Search Results (683)

This paper presents an analytical model for one-dimensional consolidation analysis of multi-layered unsaturated soils under depth-dependent initial conditions. The general solutions are derived explicitly using the Laplace transform. By combining these general solutions with interfacial continuity conditions between layers and the boundary conditions, the reduced-order system is solved via the Euler method to obtain analytical solutions in the Laplace domain. Numerical inversion of the Laplace transform is then performed using Crump’s method to yield the final analytical solutions in the time domain. The model incorporates initial conditions that account for both uniform and linear distributions of initial excess pore pressure within the soil stratum. The proposed solution is verified by reducing it to degenerated cases (e.g., uniform initial pressure) and comparing it with existing analytical solutions, showing excellent agreement. This confirms the model’s correctness and demonstrates its generalization to multi-layered systems with depth-dependent initial conditions. Focusing on a double-layered unsaturated soil system, the one-dimensional consolidation characteristics under depth-dependent initial conditions are investigated by varying the physical parameters of individual layers. The proposed solution can serve as a theoretical reference for the consolidation analysis of multi-layered unsaturated soils with depth-dependent initial conditions. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article

This research focuses on the effects of leadership styles on the implementation of Green Human Resource Management (Green HRM) initiatives in hotels in Greece by staff members, and it recognizes the lack of sustainability research in the Mediterranean hospitality sector. Employing the Full-Range Leadership Model, we explore the impact of transformational, transactional, and passive leadership on the implementation of environmental HR practices. The data for this study were obtained from 216 employees in 29 hotels in Greece, who completed the Multifactor Leadership Questionnaire (MLQ-5x) and a Green HRM instrument. Several regression analyses showed that transformational leadership was the most robust positive predictor of Green HRM practices, followed by leadership outcomes and transactional leadership. On the other hand, passive leadership was significantly inversely associated with Green HRM implementation. Demographic variables, such as gender, age, and experience, had a substantial impact on both perceptions of leadership and involvement in Green HRM as well. The results offer significant theoretical implications and practical directions for improving environmental performance in hospitality organizations through the strategic use of leadership development and human resource management intervention. Full article

Background/Objectives: Sepsis is a life-threatening condition associated with high mortality. Optimal dosing strategies for β-lactam antibiotics in sepsis remain controversial, particularly in patients with renal impairment. Cefepime (CFPM) is widely used as empiric therapy; however, its appropriate initial dosing in critically ill patients is unclear. This study aimed to evaluate whether high-dose CFPM administration during the first 48 h improves survival in patients with sepsis, irrespective of renal function. Methods: This single-center, retrospective, observational study included adult intensive care unit (ICU) patients with sepsis who received CFPM as initial therapy between January 2017 and December 2024. Patients were categorized into High-dose (12 g within 48 h; 2 g every 8 h) and Low-dose (<12 g/48 h) groups. The primary outcome was ICU survival. To address confounding, inverse probability of treatment weighting (IPTW) based on serum creatinine was applied, with sensitivity analyses using 1% trimmed and stabilized IPTW. Results: Of 122 eligible patients, 84 were analyzed (High-dose: n = 27; Low-dose: n = 57). After IPTW adjustment, high-dose CFPM was significantly associated with improved ICU survival (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.60–18.39, p = 0.0066). This association remained consistent in the 1% trimmed IPTW analysis (OR 4.07, 95% CI 1.19–13.97, p = 0.0256). Stabilized IPTW yielded a similar effect estimate, though without statistical significance (OR 5.43, 95% CI 0.72–41.16, p = 0.1017). Overall, results were consistent in direction and magnitude across models. Conclusions: High-dose CFPM administration during the initial 48 h was associated with improved ICU survival in patients with sepsis, independent of renal function. Full article

Prestack seismic waveform inversion starts with an initial model and computes synthetic or predicted seismic data using a wave equation-based approach. Then, by matching these predicted data with the observed seismic data, it iteratively modifies the initial model using an optimization method until the predicted and observed data reasonably match. This method has been demonstrated to be superior to amplitude-variation-with-angle inversion. Because of the wave equation-based approach, computational cost is, however, one major drawback of the method. In the presence of well-logs with borehole measurements of the subsurface properties such as the P-wave velocity, S-wave velocity, and density, it is possible to provide a good initial model, and the method quickly converges to the true model at well locations. However, for locations away from the wells, the initial models are obtained by interpolating the initial models at the well locations over the interpreted geological horizons. These models can be far from the true models and inverting prestack data for these locations using wave equation-based method is computationally challenging. Because of these computational challenges, amplitude-variation-with-angle inversion is the current state-of-the-art method for routine seismic inversion applications. In this work, we provide an attribute-guided framework to generate initial models and demonstrate its applicability, which can potentially overcome computational challenges of prestack seismic waveform inversion. Furthermore, we also discuss the feasibility of using this attribute-guided approach to characterize reservoirs for underground hydrogen storage. Full article

