Search Results (3,904)

Background/Objectives: Vitamin D and mean platelet volume (MPV) have been suggested as biomarkers of obstructive sleep apnea (OSA) in adults, while pediatric data remain limited. We aimed (i) to investigate associations between vitamin D and MPV with the presence and severity of pediatric OSA and (ii) to explore potential associations between vitamin D status and sleep parameters in normal-weight affected children. Methods: A total of 138 children with polysomnography-confirmed OSA and 138 age- and sex-matched controls were enrolled. All participants underwent detailed clinical, biochemical, and overnight sleep assessments. The OSA group was stratified according to disease severity. Results: Vitamin D levels were significantly lower in OSA patients (p < 0.0001), while MPV, C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were significantly higher (all p < 0.0001) than in the controls. Severe OSA was associated with elevated inflammation markers, higher insulin resistance, and lower vitamin D levels (all p < 0.0001). Vitamin D levels were inversely associated with the Apnea–Hypopnea Index (AHI) (R 0.37, adjusted r² 0.13, p < 0.0001) and Oxygen Desaturation Index (ODI) (R 0.36, adjusted r² 0.13, p < 0.0001), even after adjustments (both p < 0.0001). N1 and REM sleep were negatively associated with vitamin D in the OSA group (p = 0.01 and p = 0.02, respectively). Vitamin D deficiency was independently associated with higher odds of OSA (adjusted OR = 6.76, 95% CI: 3.97–11.51, p < 0.0001). Similarly, OSA presence was associated with lower vitamin D levels (aOR = 1.40, 95% CI: 1.06–1.94, p = 0.03). Conclusions: Vitamin D and MPV are associated with the presence and severity of pediatric OSA. Vitamin D levels were related to specific sleep architecture parameters, and MPV appeared to reflect inflammation associated with OSA, supporting their potential utility as biomarkers in pediatric OSA. Full article

Accurate and real-time monitoring of root soil water content (RSWC) is key in optimizing field irrigation decisions and enhancing crop water productivity. However, relying only on the vegetation index as the input to the inversion model may result in lower inversion accuracy due to the canopy spectral saturation effect. To break through the limitation of a single data source, this study constructed an integrated network model (ATT-LSTM) incorporating the attention mechanism based on the long and short-term memory network (LSTM) to enhance the inversion performance by integrating heterogeneous data from multiple sources. The experiment used canopy spectral data based on UAV remote sensing and weather station monitoring data as input features. A control group was set up for cross-validation to realize the accurate inversion of RSWC in kiwifruit plants. The results show that the coefficient of determination (R²) of the ATT-LSTM model on the test set reaches 0.868. This study confirms that the multi-source data fusion framework effectively overcomes vegetation index saturation, improves rhizosphere moisture monitoring accuracy, supports precision irrigation decisions in kiwifruit orchards, and provides a reference for smart agriculture water management optimization. Full article

Primary open angle glaucoma (POAG) is one of the leading causes of irreversible blindness and is associated with dysfunction of the trabecular meshwork (TM), a three-dimensional (3D) structure that regulates aqueous humor outflow and, consequently, intraocular pressure (IOP). IOP is the only modifiable factor for glaucoma. Outflow facility is the inverse of aqueous humor outflow resistance caused by the presence of the TM and adjacent tissues, and reflects the TM’s central role in IOP control, representing the most physiologically relevant measure of human trabecular meshwork (HTM) function. Therefore, development of ex vivo systems to study outflow facility and IOP regulation is critical for advancing glaucoma research. We present a comprehensive review of bioengineering approaches to generation of 3D HTM models using synthetic, natural, and hybrid hydrogels, micro- and nanofabricated synthetic substrates or porous scaffolds, and microfluidic devices. These 3D HTM systems have been designed to capture key features such as topography, stiffness, and fluid flow in the conventional outflow pathway. In particular, we highlight HTM models that recapitulate IOP regulation and allow measurement of outflow facility, which directly reflect pressure-dependent outflow resistance in dynamic HTM physiology and glaucoma pathophysiology. By integrating these bioengineering approaches with emerging stem cell technologies, this review offers an evidence-based landscape overview and framework for designing next-generation 3D human-relevant TM models for outflow physiological studies and IOP-modulating drug discovery. Full article

