Search Results (67)

Two methods of accounting for the motion of the bodies—the deforming mesh method and the method of overlapping meshes (or overset mesh method)—are compared using problems with floating bodies, which are typical for the shipbuilding industry. Three problems are considered: oscillation of the cylinder on the water surface, movement of the box under the influence of waves, and heaving and pitching of the ship model in head waves. Numerical computations are carried out in the LOGOS software package, the simulation methodology used is based on the solution of a system of Reynolds-averaged Navier-Stokes equations, and the Volume of fluid (VOF) method to take into account the free surface. In all problems, the characteristics of the movement of bodies are evaluated; the resistance force of the ship model is also determined in the third problem; control values obtained using two methods of accounting for moving bodies are compared with the available experimental data. The results of numerical simulation have shown that both methods predict body movement parameters well; the accuracy in determining the resistance force in the task of streamlining the ship’s hull is also comparable: the difference between the maximum deviations of the resistance coefficient in the computations with deformation and overlapping computation meshes is 0.5%. In the case of computations of the three-dimensional problem, the time spent when using the mesh-deformation method turned out to be 10% more; therefore, the method of overlapping meshes can be considered more optimal when solving such shipbuilding tasks as self-propelled tests and streamlining the ship’s hull with and without wind and wave loads. Full article

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T₁-weighted, T₂-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T₁/T₂-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P₂ (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P₂ values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P₂ was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema. Full article

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with a T4-selective DNA aptamer via a 3-Triethoxysilyl propylsuccinic Anhydride (TESPSA)-mediated silanization approach, enabling a streamlined two-step modification for oriented immobilization. The biosensor achieves robust real-time detection of T4 across the physiological concentration range (5–30 pM), with a limit of detection of ~5 pM and a strong linear correlation between drain current and analyte concentration (R² = 0.9931). Specificity is confirmed using non-functionalized devices and estradiol as a non-target control. All measurements were performed in undiluted phosphate-buffered saline, representing a physiologically relevant ionic environment and demonstrating stable sensor performance under realistic buffer conditions. The dose–response behavior follows a Hill model, allowing extraction of binding parameters and confirming that the electrical signal originates from specific aptamer–target interactions. These results demonstrate that aptamer-functionalized SiNW-FETs provide a highly sensitive, selective, and miniaturizable platform for quantitative thyroid hormone monitoring, with strong potential for future point-of-care applications. Full article

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

Background/Objectives: This technical report introduces an innovative hybrid digital workflow that integrates diagnostic plaster-cast scanning with intraoral scanning to produce an accurate 3D-printed model for fabricating distal-extension removable dental prostheses (RDPs). Methods: The technique aims to overcome the challenges of reproducing the mobile mucosa in free-end saddles, a critical factor for denture base accuracy and stability. The workflow began with conventional clinical procedures, including clinical examination, impression-making, and cast surveying. After performing the required mouth preparations according to the prosthetic design, the diagnostic cast was digitized and selectively modified to allow intraoral rescanning. The prepared teeth were then scanned intraorally and merged with the digitalized cast, producing a refined virtual model for CAD-based metal framework design. The framework was digitally designed, 3D-printed to verify adaptation, and cast in cobalt–chromium. Standard RDP fabrication steps were followed, including intraoral framework try-in, fabrication of acrylic bases, occlusal registration, tooth arrangement, and functional and esthetic try-in. The final prosthesis was installed and adjusted without the need for an additional impression. Results: This hybrid workflow enabled a highly accurate reproduction of the distal extension region, outperforming models derived solely from direct intraoral scanning. By digitally capturing the physiological morphology of the mobile mucosa, the method eliminates the need for the traditional altered-cast technique, reducing clinical time, technical sensitivity, and material costs. Conclusions: The proposed approach enhances denture base accuracy, improves adaptation, and promotes more uniform occlusal load distribution in free-end RDPs. This streamlined and reproducible digital protocol offers a clinically relevant advancement, with potential to improve prosthesis stability and long-term outcomes. Full article

Though deep learning has brought transformative advances to the field of modulation recognition, conventional approaches typically rely on a large amount of labeled data, which is often difficult to obtain in real-world communication scenarios. Few-shot modulation recognition (FSMR), which aims to identify modulation formats with extremely limited training samples, serves as a key enabler for next-generation cognitive radio, intelligent spectrum management, and non-cooperative communications. However, existing neural network models are not inherently designed for few-shot learning (FSL) and cannot be directly applied to FSMR tasks. To address this gap, this paper proposes a spatio-temporal prototypical network (STPN) trained within a meta-learning framework. Through a lightweight multi-module design that sequentially captures spatial patterns and temporal dependencies, STPN effectively integrates hybrid feature extraction with prototype-based classification. In contrast to existing approaches, STPN features a streamlined architecture free from intricate operations that could compromise generalization. This advantage is especially crucial when the model is trained on numerous meta-tasks with only a few samples. Comprehensive experiments on public benchmarks show that STPN achieves superior classification accuracy over several baseline models, while also offering advantages in parameter efficiency and computational cost. Further analysis investigates the key parameters influencing model performance, and ablation studies confirm the individual contribution of each module. This work not only deepens the theoretical understanding of prototype-based FSL techniques but also establishes a practical framework applicable to other signal processing tasks that demand robust performance under limited labeled data. Full article

