Search Results (1,309)

Long-term operation of drip emitters under sediment-laden water conditions readily induces particle deposition and clogging, leading to discharge reduction and deterioration of irrigation uniformity. To clarify the temporal evolution and spatial distribution of clogging and to support structure-oriented anti-clogging improvement, three integrated drip tape emitters with different labyrinth-channel geometries were tested at sediment concentrations of 1, 2, and 3 g·L⁻¹ under a constant pressure of 100 kPa. The average relative discharge ratio (Dra) and Christiansen’s uniformity coefficient (CU) were continuously monitored, and cross-sectional observation and numerical simulation were combined to identify dominant deposition hotspot regions within the labyrinth channel. The results showed that increasing sediment concentration significantly accelerated clogging development and shortened operating lifetime. At 1 g·L⁻¹, the times required for the three emitter types to reach the clogging criterion of Dra < 75% were 120, 81, and 107 h, respectively, whereas at 3 g·L⁻¹ these values decreased to 39, 42, and 39 h. CU continuously declined with operating time and, in some treatments, responded earlier than Dra to system deterioration. Sediment deposition was mainly concentrated in the inlet section and bend regions, indicating that these locations were the dominant hotspots for clogging initiation and propagation. These findings demonstrate that clogging in drip emitters is jointly regulated by sediment load and labyrinth-channel geometry, and that hotspot-based structural optimization provides an effective basis for improving anti-clogging performance under sediment-laden water conditions. Full article

Background and Clinical Significance: Decompressive hemicraniectomy (DHC) is a life-saving intervention for malignant middle cerebral artery (MCA) infarction, but postoperative secondary injury mechanisms and functional outcome remain difficult to evaluate using intracranial pressure (ICP) alone. The pressure reactivity index (PRx), calculated as the moving correlation coefficient between ICP and mean arterial pressure (MAP), provides a measure of cerebral autoregulation. The utility of PRx monitoring in ischemic stroke, especially following DHC, remains uncertain. Case Presentation: We describe two patients presenting with acute ischemic stroke in the MCA territory who underwent DHC followed by postoperative ICP and PRx monitoring. Case 1 is a 40-year-old female with a left proximal MCA occlusion initially treated with endovascular thrombectomy (EVT) who required emergent DHC due to re-occlusion. Postoperatively, ICPs remained controlled, and PRx values were favorable (<0.2), indicating preserved cerebral autoregulation. She later showed moderate neurological improvement. Case 2 was a 68-year-old female with a left proximal MCA occlusion treated with EVT who developed worsening cerebral edema and midline shift, necessitating emergent DHC. Despite adequate ICP control, PRx values remained markedly elevated (0.45 to 0.73), consistent with impaired cerebral autoregulation, and her neurologic state remained poor at discharge. Conclusions: These contrasting cases suggest that PRx may provide physiologic information not reflected by ICP metrics alone post-DHC. PRx monitoring may provide complementary physiologic insight into postoperative autoregulatory status following DHC. Further investigation is warranted to define its role in individualized post-DHC management and prognostication in malignant ischemic stroke. Full article

The Tianshan–Pamir mountain region, serving as the core “water tower” for countries in Central Asia east of the Aral Sea, is a critical bulwark for sustaining downstream socioeconomic systems. However, constrained by complex topography and harsh climatic conditions, this region suffers from a severe scarcity of long-term, continuous hydrological observation data. This study focuses on a typical data-scarce mountainous area, coupling UAV and satellite imagery-based (e.g., Landsat/Sentinel) flow inversion with a hybrid spatial regionalization method—integrating spatial proximity, basin similarity, and regression-based hydrograph reconstruction—to quantitatively estimate long-term discharge time series. The results indicate that, for the validation of instantaneous discharge inversion, the Nash–Sutcliffe efficiency coefficient (NSE) at 29 river cross-sections was consistently greater than 0.80, with the coefficient of determination (R²) reached 0.94 (p < 0.01). Subsequently, for the long-term discharge series reconstructed using the regionalization method, the NSE values at three representative verification sites—each corresponding to a distinct basin type—were 0.88, 0.84, and 0.86, respectively. These findings exhibit higher precision compared to direct temporal upscaling, confirming the reliability of the regionalization method across varying temporal scales. An analysis of monthly discharge trends from 1989 to 2020 revealed a decreasing trend in the discharge of glacier-dominated rivers, with an average rate of change of −2.89 ± 2.54% (p < 0.05); the Pamir Plateau experienced the largest decline (−4.89 ± 6.58%), which is closely linked to large-scale glacial retreat within the basins. Conversely, the discharge of non-glacier-dominated rivers showed an increasing trend, with a multi-year average rate of change of +0.32 ± 8.43% (n.s.), primarily driven by shifts in precipitation and vegetation cover. This research introduces a new approach for hydrological monitoring in data-scarce regions and provides essential data and methodological support for water resource management decisions in arid zones. Full article

