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Magnetars are believed as neutron stars (NSs) with strong magnetic fields. X-ray flares and fast radio bursts (FRBs) have been observed from the magnetar (soft gamma-ray repeater, SGR J1935+2154). We propose that the phase transition of the NS can power the FRBs and SGRs.Based on the equation of state provided by the MIT bag model and the mean field approximation, we solve the Tolman-Oppenheimer-Volkoff equations to get the NS structure. With spin-down of the NS, the hadronic shell gradually transfers to the quark shell.The gravitational potential energy released by one time of the phase transition can be achieved. The released energy, time interval between two successive phase transitions, and glitch are all consistent with the observations of the FRBs and the X-ray flares from SGR J1935+2154. We conclude that the phase transition of an NS is a plausible mechanism to power the SGRs as well as the repeating FRBs., 11 pages, 3 figures

In the current study, schisandrin B(SchB)-loaded F127 modified lipid-polymer hybrid nanoparticles(SchB-F-LPNs) were developed to improve the inhibition of breast cancer lung metastasis. Modified nanoprecipitation method was used to prepare SchB-F-LPNs. The nanoparticles were spherical in shape with shell-core structure by TEM observation. SchB-F-LPNs showed a mean particle size of(234.60±6.11) nm with zeta potential of(-5.88±0.49) mV. XRD results indicated that SchB existed in the nanoparticles in an amorphous state. The apparent permeability coefficient through porcine mucus of F-LPNs was 1.43-fold of that of LPNs as shown in the in vitro mucus penetration study. The pharmacokinetics study showed that the C_(max) of SchB was(369.06±146.94) μg·L~(-1),(1 121.34±91.65) μg·L~(-1) and(2 951.91±360.53) μg·L~(-1) respectively in SchB suspensions group, SchB-LPNs group and SchB-F-LPNs group after oral administration in rats. With SchB suspensions as the reference formulation, the relative bioavailability of SchB-F-LPNs was 568.60%. SchB-F-LPNs inhibited the morphological change during transforming growth factor-β1(TGF-β1)-induced epithelial-mesenchymal transition. In addition, SchB-F-LPNs significantly decreased the number of metastatic pulmonary nodules in 4 T1 tumor-bearing mice, suggesting that SchB-F-LPNs may inhibit the metastasis of breast cancer. These results reveal the promising potential of SchB-F-LPNs in treatment of breast cancer lung metastasis.

The optimal treatment (liver transplantation [LT] vs surgical resection [SR]) for early-stage hepatocellular carcinoma (HCC) remains controversial. A total of 209 SR patients and 129 LT patients were identified at our institution. After eliminating 27 patients with Child–Pugh C, the data from 209 SR patients and 102 LT patients were analyzed using a propensity score matching (PSM) model. Forty-six pairs were generated. A subgroup analysis was conducted based on the alpha-fetoprotein (AFP) level or platelet count (PLT). A survival analysis was performed using the Kaplan–Meier method. Gender, satellite lesions, and the treatment method were predictors of HCC recurrence. The Ishak score and treatment methods were associated with long-term survival after surgery. Before PSM, LT patients had a better prognosis than those treated by SR. Among HCC patients with childhood A/B cirrhosis, after PSM, SR achieved similar overall survival outcomes compared with LT. LT and SR resulted in comparable long-term survival for patients with or without thrombocytopenia. Patients with an AFP ≥ 400 ng/mL might achieve more survival benefits from LT. Our propensity score model provided evidence that, compared with transplantation, surgical resection could result in comparable long-term survival for resectable early-stage HCC patients, except for the AFP ≥ 400 ng/mL HCC subgroup. Surgical resection might not be a contraindication for early-stage HCC patients with thrombocytopenia due to their similar prognosis after transplantation.

The paper analysed the mobile node, home agent, and foreign agent of mobile IP network firstly, some key technique, such as mobile IP network basical principle, protocol work principle, agent discovery, registration, and IP packet transmission, were discussed. Then a network simulation model was designed, validating the characteristic of mobile IP network, and some advantages, which were brought by mobile network, were testified. Finally, the conclusion is gained: mobile IP network could realize the expectation of consumer that they can communicate with others anywhere.

QoS is essential for selecting an appropriate service from a set of services that match user’s functionality requirements. To enable this, a QoS Ontology for semantic service discovery, which provide the uniform model for QoS, is proposed in this article. With this ontology, the algorithms for QoS matchmaking and service ranking are presented. And an expanded service discovery framework is also presented.

This paper proposed an incremental textclustering algorithm based on semantic sequence. Using similarity relation of semantic sequences and calculating the cover of similarity semantic sequences set, the candidate cluster with minimum entropy overlap value was selected as a result cluster every time in this algorithm. The comparison of experimental results shows that the precision of the algorithm is higher than other algorithms under same conditions and this is obvious especially on long documents set.

