A Real-time Prediction System for Molecular-level Information of Heavy Oil Based on Machine Learning

Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.

A new interpretation and quantitative method for diplopia test： 304 cases of ocular motor nerve palsy for clinical test and verify

We introduce a new interpretation and quantitative method for computerized diplopia test. By comparing this new method to the Hess screen test, we validate its applicability among 304 patients with ocular motor nerve palsy. This new method shows great assistant value as the Hess screen test in making accurate diagnosis and quantitative evaluation the severity of diplopia. Furthermore, it is more convenient and suitable for daily clinical use.

Analysis on the Structure of Influencing Factors of Sustainable Supply Chain Implementation of Water Diversion Project

The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sustainable development of large-scale water diversion projects. Through the investigation of relevant literature, books, web pages, materials, and discussions with relevant experts and scholars, a total of 23 factors influencing the sustainable supply chain implementation of water diversion projects were identified. Then using ISM (Interpretative Structural Modeling Method) to analyze the causality of each factor, a multi-level hierarchical structure model was obtained. The results showed that: 1) The surface-level influencing factors of the sustainable supply chain implementation of the water diversion project mainly included 8 factors such as water-saving awareness and water-saving intensity in the diversion area, water quality, water pollution and other disasters, effective incentive mechanisms, etc., and surface-level influencing factors were directly related to the sustainable supply chain implementation of water diversion projects. 2) The indirect influencing factors of the sustainable supply chain of water diversion projects included 12 factors such as the water quality and quantity guarantee rate of the supply chain, the government’s enforcement of laws and regulations, water distribution, ecological compensation, and compensation mechanisms for residents in the water source area. Indirect influencing factor scan acts directly on the direct influencing factors, and intervening in the factors that can be controlled by humans is one of the important ways to improve the sustainable operation of water diversion projects. 3) The fundamental influencing factors for the sustainable supply chain implementation of water diversion projects included three factors: Resettlement policy, government financial support, and sound laws and regulations. Deep influencing factors had multi-channel influence and controllability, and intervening in them was the main means to improve the sustainable operation of water diversion projects.

Interpretation of Gastronomic Traditions within Tourism

This article presents a current EU project on the interpretation of Gastronomic European cultural heritage.Europe’s cultural heritage is highly diverse.This is by no means limited to museums,theatres,or castles;gastronomic traditions are also part of Europe’s cultural heritage.As an essential part of the collective European memory,it is essential to preserve this diversity.From an economic perspective,the preservation of cultural heritage is a crucial task for the future.Within the EU,more than 300,000 people work in the cultural heritage sector;in addition,there are about 7.8 million jobs in the EU that correlate indirectly with cultural heritage(e.g.,tourism).How could gastronomic traditions be used to support a sustainable tourisms development?

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer(GFRP)elastic gridshell structures.Machine learning(ML)approaches are implemented in this study,to predict maximum stress and displacement of GFRP elastic gridshell structures.Several ML algorithms,including linear regression(LR),ridge regression(RR),support vector regression(SVR),K-nearest neighbors(KNN),decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),extreme gradient boosting(XGBoost),category boosting(CatBoost),and light gradient boosting machine(LightGBM),are implemented in this study.Output features of structural performance considered in this study are the maximum stress as f1(x)and the maximum displacement to self-weight ratio as f2(x).A comparative study is conducted and the Catboost model presents the highest prediction accuracy.Finally,interpretable ML approaches,including shapely additive explanations(SHAP),partial dependence plot(PDP),and accumulated local effects(ALE),are applied to explain the predictions.SHAP is employed to describe the importance of each variable to structural performance both locally and globally.The results of sensitivity analysis(SA),feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f1(x)and f2(x).

Comparison of Four O-F Media and Interpretation Method for the Result.

O-F culture medium remains to be important for the detection of nonfermentative bacteria. Currently the widely used O-F culture medium adopts the Hugh-Leifson formula. The authors made a comparative study of the 4 kinds of O F methods and concluded that: the Hugh-Leifson is still a reliable formula, although sometimes

A GEOMETRICAL INTERPRETATION OF THE WITHOUT-EXCEPTION FEASIBILITY OF PL HOMOTOPY METHODS

PL homotopy metheds are effective methods to locate zerces(or fixed points) of highly nonlinearmappirgs. Due to the Jexicographical system, the methods are feasible without exceptions Thispaper presents a geemetrical interpretation of the without-exception feasibility.