Search Results (11,876)

Background/Objectives: Postoperative hypothyroidism is a common complication following hemithyroidectomy and may necessitate lifelong levothyroxine replacement therapy. Although several clinical predictors have been identified, clinically applicable risk models integrating both clinical and ultrasonographic parameters remain limited. This study aimed to identify independent predictors of postoperative hypothyroidism and to develop practical risk scoring systems with and without ultrasonographic parameters based on these predictors. Methods: This observational cohort study included 95 patients who underwent hemithyroidectomy and were followed for at least 6 months. Clinical characteristics, thyroid autoantibodies, and ultrasonographic features of the remnant thyroid lobe were evaluated. Independent predictors of postoperative hypothyroidism were identified using multivariable logistic regression analysis, and two simplified risk scoring systems were developed based on the regression coefficients. Results: Postoperative hypothyroidism developed in 39 of the 95 patients (41.1%). In the model incorporating ultrasonographic parameters, preoperative TSH, a low remnant thyroid volume-to-body surface area ratio, and heterogeneous remnant thyroid parenchyma were independent predictors of postoperative hypothyroidism. The corresponding score stratified patients into low-, intermediate-, and high-risk categories with observed hypothyroidism rates of 9.1%, 44.7%, and 100.0%, respectively, and showed an AUC of 0.858. In the model without ultrasonographic parameters, preoperative TSH and thyroid autoantibody positivity were independent predictors; the corresponding score showed an AUC of 0.745. Conclusions: The proposed risk scoring systems may serve as practical tools for identifying patients at increased risk of postoperative hypothyroidism following hemithyroidectomy according to the availability of ultrasonographic assessment. External validation in independent cohorts is warranted before routine clinical implementation. Full article

Upper-loss and lower-kick (UL–LK) events may occur in ultra-deep fractured carbonate formations when gas enters from a lower high-pressure zone while drilling fluid is lost to an upper low-pressure fracture. Because both flows can continue after shut-in, the wellbore remains incompletely closed. This study develops a transient wellbore-formation pressure model based on phase mass conservation and global volume balance, and introduces an effective gas–liquid partition coefficient to represent phase separation at the fracture inlet. The model shows that circulation loss limits bottomhole-pressure recovery, allowing gas influx to persist after shut-in. Relative to kick-only conditions, UL–LK conditions have a lower initial shut-in casing pressure (SICP) but a steeper subsequent buildup. A smaller partition coefficient, corresponding to preferential liquid loss, leaves more free gas in the wellbore and further increases the SICP buildup rate. A surface-pressure-based screening method was developed from contrasting SICP and shut-in drillpipe pressure (SIDPP) responses. When applied to five field cases, the method correctly identified three UL–LK cases and two kick-only cases. Its outcomes for five field cases agreed with the field interpretations. This framework supports post-shut-in pressure prediction and rapid screening without dedicated downhole measurements. Full article

The temperature field of a high-voltage direct-current (HVDC) cable governs its current rating and insulation lifetime and must therefore be predicted accurately across diverse operating conditions. Finite-element (FE) simulation is accurate but too costly for repeated evaluation, whereas data-driven reduced-order models (ROMs) often extrapolate poorly beyond the training-current range. This paper proposes a physics-structured POD–neural ROM to address this limitation. Specially, proper orthogonal decomposition (POD) compresses the three-dimensional temperature-rise field into a few modal coefficients, which are predicted from the operating conditions by a neural network. The key innovation is to embed the Joule-heating law directly into the architecture: the leading coefficient is represented as a current-squared factor multiplied by a learned current-independent shape. This construction guarantees the correct current scaling of the dominant mode, including its zero-current limit and extrapolation beyond the training range. On FE data for an eight-layer cross-linked polyethylene cable, the model achieves 2.4% mean relative error under current extrapolation and remains below 5% at twice the maximum training current, outperforming Gaussian-process, dynamic-mode-decomposition, autoregressive, and black-box baselines. The full field is evaluated in approximately one millisecond per condition, with a cost independent of the training-set size. Controlled ablations show that the improvement arises from structurally enforcing the scaling law rather than merely supplying $I^2$ as an input feature. Embedding known physical scaling into a surrogate architecture therefore provides a principled route to reliable extrapolation. Full article

