Parameter selection of Gaussian kernel SVM based on local density of training set

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method

National-scale mapping of building footprints using feature super-resolution semantic segmentation of Sentinel-2 images

Since buildings are closely related to human activities, large-scale mapping of individual buildings has become a hot research topic. High-resolution images with sub-meter or meter resolution are common choices to produce maps of building footprints. However, high-resolution images are both infrequently collected and expensive to obtain and process, making it very difficult to produce large-scale maps of individual buildings timely. This paper presents a simple but effective way to produce a national-scale map of building footprints using feature super-resolution semantic segmentation of sentinel-2 images. Specifically, we proposed a super-resolution semantic segmentation network named EDSR_NASUnet, which is an end-to-end network to generate semantic maps with a spatial resolution of 2.5 m from real remote sensing images with a spatial resolution of 10 m. Based on the dataset consisting of images from 35 cities in China, we quantitatively compared the proposed method with three methods under the same framework and qualitatively evaluated the identification results of individual buildings. In addition, we mapped building footprints within the entire China at 2.5 m-resolution using Sentinel-2 images of 10 m resolution. The density of building footprints varies considerably across China, with a gradual increase in building footprints from west to east, i.e. from the first step of China’s terrain to the third one. We detected over 86.3 million individual buildings with a total rooftop area of approximately 58,719.43 km2. The number of buildings increased from 5.73 million in the first step of China’s terrain, through 23.41 million in the second step of China’s terrain, to 57.16 million in the third step of China’s terrain. The area of buildings also increased from 3318.02 km2 through 13,844.29 to 41,557.12 km2. The Aihui-Tengchong line, a dividing line representing the regional distribution of China’s population, also divides the regional distribution of Chinese buildings. Our approach has a more open and practical application because of the medium-resolution images and platform with open access. Results are available to the community (https://code.earthengine.google.com/?asset=users/flower/2019_China)

Numerical Simulation of Supersonic Carman Curve Bodies with Aerospike

Drag reduction is one of the important problems for the supersonic vehicles. As one of the drag reduction methods, aerospike has been used in some equipment because of its good drag reduction effect. In this paper, the numerical simulations of Carman curve bodies with different lengths of the aerospike and different radius of the flat cylindrical aerodisk in supersonic flow freestream are investigated. Based on the numerical simulations, the mechanism of drag reduction of the aerospike is discussed. The drag reduction effect influence of the parameters of the aerodisk radius and the aerospike length on the Carman curve body is analyzed. The aerodisk radius within a certain range is helpful for the drag reduction. The change of length of the aerospike has little effect on the drag of Carmen curve bodies. The drag reduction effect of the same aerospike becomes worse with the increase of the incoming Mach number

The effects of domain division types on the performance prediction of a rim-driven thruster

The rim-driven\ua0thruster\ua0(RDT) is an innovative propulsion thruster. The rotating\ua0subdomain\ua0can contain different assemblies with different domain division types (DDT). This paper tried to figure out the effects of DDT on the performance of RDT. This paper uses the Delayed\ua0Detached Eddy Simulation\ua0(DDES)\ua0turbulence model\ua0to conduct the simulations. Three DDTs are carefully analyzed to understand their effects on predictive performance. The convergence analysis is performed by taking a typical method: Grid Convergence Index (GCI). Some theoretical results and the experimental\ua0hydrodynamics\ua0of a popular combination of Ka 4–70 and MARIN 19\ua0A are used to validate the\ua0numerical method. The numerical results demonstrate that the computational efficiency is influenced by the cell number on the interfaces between two subdomains and an inherent characteristic of DDT. Moreover, the torques acting on rim surfaces are closely accounting for the gap flow. Moreover, regarding the morphology and variables of the\ua0vortex system, the third type of DDT enhances vortices presenting in the hub region and vortex-instability region. Although the present analysis is performed for an RDT, the findings should be generally applicable for other RDT designs with similar structures and operational conditions

Anemoside A3 Inhibits Macrophage M2-Like Polarization to Prevent Triple-Negative Breast Cancer Metastasis

