Search Results (3,163)

This research explores the distribution, transport, and deposition of calcium carbonate particles in the colorful pools of the Huanglong area under varying hydrodynamic conditions. The study employs Particle Image Velocimetry (PIV) for real-time measurements of flow field velocity and computational fluid dynamics (CFD) simulations to analyze particle behavior. The findings reveal that under horizontal flow conditions, the peak concentration of calcium carbonate escalated to 1.06%, representing a 6% surge compared to the inlet concentration. Significantly, particle aggregation and settling were predominantly noted at the bottom right of the flow channel, where the flow boundary layer is most pronounced. In the context of inclined surfaces equipped with a baffle, a substantial rise in calcium carbonate concentrations was detected at the channel’s bottom right and behind the baffle, particularly in regions characterized by reduced flow velocities. These low-velocity areas, along with the interaction of the boundary layer and low-speed vortices, led to a decrease in particle velocities, thereby enhancing deposition. The highest concentrations of calcium carbonate particles were found in regions characterized by thicker boundary layers, particularly in locations before and after the baffle. Using the Discrete Phase Model (DPM 22), the study tracked the trajectories of 2424 particles, of which 2415 exited the computational channel and nine underwent deposition. The overall deposition rate was measured at 0.371%, with calcium carbonate deposition rates ranging from 4.06 mm/a to 81.7 mm/a, closely matching field observations. These findings provide valuable insights into the dynamics of particle transport in aquatic environments and elucidate the factors influencing sedimentation processes. Full article

To enhance the hydraulic performance of the Venturi injector, the effects of the structural parameters were investigated using response surface methodology (RSM) and computational fluid dynamics (CFD) simulations. The fertilizer suction chamber diameter, contraction angle, and throat diameter ratio were chosen as variables, while the suction flow rate, suction concentration, and suction efficiency were selected as performance indicators. Multiple regression models were established, and the regression models were used for parameter optimization and experimental verification. The results showed that under the same inlet-outlet differential pressure, with the increase in the fertilizer suction chamber diameter, contraction angle, and throat diameter ratio, the suction flow rate, suction concentration, and suction efficiency showed a trend of first increasing and then decreasing, and there were peaks in suction performance. Predictive regression equations were established for the suction flow rate, concentration, and efficiency within the experimental parameter range. The determination coefficients of the three regression equations were 0.9987, 0.9961, and 0.9990, respectively, which indicated that the established regression equations could be used for performance prediction. The optimized combination of structural parameters included a fertilizer suction chamber diameter of 32 mm, a contraction angle of 35°, and a throat diameter ratio of 2.93. The error between the predicted and experimental values was less than 3%, indicating a high level of reliability in the predictive regression model. The performance indicators of the optimized Venturi injector were significantly improved, with an increase of 124.1~793.7 L h⁻¹ in the suction flow rate, 9.52~16.42 percentage points in suction concentration, and 5.4~9.19 percentage points in suction efficiency. Full article

Research on the multi-field coupling effects in rocks has been ongoing for several decades, encompassing studies on single physical fields as well as two-field (TH, TM, HM) and three-field (THM) couplings. However, the environmental conditions of rock masses in deep resource extraction and underground space development are highly complex. In such settings, rocks are put through thermal-hydrological-mechanical-chemical (THMC) coupling effects under peak temperatures, strong osmotic pressures, extreme stress, and chemically reactive environments. The interaction between these fields is not a simple additive process but rather a dynamic interplay where each field influences the others. This paper provides a comprehensive analysis of fragmentation evolution, deformation mechanics, mechanical constitutive models, and the construction of coupling models under multi-field interactions. Based on rock strength theory, the constitutive models for both multi-field coupling and creep behavior in rocks are developed. The research focus on multi-field coupling varies across industries, reflecting the diverse needs of sectors such as mineral resource extraction, oil and gas production, geothermal energy, water conservancy, hydropower engineering, permafrost engineering, subsurface construction, nuclear waste disposal, and deep energy storage. The coupling of intense stress, fluid flow, temperature, and chemical factors not only triggers interactions between these fields but also alters the physical and mechanical properties of the rocks themselves. Investigating the mechanical behavior of rocks under these conditions is essential for averting accidents and assuring the soundness of engineering projects. Eventually, we discuss vital challenges and future directions in multi-field coupling research, providing valuable insights for engineering applications and addressing allied issues. Full article

