Search Results (22,114)

Background and Objectives: Malnutrition is one of the most common complications in patients undergoing hemodialysis (HD) and is closely linked to increased morbidity and mortality. This study aimed to investigate the nutritional status of HD patients and the clinical relevance of bioelectrical impedance analysis (BIA) parameters such as the percent body fat (PBF), skeletal muscle mass index (SMI), extracellular water-to-total body water ratio (ECW/TBW), and phase angle (PhA) in assessing malnutrition in Vietnamese HD patients. Materials and Methods: This cross-sectional study was conducted among 184 patients undergoing hemodialysis in Hanoi, Vietnam. The BIA parameters were measured by the InBody S10 body composition analyzer, while malnutrition was assessed by the geriatric nutritional risk index (GNRI), with a GNRI <92 classified as a high risk of malnutrition. The independent BIA variables for predicting malnutrition and its cut-off values were explored using logistic regression models and a receiver operating characteristic (ROC) curve analysis, respectively. Results: Among the study population, 42.9% (79/184) of patients were identified as being at a high risk of malnutrition. The multivariate logistic regression analysis revealed that a higher ECW/TBW was independently associated with an increased risk of malnutrition, while the PBF, SMI, and PhA expressed significant and inverse associations with the malnutrition risk after adjusting for multiple confounders. The cut-off values for predicting the high risk of malnutrition in overall HD patients were determined to be 20.45%, 7.75 kg/m², 5.45°, and 38.03% for the PBF, the SMI, the PhA, and the ECW/TBW ratio, respectively. Conclusions: BIA parameters, including the PBF, SMI, PhA, and ECW/TBW ratio, could serve as indicators of malnutrition in general Vietnamese patients with HD. Full article

The ataxia–telangiectasia-mutated (ATM) protein plays a crucial role in the DNA damage response, particularly in the homologous recombination (HR) pathway. This study aimed to assess the impact of deleterious ATM variants on homologous recombination deficiency (HRD) and response to PARP inhibitors (PARPi) in melanoma patients, using a cell line established from melanoma tissue of a patient carrying the c.5979_5983del germline ATM variant. Despite proven loss of heterozygosity, lack of ATM activation, and HRD, our model did not show sensitivity to PARPi. We assessed the potential contribution of the Schlafen family member 11 (SLFN11) helicase, whose expression is inversely correlated with PARPi sensitivity in other cancers, to the observed resistance. The ATM mutant cell line lacked SLFN11 expression and featured hypermethylation-mediated silencing of the SLFN11 promoter. While sensitive to the ATR inhibitor (ATRi), the addition of ATRi to PARPi was unable to overcome the resistance. Our findings suggest that ATM mutational status and HRD alone do not adequately account for variations in sensitivity to PARPi in our model. A comprehensive approach is essential for optimizing the exploitation of DNA repair defects and ultimately improving clinical outcomes for melanoma patients. Full article

Introduction: Chronic kidney disease (CKD) increases cardiovascular risk through mechanisms such as oxidative stress and the accumulation of advanced glycation end products (AGEs). Glycated albumin (GA) is associated with cardiovascular risk in CKD patients, but its relationship with AGEs and systemic inflammation remains unclear. This study investigated these associations in old patients with severe CKD, with and without diabetes. Methods: We conducted a cross-sectional analysis in 122 patients aged ≥ 65 years with CKD stages G3a–G5, including 67 diabetics and 55 non-diabetics. Patients with confounding comorbidities were excluded. We measured GA, AGEs, various AGEs receptors (RAGE) isoforms, and inflammatory cytokines (CRP, IL-6, TNFα, and MCP-1) using standardized assays. Statistical analyses included group comparisons, correlation coefficients, and multivariate regression. Results: Of 122 patients (mean age 77.7 ± 11.3 years), diabetics had higher GA percentages than non-diabetics (22.0 ± 7.1% vs. 17.5 ± 5.4%, p = 0.0001), while AGEs (2931 ± 763 vs. 3156 ± 809 AU; p = 0.118) and inflammatory markers (CRP 0.240[0.380] vs. 0.200[0.280] mg/dL; p = 0.142; IL-6 3.4[4.0] vs. 3.0[3.8] pg/mL; p = 0.238) were similar between groups. Overall, GA was inversely correlated with estimated glomerular filtration rate (eGFR) (ρ = −0.189, p = 0.037) and positively with glycated hemoglobin (HbA1c) (ρ = 0.525, p < 0.0001), but showed no significant correlation with AGEs, RAGE isoforms, or inflammatory cytokines. In multivariate analysis, only HbA1c remained independently associated with GA (β = 0.222, p = 0.005). Conclusions: In old patients with severe CKD, GA appears to be a more useful marker of glycemic control than glycation stress, the latter of which is the result of multiple factors, including impaired kidney function and systemic inflammation. Full article

