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The electromechanical planetary transmission system has the advantages of high transmission power and fast running speed, which is one of the important development directions in the future. However, during the operation of the electromechanical planetary transmission system, friction and other factors will lead to an increase in gear temperature and thermal deformation, which will affect the transmission performance of the system, and it is of great significance to study the influence of the temperature effect on the nonlinear dynamics of the electromechanical planetary system.

The effects of temperature change, motor speed, time-varying meshing stiffness, meshing damping ratio and error amplitude on the nonlinear dynamic characteristics of electromechanical planetary systems are studied by using bifurcation diagrams, time-domain diagrams, phase diagrams, Poincaré cross-sectional diagrams, spectra, etc.

The results show that when the temperature rise is less than 70 °C, the system will exhibit chaotic motion. When the motor speed is greater than 900r/min, the system enters a chaotic state. The changes in time-varying meshing stiffness, meshing damping ratio, and error amplitude will also make the system exhibit abundant bifurcation characteristics.

Based on the principle of thermal deformation, taking into account the temperature effect and nonlinear parameters, including time-varying meshing stiffness and tooth side clearance as well as comprehensive errors, a dynamic model of the electromechanical planetary gear system was established.

The purpose of this study is to propose a new magnetic gearing device and the proposed transmission model can be applied in the field of wind power and wave energy generation gearboxes.

A novel radial differential field-modulated two-stage magnetic gear is introduced, using a unique radial differential linkage to integrate two single-stage magnetic gears. This design incorporates a magnetic isolation ring to enhance transmission efficiency by minimizing magnetic interference and preventing power circulation. The two-stage modulating ring rotor operates synchronously via a connecting bridge, ensuring system stability and efficiency. This configuration not only boosts the gear ratio but also maintains a compact structure, improving power density and efficiency. Leveraging the magnetic field modulation and differential transmission, a finite element model of this gear is developed and its electromagnetic performance is analyzed.

The torque density of the new radial differential field-modulated two-stage magnetic gear has increased by 44.97% compared to the traditional tandem two-stage magnetic gear. It achieves a high transmission ratio of 64 and maintains comparable power density, indicating strong torque transfer capabilities suitable for low-speed, high-torque applications. During steady-state operation, the torque pulsation difference between the two stages is minimal, ensuring stable working torque.

This research not only propels the forefront of magnetic gear technology through heightened efficiency and streamlined design but also bears profound societal significance in fostering sustainable energy paradigms. By facilitating superior energy conversion efficiencies in wind turbines and wave energy converters, it plays a pivotal role in mitigating carbon emissions and accelerating the global pivot towards cleaner, renewable energy landscapes.

A magnetic gear transmission device is proposed, which can achieve high power density and large transmission ratio at the same time, and this study provides a useful reference for the design optimization of new high-performance multistage magnetic gears.

This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

This study aims to investigate the process of adjustment for international construction professionals when facing new technical contexts. It introduces a framework called construction technical intelligence (CTI) and seeks to provide valuable insights and practical guidance for professionals involved in international construction projects.

In this study, a grounded theory approach was employed, which included conducting in-depth interviews with 28 professionals engaged in international construction projects. The qualitative data gathered from these interviews underwent systematic analysis to identify important categories and develop theoretical perspectives.

The findings demonstrate the framework of CTI, which encompasses four essential dimensions that play a significant role in facilitating the successful adjustment of international construction professionals to new technical contexts. These dimensions underscore the multidimensional nature of CTI and offer valuable insights into the necessary capabilities for professionals to thrive in dynamic and globalized construction environments.

By proposing this comprehensive framework, the study contributes to the knowledge and understanding of the technical adjustment process for international construction professionals. It also establishes a foundation for future quantitative research to validate and refine the proposed model, enabling a deeper comprehension of the dynamics involved in professionals' technical adjustment.

This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.

This paper examines residential satisfaction in welfare housing facilities exclusively built for low-income single-mother households in South Korea. The main objective is to identify predictors from among various domains such as sociodemographic and psychological characteristics of residents, as well as objective and subjective characteristics of their residential environment.

A mixed-method field study evaluates data obtained via structured questionnaires administered to 233 low-income single mothers in 23 residential welfare facilities across South Korea, supplemented by facility observations and interviews with housing staff from 16 facilities.

Residential satisfaction was found to be a multidimensional construct predicted by a number of variables, with psychological characteristics being dominant. Significant predictors were also identified among physical and sociospatial environmental characteristics, such as plan type, management and service and personal space.

The results of this study highlight the significance of the human aspects of management and the sociospatial quality of housing unit spaces to provide a sense of protection and privacy for the residents, which emphasizes the importance of management, design and policy improvements to increase satisfaction in welfare facility residents.

Currently, data for special housing conditions of female-headed households in unstable housing affordability are relatively few and outdated. A critical examination of the physical and sociospatial quality of short-term subsidized public housing for low-income single-mother households in South Korea expands the current knowledge in this field to various sociodemographic and cultural contexts.