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Complex mechanical 3D computer-aided design (CAD) model embodies rich implicit design knowledge. Through discovering the key function parts and key function module in 3D CAD assembly model in advance, it can promote the designers’ understanding and reuse efficiency of 3D assembly model in design reuse.

An approach for discovering key function module in complex mechanical 3D CAD assembly model is proposed. First, assembly network for 3D CAD assembly model is constructed, where the topology structure characteristics of 3D assembly model are analyzed based on complex network centrality. The degree centrality, closeness centrality, betweenness centrality and mutual information of node are used to evaluate the importance of the parts in 3D assembly model. Then, a multi-attribute decision model for part-node importance is established, and the comprehensive evaluation for key function parts in 3D assembly model is accomplished by combining Analytic Hierarchy Process and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Subsequently, a community discovery of function module in assembly model-based Clauset–Newman–Moore (CNM)-Centrality is given in details. Finally, 3D CAD assembly model of worm gear reducer is taken as an example to verify the effectiveness and feasibility of proposed method.

The key function part in CAD assembly model is evaluated comprehensively considering assembly topology more objectively. In addition, the key function module containing key function part is discovered from CAD assembly model by using CNM-Centrality-based community discovery.

The approach can be used for discovering important design knowledge from complex CAD assembly model when reusing the assembly model. It can help designers capture and understand the design thinking and intent, improve the reuse efficiency and quality.

The paper first proposes an approach for discovering key function module in complex mechanical 3D CAD assembly model taking advantage of complex network theory, where the key function part is evaluated using node centrality and TOPSIS, and the key function module is identified based on community discovery.

The purpose of this paper is to explore the factors that influence Chinese apple farmers’ willingness to adopt organic farming, paying a special attention to the role of information acquisition.

Given that the selection bias may occur when farmers themselves decide whether or not to acquire the information to understand the essence of organic farming, this study employs a recursive bivariate probit model to address the issue of the selection bias.

The empirical results indicate that farmers’ decision to acquire information is positively affected by farmers’ environmental awareness, access to credit and access to information. In particular, information acquisition appears to increase the likelihood of farmers’ willingness to adopt organic farming by 35.9 percentage points on average.

The findings suggest that measures increasing farmers’ information exposure can be promising policy interventions to induce adoption of organic farming.

While considerable evidence indicates that organic farming provides more benefits than conventional production practice, little is known about farmers’ willingness to adopt in China. This paper provides a first attempt by examining the role of information acquisition in determining Chinese apple farmers’ willingness to adopt.

The purpose of this paper is to examine the effect of adopting certified food production on chemical fertilizer and pesticide use in China.

The authors estimate fixed effect models to track the changes in agrochemical consumption at household level over time and evaluate the effect of certified food production, using an unbalanced panel data set covering 4,830 households in six provinces over the period 2005–2013.

On average, the authors do not find significant effects of certified food production on either chemical fertilizer or pesticide consumption among Chinese farmers. The effects are heterogeneous across villages, but the heterogeneous effects show no clear pattern that is consistent with different types of certification. The findings are robust to the use of alternative panel structure and certification indicators. The lack of knowledge about certification among farmers, the price premium and differences in regulation enforcement across regions may explain why the authors do not find negative effects on agrochemical use.

This study suggests that careful inspections and strong enforcement of certified food production is needed to ensure that the environmental goals of certified food production can be achieved and the reputation of certification in China can be improved. The inspection of certification producers and the enforcement of current regulations should be stricter for the further healthy development of certified food production in China.

This study is the first attempt to systematically evaluate the impact of food certification on the use of agrochemicals in Chinese agriculture.

The integrated reports published by companies vary significantly in quality in spite of them claiming to be compliant with the integrated reporting (IR) Framework issued by the International Integrated Reporting Council (IIRC). The purpose of this paper is to develop and apply a normative benchmark against which compliance with the IR Framework, and the extent to which integrated reports make visible how organisations create value, can be evaluated.

The three pillars of the IR Framework – Capitals, Content Elements and the Guiding Principles – are operationalised by the way of a set of disclosure items that capture the extent to which they manifest within integrated reports. The created disclosure index is applied to analyse reports of five companies that are expected to be superior integrated reporters.

The normative benchmark that was created to operationalise the IR Framework identifies a vast amount of potentially communicable information and various degrees to which information may be disclosed. The integrated reports analysed differ significantly in the extent to which value-creation stories are made visible, despite some of the companies promoting to have actively engaged with IR as participants of the IIRC Pilot Program Business Network. All selected companies performed poorly in comparison to the normative benchmark.