During public health emergencies, studying the travel characteristics and influencing factors of shared bicycles during different time periods on weekdays can provide valuable insights for urban transportation planning and offer recommendations for bike-sharing systems (BSS) affected by such events. Utilizing bike-sharing data, this study initiated the analysis by scrutinizing the spatial flow patterns in the core area of Beijing, employing network indicators within the framework of complex network theory. Subsequently, influencing factors associated with bike-sharing trips were pinpointed using the exponential random graph model (ERGM). Using COVID-19 as an example, it examines the impact of public health emergencies on bike-sharing during multiple time periods. Supported by the network analysis method, our findings revealed that the majority of travel activities occurred between adjacent areas. Throughout weekdays, a consistent level of travel activity was observed, exhibiting distinct patterns during daytime and nighttime. The period from 4:00 to 8:00 emerged as the peak time, characterized by heightened traffic and temperature changes. Morning commuting extended until 8:00–12:00, followed by a transition period from 12:00–16:00. The most active travel time, encompassing various purposes, was identified as 16:00–20:00. Additionally, the presence of hospitals and train stations amplified travel within the pandemic-affected area. Finally, variants of ERGMs were employed to assess the influence of finance, shopping, dining, education, transportation, roads, and COVID-19 on bike-sharing activities. The road network emerged as the most critical factor, exhibiting a significant negative impact. Conversely, COVID-19 had the most pronounced positive influence, with transportation stops and educational institutions also contributing significantly in a positive manner. This research provides valuable transportation planning insights for addressing public health emergencies and promotes the effective utilization of bike-sharing systems. Full article

This study introduces an innovative approach based on multi-source data fusion to address the challenges of traffic operation management in cold-climate cities. Taking Harbin City as the research object, GPS trajectory data and checkpoint data were integrated to systematically analyze the seasonal fluctuation patterns and spatial distribution characteristics of traffic operations from the dimensions of time and space. The study shows that low temperatures and snow in winter significantly reduce traffic efficiency, with prominent traffic pressure during morning and evening peak hours. On weekdays, there is a clear “double peak” characteristic, while on non-working days, traffic flow is relatively stable. Moreover, compared to southern cities with a more pronounced “long-tail effect”, the long period of traffic congestion recovery significantly increases the resilience requirements of the traffic system in cold-climate cities. In terms of space, the concentrated commuting demand in the core circle leads to much higher traffic pressure than in the peripheral areas, creating a marked traffic gradient. Frequently congested road sections are mostly concentrated on commuting arteries and functional nodes, while peripheral areas have higher operational efficiency due to a balanced work–residence distribution. The study reveals the spatiotemporal characteristics of traffic operations in cold-climate cities, offering data support for precise management. By verifying the application value of multi-source data fusion under extreme climate conditions, this study provides important references for intelligent transportation management and sustainable development in cold-climate cities. Full article

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios. Full article

With the increasing frequency of extreme rainfall events, enhancing urban drainage systems’ regulation capacity is crucial for mitigating urban flooding. Existing studies primarily analyze infrastructure impacts on peak flow delay but often lack a systematic exploration of time-lag mechanisms. This study introduces the time-lag parameter, using the hysteresis curve of the water level–flow rate relationship to quantify drainage system dynamics. An SWMM-based drainage model was developed for the Rongdong area of Xiong’an New District to evaluate the independent roles of green, gray, and blue infrastructures in peak flow reduction and time-lag modulation. The results indicate that green infrastructure extends the horizontal width and reduces the vertical height of the hysteresis curve, prolonging time lag and making it effective for small-to-medium rainfall. Gray infrastructure enhances drainage efficiency by compressing the hysteresis curve horizontally and increasing its vertical height, facilitating rapid drainage but offering limited peak reduction. Blue infrastructure, by lowering outlet water levels, improves drainage capacity and reduces time lag, demonstrating adaptability across various rainfall scenarios. This study systematically quantifies the role of each infrastructure type in time-lag regulation and proposes a collaborative optimization strategy for urban drainage system design. Full article

