An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Purpose: The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes. Design/methodology/approach: The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model. Findings: This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically. Originality/value: The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses

Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images

Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer’s disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, 1) the discriminative landmarks are first automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; 2) high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine (SVM) is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the ADNI database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD vs. HC and 79.02% for MCI vs. HC, respectively

1,2-Bis[5-(2,2′-dicyano­vinyl)-2-n-pentyl-3-thien­yl]-3,3,4,4,5,5-hexa­fluoro­cyclo­pent-1-ene: a new photochromic diaryl­ethene compound

The title compound, C31H26F6N4S2, is a new photochromic dithienylethene with dicyano­vinyl subsitituents. In the crystal structure, the mol­ecule adopts a photoactive anti­parallel conformation, with two n-pentyl groups located on opposite sides of the cyclo­pentene ring. The cyclo­pentene ring assumes an envelope conformation. The distance between the two reactive C atoms on the thio­phene rings is 3.834 (7) Å. One of the n-pentyl groups is disordered over two positions; the site occupancy factors are ca 0.7 and 0.3

3-(4-{3,3,4,4,5,5-Hexafluoro-2-[5-(3-methoxy­phen­yl)-2-methyl-3-thien­yl]cyclo­pent-1-en­yl}-5-methyl-2-thien­yl)benzonitrile

The title compound, C29H19F6NOS2, is a new unsymmetrical photochromic diarylethene derivative with different meta-phenyl substituents. The distance between the two reactive (i.e. can be irradiated to form a new chemical bond) C atoms is 3.501 (4) Å; the dihedral angles between the mean plane of the main central cyclo­pentene ring and the thio­phene rings are 47.7 (5) and 45.1 (2)°, and those between the thio­phene rings and the adjacent benzene rings are 29.4 (2) and 28.4 (3)°. The three C atoms and the F atoms of hexa­fuorocyclo­pentene ring are disordered over two positions, with site-occupancy factors of 0.751 (4) and 0.249 (4)

Polymicrobial periodontal disease triggers a wide radius of effect and unique virome.

Periodontal disease is a microbially-mediated inflammatory disease of tooth-supporting tissues that leads to bone and tissue loss around teeth. Although bacterially-mediated mechanisms of alveolar bone destruction have been widely studied, the effects of a polymicrobial infection on the periodontal ligament and microbiome/virome have not been well explored. Therefore, the current investigation introduced a new mouse model of periodontal disease to examine the effects of a polymicrobial infection on periodontal ligament (PDL) properties, changes in bone loss, the host immune response, and the microbiome/virome using shotgun sequencing. Periodontal pathogens, namely Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Fusobacterium nucleatum were used as the polymicrobial oral inoculum in BALB/cByJ mice. The polymicrobial infection triggered significant alveolar bone loss, a heightened antibody response, an elevated cytokine immune response, a significant shift in viral diversity and virome composition, and a widening of the PDL space; the latter two findings have not been previously reported in periodontal disease models. Changes in the PDL space were present at sites far away from the site of insult, indicating that the polymicrobial radius of effect extends beyond the bone loss areas and site of initial infection and wider than previously appreciated. Associations were found between bone loss, specific viral and bacterial species, immune genes, and PDL space changes. These findings may have significant implications for the pathogenesis of periodontal disease and biomechanical properties of the periodontium. This new polymicrobial mouse model of periodontal disease in a common mouse strain is useful for evaluating the features of periodontal disease

Salinity stress induces the production of 2-(2-phenylethyl)chromones and regulates novel classes of responsive genes involved in signal transduction in Aquilaria sinensis calli

Gene ontology (GO) functional enrichment analysis of assembled unigenes. A total of 53514 matched unigenes were classfied into three principal categories: biological process, cellular component and molecular function. (PPTX 165 kb