Background: Velocity-based training (VBT) is used to estimate maximal strength and prescribe resistance-training loads, but evidence in untrained youth, especially early-adolescent females, is limited. In untrained early-adolescent females performing free-weight back squats, (1) the load–velocity relationship (LVR) is comparable to adult samples, albeit with greater between-subject variability, and (2) one-repetition maximum (1RM) estimates are affected by the minimum velocity threshold (MVT) anchor. Methods: Thirty-four untrained females (10–14 years) completed two progressive loading tests followed by actual 1RM attempts. Mean propulsive velocity (MPV) was recorded to model LVRs. Three MVTs were considered: (a) Actual (from Test 1), (b) General (0.30 m·s⁻¹), and (c) Optimal (individualized to minimize prediction error in Test 1). LVR-based 1RM estimates from Multi-point and Two-point approaches were generated in Test 2 using each MVT and compared with the actual 1RM. Results: MPV decreased near-linearly with load (median R² ≈ 0.996), from 1.00 ± 0.19 m·s⁻¹ at ~40%1RM to 0.30 ± 0.05 m·s⁻¹ at 100%1RM. Across MVTs, Two- and Multi-point models showed similar 1RM accuracy (≤~0.7% difference; p > 0.35). Actual and General MVTs overestimated 1RM (+5.1 kg; p < 0.001), whereas an individualized Optimal MVT (~0.38 m·s⁻¹) removed bias (+0.6 kg; p = 0.52) and reduced error (p ≈ 0.03). Conclusions: In untrained early-adolescent females, the back-squat LVR is highly linear, and 1RM estimation accuracy hinges on the MVT anchor. A streamlined Two-point LVR paired with an individualized Optimal MVT provides an efficient, accurate workflow for youth strength assessment. Full article

Subglacial topography is a critical boundary condition for ice sheet models projecting past and future ice sheet–climate interactions. Contemporary ice-sheet-wide bed topography datasets are partially derived using mass conservation, but approximately 75% of the most widely used Greenland Ice Sheet (GrIS) dataset is based on simple interpolation of airborne radio-echo sounding (RES) measurements, such as kriging or streamline diffusion. Due to limited independent validation data, the errors and biases in this approach are poorly understood, creating largely unknown uncertainties in subglacial topography. Here, we interpolated synthetic RES observations of bed topography over ice-free areas with a known topography at a 5 m spatial resolution and quantify discrepancies. We found that the absolute error in kriged bed topography increases with distance from the input data, though at a reduced rate than previously estimated. The difference between an interpolated elevation estimate and the local mean elevation is a strong predictor of real bed errors (R² = 0.72), with further improvement as input observation sparsity increases (R² > 0.82). We propose a method to quantify and reduce uncertainty in kriged bed topography in sparsely surveyed regions, reducing uncertainty for at least 56% of the kriged interior at a 99% confidence interval. Our results suggest that subglacial depth is on average 5 m deeper than previous estimates, though individual areas may be shallower or deeper (σ = 41 m). Consequently, the area grounded below sea level is likely underestimated by 2%, increasing to 29% for regions deeper than 200 m. These findings have potential implications for the future stability of the GrIS under climate change. Full article

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity. Full article

We report a solvent-free, microwave-assisted protocol for the synthesis of 2,2-disubstituted 1,3-oxathiolan-5-ones catalyzed by magnetite nanoparticles (MNPs). A design of experiments (DoE) approach was employed to optimize reaction parameters using the model reaction between acetophenone and 2-mercaptoacetic acid. Temperature, catalyst loading, and stoichiometry emerged as the most influential factors. Although the aromatic model substrate afforded modest yields (up to 24% by GC), the optimized conditions applied to aliphatic and cyclic ketones led to significantly higher yields, reaching up to 92%. This study highlights the value of combining heterogeneous nanocatalysis, microwave irradiation, and DoE to streamline optimization in heterocyclic synthesis. Full article

This study proposes a comprehensive framework for establishing an Emissions Trading System (ETS) in Thailand, addressing three core design elements: scope, cap setting, and allowance allocation. Using a mixed-methods approach that combines quantitative data analysis with qualitative insights from expert and stakeholder consultations, the research identifies a practical and strategic pathway for implementation. The proposed framework recommends a phased approach, with the initial phase covering 222 high-emitting facilities across seven key sub-industrial sectors. This scope, defined by a 25,000 tCO₂e threshold, is estimated to cover approximately 42.64% of the country’s total greenhouse gas (GHG) emissions. The ETS cap for the first phase is set at 20 MtCO₂e, aligning with national climate targets outlined in Thailand’s draft NDC 3.0. For allowance allocation, free allocation via output-based benchmarking is identified as the most suitable method for initial implementation, given its feasibility and effectiveness in incentivizing efficiency improvements. Furthermore, the standard cost model (SCM) was applied to assess compliance costs, indicating an annual administrative burden of 21,534 h and THB 42.18 million. These insights provide policymakers with a baseline for streamlining monitoring, reporting, and verification (MRV) requirements. The findings suggest that the proposed framework is a robust and strategic model, tailored to the unique economic and regulatory context of Thailand, providing a clear path to achieving the nation’s ambitious sustainable climate goals. Full article

