Design and optimization of the surfactant mixture ratio for oleic acid-based ophthalmic nanoemulsions prepared by the HPH technology - feasibility study for clotrimazole as a model drug.

Washability evaluation of coal gasification slag via an integrated sieving-water flow classification.

We present a deep learning framework that combines classical regularization and data preprocessing to improve estimation of the myelin water fraction (MWF) in the brain from magnetic resonance relaxometry data. The proposed method is developed within the context of biexponential signal modeling, a standard approach for quantifying MWF. Building on prior work on input layer regularization (ILR), we introduce several key extensions. First, we incorporate optimal regularization hyperparameter selection using either a dedicated neural network or generalized cross-validation (GCV), applied on a signal-by-signal (or pixel-by-pixel) basis to generate concatenated input features. Second, we extend the framework to directly estimate MWF in addition to exponential time constants. On synthetic data, the proposed architecture outperforms both conventional regularized fitting methods and standard multilayer perceptrons. When applied to invivo brain data, it again yields superior accuracy, with GCV-based parameter selection performing slightly better than the neural network alternative. These findings demonstrate that ILR enhances MWF estimation within the biexponential model and that classical regularization techniques, when integrated with deep learning, can substantially improve quantitative estimation of myelin content.

An elastoplastic analytical method for characterizing the plastic zones around twin circular tunnels excavated at shallow depth

Study on adsorption characteristics of dredged sediment-attapulgite vertical cutoff walls.

Kernelized linear principal component discriminant analysis.

Summary statement We compared PhotoGEA, a new software tool for analysing gas exchange data, to a previous, widely used software tool. The two approaches had excellent agreement in values of photosynthetic capacity. Care and transparency are needed in parameter selection for gas exchange analysis.

Electric phrenic nerve stimulation is employed as a method of artificial ventilation, and computational models are utilized to assist in parameter selection. The majority of models assume isotropic tissue conductivity, although muscle tissue exhibits anisotropic properties. We aim to investigate the influence of anisotropic muscle conductivity on the results of phrenic nerve activation. To calculate the potential distribution, we used an anatomically detailed multi-scale model for non-invasive electrical stimulation in the neck, incorporating realistic muscle fiber orientations. Phrenic nerve activation thresholds were calculated using the McIntyre-Richardson-Grill nerve model. Anisotropy ratios ranging from 1:1 to 1:15 (transversal:longitudinal conductivities) were analyzed at constant corresponding isotropic conductivity. Additional simulations assessed the influence of muscle volume and electrode placement and quantified possible co-activation of other nerves in the neck. Increasing anisotropy ratios resulted in consistently higher phrenic nerve activation thresholds across all axon diameters (up to + 90%). Larger muscle volumes and electrode positions directly over a muscle further elevated the anisotropy effects. Considering anisotropic muscle conductivity increases the number of co-activated nerves. High-resolution models incorporating anisotropic conductivity are recommended for research studies on phrenic nerve stimulation.

The difficulty in predicting the horizontal displacement of the support pile top in ultra-deep foundation pits within muddy formations, combined with insufficient consideration of parameter discretization characteristics in existing methods, motivates this study. Taking the Songtao Street Station project of Suzhou Metro Line 8 as a case study, this paper classifies the soil layers and implements corresponding support technologies. First, based on the engineering geological and hydrological conditions, the discrete values of earth pressure, the discrete combination of support structure thickness, and the discrete gradient of the lateral earth pressure coefficient are selected as core discrete variables. Second, a numerical model is constructed using FLAC3D software, and the soil-structure interaction is simplified via the elastic foundation beam method. The deflection differential equation of the support structure is derived to verify the accuracy of the elastic modulus conversion formula and the earth pressure calculation method. Subsequently, the influence of key parameters, such as the lateral earth pressure coefficient and the cohesion of silty clay, on displacement is analyzed. Finally, the reliability of the model is verified using on-site monitoring data from 12 monitoring points throughout the entire construction period. The results indicate that the displacement error between numerical simulation and actual measurement is ≤ 3.3%. When the lateral earth pressure coefficient is 1.0, the horizontal displacement of the pile top is minimized. The safety factor of uniformly thick shotcrete support is 1.8-2.9 times higher than that of non-uniform schemes. Significant creep characteristics are observed during the excavation and sealing of ultra-deep foundation pits in muddy formations. This study provides a quantitative basis for the discrete selection of support parameters for ultra-deep foundation pits in similar silty formations and improves the accuracy of displacement prediction.

Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user systems. Novel view synthesis techniques can address this by producing a small, interactive model of the data, requiring only a set of training images to learn from. While these models allow accessible visualization of large data and complex scenes, they do not provide the interactions needed for scientific volumes, as they do not support interactive selection of transfer functions and lighting parameters. To address this, we introduce Volume Encoding Gaussians (VEG), a 3D Gaussian-based representation for volume visualization that supports arbitrary color and opacity mappings. Unlike prior 3D Gaussian Splatting (3DGS) methods that store color and opacity for each Gaussian, VEG decouple the visual appearance from the data representation by encoding only scalar values, enabling transfer function-agnostic rendering of 3DGS models. To ensure complete scalar field coverage, we introduce an opacity-guided training strategy, using differentiable rendering with multiple transfer functions to optimize our data representation. This allows VEG to preserve fine features across a dataset's full scalar range while remaining independent of any specific transfer function. Across a diverse set of volume datasets, we demonstrate that our method outperforms the state-of-the-art on transfer functions unseen during training, while requiring a fraction of the memory and training time.

