Search Results (1,188)

Vascular steal refers to the diversion of blood flow between collateral vessels that share a common inflow restricted by arterial stenosis. Blood is diverted from the high-pressure to the low-pressure, low-resistance system. Vascular steal is associated with anatomical bypass or vasodilation in the collateral network and is called “the arterial steal”. However, we have demonstrated that in the presence of an outflow gradient (e.g., intra-extracranial), blood is shunted to a lower pressure system, a phenomenon we term “venous steal”. Using Thevenin’s equivalent, we generalized the concept of venous steal to apply it to any region of the vascular system with increased outflow pressure. Both arterial steal, caused by increased collateral network conductivity, and venous steal, resulting from lower collateral outflow pressure, reduce compartment perfusion. This occurs indirectly by increasing flow and the pressure gradient across the arterial stenosis, lowering the segmental compartment perfusion pressure—the difference between post-stenotic (inflow) and compartmental (outflow) pressures. Venous steal diverts blood flow from compartments with elevated pressure, such as intracranial, subendocardial, the ischemic core, and regions of focal edema due to inflammation, trauma, or external compression. In shock and low-flow states, it contributes to regional blood flow maldistribution. Treatment of venous steal addresses inflow stenosis, increased compartmental pressure and systemic loading conditions (arterial and venous pressure) to reverse venous steal malperfusion in the ischemic regions. Full article

Phacelia secunda J.F. Gmel. (Boraginaceae) is a widely distributed insect-pollinated perennial herb. In central Chile (33° S), it occurs from the sea level up to 3600 m in the Andes, exhibiting broad morphological variation. In this study, we developed and characterized novel polymorphic microsatellites for this species, using an Illimina MiSeq sequencing platform. Nineteen polymorphic loci were obtained, with alleles numbers ranging from 3 to 13 per locus (mean = 5.84). Observed (H_O) and expected heterozygosities (H_E) ranged from 0.050 to 0.900 and from 0.049 to 0.825, respectively. These markers were applied to assess the genetic diversity and population structure along an altitudinal spanning from 1600 to 3600 m. The highest elevation population exhibited significantly lower within-population genetic diversity compared to lower-elevation populations. Significant population differentiation was observed along the gradient. Gene flow estimates support a stepping-stone like mode of migration, with greater exchange between adjacent elevations. These new microsatellites provide a valuable tool for elucidating the influence of altitude on genetic diversity and structure, and for evaluating the roles of local adaptation and phenotypic plasticity in shaping population variation. Full article

Elevated levels of cadmium (Cd) and lead (Pb) in the edible parts of rice (Oryza sativa L.) grown in agricultural soils may enter the human body through the food chain, posing significant health risks. In this study, rice and paired rhizosphere soil samples were collected from 194 locations in Jiangsu Province, China, with 60 samples selected for diffusive gradients in thin films (DGT) extraction analysis. The findings indicate that total soil concentrations of Cd and Pb are inadequate for assessing cadmium bioavailability, implying that current soil quality standards may not accurately reflect the bioaccessible fractions of these metals. Both DGT and soil solution measurements effectively predicted crop Cd levels, with the effective concentration (C_E) derived from the DGT-induced soil flux (DIFS) model showing the strongest correlation with rice Cd content. Pearson correlation analysis and a random forest (RF) model further identified critical factors influencing rice uptake of Cd and Pb, including soil iron (Fe) content, cation exchange capacity (CEC), pH, and the levels of zinc (Zn) and selenium (Se), which antagonize Cd uptake. Full article

Open-pit mining involves the use of vehicles with high load capacity and satisfactory mobility. As experience shows, these requirements are fully met by pneumatic wheeled dump trucks, the traction drives of which can be made using thermal or electric machines. The latter are preferable due to their environmental friendliness. Unlike dump trucks with thermal engines, which require fuel to be injected into them, electric trucks can be powered by various options of a power supply: centralized, autonomous, and combined. This paper highlights the advantages and disadvantages of different power supply systems depending on their schematic solutions and the quarry parameters for all the variants of the power supply of the dumper. Each quantitative indicator of each factor was changed under conditions consistent with the others. The steepness of the road elevation in the quarry and its length were the factors under study. The studies conducted show that the energy consumption for dump truck movement for all variants of a power supply practically does not change. Another group of factors consisted of electric energy sources, which were accumulator batteries and double electric layer capacitors. The analysis of energy efficiency and the regenerative braking system reveals low efficiency of regeneration when lifting the load from the quarry. In the process of lifting from the lower horizons of the quarry to the dump and back, kinetic energy is converted into heat, reducing the efficiency of regeneration considering the technological cycle of works. Taking these circumstances into account, removing the regenerative braking systems of open-pit electric dump trucks hauling soil or solid minerals from an open pit upwards seems to be economically feasible. Eliminating the regenerative braking system will simplify the design, reduce the cost of a dump truck, and free up usable volume effectively utilized to increase the capacity of the battery packs, allowing for longer run times without recharging and improving overall system efficiency. The problem of considering the length of the path for energy consumption per given gradient of the motion profile was solved. Full article