Background/Objectives: Recent guidelines have emphasized the importance of early mobilization and rehabilitation of patients following cardiac surgery. However, studies on the optimal targets and prescription methods for phase I cardiac rehabilitation (CR) are lacking. This study aimed to evaluate the feasibility and utility of an early phase 1 submaximal cardiopulmonary exercise test (CPET) using a recumbent ergometer in patients who have undergone cardiac surgery. Methods: Twenty ambulatory patients who underwent cardiac surgery between December 2021 and February 2023 were referred to the CR department on the fifth postoperative day, and a CR program was initiated. The program was conducted five times a week, with hour-long sessions consisting of warm-up exercises, resistance training, aerobic exercises, and a cool-down period. A recumbent ergometer-based submaximal CPET was performed approximately nine days after the surgery, prior to discharge. Participants initiated the test at 0 W, and the workload was increased by 20 W after 2 min. During the test, researchers evaluated parameters including submaximal peak values of oxygen consumption (VO₂), metabolic equivalents of task, respiratory exchange ratio (RER), blood pressure, heart rate (HR), and rating of perceived exertion (RPE). The grip strength test, 6 min walk test (6MWT), Korean Activity Scale/Index (KASI), EuroQol-5 dimension (EQ-5D), and short-form 36-item health survey (SF-36) values were also measured prior to discharge. Results: Twenty patients (75% male, average age 62.50 ± 1.99 years) underwent CPET at a median of 9.0 (8.0; 12.5) days postoperative. The average exercise duration of the CPET was 411.75 ± 168.25 s. During the test, their submaximal peak VO₂ was 12.32 ± 0.75 mL/kg/min (corresponding to 46.65 ± 2.08% of VO₂ max). The submaximal peak RER was 1.01 (0.98–1.12), and the submaximal peak RPE was 15.00 ± 0.51. Furthermore, the submaximal peak HR was 111.8 ± 3.76 beats/min (equivalent to 70.95 ± 2.09% of age-predicted maximal HR). After adjustment for age and sex, statistically significant positive correlations were observed between the submaximal peak VO₂ and 6MWT, squat endurance test, KASI, EQ-5D, and the physical component summary (PCS) of the SF-36 questionnaire. The 6MWT, squat endurance test, KASI, and PCS of SF-36 showed a correlation coefficient (r) of 0.522 (p = 0.026), 0.628 (p = 0.005), 0.586 (p = 0.011), and 0.546 (p = 0.019), respectively. No significant cardiac events, such as ST elevation/depression or hemodynamic instability, were observed during the test. Conclusions: Our findings suggest that performing recumbent ergometer-based CPET during early phase 1 CR is safe and feasible. These results highlight the potential of recumbent ergometer-based CPET as a valuable tool for guiding the appropriate prescription of early CR programs following hospital discharge in patients undergoing cardiac surgery. Full article

With hydrogen as a clean but hazardous energy carrier, solid-state hydrogen storage in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. This paper reports the modelling of a metal hydride reactor during its discharge state using neural network regression. This was done by generating a validated finite element model of the reactor, which was then used to generate dynamic operational data based on the desired pressure outlet and heating fluid temperature as independent variables. The best-performing neural network model validation using the experimentally observed data achieved a regression coefficient of 0.99 and a mean squared error of less than 10⁻⁴. This predictive model, with further refinement, can be implemented to allow for predictive control, which has always been a challenge through conventional means due to the batch nature of the system. Moreover, the hydrogen concentration as stored in a solid-state measurement would be too expensive for industrial applications. Full article

