Search Results (2,834)

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 $m_V$, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services. Full article

Noninvasive imaging of circuit breakers under short-circuit testing is addressed by recording the magnetic field produced over an array of external sensors and by solving an inverse problem to identify the causing current distribution. The temporal and spatial resolution of the sensing chain are studied and implemented in a physical set-up. A wire model is adopted to describe electrical current distribution. Additionally, the simpler, more direct approach to evaluating the passage of electric current in front of sensors is proposed. The dynamics of suitable approximating models of the electric arc that forms across contacts is obtained and agrees with multi-physical simulations and with experimental time histories of current and voltage. The two methods are flexible and allow the analysis of different types of circuit breakers. Full article

Lifestyle behaviors, defined as a combination of dietary behavior, physical activity (PA), screen time (ST), and sleep duration indicators, are strongly associated with blood pressure (BP) in students. Our aim was to characterize the joint trajectories of lifestyle behaviors among middle school students and evaluate their association with BP. Data were obtained from the monitoring dataset on common diseases and health factors among students in Jiading District, Shanghai, China, conducted from 2019 to 2023. Lifestyle behavior data were collected annually from middle school students aged 12–18 years through questionnaires covering dietary behavior score, PA, ST, and sleep duration. Students’ BP was measured in 2023. Joint trajectories of lifestyle behaviors were determined using group-based multi-trajectory modeling. Associations between lifestyle trajectories and students’ BP were examined using multiple linear regression and modified Poisson regression. A total of 1378 middle school students (759 [58.98%] boys, median age 14.36 years [IQR: 13·30–13.28]) with lifestyle behaviors data assessed at least three times were included, and they were categorized into four joint lifestyle trajectories as follows: “remain unhealthy with low PA and increasing ST” (n = 141, 10.46%), “remain unhealthy with only low PA” (n = 305, 22.63%), “change towards unhealthy with decreasing sleep duration” (n = 776, 57.57%), and “relatively healthy” (n = 126, 9.35%). After adjusting for important confounders, the “remain unhealthy with low PA and increasing ST” group was associated with higher diastolic BP (DBP) [β: 3.49, 95% CI: 0.55–6.44] and higher mean arterial pressure (MAP) [β: 3.19, 95% CI: 0.37–6.01] in students compared with the “relatively healthy” group. Additionally, compared with the “relatively healthy” group, students in the “remain unhealthy with low PA and increasing ST” group had a 1.12-fold increase in the risk of hypertension (risk ratios: 1.12, 95% CI: 1.03–1.24). All trend p values in DBP, MAP, and hypertension from the “relatively healthy” group to the “remain unhealthy with low PA and increasing ST” group were less than 0.05. Four distinct lifestyle trajectories were identified among middle school students. Students who remained in the “unhealthy with low PA and increasing ST” lifestyle trajectory were associated with later elevations in BP. Full article

As in any other industry, nuclear energy results in the accumulation of some waste, which needs to be managed safely and responsibly due to its radiotoxicity. In the case of highly radioactive waste, geological disposal in stable rock is considered a broadly accepted solution. For the evaluation of the long-term safety of a geological repository, the assessment of radionuclide transport needs to be carried out. Radionuclide transport through engineered and natural barriers of the repository will highly depend on the barriers’ transport-related properties, which will be determined by coupled thermal, hydraulic, chemical, mechanical, biological, and radiation processes taking place in those barriers. In this study, the thermo-hydro-chemical (THC) state of bentonite was analysed considering CO₂ gas diffusion and temperature-dependent solubility in water. Reactive transport modelling of bentonite under non-isothermal conditions was performed with the COMSOL Multiphysics software (v6.0), coupled with the geochemical solver Phreeqc via the iCP interface. The modelling demonstrated that the consideration of chemical processes in bentonite had no significant influence on non-reactive Cl⁻ transport; however, it would be important for other radionuclides whose sorption in porous media depends on the porewater pH. Based on the modelling results, changes in the bentonite mineralogical composition and, subsequently, porosity depend on the partial CO₂ pressure at the bentonite–granite boundary. In the case of low CO₂ partial pressure at the bentonite–granite interface, the calcite dissolution led to a slight porosity increase, while higher CO₂ partial pressure led to decreased porosity near the interface. Full article

