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An air conditioning system plays an important role in regulating the indoor environment and improving the indoor thermal comfort. However, there exists hysteresis of the temperature and humidity regulating system of an air conditioning system. The coupling between temperature and humidity control circuits leads to more energy consumption of the air conditioning system. To solve this issue, an improved feed-forward decoupling model with Smith predictor was designed. The simulation results indicate that it can eliminate the coupling between temperature and humidity regulating circuits. This model can have less time-delay, decoupling between temperature and humidity control circuits. Compared with the conventional method, this decoupling model with Smith predictor has the advantages of timely dynamic response, quick increasing time and less overshoot.

Enclosure partitioning approaches, such as Treemaps, have proved their effectiveness in visualizing large hierarchical structures within a compact and limited display area. Most of the Treemaps techniques do not use node-links to show the structural relations. This paper presents a new tree visualization approach known as Drawer-Tree that can be used to present the structure, organization and interrelation of big data. By utilizing the display space with traditional node-link visualization, we have developed a novel method for visualizing tree structures with high scalability. The name "drawer" is a metaphor that helps people understand the visualization.

Expression recognition is an important subjective measurement method in emotional calculation. Considering the stability and representativeness of image algebra features, expression characteristics can be extracted by singular value decomposition. BP neural network optimized by the genetic algorithm is adopted as a classifier. Using the classifier, an expression recognition experiment was done on the JAFFE library and emotional induced experimental expression database. Comparing with the traditional BP classifier, the results of the experiment proves that the method is more effective and efficient.

Internal fruit rot, caused by Fusarium lactis, is an important disease of sweet pepper (Capsicum annuum) in Canadian greenhouses. Production of the mycotoxins fumonisin B₁ (FB₁), moniliformin (MON) and beauvericin (BEA) by F. lactis (17 isolates) and the related species F. proliferatum (three isolates) and F. verticillioides (one isolate), which are also associated with internal fruit rot, was evaluated on rice medium. All 21 isolates examined were found to produce BEA, at concentrations ranging from 13.28 to 1674.60 ppm, while 13 of 17 F. lactis isolates and two of three F. proliferatum isolates produced MON (0.23 to 181.85 ppm). Only one isolate of F. lactis produced detectable levels of FB₁ in culture, whereas all three F. proliferatum isolates and the F. verticilloides isolate produced this mycotoxin (0.28 to 314 ppm). Production of FB₁, MON and BEA was also evaluated in inoculated pepper fruits showing mild or severe symptoms of infection. FB₁ could be detected in both lightly and heavily diseased fruit tissue after inoculation with F. lactis, F. proliferatum or F. verticilloides, at concentrations ranging from 0.61 to 8.04 ppm. BEA was also detected in lightly and heavily diseased fruit tissue inoculated with F. lactis, as well as in heavily diseased tissue inoculated with F. proliferatum (3.00 to 19.43 ppm), but not in tissue inoculated with F. verticilloides. MON was detected in all tissues inoculated with F. proliferatum or F. verticilloides, and in heavily diseased tissue inoculated with F. lactis (0.03 to 0.27 ppm). The three mycotoxins were also found in naturally infected sweet pepper fruits exhibiting symptoms of internal fruit rot and collected from a commercial greenhouse. The production of MON, BEA and FB₁ alone or in combination by isolates of F. lactis suggests that development of internal fruit rot of sweet pepper is an important food safety concern, and that every effort should be made to cull infected fruit before it makes it to market.

Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets.