Search Results (18,494)

The blast furnace–basic oxygen furnace long process is the dominant steel production route in China. Increasing the scrap ratio is an effective way to reduce cost and carbon emissions, and scrap preheating is a key technology to achieve a high scrap ratio. To improve the low thermal efficiency and poor deep-bed melting performance of converter gas-based scrap preheating, an innovative process using oxygen-enriched combustion in a hot metal ladle is proposed. Numerical simulation is essential for capturing the complex multiphysics phenomena, as real-time monitoring of melting inside the packed scrap bed is extremely difficult. In this study, a novel multiphysics approach based on a User-Defined Function (UDF) is developed to dynamically track the progressive melting of the scrap skeleton, overcoming the key limitation of conventional enthalpy–porosity models that cannot capture the feedback between phase change and porous medium property evolution. A three-dimensional transient model was established, integrating turbulent combustion, gas–solid convective heat transfer in porous media, and solid–liquid phase change. The effects of impact pit depth, scrap porosity, and converter gas flow rate on temperature distribution, melting behavior, and thermal efficiency were systematically investigated. Results showed that porosity had the strongest influence; thermal efficiency increased from 33.92% to 65.59% as porosity rose from 0.6 to 0.8, due to a transition from conduction-dominated to coupled convection–conduction heat transfer. Converter gas flow rate exhibited a non-monotonic effect, peaking at 3688.14 m³·h⁻¹, highlighting a trade-off between energy input and gas residence time, while impact pit depth showed a limited effect with diminishing returns. A 600 s full-process simulation revealed stage-dependent melting, and the initial phase was crucial for process optimization. The optimal condition, with a pit depth of 64 cm, porosity of 0.8, and converter gas flow rate of 3688.14 m³·h⁻¹, achieved a 1.23% melting fraction and 65.59% thermal efficiency within 120 s. These findings clarify the combined roles of geometric confinement, permeability, and energy-residence time interactions, providing guidance for industrial scrap preheating design. Full article

Oncolytic viruses represent a promising class of anticancer therapeutics, and rapid, accurate quantification of viral titers is critical for ensuring both efficacy and safety during clinical development. Conventional viral titering methods, such as 50% cell culture infectious dose (CCID₅₀), are time-consuming and limited in sensitivity, thereby restricting their application in real-time clinical monitoring. This study aimed to develop and validate a rapid titer assay for oHSV2-PD-L1/CD3-BsAb, an oncolytic herpes simplex virus expressing a PD-L1/CD3 bispecific antibody, to support preclinical and clinical monitoring. A dual-reporter cell system was established using Vero-PD-L1-GFP (Vero cells expressing PD-L1 and GFP) cells as target cells and Jurkat-NFAT-Fluc (Jurkat cells expressing NFAT and Fluc) cells as effector cells. Viral infection activates the NFAT signaling pathway, driving Fluc expression, thereby enabling rapid quantification of infectious virus. The assay was evaluated for specificity, limit of detection (LOD), and lower limit of quantification (LLOQ), and compared with the conventional CCID₅₀ method. Its applicability was further assessed using clinical simulation samples, including PBMCs and swabs. The rapid titer assay accurately quantified virus at 10³ CCID₅₀/mL after 8 h of incubation, consistent with CCID₅₀ results, while extending the incubation to 18 h improved the LLOQ to 10^2.5 CCID₅₀/mL, demonstrating enhanced sensitivity. The assay exhibited high reproducibility and stability in both PBMC and swab samples, enabling reliable quantification of low-titer virus in complex biological matrices. Compared with CCID₅₀, the method substantially reduced assay time (from 3–5 days to 8–18 h) while improving sensitivity and specificity. The developed rapid titer assay for oHSV2-PD-L1/CD3-BsAb provides a sensitive and specific platform for viral quantification. It offers a valuable tool for oncolytic virus development, production quality control, and clinical monitoring, facilitating efficient safety evaluation and risk management in ongoing and future clinical applications. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