Microwave continuous-flow heating has been proven to reduce the time of chemical reaction, increase the conversion rate, and improve product purity effectively. However, there are still problems such as relatively low heating efficiency, unideal heating homogeneity, and poor compactness, which brings further drawbacks like difficulty in fabrication and integration. In this study, a compact microwave continuous-flow heater based on six fractal antennas is proposed to address the problems above. First, a multi-physics simulation model is built, while heating efficiency and the volumetric coefficient of variance (COV) are improved through adjusting the geometric structure of this heater and the phase assignment of each radiator. Second, an experiment is conducted to verify the simulation model, which is consistent with the simulation. Third, a method of fast varying phases to achieve greater heating efficiency and heating homogeneity is adopted. The results show that the single-phase radiator improved efficiency by 31.1%, and COV was significantly optimized, reaching 64%. Furthermore, 0–100% ethanol–water solutions are processed by the heater, demonstrating its strong adaptability of vastly changing relative permittivity of liquid load. Moreover, an advance of this microwave continuous-flow heater is observed, compared with conventional multi-mode resonant cavity. Last, the performance of this microwave continuous-flow heater as the chemical reactor for biodiesel production is simulated. This design enables massive chemical production in fields like food industry and biodiesel production, with enhanced compactness, heating efficiency, and heating homogeneity. Full article

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups. Full article

The Polymer Electrolyte Membrane Water Electrolyser (PEMWE) has gained significant interest among various electrolysis methods due to its ability to produce highly purified, compressed hydrogen. The spatial configuration of bipolar plates and their flow channel patterns play a critical role in the efficiency and longevity of the PEM water electrolyser. Optimally designed flow channels ensure uniform pressure and velocity distribution across the stack, enabling high-pressure operation and facilitating high current densities. This study uses flow channel geometry inspired by authentic vine leaf patterns found in biomass, based on various plant leaves, including Soybean, Victoria Amazonica, Water Lily, Nelumbo Nucifera, Kiwi, and Acalypha Hispida leaves, as a novel channel pattern to design a PEM bipolar plate with a circular cross-section area of 13.85 ${\mathrm{c}\mathrm{m}}^2$. The proposed bipolar design is further analysed with COMSOL Multiphysics to integrate the conservation of mass and momentum, molecular diffusion (Maxwell–Stefan), charge transfer equations, and other fabrication factors into a cohesive single-domain model. The simulation results showed that the novel designs have the most uniform velocity profile, lower pressure drop, superior pressure distribution, and heightened mixture homogeneity compared to the traditional serpentine models. Full article

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method. Full article

Background: A Special Health Care Need (SHCN) is characterized by any type of physical, mental, sensorial, cognitive, emotional, or developmental condition that requires medical treatment, specialized services, or healthcare interventions. These conditions can negatively impact oral health as SHCN children can hardly cooperate or communicate and experience higher levels of dental fear/anxiety, which interfere with regular appointments. The present narrative review aims to analyze the use of audiovisual (AV) tools in dental setting for the management of SHCN children during dental treatment and to evaluate their effectiveness in anxiety/behavior control from the child, dentist, and care-giver perspectives. This analysis leads to the proposal of a new multi-session model for the behavioral management of SHCN pediatric subjects. Methods: An electronic search on the MEDLINE/Pubmed, Scopus, and Web of Science databases was carried out and through this analysis, a new model was proposed, the “UNISA-Virtual Stepwise Distraction model”, a multi-session workflow combining traditional behavior management and the progressive introduction of AV media to familiarize the SHCN child with dental setting and manage behavior. Results: AV tools helped in most cases to manage SHCN behavior and decreased stress in both the dentist and child during dental treatments. Care-givers also welcomed AV distractors, reporting positive feedback in using them during future treatments. Conclusions: The present narrative review found increasing evidence of the use of AV media for SHCN pediatric subjects as distraction tools during dental treatment. In the majority of the studies, AV tools proved to be effective for the management of anxiety, dental fear, and behavior in dental setting. Full article

This article studies the migration and settlement laws of backflow proppants after fracturing tight sandstone. This paper proposes a fitting method based on a multi-task learning network to address the issue of interference from multiple physical parameters during the transport and settlement processes of proppants. This method can effectively handle multi-dimensional interference factors and fit the mapping logic of multiple engineering parameters to transport patterns through the continuous correction of multi-layer networks. We first introduce the characteristics of tight sandstone reservoirs and their important value in mining, as well as the status of current research on the migration and settlement laws of proppants at home and abroad. Based on this, we then deeply analyze the sedimentation rate model of proppants in tight sandstone backflow and the equilibrium height of proppants under multiple factors of interference while considering the distribution characteristics of proppants. In order to more accurately simulate the transport and settlement laws of proppants, this paper introduces a multi-task learning network. This network can comprehensively consider multi-dimensional parameters, learn the inherent laws of data through training, and achieve accurate fitting of the transport and settlement laws of proppants. This study trained and tested the model using actual production data, and the results showed that the proposed model can fit the input–output relationship well, thus effectively supporting the study of proppant transport and settlement laws. Full article