Strong broadband noise can mask the weak impact responses produced by bearing faults, while wavelet reconstructions selected only by signal-domain scores may not provide useful inputs for diagnosis. To address this problem, this paper presents WPMSR-1D-ResNet, a training-aware wavelet-domain controlled-noise augmentation framework. PKPM-guided particle swarm optimization first generates scale-specific perturbation candidates in the DWT detail coefficients. Signal-fidelity constraints remove distorted reconstructions, and a proxy CNN with identity fallback selects a global candidate according to validation Macro-F1. The final 1D-ResNet is trained jointly with the measured waveform and the selected augmented view, whereas inference uses only the raw signal. Under one fixed data construction and a common fixed 20-epoch budget, WPMSR-1D-ResNet achieved $0.8311\pm 0.0607$ Macro-F1 at the predefined CWRU low-SNR endpoint and ranked third among eleven methods, placing it within the leading statistical group. Its paired mean exceeded raw-signal 1D-ResNet and per-slice PKPM replacement by 0.05582 and 0.05227, respectively, with gains in nine of ten paired computational seeds. Mixed-SNR training increased mean Macro-F1 across eight mismatched-noise conditions from $0.5463\pm 0.1320$ to $0.6105\pm 0.1292$. The method ranked second on PU and third on acquisition-held-out AT data; on AT, it reduced the normal-state false-alarm rate from 0.2854 to 0.1646 while maintaining 0.9708 fault sensitivity. Raw-only inference required 0.266 ms per slice. WPMSR-1D-ResNet, therefore, aligns wavelet augmentation with diagnostic performance, preserves useful waveform information, and removes wavelet reconstruction and PSO from the deployment path. Full article

Background and Objectives: Pain has been proposed as a pathway linking chronic disease to healthcare spending, but existing estimates measure exposure, pain and cost in the same period and combine coefficients on incompatible scales. We asked how much of the disease–expenditure association runs through pain when exposure, pain and cost are separated in time. Materials and Methods: In the Korea Health Panel Survey (2019–2023; 54,845 adult person-years), chronic disease at year t, EQ-5D pain/discomfort at t + 1 and out-of-pocket payments during t + 2 were linked, conditioning on pain and payments at t. Direct and indirect effects were estimated by parametric g-computation (randomised-interventional analogues, two-part outcome model, exposure-specific adjustment sets, survey and censoring weights, false discovery rate control). Results: Among 25,692 triplets, pain/discomfort predicted the amount spent among healthcare users (cost ratio, CR 1.099, 95% confidence interval, CI 1.023–1.180) but showed no detectable association with whether care was used (odds ratio, OR 1.136, 0.918–1.406); the contemporaneous cost ratio was substantially larger (1.296), inflated by simultaneity. Three musculoskeletal conditions prospectively predicted pain (false discovery rate q < 0.001). Six conditions had indirect-effect intervals excluding zero, but none survived false discovery rate correction (minimum q = 0.19); where estimable, the proportion mediated was small (2.8% to 4.8%). Conclusions: The disease → pain and pain → payment associations are each established, but the disease-specific mediated amount is not confirmed after multiplicity correction. Contemporaneous designs overstate the indirect association; claims that pain causes a specified share of chronic disease expenditure are not supported. Full article

