Search Results (6,029)

Driven by the global energy transition and the dual carbon goals, developing low-carbon and zero-carbon alternative fuels has become a core issue for sustainable development in the internal combustion engine sector. Ammonia is a promising zero-carbon fuel with broad application prospects. However, its inherent combustion characteristics, including slow flame propagation, high ignition energy, and narrow flammable range, limit its use in internal combustion engines, necessitating the addition of auxiliary fuels. To address this issue, this paper proposes a composite injection technology combining “ammonia duct injection + hydrogen cylinder direct injection.” This technology utilizes highly reactive hydrogen to promote ammonia combustion, compensating for ammonia’s shortcomings and enabling efficient and smooth engine operation. This study, based on bench testing, investigated the effects of hydrogen direct injection timing (180, 170, 160, 150, 140°, 130, 120 °CA BTDC), hydrogen direct injection pressure (4, 5, 6, 7, 8 MPa) on the combustion and emissions of the ammonia–hydrogen engine. Under hydrogen direct injection timing and hydrogen direct injection pressure conditions, the hydrogen mixture ratios are 10%, 20%, 30%, 40%, and 50%, respectively. Test results indicate that hydrogen injection timing that is too early or too late prevents the formation of an optimal hydrogen layered state within the cylinder, leading to prolonged flame development period and CA10-90. The peak HRR also exhibits a trend of first increasing and then decreasing as the hydrogen direct injection timing is delayed. Increasing the hydrogen direct injection pressure to 8 MPa enhances the initial kinetic energy of the hydrogen jet, intensifies the gas flow within the cylinder, and shortens the CA0-10 and CA10-90, respectively. Under five different hydrogen direct injection ratios, the CA10-90 is shortened by 9.71%, 11.44%, 13.29%, 9.09%, and 13.42%, respectively, improving the combustion stability of the ammonia–hydrogen engine. Full article

To investigate the influence of salt transport in water–steam mixtures on flow and heat transfer and to ensure the operational safety of steam injection boilers, this study simulated the behavior of high-dryness steam carrying salts in U-tubes. The analysis focused on three representative substances—silica, hematite, and calcium carbonate—to evaluate their effects on flow and heat transfer characteristics under varying conditions. The simulation results show that under specified operating conditions, vortices induced by rotational flow lead to complex flow behavior in U-tubes, with transitions from stratified flow to annular flow and back to stratified flow. The effects of salt precipitation on the temperature, velocity, and pressure fields of the boiling flow were also examined. The findings indicate that for pure water, large gradients and multiple vortices adversely affect flow stability, whereas the introduction of small amounts of salts provides localized stabilization in regions of the fluid away from the wall. Full article

A mathematical model was established to describe the sublimation and diffusion of water molecules and their adsorption onto cold traps. This model was used to analyze the combined influence mechanisms of sublimation temperature and ambient pressure on the vapor deposition process of water ice. Tunable Diode Laser Absorption Spectroscopy (TDLAS) was employed to provide real-time feedback on water vapor concentration within the experimental apparatus. Based on this feedback, the sublimation temperature was dynamically adjusted to maintain the concentration dynamically stabilized around the target value. A dedicated apparatus for generating controlled water vapor flow fields and detecting concentration was constructed. The accuracy of both the mathematical model and Finite Element Analysis (FEA) simulations was verified through comparative experiments. Laser triangulation was utilized as a method to detect the thickness of the adsorbed ice film on the sample surface. Leveraging this technique, a water vapor deposition and adsorption verification system was developed. This system was used to test the differences in water adsorption performance across various materials and to measure the correlation between the thickness of the adsorbed/deposited ice film on the samples and both deposition time and sublimation temperature. Full article

