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This paper aims to determine whether domestic cotton support permitted by the current or potential World Trade Organization (WTO) rules would be sufficient to compensate the cost to China's cotton farmers if the sliding scale duty (SSD) on cotton imports is removed.

The simulation was conducted using a static spatial equilibrium model (SEM) of the world cotton market. First, a base model was specified to provide a good representation of the world cotton market's conditions. Second, simulations were conducted to evaluate the effects of replacing the SSD and subsidizing cotton producers pursuant to the current or potential WTO rules on domestic cotton support.

The results of the simulations suggest that China's cotton farmers are bound to incur losses. In either case, cotton subsidies permitted by the current or potential WTO rules are not sufficient to compensate for the cost to China's cotton producers if the SSD is eliminated.

It should be pointed out that these findings could suffer from a bias, primarily because the authors assumed that the WTO's blue box subsidies have no incentives for farmers to produce, and no substitution between cotton and alternative products. Thus, additional work is needed to reflect a more realistic situation in future studies.

The simulation estimation contributes to a better understanding of the issue of whether China should replace the current SSD with cotton subsidies to protect cotton producers.

The paper aims to assess the impact of agricultural output changes on the general price level over time with China as an example.

A simple numerical global general equilibrium (GE) model of two regions (China and the rest of the world) and three commodities (agriculture, manufacturing goods, and services) is used to assess the impacts of agricultural output changes on the overall economy price changes. The numerical GE model of this paper consists of production, final consumption, and market clear conditions. The results are generated with the GE model calibrated to aggregated China's input‐output tables of 1987, 1997, and 2005.

The results suggest that China witnessed a declining influence of agricultural output changes on general price changes. The contribution of given agricultural output change on the general price change in 2005 was merely less than 60 percent of that in 1987, which in turn implies that macro policies targeting to curb general inflation via boosting agricultural output will be less effective as those of 20 years ago.

China's policy makers should rely less and less on promoting agricultural output policies to fight against general inflation and should resort to non‐agricultural policies.

The paper argues that the influence of agriculture on the China's general price indices has been weakening along with China's economic development with a numerical GE model calibrated to aggregated China's input‐output tables of 1987, 1997, and 2005.

The purpose of this paper was to improve the frictional wear resistance properties of piston skirts caused by the low viscosity lubricant by studying the tribological performance of three novel coating materials.

Comparative tribological examinations were performed in a tribological tester using the ring-block arrangement under two viscosity lubricants, the loading force was applied as 100 N, the speed was set to 60 r/min and the testing time was 180 min.

Under low viscosity lubricant, the friction coefficient and wear of the three coatings all increase, and the friction coefficient and wear of the PTFE coating are the largest, while the MoS₂ coating has the lowest friction coefficient and wear. Under low viscosity lubricant, the friction coefficient of the MoS₂ coating is 2.1%–5.4% and 20.0%–24.3% lower than that of the SiO2 and PTFE coating, respectively. The friction coefficient and wear fluctuation rate of the MoS₂ coating is the smallest when the lubricant viscosity decreases, which indicates that the MoS₂ coating has excellent stability and adaptability under low viscosity lubricant.

To reduce the piston skirt wear caused by low viscosity lubricant in heavy-duty diesel engines, the friction and wear adaptability of three novel composite coating materials for piston skirts were compared under 0 W-20 low viscosity lubricant, which could provide a guidance for the application of wear-resistant materials for heavy-duty diesel engine piston skirt.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

The purpose of this paper is to propose a approach for data visualization and industrial process monitoring.

A deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.

The proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.

This paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

Glass is a promising interposer substrate for 2.5 D integration; yet detailed analysis of the interfacial reliability of through-glass vias (TGVs) has been lacking. The purpose of this paper is to investigate the design and material factors responsible for the interfacial delamination in TGVs and identify methods to improve reliability.

The interfacial reliability of TGVs is studied both analytically and numerically. An analytical solution is presented to show the dependence of the energy release rate (ERR) for interfacial delamination on the via design and the thermal mismatch strain. Then, finite element analysis (FEA) is used to investigate the influence of detailed design and material factors, including the pitch distance, via aspect ratio, via geometry and the glass and via materials, on the susceptibility to interfacial delamination.

ERR for interfacial delamination is directly proportional to the via diameter and the thermal mismatch strain. Thinner wafers with smaller aspect ratios show larger ERRs. Changing the via geometry from a fully filled via to an annular via leads to lower ERR. FEA results also show that certain material combinations have lower thermal mismatch strains, thus less prone to delamination.

The results and approach presented in this paper can guide the design and development of more reliable 2.5 D glass interposers.

This paper represents the first attempt to comprehensively evaluate the impact of design and material selection on the interfacial reliability of TGVs.