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Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO).Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups.Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups (p < 0.001 for the training group, and p = 0.016 for the validation group).Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS.

In this paper, a fall detection method is proposed by employing deep learning and multi-sensors fusion. Continuous wave radar and optical cameras are used simultaneously to capture human action information. Based on the abstraction of both the microwave and optical characteristics of the captured information, multiple convolutional neural network (CNN) is used to realize the information training and fall action recognition. Due to the fusion of multi-sensor information, the overall performance of the fall detection system can be improved remarkably. Detailed experiments are given to validate the proposed method.

Aiming at solving the problem of forecasting the dynamics of express delivering quality elements classification, the paper proposed a novel method integrating Kano model with GM(1,1) model which is effective to perform fitting prediction. An approach of fuzzy questionnaires to modify Kano questionnaires which considered as subjective is suggested, and the GM (1,1) model is used to estimate and predict data according to the quality classification of fuzzy Kano model. An empirical study in express industry is provided to testify its feasibility and validity.

Least-squares fitting was employed to interpolate the reflect point, ghost body-cell method (GBCM) was applied to quadtree-based adaptively refined Cartesian grid approach, and finite volume method was employed to solve Euler equations. Shape of cut cell kept in accordance with that in the flow field which efficiently relieved the time step restriction of small cut cells. Flows around a circular cylinder and the double NACA0012 airfoils are simulated and compared with published structured grid results, which show flow field around complex geometries is adequately resolved resort to GBCM approach on adaptively refined Cartesian grid in computational fluid dynamics (CFD).

A novel adaptive method for singular geometric regions was proposed. Combined with conventional cut cell adaption, curvature cell adaption and solution adaption, adaptively refined Cartesian grid was automatically generated after geometries had been specified by users. Solid wall boundary was implemented by ghost body-cell method which efficiently relieved the time step restriction of small cut cells. Flow field was well resolved on adaptively refined Cartesian grid with finite volume method. Transonic flow around the RAE2822 airfoil and subsonic flow around the Suddhoo four-element airfoil are carefully investigated, which validate the efficiency of current approach and its feasibility in complicated geometric regions.

In this investigation, a novel water-insoluble slow-release fertilizer, biuret polyphosphoramide (BPAM), was formulated and synthesized from urea, phosphoric acid (H(3)PO(4)), and ferric oxide (Fe(2)O(3)). The structure of BPAM was characterized by Fourier transform infrared (FTIR) spectroscopy. Subsequently, a coated slow-release BPAM fertilizer with superabsorbent was prepared by ionic cross-linked carboxymethylchitosan (the core), acrylic acid, acrylamide, and active carbon (the coating). The variable influences on the water absorbency were investigated and optimized. Component analysis results showed that the coated slow-release BPAM contained 5.66% nitrogen and 11.7% phosphorus. The property of water retention, the behavior of slow release of phosphorus, and the capacity of adsorption of cations were evaluated, and the results revealed that the product not only had good slow-release property and excellent water retention capacity but also higher adsorption capacities of cations in saline soil.

In the present paper the effects of different digestion methods on determining the amount of metal elements in the plant platycodon grandi florum (Jacq.) A. DC. by flame atomic absorption spectrophotometry(FAAS) are reported. FAAS method was established for the determination of K, Mg, Ca, Cu, Zn, Mn, Fe, Co, Ni, Cd and Cr and Pb in the plant platycodon grandi florum (Jacq.) A. DC. The samples were incinerated and followed by digestion with HNO3-HClO (phi, 4 : 1) at 90-95 degrees C and normal pressure. In the meantime, the optimum parameters of FAAS and effects of solution medium on the results were discussed. The analytical results of K, Mg, Ca, Mn, Fe, Cu and Zn were 13 226.32, 922.57, 1 710.72, 9.23, 336.66, 8.75 and 19.62 microg x g(-1) respectively, while Co, Ni, Cd, Cr and Pb were not checked out in the samples. The results showed that the recovery of standard addition was 95.45Y-105.50% and the relative standard deviation (n = 9) was 0.34%-5.78%. The method is quick, simple and convenient, and the results are satisfactory.

The objective of the paper is to determine the amount of metal elements of Na, K, Mg, Ca, Cu, Zn, Mn, Fe, Co, Ni, Cd, Cr and Pb in the planted Foeniculum vulgare Mill. by flame atomic absorption spectrophotometry (FAAS), after the cinefaction and the digestion with HNO3-HClO4 (phi 4:1) at 90-95 degrees C and normal pressure. The optimum parameters of FAAS and the effects of solution medium on the results were investigated. The analytical results show that the amount of Na, K, Mg, Ca, Mn, Fe, Cu, Zn and Pb was 1508.7, 27653.0, 2036.0, 4848.1, 24.8, 323.5, 15.2, 23.7 and 10.8 microg x g(-1), respectively, and that of Co, Ni, Cd and Cr was not checked out in the samples. The recovery of standard addition is 97.45%-102.50%, the relative standard deviation (n=9) was 0.34%-2.77%. The characteristic method is quick, simple and convenient and the results are satisfactory.

A FAAS method was established for the determination of Na, K, Mg, Ca, Cu, Zn, Fe, Mn, Ni, Co, Pb, Cr, Cd, Ag and Au in Cynomorium songaricum after digestion with HNO3-HClO4 (phi 4:1) at 90-95 degrees C and at normal pressure. In this paper, the parameters in FAAS were studied. The analytical results of Na, K, Mg, Ca, Fe, Zn, Cu, Mn, Pb, Ni and Ag were 13,572.0, 14,260.0, 358.3, 168.3, 238.5, 19.4, 5.9, 3.4, 2.6, 1.3 and 0.4 microg x g(-1) respectively and Co, Cr, Cd and Au were not found in the samples. The results showed that the recovery of standard addition was 97.8%-104.5%, and the relative standard deviation (n = 9) was 0.2%-5.0%. The method is fast, simple and convenient. The results were satisfactory.

Reliability optimization for FMS (flexible manufacturing system) is very important. Traditional optimal algorithms applying to FMS reliability have many shortcomings. In this article, the authors take into account the characteristics of FMS and bring forward modular neural networks (NN) model based on genetic algorithm (GA) to optimize FMS reliability. Modular NN based on GA has good convergence and short learning time. The example shows that the new algorithm is feasible and effective.

The evaluation of credit guarantee risk (CGR) involves many complex problems such as how to quantify risk, how to express causal relationships of risk factors, how to fuse all kinds of information and evaluate risk with uncertainties. Thus the evaluation of CGR is very difficult because of its uncertainty and diverse information. Bayesian belief networks (BBN) are basically a framework for reasoning uncertainty and diverse information. This paper presents BBN as a new methodology to evaluate CGR. An example is presented by using the BBN method.