Due to its high-temperature resistance, high thermal conductivity, electrical conductivity, excellent chemical stability, and outstanding mechanical and electrochemical properties, graphite has been widely applied in various fields. However, the current production process of high-purity graphite is faced with issues such as high energy consumption and insufficient reduction degree. This study utilized COMSOL Multiphysics 6.0 to couple the electromagnetic field, temperature field, velocity field, and flow field during the purification process of graphite. The dimensionless analysis method is adopted to investigate the influence of parameters such as current intensity, magnetic field frequency and concentration on the reduction degree of graphite feedstock, and the energy consumption in the furnace. Through numerical simulation, the interaction mechanism among various parameters under different parameter combinations is compared and analyzed, and the temperature change and fluid motion state of graphite feedstock during the electromagnetic induction heating process are predicted. When the current is 500 A, the average temperature inside the furnace gradually rises with the increase in the magnetic field frequency. This is because the energy input from induction coil and the energy output due to radiative heat loss gradually reach a dynamic equilibrium state. Furthermore, the average temperature inside the furnace continuously increases with the enhancement of the current, and for every increase of 50 A, the average temperature rises by approximately 200 K. Additionally, through dimensionless analysis, the optimal operating conditions for this induction furnace were determined to be a current intensity of 600 A and a magnetic field frequency of 14 kHz. Under these conditions, the reduction degree of the material reaches 99.69%, which achieves efficient purification and economical energy consumption. This study provides a theoretical basis for the optimization of operating parameters in graphite purification process, which is of great significance for improving production efficiency, reducing energy consumption, and promoting the application of high-purity graphite. Full article

Biodegradable zinc-based alloys have recently emerged as promising candidates for temporary biomedical implants due to their favorable biocompatibility, appropriate degradation rate, and relatively simple processing. In this study, the Zn-0.1Mg alloy was investigated after being processed by means of a two-step equal-channel angular pressing (ECAP) route, consisting of the first pass at 150 °C followed by a second pass at room temperature. The mechanical properties were evaluated using uniaxial tensile tests at different strain rates, while the microstructure and phase composition were analyzed using synchrotron hard X-ray diffraction and transmission electron microscopy (TEM). The processed alloy exhibited a remarkable enhancement in both strength and ductility compared to the annealed state. At the lowest applied strain rate, a fracture elongation of up to 240% was achieved at room temperature, representing a unique manifestation of superplasticity under ambient conditions. Diffraction analysis confirmed the stability of the supersaturated Zn matrix with minor Mg₂Zn₁₁ intermetallic phase. TEM observations revealed an ultrafine-grained microstructure and activation of non-basal slip systems, which enabled efficient plastic flow. These findings demonstrate that controlled severe plastic deformation provides an effective pathway for tailoring Zn-Mg alloys, opening opportunities for their use in the next generation of bioresorbable low-to-moderate load orthopedic fixation devices, e.g., plates, screws, suture anchors and craniofacial miniplates. Full article

Background: Despite advances in temporary mechanical circulatory support (tMCS), patients with cardiogenic shock (CS) who are treated with a microaxial flow pump (mAFP; Impella^®, Abiomed) still have a high mortality rate. A dysregulated systemic inflammatory response significantly contributes to multiorgan failure in this population. CytoSorb^® hemadsorption has emerged as a potential adjunctive therapy for modulating inflammation, but data on its use in CS are limited. Methods: This retrospective, single-center study used propensity score matching analysis (1:1 matching; n = 15 per group) to compare the outcomes of patients receiving mAFP support with and without concomitant CytoSorb therapy. Baseline data (T0), including comorbidities and clinical status at ICU admission, were collected for all patients. In the CytoSorb group, data were collected at two additional time points: 24 h before the start of CytoSorb therapy (T1), and 24 h after its completion (T2). At these time points, laboratory values and parameters on respiratory, hemodynamic, and organ function were assessed. Corresponding data were also collected for matched patients in the non-CytoSorb group at equivalent time points relative to their matched counterparts. Results: In the propensity score-matched cohort, patients treated with CytoSorb exhibited significant improvements between T1 and T2. Specifically, reductions were observed in the vasoactive-inotropic score (p = 0.035), procalcitonin levels (p = 0.041), peak inspiratory pressure (p = 0.036), and positive end-expiratory pressure (p = 0.016). Flow rates through the mAFP declined significantly (p = 0.014), suggesting stabilization of hemodynamics. These changes were not observed in the non-CytoSorb group, where most parameters remained unchanged or exhibited less pronounced trends. We observed a lower in-hospital mortality rate in the CytoSorb group (33.3% versus 46.7%), though the difference was not significant, potentially due to limited statistical power. Conclusions: CytoSorb hemadsorption in mAFP-supported CS was associated with improved hemodynamic stability and reduced inflammatory burden. These findings suggest a potential therapeutic benefit of adjunctive hemadsorption in this high-risk population. Full article