CO₂ methanation with renewable hydrogen is a promising strategy for carbon valorization and synthetic natural gas (SNG) production. However, the molecular mechanisms behind catalyst-dependent adsorption and mass transport in zeolite-confined spaces are still not fully elucidated. Herein, we performed comparative molecular simulations on HZSM-5, Ni/ZSM-5 and Ru/ZSM-5 by combining density functional theory (DFT), grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) methods, aiming to clarify the thermodynamic and mass transport mechanisms of reactant enrichment and product desorption in CO₂ methanation. The electronic structures of the three systems were systematically evaluated via Mulliken charge analysis, differential charge density mapping, and frontier molecular orbital calculations. We further quantified the adsorption thermodynamics and diffusion kinetics of reactants and products, focusing specifically on the effects of temperature and framework Si/Al ratio for Ni/ZSM-5. The results show that Ni doping greatly modulates the local electronic environment of the ZSM-5 framework, enhancing the adsorption of CO₂ (−121.9 kJ·mol⁻¹) and H₂ (−81.6 kJ·mol⁻¹) and weakening the adsorption of CH₄ and H₂O. A higher Si/Al ratio reduces CO₂ adsorption capacity, while elevated temperatures inhibit reactant adsorption and lower the diffusion selectivity of CH₄. This demonstrates that moderately low temperatures and moderate Si/Al ratios can optimize the adsorption and diffusion behaviors of reactants and products. This work provides molecular-level insights into the adsorption and diffusion behaviors of Ni/ZSM-5 and offers theoretical references for the rational development of high-performance CO₂ methanation catalysts. Full article

Horticultural intensification in Mediterranean areas has increased the risk of nitrate pollution due to inefficient irrigation and nitrogen fertilisation management. The availability of simulation models aimed at rational nitrogen management in outdoor crops is limited. The objective of this study is to adapt the VegSyst model, initially developed for greenhouse vegetables, for use in open-field conditions in relevant crops, such as processing peppers and broccoli in Extremadura. VegSyst simulates dry matter production and nitrogen uptake by incorporating the influence of evaporative demand (TUE approach) in addition to the effect of radiation (RUE approach). Experimental field data obtained in five campaigns (peppers: 2020–2022; broccoli: 2020 and 2022) under different nitrogen doses were used. The model was calibrated, and critical N dilution curves were developed for each crop. Subsequently, the simulation of fi-PAR, dry matter production (DMP) and N uptake was validated using statistical indices (RMSE, RE, d, EF) and regression analysis. The model showed a high predictive capacity for N uptake in both crops, with values of d ≥ 0.98 and EF ≥ 0.90 in the validation campaigns. The fi-PAR simulation was acceptable in peppers and excellent in broccoli. In contrast, the DMP prediction showed notable deviations in peppers, especially in 2022, attributable to interannual variations in weather conditions and physiological limitations not considered by the model. In both crops, the TUE-based strategy was a better fit for the measurements than the RUE-based strategy, indicating that under semi-arid Mediterranean conditions, transpiration is the limiting factor for biomass production. The adaptation of the VegSyst-Outdoors model proved to be robust for simulating N uptake and sufficiently accurate to be integrated into decision support tools aimed at efficient fertilisation and irrigation management. Full article

The integration of photovoltaic (PV) generation with alkaline water electrolyzers (AWE) in DC microgrids offers a highly promising pathway for green hydrogen production. However, the inherent volatility of solar power often induces transient voltage ripples and power surges, degrading the electrolyzer stack and destabilizing the common DC bus. To overcome this, this study proposes a hierarchical multimodal coordinated control strategy tailored for a four-port (PV–Storage–Grid–Hydrogen) DC microgrid. The proposed framework leverages multi-port synergetic coordination among the PV array, battery storage, and grid-interfacing converters to actively buffer extreme power mismatches, thereby ensuring the constant regulation of the DC bus voltage. Through comprehensive time-domain simulations under worst-case step-change boundary conditions, the large-signal transient stability of the proposed strategy is quantitatively verified. Under extreme disturbances, the system successfully confines DC bus voltage deviations to within safe operational boundaries with a rapid settling time, effectively avoiding typical inverter overvoltage trip thresholds. Furthermore, the adaptive power regulation algorithm maintains precise steady-state power tracking. By utilizing a gradient-based flag variable, the system seamlessly transitions between maximum power point tracking (MPPT) and active power-limiting modes, ensuring continuous equipment protection, stable high-purity hydrogen yield, and uninterrupted microgrid stability. Full article