Background/Objectives: This systematic review evaluates the efficacy of quail egg-based supplements (QES) as an integrative remedy for treating allergic rhinitis. Methods: A comprehensive search of PubMed, Scopus, EMBASE, Cochrane Library, and Google Scholar was conducted up to January 2025 to address the research question. Results: A total of 294 studies were initially identified, with five clinical reports meeting the inclusion criteria. Participant numbers ranged from 40 to 180 (median: 77), with a balanced gender ratio. Four reports focused on allergic rhinitis, and one investigated nonsymptomatic atopic individuals exposed to volatile allergens. The findings suggest that a combination of QES and zinc significantly improves peak nasal inspiratory flow, mucociliary transport time, and symptoms such as rhinorrhea, nasal congestion, itchy nose and eyes, and sneezing in patients with allergic rhinitis. Additionally, QES may reduce the reliance on standard symptomatic medications. The intervention was generally well tolerated, with side effects being rare, mild, and transient; however, QES should be avoided in patients with egg allergies. Conclusions: The reviewed studies indicate that QES with zinc can serve as an effective integrative approach to alleviating symptoms of allergic rhinitis. Further research is recommended to confirm these findings. Full article

Composite phase-change materials (PCMs) exhibit significant potential for enhancing the thermal performance of building walls. However, previous studies have generally lacked detailed investigations of the performance of PCM-integrated walls under cold climate conditions. Therefore, in order to evaluate the thermal performance and wall adaptability of hollow bricks with composite PCMs in cold climates, a brick model was created by filling the hollow bricks with PCMs. Then a comparative test was conducted between the PCM-filled bricks and the conventional non-PCM-filled hollow bricks. The comparative experimental method and the thermal performance index evaluation method resulted in the following: (1) Compared with conventional hollow bricks, PCM-filled bricks showed an increase of approximately 0.99 °C in inner surface temperature and 3.85 °C in midsection temperature. This demonstrates that PCM-filled bricks can retard the rate of temperature drop, significantly enhancing the insulation performance of walls. This improvement contributes to enhance indoor thermal comfort and reduce energy consumption. (2) The temperature difference between the interior and exterior surfaces of the non-PCM-filled hollow bricks is 23.54 °C, which is 5.62 °C higher than that of the PCM-filled bricks. This indicates that bricks filled with PCMs possess superior heat storage capacity, effectively reducing indoor heat loss, which aligns with the principles of green building design. (3) Compared with the conventional non-PCM-filled hollow bricks, the heat flow on the inner surface of the PCM-filled bricks is significantly lower, with the average heat flow reduced by 8.57 W/m². This suggests the ability of bricks filled with PCMs to moderate heat flux fluctuations through a “peak-shaving and valley-filling” effect, contributing to reduced energy consumption and enhanced occupant thermal comfort. Full article

The narrow bandgap InN material, with exceptional physical properties, has recently gained considerable attention, encouraging many scientists/engineers to design infrared photodetectors, light-emitting diodes, laser diodes, solar cells, and high-power electronic devices. The InN/Sapphire samples of different film thicknesses that we have used in our methodical experimental and theoretical studies are grown by plasma-assisted molecular-beam epitaxy. Hall effect measurements on these samples have revealed high-electron-charge carrier concentration, η. The preparation of InN epifilms is quite sensitive to the growth temperature T, plasma power, N/In ratio, and pressure, P. Due to the reduced distance between N atoms at a higher P, one expects the N-flow kinetics, diffusion, surface components, and scattering rates to change in the growth chamber which might impact the quality of InN films. We believe that the ionized N, rather than molecular, or neutral species are responsible for controlling the growth of InN/Sapphire epifilms. Temperature- and power-dependent photoluminescence measurements are performed, validating the bandgap variation (~0.60–0.80 eV) of all the samples. High-resolution X-ray diffraction studies have indicated that the increase in growth temperature caused the perceived narrow peaks in the X-ray-rocking curves, leading to better-quality films with well-ordered crystalline structures. Careful simulations of the infrared reflectivity spectra provided values of η and mobility μ, in good accordance with the Hall measurements. Our first-order Raman scattering spectroscopy study has not only identified the accurate phonon values of InN samples but also revealed the low-frequency longitudinal optical phonon plasmon-coupled mode in excellent agreement with theoretical calculations. Full article