Wearable inertial measurement units (IMUs) offer an accessible alternative to optical motion capture (MoCap) gait analysis, but their performance in Progressive Supranuclear Palsy (PSP) requires validation. We assessed the concurrent validity of IMU-derived versus MoCap-derived gait metrics and static postural sway in 30 patients with PSP using Bland–Altman analysis, Intraclass Correlation Coefficients (ICC), and Spearman rank correlations. Finally, we assessed equivalence using the Two one-sided tests (TOST) procedure. Multivariable linear regression was used to determine whether clinical severity, as measured by the PSP Rating Scale (PSPRS), independently predicted absolute IMU measurement error while controlling for patient age and gait velocity. IMUs demonstrated excellent between-system agreement for parameters such as cadence (100.76 ± 11.42 vs. 100.52 ± 11.59) and cycle time (1.21 ± 0.15 vs. 1.22 ± 0.15; ICC > 0.98), despite a systematic underestimation of gait velocity (p < 0.05). Agreement significantly diminished for micro-phases (e.g., single/double support times) and spatial asymmetry. Interestingly, the TOST procedure revealed that only sagittal and transverse trunk kinematics were equivalent between systems, with all other measures failing to find equivalency. For static sway, the IMU demonstrated strong rank-order correspondence for tracking relative postural instability (ρ = 0.82, p < 0.05). Multivariable analysis revealed that higher PSPRS scores are independently associated with greater between-system discrepancies in support phases and pelvic and trunk kinematics (p < 0.05), irrespective of reduced gait speed. These findings highlight the need to develop disease-specific algorithms, rather than relying on normative commercial models, to establish reliable digital biomarkers for monitoring progressive motor decline. Full article

Vessel segmentation is a fundamental task in medical image analysis and plays an important role in disease diagnosis and treatment assessment. However, existing segmentation methods often show limited adaptability to feature extraction from single-channel X-ray coronary angiograms, which restricts their performance in segmenting small vessels and low-contrast vascular regions. To address these limitations, this study proposes a Channel-Attention U-Net, termed CA-UNet, which integrates residual connections and a channel attention mechanism. Based on the conventional encoder–decoder architecture of U-Net, the proposed method introduces a residual-enhanced double-convolution block to alleviate gradient vanishing in deeper networks. In addition, a dual-pooling channel attention module is incorporated to enhance the selection of discriminative vascular features. Furthermore, the data loading, normalization, and augmentation strategies are optimized to improve the adaptability of the network to single-channel PGM grayscale images. Under three-fold out-of-fold evaluation, CA-UNet achieved the highest mean Dice coefficient (0.7450) and IoU (0.5972) among the evaluated models, while maintaining real-time-rate inference at 41.7 frames per second. These results indicate that CA-UNet provides an effective balance of segmentation accuracy, stability, and computational efficiency for vessel segmentation. Full article

Constructed wetlands (CWs) are widely used as an ecological technology for wastewater treatment. However, the accumulation of microplastics (MPs) in their substrates may impair long-term performance and threaten the operational sustainability of these nature-based treatment systems. To elucidate the transport behaviour of MPs in wetland substrates, this study developed a numerical model based on the lattice Boltzmann method (LBM) to simulate advection, hydrodynamic dispersion, and reversible first-order adsorption/desorption of MPs in saturated porous media. The model incorporates a salinity-dependent non-linear attachment rate coefficient, which captures the compression of the electrical double layer and the enhanced attachment efficiency with increasing salinity. Pore-scale flow is solved using the LBM with an Ergun-type drag term to represent the resistance of the porous matrix. The model was validated against experimental breakthrough curves from column studies using quartz sand and coastal wetland soils under five salinity levels (0–35 PSU) reported in the literature. Quantitative validation yielded coefficients of determination (R²) ranging from 0.782 to 0.960 (RMSE = 0.024–0.045) for calibration cases and 0.741 to 0.946 (RMSE = 0.027–0.048) for independent validation cases across both substrates, excluding the soil cases at 3.5 and 35 PSU. Here, both observed and simulated effluent concentrations were identically zero, resulting in the statistically forced R² = 1.000 and RMSE = 0, which are mathematical artefacts rather than indicators of predictive performance. The simulations reproduce the observed reduction in peak relative concentration by over 50% in sand and near-complete retention (C/C₀ ≈ 0) in soil at high salinities (3.5 and 35 PSU). Results demonstrate that the model successfully reproduces the differences in MP breakthrough behaviour across different substrate types and salinity levels. By linking salinity-enhanced retention to the risk of irreversible clogging and shortened wetland lifespan, the model provides a predictive tool for evaluating the sustainability of CWs under saline stress. This study offers a scientific basis for optimizing hydraulic management (e.g., flushing strategies) to mitigate microplastic pollution and enhance the long-term sustainability and resilience of constructed wetlands in coastal and saline environments. Full article

