Search Results (695)

The ammonia-n-heptane reaction mechanism is essential for simulation of the in-cylinder process for diesel-ignited ammonia engines. To gain insight into the differences in predictive performance among various ammonia-n-heptane reaction mechanisms, four mechanisms were comprehensively evaluated and analyzed based on the modeling of ignition, oxidation, laminar flame propagation and in-cylinder combustion processes. The result shows that only under high ammonia blending ratios and elevated temperatures are discrepancies in predicted ignition delay times observed among the studied reaction mechanisms. Regarding the oxidation process, on the whole, the concerned mechanisms can reasonably predict concentrations of reactants and complete combustion products. However, significant discrepancies exist among the mechanisms in predicting concentrations of intermediate species and other products. For laminar burning velocity, the modeled values from the studied mechanisms are consistent with experimental results under both fuel-lean and -rich conditions. The Wang mechanism exhibits significant deviations from the other three mechanisms in predicting reaction pathways of ammonia and n-heptane. From the perspective of reaction class, the studied mechanisms are similar to each other, to some extent, in the key reactions governing consumption of ammonia and n-heptane. For the engine simulation, the predicted in-cylinder pressure and temperature profiles show minimal variations across different reaction mechanisms. In conclusion, the Fang mechanism can be selected to understand more accurately ignition, oxidation and flame characteristics of ammonia-n-heptane mixtures, while to reduce the engineering computational cost of the engine simulation, the Wang mechanism tends to be a good choice. Full article

Air pollution, particularly particulate matter (PM₁, PM_2.5, and PM₁₀), poses a significant environmental health risk globally. This study evaluates the predictive performance of three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), for forecasting particulate matter concentrations in four Brazilian cities (Porto Alegre, Recife, Goiânia, and Belém), which share similar demographic and urbanization characteristics but differ in geographic and climatic conditions. Using data from the Copernicus Atmosphere Monitoring Service, daily concentrations of PM₁, PM_2.5, and PM₁₀ were modeled based on meteorological variables, including air temperature, relative humidity, wind speed, atmospheric pressure, and accumulated precipitation. The models were tested under two climate change scenarios (+2 °C and +4 °C temperature increases). The results indicate that RF consistently outperformed the other models, achieving low RMSE values, around 0.3 µg/m³, across all cities, regardless of their geographic and climatic differences. KNN showed stable performance under moderate temperature increases (+2 °C) but exhibited higher errors under more extreme warming, while SVM demonstrated higher sensitivity to temperature changes, leading to greater variability in bivariate contexts. However, in multivariate contexts, SVM adjusted better, improving its predictive performance by accounting for the combined influence of multiple meteorological variables. These findings underscore the importance of selecting suitable machine learning models, with RF proving to be the most robust approach for particulate matter prediction across diverse environmental contexts. This study contributes valuable insights for the development of region-specific air quality management strategies in the face of climate change. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

With the growing demand for energy, oil and gas exploration and development are progressively moving into deep and ultra-deep formations, where extreme temperatures and pressures create complex challenges for drilling operations. While drilling fluids are critical for controlling bottom-hole pressure, cooling drill bits, and removing cuttings, accurately characterizing their rheological behavior under high-temperature and high-pressure (HTHP) conditions remains a key focus, as existing research has limitations in model applicability and parameter prediction range under extreme downhole environments. To address this, the study aims to determine the optimal rheological model and establish a reliable mathematical prediction model for drilling fluid rheological parameters under HTHP conditions, enhancing the precision of downhole temperature and pressure calculations. Rheological experiments were conducted on eight field-collected samples (4 water-based and four oil-based drilling fluids) using a Chandler 7600 HTHP rheometer, with test conditions up to 247 °C and 140 MPa; nonlinear fitting via a hybrid Levenberg–Marquardt and Universal Global Optimization algorithm and multivariate regression were employed for model development. Results showed that oil-based and water-based drilling fluids exhibited distinct rheological responses to temperature and pressure, with the Herschel–Bulkley model achieving superior fitting accuracy (coefficient of determination > 0.999). The derived prediction model for Herschel–Bulkley parameters, accounting for temperature-pressure coupling, demonstrated high accuracy (R² > 0.95) in validation. This research provides an optimized rheological modeling approach and a robust prediction tool for HTHP drilling fluids, supporting safer and more efficient deep and ultra-deep drilling operations. Full article

