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An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

Radial vibration of horizontal centrifugal pump has a close association with radial exciting forces. The purpose of this paper is to analyze the unsteady radial force in multistage centrifugal pump with double volute in detail and investigate the relevance of static pressure, radial force and radial vibration.

The unsteady numerical simulation with realizable k-ε turbulence model was carried out for a multistage centrifugal pump with double volute using computational fluid dynamics codes Fluent. The performance tests were conducted by use of a closed loop system and performance curves from numerical simulation agree with that of experiment. Vibration tests were carried out by vibration probes instrumented on the bearing cover of pump near no-driven end. Fast Fourier transform was used to obtain the frequency components of radial forces on the impellers from numerical simulation, which are compared with ones of radial vibration from experiment in Y and Z direction. And the static pressure distributions in the impeller were analyzed under different flow rates.

The symmetrical double volute can effectively balance radial forces. The maximum radial force and vibration velocity appear at 0.6 Q among the three flow rates 0.6 Q, Q and 1.2 Q. The frequencies corresponding to relatively large amplitude of vibration velocities and radial forces on the impellers in Y direction are blade passing frequency of the impellers. Blade passing frequency of first-stage impeller and shaft frequency are predominating in Z direction. It indicates that the radial vibration of centrifugal pump is closely related to the unsteady radial force.

The unsteady radial forces of the impeller in multistage centrifugal pump with double volute were comprehensively analyzed. The radial forces should be considered to balance during the design of multistage centrifugal pump.

This study aims to establish a centralized decision-making game model and manufacturer-led Stackelberg game model based on factors of risk aversion of supply chain members and product greenness. The research aims to study whether the introduction of the “cost + risk sharing” contract affects coordination of this type of green supply by calculating the optimal decision of each mode.

This research designs a supply chain model under centralized and decentralized decision-making. This model uses the Stackelberg game to calculate the optimal decision under decentralized decision-making to evaluate the effect of a green supply chain and then analyze the “cost + risk sharing” contract and the degree of coordination of the supply chain. A sensitivity analysis is conducted on the centralized mode for the impact of variables on the supply chain.

This research finds a double marginalization effect in decentralized decision-making, and the risk aversion coefficient plays a decisive role in the utility of supply chain members. The specific range of risk- and cost-sharing factors allows supply chain members to achieve Pareto improvements and provides decision-making based on the corresponding management strategies according to each other’s risk preference degree.

The influence of each variable on the green supply chain in the centralized mode is studied by MATLAB numerical simulation. It provides reference for green supply chain members to formulate corresponding management strategies according to each other's risk preference degree.

This research innovatively considers manufacturers and retailers to explore the market demand for product greenness. It introduces a novel “cost + risk sharing” contract to coordinate the green supply chain.

The purpose of this study aims to modify a self-mixing laser mouse as an extremely cost-effective displacement sensor to measure the mechanical oscillation of a commercial shaker and a nano-positioning stage.

This kind of laser mouse, mostly consisting of a pair of vertical cavity surface emitting lasers, two photodiodes and an integrated signal processing unit, is capable of directly giving the x-axis and y-axis components of the measured vibrating displacement. Based on the laser self-mixing interference, the velocity of the object is coded into the Doppler frequency shift of the feedback light, which allows accurate determination of the vibration of the object.

A commercial shaker has been used to provide standard harmonic oscillation to test the displacement sensor. Within a vibrating frequency range of 110 Hz, the experimental results show that the micrometer scale resolution has been achieved at the velocity of up to 2 m/s, which is much improved compared with the image-based optical mouse. Furthermore, the measurements of the two dimensional displacement of a nano-positioning stage are performed as well. The minimum measurable velocity limit for this sensor has been discussed in detail, and the relative measurement error can be greatly reduced by appropriate selection of the modulation frequency of the triangular injection current.

These results demonstrate the feasibility of this device for the industrial vibration sensing applications.