Background: This study aimed to develop and evaluate a deep learning (DL) algorithm based on magnetic resonance imaging (MRI) for the classification of temporomandibular joint degenerative joint disease (TMJDJD), thereby exploring its potential role in assisting cone-beam computed tomography (CBCT) examinations and minimizing patient radiation exposure. Methods: A retrospective analysis was conducted on 104 patients who had undergone both MRI and CBCT examinations of the temporomandibular joint. A total of 1769 sagittal and 1448 coronal MRI images were collected. After image preprocessing, the datasets were grouped according to different imaging features. Three DL frameworks—ResNet101, DenseNet201, and MobileNetV2—were developed to classify TMJDJD using various MRI image categories. The classification performance of these models was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), and the results were compared with classifications made by two senior clinical experts in a blinded image-level reader experiment. Results: DenseNet201 achieved the best performance in the sagittal (Sg) group, with an accuracy of 80.11%, precision of 80.26%, recall of 75.31%, and an MCC of 0.60. DenseNet201 achieved the best performance among the evaluated DL models, with higher overall performance metrics than ResNet101 and MobileNetV2. In a constrained image-level reader comparison, DenseNet201 demonstrated comparable overall agreement metrics to the two experts, although one expert achieved higher recall and a lower false-negative rate. t-Distributed Stochastic Neighbor Embedding (t-SNE) and training curve analyses confirmed that DenseNet201 demonstrated superior feature extraction capability and a more stable training process. Grouping MRI images, however, did not improve model accuracy. Conclusions: DenseNet201 showed preliminary potential for MRI-based TMJDJD classification, particularly on sagittal images. Based on image-level classification results, the model may provide preliminary support for identifying MRI findings associated with TMJDJD and assisting decisions regarding further CBCT evaluation. Full article

This paper proposes a spectral-grid method based on Chebyshev polynomials of the first kind for the numerical solution of second-order singularly perturbed boundary value problems containing two boundary layers. The proposed method possesses several important advantages, including high numerical accuracy, computational efficiency in terms of the number of arithmetic operations, reduced memory requirements, accurate localization and resolution of boundary layers, and applicability to singularly perturbed boundary value problems containing one, two, or multiple boundary layers. In the proposed approach, the computational domain is partitioned into several grid elements, and the solution on each element is approximated by a truncated series of Chebyshev polynomials. Continuity conditions for the solution and its derivatives are imposed at the interfaces between adjacent elements, resulting in a system of algebraic equations for the unknown expansion coefficients. The principal advantage of the method lies in its ability to accurately localize boundary layers by appropriately selecting the lengths of the grid elements and the degrees of the approximation polynomials. Numerical experiments for a wide range of perturbation parameters are presented in the form of tables and graphical illustrations and are compared with existing results available in the literature. The obtained results demonstrate that the proposed spectral-grid method provides highly accurate numerical solutions even for very small values of the perturbation parameter while significantly reducing the maximum absolute error. The convergence of the proposed method has been theoretically established, and its convergence rate has been analyzed. The numerical results confirm the accuracy, computational efficiency, robustness, and reliability of the proposed method for solving singularly perturbed boundary value problems. Full article

Pump-as-Turbine (PaT) systems represent a cost-effective solution for hydraulic energy recovery in existing water networks and industrial processes. However, the prediction of their performance in turbine mode remains challenging, especially under off-design conditions where unsteady flow structures and internal losses strongly affect the machine efficiency. In this work, transient CFD simulations of a real industrial centrifugal pump operating as a turbine are performed using OpenFOAM and ANSYS CFX and compared with available experimental data. The investigated operating range extends from $0.7Q_{\mathrm{BEP}}$ to $1.3Q_{\mathrm{BEP}}$. A mesh independence analysis is first carried out using the Grid Convergence Index method, leading to the selection of a mid-size computational mesh as a compromise between accuracy and computational cost. The transient OpenFOAM results show close agreement with the ANSYS CFX predictions over the complete operating range. Both numerical frameworks reproduce the experimental hydraulic-efficiency trend and the location of the BEP, while systematic deviations in hydraulic head and mechanical power are mainly attributed to the geometrical and physical simplifications adopted in the common computational model. The local pressure coefficient monitored at the tongue region shows that both the mean pressure level and the fluctuation amplitude increase with flow rate, indicating stronger transient behavior under high-flow conditions. Beyond the global performance comparison, the flow field is analyzed using $Q_{crit}$ iso-surfaces, mean circumferential velocity, the swirl-intensity parameter $S_{\mathrm{int}}$, relative velocity fields at the PaT operational leading edge, and volute head-loss evaluation. The results show that part-load operation is characterized by strong outlet vortical structures and high residual swirl intensity, while the BEP region corresponds to reduced outlet rotational content. Under overload conditions, the outlet swirl remains limited, but the volute head loss increases significantly, becoming a dominant contributor to the efficiency drop. The study demonstrates that PaT performance cannot be interpreted from outlet swirl alone, but results from the combined effect of residual rotational structures, tongue-region unsteadiness, impeller incidence conditions, and volute dissipation. Full article

Groundwater-storage anomalies (GWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) mission provide valuable information for regional groundwater monitoring. Improving the spatial representation and temporal continuity of GRACE-derived GWSA is important for supporting groundwater assessment at subregional scales. A sequential framework combining Gaussian Process (GP) temporal gap filling and Random Forest (RF) spatial downscaling was developed for GWSA reconstruction in Henan Province, China, during 2002–2022. The GP model was used to reconstruct missing observations and characterize temporal variations, while the RF model statistically redistributed the GRACE-based GWSA signal using multi-source hydroclimatic predictors. The resulting dataset comprises model-derived GWSA estimates on a 1 km output grid constrained by the coarse spatial support of GRACE observations and the relationships learned from the auxiliary variables. Therefore, the 1 km grid spacing should not be interpreted as an independent 1 km resolving capability for groundwater-storage variations. Agreement with the parent GRACE-based GWSA product was used to assess coarse-scale reconstruction consistency rather than independent fine-scale accuracy. Comparison with groundwater-level anomalies from 63 monitoring wells yielded a correlation coefficient of 0.88, indicating temporal agreement at the sampled locations. Because the groundwater-level observations were not converted into storage anomalies using specific yield, this comparison does not establish absolute GWSA accuracy or independently validate the model-derived fine-scale spatial patterns. The reconstructed estimates revealed pronounced spatial heterogeneity in groundwater-storage changes, with persistent depletion concentrated in northern Henan, where groundwater decline rates exceeded 20 mm yr⁻¹. Overall, the framework improved the temporal continuity and spatial representation of GRACE-based groundwater-storage estimates while retaining the fundamental spatial constraints of satellite gravimetry. The results demonstrate the potential of integrating GRACE observations, machine learning, and multi-source Earth observation data to support regional groundwater assessment. Full article

Coal gangue concrete (CGC) is an effective green building material that can promote the resource utilization of solid waste. To study its durability performance and degradation mechanism under sulfate attack with dry-wet cycles, and to realize the intelligent prediction of mechanical properties, this study prepared CGC specimens with a water-to-binder ratio of 0.4, a fine aggregate replacement rate of 20%, and coarse aggregate replacement rates of 0%, 20%, 50%, 80%, and 100%. The specimens were tested under 30, 60, 90, and 120 dry-wet cycles in 10% MgSO₄ solution. Mass loss, relative dynamic elastic modulus, and compressive and flexural strength corrosion resistance coefficients were used as evaluation indices, and SEM and XRD were adopted to analyze microstructural deterioration. A database compiled from literature data was established, and six machine learning models-random forest (RF), artificial neural network (ANN), decision tree (DT), support vector machine (SVM), particle swarm optimization-artificial neural network (PSO-ANN), and particle swarm optimization-support vector machine (PSO-SVM) were constructed to predict the strength corrosion resistance coefficients. Test results indicate that all macroscopic indices first increased and then decreased with the number of dry-wet cycles. Early ettringite and gypsum products filled internal pores, while prolonged sulfate attack caused decalcification and structural degradation of C-S-H gel, resulting in obvious performance loss. The PSO-SVM model showed the best prediction accuracy, with R² values of 0.912 and 0.981 for compressive and flexural strength corrosion resistance coefficients, respectively. Feature importance analysis shows that dry-wet cycles had the most significant negative impact, followed by the coal gangue fine aggregate replacement rate. This study provides support for the durability evaluation and intelligent prediction of coal gangue concrete in sulfate environments. Full article