The multiway valve is the core control element of the hydraulic system in construction machinery, such as excavators. Its complex internal structure, especially the flow channels, significantly impacts the machine’s efficiency and reliability. This study focuses on the boom flow channel of excavator multiway valves and establishes a multi-physical field coupling simulation model. We propose six key flow channel structural parameters and analyze changes in the valve’s flow field, temperature field, and structural field using orthogonal test simulation data. The range analysis method identifies the primary and secondary influences of structural parameters on pressure loss, temperature, stress, and strain. A multi-objective optimization model was developed using a neural network and the Non-dominated Sorting Genetic Algorithm II(NSGA-II), with pressure loss and maximum stress as the optimization objectives. The Pareto front solution set for key flow channel parameters was calculated. The optimization results showed a 9.0% reduction in pressure loss and a 40.7% reduction in maximum stress. A test bench verified the simulation model, achieving prediction accuracies of 94.8% for pressure loss in the inlet area and 92.3% in the return area. This method can provide a reference for the optimal design of the dynamic characteristics of high-pressure multiway valves. Full article

An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about $5\%$, but individual comparisons resulted in an $R^2$ of $0.36$. Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to $R^2>0.95$. This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations. Full article

To address the problems of the inaccurate seeding rate and uneven seeding in the process of dense planting of winter wheat in Xinjiang, according to the physical characteristics of the wheat seeds and the agronomic requirements of the high-yield cultivation techniques for the winter wheat “well” type, a double-hole wheel-type densely planted wheat hole sower was designed and produced. Through theoretical design and research, the structural design of the overall hole seeder and its key components was completed. The findings indicated that 5–7 wheat seeds could be planted in each hole at a 9.2 mm nest depth and 610 mm³ nest volume, which was consistent with the “well”-type high-yield dense-planting cultivation technology’s need for 400,000–500,000 basic seedlings per mu. The rotation speed and the quantity of the wave guide teeth were used as test factors and the qualifying index, replay index, and missed sowing index were used as test indicators to create the two-factor, three-level central composite design center combination test. It was possible to derive the mathematical model connecting the test factors and test indexes. The regression model underwent multi-objective optimization using the Design-Expert 13 program to determine the optimal parameters: the qualifying index was 91.24%, the replay index was 6.14%, and the missed seeding index was 2.62% when the wave guide rail had four teeth and the seed drill rotated at a speed of 40 revolutions per minute. The best parameter combinations were used for a bench verification test, and the test indicated that the qualified index was 90.25%, the replay index was 4.59%, and the missed broadcast index was 5.16%. The results demonstrated that the densely planted wheat hole seeder performs well, satisfies the requirements for winter wheat dense-planting and sowing operations, and serves as a model for the densely planted wheat hole seeders that will be optimized in the future. Full article

Increasing extreme weather events and climate change can significantly affect soil moisture regimes, particularly soil suction, leading to additional challenges associated with unsaturated soils, including the collapse phenomenon. The collapsibility of soils poses significant engineering and geotechnical risks globally, necessitating urgent attention from engineers. This work establishes a numerical model of a shallow foundation subjected to rainfall and load using COMSOL Multiphysics. A hydromechanical model (H-M) is introduced which incorporates The Richards’ module and the Extended Basic Barcelona Model (EBBM) as a constitutive model to predict settlements in shallow foundations influenced by climate change and intense rainfall. The validation of the model is conducted through experimental tests, ensuring its accuracy. Additionally, in the practical application, the hydromechanical model is applied to anticipate the effect of infiltration on settlements of shallow foundations. The simulation results show that infiltration leads to an increase in the pressure head above the water table, decreasing soil suction, which induces additional settlement due to wetting-induced collapse. The maximum settlement happened at the corners of the footing due to increased exposure to infiltration and a greater reduction in suction. The collapse potential calculated from the numerical simulation was found to be consistent with the predictions established via analytical models, validating the accuracy of the numerical approach. Full article

The construction sector is among the most polluting industries globally, accounting for approximately 37.5% of the European Union’s total waste generation in 2020. Therefore, it is imperative to develop strategies to enhance the sustainability of this sector. This paper proposes a multiscale COMSOL Multiphysics numerical model for an ex situ mineral carbonation process of hydrated lime. The carbonation process is characterized at both the micro- and macroscale levels, encompassing interactions within and between the particles. This model incorporates both reaction and diffusion phenomena, considering the effects of porosity and liquid-water saturation parameters. Generally, liquid-water saturation enhances the reaction kinetics but not CO₂ diffusion, while porosity improves CO₂ diffusion throughout the granular bed. The model has been experimentally validated, showing promising results by accurately characterizing carbonation tendencies and the influence of the CO₂ flow rate and the initial water-to-solid ratio on the carbonation process. The proposed mathematical model facilitates the study of various parameters, including particle radius, reactor geometry, and material porosity. This analysis is valuable for both current and future projects, as it aims to identify the most profitable configurations for the hydrated lime carbonation process. Full article