Unmanned Aerial Vehicle (UAV) fertilization is important for precision agriculture. However, multi-rotor UAVs show a lot of inconsistencies in homogeneity and unclear deposition patterns when they spread granular fertilizer in different operational situations. This study utilized the DJI T40 UAV to measure discharge rates and create a correlation model. An orthogonal design combined DEM simulation with field experiments to look at how flight height and disc speed affected spreading uniformity and effective swath for single and overlapping flight paths. The discharge rate has a strong linear relationship with control parameters (R² > 0.94), which means that it is very easy to predict for all particle sizes. Single-pass deposition shows an “M-shaped” bimodal profile with particles of different sizes arranged in a radial pattern. The best values for H and n were found to be 7 m and 1200 rpm, respectively, and gave a 10 m effective swath width and a coefficient of variation (CV) of 13.79%. Deposition patterns change nonlinearly with flight height and disc speed. Particle size consistency is critical for distribution stability, with flight height being the key quality determinant and particle size variation the primary source of instability. Full article

This study investigates the applicability of alternative low-global warming potential (GWP) refrigerant blends in water-source heat pump systems. Binary and ternary refrigerant mixtures were generated using REFPROP 10 to identify suitable candidates. Among 379 novel blends, 18 mixtures with glide temperatures below 10 °C, high critical temperatures, and GWP values lower than 750 were selected for analysis. Thermodynamic analyses were conducted for the selected refrigerants at target water outlet temperatures ranging from 35 to 75 °C, with a heat source temperature of 15 °C and an evaporation temperature of 5 °C. In addition, compressor discharge temperature, volumetric heating capacity, and coefficient of performance (COP) were evaluated. Among the refrigerants, MX1 was recommended for condenser temperatures of 40–80 °C in large-scale heat pump and district heating applications. For refrigerants with GWP values below 150, MX7 exhibited the highest COP and second-law efficiency (η_II) and is therefore suitable for small-capacity systems. In the GWP range of 150–750, MX16 demonstrated the highest COP and η_II values over the entire temperature range. Overall, MX7 achieved the highest COP and η_II among all refrigerants considered, while MX4 emerged as the most favorable mixture in terms of low GWP (below 150) and thermophysical performance. Full article

The discharge of untreated paper mill effluent poses significant ecological risks due to its high organic and nutrient loads. This study aimed to assess the phytoremediation potential of Azolla pinnata for treating paper mill effluent. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling approaches were applied and optimization was used for pollutant removal and plant biomass production. Experiments were designed using a Central Composite Design with two independent variables: effluent concentration (0, 50, and 100%) and plant density (10, 20, and 30 g per container). The responses measured were biochemical oxygen demand (BOD), chemical oxygen demand (COD) removal efficiencies, and final biomass yield after 16 days of exposure. RSM produced statistically significant (p < 0.05) second-order regression models for all three responses (coefficient of determination; R² > 0.98), while ANN showed slightly lower prediction errors within the experimental range studied. Maximum observed removal efficiencies were 91.74% for BOD, 80.91% for COD, and 92.66 g biomass yield under 50% effluent concentration and 30 g plant density. Optimization via both models suggested closely comparable operating conditions (79% effluent concentration and 29 g biomass) for optimal performance. The results indicate that A. pinnata demonstrates potential as a low-cost, nature-based treatment system for industrial effluent remediation under controlled conditions. The integration of data-driven optimization with biological treatment contributes to sustainable effluent management strategies by reducing chemical inputs, minimizing energy demand, and enabling biomass generation with potential downstream valorization. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

This article presents the results of a study on an axisymmetric gravity-driven slender free-surface flow down a cone by deriving depth-averaged conservation equations on a cone-adapted coordinate system and obtaining a backwater-type differential equation for steady, axisymmetric films with prescribed apex discharge. Analysis of this equation reveals a location-dependent critical condition separating supercritical and subcritical regimes and shows that a classical constant normal depth does not exist; instead, the flow approaches an equilibrium between gravity and resistance forces as it develops downstream. Asymptotic expansions for the flow and critical depths recover previously established results for the laminar leading-order and first-order corrections under consistent velocity shape coefficients, confirming that capillarity affects only first-order terms. The framework predicts a critical length beyond which the flow must be subcritical, Reynolds number decays inversely with the distance, leading to inevitable relaminarization on sufficiently long cones, and the potential need for hydraulic jumps to compatibilize supercritical and subcritical flow regimes, paralleling open-channel hydraulics on mild slopes. Numerical solutions of the backwater equation agree with existing measurements where the slender-film assumptions hold, providing a practical basis to compute flow depth and regime transitions on conical surfaces. Full article