Background/Objectives: The simulation of human movement offers transformative potential for the design of medical devices, particularly in understanding the cause–effect dynamics in individuals with neurological or musculoskeletal impairments. This study presents a simulation-driven framework to determine the optimal ankle–foot orthosis (AFO) stiffness for mitigating the risk of ankle sprains due to excessive subtalar inversion during high-impact activities, such as landing from a free fall. Methods: We employed biomechanical simulations to assess the influence of translational stiffness on subtalar inversion control, given that inversion angles exceeding 25 degrees are strongly correlated with injury risk. Simulations were conducted using a musculoskeletal model with and without a passive AFO; the stiffness varied in three anatomical directions. A Design of Experiments (DoE) approach was utilized to capture nonlinear interactions among stiffness parameters. Results: The results indicated that increased translational stiffness significantly reduced inversion angles to safer levels, though direction–dependent effects were noted. Based on these insights, we developed a 4D visualization tool that integrates simulation data with an interactive color–coded interface to depict ”safe design” zones for various AFO stiffness configurations. This tool supports clinicians in selecting stiffness values that optimize both safety and functional performance. Conclusions: The proposed framework enhances clinical decision-making and engineering processes by enabling more accurate and individualized AFO designs. Full article

The prevailing environmental conditions before and during the 28 Large Forest Fires (LFFs) that have occurred in the Canary Islands since 1983 are analyzed. These conditions are often associated with episodes characterized by the advection of continental tropical air masses originating from the Sahara, which frequently result in intense heatwaves. During the onset of the LFFs, the base of the subsidence thermal inversion layer—separating a lower layer of cool, moist air from an upper layer of warm, dry air—is typically located at an altitude of around 350 m above sea level, approximately 600 m below the usual average. Understanding these Saharan air advection events is crucial, as they significantly alter the vertical thermal structure of the atmosphere and create highly conducive conditions for wildfire ignition and spread in the forested mid- and high-altitude zones of the archipelago. Analysis of meteorological records from various weather stations reveals that the average maximum temperature on the first day of fire ignition is 30.3 °C, with mean temperatures of 27.4 °C during the preceding week and 28.9 °C throughout the fire activity period. Relative humidity on the ignition days averages 24.3%, remaining at around 30% during the active phase of the fires. No significant correlation has been found between dry or wet years and the occurrence of LFFs, which have been recorded across years with widely varying precipitation levels. Full article

A method for configuration synthesis of a reconfigurable decoupled parallel mechanical leg is proposed. In addition, a configuration evaluation index is proposed to evaluate the synthesized configurations and select the optimal one. Kinematic analysis and performance optimization of the selected mechanism’s configuration are carried out, and the motion mode of the robot’s reconfigurable mechanical leg is selected according to the task requirements. Then, the robot’s gait in walking mode is planned. Firstly, based on bionic principles, the motion characteristics of a mechanical leg based on a mammalian model and an insect model were analyzed. The input and output characteristics of the mechanism were analyzed to obtain the reconfiguration principle of the mechanism. Using type synthesis theory for the decoupled parallel mechanism, the configuration synthesis of the chain was carried out, and the constraint mode of the mechanical leg was determined according to the constraint property of the chain and the motion characteristics of the moving platform. Secondly, an evaluation index for the complexity of the reconfigurable mechanical leg structure was developed, and the synthesized mechanism was further analyzed and evaluated to select the mechanical leg’s configuration. Thirdly, the inverse position equations were established for the mechanical leg in the two motion modes, and its Jacobian matrix was derived. The degrees of freedom of the mechanism are completely decoupled in the two motion modes. Then, the workspace and motion/force transmission performance of the mechanical leg in the two motion modes were analyzed. Based on the weighted standard deviation of the motion/force transmission performance, the global performance fluctuation index of the mechanical leg motion/force transmission is defined, and the structural size parameters of the mechanical leg are optimized with the performance index as the optimization objective function. Finally, with the reconfigurable mechanical leg in the insect mode, the robot’s gait in the walking operation mode is planned according to the static stability criterion. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

To investigate the influence of different water quality conditions on the underwater transmission performance of laser communication signals, this paper systematically analyzes the absorption and scattering characteristics of the underwater laser communication channel, and constructs a transmission model of laser propagation in water, so as to explore the transmission influence mechanism under typical water quality environments. On this basis, a system of in situ measurements for underwater laser channel attenuation is designed and constructed, and several sets of experiments are carried out to verify the rationality and applicability of the model. The collected experimental data are denoised by the fusion of wavelet analysis and adaptive Kalman filtering (DWT-AKF in short) algorithm, and compared with the data measured by an underwater hyperspectral Absorption Coefficient Spectrophotometer (ACS in short), which shows that the channel attenuation coefficients of the model inversion and the measured values are in high agreement. The research results provide a reliable theoretical basis and experimental support for the performance optimization and engineering design of the underwater laser communication system. Full article

