Model Of Mechanism Research Articles

The impact of environmental literacy on the health level of rural residents: evidence from the mountainous areas of Sichuan, China.

Environmental pollution and health issues are hot topics of discussion in modern society. However, there is a lack of research from the perspective of subjective factors such as environmental protection to study the impact of environmental literacy on health, especially in rural areas. First, through field research in the mountainous rural areas of Sichuan Province, 396 data points were collected. Based on the KAP model, we constructed an interactive impact mechanism model for the health of rural residents. We used chi-square tests and t-tests to examine the relationship between the background characteristics of rural residents and environmental protection. All environmental literacy variables were classified into strong and weak observational variables, and a multiple linear regression model was employed to explore the impact mechanism of environmental literacy (divided into environmental protection awareness, attitudes, behaviors, and perceptions of environmental pollution) on the health of rural residents. (1) Young village officials with higher education levels are more likely to have stronger environmental literacy. (2) The health status of rural residents is positively affected by their positive attitude towards environmental protection and negatively affected by their environmental protection behaviors. (3) Age, gender, marital status, and party membership have a significant impact on the health of rural residents. The results of this study can enhance the attention to the living environment and health in rural areas, and provide a scientific basis for improving the environmental literacy and health level of rural residents.

Exploring the reaction dynamics of alanine racemase using serial femtosecond crystallography

Alanine racemase (Alr) catalyzes the pyridoxal 5′-phosphate (PLP)-dependent racemization between l- and d-alanine in bacteria. Owing to the potential interest in targeting Alr for antibacterial drug development, several studies have determined the structures of Alr from different species, proposing models for the reaction mechanism. Insights into its reaction dynamics may be conducive to a better understanding of the Alr reaction mechanism. In this study, we determined the structures of the apo and reaction states of Bacillus subtilis Alr (BsAlr) at room temperature using a fixed-target based X-ray free-electron laser. The 2.3 Å resolution structures revealed the alanine substrate or intermediate in various positions at the active site. Conformational change between the N- and C-terminal domains of BsAlr expanded the entryway for substrate binding. In the reaction state of BsAlr, two main alanine binding states were observed: one alanine molecule is positioned away from PLP, whereas the other alanine molecule is covalently bonded to PLP. These structures might represent the dynamic states of the substrate for entrance into, reaction with, or exit from the active site. Our approach provides a simple and rapid method for elucidating the intermediate structure of Alr, which can be expanded to other enzymes.

Modeling the Influence Mechanism of Hotel Consumers' Purchase Intention Based on Bayesian Networks

This article aims to explore the influencing mechanism of hotel consumers' purchase intention and provide scientific decision-making basis for hotel managers. The research adopts Bayesian network as the theoretical framework and modeling tool, and through multi-channel and multi-level data collection methods, it covers many dimensions such as price, service quality, hotel facilities, geographical location, brand reputation, customer evaluation, personalized service and social media influence. By constructing and verifying the influence mechanism model of hotel consumers' purchase intention based on Bayesian network, this article finds that service quality and customer evaluation have a direct and significant impact on consumers' purchase intention; The price, hotel facilities, geographical location and other factors indirectly affect the purchase intention through the interaction with other factors. Word-of-mouth communication on social media has also proved to play an important role in consumer decision-making. The results of this study deepen our understanding of hotel consumer behavior and provide targeted marketing strategy suggestions for hotel managers.

Синтез нейрорегулятора для системы, содержащей существенно нелинейный блок

In some cases, in order to improve the quality indicators of the transients of the automatic control system, it is necessary to take into account in the model of the control object various mechanisms that drive the control object itself (DC motor, wheels, amplifier-converting devices). When calculating controllers by analytical methods, difficulties arise due to the presence of various kinds of irregularities in such systems, including "significant" ones ("backlash", "friction", etc.). In this case, the solution of this issue may be related to the use of artificial neural networks as part of the simulator. This paper shows the application of a neurofeedback scheme using a neuro-emulator and a neurocontroller. This allows you to form a training sample and train a neural network controller in the operating modes of the system that are beyond the control capabilities of a nonlinear model of the control object using a controller calculated by an analytical method. This scheme is considered as an addition to the algorithm of synthesis of neural network controllers with a deterministic way of choosing the architecture and weighting coefficients of a neural network using a scheme of imitating neural control. An example is given of improving the qualitative characteristics of the transients of a system by means of fine-tuning a neurocontroller for a nonlinear system "inverse pendulum on a movable base", taking into account the presence in the system of an inertial link containing a significant non-linearity of the "backlash" type. The purpose of the control was indicated, i.e. stabilization of the inverted pendulum in a vertical position and moving the mobile base to a set value. To achieve these goals, a neurocontrol scheme is used, which contains two neural networks: a neurocontroller (performs the function of forming a control effect on an object) and a neuroemulator (performs the function of simulating a model of the control object and is necessary to calculate the error back pass and adjust the weighting coefficients of the neurocontroller). As a result, it is possible to obtain an automatic control system capable of controlling the specified object.

Experimental Study of the Kinematics of a Double-Row Planetary Mechanism Using Two Elliptical External Gears

Introduction. Mechanisms with non-circular gears are of wide interest to researchers and inventors due to their compactness and the implementation of a wide range of transfer functions. The development of this area is stimulated by the advancements and reduction in cost of mechanical processing and additive manufacturing technologies, as well as the use of applied mathematical modeling packages for the analysis and synthesis of non-circular gears. Traditionally, noncircular gears are used to transmit rotational motion between parallel axes with a variable ratio of angular velocities. However, their use in planetary gear schemes provides implementing various types of output link motion. The analysis of the papers on the research area shows that gears with movable rotation axes have not been sufficiently studied from the point of view of kinematics and dynamics. Most research papers reveal the theory of such mechanisms without verifying the results obtained in practice. This work is aimed at the experimental verification of the kinematics of a planetary mechanism with two external engagements, which contains elliptical gears.Materials and Methods. The kinematic model of the mechanism under study is built on the basis of the velocity diagram of its links, which made it possible to obtain expressions for finding an analogue of the angular velocity and the position function of the output shaft. The experimental study of kinematics was performed on a laboratory stand containing a model of a planetary mechanism with a set of replaceable gear wheels, absolute encoders on the input and output shafts of the mechanism, a controller, and a PC for recording and processing the signal. The analysis of the obtained results was performed on a computer using statistical analysis methods.Results. As a result of kinematic analysis, position functions were constructed for three alternative planetary mechanisms, which had different geometric parameters of the gears and made it possible to implement various types of motion of the output shaft: swinging motion, discontinuous motion, and unilateral uneven rotation.Discussion and Conclusion. The analysis of the experimental results showed the adequacy of the constructed mathematical model of kinematics to real mechanisms. The confidence interval of measuring errors at a reliability level of 95% was 0.16±0.08° for the first version of the mechanism, 0.57±0.22° — for the second version, and 0.08±0.26° — for the third. The proposed planetary mechanism with elliptical gears for implementing various types of motion can be used in drives of process equipment in numerous industries: chemical and food (mixers), oil refining (pumping units for crude production), mechanical engineering (compressors, pumps, automated machines), and others. The conducted kinematic studies of the planetary mechanism and their experimental analysis are needed for further dynamic and force investigations, as well as for the design of drives based on the proposed transmission.

Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN.

The fuel system serves as the core component of marine diesel engines, and timely and effective fault diagnosis is the prerequisite for the safe navigation of ships. To address the challenge of current data-driven fault-diagnosis-based methods, which have difficulty in feature extraction and low accuracy under small samples, this paper proposes a fault diagnosis method based on digital twin (DT), Siamese Vision Transformer (SViT), and K-Nearest Neighbor (KNN). Firstly, a diesel engine DT model is constructed by integrating the mathematical, mechanism, and three-dimensional physical models of the Medium-speed diesel engines of 6L21/31 Marine, completing the mapping from physical entity to virtual entity. Fault simulation calculations are performed using the DT model to obtain different types of fault data. Then, a feature extraction network combining Siamese networks with Vision Transformer (ViT) is proposed for the simulated samples. An improved KNN classifier based on the attention mechanism is added to the network to enhance the classification efficiency of the model. Meanwhile, a Weighted-Similarity loss function is designed using similarity labels and penalty coefficients, enhancing the model's ability to discriminate between similar sample pairs. Finally, the proposed method is validated using a simulation dataset. Experimental results indicate that the proposed method achieves average accuracies of 97.22%, 98.21%, and 99.13% for training sets with 10, 20, and 30 samples per class, respectively, which can accurately classify the fault of marine fuel systems under small samples and has promising potential for applications.

Modeling and Control of a Coaxial Parallel Spherical Mechanism

This paper presents a method for modeling and controlling a coaxial parallel spherical mechanism. This mechanism consists of elements connected to allow them to move relative to each other in a parallel and coaxial manner, forming a spherical structure capable of rotating and moving in multiple directions. The operation of the mechanism is controlled by a high-order sliding controller to improve control performance, reduce chattering phenomena, and mitigate disturbances during operation. The system’s stability using the suggested control approach is demonstrated and analyzed in detail through experimental validation on a real-time controller, TMS320F28379D.

Research on the mechanism and prediction model of gas leakage and diffusion from shallow-buried natural gas pipelines

As a crucial component of urban lifeline projects, shallow-buried gas pipelines pose a significant threat to society and people's lives, as well as property in the event of a leakage accident. To investigate the diffusion mechanism of gas leakage out of pipelines, the CFD method is employed to establish a validated numerical model for gas diffusion in backfilled soil. The effects of operating pressure, burial depth, leakage direction of pipelines, and soil resistance coefficient were discussed. The calculated results indicate that the numerical model can accurately predicte the diffusion process and the resistance coefficient of methane in soil-sand, loam, and clay-based. It was found that the burial depth affects the speed of gas diffusion to the soil surface but has little impacts on the concentration distribution range. The diffusion process in the same type of backfilled soil was found very similar under varying pipeline operating pressures, though the pipeline pressure significantly impacts the diffusion range. The shape of the leaked gas cloud in the soil is affected by soil resistance, leak direction, and air buoyancy. A new diffusion model for methane leaking in soil was proposed by introducing two time-related concentration adjustment parameters to the existing natural gas diffusion model. The average error between the calculated results of the proposed diffusion model and the established numerical model was found to be within 10%.

Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area

In this study, the JiaoGang Basin in the Yangtze River Delta plains of the river network area was the research object. A basin water level simulation model was constructed based on the physical mechanism model and Mike software, and the parameters were calibrated and validated. Based on the dataset produced by the physical model, three types of ML models, Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were constructed, trained, validated, and compared with the physical model. The results showed that the physical mechanism model met the water level simulation accuracy requirements at most stations. In the training and validation periods, the RF water level simulation and GBDT water level simulation models had root mean square errors (RMSEs) of all stations less than 0.25 and the Nash–Sutcliffe coefficient (NSE) of all stations was greater than 0.7. The physical mechanism model and ML water level simulation models can simulate the water level in the JiaoGang Basin better. The RF and GBDT models considerably outperform the physical mechanism model in terms of the peak simulation errors and peak present time errors, and the fluctuations of the ML water level simulation models (RMSE and NSE) are minor compared to those of the physical mechanism model.

Dynamic analysis of spacecraft truss deployment mechanism based on conformal contact

The spacecraft deployment mechanism has phenomena such as impact, vibration and creep during the unfolding process, which has an important influence on the successful unfolding of the structure and the dynamic stability of the load after unfolding. Aiming at the shortcomings of the traditional conformal contact force calculation model, the viscous damping factor is introduced into the model to obtain an improved conformal contact force calculation model. The modified contact force model is used to calculate the normal contact force of the mechanism joints, and the improved Coulomb friction model is used to calculate the tangential contact force of the mechanism joints. Then, a multi-body system dynamic model of the truss deployment mechanism with multi-clearances is established. This model is used to numerically study the effect of clearance on spacecraft’s multi-clearances deployable truss system. The simulation results show that the clearance parameters have regular effects on the dynamic characteristics of the truss unfolding mechanism. At the same time, there is a very obvious coupling effect between different clearances.

Family Tourist Emotional Experience: Emotion-Generative and Regulatory Process

Family tourism has received much scholarly attention, but few studies have examined family tourists’ experience by viewing them as active agents that generate and regulate emotions. This study aims to disclose family tourists’ dynamic emotional experiences through probing into their emotion generative and regulatory process. An extended Chinese family taking a 6-day overseas trip was recruited and each member was interviewed multiple times throughout the pre-, during- (assisted by participatory observation and psychophysiological data), and post-trip phrases. The family tourists reported 64 emotional episodes, 32 discrete emotions, and 13 emotion regulation strategies. Their emotion-generation and regulation were revealed as an interdependent and cyclical cognitive appraisal process that occurs in complex social settings. The proposed psychological mechanism model contributes to tourism and psychological literature by highlighting the dynamic and social nature of family tourists’ emotions, and the role of memory in their emotion dynamics. Future research directions on family tourists’ emotion regulation are suggested.

