Search results

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Multilayer composite liner structures are the primary structural form of hydraulic tunnels. However, the bearing mechanism of multilayer composite liners has not been investigated thoroughly. Many existing design schemes do not properly achieve a balance between structural safety, anti-seepage capacity, and cost effectiveness. Thus, a new composite liner structure type and its theoretical model was proposed.

A novel hydraulic tunnel composite liner structure with a polyurea spray coating interlayer was proposed in this study. A theoretical model based on the state-space method was developed and verified using FEM models and existing theoretical models. Parametric analysis was conducted based on the theoretical model to investigate the influence of various variables, including interfacial shear stiffness, inner liner thickness, and outer liner elastic modulus.

It was concluded that the proposed theoretical model can be used successfully to calculate multilayer composite liner structures with high calculation efficiency. The overall deformation stiffness of the composite liner system increased with the interfacial shear stiffness. The sprayed coating interlayer significantly affects the residual force distribution between the outer and inner liners, which can also be affected by the adjustment of the thickness of the outer and inner liners. Thus, attention should be paid to these factors in the rational design of the proposed composite liner system.

With the development of China’s water conservancy projects, complex geological conditions, high surrounding rock stress, high internal and external water pressures, and other unique application scenarios have gradually increased. This places higher requirements on the bearing performance and impermeability of hydraulic tunnel lining structures. On the other hand, conventional hydraulic tunnel lining structures can hardly achieve a satisfactory balance between economy, structural safety, and impermeability. Thus, the proposed structure has the potential to be used in a wide range of applications.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

A physically-based elasto-viscoplastic constitutive model is proposed to examine the size effects of the precipitate and blocks on the creep for martensitic heat-resistant steels with both the dislocation creep and diffusional creep mechanisms considered.

The model relies upon the initial dislocation density and the sizes of M₂₃C₆ carbide and MX carbonitride, through the use of internal variable based governing equations to address the dislocation density evolution and precipitate coarsening processes. Most parameters of the model can be obtained from existing literature, while a small subset requires calibration. Based on the least-squares fitting method, the calibration is successfully done by comparing the modeling and experimental results of the steady state creep rate at 600° C across a wide range of applied stresses.

The model predictions of the creep responses at various stresses and temperatures, the carbide coarsening and the dislocation density evolution are consistent with the experimental data in literature. The modeling results indicate that considerable effect of the sizes of precipitates occurs only during the creep at relatively high stress levels where dislocation creep dominates, while the martensite block size effect happens during creep at relatively low stress levels where diffusion creep dominates. The size effect of M₂₃C₆ carbide on the steady creep rate is more significant than that of MX precipitate.

The present study also reveals that the two creep mechanisms compete such that at a given temperature the contribution of the diffusion creep mechanism decreases with increasing stress, while the contribution of the dislocation creep mechanism increases.

This paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme.

To begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach.

Based on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed.

The output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

人工智能（AI）设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

Based on social cognitive theory, this study aims to explore the influence of supervisor bottom-line mentality (SBLM) on employee knowledge behavior (knowledge territorial behavior and knowledge sabotage behavior). The study first investigates the role of an ethical decision-making mechanism (moral disengagement) in mediating this relationship. In addition, it considers the possible boundary conditions to supplement research on the influence of SBLM in the knowledge management field.

The authors collected 256 data points from employees across three stages using convenience sampling. The authors then tested the proposed hypothesis using hierarchical regression and bootstrap methods.

The results demonstrated that SBLM promotes employees’ moral disengagement, leading to more knowledge territorial behavior and knowledge sabotage behavior. Furthermore, high power distance orientation among employees exacerbates the ill effects of SBLM according to the first stage of a moderated mediation model. Employees with such an orientation are more likely to respond to a SBLM by exhibiting a higher level of moral disengagement, thus increasing their knowledge territorial behavior and knowledge sabotage behavior.

Research on the influence of SBLM in the knowledge management field is limited. This study not only clarifies the relationships between SBLM and two types of knowledge behavior (knowledge territorial behavior and knowledge sabotage behavior) but also enriches the research on the antecedents of these two types of knowledge behavior.