Fermentative processes are considered one of the most important technological developments in the modern transforming industry, due to this, the applied research to reach high performance standards with a crucial focus on system intensification, which is the the analysis, optimization, and control issues, are a cornerstone. The goal of this proposal is to show a novel nonlinear feedback control structure to assure a stable closed-loop operation in a continuous flow enzymatic–fermentative bioreactor with chaotic dynamic behavior. The proposed structure contains an adaptive-type gain, which, coupled with a proportional term of the named control error, can lead the feedback control trajectories of the bioreactor to the required reference point or trajectory. The Lyapunov method is used to present the stability analysis of the system within a closed loop, where an adequate choice of the controller gains assures asymptotic stability. Moreover, analyzing the dynamic equation of the control error, under some properties of boundedness of the system, shows that the control error can be diminished to close to zero. Numerical experiments are carried out, where a well-tuned standard proportional–integral (PI) controller is also implemented for comparison purposes, the satisfactory performance of the proposed control scheme is observed, including the diminishing offsets, overshoots, and settling times in comparison with the PI controller. Full article

Renewable energy sources, such as photovoltaic panels and wind turbines, are increasingly integrated into domestic systems to address energy scarcity, rising demand, and climate change. However, their intermittent nature requires efficient energy storage systems (ESS) for stability and reliability. This systematic review, conducted in accordance with PRISMA guidelines, aimed to evaluate the size and chemical composition of battery energy storage systems (BESS) in household renewable energy applications. A literature search was conducted in Scopus in August 2025 using predefined keywords, and studies published in English from 2015 onward were included. Exclusion criteria included book chapters, duplicate conference proceedings, geographically restricted case studies, systems without chemistry or size details, and those focusing solely on electric vehicle batteries. Of 308 initially retrieved records, 83 met the eligibility criteria and were included in the analysis. The majority (92%) employed simulation-based approaches, while 8% reported experimental setups. No formal risk-of-bias tool was applied, but a methodological quality check was conducted. Data were synthesized narratively and tabulated by chemistry, nominal voltage, capacity, and power. Lithium-ion batteries were the most prevalent (49%), followed by lead–acid (13%), vanadium redox flow (3.6%), and nickel–metal hydride (1.2%), with the remainder unspecified. Lithium-ion dominated due to high energy density, long cycle life, and efficiency. Limitations of the evidence include reliance on simulation studies, heterogeneity in reporting, and limited experimental validation. Overall, this review provides a framework for selecting and integrating appropriately sized and composed BESS into domestic renewable systems, offering implications for stability, efficiency, and household-level sustainability. The study was funded by the PNRR NEST project and Sapienza University of Rome Grant. Full article

“Wind theft”, the extraction of upstream wind resources by neighboring wind farms on account of wind farm or cluster wakes, is receiving wider popular attention. Cluster wakes need to be accounted for in wider planning strategies, for which measurements and wake models can be deployed to aid this process. To contribute to such planning measures, a flight campaign for investigating cluster waking and other phenomena in the North Sea was conducted in 2020 and 2021 to contribute extra flight data obtained during the first flight campaign of 2016 and 2017. We report the latest results of the 2020–2021 flight campaign following the work and methodology of Cañadillas et al. (2020), where, using the 2016–2017 flight measurements, wake lengths extending up to approximately 60 km in stable stratification were inferred, consistent with an explicit stability-dependent analytical model. Analysis of the recent 2020–2021 flight data is approximately consistent with the results of Cañadillas et al. (2020) in stable conditions, albeit with greater scatter. This is because Cañadillas et al. (2020) analyzed only flights in which the wind conditions remained nearly constant during the measurement period, whereas the current dataset includes more variable conditions. Comparisons with the analytical-based engineering model show good first-order agreement with the flight data, but higher-order effects, such as flow non-homogeneity, are not accounted for. The application of these results to the stability information for developing offshore wind energy regions such as the East Coast of the USA and Bass Strait, Australia gives an outline of the expected wake lengths there. Simple engineering models, such as that demonstrated here, though primarily designed for commercial applications, need to be further developed into advanced spatial planning frameworks for offshore wind energy areas. Full article