Triple negative breast cancer (TNBC) exhibits the characteristics of strong metastatic ability and a high recurrence rate, and M2-type macrophages play an important role in this process. Previous research data suggested that Anemoside A3 (A3), a monomeric component of Pulsatilla Chinensis, could prevent and treat TNBC by converting M0 macrophages into M1 immunogen phenotypes. This study showed that A3 significantly restrained the lung metastases of 4 T1-Luc cells with bioluminescence imaging in vivo and Hematoxylin and Eosin (H&E) staining. Meanwhile, the percentage of M2-type macrophages (CD206+ labeled cells) in the lung tissues was evidently decreased through immunohistochemical assay. We further proved that A3 markedly prevented M2-type polarization induced by IL-4 in vitro, as illustrated by the down-regulated expression of the cell surface marker CD206 protein by FACS and Arg-1, and of the Fizz1 and Ym1 genes by RT-PCR in M2-type macrophages. Furthermore, the invasion and migration of 4 T1 cells, which was promoted by the conditioned medium from M2-type macrophages, could be suppressed by A3. Luminex assay demonstrated that A3 treatment resulted in a reduction of the levels of CCL2, VEGF, CCL7, and MMP-9 in conditioned medium. Additionally, the expression of phosphorylated-STAT3 protein was inhibited by A3, which resulted in the macrophage M2-type polarization arrest, while no significant difference in JAK2 phosphorylation was detected. SiRNA transfection experiments suggested that STAT3 might be the target of A3 inhibiting M2-type polarization of macrophages. In conclusion, these results indicate that A3 could attenuate the metastasis of TNBC by inhibiting the M2-type polarization of macrophages, which may be related to the STAT3 pathway

Controlled growth of bismuth antimony telluride BixSb2 − xTe3 nanoplatelets and their bulk thermoelectric nanocomposites

Solution synthesis as a scalable bottom-up growth method shows considerable advantages for designing novel nanostructured bulk composites with augmented thermoelectric performance. Tuning the composition of synthesized materials in the solution process is important for adjusting the carrier type and concentration. Here, we report a modified solvothermal synthesis method for the controlled growth of BixSb2−xTe3 nanoplatelets, which can be sintered into nanostructured bulk pellets by using the spark plasma sintering process. We further demonstrate the tuning of the stoichiometric composition in ternary BixSb2−xTe3 nanoplatelets with high crystallinity and homogenous phase purity, which is proved by X-ray diffraction and Raman spectroscopy. The composition dependence of the thermoelectric performance of p-type BixSb2−xTe3 pellets is also systemically studied. The optimized nanostructured bulk Bi0.5Sb1.5Te3 sample is found to have ZT ~0.51 at 375 K, which shows great potential for further improving the thermoelectric performance by this solution synthesis method. Considering the progress in n-type Bi–Te–Se composites, our results advocate the promise of bismuth/antimony chalcogenide nanocomposites towards practical thermoelectric applications.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio

Cuproptosis‐related molecular patterns and gene (ATP7A) in hepatocellular carcinoma and their relationships with tumor immune microenvironment and clinical features

Abstract Background Cuproptosis has been studied in various aspects as a new form of cell death. Aims We hope to explore the molecular patterns and genes related to cuproptosis in evaluating and predicting the prognosis of hepatocellular carcinoma (HCC), as well as the impact of tumor immune microenvironment. Methods and Results Sixteen cuproptosis related gene (CRGs) and cuproptosis related molecular and gene characteristics were comprehensively analyzed from 492 HCC samples. Cuproptosis related molecular patterns were generated by consensus clustering algorithm, including cuproptosis clusters, cuproptosis gene clusters (CGC) and cuproptosis score (CS). The characteristics of tumor microenvironment (TME) and tumor immune cells were described by the ssGSEA and ESTIMATE algorithms. Cuproptosis score was established to assess the clinical characteristics, prognostic and immunotherapy. The role and mechanism of CRG (ATP7A) in HCC, as well as its relationship with TME and immune checkpoints, have been further explored. The results of somatic mutation, copy number variations (CNV), and CRGs expression in HCC suggested the CRGs might participate in the HCC oncogenesis. The cuproptosis clusters were closely related to the clinical pathological characteristics, biological processes, and prognosis of HCC. The three CGC was revealed to be consistent with the three immune infiltration characterizations, including immune‐high, immune‐mid, and immune‐low subtypes. Higher CS was characterized by decreased TMB, activated immunity, higher immune cell proportion score (IPS) and better overall survival (OS), which indicated higher CS was immune‐high type and with better treatment effect and prognosis. The ATP7A had the highest hazard ratio (HR = 1.465, p < .001), was high expression in HCC tissues and with a shorter 5‐year OS. Knocking down ATP7A could enhance intracellular copper concentration, cause a decrease in DLAT expression, and induce cuproptosis and inhibit cell proliferation and migration. ATP7A was also positively correlated with most cancer immune cells and immune checkpoints. Conclusion Taken together, this research revealed the cuproptosis related molecular patterns and genes associated with the clinical pathological characteristics, TME phenotype and prognosis of HCC. The CS will further deepen our understanding of the TME characteristics of HCC, and the involvement of ATP7A in cuproptosis will provide new ideas for predicting HCC prognosis and immunotherapy