Comprehensive multimodal management of borderline resectable pancreatic cancer:Current status and progress

Borderline resectable pancreatic cancer(BRPC)is a complex clinical entity with specific biological features.Criteria for resectability need to be assessed in combination with tumor anatomy and oncology.Neoadjuvant therapy(NAT)for BRPC patients is associated with additional survival benefits.Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT.More attention to management standards during NAT,including biliary drainage and nutritional support,is needed.Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period,including NAT responsiveness and the selection of surgical timing.

Tribological properties and thermal-stress analysis of C/C-SiC composites during braking

The tribological properties and thermal-stress behaviors of C/C-SiC composites during braking were investigated aiming to simulate braking tests of high-speed trains. The temperature and structural fields of C/C-SiC composites during braking were fully coupled and simulated with ANSYS software. The results of tribological tests indicated that the C/C-SiC composites showed excellent static friction coefficient (0.68) and dynamic friction coefficient (average value of 0.36). The highest temperature on friction surface was 445℃. The simulated temperature field showed that the highest temperature which appeared on the friction surface during braking was about 463℃. Analysis regarding thermal-stress field showed that the highest thermal-stress on friction surface was 11.5 MPa. The temperature and thermal-stress distributions on friction surface during braking showed the same tendency.

Multifractal methods for rapeseed nitrogen nutrition qualitative diagnosis modeling

Nutrition diagnosis plays a key role in the crop＇s growth, which has mainly been car- ried out in the field by agricultural workers. Currently, automatic nutrition recognition technologies have been widely used in this field. A procedure is proposed in this paper to diagnose nitrogen nutrition non-destructively for rapeseed qualitatively based on the multifractal theory. Twelve texture parameters are given by the method of multifractal detrended fluctuation （MF-DFA）, which contains six generalized Hurst exponents and six relative multifractal parameters that are used as features of the rapeseed leaf images for identifying the two nitrogen levels, namely, the N-mezzo and the N-wane. For the base leaves, central leaves and top leaves of the rapeseed plant and the three-section mixed samples, three parameters combinations are selected to conduct the work. Five classifiers of Fisher＇s linear discriminant algorithm （LDA）, extreme learning machine （ELM）, support vector machine and kernel method （SVMKM）, random decision forests （RF） and K-nearest neighbor algorithm （KNN） are employed to calculate the diagno- sis accuracy. An interesting finding is that the best diagnose accuracy is from the base leaves of the rapeseed plant. It is explained that the base leaf is the most sensitive to the nitrogen deficiency. The diagnose effect by the base leaves samples is outshining the existing result significantly for the same leaves samples. For the mixed samples, the aver- aged discriminant accuracy reaches 97.12% and 97.56% by SVMKM and RF methods with the 10-fold cross-validation respectively. The resulting high accuracy on N-levels identification shows the feasibility and efficiency of our method.

Analysis of the vortices in the inner flow of reversible pump turbine with the new omega vortex identification method

Reversible pump turbines are widely employed in the pumped hydro energy storage power plants. The frequent shifts among various operational modes for the reversible pump turbines pose various instability problems, e.g., the strong pressure fluctuation, the shaft swing, and the impeller damage. The instability is related to the vortices generated in the channels of the reversible pump turbines in the generating mode. In the present paper, a new omega vortex identification method is applied to the vortex analysis of the reversible pump turbines. The main advantage of the adopted algorithm is that it is physically independent of the selected values for the vortex identification in different working modes. Both weak and strong vortices can be identified by setting the same omega value in the whole passage of the reversible pump turbine. Five typical modes（turbine mode, runaway mode, turbine brake mode, zero-flow-rate mode and reverse pump mode） at several typical guide vane openings are selected for the analysis and comparisons. The differences between various modes and different guide vane openings are compared both qualitatively in terms of the vortex distributions and quantitatively in terms of the areas of the vortices in the reversible pump turbines. Our findings indicate that the new omega method could be successfully applied to the vortex identification in the reversible pump turbines.

Local multifractal detrended fluctuation analysis for tea breeds identification

In recent years, the popular multifractal detrended fluctuation analysis （MF-DFA） is extended to two-dimensional （2D） version, which has been applied in some field of image processing. In this paper, based on the 2D MF-DFA, a novel multifractal estimation method for images, which we called the local multifractal detrended fluctuation analysis （LMF-DFA）, is proposed to recognize and distinguish 20 types of tea breeds. A set of new multifractal descriptors, namely the local multifractal fluctuation exponents is defined to portray the local scaling properties of a surface. After collecting 10 tea leaves for each breed and photographing them to standard images, the LMF-DFA method is used to extract characteristic parameters for the images. Our analysis finds that there are significant differences among the different tea breeds＇ characteristic parameters by analysis of variance. Both the proposed LMF-DFA exponents and another classic parameter, namely the exponent based on capacity measure method have been used as features to distinguish the 20 tea breeds. The comparison results illustrate that the LMF-DFA estimation can differentiate the tea breeds more effectively and provide more satisfactory accuracy.