BackgroundComputed Tomography Ventilation Imaging (CTVI) is an investigational technique that has its basis in functional lung avoidance radiotherapy. It offers a cost‐effective and accessible alternative to nuclear medicine imaging by generating lung ventilation maps from 4DCT or paired inhale/exhale breath‐hold CT (BHCT) scans. Despite over a decade of clinical validation, there is still no consensus on how algorithm parameters and patient‐specific factors influence CTVI accuracy. Further research is needed to understand CTVI's sensitivity to these variables and to standardize its implementation for clinical use.PurposeThis study evaluates how key algorithm parameters and patient‐specific factors affect the accuracy of CTVI.Materials and methodsCT ventilation images were generated from BHCT scans and compared to Galligas PET ventilation scans. The VESPIR toolkit was used to compute ventilation based on deformable image registration (DIR) evaluation of volume change (CTVIJac) or change in Hounsfield Unit (HU) value (CTVIHU). CTVI accuracy was characterized as the voxel‐wise Spearman correlation (rS) with Galligas PET. Algorithm parameters common to many CTVI implementations were investigated with a baseline determined from existing literature: lung segmentation threshold (−600 HU to −150 HU), DIR regularization parameter (λ = 0.05 to 100), and smoothing filter diameter (0 voxels to 9 voxels). Robust parameter ranges were defined as those yielding rS within 10% of the maximum cohort average observed through parameter variation, and no negative Jacobian values for the registration. Patient‐specific lung volume and density metrics were also analyzed to explain inter‐patient variability in CTVI accuracy.ResultsThe correlation between CTVI and Galligas PET was demonstrated to be robust within identified parameter ranges: lung segmentation threshold −600 HU to −150 HU for CTVIJac and CTVIHU, DIR regularization parameter (λ) 1.25 to 5 for CTVIJac and CTVIHU, and smoothing filter diameter 0 to 9 voxels for CTVIJac and 7 to 9 voxels for CTVIHU. No significant correlation was found between the accuracy of CTVIJac and any patient‐specific lung volume or density parameters. Significant correlations were found between the accuracy of CTVIHU and the percentage change in lung volume during inspiration (r = 0.72, p < 0.01) and the lung volume in the exhale phase (r = −0.63, p < 0.01). The correlation between CTVIJac and CTVIHU was found to be strongly correlated to CTVI accuracy.ConclusionsCTVI accuracy was relatively stable across the range of parameter values tested with no strong indication of the need for patient‐specific parameter sets. Patient‐specific differences appear to be a driving factor for inter‐patient variability in CTVI accuracy as parameter selection alone was insufficient to explain the variability. The strong association of CTVIJac and CTVIHU agreement and CTVI accuracy suggests that CTVIJac and CTVIHU agreement is a useful predictor of CTVI accuracy and quality metric for parameter optimization.

Crowdsourced data, such as that from mobile fitness apps (MFAs), has the potential to transform tourism research. This opportunity is particularly valuable as tourism to protected areas (PAs) increases, making it more difficult to manage their environmental impacts. However, such data sources come with challenges. Therefore, their representativeness should be carefully evaluated, particularly as their use continues to grow. We use the Paiva Walkways (Portugal) to assess MFAs as a tourism proxy using Spearman’s rank correlation, to evaluate kernel density and fishnet methods for spatialising visitor movements, and to analyse the visitors’ spatiotemporal behaviour using GIS, kernel density estimation, and frequency distribution analysis. Our findings show that the digital records of MFAs correlate with the analogue ticket records; that both kernel density and fishnet methods are effective, but parameter selection affects the detail level and computing processing time; and that visitation to the Paiva Walkways exhibits temporal fluctuations, with higher numbers during summer, August, and on weekends. MFAs effectively represent visitors’ spatiotemporal behaviour, for example, by measuring peak visitation periods. However, MFA data do not increase proportionally with ticket data, suggesting that high-pressure visiting periods may be underrepresented in MFAs. Overall, MFAs provide detailed data that offer new opportunities for tourism geographies research. MFAs data can be used to quantify tourism in PAs, to understand how visitors use space and interact with the surrounding environment, and to analyse how these spatial patterns change over time. MFAs also excel at detecting unauthorised activities, but require careful data curation and a transparent explanation of the selection approach to achieve high-quality, consistent, and replicable results. MFA-based monitoring is an important proxy that expands the information available to stakeholders and park managers about tourism and leisure activities, thereby enabling effective management and promoting sustainable tourism.

The central adjustment coil of a gasdynamic Electron Cyclotron Resonance (ECR) ion source requires wide-range bipolar current regulation over ±100 A with flat-top stability within 0.1% (1000 ppm) and a current rise time below 4 ms. Conventional fully controlled H-bridge converters operating under hard-switching conditions are unable to satisfy these requirements simultaneously, as the switching loss penalty restricts the control bandwidth and degrades flat-top stability. This paper presents an Asymmetrical Half-Bridge Flyback (AHB-Flyback) converter specifically designed for this application. By incorporating a dedicated resonant branch Lr–Cr on the primary side, the converter achieves primary-side Zero-Voltage Switching (ZVS) and secondary-side Zero-Current Switching (ZCS) over the full operating range, enabling 100 kHz operation without incurring the switching losses that would otherwise limit control bandwidth. A decoupled energy management architecture is adopted in which the primary circuit pre-charges an energy storage capacitor during idle intervals, and the coil current is subsequently established through an autonomous capacitor-to-coil discharge, effectively decoupling the peak power demand from the upstream supply network. The operating modes of the flat-top maintenance stage are analyzed through time-domain state equations, yielding an explicit closed-form expression for the Mode 3 duty cycle DT3. This expression demonstrates that DT3 is determined solely by the switching frequency and circuit parameters, independent of the load current setpoint, which is the fundamental mechanism enabling stable wide-range current regulation without parameter re-tuning. Parameter selection guidelines are derived from this result. Simulation results across the 20–100 A operating range and experimental validation on a scaled prototype confirm flat-top current stability within 1000 ppm and a current rise time of 4 ms, demonstrating the suitability of the proposed converter for precision ECR ion source power supply applications.

Gray-Box (GB) models are being considered for risk monitoring of Small Modular Reactors (SMRs). Their effectiveness is linked to the proper selection of the model parameters. This paper proposes a systematic methodology for identifying the most influential parameters of a GB model for estimating safety-critical variables of an SMR during normal operation and accident scenarios. The GB integrates a reduced-order physics-based model (White-Box, WB) with a data-driven (Black-Box, BB) model that corrects the outputs of the WB using the condition-monitoring data collected by sensors positioned onto the SMR. The proposed method combines signal decomposition, specifically the Hilbert–Huang Transform (HHT), and global sensitivity analysis (SA), based on first-order Kucherenko indices, to quantify the contribution of non-stationary, correlated GB input parameters to the variability of the safety-critical output parameters of interest. The proposed approach is applied to the Small Modular Dual Fluid Reactor (SMDFR), and the obtained results demonstrate its effectiveness in identifying informative and physically interpretable features, reducing complexity and computational burden to enable real-time risk monitoring.

Apparent tissue sodium concentration quantification across reconstruction methods with different partial volume effect reduction - A preliminary study.

Investigation of critical buckling load enhancements via ultrasonic vibration assisted implantation of microwire-based brain electrodes.

A novel hand gesture recognition framework based on surface electromyography (sEMG) is proposed for soldier operational scenarios under small-sample conditions. The framework integrates Empirical Mode Decomposition (EMD) for signal reconstruction, histogram-based features, and the Deep Forest (DF) classifier. Evaluations are conducted under two protocols: subject-wise evaluation and mixed-subject nested 8-fold cross-validation. Under subject-wise evaluation, the proposed EMD-HIST-DF method achieves 99.94% accuracy with 0.00027 ms per sample. Under mixed-subject nested 8-fold cross-validation, 98.41% accuracy is maintained with 0.00053 ms per sample. Ablation studies confirm the significant contribution of EMD-based signal enhancement in the mixed-subject setting (approximately 10.6 percentage points, p &lt; 0.001). Parameter sensitivity analysis guides optimal parameter selection, and statistical tests confirm significant performance gains over baseline methods. Confusion matrices illustrate high per-class accuracy with minimal inter-class confusion. The framework shows potential as a promising solution for accurate, efficient, and sample-sparing gesture recognition in resource-constrained environments such as supernumerary robotic limb control.

ABSTRACT Noncontact vibration-measurement methods based on machine vision have been widely applied for their high scene adaptability, low cost, and easy operation. Directly obtaining vibration signals of rotating shafts using machine vision is an innovative and efficient way to assess the vibration intensity and operational status of rotating mechanical systems. However, their measurement accuracy is limited by lighting, noise, image resolution, and other factors. This work introduces a visual vibration-measurement method utilising optical flow enhanced by single-component image phase (SCIPOF). This method separates single-component images from the original images by designing a narrowband bandpass image filter with highly concentrated frequency components. Subsequently, analytic signals are constructed using these single-component images and their first order derivatives, from which the phase of the images is extracted. Finally, the phase variations from the reference image to other images in the videos are converted to the actual displacement signal of shafts. The proposed method avoids the effect of manual parameter selection of existing methods on measurement accuracy, which improves its robustness. A series of numerical simulations and actual rotor experiments demonstrate that SCIPOF outperforms existing methods in accuracy and reliability, and maintains high accuracy even with low signal-to-noise ratio video data.

Thermal–microstructural–mechanical relationships in Fe-based tool steel coatings fabricated by laser directed energy deposition

Production of TEMPO-oxidized cellulose nanofibers: Tuning the physio-chemical properties by controlling the process.