This paper is the first to provide a comprehensive normative benchmark for analysing and evaluating compliance with the IR Framework and the extent to which integrated reports make visible how organisations create value.

Current measurement methods for fabric comfort attributes generally suffer from either complicated testing processes and intricate measuring equipment or partial evaluation objectives and thus are difficult for effectively evaluating multidimensional human perceptions towards the comprehensive comfort of fabrics. The purpose of this paper is to develop a facile test device, namely fabric comfort tester, to achieve a comprehensive evaluation of human sensations in terms of sensorial, thermal and acoustic comfort in clothing.

The prototype of the designed device was introduced, which enables a simultaneous test for multiple physical and mechanical properties of fabrics based on a force sensor and a set of infrared sensors via constructing multi-deformation states of the measured fabrics. Eleven measurement indices extracted from the measurement curves are defined and interpreted based on correlation analysis. A series of regression models are developed by relating the measurement indices with subjective evaluation results and validated by a set of independent samples.

Human perceptions of sensorial, thermal and acoustic comfort in clothing can be predicted by the measured physical indices and the designed test device with the developed regression models provides an alternative method to characterize the fabric comfort attributes effectively.

The work develops a novel device for objective evaluation of fabric comfort properties by a simultaneous test, integrating the mechanical measurement with thermal test and thereby filling the gap between the existing evaluation methods and practical requirements for the digitalization of fabric comfort in present textile and garment trade.

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.

First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.

The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.

This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

The dryer feed chute of the pellet plant plays an important role in the pelletizing process. The chute discharges sticky and moist iron ore fines (<1 mm) to the inline rotary dryer for further processing. Since the inception of the installation of the dryer feed chute, the poor flowability of the feed materials has caused severe problems such as blockages and excessive wear of chute liners. This leads to high maintenance costs and reduced lifetime of the liner materials. Constant housekeeping is needed for maintaining the chute and reliable operation. The purpose of this study is to redesign the dryer feed chute to overcome the above challenges.

The discrete element method (DEM) has been used to model the flow of cohesive materials through the transfer chute. Physical experiments have been performed to understand the most severe flow conditions. A DEM material model is also developed for replicating the worst-case material condition. After identifying the key problem areas, concept designs were proposed and simulated to assess the design improvements to increase the reliability of chute operation.

Flow simulations correlated well with the existing flow behavior of the iron ore fines inside the chute. The location of the problematic areas has been validated with that of the previously installed chute. Subsequently, design modifications have been proposed. This includes modification of deflector plate and change in slope and cross-section of the chute. DEM simulations and analysis were conducted after incorporating these design changes. A comparison in the average velocity of particle and force on chute wall shows a significant improvement using the proposed design.

Method to calibrate DEM material model was found to provide accurate prediction and modeling of the flow behavior of bulk material through the real transfer chute. DEM provided greater insight into the performance of the chute especially modeling cohesive materials. DEM is a valuable design tool to assist chute designers troubleshoot and verify chute designs. DEM provides a greater ability to model and assess chute wear. This technique can help in achieving a scientific understanding of the flow properties of bulk solids through transfer chute, hence eliminate challenges, ensuring reliable, uninterrupted and profitable plant operation. This paper strongly advocates the use of calibrated DEM methodology in designing bulk material handling equipment.

The purpose of this paper is to explore the impact of using computers to obtain information on the farm household’s production and consumption based on a field survey of farm households in the northern China.

The most important methods applied are instrumental variable (IV) method and propensity score matching (PSM) method. Estimators of IV, PSM and nearest neighborhood matching approaches are considered together to check the robustness of empirical results.

This paper careful impact evaluation results suggest that the use of computer not only improves the size of arable land rented in but also reduces family labor input intensity and the probability of selling agricultural outputs at farm-gate markets. Moreover, it also stimulates transportation, garment, housing and insurance expenditures per capita.

The database of this research comprises cross-section data, which does not support a cross-time comparison.

These results imply that it is vital to expand the coverage of computer use in rural areas. This may suggest that the importance of improving computer access is crucial for stimulating rural consumption increase. Furthermore, the need for the expansion of internet network coverage in western areas is also of importance.

First, the authors directly estimate computer usage impacts on a broader range of production and consumption indicators by including land-relative investments, variable investments, labor input and household’s expenditure and provide rigorous impact evaluations on the impact of access to computer. Second, the authors use IV and PSM methods to correct self-selection bias, going beyond the single equation approach in other studies. This enables us to identify the causal relationship between computer usage and farmer’s production and consumption decisions.