It is very important to create mathematical models for real world problems and to propose new solution methods. Today, symmetry groups and algebras are very popular in mathematical physics as well as in many fields from engineering to economics to solve mathematical models. This paper introduces a novel methodological framework based on the SO(3) Lie method to estimate time-dependent correlation matrices (correlation flows) among three variables that have chaotic, entropy, and fractal characteristics, from 11 April 2011 to 31 December 2024 for daily data; from 10 April 2011 to 29 December 2024 for weekly data; and from April 2011 to December 2024 for monthly data. So, it develops the stochastic SO(2) Lie method into the SO(3) Lie method that aims to obtain the correlation flow for three variables with chaotic, entropy, and fractal structure. The results were obtained at three stages. Firstly, we applied entropy (Shannon, Rényi, Tsallis, Higuchi) measures, Kolmogorov–Sinai complexity, Hurst exponents, rescaled range tests, and Lyapunov exponent methods. The results of the Lyapunov exponents (Wolf, Rosenstein’s Method, Kantz’s Method) and entropy methods, and KSC found evidence of chaos, entropy, and complexity. Secondly, the stochastic differential equations which depend on S² (SO(3) Lie group) and Lie algebra to obtain the correlation flows are explained. The resulting equation was numerically solved. The correlation flows were obtained by using the defined covariance flow transformation. Finally, we ran the robustness check. Accordingly, our robustness check results showed the SO(3) Lie method produced more effective results than the standard and Spearman correlation and covariance matrix. And, this method found lower RMSE and MAPE values, greater stability, and better forecast accuracy. For daily data, the Lie method found RMSE = 0.63, MAE = 0.43, and MAPE = 5.04, RMSE = 0.78, MAE = 0.56, and MAPE = 70.28 for weekly data, and RMSE = 0.081, MAE = 0.06, and MAPE = 7.39 for monthly data. These findings indicate that the SO(3) framework provides greater robustness, lower errors, and improved forecasting performance, as well as higher sensitivity to nonlinear transitions compared to standard correlation measures. By embedding time-dependent correlation matrix into a Lie group framework inspired by physics, this paper highlights the deep structural parallels between financial markets and complex physical systems. Full article

Background/Objectives: This study investigated how selected functionality-related characteristics (FRCs) of hypromellose (HPMC)—namely viscosity, hydroxypropoxy substitution, particle size, and the ratio of water-soluble (FlowLac^® 100) to water-insoluble (Avicel^® PH-102) fillers— affect the release of carvedilol from matrix tablets. Methods: Using a Central Composite Design (CCD) Design of Experiments (DoE), mixtures of HPMC QbD samples were prepared to achieve target HPMC FRC levels. Within the CCD, levels of FlowLac^® 100 and Avicel^® PH-102 were also varied. The mean and standard deviation of carvedilol release at each analyzed time point of the release profile were used as target variables for individual multiple linear regression (MLR) models. Results: Lactose, the water-soluble filler, significantly accelerated carvedilol release, whereas the water-insoluble MCC slowed and stabilized release by improving gel integrity. Among the HPMC FRCs, particle size had the strongest influence during the early release phase, while HPMC viscosity and hydroxypropoxy substitution degree became more important in later phases. Analysis of the results using optimized multiple linear regression (MLR) models revealed key interaction effects, particularly between HPMC viscosity and lactose content, and between viscosity and particle size, demonstrating their combined role in modulating release kinetics. Conclusions: These findings provide valuable insight into how controlling HPMC’s FRCs and filler composition can reduce interbatch variability in drug release and support the rational design of robust controlled release formulations. Full article