The nonlinear Bagley–Torvik equation is of fundamental importance, as it captures a realistic and intricate interplay among memory effects, nonlinearity, and functional dependence—making it a powerful model for a wide range of natural and engineered systems. Its analysis contributes significantly to both the theoretical development of fractional differential equations and their practical applications across science and technology. In this paper, we employ the inverse operator method, the multivariate Mittag-Leffler function, and several classical fixed-point theorems to establish sufficient conditions for the existence, uniqueness, and Hyers–Ulam stability of solutions to the nonlinear Bagley–Torvik equation with functional initial conditions. Finally, we present several examples by explicitly computing values of the multivariate Mittag-Leffler functions to illustrate the main results. Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

This study analyzed the spatial distribution of large green algae (LGA) in the northern coastal waters of China, including the Yellow Sea and Bohai Sea, using Coastal Zone Imager (CZI) data from the HY-1C/D satellites. An inversion model (coastal zone imager model) of LGA was established, based on which the distribution details of large green algae in the Yellow Sea and Bohai Sea were investigated. The results indicated the following: (1) LGA exhibits a clearly seasonal pattern from May to August. Initially occurrences are detected in May in the southern Yellow Sea (32–34° N), followed by a rapid expansion and intensification from June to mid-July, with peak distribution around 35° N near the Shandong Peninsula. The affected area subsequently decreases in late August. (2) High LGA coverage is mainly concentrated along the Subei Shoal and the Shandong Peninsula in the Yellow Sea, as well as the coastal regions of Yantai, Qinhuangdao, and Yingkou in the Bohai Sea. (3) The LGA-M inversion model demonstrates stable performance in nearshore waters with similar optical characteristics and is applicable to LGA extraction in adjacent coastal seas, highlighting the potential of HY-1C/D satellite data in marine environmental monitoring and protection. Full article

Background. The aim of this study was to develop a predictive model of anticancer drug therapy toxicity in patients with gastric cancer. Methods. The retrospective study included 100 patients with stage II–IV gastric cancer who underwent 4 chemotherapy cycles. Initial significant toxicity factors included age, gender, height, body mass, body mass index, disease stage, skeletal muscle index (SMI), as well as plasma levels of trace elements (copper, zinc, selenium, manganese) and thyroid-stimulating hormone, cancer histology type and treatment regimen. The CTCAE v5.0 scale was employed to assess the severity of adverse events. Statistical analysis and building of mathematical neural network models were carried out in SPSS Statistics (v19.0). Results. Lower SMI values were associated with higher rates of toxicity-related complications of anticancer drug therapy (p < 0.05): leukopenia, hypoproteinemia, nausea, vomiting, cardiovascular events. Anemia, thrombocytopenia, hepatic cytolysis syndrome, nausea, diarrhea, constipation and stomatitis showed a weaker correlation with SMI. An increase in TSH was associated with higher rates of thrombocytopenia, nausea and vomiting. A decrease in Cu/Zn in plasma correlated with the severity of leukopenia and diarrhea, whereas Se/Mn showed an inverse correlation with the severity of anemia. Conclusions. Sarcopenia, abnormal thyroid status and imbalances in copper, zinc, selenium and manganese in blood plasma of patients with gastric cancer may be used as predictors of increased toxicity of anticancer drug therapy. Full article

This study presents a novel experimental methodology enabling the synchronous observation of strain and stress evolution in granular backfill subjected to active earth pressure. A physical model of plane deformation was used in which a rigid retaining wall was gradually moved away from the ground while simultaneously recording, at each step, both displacement-based images for digital image correlation (DIC) and photoelastic pictures of the force-chain rearrangements. The results show that active failure develops gradually through narrow shear bands, initiated near the wall base and propagating towards the ground surface. A consistent inverse relationship between shear-strain location and photoelastic stress concentration was identified: low-strain zones within the shear wedge in the shear and volumetric strain images correspond to strong force-chain development, whereas high-strain zones (strain localization) correspond to local stress release. These findings provide new experimental evidence regarding the micromechanics of active pressure and offer comparative data for calibrating DEM (discrete element method) models and interpreting the reduced active pressures reported in confined granular backfills. Full article

The design of smart skin with lightweight requirements utilizes high-performance composite materials, resulting in thin structural characteristics. When subjected to complex aerodynamic loads, the smart skin structure experiences warping deformation, which significantly impacts both flight efficiency and structural integrity. However, this deformation behavior has been largely overlooked in current shape sensing methods embedded within the structural health monitoring (SHM) systems of smart skin, leading to insufficient monitoring capabilities. To address this issue, this paper proposes a novel shape sensing methodology for the real-time monitoring of warping deformation in smart skin. Initially, the structural displacement field of the smart skin and the warping function are mathematically defined, incorporating constitutive relations and considering the influence of material parameters on sectional strains. Subsequently, the inverse finite element method (iFEM) is employed to establish a shape sensing model. The interpolation function and the actual sectional strains, derived from discrete strain measurements, are calculated based on the current constitutive equations. Finally, to validate the accuracy of the proposed iFEM for monitoring warping deformation, numerical tests are conducted on curved skin structures. The results indicate that the proposed methodology enhances reconstruction capability, with a 10% improvement in accuracy compared to traditional iFEM methods. Consequently, the shape sensing algorithm can be seamlessly integrated into the SHM system of smart skin to ensure the predicted performance. Full article