To the authors’ knowledge, this is the first industrial machine learning (ML) study focused on wet vertical stirred media milling. The study develops and validates machine learning (ML) models to predict the key operating parameters, namely mill discharge product size, mill feed slurry flow rate, mill power draw, and the specific energy consumption of an industrial wet vertical stirred media mill operating at a copper plant. A physics-guided workflow was adapted, combining relief coefficient-based variable screening with fundamental stirred milling principles to define 20 different structured model input scenarios. In the scope, six regression approaches, linear regression (LR), fine tree regression (FTR), support vector regression (SVR), random forest regression (RFR), artificial neural network regression (ANN), and Gaussian process regression (GPR), were trained and validated using plant sensor data and evaluated using R² and RMSE. Overall performance was reasonable, with GPR providing the highest predictive accuracy, followed by RFR/ANN, while LR, SVR, and FTR performed lower. The potential benefit of feed size was also assessed conceptually through an upper-bound sensitivity analysis, representing a best-case scenario where an online feed size measurement would be available. Because the feed size descriptor (F₈₀) was not independently measured but derived from an energy–size relationship, the associated accuracy gains are reported as theoretical upper-bound indications rather than independent predictive capability. Overall, the findings support ML-based decision support in stirred milling operations and motivate future work using independently measured feed size (or reliable proxy sensing). Full article

Given the growing concern over the operational safety and long-term reliability of lithium-ion batteries, the accurate assessment of battery state of health (SOH) is of paramount importance. With the aim of elevating the SOH estimation exactitude and remedying the model degradation induced by data paucity, this paper proposes an SOH estimation method that integrates a data-augmentation strategy with a Long Short-Term Memory (LSTM)-iTransformer model. Specifically, multiple health characteristic factors characterizing the aging behavior are first extracted from the battery charge–discharge curves and incremental capacity (IC) curves, and the features that are highly correlated with the SOH are screened by a Pearson correlation coefficient analysis. Subsequently, the data augmentation technique is used to extend the degradation sample set. The LSTM-iTransformer model is trained based on the extended samples and evaluated on multiple performance metrics. A comparative analysis reveals a marked enhancement in predictive accuracy achieved by this method over the baseline model trained with the initial data, which validates the effectiveness of the data augmentation strategy in improving the performance of SOH estimation models. Additionally, in scenarios characterized by abundant data availability, the direct application of this model facilitates enhanced predictive precision. Full article

The Lijiang River is a typical karst landscape river and an important drinking water source for Guilin City. To evaluate its contamination of metals and metalloids, water, surface sediment and four sediment profiles were systematically collected from the Guilin urban segment in April 2023, and the distribution, mobility and potential sources of nine elements (Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb) were analyzed. Results show that metal and metalloid concentrations in the river water are low and water quality is good, whereas sediment concentrations of Cd, Zn, As and Pb are markedly higher than the background values. Compared with other elements, Ni, Cu, As and Cd are more readily mobilized in the aqueous phase and exhibit higher bioavailability. Vertical variation coefficients of all elements in the sediment profiles are mostly below 15%, indicating a relatively stable depositional environment. Correlation analysis and positive matrix factorization identify four main sources: industrial discharge (12.5%), mixed agricultural–geogenic origin (34.3%), traffic emissions (11.9%) and geological background (41.3%). Overall, metal and metalloid contamination in the urban Lijiang River is controllable, but accumulation of Cd and other elements in sediments requires continued attention. Full article

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

UAV-based rice direct seeding offers high operational efficiency and reduced labor demand, yet seed distribution uniformity remains a major limitation for centrifugal spreading devices. This study aims to design and optimize a novel centrifugal drone rice direct seeding device to improve seed lateral distribution uniformity. In this study, a centrifugal drone rice direct seeding device was developed with a concave perforated disc and double-arc seed-pushing blades to regulate seed motion and improve lateral distribution uniformity. Discrete element method (DEM) simulations were conducted to examine the effects of disc tilt angle, blade type, and blade number. Single-factor and response-surface simulation results identified an optimal parameter combination of a 29.0° disc tilt angle, double-arc blades with a 110° arc angle, and six blades. Based on these results, the disc structure was further refined, and the simulated lateral coefficient of variation (CV) of seed distribution reached 18.22%. Bench tests yielded a minimum CV of 16.34%, an average CV of 19.36%, and a total discharge coefficient of variation of 0.276%, which agrees with the simulation outcomes and supports the validity of the DEM model. Overall, the proposed device demonstrates improved seeding uniformity and meets agronomic requirements for rice cultivation, offering farmers a high-efficiency planting solution and providing UAV manufacturers with a validated double-arc disc design for equipment optimization. Full article