Storm Daniel is arguably one of the most severe Mediterranean tropical-like cyclones (medicanes) ever recorded. Greece was one of the most affected areas, especially the central part of the country. The extreme precipitation that was observed along with the subsequent extensive flooding was considered a critical challenge to validate the regional version of the ICON (Icosahedral Non-Hydrostatic) numerical weather prediction (NWP) model. From a methodological standpoint, the short-range nature of the model was realized with 48 h runs over a sequence of cases that covered the storm period. The development of the medicane was highlighted via the tracking of the minimum mean sea level pressure (MSLP) in reference to the corresponding analysis of the European Center for Medium-Range Weather Forecasts (ECMWF). In a similar fashion, snapshots regarding the 500 hPa geopotential associated with the 850 hPa temperature were addressed at the 24th forecast hour of the model runs. Although the model’s performance over the four most affected synoptic stations of the Hellenic National Meteorological Service (HNMS) was mixed, the overall accumulated forecasted precipitation was in very good agreement with the corresponding total value of the observations over all the available synoptic stations. Full article

Mechanical damage reduces the marketability of Korla fragrant pears, severely restricting industry development. To enhance the commercial value of pears, this study investigated the effects of impact, compressive, and combined impact-compressive damage types on the weight loss rate, L*, a*, and b* of pears, and constructed a multi-output prediction model for the weight loss rate, L*, a*, and b* of damaged pears during storage by integrating partial least squares regression (PLSR), support vector regression (SVR), and long short-term memory (LSTM), from which the optimal prediction model was selected to achieve synchronous detection of the physical quality of damaged pears during storage. The results indicated that during storage, the weight loss rate, a*, and b* of pears subjected to different damage types gradually increased with prolonged storage time, while L* gradually decreased. Under the same damage volume situation, pears subjected to impact-static pressure combined action exhibited the fastest storage quality change speed, followed by impact action, static pressure action. The SVR multi-output model demonstrated optimal performance in predicting the weight loss rate, L*, a*, and b* of damaged pears during storage, achieving mean coefficient of determination R², root mean square error (RMSE), and residual prediction deviation (RPD) values of 0.988, 0.513, and 10.072, respectively, for these four quality indicators. These results establish a theoretical foundation for the development of simultaneous monitoring techniques for fruit storage quality. Full article

Wetland ecosystems are critical for biodiversity conservation and carbon sequestration, underpinning climate regulation and sustainable development. Accurate prediction of wetland evolution is therefore essential for informed regional planning, particularly in International Wetland Cities. As one of the first designated International Wetland Cities, Haikou exemplifies the intensifying pressures faced by coastal wetlands in rapidly urbanizing regions, balancing economic development imperatives with ecological conservation. This study addresses this challenge by employing the PLUS model to simulate the spatiotemporal dynamics of wetland evolution in Haikou from 2010 to 2030 under four distinct scenarios: Business-as-Usual (BAU), Ecological Conservation (EC), Economic Development (ED), and Multi-Objective Development (MOD). The integrated approach combines landscape pattern dynamics analysis, land-use transition matrices, and quantitative assessment of driving factor contributions. Key findings reveal significant historical wetland loss between 2010 and 2020 (21.01 km²), characterized by substantial declines in artificial wetlands (paddy fields: −14.43 km²; agricultural ponds: −8.99 km²) alongside resilient growth in natural wetlands (rivers: +2.70 km²; mangroves: +1.25 km²), highlighting fundamental trade-offs between economic and ecological priorities. Scenario projections indicate that unregulated development (ED) would exacerbate wetland loss (−26.33 km²; dynamic change rate: −0.61%), including unprecedented river fragmentation (−16.0%). Conversely, strict conservation (EC) achieves near net-zero wetland loss (−0.05%) but constrains economic development capacity by 24%. Critically, the MOD scenario demonstrates an effective balance, maintaining 86% of EC’s wetland preservation efficacy while satisfying 73% of ED’s development demand. This is achieved through strategic interventions including establishing wetland protection constraints and optimizing bidirectional land conversion rules, yielding synergistic benefits. Spatial analysis identifies key conflict hotspots such as Nandu River shoreline, Dongzhai Port mangroves, necessitating targeted management strategies aligned with the heterogeneity of driving factors. This study advances the framework for sustainable wetland governance by demonstrating how multi-objective spatial planning can transform ecological-economic trade-offs into synergistic co-benefits. It provides a transferable methodological approach for coastal cities in the Global South and other International Wetland City. Full article

Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and $R^2$ values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching. Full article

Produced water treatment (PWT) is a critical process in offshore oil and gas production, ensuring compliance with stringent environmental discharge regulations and minimizing environmental impact. This process is characterized by inherent nonlinearities, coupled system dynamics, and the presence of significant disturbances that can impede operational efficiency and separation performance. Effective control strategies are essential to maintain stable operation and high separation efficiency under dynamic and uncertain conditions. This paper presents a comprehensive evaluation of advanced control methods applied to a pilot-scaled PWT facility designed to replicate offshore conditions. Four control solutions are assessed, i.e., (i) baseline approach using PID controllers; (ii) Multi-Input–Multi-Output (MIMO) $H_{\infty }$ control; (iii) MIMO Model Predictive Control (MPC); and (iv) MIMO Model Reference Adaptive Control (MRAC). The motivation lies in their differing capabilities for disturbance rejection, tracking accuracy, robustness, and computational feasibility. Real-world operational data were used to assess each strategy in regulating critical process variables, the interface water level in the three-phase gravity separator, and the pressure drop ratio (PDR) in the hydrocyclone, both closely linked to de-oiling efficiency. The results highlight the distinct advantages and limitations of each method. In general, the baseline PID solution offers simplicity but limited adaptability, while advanced strategies such as MIMO $H_{\infty }$, MPC, and MRAC solutions demonstrate enhanced reference-tracking and de-oiling performances subject to diverse operating conditions and disturbances, though different control solutions still exhibit different dynamic characteristics. The findings provide systematic insights into selecting optimal control architectures for offshore PWT systems, supporting improved operational performance and reduced environmental footprint. Full article

Pressure-sensitive adhesion arises at a specific rheological behavior of polymer systems, which should correlate with their relaxation properties, making them potentially useful for predicting and altering adhesive performance. This work systematically studied the rheology of eco-friendly pressure-sensitive adhesives based on non-crosslinked polyisobutylene ternary blends free of solvents and byproducts, which serve for reversible adhesive bonding. The ratio between individual polymer components differing in molecular weight affected the rheological, relaxation, and adhesion properties of the constituted adhesive blends, allowing for their tuning. The viscosity and viscoelasticity of the adhesives were studied using rotational rheometry, while their adhesive bonds with steel were examined by probe tack and shear lap tests at different temperatures. The adhesive bond durability at shear and pull-off detachments depended on the adhesive composition, temperature, and contact time under pressure. The double differentiation of the continuous relaxation spectra of the adhesives enabled the accurate determination of their characteristic relaxation times, which controlled the durability of the adhesive bonds. A universal linear correlation between the reduced failure time of adhesive bonds and their reduced formation time enabled the prediction of their durability with high precision (Pearson correlation coefficient = 0.958, p-value < 0.001) over at least a four-order-of-magnitude time range. The reduction in the formation/failure times of adhesive bonds was most accurately achieved using the longest relaxation time of the adhesives, associated with their highest-molecular-weight polyisobutylene component. Thus, the highest-molecular-weight polymer played a dominant role in adhesive performance, determining both the stress relaxation during the formation of adhesive bonds and their durability under applied load. In turn, this finding enables the prediction and improvement of adhesive bond durability by increasing the bond formation time (a durability rise by up to 10–100 times) and extending the adhesive’s longest relaxation time through elevating the molecular weight or proportion of its highest-molecular-weight component (a durability rise by 100–350%). Full article