Shear capacity model of RC beams based on MCMC approach and reliability analysis

The research question revolves around accurately predicting the shear capacity of RC beams and enhancing the reliability of such prediction models. This study aims to refine the accuracy of the RC beam shear capacity model. To augment the safety margin, the enhanced model underwent further calibration through a reliability analysis. The shear strength models, rooted in Bayesian theory’s Markov Chain Monte Carlo (MCMC) method and the Modified Compressive Field Theory (MCFT), were introduced. A research database was curated from test data of 782 RC beams sets under shear stress. From this database, six mechanism models were juxtaposed with the MCMC models for comparison. To meet the desired reliability indexes ([Formula: see text] = 3.2, 3.7, 4.0) in these models, resistance factors ([Formula: see text]) were incorporated. Both the Monte Carlo and First Order Second Moment (FOSM) methods were employed for the reliability analysis, considering inherent model errors and the probability distribution of primary variables. The effect of different elements on the target reliability index was assessed using a tornado diagram. Findings showed that the refined shear model displayed minimal variance when benchmarked against models from existing literature and national codes, yielding a Vtest/ Vcal ratio close to 1.0. The shear components aligned with established mechanical mechanism. Depending on various operational conditions and load combinations, resistance factors fluctuated from 0.377 to 0.516 in the RC beams without stirrup, while resistance factors fluctuated from 0.479 to 0.621 in the RC beams with stirrup. Key determinants, such as the inherent uncertainty in computing model, the live loads, and the cylinder compression strength, significantly influenced the reliability index.

Hybrid prediction model for strip width based on improved mechanism and data-driven model

Hybrid prediction model for strip width based on improved mechanism and data-driven model

An Analytical Framework for Global Dynamic Modeling of Flexible Variable Topology Mechanisms

The coupling of topology transition with flexible deformation and rigid motion presents significant challenges in the dynamic modeling of flexible variable topology mechanisms. Existing dynamics models are mostly special-purpose models for their particular cases and thus struggle to completely depict the general topology transition characteristics. To address this gap, this paper proposes an analytical framework for the global dynamic modeling of flexible variable topology mechanisms, focusing on general cases. Initially, the flexible variable topology mechanisms are rigorously defined by the ordered triples and the general topology transition approaches are presented. A novel concept, the “basic flexible kinematic chain set”, is suggested, which can easily transform into the topology of each submechanism by slightly extending. Based on this concept, basic and conditional constraints are established. The continuous dynamic modeling method for each topology is developed using Jourdain’s principle and the Lagrange multiplier method. Additionally, three classes of constraints related to topology transition are identified, and their equations are formulated, elucidating the topology transition nature. Compatibility equations are proposed to define the new coordinate system for describing the deformation of flexible components after the topology transition. An impact dynamic equation is established to describe abrupt velocity change. Integrating compatibility and impact equations, a discontinuous dynamic modeling method for topology transition is developed. Finally, a flexible variable topology mechanism example is studied, and simulations and experiments are conducted to validate the proposed framework. This analytical framework is general-purpose and efficient, guiding the global dynamic modeling of various flexible variable topology mechanisms and the development of sophisticated control techniques.

On the results of tribological studies of railway rails

Metal resistance to the formation of contact fatigue defects and wear development has a great influence on the consumer properties of rails. The most significant factors limiting the service life of rails in curved sections of the railway track are wear of rails of the outer threads and development of contact fatigue defects in the inner threads of the track. In this regard, methods of reliable laboratory assessment of the rail metal resistance become important in the development of new products. The paper describes the change in the nature of damage to rails of various hardness categories by contact fatigue defects, and evaluates their wear resistance. The study of defects and forecasting of the rail resource require an integrated approach. The paper provides a brief description of modeling the conditions of formation and accumulation of contact fatigue defects. The parameters under consideration have an effect on the wear resistance of rail metal of various chemical compositions. During the testing, the rails microstructure and the nature of crack growth change. The authors made a comparative analysis of the data obtained characterizing the wear resistance of rail steels of various hardness categories. The basis of the methodology for assessing the wear resistance of railway rails is the physical modeling of adhesion-deformation mechanism of friction of the samples on a roller friction machine (tribometer). During laboratory tests of the studied categories of rails, the friction machine automatically outputs and records a number of computational parameters shown in the work. The conducted research is promising from a practical point of view. The results obtained can be used to develop a theory to increase the service life of differentially hardened rails produced by JSC EVRAZ United West Siberian Metallurgical Plant.

Iv3-MGRUA: a novel human action recognition features extraction using Inception v3 and video behaviour prediction using modified gated recurrent units with attention mechanism model

Iv3-MGRUA: a novel human action recognition features extraction using Inception v3 and video behaviour prediction using modified gated recurrent units with attention mechanism model

Deterioration Mechanism and Statistical Damage Constitutive Model of Red-Bed Soft Rock with Different Moisture Content

Deterioration Mechanism and Statistical Damage Constitutive Model of Red-Bed Soft Rock with Different Moisture Content

Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model

The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can transform research into customized and personalized healthcare. The widespread adoption of DT technology relies on ample patient data to ensure precise monitoring and decision-making, leveraging Machine Learning (ML) and Deep Learning (DL) algorithms. Given the non-stationarity of EEG recordings, characterized by substantial frequency variations over time, there is a notable preference for advanced time-frequency methods in seizure prediction. This research proposes a DT-based seizure prediction system by applying an advanced time-frequency analysis approach known as Time-Reassigned MultiSynchroSqueezing Transform (TMSST) to EEG data to extract patient-specific impulse features and subsequently, a Deep Learning strategy, CNN-BiLSTM-Attention mechanism model is utilized in learning and classifying features for seizure prediction. The proposed architecture is named as 'Digital Twin-Net'. By estimating the group delay in the time direction, TMSST produces the frequency components that are responsible for the EEG signal's temporal behavior and those time-frequency signatures are learned by the developed CNN-BiLSTM-Attention mechanism model. Thus the combination acts as a digital twin of a patient for the prediction of epileptic seizures. The experimental results showed that the suggested approach achieved an accuracy of 99.70% when tested on 22 patients from the publicly accessible CHB-MIT dataset. The proposed method surpasses previous solutions in terms of overall performance. Consequently, the suggested method can be regarded as an efficient approach to EEG seizure prediction.

Laser melting, evaporation, and fragmentation of nanoparticles: Experiments, modeling, and applications

This review examines the processes of laser heating, melting, evaporation, fragmentation, and breakdown of metal nanoparticles, as well as the dependences and values of the threshold laser parameters that initiate these processes. Literature results are analyzed from experimental studies of these processes with gold, silver, and other nanoparticles, including laser surface melting and evaporation of nanoparticles and Coulomb fragmentation of nanoparticles by ultrashort laser pulses. A theoretical model and description of the thermal mechanisms of mentioned processes with metal (solid) nanoparticles in a liquid (solid) medium, initiated by the action of laser pulses with the threshold fluences, are presented. Comparison of the obtained results with experimental data confirms the accuracy of the model and makes it possible to use them to evaluate the parameters of laser thermal processing of nanoparticles. Applications of the processes include the laser melting, reshaping, and fragmentation of nanoparticles, the formation of nanostructures and nanonetworks, the laser processing of nanoparticles located on substrates, and their cladding on surfaces in various laser nanotechnologies. The use of laser ignition, combustion, and incandescence of nanoparticles is discussed, as is the use of nanoparticle-triggered laser breakdown for spectroscopy. These laser processes are used in photothermal nanotechnologies, nanoenergy, laser processing of nanoparticles, nonlinear optical devices, high-temperature material science, etc. In general, this review presents a modern picture of the state of laser technology and high-temperature processes with nanoparticles and their applications, being focused on the latest publications with an emphasis on recent results from 2021–2024.

Benefit-Sharing Mechanism in Cross-Regional Agricultural Product Supply Chain: A Grounded Theory Approach

The accelerated development of economic globalization has given rise to the formation of supply chain networks for cross-regional agricultural products. However, the unequal rights and status of the partners and an imperfect benefit linkage mechanism have made it difficult to reasonably distribute and share benefits. Understanding how to establish a benefit-sharing mechanism among partners has been a popular topic in the industry, but thematic research and discussions are lacking in the academic community. Therefore, in this study, we utilized a grounded theory approach and selected five supply chain master enterprises with different supply chain systems as samples. We constructed a theoretical model of a benefit-sharing mechanism and systematically revealed the key elements and theoretical logic of benefit sharing. The results of this study showed that (1) the five elements of benefit creation, benefit integration, benefit distribution, benefit constraint, and benefit coordination have important impacts on benefit sharing in the supply chain; (2) the dimensions within these five elements are independent of each other, and these elements can be combined to demonstrate a common impact on benefit sharing. The dynamic interaction among the five elements constituted a self-regulating and self-optimizing cyclic system; (3) benefit integration and benefit distribution have a dynamic cyclic facilitating effect on benefit sharing. This study has refined the key elements of the benefit-sharing mechanism, and the results provide theoretical references for benefit sharing and in-depth collaboration among supply chain members.