A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Absorption properties of radar absorbing structure laminate composites filled with Carbon Nanotubes

Radar absorbing structure laminate composites composed of glass fibers, carbon fibers and epoxy resin filled with carbon nanotubes were fabricated. Two optimal double-layer radar absorbing structures were obtained, the smallest reflection loss of them was -19.23 dB and -26.60 dB respectively, and the absorbing bandwidth was 4.2 GHz and 4.0 GHz separately. It was possible to achieve out the reflection loss was smaller than -10 dB in whole 8.2 - 12.4 GHz by adopting double-layered radar absorbing structure, adjusting to the permittivity of the composites and controlling the thickness of each layer materials

The effect of PTSA on preparation of mesophase carbon spheres.

Mesophase spheres have been synthesized by heat-treating a medium coal tar pitch at 420 ºC for 2 hours in the presence of P-toluene sulphonic acid (PTSA). The effect of PTSA on synthesis of mesophase spheres had been studied. It was found that PTSA promotes the formation of mesophase spheres in coal tar pitch through acceleratingpolymerization of aromatic hydrocarbons. PTSA content between 3 and 5 wt % gave similar size spheres, beyond which as the PTSA content increases, the size of spheres increases. 5 wt % PTSA gives uniform spheres with small size, good spherical shape and smooth surface

Mechanism of multi-stage sand filling stimulation in horizontal shale gas well development

Fracturing operations in shale gas reservoirs of the Sichuan–Chongqing area are frequented by casing deformation, failures in delivery of mechanical staging tools and other down-hole complexities. In addition, limitation in volumes of tail-in proppant in the matrix area significantly restricts the conductivity in the near zones of the wellbore. Eventually, flowback performance and productivity of shale gas horizontal wells are negatively affected. With consideration to the limitations in the implementation of the mechanical staging technique with bridge plug for shale gas development in the Sichuan–Chongqing area, the technique of multi-stage sand filling stimulation in horizontal wells was proposed to solve the above-mentioned problems. By filling sands in fractures, it is possible to divert fluids to maintain long-term high conductivity of fractures, which is the key to satisfactory EOR performances. By introducing the Hertz contact and fractal theory in the analysis of sand plug strength, and in combination of lab engineering simulation test results, the mechanical model for sand plugs in fractures with proppant was constructed. In terms of strength criteria and friction, the stability criteria of sand plug were put forward. Thus, the permeability fractural model for sand plugs in fractures was perfected. Test results show that the stability of sand plug in the earlier stage of production is mainly affected by fluid washing during flowback, so it is necessary to control the flowback rate strictly. In the later stage of production, the stability is mainly affected by fracture closure stress and flow pressure, so it is necessary to enhance the yield strength of proppant to maintain high conductivity of fractures. In conclusion, the multi-stage sand filling stimulation provides a new technique for multi-stage clustering fracturing operations in shale gas horizontal well development. Keywords: Shale gas, Horizontal well, Staged fracturing, Sand filling, Reservoir stimulation, Conductivity, Stimulation mechanism, Sichuan–Chongqing are