Reasonably configuring the concentration distribution of the mixture to achieve partially premixed combustion has been proven to be an effective method for improving energy utilization efficiency. However, due to the significant influence of concentration non-uniformity and flow field disturbances, the combustion behavior and mechanisms of partially premixed combustion have not been fully understood or systematically analyzed. In this study, the partially premixed combustion characteristics of methane–hydrogen–air mixtures in a confined space were investigated, focusing on the combustion behavior and key parameter variation patterns under different equivalence ratios (0.5, 0.7, 0.9) and hydrogen contents (10%, 20%, 30%, 40%). The global equivalence ratio and degree of partial premixing of the mixture were controlled by adjusting the fuel injection pulse width and ignition timing, thereby regulating the concentration field and flow field distribution within the combustion chamber. The constant-pressure method was used to calculate the burning velocity. Results show that as the mixture formation time decreases, the degree of partial premixing increases, accelerating the heat release process, increasing burning velocity, and shortening the combustion duration. It exhibits rapid combustion characteristics, particularly during the initial combustion phase, where flame propagation speed and heat release rate increase significantly. The burning velocity demonstrates a distinct single-peak profile, with the peak burning velocity increasing and its occurrence advancing as the degree of partial premixing increases. Additionally, hydrogen’s preferential diffusion effect is enhanced with increasing mixture partial premixing, making the combustion process more efficient and concentrated. This effect is particularly pronounced under low-equivalence-ratio (lean burn) conditions, where the combustion reaction rate improves more significantly, leading to greater combustion stability. The peak of the partially premixed burning velocity occurs almost simultaneously with the peak of the second-order derivative of the combustion pressure. This phenomenon highlights the strong correlation between the combustion reaction rate and the dynamic variations in pressure. Full article

We investigated the orthogonal cutting process by using machine learning models to predict its performance. This study used the AZ91 magnesium alloy as the workpiece material, and machining was performed under the Minimum Quantity Lubrication (MQL) technique. The input parameters were the feed, cutting speed and MQL flow rate. Additionally, the outputs were flank tool wear, the chip contact length, peak distance, valley distance, pitch distance, chip segmentation ratio, compression ratio and shear angle. Studies on machine learning (ML) models being employed to evaluate the performance of the MQL-assisted orthogonal machining of AZ91 are very rarely found in the literature. This study explored machine learning (ML) as a data-driven alternative, evaluating decision tree regression, Bayesian Optimization, Random Forest Regression and XGBoost for predicting machinability. A comprehensive dataset of the cutting parameters and outcomes was utilized to train and validate these models, aiming to enhance the accuracy of the predictive analysis. The performance of each model was evaluated based on error metrics such as the mean squared error (MSE) and R-squared values. Among these models, XGBoost demonstrated a superior predictive accuracy, outperforming the other methods in terms of its precision and generalizability. These findings suggest that XGBoost provides a more robust solution for modeling the complexities of the orthogonal cutting process, offering valuable insights into process optimization. The analysis supports that the XGBoost model is the most accurate, with a 34.1% reduction in the mean squared error and a 17.1% reduction in the mean absolute error over these values for the Decision Tree. It also outperforms the Random Forest Regression model, achieving a 19.8% decrease in the mean squared error and a 7.1% decrease in the mean absolute error. Full article