Slowly moving active landslides threaten infrastructure, particularly along highway routes traversing active mountainous ranges. Detecting and characterizing such landslides in highly elevated mountainous terrains is challenging due to their inaccessibility, wide area coverage, limited approaches, and the complex nature of mass movements. In this study, we processed Sentinel-1 Synthetic Aperture Radar data acquired from 2015 to 2024 to detect active landslides along the Karakoram Highway alternate route (Chitral-Gilgit) and the Karakoram Highway part (Gilgit-Khunjerab). We detected 1037 active landslides in the study region using phase gradient stacking and a deep learning network. Based on the detection, we applied time series InSAR analysis to reveal the velocity and deformation series for some large-scale landslides, revealing high displacement rates with line-of-sight velocities reaching up to −81 mm/yr. We validated our detections by comparing them with Google Earth imagery and the previously published landslide inventories along the Karakoram Highway. This study reveals the spatial distribution of active landslides along the uplifted mountainous terrain, highlighting potentially unstable zones, and offers insights into hazard mitigation and risk analysis, especially for less monitored economic roads in orogenic zones. Full article

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

Background and Objectives: Transcatheter aortic valve implantation is associated with a higher risk for elevated trans-prosthetic gradients and prosthesis-patient mismatch in patients with a small aortic annulus. We aimed to assess the short-term hemodynamic performance of self-expanding transcatheter aortic valves with an intra-annular leaflet position in patients with small aortic anatomies. Materials and Methods: Consecutive patients with small aortic annuli (annular area < 430 mm²), who underwent transcatheter aortic valve implantation with a self-expanding Portico or Navitor (Abbott Medical, St. Paul, MN, USA) transcatheter aortic valve between October 2017 and August 2024 at the University Medical Centre Ljubljana, Slovenia, were analyzed. The main endpoints were the post-procedural mean trans-prosthetic gradient, the presence of moderate or severe prosthesis-patient mismatch or paravalvular regurgitation. Results: Overall, 37 patients were included in the study (29 patients with a native aortic valve and 8 patients undergoing valve-in-valve transcatheter aortic valve implantation). The mean age was 81.6 ± 4.3 years, 32 patients (86.5%) were female. The median annular perimeter was 70.8 mm (interquartile range 67.3–74.1 mm) and the median annular area was 379 mm² (interquartile range 355–412 mm²). The post-procedural mean trans-prosthetic gradient was 9.0 ± 3.5 mmHg, with no cases with a mean gradient > 20 mmHg. Moderate and severe prosthesis-patient mismatch was observed in 21.2% and 3.0% of patients, respectively. Mild paravalvular regurgitation was noted in 44.1% of patients, there were no cases of moderate or severe paravalvular regurgitation. One patient (3.0%) had moderate valvular regurgitation. Conclusions: Self-expanding transcatheter aortic valves with an intra-annular leaflet position are associated with favorable hemodynamic performance in patients with a small aortic annulus. Full article

The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a cloud-driven data analytics pipeline for indoor agriculture, using lettuce as a test crop due to its suitability for controlled environments. Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. Random Forest analysis identified light intensity as the most influential factor (importance: 0.7), while multivariate regression highlighted phosphorus (0.54) and temperature (0.23) as key contributors to plant growth. Nitrogen exhibited a strong positive correlation (0.85) with growth, whereas excessive moisture (–0.78) and slightly elevated temperatures (–0.24) negatively impacted plant development. To enhance resource efficiency, this study introduces the Integrated Agricultural Efficiency Metric (IAEM), a novel framework that synthesizes key factors, including resource usage, alert accuracy, data latency, and cloud availability, leading to a 32% improvement in resource efficiency. Unlike traditional productivity metrics, IAEM incorporates real-time data processing and cloud infrastructure to address the specific demands of modern indoor farming. The combined approach of scalable ETL (Extract, Transform, Load) pipelines with predictive analytics reduced light use by 25%, water by 30%, and nutrients by 40% while simultaneously improving crop productivity and sustainability. These findings underscore the transformative potential of integrating IoT, AI, and cloud-based analytics in precision agriculture, paving the way for more resource-efficient and sustainable farming practices. Full article

