root-Mean-Square-Error (RMSE) of 115 kg/m², a correlation of 0.3, and a reduction of 73%. However, in dense simulated GNSS areas, TWS and hydrological input show strong agreement, with an RMSE of 41 kg/m², a correlation of 0.83, and a reduction of 92%. The results confirm LSC potentiality for assessing TWS changes and improving water quantification in dense GNSS station region. Full article

Solvent-exchange-induced in situ forming gel (ISG) refers to a drug delivery system that transforms from a solution state into a gel or solid matrix upon administration into the body and exposure to physiological aqueous fluid. This study investigates the molecular behavior and phase inversion process of cellulose acetate butyrate (CAB)-based in situ forming gel (ISG) formulations containing moxifloxacin (Mx) or benzydamine HCl (Bz) as model drugs dissolved in N-methyl pyrrolidone (NMP) using molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The simulations reveal a solvent exchange mechanism, where the diffusion of water molecules replaces NMP, driving the formation of the CAB matrix. Bz exhibited faster diffusion and a more uniform distribution compared to Mx, which aggregated into clusters due to its larger molecular size. The analysis of the root mean square deviation (RMSD) and radius of gyration confirmed the faster diffusion of Bz, which adopted a more extended conformation, while Mx remained compact. The phase transformation was driven by the disruption of CAB-NMP hydrogen bonds, while CAB–water interactions remained limited, suggesting that CAB does not dissolve in water, facilitating matrix formation. The molecular configuration revealed that drug–CAB interactions were primarily governed by hydrophobic forces and van der Waals interactions rather than hydrogen bonding, controlling the release mechanism of both compounds. DFT calculations and electrostatic potential (ESP) maps illustrated that the acetyl group of CAB played a key role in drug–polymer interactions and that differences in CAB substitution degrees influenced the stability of drug-CAB complexes. Formation energy calculations indicated that Mx-CAB complexes were more stable than Bz-CAB complexes, resulting in a more prolonged release of Mx compared to Bz. Overall, this study provides valuable insights into the molecular behavior of CAB-based Mx-, Bz-ISG formulations. Full article

The ability to quantify equine kinematics is essential for clinical evaluation, research, and performance feedback. However, current methods are challenging to implement. This study presents a motion capture methodology for horses, where three-dimensional, full-body kinematics are calculated without instrumentation on the animal, offering a more scalable and labor-efficient approach when compared with traditional techniques. Kinematic trajectories are calculated from multi-camera video data. First, a neural network identifies skeletal landmarks (markers) in each camera view and the 3D location of each marker is triangulated. An equine biomechanics model is scaled to match the subject’s shape, using segment lengths defined by markers. Finally, inverse kinematics (IK) produces full kinematic trajectories. We test this methodology on a horse at three gaits. Multiple neural networks (NNs), trained on different equine datasets, were evaluated. All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. NNs trained over a larger variety of data improved joint angle RMSE and curve similarity. Marker prediction error, the average distance between ground truth and predicted marker locations, and IK marker error, the distance between experimental and model markers, were used to assess network, scaling, and registration errors. The results demonstrate the potential of markerless motion capture for full-body equine kinematic analysis. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extensive numerical simulations confirm a power-law relationship between thermalization time (T_eq) and perturbation strength for the AH chain, revealing an exponent larger than the previously observed inverse-square law in the thermodynamic limit. Upon adding symmetric quartic nonlinearity into the AH model, we systematically study thermalization under combined asymmetry and nonlinearity. Matthiessen’s rule provides a good estimate of T_eq in this case. Our results demonstrate that asymmetry plays a distinct role in enhancing higher-order effects and governing relaxation dynamics. Full article