Background: Despite the ongoing innovations and the availability of numerous effective inhaled treatment options, achieving optimal disease control in most patients frequently remains disappointing. Unfortunately, although inhaled therapy is the cornerstone of respiratory disease management, the selection of the most appropriate inhaler is still overlooked or underestimated by some healthcare professionals, and inhaler misuse remains a significant challenge in managing chronic respiratory diseases which directly influences patients’ quality of life, clinical outcomes, and risk of disease progression. Materials and Methods: This is a unicentric, observational, cross-sectional study designed to evaluate the inhaled therapy prescribed in hospitalized patients and to analyze device changes after hospitalization, as well as the factors associated with these changes. A single face-to-face visit was performed during the patient’s hospitalization, where the inhaled therapy used prior to hospitalization was evaluated: technique (critical errors), compliance (TAI questionnaire), maximum peak inspiratory flow [PIF (L/min)], and level of inhaler handling-related knowledge. A binary logistic regression model was used to explore the association between changing device at discharge and the other independent variables Results: The inhaler most used during hospitalization was the metered-dose inhaler (MDI) with a chamber (51.9% of patients), with the dry powdered inhalers (DPI) being the inhalers used in 43% of maintenance inhaled therapies in the community setting prior to hospitalization. In addition, 90% of patients showed a maximum PIF ≥ 30 L/min, and 35.6% performed critical inhaler errors. These patients had statistically significantly lower maximum PIF values (52.1 L/min in patients with critical inhaler errors vs. 60.8 L/min without critical inhaler errors; p > 0.001) and were more likely to exhibit poor inhaler compliance compared to those without critical errors (50.5% vs. 31.0%, respectively). More than half of the patients who used MDI with spacer chamber made critical inhaler errors; 69.9% showed regular or poor treatment adherence, although 75.6% demonstrated good knowledge about inhaler handling. Only in 27% of the patients did the healthcare professional change the type of inhaler after hospitalization within clinical practice. The medical and nursing staff responsible for the patient’s hospitalization were not informed of the assessment carried out in the study. The probability of not performing a device change at discharge was lower in patients with previous at-home treatment with combined inhaled therapy with LABA + ICS (OR 0.3 [0.18–0.83], p = 0.016) and in patients under triple inhaled therapy (OR 0.3 [0.17–0.76], p = 0.007). No significant differences were observed in inhaler changes when considering the frequency of critical inhaler errors, inhaler handling-related knowledge or maximum PIF values. Conclusions: Our study highlights the urgent need for a more personalized inhaler selection and consistent monitoring by healthcare professionals to minimize inhaler misuse, increase treatment compliance and adherence, and improve disease management outcomes. It is essential to provide training and promote the role of nursing in the evaluation and education of inhaled therapy. Additionally, the use of standardized approaches and tools, such as the CHECK DIAL, is crucial to facilitate the adaptation of devices to patients’ needs. Full article

The integration of renewable energy sources and electric vehicles has become a focal point for industries and academia due to its profound economic, environmental, and technological implications. These developments require the development of a robust intelligent home energy management system (IHEMS) to optimize energy utilization, enhance transaction security, and ensure grid stability. For this reason, this paper develops an IntelliGrid AI, an advanced system that integrates blockchain technology, deep learning (DL), and dual-energy transmission capabilities—vehicle to home (V2H) and home to vehicle (H2V). The proposed approach can dynamically optimize household energy flows, deploying real-time data and adaptive algorithms to balance energy demand and supply. Blockchain technology ensures the security and integrity of energy transactions while facilitating decentralized peer-to-peer (P2P) energy trading. The core of IntelliGrid AI is an advanced Q-learning algorithm that intelligently allocates energy resources. V2H enables electric vehicles to power households during peak periods, reducing the strain on the grid. Conversely, H2V technology facilitates the efficient charging of electric cars during peak hours, contributing to grid stability and efficient energy utilization. Case studies conducted in Tunisia validate the system’s performance, showing a 20% reduction in energy costs and significant improvements in transaction efficiency. These results highlight the practical benefits of integrating V2H and H2V technologies into innovative energy management frameworks. Full article

The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras TensorFlow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m⁻² root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R² also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m⁻². The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth. Full article