Calcareous interbeds are widely developed in marine clastic sequences, where laterally continuous, tight calcareous interbeds act as critical controls on the formation of lithologic traps and the distribution of oil. However, the genetic mechanisms and development models of these interbeds, particularly under deep-burial conditions subject to complex fluid interactions, remain poorly understood. Using the Donghe Sandstone in the Hade Oilfield (Tarim Basin) as a case study, this paper investigates the genetic evolution of calcareous interbeds via an integrated approach combining core observation, thin-section petrography, scanning electron microscopy (SEM), stable isotope analysis, fluid inclusion microthermometry, and heavy fraction analysis. The results indicate that: (1) The carbonate cements within the interbeds are compositionally complex, dominated by calcite but characterized by a diagnostic assemblage of anhydrite, ferroan calcite, and ankerite. (2) During the depositional to shallow burial stages, seawater evaporation and meteoric freshwater influx led to the supersaturation of calcium-rich pore waters near the surface. This facilitated the precipitation of early cement assemblages, which are predominantly of freshwater origin and consist mainly of non-ferroan calcite nodules, dolomite, and anhydrite. (3) During the deep burial stage, the injection of high-salinity brines and organic acid decarboxylation triggered Thermochemical Sulfate Reduction (TSR). This process caused the extensive consumption of the pre-existing anhydrite and the formation of authigenic pyrite, followed by the tight occlusion of remaining porosity through the precipitation of late-stage ferroan calcite and ankerite. (4) In the broad slope setting, these tight calcareous interbeds constitute effective flow barriers, resulting in a stepped distribution of the oil–water contact. Within the reservoir compartments segmented by these interbeds, crude oil maturity exhibits a distinct inversion (i.e., higher maturity below the interbeds and lower maturity above), confirming the critical sealing capacity of the interbeds during hydrocarbon accumulation. Ultimately, this study establishes a genetic model coupling calcareous interbed development with deep-burial fluid alteration, providing new geological insights for predicting subtle traps in marine sandstone reservoirs. Full article

The adsorption and desorption behavior of arsenic (As) in agricultural soils is a critical process controlling its migration, transformation, and bioavailability, with direct implications for food safety and environmental risk. Although black soil regions are major grain-producing areas in China, the roles of different soil microaggregate fractions and their components in As retention remain poorly understood. Therefore, batch equilibrium adsorption experiments were performed to study the adsorption and desorption behaviors of As(V) on the different microaggregates and to explore the effects of soil particle size, organic matter and iron oxide on the adsorption performance of As(V). The results show that as the concentration of As(V) increases, the adsorption capacity gradually reaches equilibrium, and the Freundlich equation fits the results well. The order of the As adsorption capacity of microaggregates of different particle sizes in black soil is as follows: (<0.002 mm) > 0.005–0.05 mm > 0.002–0.005 mm > 0.05–0.25 mm > 0.25–2 mm. The maximum adsorption capacity occurs in the microaggregates of the soil with the smallest particle size. The order of the As desorption capacity of microaggregates of each particle size is opposite to their adsorption capacity and inversely proportional to the content of each component in the soil. Removal of soil organic matter (SOM) and free iron oxide (Fe_d) significantly reduced the specific adsorption and immobilization capacity of black soil for As(V), while enhancing non-specific adsorption. This study elucidates the differential contributions of soil microaggregates and key components to As(V) retention and provides an experimental foundation for further research on the occurrence and migration mechanisms of As in black soil. Full article

Surgical robots require sub-millimeter accuracy and reliable inverse kinematics across anatomies. Population-based metaheuristics address this, but static parameters may limit achieving the needed precision for clinical use. This study introduces the Rough Sets Meta-Heuristic Schema (RSMS) for dynamic, context-aware control. RSMS categorizes agents (Elite, Boundary, Poor) via Rough Set discretization based on fitness and distribution, allocating resources accordingly without problem-specific heuristics. To demonstrate the approach’s effectiveness, RSMS was implemented within Particle Swarm Optimization and evaluated as a surgical robotics inverse kinematics solver and path planner. In simulations using three surgical problems, RS-PSO allowed upgrading of the performance of the standard PSO in terms of consistent convergence and success in tight search spaces. Statistical tests confirmed these improvements. Using a 7-DOF KUKA LBR iiwa robot and surgical benchmarks of landmark acquisition, spiral trajectory tracking, and constrained path, RS-PSO achieved success rates of 100%, 67%, and 100%, respectively, meeting surgical requirements. The results demonstrate clinical gains in accuracy, consistency, and reproducibility for minimally invasive surgery. These findings support the practical advantages of RS-PSO and, more importantly, show that the RS-MH framework can be used as a general, reusable tool to improve the robustness, precision, and reproducibility of many swarm-based meta-heuristics for surgical robotics and other applications. Full article