Background: Clinical diagnostics, food industries, and biotechnological processes typically use an enzyme called alpha-amylase to metabolize carbohydrates. Objective: The aim of this study is to investigate how low-level laser irradiation (LLLI) affects alpha-amylase activity towards determining the usability of LLLI in non-invasive enzymatic modulation. Methods: Enzyme solutions were irradiated at 10, 20, 30, and 40 J/cm² utilizing 589 nm and 532 nm diode-pumped solid-state lasers. The iodine–starch colorimetric method was used to quantify post-irradiation enzymatic activity, with inverse correlations found between absorbance and activity levels. Modulation was determined by the wavelength and dosage. Results: Enzymatic activity significantly improved when utilizing 589 nm irradiation at lower doses, maximizing at 120% at 20 J/cm² (p < 0.01). Neutral or inhibitory effects were revealed when higher doses were applied. Enzymatic activity showed progressive inhibition when 532 nm irradiation was applied, declining to 75% at 40 J/cm² (p < 0.01). Conclusions: These outcomes indicate that conformational flexibility and catalytic efficiency occur when applying lower-energy photons at 589 nm, whilst oxidative stress and impaired enzymatic function are induced by higher-energy photons at 532 nm. This is consistent with the biphasic dose–response characteristic of photobiomodulation. 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

Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in the Ulsan petrochemical complex, South Korea. A vehicle-mounted Me-DOAS system conducted monthly measurements throughout 2024, capturing data during four daily intervals to evaluate diurnal variation. Routes included perimeter loops and grid-based transects within core industrial zones. The highest benzene concentrations were observed in February (mean: 64.28 ± 194.69 µg/m³; geometric mean: 5.13 µg/m³), with exceedances of the national annual standard (5 µg/m³) in several months. Notably, nighttime and early morning sessions showed elevated levels, suggesting contributions from nocturnal operations and meteorological conditions such as atmospheric inversion. A total of 179 exceedances (≥30 µg/m³) were identified, predominantly in zones with benzene-handling activities. Correlation analysis revealed a significant relationship between high concentrations and specific emission sources. These results demonstrate the utility of Me-DOAS in capturing spatiotemporal emission dynamics and support its application in exposure risk assessment and industrial emission control. The findings provide a robust framework for targeted management strategies and call for integration with source apportionment and dispersion modeling tools. Full article

Transpiration cooling that utilizes the phase change of a liquid coolant is recognized as an effective thermal protection technique for extreme environments. However, the introduction of phase change within the porous structure brings about challenges, such as vapor blockage, pressure fluctuations, and temperature inversion, which critically influence system reliability. This study conducts numerical analyses of coupled processes of heat transfer, flow, and phase change in transpiration cooling using a Two-Phase Mixture Model. The simulation incorporates a Local Thermal Non-Equilibrium approach to capture the distinct temperature fields of the solid and fluid phases, enabling accurate prediction of the thermal response within two-phase and single-phase regions. The results reveal that under low heat flux, dominant capillary action suppresses dry-out and expands the two-phase region. Conversely, high heat flux causes vaporization to overwhelm the capillary supply, forming a superheated vapor layer and constricting the two-phase zone. The analysis also explains a paradoxical pressure drop, where an initial increase in flow rate reduces pressure loss by suppressing the high-viscosity vapor phase. Furthermore, a local temperature inversion, where the fluid becomes hotter than the solid matrix, is identified and attributed to vapor counterflow and its subsequent condensation. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

Traps are central to petroleum exploration, where hydrocarbons accumulate during migration. Reservoirs are likewise an essential petroleum system element and serve as the primary medium for hydrocarbon storage. The buried hill is a geological formation highly favorable for reservoir development. However, the factors influencing hydrocarbon accumulation in buried hill reservoirs are highly diverse, especially in areas with complex, active fault systems. Fault systems play a dual role, both in the formation of reservoirs and in the migration of hydrocarbons. Therefore, understanding the impact of complex fault systems helps enhance the exploration success rate of buried hill traps and guide drilling deployment. In the Beibuwan Basin in the South China Sea, buried hill traps are key targets for deep-buried hydrocarbon exploration in this faulted basin. The low level of exploration and research in buried hills globally limits the understanding of hydrocarbon accumulation conditions, thereby hindering large-scale hydrocarbon exploration. By using drilling data, logging data, and seismic data, stress fields and tectonic faults were restored. There are two types of buried hills developed in the Beibuwan Basin, which were formed during the Late Ordovician-Silurian period and Permian-Triassic period, respectively. The tectonic genesis of the Late Ordovician-Silurian period buried hills belongs to magma diapirism activity, while the tectonic genesis of the Permian-Triassic period buried hills belongs to reverse thrust activity. The fault systems formed by two periods of tectonic activity were respectively altered into basement buried hills and limestone buried hills. The negative structural inversion controls the distribution and interior stratigraphic framework of the deformed Carboniferous strata in the limestone buried hill. The faults and derived fractures of the Late Ordovician-Silurian period and Permian-Triassic period promoted the diagenesis and erosion of these buried hills. The faults formed after the Permian-Triassic period are not conducive to calcite cementation, thus facilitating the preservation of the reservoir space formed earlier. The control of hydrocarbon accumulation by the fault system is reflected in two aspects: on the one hand, the early to mid-Eocene extensional faulting activity directly controlled the depositional process of lacustrine source rocks; on the other hand, the Late Eocene-Oligocene, which is closest to the hydrocarbon expulsion period, is the most effective fault activity period for connecting Eocene source rocks and buried hill reservoirs. This study contributes to understanding of the role of complex fault activity in the formation of buried hill traps within hydrocarbon-bearing basins. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article