Pastures are the primary food source for cattle, yet their productivity is often limited by management practices and water scarcity. In this context, approaches capable of representing nonlinear relationships and handling uncertainties can support sustainable water management. The objective of this study was to develop and compare fuzzy inference systems (FISs) to predict the instantaneous water use efficiency (iWUE) in a forage species subjected to organic and mineral fertilization under different levels of water deficit. The models were built in MATLAB R2024a using Mamdani and Sugeno inference methods. Input variables (fertilization and water deficit) were represented by triangular, trapezoidal, and Gaussian membership functions, while the output variable (iWUE) was modeled using triangular, trapezoidal, and Gaussian membership functions in the Mamdani system and singleton functions in the Sugeno system. Different defuzzification strategies were evaluated, resulting in 21 fuzzy systems. The results showed satisfactory model performance, with coefficients of determination above 0.90 and strong agreement between observed and simulated values. The Mamdani system with trapezoidal membership functions and centroid defuzzification achieved the best predictive performance (R² = 0.9846, NSE = 0.9887, RMSE = 0.0923). The response surface generated by the best-performing fuzzy system indicated a smaller reduction in iWUE under organic fertilization compared to mineral fertilization as water deficit intensified. The developed fuzzy systems demonstrated potential to represent the interaction between nutritional management and water availability, supporting decision-making in forage production systems. Full article

This study investigated an efficient approach for extracting saponins from Panax japonicus using deep eutectic solvents (DES) coupled with ultrasound-assisted (UA) extraction, and compared its performance with the methanol extraction method. Twenty-six DES were screened, and choline chloride–urea was selected as the optimal solvent. The total extraction yield was evaluated based on the sum of the yields of chikusetsusaponin IVa (CS-IVa) and ginsenoside Ro (G-Ro). The extraction process was optimized using single-factor experiments combined with an orthogonal array design. Molecular dynamics (MD) simulation was applied to reveal the extraction mechanism at the molecular level. The results showed that the optimal conditions were as follows: a choline chloride-to-urea molar ratio of 1:3, a solid-to-liquid ratio of 1:50, a water content of 60%, an ultrasonic temperature of 40 °C, and an ultrasonic time of 60 min. Under these conditions, the total extraction yield of Panax japonicus saponins reached 7.4%, which was 13% higher than that obtained with the pharmacopeia methanol extraction method. MD simulation demonstrated that DES weakens intermolecular interactions among saponins through hydrogen bonds and van der Waals forces, promoting the dispersion of saponin aggregates and enabling efficient dissolution. Compared with CS-IVa, G-Ro displayed a more pronounced solvation effect, which was likely attributed to the difference in the number of polar sites in their molecular structures. The UA-DES extraction method established herein is green and efficient. It provides a practical reference for the industrial extraction of Panax japonicus saponins and a theoretical foundation for mechanistic studies on natural product extraction using DES. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Predicting thermal breakthrough and optimizing injection-production parameters are essential for sustainable geothermal development. Traditional hydrothermal coupled simulations in porous media entail substantial computational costs, which limits their use in dense multi-parameter screening. This study develops a physics-constrained surrogate workflow for the Qihe geothermal doublet system by using COMSOL to generate hydrothermal simulation data and a deep neural network (DNN) to emulate the simulator response within a predefined operating domain. The DNN was trained on physics-driven synthetic outputs rather than independent field observations, and a 2.0 °C decrease in production temperature was used as the thermal breakthrough criterion. Under scenario-wise validation, the surrogate model achieved a test-set R² of 0.9995 and an RMSE of 0.0351 °C, indicating accurate approximation of the deterministic simulator response within the bounded parameter space. The surrogate-based global scan identified a favorable operating region near a well spacing of 462 m, a reinjection temperature of 20 °C, and a reinjection rate of 150 m³/h. To evaluate whether this result was affected by sparse well-spacing sampling, additional COMSOL simulations were performed at 430, 440, 450, 460, 462, 470, 480, 490, and 500 m under the same reinjection temperature and rate. These simulator-based validation cases showed a continuous thermal response with increasing well spacing. The 2.0 °C thermal breakthrough time increased from 46 yr at 430 m to 61 yr at 500 m, while the 50-year cumulative heat extraction increased from 6594.2 to 6722.9 TJ. The 430 and 440 m cases experienced thermal breakthrough before the 50-year design life, whereas the 450 m case was close to the design boundary. The 460 and 462 m cases did not reach the 2.0 °C decline threshold within the 50-year design life and retained relatively high heat-extraction efficiency per unit well spacing. Therefore, the engineering recommendation is revised from a single precise optimum to a locally validated spacing interval of approximately 460–462 m under the present equivalent-porous-medium assumption. The proposed workflow does not replace hydrothermal simulation; instead, it provides a rapid screening tool that narrows the design space before targeted simulator verification and field calibration. Full article