Long-term influent flow projection governs the sizing of wastewater treatment plants (WWTPs), yet conventional practice estimates future flows using per capita generation rates and coefficients assumed to remain constant over time, neglecting the fact that, in urbanizing catchments, the fraction of generated wastewater that reaches the treatment plant increases as the sewer network expands and densifies. This study develops and validates an explicit-structure model that separates demographic wastewater generation from hydraulic conveyance, disaggregates the service area into fully contributing and partially contributing sectors, and introduces a time-dependent transport coefficient, $k\left(t\right)$. Applied to the La Chira WWTP (Lima, Peru; approximately 2.6 million inhabitants), the model was evaluated through leave-one-year-out cross-validation against both a static transport model and an aggregated formulation. The proposed formulation consistently outperformed the alternatives in out-of-sample prediction (Nash–Sutcliffe efficiency of 0.965 and mean absolute percentage error of 1.88%, compared with 0.825 and 0.799 for the benchmark models), providing falsifiable evidence of the value of spatial disaggregation and time-varying transport representation. The transport coefficient increases from 0.491 in 2017 to 0.813 in 2040 under the linear reference specification, with a logistic alternative—statistically indistinguishable in calibration—bounding the projection from below; a formal Shapley decomposition attributes 44% of the projected flow increase to this coefficient. The observed flow rate in 2025 (7.341 m³ s⁻¹) provides an external validation point, predicted with a relative error of 0.7%. Mean influent flow is projected to reach 10.12 m³ s⁻¹ by 2040 (95% CI: 8.55–11.69), representing a 61% increase above the design average flow and approaching the design peak capacity (11.3 m³ s⁻¹), with an exceedance probability of the annual mean of approximately 5%. These results indicate a progressive approach to hydraulic saturation within the planning horizon. The proposed framework is robust to alternative per capita generation assumptions, mechanistically grounded, interpretable, and transferable to sanitation systems characterized by evolving coverage and network connectivity. Full article

Conventional spoon-type seed-metering devices have fixed spoon-cavity geometry, which makes it difficult to achieve both stable sowing of small-sized seeds and precise seeding-rate regulation. To address this problem, a coordinated variable-volume adjustment method based on an adjustment ring-seed-picking spoon structure was proposed, and a variable-rate spoon-type millet seed-metering device was designed. Through circumferential rotation of the adjustment ring, the effective volume of the scooping spoon and the length of the transition groove were synchronously adjusted, enabling precise regulation of the number of seeds per hill without replacing components. The zoning characteristics of seed-population flow in the seed-metering chamber and the effects of operating speed on seed-filling mechanical behaviour were investigated through theoretical analysis and discrete element simulation. The seed-filling and seed-clearing processes were clarified, and key parameter ranges were determined. Through response surface methodology (RSM) experiments, the optimal parameter combination was determined as an operating speed of 3.43 km∙h⁻¹, a scooping-spoon inclination angle of 35.25° and a transition-groove inclination angle of 62.47°. Under these conditions, the qualified rate of seeds per hill was 93.11%, the average number of seeds per hill was 5.51 seeds and the coefficient of variation in seeds per hill was 23.71%. When the seeding-rate adjustment ring was adjusted within 0–10°, the average number of seeds per hill increased from 5.7 to 11.2 seeds, while the coefficient of variation decreased from 22.71% to 19.97%. The device can adapt to differences in grain shape among different millet varieties and to seeding-rate adjustment requirements under varying soil fertility conditions across different fields, achieving precise seeding-rate regulation and uniform seed metering. This study provides a reference for improving the variable-rate operating precision of precision hill-drop seeding devices for small-sized seeds. Full article