Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes.

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

A methodology for energy dissipation prediction of the bolt group with non-uniform preload.

The bolted joints exhibit typical nonlinear hysteresis under tangential loading, and the deviations of the preload in the bolt group add to the complexity of the model describing this behavior. In this paper, based on the mechanical analysis of the bolt group, a new methodology for predicting hysteresis behavior under non-uniform preload was proposed. Firstly, based on the moment equilibrium within the framework of material mechanics and the tangential stiffness model of the joint interface, a new coordination equation of displacement and force between the bolted joints was deduced, which could realize the calculation of the bolt group loading curve. Then, the PCOM (Preload Co-Occurrence Matrix) was constructed considering the arrangement of the joints, and the indices related to the spatial distribution of the preload were extracted from PCOM. The results of GCA (Grey Correlation Analysis) showed that the indices of PCOM were closely related to the hysteresis behavior of the bolt group. Finally, the prediction of energy dissipation could be realized by using the indices extracted from PCOM and the SVM(Support Vector Machine) regression model. The prediction results were in good agreement with the simulation and experiment, which verified the validity of the methodology proposed in this paper.

Characterization of the Volatile Compounds in Camellia oleifera Seed Oil from Different Geographic Origins.

Volatile flavor of edible oils is an important quality index and factor affecting consumer choice. The purpose of this investigation was to characterize virgin Camellia oleifera seed oil (VCO) samples from different locations in southern China in terms of their volatile compounds to show the classification of VCO with respect to geography. Different samples from 20 producing VCO regions were collected in 2020 growing season, at almost the same maturity stage, and processed under the same conditions. Headspace solid-phase microextraction (HS-SPME) with a gas chromatography-mass spectrometer system (GC-MS) was used to analyze volatile compounds. A total of 348 volatiles were characterized, including aldehydes, ketones, alcohols, acids, esters, alkenes, alkanes, furans, phenols, and benzene; the relative contents ranged from 7.80-58.68%, 1.73-12.52%, 2.91-37.07%, 2.73-46.50%, 0.99-12.01%, 0.40-14.95%, 0.00-27.23%, 0.00-3.75%, 0.00-7.34%, and 0.00-1.55%, respectively. The VCO geographical origins with the largest number of volatile compounds was Xixiangtang of Guangxi (L17), and the least was Beireng of Hainan (L19). A total of 23 common and 98 unique volatile compounds were detected that reflected the basic and characteristic flavor of VCO, respectively. After PCA, heatmap and PLS-DA analysis, Longchuan of Guangdong (L8), Qingshanhu of Jiangxi (L16), and Panlong of Yunnan (L20) were in one group where the annual average temperatures are relatively low, where annual rainfalls are also low. Guangning of Guangdong (L6), Yunan of Guangdong (L7), Xingning of Guangdong (L9), Tianhe of Guangdong (L10), Xuwen of Guangdong (L11), and Xiuying of Hainan (L18) were in another group where the annual average temperatures are relatively high, and the altitudes are low. Hence, volatile compound distributions confirmed the differences among the VCO samples from these geographical areas, and the provenance difference evaluation can be carried out by flavor.

The Effect of Camellia oleifera Cake Polysaccharides on Growth Performance, Carcass Traits, Meat Quality, Blood Profile, and Caecum Microorganisms in Yellow Broilers.

The study was carried out to evaluate the influence of polysaccharides from Camellia oleifera cake (CCP) in Lingnan yellow broilers diet from 1 to 50 days. Growth performance, carcass traits, meat quality, blood profile, and caecum microorganisms were characterized by three different levels of 0, 200 and 800 mg/kg CCP supplementation. Dietary treatment did not affect the productive trait from 1 to 50 days of age, except that average daily feed intake decreased at 42 days of age (p < 0.05). Additionally, the effects of CCP on various organs were different. The weight (p < 0.01) and index (p < 0.05) of bursa of Fabricius gradually decreased with the higher CCP supplementation at 21 days of the broilers diet. The gizzard weights were all higher when the broilers were fed with higher CCP concentration at 21, 42, and 50 days, respectively (p < 0.05). The weight and index of the spleen increased most with low CCP concentration (200 mg/kg) at 42 and 50 days. Moreover, CCP addition had no significant effect on meat quality except cooking loss (P < 0.05) and yellowness of meat color (p < 0.05). In the study of blood metabolism at 50 days of broilers, the concentration of calcium (p < 0.01), total cholesterol (p < 0.05) and uric acid (p < 0.01) decreased with higher CCP supplementation. CCP increased the albumin concentration (p < 0.001) that was highest at 200 mg/kg CCP supplementation. The addition of CCP increased the number of Lactobacillus and Enterococcus faecalis (p < 0.01) in the caecum of broilers, and had the potential to inhibit the growth of Escherichia coli (p = 0.11). Results showed that CCP played a role in improving intestinal flora and the immunity of yellow broilers.

