Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature.

PurposeThe aim of this study was to investigate and compare impulsiveness, negative emotion, cognitive function, and P300 components among gamma-hydroxybutyrate (GHB)-addicted patients, heroin-dependent patients, and methadone maintenance treatment (MMT) subjects.MethodsA total of 48 men including 17 GHB addicts, 16 heroin addicts, 15 MMT subjects, and 15 male mentally healthy controls (HC) were recruited. All subjects were evaluated for symptoms of depression, anxiety, impulsiveness, and cognitive function through the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder 7-item (GAD-7), the Barratt Impulsiveness Scale version II (BIS-II), the Beijing version of the Montreal Cognitive Assessment (BJ-MoCA), the behavioral test (response time), and event-related potential P300 detection.Results(1) The mean scores of BIS-II in the GHB addiction group, heroin dependence group, and MMT group were significantly higher than those of the HC group (F = 30.339, P = 0.000). (2) The total scores of BJ-MOCA in GHB addiction group was the worst among the four groups, followed by heroin addiction, MMT group and HC group (F = 27.880, P = 0.000). (3) The response time in the GHB addiction group was the longest among the four groups, followed by the heroin addiction, MMT, and HC groups (F = 150.499, P = 0.000). (4) The amplitude and latency of P300 in GHB addiction subjects were significantly lower and longer than those of the MMT group and the HC group. (5) For the three types of addiction, the P300 amplitudes at Fz, Cz, Pz, T5, and T6 were negatively correlated with the scores of GAD-7, PHQ-9, and BIS-II; the P300 latencies were positively correlated with the response time and negatively correlated with the scores of the BJ-MoCA.ConclusionPeople with an addiction were likely to have increased impulsiveness. The cognitive function of the GHB and heroin-addicted subjects, including the heroin detoxification and the MMT groups, was severely impaired, especially for the GHB-addicted patients. The impairment manifested as abnormalities of BJ-MoCA, response time, and P300 components.

Single-pass corner milling is common in machining process. Machining quality and cost are the two most important goals for machining process. Machining quality is greatly affected by deformation and mechanical property. Cutting temperature has a very important effect on tool wear, mechanical property, deformation and machining precision. In this paper, cost per volume (CPV) and machining temperature are selected as objectives. To reduce the cost of research, AdvantEdge simulation software was used to simulating the machining process and examined for its accuracy. Comparing the result of AdvantEdge with physical experiments, it showed an average 8% error on CPV and a 6% error on temperature. Given this comparison, simulation data were used to train Kriging model as the surrogate model. Kriging model manifested high accuracy, showing mean error of 6% and 3% in terms of CPV and temperature on validation points. To solve the multi-objective problem, a K-means particle swarm optimization (PSO) was constructed, which outperformed traditional adaptive gird algor (AGA) by finding the better optimal solutions, meanwhile consuming lesser time. Optimization result showed that corner milling should have a low-level spindle speed to ensure low cost and low temperature. High feed rate, cutting depth and cutting width will result in low cost along with high temperature. Optimal parameters should be chosen from Pareto set according to different practical needs. This integrated method manifested its applicability in solving multi-objective problems.

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Recently, there are many serious problems in welfare environment for the aged and the handicapped including women, infants, children and foreigners. These problems are also serious in architectural field. However, it is not easy to think that university students who are majoring in architecture consider these problems seriously. Furthermore, programs of university have changed for ten years. We have conducted questionnaire surveys to the first and the third grade of the university students majoring in architectural field in Kansai University from 2006 to 2015. In this study, we clarified the change in "consciousness", "action" and "knowledge" of the students to the aged and the handicapped for ten years.