The offshore reservoir development involves large injection and production rates and high injection pressures. High-permeability flow channels usually occur in offshore unconsolidated heavy-oil reservoirs during long-term water flux, substantially impacting the production performance. As one important method for identifying channeling, the numerical simulation method with a full-fidelity model is hampered by the low computational efficiency of the history matching process. The GPSNet model is extended for polymer flooding simulations, incorporating complex mechanisms including adsorption and shear-thinning effects, with solutions obtained through a fully implicit numerical scheme. Four flow channel characteristic parameters are proposed, and an evaluation factor M for flow channel identification is established with the comprehensive evaluation method. Finally, the field application of the GPSNet model is made and validated by the tracer interpretation result. The history matching speed based on the GPSNet model is 58 times faster than the full-fidelity ECLIPSE model. In addition, the application demonstrates a high degree of consistency with tracer monitoring results, confirming the accuracy and field feasibility. The new method enables rapid and accurate identification and prediction of large and dominant channels, offering effective guidance for targeted treatment of channels and sustainable development of polymer flooding. Full article

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity—particularly through incorporation of the dicrotic notch and other waveform parameters—are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Freeze–thaw damage is one of the most important factors affecting the durability of concrete in cold regions, and how to quantitatively characterize the effect of freeze–thaw cycles on the degree of damage of concrete is a widely concerning issue among researchers. Based on the hydraulic pressure theory, a new concrete freeze–thaw damage model was proposed by assuming the defect development mode of concrete during freeze–thaw cycles. The model shows that the total amount of defects due to freeze–thaw damage is related to the initial defects and the defect development capacity within the concrete. Based on the new freeze–thaw damage model, an equation for the loss of relative dynamic elastic modulus of concrete during freeze–thaw cycles was established using the relative dynamic elastic modulus of concrete as the defect indicator. In order to validate the damage model using relative dynamic elastic modulus as the defect index, freeze–thaw cycle tests of four kinds of concrete with different air content were carried out, and the rationality of the model was verified by the relative dynamic elastic modulus of concrete measured under different freeze–thaw cycling periods. On this basis, a freeze–thaw damage model of concrete was established considering the effect of air content in concrete. In addition, the model proposed in this paper was supplemented and validated by experimental data from other researchers. The results show that the prediction model proposed in this study is not only easy to apply and has clear physical meaning but also has high accuracy and general applicability, which provides support for predicting the degree of freeze–thaw damage of concrete structures in cold regions. Full article

Introduction: A validated clinical decision tool predictive of favorable functional outcomes following endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) remains elusive. We performed a retrospective case series of patients at our regional Comprehensive Stroke Center, over a four-year period, who have undergone EVT to elucidate patient characteristics and factors associated with a favorable functional outcome after EVT. Methods: We reviewed all cases of EVT at our institution between February 2018 and February 2022 in the extended time window from 6–24 h. Demographic, clinical, imaging, and procedure co-variates were included. A favorable clinical outcome was defined as a modified Rankin scale of 0–2. We included patients with M1 or internal carotid artery occlusion treated with EVT within 6–24 h after symptom onset. We used a univariate and multivariate logistic regression analysis to identify patient factors associated with a favorable clinical outcome at 90 days. Results: Our study included evaluation of 121 patients who underwent EVT at our comprehensive stroke center. Our analysis demonstrates that a higher recanalization score based on the modified Thrombolysis In Cerebral Infarction (mTICI) scale (2B-3) was a strong indicator of a favorable outcome (OR 7.33; CI 2.06–26.07; p = 0.0021). Our data also showed that a higher baseline National Institutes of Health Stroke Scale (NIHSS) score (p = 0.0095) and the presence of pre-existing hypertension (p = 0.0035) may also be predictors of an unfavorable outcome (mRS > 2) per our multivariate analysis. Conclusion: Patients without pre-existing hypertension had more favorable outcomes following EVT in the expanded time window. This is consistent with other multicenter data in the expanded time window that demonstrates greater odds of a poor outcome with elevated pre-, peri-, and post-endovascular-treatment blood pressure. Our data also demonstrate that the mTICI score is a strong predictor of favorable outcome, even after controlling for other variables. A lower baseline NIHSS at the time of thrombectomy may also indicate a favorable outcome. Furthermore, the presence of clinical or radiographic mismatch based on the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) and NIHSS per DAWN and DEFUSE-3 criteria did not emerge as a predictor of favorable outcome, which is congruent with recent randomized controlled trials and meta-analyses. Full article