Rapid Determination of the Oil and Moisture Contents in Camellia gauchowensis Chang and Camellia semiserrata Chi Seeds Kernels by Near-infrared Reflectance Spectroscopy.

A fast and effective determination method of different species of vegetable seeds oil is vital in the plant oil industry. The near-infrared reflectance spectroscopy (NIRS) method was developed in this study to analyze the oil and moisture contents of Camelliagauchowensis Chang and C.semiserrata Chi seeds kernels. Calibration and validation models were established using principal component analysis (PCA) and partial least squares (PLS) regression methods. In the prediction models of NIRS, the levels of accuracy obtained were sufficient for C.gauchowensis Chang and C.semiserrata Chi, the correlation coefficients of which for oil were 0.98 and 0.95, respectively, and those for moisture were 0.92 and 0.89, respectively. The near infrared spectrum of crush seeds kernels was more precise compared to intact kernels. Based on the calibration models of the two Camellia species, the NIRS predictive oil contents of C.gauchowensis Chang and C.semiserrata Chi seeds kernels were 48.71 ± 8.94% and 58.37 ± 7.39%, and the NIRS predictive moisture contents were 4.39 ± 1.08% and 3.49 ± 0.71%, respectively. The NIRS technique could determine successfully the oil and moisture contents of C.gauchowensis Chang and C.semiserrata Chi seeds kernels.

Hyperhomocysteinemia-induced upper extremity deep vein thrombosis and pulmonary embolism in a patient with methyltetrahydrofolate reductase mutation: a case report and literature review.

The study highlights pulmonary embolism and deep vein thrombosis by methylene tetrahydrofolate reductase (MTHFR) deficiency-related hyperhomocysteinemia occurring in rare locations of left veins superior to the heart extensively. A 59-year-old white man with history of leg pain, smoking, weight loss, benign prostatic hyperplasia, lipoma and panic attack presented with shortness of breath and chest pain for 2 days precipitated by not feeling well for months. The diagnostic workup revealed pulmonary embolism and deep vein thrombosis in the left subclavian vein which extended throughout the left brachiocephalic vein to the superior vena cava and left jugular vein. Further workup showed moderate hyperhomocysteinemia with normal levels of vitamin B6, B12 and folic acid. Methylene tetrahydrofolate reductase genetic study found the patient to be homozygous for G677T variant. He was started on low-molecular-weight heparin and was discharged on oral anticoagulant. No recurrent thrombotic episodes were witnessed after 4 months of follow-up after discharge.

Serum parathyroid hormone as a potential novel biomarker of coronary heart disease.

OBJECTIVE: This study aimed to evaluate the relationships between serum parathyroid hormone (PTH) and coronary heart disease (CHD). METHODS: From July 2011 to February 2013, a total of 79 CHD patients and 94 normal control patients with ages ranging from 25 to 79 years were included in this study. Serum PTH level and common risk factors of CHD (age, gender, cholesterol, glycosylated hemoglobin [HbA1c], blood pressure [BP], history of diabetes, smoking, and body mass index) were investigated. Pearson's correlation and multiple regression analyses were used to evaluate the relationships between serum PTH level and CHD risk factors. All statistical analyses were performed using the SPSS 18.0 software. RESULTS: RESULTS from Pearson's correlation analysis indicated that age, systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), HbA1c, history of smoking, and serum PTH level were risk factors for CHD (all p<0.05). Serum PTH levels were positively correlated with DBP (r=0.256, p=0.010) and HbA1c (r=0.223, p=0.003), while not being related to other risk factors of CHD (all p>0.05). Multiple linear regression analysis showed that SBP, DBP, LDL-c, and HDL-c may be important determinants of CHD (all p<0.05). Further, serum PTH level is also an independent risk factor for CHD (p<0.001). CONCLUSION: Our results provide evidence that serum PTH level may be involved in the pathogenesis of CHD. Thus, PTH could be used as an important biomarker in the diagnosis of CHD.