The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure−property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C 3 −C 64 ), covering n-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named B and T 0 ). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trialand-error process. An artificial neural network was trained to correlate the Andrade model parameters to the selected chemical features. The average relative errors for B and T 0 predictions are 2.87 and 1.05%, respectively. The viscosity versus temperature profile was calculated from the predicted Andrade model parameters, reaching the mean absolute error at a value of 0.10 mPa s. We also proved that the established QSPR model can describe the viscosity versus temperature profile of different isomers, such as isoparaffins, with different branch degrees and aromatic hydrocarbons with different substituent positions. At last, we applied the QSPR model to predict gasoline and diesel viscosities based on the measured molecular composition. A good agreement was observed between predicted and experimental data (absolute mean deviation equals 0.21 mPa s), demonstrating that it has capacity to calculate viscosity of hydrocarbon mixtures.

Drug-induced immune thrombocytopenia should be considered in cases of acute thrombocytopenia in patients undergoing meropenem treatment. Clinicians should be cognizant of DITP, and a definitive diagnosis should be pursued, if feasible.

Rationale:Pain in the hip joint is a common symptom in children. The common diseases leading to pain in the hip joint in children include transient synovitis of the hip, septic arthritis of the hip, and Legg–Calve–Perthes disease.Patient concerns:A 7-year-old boy was admitted due to pain in the right hip joint and limping for more than 1 month.Diagnosis:Synovial chondromatosis.Interventions:The patient underwent a hip open surgery, all the loose bodies in articular capsule were removed.Outcomes:At the 6-month follow-up, pain and limping disappeared, and the range of activity of the hip joint was restored to a normal level.Conclusions:Synovial chondromatosis is an uncommon disease which can cause pain of hip joint in children.Lessons:When the pediatric orthopedic surgeon treats the children suffered with hip pain the surgeon should be aware of this rare disease.

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Petroleum molecular reconstruction method can be used to calculate molecular composition from limited analytical data, which is the basis of the molecular‐level process modeling of petroleum refining. However, due to the problem of multiple solutions, it is difficult to obtain accurate and stable molecular compositional models. Therefore, based on the traditional bulk property constraints, composition similarity was proposed and a new petroleum molecular reconstruction method was proposed. In this article, diesel is taken as the study object, and the method was applied to construct various diesel compositional models. The results showed that the models' bulk properties and molecular fraction distribution were consistent with the experimental data. Finally, the method was applied to construct diesel compositional models of an actual refinery. With the reference oil compositional model as the molecular reference data, the problem of accurate construction of compositional models in a long‐term production process without detailed characterization data was solved.