In this study, low-alloy structural steels with different La and Ce contents were prepared via vacuum smelting and controlled rolling and controlled cooling technologies, and their microstructures were characterized. The influence of La and Ce on the corrosion resistance of low-alloy steels was compared through indoor cyclic-immersion accelerated tests simulating tropical marine atmospheres. The corrosion mechanism of low-alloy steels with different La and Ce contents in simulated tropical marine atmospheres was investigated using electrochemical measurements and corrosion product analysis. The results show that La and Ce improve the uniform corrosion resistance of low-alloy steels. With increasing La/Ce content, the corrosion current density decreased from 1.8936 × 10⁻⁶ A cm⁻² for 0LaCe to 1.29 × 10⁻⁶ A cm⁻² for 0.3LaCe, corresponding to a reduction of approximately 31.9%. This is attributed to the fact that La/Ce addition promotes rust layer stabilization and densification, as suggested by the evolution of major rust phases and the presence of La/Ce-related oxidized species. Meanwhile, alloying with La and Ce improves the cracking of the rust layer, reduces the number of pores, and stabilizes the rust layer structure. Full article

Reliable carbon emission data are essential for the effective operation of emissions trading systems (ETS), especially as China’s ETS expands to include energy-intensive industries. This study proposes a hybrid, risk-based anomaly detection framework for high-frequency CO₂ emission data by cross-validating material-based emissions with flue gas-based monitoring data. Under normal operating conditions, the ratio of material-based to flue gas-based emissions is expected to remain within a relatively stable distribution. Potential high-risk periods can therefore be identified when this relationship is distorted or when local temporal patterns deviate from expected behavior. The framework combines Hartigan’s dip test with a window-based Random Forest (RF) classifier, which is suitable for continuous monitoring data that may exhibit temporal dependence. The framework was evaluated using 15-min CO₂ emission data from a cement production facility, with simulations of anomaly magnitude, duration, and mode. Results show that the dip test performs well for long-lasting or strong anomalies, whereas the RF model is more sensitive to subtle, short-term deviations. In the integrated framework, 94.7% of anomalous periods were detected by at least one method and flagged as potential data-quality risks, whereas normal periods were not flagged, supporting its use to prioritize verification efforts. Full article