The Gross Primary Productivity (GPP) of Mediterranean forest is expected to change over the 21st century due to the warmer and drier conditions. In this study, we present a process-based forest carbon-flux model, where stand structure and soil heterotrophic respiration have been parameterized with long-term monitoring data in a Mediterranean Pinus brutia. Ten. forest. The developed model was validated using an independent annual tree-ring increment dataset from the 1980–2020 period (baseline climate) across a post-fire gradient (four plots) and an elevation gradient (five plots). Additionally, the model was forced with two downscaled climate change scenarios (RCP4.5 and RCP8.5) for the 2020–2100 period. Average GPP, Net Primary Productivity (NPP), ecosystem Respiration (R_eco) and Net Ecosystem Productivity (NEP) were calculated for two future time periods (2051–2060 and 2091–2100) under the two climate change scenarios and compared along the two gradients. Under baseline climate conditions, our simulations suggest a temperature sensitivity of GPP and R_eco, as expressed along the elevation gradient. However, the effect of stand structure (represented through the site-specific leaf area index (LAI)) was more prominent, both along the elevation gradient and the post-fire chronosequence. Under the two climate change scenarios, a reduced GPP and an increased R_eco lead to reduced NEP compared to baseline climate conditions across all study plots. Full article

Foliar endophytic fungi (FEF) live within leaves without causing visible signs of disease. FEF occur in all vascular plants, yet the exact nature of interactions between specific FEF and their hosts is not well understood. Some FEF are associated with enhanced water use efficiency, nutrient acquisition, and defense. However, others may have negative effects under high-stress conditions. We examined a series of gas exchange traits in sweet birch (Betula lenta, Fagaceae) along an elevation gradient in the Pisgah National Forest Asheville, North Carolina, USA. From these leaves, we cultured surface-sterilized samples to examine FEF frequency and diversity. FEF cultures were categorized by morphotype and identified through analysis of internal transcribed spacer (ITS) sequences. FEF colonization frequency was 100% across all sites, and we identified 68 distinct morphotypes. Genetic identification of a subset of cultures suggests highly diverse FEF communities within this study system. Leaf gas exchange traits showed significant correlations with elevation at the site level, supporting the hypothesis that water stress increases with increasing elevation. However, further research is needed to determine associations between FEF communities and elevation. These findings, especially considering the limited sample size and small spatial scale of this study, indicate that the southern Appalachians are a promising region for future studies of FEF in forest systems. Full article

Background: Atherosclerotic diseases, including coronary heart disease and cerebrovascular disease, are leading causes of morbidity and mortality worldwide. Atherosclerosis is a chronic vascular condition marked by the accumulation of lipid plaque within arterial walls. These plaques can become unstable and rupture, leading to thrombosis and subsequent cardiovascular events. Therefore, early identification of vulnerable plaque is critical for preventing such events. Objectives: This study aims to develop a novel imaging platform for atherosclerotic plaque by designing a molecular imaging probe based on fluorescent molecules that target lipid necrotic cores. The goal is to specifically detect high-risk plaque, enabling early diagnosis and intervention. Methods: Bioinformatic analysis and immunofluorescence were used to detect CD36 expression in human carotid plaque. CD36pep-ICG was synthesized using the Fmoc solid-phase peptide method. A series of experiments was conducted to characterize the probe’s properties. To assess imaging performance, probe concentration gradients were tested using FLI equipment. Ex vivo imaging was performed on atherosclerotic mice and treatment models to evaluate the probe’s targeting ability and effectiveness in monitoring disease progression. Results: The CD36 expression was significantly elevated in the core of plaque compared to distal regions. The CD36pep-ICG probe, specifically designed to target lipids, was successfully synthesized and exhibited excellent fluorescence properties. In animal models, FLI imaging demonstrated that the CD36pep-ICG probe selectively accumulated in atherosclerotic plaque, enabling precise plaque detection. Moreover, the probe was used to monitor the therapeutic efficacy of anti-atherosclerotic drugs. Conclusions: The CD36pep-ICG probe developed in this study is an effective molecular imaging tool for the specific identification of vulnerable atherosclerotic plaque, offering a novel approach for early diagnosis and treatment. Additionally, the probe shows promise in tracking the therapeutic effects of the drug, potentially advancing the precision treatment of cardiovascular diseases. Full article

