Search Results (1,492)

Background/Objectives: Preoperative discrimination of complicated appendicitis in children remains challenging but crucial for timely surgical decision-making and perioperative management. We aimed to develop a simple, clinically applicable scoring system for preoperative risk stratification of complicated appendicitis in children. Methods: This retrospective single-center study included children aged 5 to <16 years who underwent emergency appendectomy between 2015 and 2024. Complicated appendicitis was defined as gangrene, perforation, or intra-abdominal abscesses on intraoperative or pathological evaluation. Literature-based prespecified predictors included body temperature ≥ 38.0 °C, presence of periappendicular fluid, serum sodium level < 135 mEq/L, white blood cell (WBC) count > 12,000/μL, and C-reactive protein (CRP) level ≥ 3.0 mg/dL. A multivariable logistic regression model using Firth’s penalized likelihood was developed and internally validated using bootstrap resampling. An additive scoring system was derived from the final model. Results: Among 301 patients, 102 (33.9%) had complicated appendicitis. Based on the Firth penalized multivariable model, integer point values were assigned in proportion to the regression coefficients. Serum sodium level < 135 mEq/L and CRP level ≥ 3.0 mg/dL assigned 2 points each; body temperature ≥ 38.0 °C, presence of periappendicular free fluid, and WBC count > 12,000/μL assigned 1 point each. The scoring system (range, 0–7) demonstrated good discrimination (area under the receiver operating characteristic curve, 0.880; 95% confidence interval, 0.840–0.920), minimal optimism, and good calibration. Stratification into low-, intermediate-, and high-risk categories showed an increasing prevalence of complicated appendicitis. Conclusions: This simple preoperative scoring system enables reliable and clinically interpretable risk stratification for complicated appendicitis in children aged 5 to <16 years and may support early decision-making in routine pediatric surgical practice. Full article

To investigate the rheological properties of geopolymer grouting materials, a systematic study was conducted on a slag–red mud–fly ash ternary solid waste geopolymer grouting material (TSWGGM). The effects of liquid-to-solid ratio (0.5–1.5), slurry temperature (10–50 °C), and alkali activator concentration (1.0–1.8 mol/L) on the rheological model, yield stress, time-dependent viscosity behavior, and thixotropy were examined. The results show that the liquid-to-solid ratio is the dominant factor determining the rheological model. With increasing liquid-to-solid ratio, the slurry exhibits Herschel–Bulkley (shear thinning), Bingham, and Newtonian fluid behaviors in sequence. The yield stress decreases significantly with increasing liquid-to-solid ratio and approaches zero at high liquid-to-solid ratios (≥1.0), while it increases with rising temperature. In the temperature range of 20–30 °C, the time-dependent viscosity curves follow an exponential growth law, whereas at 40–50 °C, competition between early reaction and shear destruction leads to an initial decrease followed by an increase in viscosity. At low alkali activator concentrations (≤1.4 mol/L), the time-dependent viscosity curves still obey the exponential model, but at excessively high concentrations (≥1.6 mol/L) a non-monotonic change (an initial increase followed by a decrease, and final steady growth) occurs. The thixotropic loop area decreases with increasing liquid-to-solid ratio, first decreases and then increases with rising temperature, and exhibits a critical phenomenon of first increasing and then decreasing with increasing alkali activator concentration. Full article

Crystallization can be exploited as an architecture-forming step for polymer membranes because it builds a load-bearing semicrystalline scaffold while simultaneously defining amorphous regions that later become transport pathways. Herein, we quantify how thermal history programs isotactic polystyrene (iPS) crystallization and translate the resulting microstructures into membrane-relevant design rules. Lux-calibrated digitally extracted pixel intensity (DPI) from polarized optical microscopy provides a quantitative, spatially resolved crystallinity proxy; benchmarking against differential scanning calorimetry confirms that the DPI proxy exhibits the same onset, peak, and completion signatures under matched temperature programs. The DPI–DSC agreement yielded R² = 0.98 under matched programs. We compared crystallization initiated from molten and glassy states across a wide range of melt pretreatments and crystallization temperatures. Molten-state pathways display pronounced melt-memory behavior: modest changes in melt pretreatment shift induction time and half-time and drive textures from dense, fine spherulitic fields to sparse, coarser morphologies. In contrast, glassy-state crystallization largely suppresses melt history, yielding overlapping sigmoidal crystallinity curves and stable kinetic parameters consistent with relaxation-mediated nucleation. Avrami analyses indicate three-dimensional growth in both routes but highlight the strong melt-history sensitivity of apparent rate constants in the molten state. The crystallization rate and half-life show bell-shaped temperature dependence. Finally, saturated nucleation density correlates with the melting response, providing a practical link between kinetic observables and morphology. The processing–morphology map provides membrane-relevant design rules by linking thermal history to nucleation density and scaffold texture, which are expected to influence transport and mechanical stability in downstream membrane fabrication. In this study, “membrane architecture” is used in a pre-fabrication sense to denote the crystallization-programmed semicrystalline scaffold expected to govern subsequent pore-generation behavior and mechanical stability. Accordingly, the present work establishes a quantitative process–structure map for iPS scaffold design. Full article

