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The sulfuric-acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods. So this paper turns to a kinetic-free black-box model, Gaussian process regression (GPR) model via 24 experiments, as a more adaptable approach. Besides, the best GPR model is combined with traditional heat balance model to generate a hybrid gray-box model, which can give complete knowledge of reaction process. The hybrid gray-box model finally achieves maximum root-mean-square error (RMSE) of 0.0069 for conversion, 0.6535 for temperature, and 10.6087 for heat flow rate, underscoring its pretty good predictive ability.

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Aims: Somatic Symptom Scale-China (SSS-CN) has been applied to assess the presence and severity of somatization symptom disorders (SSD) in Chinese patients. However, there was no study comparing SSS-CN with Patient Health Questionnaire-15 (PHQ-15). The aim of this study was to compare the consistency of the SSS-CN with the PHQ-15 in evaluating SSD in patients with suspected psychological disorders in cardiovascular medicine and to explore the relationship between scores on the two SSD self-rating scales and scores on self-rating scales for anxiety or depression. Methods: In this study, 1,324 subjects were enrolled by using a "three-question method." Then, they completed four self-assessment scales, i.e., SSS-CN, PHQ-15, Patient Health Questionnaire-9 (PHQ-9), and General Anxiety Disorder-7 (GAD-7), in turn. The ability of SSS-CN to diagnose SSD was analyzed by the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) value, sensitivity, and specificity were calculated. Reliability analysis was performed with the Kappa statistic to determine consistency between SSS-CN and PHQ-15. The relationship between two qualitative variables was analyzed by Spearman correlation analysis. Results: The proportions of SSD evaluated by SSS-CN and PHQ-15 were 83.2 and 87.0%, respectively. SSS-CN score was significantly correlated with PHQ-15 one (r = 0.709, p < 0.001). The AUC of the SSS-CN for the diagnosis of SSD was 0.891, with a high sensitivity and acceptable specificity. There was a moderate agreement between SSS-CN and PHQ-15 in assessing SSD, with a Kappa value of 0.512. Anxiety and/or depression were detected in about 70% of patients with SSD. There was significant correlation between the score of each SSD scale and that of GAD-7 or PHQ-9 (SSS-CN: r = 0.614 or 0.674; PHQ-15: r = 0.444 or 0.582, all p < 0.001). In addition, the SSS-CN score was more closely correlated with the GAD-7 or PHQ-9 score than the PHQ-15 score, and a higher proportion of patients with anxiety or depression was detected in those with moderate and severe SSD evaluated by SSS-CN. Conclusion: The SSS-CN could be one of the ideal scales for the rapid screening of patients with suspected psychological disorders in cardiovascular medicine.

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High-sensitivity C-reactive protein (hs-CRP) is a key inflammatory factor in atherosclerotic cardiovascular diseases. In Chinese patients with coronary heart disease (CHD), the changes in hs-CRP levels after a daily meal and the effect of statins on those were never explored. A total of 300 inpatients with CHD were included in this study. Hs-CRP levels were measured in the fasting and non-fasting states at 2 h and 4 h after a daily breakfast. All inpatients were divided into two groups according to fasting hs-CRP ≤ 3 mg/L or not. Group with fasting hs-CRP ≤ 3 mg/L had a significantly higher percentage of patients with statins using ≥ 1 month (m) before admission than that with fasting hs-CRP > 3 mg/L (51.4% vs. 23.9%, P < 0.05). Hs-CRP levels increased significantly in the non-fasting state in two groups (P < 0.05). About 32% of patients with non-fasting hs-CRP > 3 mg/L came from those with fasting hs-CRP ≤ 3 mg/L. In conclusion, hs-CRP levels increased significantly in CHD patients after a daily meal. It suggested that the non-fasting hs-CRP level could be a better parameter to evaluate the inflammation state of CHD patients rather than fasting hs-CRP level.

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Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.

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Background: Xuezhikang, an extract of red yeast rice, effectively lowers fasting blood lipid levels. However, the influence of Xuezhikang on the non-fasting levels of low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) has not been explored in Chinese patients with coronary heart disease (CHD). Methods: Fifty CHD patients were enrolled and randomly divided into two groups (n = 25 each) to receive 1,200 mg/d of Xuezhikang or a placebo for 6 weeks as routine therapy. Blood lipids were repeatedly measured before and after 6 weeks of treatment at 0, 2, 4, and 6 h after a standard breakfast containing 800 kcal and 50 g of fat. Results: The serum LDL-C levels significantly decreased, from a fasting level of 3.88 mmol/L to non-fasting levels of 2.99, 2.83, and 3.23 mmol/L at 2, 4, and 6 h, respectively, after breakfast (P < 0.05). The serum non-HDL-C level mildly increased from a fasting level of 4.29 mmol/L to non-fasting levels of 4.32, 4.38, and 4.34 mmol/L at 2, 4, and 6 h post-prandially, respectively, and the difference reached statistical significance only at 4 and 6 h after breakfast (P < 0.05). After 6 weeks of Xuezhikang treatment, the patients had significantly lower fasting and non-fasting serum levels of LDL-C and non-HDL-C (P < 0.05) than at pretreatment. The LDL-C levels were reduced by 27.8, 28.1, 26.2, and 25.3% at 0, 2, 4, and 6 h, respectively, and the non-HDL-C levels were reduced by 27.6, 28.7, 29.0, and 28.0% at 0, 2, 4, and 6 h, respectively, after breakfast. No significant difference was found in the percent reductions in the LDL-C and non-HDL-C levels among the four different time-points. Conclusions: Six weeks of Xuezhikang treatment significantly decreased LDL-C and non-HDL-C levels, with similar percent reductions in fasting and non-fasting states in CHD patients, indicating that the percent change in non-fasting LDL-C or non-HDL-C could replace that in the fasting state for evaluation the efficacy of cholesterol control in CHD patients who are unwilling or unable to fast.

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The increasing interest in the effects of emotion on cognitive, social, and neural processes creates a constant need for efficient and reliable techniques for emotion elicitation. Emotions are important in many areas, especially in advertising design and video production. The impact of emotions on the audience plays an important role. This paper analyzes the physical elements in a two-dimensional emotion map by extracting the physical elements of a video (color, light intensity, sound, etc.). We used k-nearest neighbors (K-NN), support vector machine (SVM), and multilayer perceptron (MLP) classifiers in the machine learning method to accurately predict the four dimensions that express emotions, as well as summarize the relationship between the two-dimensional emotion space and physical elements when designing and producing video.

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