Aronia melanocarpa (black chokeberry) is a medicinally valuable small fruit species, yet its commercial propagation remains limited by low rooting and genotype-specific responses. This study developed an efficient, callus-free micropropagation and rooting protocol using a Shrub Plant Medium (SPM) supplemented with 5 mg/L BAP in large 660 mL jars, which yielded up to 27 shoots per explant. Optimal rooting (100%) was achieved with 0.5 mg/L NAA + 0.25 mg/L IBA in half-strength SPM. In the second phase, supervised machine learning models, including Random Forest (RF), XGBoost, Gaussian Process (GP), and Multilayer Perceptron (MLP), were employed to predict morphogenic traits based on culture conditions. XGBoost and RF outperformed other models, achieving R² values exceeding 0.95 for key variables such as shoot number and root length. These results demonstrate that data-driven modeling can enhance protocol precision and reduce experimental workload in plant tissue culture. The study also highlights the potential for combining physiological understanding with artificial intelligence to streamline future in vitro applications in woody species. Full article

This paper presents a three-dimensional (3D) free surface model to predict incompressible flow around a spur dike in a meandering channel bend, which is highly 3D due to the presence of curvature effects. The model solves the Reynolds-averaged Navier–Stokes (RANS) equations using an explicit projection method. The 3D grid system is built from a two-dimensional grid by adding dozens of horizontal layers in the vertical direction. Numerical simulations consider four test cases with different spur dike locations in the same meandering channel bend with the same Froude numbers as 0.22. Four turbulence models, the standard k-ε model, the k-ω model, the RNG k-ε model and a nonlinear k-ε model, are implemented in our three-dimensional free surface model. The performance of these turbulence models within the RANS framework is assessed. Comparisons between the model results and experimental data show that the nonlinear k-ε model behaves better than the three other models in general. Based on the results obtained by the nonlinear k-ε model, the highly 3D flow field downstream of the spur dike was revealed by presenting velocity vectors at representative cross-sections and streamlines at the surface and bottom layers. Meanwhile, the 3D characteristics of the downstream separation zone were also investigated. In addition, to highlight the advantage of the nonlinear turbulence model, comparisons of velocity vectors at representative cross-sections between the results obtained by the linear and nonlinear $k$-$\epsilon$ models are also presented. Full article

The Farmworker Family Health Program (FWFHP) annually supports 600 farmworkers in connectivity-challenged rural areas. Traditional paper-based data collection poses validity concerns, prompting a pilot of direct data entry using tablets and satellite internet to enhance efficiency. The purpose of this article is to describe, using the TIDier checklist, a real-time, live data-entry EMR intervention made possible by satellite internet. Utilizing a customized REDCap database, direct data entry occurred through tablets and satellite internet. Patients received a unique medical record number (MRN) at the mobile health clinic, with an interprofessional team providing care. Medication data, captured in REDCap before the mobile pharmacy visit, exhibited minimal defects at 6.9% of 319 prescriptions. To enhance data collection efficiency, strategies such as limiting free text variables and pre-selecting options were employed. Adequate infrastructure, including tablets with keyboards and barcode scanners, ensured seamless data capture. Wi-Fi extenders improved connectivity in open areas, while backup paper forms were crucial during connectivity disruptions. These practices contributed to enhanced data accuracy. Real-time data entry in connectivity-limited settings is viable. Replacing paper-based methods streamlines healthcare provision, allowing timely collection of occupational and environmental health metrics. The initiative stands as a scalable model for healthcare accessibility, addressing unique challenges in vulnerable communities. Full article

This work concentrates on an experimental project for the integration of Large Language Models (LLMs) inside a Historic Building Information Modeling (HBIM) workflow. In particular, this evaluation was carried out by using open source solutions as concerns parametric modeling of BIM elements. This experimental test focuses on how Python scripts, generated by AI agents, can create parametric models for HBIM purposes and archaeology: starting from the archaeological plan, the parametric modeling of the Parthenon temple was carried out via a text-to-BIM workflow based on OpenAI and open source tools. The use of AI in generating these scripts can potentially automate and streamline the modeling process, making it more efficient and less prone to human error (or almost). FreeCAD, being a Python-based software, is identified as the perfect fieldwork for this test. Its open source nature allows extensive customization and experimentation, making it an ideal platform for integrating AI-generated Python scripts. In addition to proving a flexible and operative BIM platform, this approach could achieve the same results by parametric modeling via Python scripts generated by LLMs. By harnessing the power of LLMs, FreeCAD could serve not only as a robust BIM tool but also as a testbed for pushing the boundaries of what AI can achieve in the realm of parametric modeling and HBIM. This project opens new possibilities for automating the creation of detailed, accurate BIM models, ultimately contributing to the preservation and management of heritage buildings. Full article