Background: There are conflicting epidemiological studies regarding the association between selenium (Se) and metabolic disorders. Furthermore, the pathophysiological links between Se and metabolic dysfunction-associated steatotic liver disease (MASLD) have not yet been fully elucidated. Therefore, we evaluated the association between serum Se levels and the clinical features of MASLD and the inflammatory and oxidative stress markers in these patients as potential risk factors for the progression of this disease. Methods: This cross-sectional study involved 150 patients aged 20 to 50 years who were newly diagnosed with MASLD. Oxidative stress was evaluated by measuring serum thiobarbituric acid reactive substances (TBARS), total antioxidant capacity (TAC), and the activities of erythrocyte superoxide dismutase (SOD) and glutathione peroxidase (GPx). Tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and transforming growth factor beta (TGF-β) were measured as inflammatory markers. A one-way analysis of variance (ANOVA), Pearson chi-square test, Kruskal–Wallis test, and multiple linear regression were employed for data analysis. Results: We observed a significant inverse association between serum Se concentrations and liver steatosis severity in the participants. There was a significant decrease in serum concentrations of insulin and the homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, TNF-α, and TBARS with ascending quartiles of serum Se. Conversely, the mean serum levels of TAC and erythrocyte GPx activities exhibited a consistent increasing trend in relation to rising serum Se concentrations. However, no significant trends were identified for serum FSG, IL-6, TGF-β, or erythrocyte SOD activities across the varying levels of serum Se. Conclusions: Our results demonstrate that decreased serum selenium levels in Iranian patients with MASLD correlate with elevated inflammatory markers, increased oxidative stress, and more severe liver steatosis. Full article

In the assembly of spacecraft cabins, the presence of uncertain and time-varying temperature environments can induce thermal deformation in bulkheads, potentially affecting dimensional stability. Online sensing of thermal deformation is critical for mitigating such risks. However, conventional finite element methods (FEMs) rely on cascading thermal and structural analyses, which suffer from inefficiency. To address this issue, we propose a methodology that integrates a physical model with a data-driven temperature field measurement technique, demonstrated through case studies involving a spacecraft porthole bulkhead. First, leveraging the geometric invariance of the bulkhead during assembly, a purely static FE model is established offline. Second, multi-point temperature measurements combined with Kriging estimation are employed to directly reconstruct the temperature field, circumventing the computationally intensive FEM-based thermal analysis process. Finally, by utilizing the precomputed inverse stiffness matrix and performing an online conversion from temperature to equivalent forces, thermal deformation is rapidly resolved. The numerical results demonstrate that the root-mean-square errors of the predicted full-field deformation are maintained at the micron level, with an average computation time of less than 0.14 s. Furthermore, a meticulously designed experiment was conducted, where the predicted thermal displacements of several key points showed good agreement with measurements by means of a laser tracker. This research provides a promising tool to achieve digital twinning of thermal deformation states for aerospace components. Full article

High grain quality is a key target in wheat breeding and is influenced by genetic and environmental factors. This study evaluated 94 recombinant inbred lines (RILs) from a Pamyati Azieva × Paragon (PA × P) mapping population grown in two regions in Kazakhstan to assess the genetic basis of six grain quality traits: the test weight per liter (TWL, g/L), grain protein content (GPC, %), gluten content (GC, %), gluten deformation index in flour (GDI, unit), sedimentation value in a 2% acetic acid solution (SV, mL), and grain starch content (GSC, %). A correlation analysis revealed a trade-off between protein and starch accumulation and an inverse relationship between grain quality and yield components. Additionally, GPC exhibited a negative correlation with yield per square meter (YM2), underscoring the challenge of simultaneously improving grain quality and yield. With the use of the QTL Cartographer statistical package, 71 quantitative trait loci (QTLs) were identified for the six grain quality traits, including 20 QTLs showing stability across multiple environments. Notable stable QTLs were detected for GPC on chromosomes 4A, 5B, 6A, and 7B and for GC on chromosomes 1D and 6A, highlighting their potential for marker-assisted selection (MAS). A major QTL found on chromosome 1D (QGDI-PA × P.ipbb-1D.1, LOD 19.4) showed a strong association with gluten deformation index, emphasizing its importance in improving flour quality. A survey of published studies on QTL identification in common wheat suggested the likely novelty of 12 QTLs identified for GDI (five QTLs), TWL (three QTLs), SV, and GSC (two QTLs each). These findings underscore the need for balanced breeding strategies that optimize grain composition while maintaining high productivity. With the use of SNP markers associated with the identified QTLs for grain quality traits, the MAS approach can be implemented in wheat breeding programs. Full article