To meet the conflicting demands of real-time simulation and high fidelity for thermal modeling of IGBT modules in digital twin applications, this paper presents a dynamic thermal network parameter updating strategy. A hybrid thermal model is constructed by combining a high-fidelity finite-element-method reference model with a 3-D compact network. Initial thermal resistance and capacitance parameters are obtained via offline calibration and validated against the transient thermal impedance curve. A dynamic identification method based on recursive least squares with precomputed sensitivity matrices is then proposed. It dynamically updates each independent thermal branch using only real-time chip junction temperature measurements. The Vincotech full-bridge IGBT module is used for simulation validation. The proposed method achieves steady-state identification errors of 3.2% for the IGBT chip thermal resistance and 4.5% for the freewheeling diode chip thermal resistance, outperforming particle swarm optimization and dual Kalman filter in both convergence speed and steady-state accuracy. Thus, it satisfies the requirements of real-time tracking and dynamic evolution for thermal models in digital twin systems. Full article

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. Full article

Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque–speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM based on the short-term dynamic feasible region constraint is proposed to effectively improve the response speed. Firstly, the dynamic trapezoidal domain operation boundary is obtained by analyzing the motor working point variation curve and considering factors such as the working temperature and trajectory control, which constitutes the torque–speed value and the dynamic constraint mechanism of trajectory planning. Secondly, based on the energy consumption model, the average thermal power is used to represent the torque overload limit condition, and a dynamic constraint method based on the short-term dynamic torque–speed operation boundary is proposed. Then, in order to reduce the computational load in the online millisecond-level response, a time-optimal trajectory optimization algorithm based on sequential least squares is proposed to calibrate the positioning time of the time-optimal trajectory under different working temperatures and angles. Finally, a simulation and experimental comparisons of the time-optimal trajectories under different angles and working temperatures are carried out to verify the effectiveness of the proposed method. Full article

Deep and ultra-deep carbonate reservoirs commonly experience fracture closure and conductivity reduction under high-temperature and high-stress conditions. In this study, triaxial creep tests were conducted on unacid-etched and acid-etched carbonate cores under different stress levels to investigate their time-dependent deformation behavior and the influence of acid etching on rock rheology. The results indicate that carbonate rocks exhibit pronounced creep behavior, including instantaneous elastic deformation, primary creep, and steady-state creep. Acid etching significantly altered the creep characteristics and rheological parameters of carbonate rocks, leading to distinct time-dependent deformation responses compared with the unacid-etched core. The Burgers constitutive model was employed to characterize the creep behavior, and all fitting correlation coefficients exceeded 0.9. Finite element simulations based on the fitted parameters successfully reproduced the experimental creep curves, verifying the reliability of the constitutive model. This study provides a theoretical and numerical basis for evaluating the long-term deformation behavior of acid-etched carbonate rocks and its implications for fracture closure and conductivity evolution. Full article

Background: The lactate-to-albumin ratio (LAR) and C-reactive protein-to-albumin ratio (CAR) are biomarkers for metabolic stress and inflammation. However, their prognostic significance after return of spontaneous circulation (ROSC) in out-of-hospital cardiac arrest (OHCA) remains unclear. Therefore, this study aims to investigate the association between serial LAR/CAR measurements and in-hospital mortality. Methods: This retrospective observational cohort study included adult comatose patients with OHCA treated with targeted temperature management between January 2022 and December 2025. Serum lactate, albumin, and C-reactive protein levels were measured at admission and at 24, 48, and 72 h after ROSC. The primary outcome was in-hospital mortality. Multivariable logistic regression analyses were performed to assess independent associations of LAR and CAR with in-hospital mortality, and discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUC). Results: Of the 284 eligible patients, 253 were included in the final analysis. Of these, 80 patients died in hospital, corresponding to an in-hospital mortality rate of 31.6%. LAR and CAR were significantly higher in non-survivors than in survivors at admission and at 24, 48, and 72 h after ROSC. After adjustment for potential confounders, LAR was associated with in-hospital mortality at all assessed time points. CAR was independently associated with in-hospital mortality at admission and at 48 and 72 h after ROSC, but not at 24 h. The AUCs of LAR for predicting in-hospital mortality ranged from 0.702 to 0.734, whereas those of CAR ranged from 0.640 to 0.690. Conclusions: In this single-center retrospective cohort of post-ROSC OHCA patients, sequential tracking of LAR and CAR profiles during the first 72 h after ROSC provided meaningful insights into in-hospital mortality. LAR showed a more consistent independent association with mortality and fair discriminatory performance, whereas CAR demonstrated limited prognostic value despite its association with mortality. Full article