The dynamic evolution of microbubbles under ultrasonic excitation is fundamental to applications ranging from targeted drug delivery to acoustic cleaning. This study employs a synchronous high-speed microscopic imaging system to systematically investigate the size-dependent stability and fragmentation of air microbubbles (R₀ = 25–82.5 μm) in a free field at a driving frequency of 16.6 kHz. Our results demonstrate a clear mechanistic transition from stable radial oscillations to complex surface instabilities and, eventually, deterministic fragmentation. Smaller bubbles (R₀ < 55 μm) exhibit long-term stability, transitioning through higher-order surface modes (n = 3 to n = 4) as surface energy accumulates. In contrast, larger bubbles (R₀ > 60 μm) undergo violent non-spherical deformations characterized by centripetal necking and high-speed micro-jetting. Notably, we identify an inverse relationship between initial radius and fragmentation onset time, with larger bubbles reaching instability thresholds significantly earlier. Furthermore, a transition from stochastic breakup to bimodal, volume-symmetric splitting was observed as R₀ increased, where daughter bubbles reached comparable volumes. These findings provide a theoretical and empirical basis for the controlled generation of monodisperse microbubble clouds, offering significant potential for enhancing the efficacy of ultrasonic contrast agents and therapeutic cavitation. Full article

Time-series interferometric synthetic aperture radar (TS-InSAR) has become a widely used technique for monitoring surface deformation with high spatial and temporal resolution. The recent rise in cloud-based InSAR platforms has significantly accelerated the production of interferograms. However, the accuracy of deformation inversion remains limited by fundamental issues affecting interferogram quality, including temporal and spatial decorrelation and phase unwrapping errors. These degrading effects are most pronounced in vegetated, desert, and snow-covered terrains, which are common in active tectonic zones and thereby exert a major impact on the quality of the unwrapped phase. Traditional quality control methods are inefficient or inadequate for large-scale analysis, and discarding low-quality data reduces the inversion accuracy. To address these limitations, we developed a deep learning-based approach to automatically assess interferogram quality and integrate it into the time-series InSAR inversion workflow. We utilized Sentinel-1 interferograms generated by the COMET-LiCSAR system as the primary data source. Based on this dataset, we developed a multi-stage selection strategy for interferogram quality control, integrating loop phase closure analysis, statistical indicators (including coherence and phase standard deviation), and manual verification. As a result, we constructed a high-quality labeled dataset comprising approximately 20,000 samples. An improved ConvNeXt-InSAR model was designed and trained to automatically quantify the quality of each pixel in individual interferograms. The model generates pixel-wise quality maps, which are then incorporated as weight constraints in the time-series InSAR network inversion. The proposed method was applied to the interseismic deformation reconstruction in the central-southern Tibetan Plateau region. This study highlights the potential of deep learning-based interferogram quality assessment in facilitating large-scale, automated time-series InSAR processing. Full article

High-precision inversion of shallow-water depth is crucial to marine resource development, ecological protection, and national defense security. Traditional acoustic detection, LiDAR, and empirical models are limited by high cost, low efficiency, or water quality dependence, struggling to meet people’s growing demand for shallow-water depth. With the rapid development of theories and technologies such as remote sensing information, computer science, and artificial intelligence, bathymetric inversion based on remote sensing images and deep learning models has become a research hotspot. In this study, journal articles and conference papers were searched in the Web of Science (WOS) and Google Scholar databases using keywords such as “remote sensing image”, “bathymetry”, and “deep learning model”. The publication time of the papers ranges from January 2021 to September 2025. A total of 309 relevant studies were retrieved and, after screening and quality control, 132 core studies were finally selected as the research objects for this review. These studies were classified according to deep learning models, including CNN, U-Net, MLP, and RNN. The study analyzed and summarized the characteristics of different deep learning models in bathymetric inversion, as well as their data source selection, inversion accuracy, and limitations. Additionally, the future development trends were discussed in combination with the latest research results. Full article

