Improved Feature Weight Algorithm and Its Application to Text Classification

Text preprocessing is one of the key problems in pattern recognition and plays an important role in the process of text classification. Text preprocessing has two pivotal steps: feature selection and feature weighting. The preprocessing results can directly affect the classifiers’ accuracy and performance. Therefore, choosing the appropriate algorithm for feature selection and feature weighting to preprocess the document can greatly improve the performance of classifiers. According to the Gini Index theory, this paper proposes an Improved Gini Index algorithm. This algorithm constructs a new feature selection and feature weighting function. The experimental results show that this algorithm can improve the classifiers’ performance effectively. At the same time, this algorithm is applied to a sensitive information identification system and has achieved a good result. The algorithm’s precision and recall are higher than those of traditional ones. It can identify sensitive information on the Internet effectively

Structural organization of the C1a-e-c supercomplex within the ciliary central apparatus

Nearly all motile cilia contain a central apparatus (CA) composed of two connected singlet microtubules with attached projections that play crucial roles in regulating ciliary motility. Defects in CA assembly usually result in motility-impaired or paralyzed cilia, which in humans causes disease. Despite their importance, the protein composition and functions of the CA projections are largely unknown. Here, we integrated biochemical and genetic approaches with cryo-electron tomography to compare the CA of wild-type Chlamydomonas with CA mutants. We identified a large ( \u3e 2 MD) complex, the C1a-e-c supercomplex, that requires the PF16 protein for assembly and contains the CA components FAP76, FAP81, FAP92, and FAP216. We localized these subunits within the supercomplex using nanogold labeling and show that loss of any one of them results in impaired ciliary motility. These data provide insight into the subunit organization and 3D structure of the CA, which is a prerequisite for understanding the molecular mechanisms by which the CA regulates ciliary beating

FcpB Is a Surface Filament Protein of the Endoflagellum Required for the Motility of the Spirochete Leptospira

International audienceThe spirochete endoflagellum is a unique motility apparatus among bacteria. Despite its critical importance for pathogenesis, the full composition of the flagellum remains to be determined. We have recently reported that FcpA is a novel flagellar protein and a major component of the sheath of the filament of the spirochete Leptospira. By screening a library of random transposon mutants in the spirochete Leptospira biflexa, we found a motility-deficient mutant harboring a disruption in a hypothetical gene of unknown function. Here, we show that this gene encodes a surface component of the endoflagellar filament and is required for typical hook- and spiral-shaped ends of the cell body, coiled structure of the endoflagella, and high velocity phenotype. We therefore named the gene fcpB for flagellar-coiling protein B. fcpB is conserved in all members of the Leptospira genus, but not present in other organisms including other spirochetes. Complementation of the fcpB− mutant restored the wild-type morphology and motility phenotypes. Immunoblotting with anti-FcpA and anti-FcpB antisera and cryo-electron microscopy of the filament indicated that FcpB assembled onto the surface of the sheath of the filament and mostly located on the outer (convex) side of the coiled filament. We provide evidence that FcpB, together with FcpA, are Leptospira-specific novel components of the sheath of the filament, key determinants of the coiled and asymmetric structure of the endoflagella and are essential for high velocity. Defining the components of the endoflagella and their functions in these atypical bacteria should greatly enhance our understanding of the mechanisms by which these bacteria produce motility

Approaches to denoise the diffuse optical signals for tissue blood flow measurement

Various diseases are relevant to the abnormal blood flow in tissue. Diffuse correlation spectroscopy (DCS) is an emerging technology to extract the blood flow index (BFI) from light electric field temporal autocorrelation data. To account for tissue heterogeneity and irregular geometry, we developed an innovative DCS algorithm (i.e., the Nth order linear algorithm, or simply the NL algorithm) previously, in which the DCS signals are fully utilized through iterative linear regressions. Under the framework of NL algorithm, the BFI to be extracted is significantly influenced by the linear regression approach adopted. In this study, three approaches were proposed and evaluated for performing the iterative linear regressions, in order to understand what are the appropriate regression methods for BFI estimation. The three methods are least-squared minimization (L2 norm), least-absolute minimization (L1 norm) and support vector regression (SVR), where L2 norm is a conventional approach to perform linear regression. L1 norm and SVR are the approaches newly introduced here to process the DCS data. Computer simulations and the autocorrelation data collected from liquid phantom and human tissues are utilized to evaluate the three approaches. The results show that the best performance is achieved by the SVR approach in extracting the BFI values, with an error rate of 2.23% at 3.0 cm source-detector separation. The L1 norm method gives a medium error of 2.81%. In contrast, the L2 norm method leads to the largest error (3.93%) in extracting the BFI values. The outcomes derived from this study will be very helpful for the tissue blood flow measurements, which is critical for translating the DCS technology to the clinic