High-temperature structural components are susceptible to creep deformation, which can ultimately lead to failure. In this work, an AI-based framework was developed capable of predicting the creep time of 316 austenitic stainless steel. Here, creep time refers to both the time to reach specific strain levels and the time to rupture. However, the scope of the present work is limited to rupture-time prediction, while the application of the framework to strain-level prediction will be reported in future work. The dataset consisted of creep strain curves from four heats, including both rupture and non-rupture curves. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Support Vector Regressor (SVR), Gaussian Process Regressor (GPR), and Neural Network (NN) were employed. The effects of square-root and cube-root transformations on data distribution and model learning behaviour were analysed using model learning curves. An Optuna (version 4.3.0)-based hyperparameter tuning strategy was employed. The cube-root transformation improved the learning performance of SVR, GPR, and NN, whereas RF, GB, and XGB remained unaffected. Learning curves revealed mild overfitting for RF, GB, and XGB, and very minimal overfitting for SVR, GPR, and NN. NN achieved the best predictive performance (${\mathrm{R}}^2=0.92,\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.195$, deviation factor of 1.57). The findings demonstrated that the combined useof creep strain curves, data transformation, learning curve guided model selection, and rigorous hyperparameter tuning can improve the prediction accuracy under a limited dataset. Full article

In this work, Direct Numerical Simulation (DNS) investigates the combustion behaviour of a reactive transverse lean premixed jet of an ammonia blend (10% NH₃, 11% H₂, 16% O₂ and 63% N₂ by volume) injected through a rectangular nozzle in a pre-heated non-vitiated air crossflow at a pressure of 5 bar. The configuration has been chosen from a Reynolds-Averaged Navier–Stokes (RANS) test campaign to ensure low NO and low unburned fuel, while maintaining a high temperature profile at the turbine inlet. The DNS shows that the flame stabilises on the leeward side of the rectangular jet, within and downstream of the recirculation region, while high scalar dissipation and short residence times prevent persistent anchoring on the windward side. Joint statistics reveal that the reaction does not follow a constant equivalence ratio path, since intermediate progress states are shifted towards leaner mixtures by entrainment, dilution and differential diffusion. The strongest heat-release and displacement-speed events occur in localised regions where mixture state, stretch and flame-front geometry act jointly. The displacement-speed budget is mainly controlled by the chemical source term, with diffusion reducing the net propagation speed and stratification-induced cross terms remaining small. Under intense stretch, positively curved flame elements exhibit larger displacement speeds, indicating a coupled effect of curvature, preferential diffusion and local radical transport. NO formation is dominated by fuel-nitrogen chemistry: HNO and NH₂ are the main NO-producing routes, whereas N₂ and N₂O provide the dominant NO-sink channels. The DNS predicts an outlet-averaged NO level of 400 dppm, while extended-domain RANS calculations indicate that longer residence times could reduce it below 100 dppm. Full article

Reliable temperature monitoring is essential for the thermal management and safe operation of modern telecommunication equipment. However, conventional temperature sensors are often relatively large and rigid, which limits their applicability for localized temperature measurement on compact electronic components. In this study, a flexible thin-film thermocouple based on NiCr–NiSi thermoelectric materials was developed for temperature monitoring of telecommunication equipment. The sensor adopts a multilayer structure consisting of a polyimide (PI) flexible substrate, an Al₂O₃ insulating layer, NiCr and NiSi thermoelectric films, and a SiO protective layer and was fabricated using magnetron sputtering. Static calibration experiments show that the fabricated sensor exhibits a thermoelectric sensitivity of approximately 40.45 µV/°C, which is close to the reference value of conventional K-type thermocouples, with a relative error of about 1.34%. Repeated heating–cooling cycles demonstrate good repeatability and stable thermoelectric characteristics. Dynamic tests under representative transient thermal conditions showed that the sensor could continuously capture temperature variations without signal interruption or abnormal fluctuations. To further quantify its dynamic behavior, a numerical step-response simulation was performed for the PI/Al₂O₃/NiCr–NiSi/SiO multilayer structure. The simulated thermal time constant and curve-extracted 90% response time were 0.0343 s and 0.0803 s, respectively, under the specified boundary conditions. Owing to its small thickness, low thermal mass, and good mechanical flexibility, the proposed thin-film thermocouple can be conformally attached to compact and curved electronic surfaces, indicating promising potential for real-time localized temperature monitoring of telecommunication equipment and other compact electronic systems. Full article