This study develops exact analytical solutions for transient elliptical groundwater flow toward an extended well in an anisotropic fractured confined aquifer and then discusses how the resulting hydraulic response can support groundwater-head interpretation in shale-gas development areas. The environmental connection is made at the aquifer-protection scale: the model is not a shale-gas reservoir production model, and it does not solve contaminant transport directly. Instead, it provides a hydraulic interpretation framework for estimating anisotropy, equivalent fracture length, wellbore-storage effects, and the preferential direction of head propagation around possible leakage points, old wells, fractures, or monitoring wells. Based on Mathieu-function theory and the separation-of-variables method, constant-rate and constant-head solutions are derived in Laplace space and inverted to the time domain with the Stehfest algorithm. The analytical results are validated against COMSOL5.2 finite-element simulations, and the effects of anisotropy coefficient and wellbore storage are analyzed through drawdown and flow-rate type curves. A synthetic but field-style water-head example is included to demonstrate how monitoring records can be converted to drawdown, fitted to the elliptical-flow solution, and used to delineate a preliminary hydraulic response zone. The results show that anisotropy mainly controls early-to-middle time response, whereas wellbore storage may obscure early head changes and delay the recognition of fracture connectivity. Therefore, the solution is best regarded as a rapid hydraulic-screening and monitoring-design tool that can precede, but not replace, site-specific contaminant-transport modeling in shale-gas groundwater-protection studies. The relevant technical issues are possible head disturbances and preferential groundwater pathways associated with surface spills, flowback-water handling, old wells, faults, and fracture-connected water-bearing zones. Because verified local field-monitoring records were not available for us, the application example is explicitly described as a synthetic field-style demonstration; it is used to show the workflow and its limitations, not to claim site-specific prediction of contaminant concentration. Full article

To address the challenge of quantifying multi-unit synergy effects in road-area high-entropy energy systems, this paper proposes a regional availability evaluation model based on synergy factors. In the revised model, regional availability is decomposed into the product of a capacity-weighted health baseline (capacity-weighted mean unit availability), weighted temporal synergy, and weighted spatial consistency coefficient. Capacity weights and pairwise coupling coefficients are introduced to extend the model from equal-capacity isomorphic units to heterogeneous road-area energy units. Simulation results demonstrate that the model can distinguish different synergy levels, and parameter sensitivity analysis verifies its robustness. An open-data-based quasi-real verification using Caltrans PeMS traffic records further shows that the model can process measured time-series inputs. The proposed model provides a theoretical basis for the regional-level operation evaluation of road-area energy systems. Full article

Background: Breastfeeding enhances infant and maternal health, but global breastfeeding rates remain suboptimal. Virtual reality (VR) emerges as a promising tool for breastfeeding education. The objective of this review was to assess the effectiveness of VR-based interventions on breastfeeding outcomes in pregnant and postpartum women. Methods: PubMed, Embase, Web of Science, Scopus, and CENTRAL were searched until 10 January 2026, for randomized controlled trials (RCTs) and quasi-experimental studies comparing VR-based interventions (immersive simulations, 360° videos, or head-mounted displays) with standard care or non-VR comparators in pregnant or postpartum women. Primary outcomes included breastfeeding self-efficacy, motivation, and breastfeeding technique (LATCH score). Secondary outcomes included exclusive breastfeeding rates, milk production, and maternal anxiety. Risk of bias was assessed using the RoB 2.0 and ROBINS-I tools for RCTs and non-RCTs, respectively. A random-effects meta-analysis was conducted, with results reported as mean differences (MD) or risk ratios (RR), along with 95% confidence intervals (CIs). Certainty of the evidence was assessed using the GRADE approach. Results: Five studies (4 RCTs and 1 quasi-experimental; n = 344) were included. VR improved prenatal breastfeeding self-efficacy (2 studies, MD: 13.93; 95% CI: 10.96–16.90), motivation (1 study, MD: 2.88; 95% CI: 1.66–4.10), and LATCH score (1 study, MD: 1.72; 95% CI: 1.37–2.07), and reduced time to breastfeeding initiation (1 study, MD: −22.4 min; 95% CI: −29 to −15.9), the certainty of evidence was low to very low for these outcomes. No significant effects were observed for postnatal self-efficacy, exclusive breastfeeding, or maternal anxiety. Formal assessment of publication bias could not be done. The small sample sizes for most outcomes, heterogeneity, the open-label nature of the trials, and the subjective nature of the outcomes should be considered when interpreting these results. Conclusions: VR-based interventions may improve process outcomes, such as prenatal breastfeeding self-efficacy, motivation, breastfeeding technique, and early breastfeeding initiation; the certainty of evidence is low to very low. Evidence for clinically important outcomes, including exclusive breastfeeding and maternal anxiety, remains inconsistent. Larger, well-designed RCTs are warranted before these interventions can be considered in routine practice. Full article