The financial industry is becoming more intelligent and digital, and the adoption of new technologies is promoting financial innovation while making financial security subject to disruption. Internet finance, as a product of the rapid development of information technology and the financial industry, has ushered in major changes in the development of the financial industry. The application of new technologies in the financial sector will bring about the development of intelligent investment consulting businesses for financial institutions The development of such a business reduces the threshold at which a customer can obtain financial services and improves the convenience and accessibility of financial services. Under the complex domestic and international economic situation, enterprises need to pay attention to financial risks and reasonably control financial risks. Applying blockchain technology to supply chain financial risk management has a natural match for solving the traditional difficulties in supply chain risk. This chapter mainly describes the types, assessment methods and existing problems of financial risks, as well as the prevention and control of network security risk management and Internet financial risk management arising therefrom, and also involves stress testing and scenario planning, blockchain-based financial risk management and risk culture, among which financial risk assessment and Internet financial risk management are mainly the content. With the help of information technology, we can effectively identify and prevent all kinds of risks and effectively promote the sustainable and healthy development of the financial industry.

Adaptive slicing is a key step in 3D printing as it is closely related to the building time and the surface quality. This study aims to develop an adaptive layering algorithm that can coordinate the optimization of printing quality and efficiency to meet different printing needs.

A multiobjective optimization model is established for printing quality, printing time and layer height based on the variation of surface features, profile slope and curvature of the model. The optimal solution is found by an improved method combining Newton's method and gradient method and adapts to different printing requirements by adjusting the parameter thresholds.

Several benchmarks are applied to verify this new method. The proposed method has also been compared with the uniform layering method, it reduces the volume error by 46.4% and shortens the printing time by 28.1% and is compared with five existing adaptive layering methods to demonstrate its superior performance.

Compared with other methods with only one layered result, this method is a demand-oriented algorithm that can obtain different results according to different needs and it can reach a trade-off between the building time and the surface quality.

This study aims to understand how product attributes (object stimuli), social influences (social-psychological stimuli) and internal factors (internal stimuli) contribute to Chinese Generation Z’s purchase intentions (responses) for new Chinese style apparel (NCSA) through NCSA attitudes (cognitive state) and cultural pride (affective state) based on the stimulus-organism-response (S-O-R) model.

Data were collected from 989 respondents aged between 18 and 29 years through self-administrated questionnaires via a professional survey panel, Credamo, in China. Data were analyzed using structural equation modeling.

Results showed that among the seven stimuli, NCSA’s design, online social networking communities, cultural identity and personal norms significantly influenced Generation Z’s purchase intentions through both NCSA attitudes and cultural pride. Further, NCSA’s cultural connotations and celebrity influences elicited purchase intentions merely through the affective state – cultural pride. In contrast, NCSA’s functionality did not have a significant influence on NCSA attitudes and adversely affected cultural pride.

This study fills the research gap and extends the application of the S-O-R model within the NCSA context. The findings of this study shed light on the practical implications for marketers, brands and policymakers with regard to a better understanding of Chinese Generation Z’s NCSA consumption.

The purpose of this paper is to compare the effects of two different configurations of plasma streamwise vortex generators (PSVG), including comb-type and mesh-type in controlling flow. This is demonstrated on the NACA 0012 airfoil.

The investigations have been done experimentally at the various electric and aerodynamic conditions. The surface oil flow visualization method has been used to the better understanding of the flow physics and the interaction of the oncoming flow passing over the airfoil and the vortex generated by comb-type PSVG.

This paper demonstrates the potential capabilities of the mesh-type and comb-type PSVGs in controlling flow in unsteady operation. It was found that the vortex generated by the mesh-type PSVG in unsteady operation was an order of magnitude stronger than comb-type PSVG. The flow visualisation technic proved that only a part of the plasma actuator is effective in the condition that the actuator is installed only on a portion of the upper surface of the airfoil.

This paper experimentally confirms the capabilities of the mesh-type PSVG unsteady operation in compare with comb-type PSVG in controlling flow, whereby recommends using mesh-type PSVG in the leading edge in front of comb-type PSVG on the entire wingspan to prevent the stall.