To address the lack of systematic quantitative characterization of oil bank dynamic evolution and unclear dominant controlling factors in polymer flooding, this study combines reservoir numerical simulation with Python-based quantitative analysis and a machine learning framework (random forest + SHAP). We established 1D and 2D reservoir models: the 1D model develops a precise quantitative characterization method for oil bank width (defined by front/rear edge saturation offsets Pf < 1.0% and Pb < 1.0%, fitted with a cubic polynomial, R² > 0.95) and height (derived from optimal oil saturation difference time curves and integral calculation); the 2D model investigates the regulatory mechanism of reservoir heterogeneity. Based on 15,000 sets of physically consistent simulation data, the random forest model achieves high prediction accuracy (R² = 0.98). Sensitivity analysis reveals that main flow direction permeability, reservoir temperature, and water-phase exponent (nw) of the Corey model are the dominant controlling parameters, exhibiting substantially higher sensitivity than polymer adsorption capacity and residual resistance coefficient. The oil bank height shows a negative correlation with the first two parameters, while it displays a peak-type variation with the water-phase exponent. Under heterogeneous conditions, permeability anisotropy amplifies the regulatory effect of relative permeability exponents, leading to unbalanced oil bank migration (quantified by front ratio R). This study breaks through the limitations of traditional qualitative characterization, elucidates the spatiotemporal evolution laws and heterogeneous regulatory mechanisms of the oil bank, and provides reliable theoretical and dataset support for optimizing polymer flooding schemes. Full article

During continuous casting, the flow behavior of liquid steel in the tundish directly affects the temperature distribution of liquid steel, inclusion removal, and billet quality. In tundish-related research, water model experiments remain an intuitive method for investigating the flow process in the tundish. However, water model experiments are often conducted in different seasons, and variations in experimental temperature can change fluid properties such as density and viscosity, thereby affecting flow characteristics and the comparability of experimental results. In this study, a 1:3.57 transparent bare single-strand tundish model made of acrylic was used, and the differences in tracer transport processes at 7 °C and 20 °C, as well as the influence of different tracer dosages on the experimental results, were systematically investigated through flow visualization and stimulus-response experiments. The results showed that, under the 7 °C condition, the upward transport tendency of the pure ink tracer was weakened, the overall flow remained closer to the tundish bottom, the transport speed decreased, and the time required to reach the outlet was significantly prolonged. For the saturated KCl solution tracer, a lower temperature enhanced its transport along the bottom toward the outlet and suppressed its diffusion toward the liquid surface. The RTD results showed that, after the temperature was increased, the curves shifted to the left as a whole, and both the peak time and the mean residence time were shortened. The outflow percentage of tracer results showed that the difference for the 10 mL saturated KCl solution between the 7 °C and 20 °C conditions was the most significant. At 7 °C, the total outflow percentage of the 10 mL salt solution tracer at 1500 s was 76.86%, which was 22.97% lower than that at 20 °C. As the tracer dosage increased, the differences in the transport process, RTD curves, and outflow percentage curves under different temperature conditions gradually decreased, indicating that the effect of dosage on the experimental results gradually became stronger than that of temperature. These results indicate that the combined effects of experimental temperature and tracer dosage cannot be neglected in tundish water model experiments. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

In this study, NiO/ZnO heterojunction materials were prepared by calcining metal–organic frameworks (MOFs). The structural and morphological characteristics of the NiO/ZnO composite were investigated using various characterization methods, including X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy. Gas-sensing tests showed that at the operating temperature of 190 °C, the NiO/ZnO heterojunction (with a molar ratio of 1:1) exhibited the highest response value (R_a/R_g = 201.7) and good selectivity toward 100 ppm n-propanol. Compared to pure ZnO and NiO, the response of NiO/ZnO was significantly improved (ZnO: 6, NiO: 14.6), with increases of 33.5-fold and 13.8-fold, respectively. The response and recovery times were 92 and 30 s, respectively. Additionally, to enable rapid identification of n-propanol gas concentrations, this study developed and validated a method by training and predicting response curves using a random forest algorithm, achieving identification of n-propanol gas at different concentrations (2–100 ppm) within 7 s. Finally, the enhanced sensing performance was mainly attributed to the formation of the interfacial p-n heterojunction between NiO and ZnO, together with increased surface active sites, oxygen vacancies, and chemisorbed oxygen species. Full article