This study systematically explores the structural stability, mechanical properties, elastic anisotropy, fracture toughness, and thermophysical characteristics of Au₃In and Au₃In₂ intermetallic compounds (IMCs) through density functional theory (DFT) simulations. Employing the generalized gradient approximation (GGA) and the Voigt–Reuss–Hill approximation enables precise predictions of polycrystalline elastic behavior, providing critical insights into the intrinsic stability and mechanical anisotropy of these IMCs. Structural optimization identifies the equilibrium lattice parameters and cohesive energies, indicating stronger atomic bonding and superior structural stability in Au₃In relative to Au₃In₂. Elastic constant calculations confirm mechanical stability and reveal pronounced anisotropic elastic behavior; Au₃In exhibits significant stiffness along the [010] crystallographic direction, while Au₃In₂ demonstrates notable stiffness predominantly along the [001] direction. Both Au₃In and Au₃In₂ exhibit ductile characteristics, confirmed by positive Cauchy pressures and elevated bulk-to-shear modulus (K/G) ratios. Fracture toughness analysis further establishes that Au₃In offers greater resistance to crack propagation compared to Au₃In₂, suggesting its suitability in mechanically demanding applications. Thermophysical property evaluations demonstrate that Au₃In possesses higher thermal conductivity, elevated Debye temperature, and superior volumetric heat capacity relative to Au₃In₂, reflecting its enhanced capability for effective thermal management in electronic packaging. Anisotropy assessments, utilizing both universal and Zener anisotropy indices, reveal significantly higher mechanical anisotropy in Au₃In₂, influencing its practical applicability. Full article

Litter decomposition is a fundamental ecological process that drives nutrient cycling and energy flow. However, little is known about the elevational patterns of this process in different stages. We established ten sites on Dongling Mountain in Beijing, China, to investigate the elevational patterns of oak leaf (Quercus liaotungensis) decomposition, as well as the underlying mechanisms. Our results revealed distinct elevational patterns of litter decomposition in different stages. There was no significant altitudinal pattern in the mass loss of Q. liaotungensis leaves at the 2nd, 4th, and 6th months of decomposition. By the 16th month, the mass loss decreased significantly along the elevation gradient (p = 0.008). By the 28th month, a reverse pattern emerged, with greater mass loss observed at higher elevations (p < 0.001). A similar change also took place in the altitudinal pattern of the abundance of invertebrates within the litter bag, which was lower at higher elevations at the 16th month (p = 0.002), but higher at higher elevations at the 28th month (p = 0.002). In addition, we examined the elevational patterns of carbon and nitrogen concentrations in different stages. The results of the structural equation model revealed that the invertebrate abundance at the 4th month influenced the litter residues at the 16th month (p < 0.001), yet nitrogen content at the 16th month affected litter residues at the 28th month(p < 0.001). This study provides novel insights into the temporal dynamics of litter decomposition along an elevational gradient and highlights the underlying mechanisms by which litter chemistry and biological factors regulate this process. Full article

Recently, the concept of cell-free massive multiple-input multiple-output (CF-mMIMO) has been implemented in low-Earth-orbit (LEO) constellations to enhance energy efficiency. However, signal distortion caused by nonlinear power amplifiers (NPAs) significantly degrades the performance of LEO satellite communication (SATCOM) links. In this paper, we propose a serving cluster scheme and a hybrid precoding framework for CF-mMIMO-assisted LEO SATCOM systems, aiming to suppress the impact of NPAs while reducing onboard hardware complexity and power consumption. Specifically, we first develop a user-centric access point clustering strategy that incorporates elevation constraints and channel fading. Then, we formulate a precoding optimization problem to maximize energy efficiency by treating the product of tightly coupled analog and digital matrices as a single fully digital precoder. The proposed distortion-aware precoding design is achieved by integrating the weighted minimum mean square error (WMMSE) approach with a gradient descent method enhanced by Nesterov’s accelerated momentum. Furthermore, an efficient hybrid precoding method based on alternating minimization is proposed to address the matrix decomposition challenge. Simulation results validate the effectiveness of our proposed user-centric serving clustering algorithm and the WMMSE-based precoding algorithm in CF-mMIMO-assisted LEO SATCOM systems. Full article

In the maritime industry, hydrogen fuel cell ships demonstrate significant potential for development due to their environmental friendliness and high efficiency. However, the risks of fire and explosion caused by hydrogen leakage pose severe challenges to their safety. To enhance the safety of hydrogen fuel cell ships and mitigate the explosion hazards caused by leakage, this study employs the XiFoam solver in the OpenFOAM v9 to establish an explosion model for a full-scale hydrogen fuel cell compartment within a hydrogen fuel cell ship. The model simulates the transient explosion process following high-pressure hydrogen leakage under varying initial hydrogen concentrations and premixed fuel conditions. By analyzing the temporary evolution of temperature distribution, flame front propagation, and explosion pressure, the study provides a comprehensive understanding of the safety implications of hydrogen leakage at different locations within the fuel cell. Specifically, increasing the hydrogen concentration from ΦH₂ = 0.10 to ΦH₂ = 0.18 and ΦH₂ = 0.20 significantly elevates the overpressure peak and accelerates the flame speed from 250 m/s to 370 m/s, with local pressure gradients approaching the deflagration to detonation transition threshold. The simulation results contribute valuable insights into optimizing hydrogen fuel cell design, formulating effective fire safety strategies, and improving overall ship safety. Full article