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Background. Congenital hepatic fibrosis is a hereditary fibropolycystic disease caused by ductal plate malformation. It is characterized by portal hypertension, but the manifestations, management, and outcome vary in children and adults. To raise awareness of medical staff, we have comprehensively compared the clinical features of congenital hepatic fibrosis between children and adults. Methods. We retrospectively enrolled all patients diagnosed with congenital hepatic fibrosis at the Huashan Hospital from August 2015 to August 2017 and analyzed their familial, clinical, laboratory, imaging, treatment, and follow-up data in detail. In addition, we reviewed cases with congenital hepatic fibrosis reported in the past 20 years in China and analyzed them according to the patients’ age. Results. A total of eight patients were diagnosed with congenital hepatic fibrosis in the study, including four children and four adults. The onset age of the children, who suffered from severe complications of portal hypertension and needed liver transplantation, ranged from 1 to 15 years old. The disorder developed in adults aged 26 to 60 years old. Three adults complained of recurrent abnormal liver function at the onset of illness, and they mainly received conservative treatments. The literature review included 30 children and 33 adults. In comparison, hepatomegaly was more common in children than in adults (57% vs. 21%, p=0.004). Malformation of kidneys and bile duct abnormalities were common, and multisystem involvement included eyes, other digestive organs, and genital and central nervous systems. Conclusions. Serious complications of portal hypertension developed in children requiring liver transplantation, while adults often had mild-to-moderate liver injuries upon onset. Adults with CHF varied a lot in clinical manifestations. Multiorgan involvement and unusual course are helpful to make a diagnosis. Timely histological assessment by liver biopsy and multidisciplinary cooperation are crucial for definitive diagnosis and early intervention.

The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data andnseries related, abbreviated as GDMC(1,n), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n),ninterpolation coefficients (taken as unknown parameters) are introduced into the background values of thenvariables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n)model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n)model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

The grey prediction model with convolution integral GMC (1,n) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC (1,n) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC (1,n). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved grey model. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. The GMC (1, 2) and NGMC (1, 2) models were used to predict China’s industrial SO2emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China’s economic output and SO2emissions with an improved accuracy over current GMC (1, 2) models.