Emptying pressure pipelines is a routine operation during pipeline maintenance. This study investigates the emptying characteristics of pressurized pipelines with air valves under unsteady flow conditions. A mathematical model for the emptying process is developed using the rigid water column theory, exploring the influence of drain valve opening, initial air pocket length, and valve opening patterns on the transient flow behavior. The results indicate that, compared with the linear valve opening pattern, a nonlinear power function opening increases the minimum air pocket pressure head by 0.1014 m and delays its occurrence by 0.655 s. The maximum emptying velocity rises by 0.48 m/s when the opening is increased from 10% to 30%, thereby shortening the emptying time by 65.4%. However, the pressure head inside the air pocket decreases accordingly. When the air valve diameter is enlarged from 0.003 mm to 0.008 mm, the pressure recovery time is markedly reduced and the initial pressure fluctuations are attenuated. Numerical simulations based on the Heihe emptying case demonstrate that a well-planned layout of multiple air valves effectively shortens the duration of negative pressure heads. Replacing the first air valve with a 50 cm diameter circular orifice significantly raises the minimum pressure head of the pipeline and dramatically enhances the stability of emptying pressurized pipeline. Full article

Accurate traffic flow prediction is pivotal for intelligent transportation systems; yet, existing spatial-temporal graph neural networks (STGNNs) struggle to jointly capture the long-term structural stability, short-term dynamics, and multi-scale temporal patterns of road networks. To address these shortcomings, we propose FISTGCN, a Frequency-Aware Interactive Spatial-Temporal Graph Convolutional Network. FISTGCN enriches raw traffic flow features with learnable spatial and temporal embeddings, thereby providing comprehensive spatial-temporal representations for subsequent modeling. Specifically, it utilizes an interactive dynamic graph convolutional block that generates a time-evolving fused adjacency matrix by combining adaptive and dynamic adjacency matrices. It then applies dual sparse graph convolutions with cross-scale interactions to capture multi-scale spatial dependencies. The gated spectral block projects the input features into the frequency domain and adaptively separates low- and high-frequency components using a learnable threshold. It then employs learnable filters to extract features from different frequency bands and adopts a gating mechanism to adaptively fuse low- and high-frequency information, thereby dynamically highlighting short-term fluctuations or long-term trends. Extensive experiments on four benchmark datasets demonstrate that FISTGCN delivers state-of-the-art predictive accuracy while maintaining competitive computational efficiency. Full article

Environmental, Social, and Governance (ESG) performance has become a pivotal driver of firm valuation, investment flows, and capital market stability and a critical dimension of corporate sustainability and investor decision-making. Yet, emerging markets face structural barriers to standardized ESG measurement due to limited data availability and inconsistent disclosures. This study addresses this gap by developing a simplified, transparent and indicator-based ESG assessment model tailored to the Moroccan capital market using publicly available data from 20 companies listed in the MASI ESG Index on the Casablanca Stock Exchange. The framework evaluates 12 equally weighted indicators across environmental, social, and governance pillars, and employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) method, to generate firm-level ESG scores and rankings. In addition to equal-weighted rankings, the model was stress-tested using entropy-based and expert-informed weights. Results reveal a wide disparity in ESG maturity: while environmental reporting is relatively advanced, social and governance disclosures lag behind. Top-ranking firms align closely with international frameworks such as GRI, whereas others lack fundamental transparency. By offering a replicable, low-data ESG scoring method applicable to other emerging markets, this research provides actionable insights for investors, regulators, and corporate leaders. The findings contribute to the financial literature on ESG integration, support the design of sustainable investment strategies, and advance policy efforts to strengthen capital market resilience across the MENA region. Full article

Small hydropower-dominated microgrids enable power exchange with the main grid during grid-connected operation but face frequency stability challenges during sudden islanding (e.g., line faults), requiring prompt generation curtailment or load shedding. In communication-constrained mountainous regions, conventional methods such as high-frequency tripping or low-frequency load shedding often struggle to achieve precise frequency regulation A hierarchical strategy integrating master station centralized decision making and substation local control is proposed. This study theoretically analyzes the post-islanding frequency dynamics of small hydropower microgrids. The master station formulates optimal shedding decisions using regional power flow data, while substations execute decisions via local measurements to mitigate communication issues. A constrained mathematical model is established, solved using a heuristic algorithm, validated through electromagnetic transient simulations, and compared with traditional schemes. The proposed scheme achieves precise surplus capacity shedding, enhancing frequency stability during abrupt islanding with reduced over-/under-tripping compared to that of conventional methods. This hierarchical strategy enhances frequency regulation capability under communication constraints, ensuring reliable operation of small hydropower microgrids during sudden islanding and providing a practical solution for remote regions with limited communication infrastructure. Full article