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The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

The purpose of this paper is to investigate how trade liberalization influences rural poverty reduction in China.

The authors make use of China Family Panel Studies survey data, take annual income of farmers of RMB2,300 and RMB3,450 as the poverty lines (poverty line 1 and poverty line 2, respectively). Residents below poverty line 1 and poverty line 2 are 2,580 and 2,661, respectively. Probit model is used to estimate the impact of trade liberalization on the poverty probability. Income-deciding equation is used to estimate the impact of trade liberalization on the income level of poor residents in rural areas. Income-deciding equation is also used to examine the transmission mechanism of trade liberalization affecting rural poverty.

This study finds that trade liberalization can reduce the poverty probability of rural residents and promote the income growth of poor residents in rural areas. Trade liberalization increases the income of poor residents and reduces poverty through transmission mechanisms such as promoting economic growth and financial expenditure.

To the authors’ knowledge, this is the first empirical study to quantitatively model the impact of trade liberalization on rural poverty reduction in China using residents’ survey data.

The purpose of this paper is to present a novel algorithm to handle space environment induced errors in the space‐robot software.

The radiations in outer space may induce transient errors in micro‐processors, this phenomena will make software behavior unpredictable, and the existing software fault tolerance methods have been restricted in non‐multi‐threaded operation systems, non‐component‐based frameworks, non‐cacheable micro‐processors, non‐distributed environments, etc. A software model for space‐robot software, based on adaptive redundancy, is developed and a corresponding run‐time error detection algorithm is presented in this paper. Software was monitored and run‐time transient error would be detected and processed.

Experiments indicate that this method introduces about 30‐35 percent time overhead and about 200‐230 percent memory overhead. It also increases the fault detection rate to 84‐92.5 percent. Moreover, the model and algorithm is effective in a realistic space robot environment.

A redundancy model is developed and an error detection algorithm is introduced in this paper. Experiments demonstrate it can provide space‐robot software with good protection against the radiation induced transient errors.

This paper aims to provide a series of new methods for projecting a three-dimensional (3D) object onto a free-form surface. The projection algorithms presented can be divided into three types, namely, orthogonal, perspective and parallel projection.

For parametric surfaces, the computing strategy of the algorithm is to obtain an approximate solution by using a geometric algorithm, then improve the accuracy of the approximate solution using the Newton–Raphson iteration. For perspective projection and parallel projection on an implicit surface, the strategy replaces Newton–Raphson iteration by multi-segment tracing. The implementation takes two mesh objects as an example of calculating an image projected onto parametric and implicit surfaces. Moreover, a comparison is made for orthogonal projections with Hu’s and Liu’s methods.

The results show that the new method can solve the 3D objects projection problem in an effective manner. For orthogonal projection, the time taken by the new method is substantially less than that required for Hu’s method. The new method is also more accurate and faster than Liu’s approach, particularly when the 3D object has a large number of points.

The algorithms presented in this paper can be applied in many industrial applications such as computer aided design, computer graphics and computer vision.

Owing to the numerous part models and massive datasets used in automobile assembly design, virtual assembly software cannot simulate a whole vehicle smoothly in real time. For this reason, implementing a new virtual assembly environment for massive complex datasets would be a significant achievement. The paper aims to focus on this problem.

A new system named “Grid‐enabled collaborative virtual assembly environment” (GCVAE) is proposed in the paper, and it comprises three parts: a private grid‐based support platform running on an inner network of enterprise; a service‐based parallel rendering framework with a sort‐last structure; and a multi‐user collaborative virtual assembly environment. These components would aggregate the idle resources in an enterprise to support assembly simulation with a large complex scene of whole vehicle.

The system prototype proposed in the paper has been implemented. The following simulations show that it can support a complex scene in a real‐time mode by using existing hardware and software, and can promote the efficient usage of enterprise resources.

Using the GCVAE, it is possible to aggregate the idle resources in an enterprise to run assembly simulations of a whole automobile with massively complex scenes, thus observably reducing fault occurrence rates in future manufacturing.

The paper introduces a new grid‐enabled methodology into research on collaborative virtual assembly system which can make the best use of idle resources in the enterprise to support assembly simulations with massively complex product models. A video‐stream‐based method was used to implement the system; this enables designers to participate ubiquitously in the simulation to evaluate the assembly of the whole automobile without hardware limitations.

Automobile development needs more and more collaborative work involving geographical dispersed designers, that brings difficulty for model verification, conception review and assembly process evaluation, so a collaborative virtual environment for automobile based on network is required. In this kind of environment, designers can do interactive assembly operations collaboratively, such as grasp, move, release, collision detection (CD), assembly evaluation report generation, etc. Furthermore, automobile structure becomes more complicated, how to process this large real‐time data effectively in real‐time interactive virtual environment is a great challenge. The purpose of this paper is focus on this.

A distributed parallel virtual assembly environment (DPVAE) is developed. In this environment, the mechanism of event synchronization based on high‐level architecture/run‐time infrastructure) is applied to realize multi‐user collaboratively interactive operation. To meet the large data set real‐time processing demand, a creative parallel processing approach supported by a single supercomputer or a parallel processing environment composed of common personal computer in a high‐speed local area network is developed. The technologies such as real‐time CD, multiple interactive operation modals are applied in DPVAE and several auxiliary tools are provide to help achieving whole scheme review, component model verification and assembly evaluation.

This paper finds that DPVAE system is an available and efficient tool to support automobile collaborative assembly design.

Designers can discuss and verify the assembly scheme to realize the previous design scenario in DPVAE, so it is useful for reducing costs, improving quality and shortening the time to market, especially for new type automobile development.

A combination of distributed technology and parallel computing technology is applied in product virtual assembly, solving the problems including collaborative work of multi‐user and large data real‐time processing successfully, that provides a useful tool for automobile development.

Supplier selection for defence procurement is a crucial function of a Ministry of Defence. The Ministry spends huge amounts of money each year to procure a vast array of equipment, goods and services. The ongoing financial crisis demands less subjective and more cost-saving methods for selecting a supplier. The approach advocated in this article integrates Analytic Hierarchy Process (AHP) with Goal Programming (GP) in order to combine conflicting criteria to select the best suppliers and allocate optimum order quantities among them. This paper presents a model close to real-world situations. Findings demonstrate that cost savings is a feasible result along with a viable combination of conflicting criteria in the suppliers' selection area.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.