Study of SOA Component Dynamic Scheduling Based on Mobile Agent Coalition

Service-oriented components differ greatly with the traditional ones in the Service-Oriented Architecture. The ways of scheduling components seamlessly according to the agile computing needs to fit the e-business requirements is the key technology in the highly distributed, paralleled environment. In this paper, Based on the Multi-Agent Coalition, a new service-oriented component dynamic scheduling model is proposed, including the Multi-Agent Organization to schedule and coordinate the component assembly, the design of virtual execution task list table and self-learning algorithm, the definition of the Services component model, and the mechanism of collaboration Agents to search, discovery, concurrent schedule, dynamic assembly when execution in an heterogeneous network environment. To a large extent, the thesis solves the traditional problem of over-emphasis on centralized control logic, which leads to lacking flexibility in e-Business computing presently, and helps e-business service-oriented components become more adaptive, mobility and intelligence

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Neutrino decay as a possible interpretation to the MiniBooNE observation with unparticle scenario

In a new measurement on neutrino oscillation $\nu_{\mu}\to\nu_e$, the MiniBooNE Collaboration observes an excess of electron-like events at low energy and the phenomenon may demand an explanation which obviously is beyond the oscillation picuture. We propose that heavier neutrino $\nu_2$ decaying into a lighter one $\nu_1$ via the transition process $\nu_{\mu}\to \nu_e+X$ where $X$ denotes any light products, could be a natural mechanism. The theoretical model we employ here is the unparticle scenario established by Georgi. We have studied two particular modes \nu_\mu\to \nu_e+\Un and $\nu_\mu\to \nu_e+\bar\nu_e+\nu_e$. Unfortunately, the number coming out from the computation is too small to explain the observation. Moreover, our results are consistent with the cosmology constraint on the neutrino lifetime and the theoretical estimation made by other groups, therefore we can conclude that even though neutrino decay seems plausible in this case, it indeed cannot be the source of the peak at lower energy observed by the MiniBooNE collaboration and there should be other mechanisms responsible for the phenomenon.Comment: 14 pages, conclusions are changed; published version for EPJ

Subjective evaluation of the frequency of coffee intake and relationship to osteoporosis in Chinese men

Background: The main purpose of this study was to evaluate the associations between frequency of coffee intake and osteoporosis (OP) in a general Chinese male sample. Methods: We conducted a large-scale, community-based, cross-sectional study to investigate the associations by using a self-report questionnaire to estimate the frequency of coffee intake. A total of 992 men were available for data analysis in this study. Multiple regression models controlling for confounding factors to include frequency of coffee intake variable were performed to investigate the relationships for OP. Results: Positive correlations between frequency of coffee intake and T-score were reported (\u3b2 = 0.211, P = 0.024). Multiple regression analysis indicated that the frequency of coffee intake was significantly associated with OP (P < 0.05 for model 1 and model 2). The men with moderate frequency of coffee intake had a lower prevalence of OP. Conclusions: The findings indicated that consumption of coffee was independently and significantly associated with OP. The prevalence of OP was less frequent in Chinese men with moderate coffee intake. Trial registration: ClinicalTrials.gov, NCT0245139