Onboard models serve as the foundation for the advanced control and fault diagnosis of aero-engines. Currently, to address the issues of high computational complexity and insufficient real-time performance in component-level aero-engine models, three improvement methods are proposed: constructing the Jacobian matrix along the reverse flow path to avoid redundant calculations; reducing the number of initial guess variables and equations in the engine co-working system through aerothermodynamic analysis, thereby achieving dimensionality reduction in the nonlinear equation sets; and leveraging the minimal variation in Jacobian inverse elements across the full flight envelope to replace them with fixed gains, thus simplifying transient performance calculations. Simulation results demonstrate that, compared to the regular Newton-Raphson method, the reverse flow method reduces the steady-state, regular transient, and small-step transient calculation time by 27.6%, 33.9%, and 30.8%, respectively, with a maximum relative error within 1.6%; the dimensionality reduction method for equations cuts the steady-state, regular transient, and small-step transient calculation time by 20.1%, 11.4%, and 11.8%, with a maximum relative error within 1.4%; and the constant Jacobian matrix inverse method reduces the calculation time by 50.9% during full flight envelope transient performance simulation, with a maximum relative error below 1.6%. All methods improve real-time performance under rated operating conditions. However, only the reverse flow method preserves both high efficiency and accuracy under off-design operating conditions. Full article

This study reveals the distribution of floating macroalgae Sargassum in the East China Sea and Yellow Sea using HY-1C/D Coastal Zone Imager (CZI) data. A new inversion model, utilizing green and near-infrared bands, was developed for the 50 m resolution CZI data. This model effectively distinguishes Sargassum from Ulva prolifera and is effective in turbid coastal waters. Sargassum spatiotemporal distribution and drift patterns over five years were analyzed. Key findings demonstrate that (1) floating Sargassum exhibits distinct spatiotemporal distribution patterns. Sargassum initially emerges along Zhejiang’s eastern coast in February. During March and April, it concentrates east of Hangzhou Bay. While in May, Sargassum appears in the Yellow Sea, and is distributed near the Shandong Peninsula by June. Small patches of Sargassum are also found in the Yellow Sea from November to January. (2) Its distribution is influenced by various factors like nutrients, temperature, salinity, currents, and winds. Suitable nutrients, temperature, and salinity promote growth, while currents and winds, particularly in April–May, drive its northward drift from the East China Sea into the Yellow Sea. The Yellow Sea population originates from both drifting populations and local growth. (3) This research highlights the utility of HY-1C/D satellite data in coastal zone research, facilitating ecological monitoring and protection. Full article

The integration of an inertial navigation system (INS) with the Global Navigation Satellite System (GNSS) is crucial for suppressing the error drift of the INS. However, traditional fusion methods based on the extended Kalman filter (EKF) suffer from geometric inconsistency, leading to biased estimates—a problem markedly exacerbated under large initial misalignment angles. The invariant extended Kalman filter (IEKF) embeds the state in the Lie group SE₂(3) to establish a more consistent framework, yet two limitations remain. First, its standard update fails to synergize complementary error information within the left-invariant formulation, capping estimation accuracy. Second, velocity and position states converge slowly under extreme misalignment. To address these issues, a multiplicative federated IEKF (MF-IEKF) was proposed. A geometrically consistent state propagation model on SE₂(3) is derived from multiplicative IMU pre-integration. Two parallel, mutually inverse left-invariant error sub-filters (ML₁-IEKF and ML₂-IEKF) cooperate to improve overall accuracy. For large-misalignment scenarios, a short-term multiplicative right-invariant sub-filter is introduced to suppress initial position and velocity errors. Extensive Monte Carlo simulations and KITTI dataset experiments show that MF-IEKF achieves higher navigation accuracy and robustness than ML₁-IEKF. Full article

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on the protection characteristics of traditional distribution networks. With protection selectivity and the physical constraints of protection devices as conditions, an optimization model for inverse-time overcurrent protection is established, aiming to minimize the total operation time. To enhance the solution capability for this complex optimization problem, the standard Osprey Optimization Algorithm (OOA) is improved through the incorporation of three strategies: arccosine chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic spiral search strategy combining mechanisms from the Whale and Marine Predators algorithms. Based on this improved algorithm, an optimized protection scheme for distribution networks with distributed PV is proposed. Simulations conducted in PSCAD/EMTDC (V4.6.2) and MATLAB (R2023b) verify that the proposed method effectively prevents protection maloperation and failure-to-operate under both fault current contribution and extraction scenarios of PV, while also reducing the overall relay operation time. Full article