Monitoring the hydrochemistry of groundwater and the H-O isotopes in the Jingpo Lake volcanic area, China, is fundamental to studying the mechanisms of volcanic and seismic events, as well as the associated hazards. To study the hydrogeochemistry of fluids in the Jingpo Lake volcanic area, water samples from seven sites were tested for hydrogeochemistry, H-O isotopes, and radon (Rn) content. The genesis and evolution of the groundwater system were elucidated through an integrated approach employing Gibbs diagrams, ionic ratio analyses, reservoir temperature estimation (silica–enthalpy method), and inverse geochemical modeling with PHREEQC. The results showed that the dominant water chemistry type was HCO₃⁻, primarily influenced by volcanic rock weathering and deep hydrothermal activity. Spring and well water were influenced by cation exchange, adsorption, and rock weathering dissolution. The H-O isotope composition and radon content indicate that atmospheric precipitation is the main source of supply, while well water is influenced by deep fluids. According to the Na-K-Mg triangle diagram, most of the groundwater was shallow and immature, whereas the well water was partially balanced. The temperature of the geothermal water was controlled by the geothermal gradient, depending on its occurrence and circulation depth. Additionally, the equilibrium temperature of the thermal reservoir was calculated using the silica–enthalpy equation method, with the concentrations of dissolved components in the water taken into account. The temperature of the thermal reservoir of the well water and the depth of groundwater circulation were estimated. The original reservoir temperature in the study area was calculated to range from 108 °C to 156 °C, with a geothermal water-to-shallow groundwater mixing ratio of between 71% and 85%. The estimated shallow temperature ranged from 64.9 °C to 74.9 °C. These hydrogeochemical signatures reflect active water–rock interactions and the contribution of deep-seated geothermal fluids, providing robust evidence for ongoing geothermal activity in the Jingpo Lake volcanic system. The findings enhance our understanding of the recent geological evolution and present-day hydrothermal processes of this potentially active volcanic field, which establishes a crucial hydrogeochemical baseline for future monitoring and hazard assessment studies. Full article

Analytical models of transdermal drug delivery (TDD) often represent deeper skin layers using ideal sink assumptions or phenomenological interfacial resistances. While mathematically convenient, these approaches obscure the physical role of the dermis and hypodermis in controlling molecular transport. Here, we develop an impedance-based analytical model for diffusion across multilayer skin membranes, in which the epidermal barrier is dynamically coupled to a finite diffusive backing layer representing the dermis–hypodermis composite. Diffusion impedance links transport conductivity, storage capacity, and layer thickness, while preserving continuity of concentration and flux at all interfaces. Closed-form expressions in the Laplace domain describe concentration fields and interfacial fluxes, and cumulative drug uptake is computed in the time domain via inverse Laplace transformation. The model identifies distinct short- and long-time transport regimes. Commonly used Dirichlet and Robin boundary conditions emerge as limiting cases but cannot reproduce the regime-dependent behavior of a backing layer. In particular, Robin formulations reduce the backing layer to a constant effective resistance, neglecting its storage capacity and time-dependent impedance. By replacing ad hoc boundary conditions with a physically grounded impedance framework, this approach provides a unified and extensible method for analyzing multilayer transport systems, including extensions to anomalous or memory-dependent diffusion. Full article

