Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Extraction and purification of total flavonoids from Eupatorium lindleyanum DC. and evaluation of their antioxidant and enzyme inhibitory activities

Abstract: The health benefits and promising medical treatment potential of total flavonoids from Eupatorium lindleyanum DC. (TFELDC) have been recognized. The process parameters of extracting total flavonoids from Eupatorium lindleyanum DC. by ultrasonic-microwave synergistic extraction (UMSE) were optimized, and they were purified by AB-8 macroporous resin in the current study. In addition, the antioxidant and enzyme inhibitory activities of the purified TFELDC (PTFELDC) were evaluated. The results showed that the optimal parameters of UMSE were as follows: ethanol volume fraction 71.5%, L/S ratio 12.2 ml/g, microwave power 318 W, and extraction time 143 s. After TFELDC were purified by AB-8 macroporous resin, the total flavonoid contents of PTFELDC increased from 208.18 ± 1.60 to 511.19 ± 3.21 mg RE/g FDS. Compared with TFELDC, the content of total flavonoids in PTFELDC was increased by 2.46 times. The antioxidant activities of PTFELDC were assessed using DPPH radical, superoxide anion radical, reducing power, and ferric reducing antioxidant power assays, and the IC50 values were found to be 37.13, 19.62, 81.22, and 24.72 μg/ml, respectively. The enzyme inhibitory activities of PTFELDC were measured using lipase, α-amylase, α-glucosidase, and acetylcholinesterase assays with the IC50 values 1.38, 2.08, 1.63, and 0.58 mg/ml, respectively. By comparing with their positive controls, it was found that PTFELDC had good antioxidant activities, and lipase, α-amylase, and α-glucosidase inhibitory activities, However, the acetylcholinesterase inhibitory activity was relatively weaker. These results suggested that PTFELDC have a promising potential as natural antioxidant, antilipidemic, and hypoglycemic drugs used in functional foods or pharmaceuticals.

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors

Abstract: This work investigates possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods for the prediction of standard enthalpy of formation (Δ_f H^o) through the use of artificial neural network (ANN) with molecular descriptors. A total number of 142 organic compounds with enough structural diversity has been considered in the training set. Standard enthalpy of formation for the selected compounds at the semiempirical PM3 and PM6 quantum chemistry methods is collected from literature, and is calculated by using semiempirical PM7 method in this work. The multiple stepwise regression is first employed to screen effective molecular descriptors, which are highly correlated with the error terms of the standard enthalpy of formation compared with experimental values. The obtained 7 effective molecular descriptors are then used as input set to establish three 7-11-1 neural network-based correction models to improve the accuracy of SQC methods. By using the developed correction models, the mean absolute errors (MAE) for Δ_f H^oof PM3, PM6, and PM7 methods are reduced from 22.36, 18.60, 17.27to 9.86, 9.83, 8.95, respectively in kJ/mol. Meanwhile, the results of the test set show that the neural network does not have the problem of over-fitting. Detailed analysis of the 7 effective molecular descriptors indicates that the major source to the correction models is from the electron withdrawing effect. The developed ANN models for the three selected SQC methods provide an efficient method for the quick and accurate prediction of thermodynamic properties.

Comparative Analysis of Coal Miner’s Fatalities by Fuzzy Logic

Abstract: In this work, we employ the fuzzy logic technique to achieve and present, for the first time, a proper analysis of the actual intensity of the increase in the coal miners’ fatality rates in Pakistan from 2010 to 2018, compared with China and India, with an objective to minimize the impact of incidents on the miners’ fatalities. The average and yearwise fatality rates in Pakistan, compared with China and India, are used for the fuzzy logic technique in order to calculate the actual degree of flexibility for the surging fatalities. The findings show that both the average (2010-2018) and yearwise fatality rates in 2011, 2015, and 2018 are 2.44, 1.74, and 1.6, respectively. In the fuzzy logic technique, the variables whose membership function (µ) values are ≥ 1 represent the absolute truth. The membership function values for the years 2011, 2015, and 2018 are alarmingly high for the fatalities of coal miners. Similarly, except for 2014 and 2010, where 0 represents the absolute falseness, the results for the remaining years indicate high fatality rates with a flexibility (or small extent) of its corresponding membership function (µ) values such as 0.623, 0.739, 0.219, 0.173 and 0.115, and 0.714, 0.24, 0.01, 0.324 and 0.317 using the average and yearwise analysis, respectively, compared with China. Likewise, the fuzzy logic membership function (µ) values compared with India in the remaining years are 0.704, 0.795, 0.386, 0.159, 0.352 and 0.306, and 0.675, 0.795, 0.386, 0.186, 0.321 and 0.322, respectively. The proposed fuzzy logic analysis has been founded based on the theory of fuzzy sets to properly identify the miners’ fatalities, and also to suggest the implementation of appropriate recommendations to reduce the fatalities in the coal mines in Pakistan.

Stochastic programming for flexible global supply chain planning

Abstract: When ocean transportation is used, possible disruptions both at sea and on land should be taken into account in the planning process of the affected supply chain. In this paper, a framework to enable flexible global supply chain operational planning in stochastic environments is presented. In order to cope with unexpected events like natural or man-made disasters, flexible international long-distance transportation modes and postponement strategies are taken into account in our supply chain model. In order to balance supply chain costs and the flexibility of supply chains, a two-stage multi-scenario stochastic programming model is developed where the stochastic events are represented by corresponding scenarios. High quality solutions of all our problem instances are generated by using a Python based stochastic programming framework to solve the model. Finally, managerial insights related to flexible supply chain planning in stochastic environments are derived from our computational results.