Optimization Design and Experiment of Ear-Picking and Threshing Devices of Corn Plot Kernel Harvester

In order to solve the problems of easy-to-break kernels and substantial harvest losses during kernel harvesting in breeding trials plot of corn, an ear-picking device and a threshing device of corn plot kernel harvester has been optimized. To automatically change the gap of the ear-picking plate, a self-elastic structure with compression spring and connecting rod is used. The ear-picking plate is glued, and an elastic rubber gasket is placed underneath it, which effectively improves the adaptability of the ear-picking device and reduces corn kernel collision damage during ear-picking. To ensure the self-purification of the ear-picking device, a combination of auger sieve hole cleaning device and lateral pneumatic auxiliary cleaning system is used. A dual-axial flow threshing device is designed, which uses a “U”-shaped conveying system to transport maize ears in the threshing chamber. The spacing of the concave sieve may be adjusted, and the residual kernels in the threshing chamber can be cleaned up after harvesting one plot by combining three cleanings, which meets the requirements of no mixing between plots. The force analysis of corn ears in the threshing chamber determines the best design plan for the forward speed, the speed of the second threshing drum, and the threshing gap. The breakage rate and non-threshing rate regression models were created using the quadratic regression orthogonal combination test, and the parameters were optimized using MATLAB. The verification test results showed that when the forward speed was 0.61 m/s, the second threshing drum speed was 500 r/min, and the threshing gap was 40 mm, the breakage rate was 1.47%, and the non-threshing rate was 0.89%, which met the kernel harvesting requirements in corn plots

Video SAR Moving Target Shadow Detection Based on Intensity Information and Neighborhood Similarity

Video Synthetic Aperture Radar (SAR) has shown great potential in moving target detection and tracking. At present, most of the existing detection methods focus on the intensity information of the moving target shadow. According to the mechanism of shadow formation, some shadows of moving targets present low contrast, and their boundaries are blurred. Additionally, some objects with low reflectivity show similar features with them. These cause the performance of these methods to degrade. To solve this problem, this paper proposes a new moving target shadow detection method, which consists of background modeling and shadow detection based on intensity information and neighborhood similarity (BIIANS). Firstly, in order to improve the efficiency of image sequence generation, a fast method based on the Back-projection imaging algorithm (f-BP) is proposed. Secondly, due to the low-rank characteristics of stationary objects and the sparsity characteristics of moving target shadows presented in the image sequence, this paper introduces the low-rank sparse decomposition (LRSD) method to perform background modeling for obtaining better background (static objects) and foreground (moving targets) images. Because the shadows of moving targets appear in the same position in the original and the corresponding foreground images, the similarity between them is high and independent of their intensity. Therefore, using the BIIANS method can obtain better shadow detection results. Real W-band data are used to verify the proposed method. The experimental results reveal that the proposed method performs better than the classical methods in suppressing false alarms, missing alarms, and improving integrity

Porous Thermal Insulation Polyurethane Foam Materials

Porous thermal insulation materials (PTIMs) are a class of materials characterized by low thermal conductivity, low bulk density and high porosity. The low thermal conductivity of the gas enclosed in their pores allows them to achieve efficient thermal insulation, and are they among the most widely used and effective materials in thermal insulation material systems. Among the PTIMs, polyurethane foam (PUF) stands out as particularly promising. Its appeal comes from its multiple beneficial features, such as low density, low thermal conductivity and superior mechanical properties. Such attributes have propelled its broad application across domains encompassing construction, heterogeneous chemical equipment, water conservation and hydropower, and the aviation and aerospace fields. First, this article outlines the structure and properties of porous thermal insulation PUF materials. Next, it explores the methods of preparing porous thermal insulation PUF materials, evaluating the associated advantages and disadvantages of each technique. Following this, the mechanical properties, thermal conductivity, thermal stability, and flame-retardant characteristics of porous thermal insulation PUF materials are characterized. Lastly, the article provides insight into the prospective development trends pertaining to porous thermal insulation PUF materials

Theoretical study of OCCHCN as a potential alternative insulation gas for SF6

Cyanoketene (OCCHCN) has been reported as a potential alternative insulation gas for SF6 in Patent US0135817. Stationary point equilibrium geometries on the ground state have been optimized at the B3LYP/6-311+G(d,p) level, and the harmonic vibration frequencies are calculated at the same level. The HOMO-LUMO energy gaps (Eg), ionization potentials (IP), and electron affinities (EA) of the studied molecules are obtained. The minimum energy path (MEP) is obtained by the intrinsic reaction coordinate (IRC) theory, and the energetic information is further refined by QCISD(T) (single-point) method. The results show that OCCHCN can be used as SF6 alternative insulation gas in high voltage equipment according to potential energy surface analysis. As the isomerization and the cleavage reactions potential barriers are lower than the Eg and IP values, resulting in OCCHCN is not easy to be ionized and excited