Interconnected fractures induced by coal mining, known as water-conducting fracture zones (WCFZs), form a fractured zone where water from overlying aquifers flows into the goaf. Substantial findings have been established on the development height of WCFZs; however, these analyses have been based on intact structures or rock masses. Research on how primary fissures or other water-conducting structures influence the development of WCFZs remains limited. The mining seam of the Gaojiapu Coal Mine in the Ordos Basin, China, is overlaid by a gigantic and highly confined Cretaceous aquifer. Additionally, the primary fissures of the overlying strata are highly developed. Geophysical inversion of the primary fissures and vertical and horizontal drilling were undertaken in order to systematically investigate the characteristics of WCFZ development in the overlying strata. The results show that a dense network of primary fissures is connected with the middle and lower Cretaceous aquifer developed in Mining Zone 1. These fissures are prone to connecting with mining-induced fractures to form the highly developed WCFZs observed and verified in this study. A grouting engineering approach was adopted at the Gaojiapu Coal Mine to block the primary fissures in advance, as this can effectively control the abnormal development of the WCFZs and decrease the discharge of mine water, ultimately protecting the water resources of the Cretaceous aquifer. Our research clarifies the significant role of primary fissures in the development of water-conducting fracture zones, and provides important theoretical guidance for the accurate prediction and prevention of mine roof water hazards in areas with similar mining conditions. Full article

Many cancer-associated deaths result from metastases rather than primary tumors. Growing evidence suggests that DNA methylation alterations are crucial for inducing a plastic phenotype that allows cancer cells to adapt to the metastatic microenvironment. Brain metastases of melanoma (MBM) and glioblastoma (GB) share a neuroectodermal origin and the brain as tissue of residence, but their epigenetic regulation is poorly understood. Aiming at elucidating shared and tumor-distinct features, we analyzed the methylation of MGMT regulatory elements. We focused on MGMT because MGMT promoter methylation is used as a predictive marker for temozolomide response in GB, but its role in MBM has been discussed controversially. By targeting 12 CpG dinucleotides (CpGs) in the promoter, 68 CpGs in intergenic enhancers, and 31 CpGs in intragenic enhancers, we identified shared features, including an L-shaped relationship between promoter methylation and MGMT protein expression and an inverse L-shaped relationship between intragenic enhancer methylation and MGMT protein expression. GB exhibited higher methylation, particularly in promoter and intergenic enhancers, and stronger associations between methylation and overall survival than MBM. These results highlight both conserved and tumor-specific MGMT regulation, reflecting the complexity of epigenetic control in brain malignancies and emphasizing divergent evolution between MBM and GB. Full article

In-line cavitation is relevant to many continuous processes; however, its intensity depends on flow rate, available pressure, temperature, fluid properties, and plant conditions, complicating the maintenance of a repeatable regime within a prescribed band. This paper presents the DVRA, an actuated Venturi module with a Reuleaux triangular cross-section for in-operation regulation of hydrodynamic cavitation through device configuration. The novelty lies in combining two degrees of freedom—an in-operation adjustable hydraulic throat and boundary-imposed swirl forcing—within a compact in-line device: all rotation is confined to the module, and no rotation of the process line is required. The hydraulic throat is tuned via an actuated elastomeric liner, while swirl is generated by external end collars. Reproducible operational conventions are introduced together with a normalized input set and a configuration-space formalism that distinguishes admissible from achievable configurations. Regulation is cast as a control-oriented inverse mapping given a target band for an in-line estimated cavitation indicator and standard industrial measurements of flow rate, pressure, and temperature; configuration commands are selected to keep the indicator within bounds. The contribution is methodological and provides an implementable basis; comprehensive validation and performance benchmarking are outside the scope of this paper and will be reported separately. Full article

This paper proposes a learning-based contact force controller using deep neural networks (DNN) and a PI controller. Stable contact force control between the foot and the ground is essential for humanoid robots to maintain balance during bipedal walking. While admittance controllers have been extensively employed for contact force control in humanoid robots, their performance is limited by the high nonlinearity inherent in robot systems. To overcome these limitations, we propose a deep neural network (DNN)–based inverse model, which leverages input–output data that inherently capture system nonlinearities. The proposed learning-based contact force controller computes the target foot height based on the target force, measured force, and measured foot height, without relying on a dynamic model of the articulated robotic leg. Furthermore, a PI controller is integrated to mitigate steady-state errors. Experimental comparisons between the proposed controller and an admittance controller were conducted using an articulated robotic leg. Compared with an admittance controller, the proposed method reduced overshoot by 96% and settling time by 61% on average in step responses and decreased force-tracking RMSE by 66.3% on average across both step and sinusoidal experiments. Full article