Aiming at the characteristic of the dependency between the application components and the application server platform, a rejuvenation strategy with two different levels of rejuvenation granularities is put forward in this paper including the application component rejuvenation and the application server system rejuvenation. The availability and maintenance cost functions are obtained by means of establishing the application server aging model and the boundary condition of the optimal rejuvenation time is analyzed. Theory analysis indicates that the two-level rejuvenation strategy is superior to the traditional single level one. Finally, evaluation experiments are carried out and numerical result shows that compared with the traditional rejuvenation policy, the rejuvenation strategy proposed in this paper can further increase availability of the application server and reduce maintenance cost.

With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then autonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to incorporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data source by ontology. Experiments suggest that the framework should be useful in guiding the continous mining process.

With the development of middleware technology, there are some new features to be adopted on demand in middleware platform based on container component. Aiming at the adaptation of middleware platform, from the nature of component container, this paper begins with behaviors features of EJB (Enterprise Java Bean) container, extracts general behaviors protocol of container, and defines its formal behaviors specification. In the end, the interactive relationship of components is described by WRIGHT language, and the configuration of whole system is shown. The method proposed in this paper mines the hidden features of framework of component container, and applies to refactor middleware platform in reflection and aspect-oriented programming technology.

Requests distribution is an key technology for Web cluster server. This paper presents a throughput-driven scheduling algorithm (TDSA). The algorithm adopts the throughput of cluster back-ends to evaluate their load and employs the neural network model to predict the future load so that the scheduling system features a self-learning capability and good adaptability to the change of load. Moreover, it separates static requests from dynamic requests to make full use of the CPU resources and takes the locality of requests into account to improve the cache hit ratio. Experimental results from the testing tool of WebBenchTM show better performance for Web cluster server with TDSA than that with traditional scheduling algorithms.

Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization.

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time.

Accurately predicting Web user access behavior can minimize user-perceived latency, which is crucial in the rapidly growing World Wide Web. Although traditional Markov models have helped predict user access behavior, they have serious limitations. Hybrid-order treelike Markov models predict Web access precisely while providing high coverage and scalability.

Considering the requirement to build space and ground integrated training, mission command and decision-making space flight simulator, with the development of manned space engineering, a standards-based HLA astronaut training, command and decision-making integrated space and ground simulator distributed architecture was presented. Firstly the system structure composed of the spacecraft, telemetry stations, command and control centers and ground-based training simulator is introduced. space and ground data link nodes and the data conversion process were elaborated. Then the distributed interactive implementation strategies between simulation data and flight data under three modes of space and ground online, offline and mixed-mode are analyzed. The causes and solutions of difficulties in application of the system are analyzed, discussed and tested. The system will further improve the existing simulator architecture, based on real flight data, the super-real-time flight missions is forecasted and fit, also can provide an important guarantee for collaborative mission training and real-time command and decision-making.

QoS is essential for selecting an appropriate service from a set of services that match user’s functionality requirements. To enable this, a QoS Ontology for semantic service discovery, which provide the uniform model for QoS, is proposed in this article. With this ontology, the algorithms for QoS matchmaking and service ranking are presented.

User behavior history has been employed to discover userpsilas preferences and interests for personalized systems. In this paper we propose a framework for extracting user interested items by analyzing user behavior history in digital library environment. Firstly, this paper models every userpsilas behaviors as a user behavior forest (UBF). Then user behavior log is formatted with several additional attributes, and we show how to construct UBF from such logs. At last, an algorithm is presented for measuring user interests on items by considering userpsilas behavior sequence, userpsilas behavior type, and the metadata of literature.

We present semantic sequence kernel (SSK) to detect document plagiarism, which is derived from string kernel (SK) and word sequence kernel (WSK). SSK first finds out semantic sequences in documents, and then it uses a kernel function to calculate their similarity. SK and WSK only calculate the gap between the first word and the last one. SSK takes into account each common word's position information. We believe SSK contains both local and global information so that it makes a great progress in small partial plagiarism detection. We compare SSK with relative frequency model and semantic sequence model, which is a word frequency based model. The results show that SSK is excellent on nonrewording corpus. It is also valid on rewording corpus with some impairment on the performance.

In this paper we present a frequent sequence pattern mining-based algorithm used for network intrusion detection, which is an application and extension of the SPADE algorithm. It is based on the idea that much behavior on the network appears as sequences of activities, according to the sequence patterns we computed, we can construct the intrusion rule base and legal action rule base, then we can detect known and novel intrusion activities by rule matching. In addition, when the system is running, we use an incremental sequence pattern mining algorithm to complement the rule library in order to avoid re-executing the algorithm on the entire dataset, thereby reducing execution time. The experimental results indicate that this algorithm is efficient enough to meet the needs for active detection of intrusion. Compared with most existing methods used in commercial systems which are built using purely knowledge engineering approaches, our algorithm is more intelligent and adaptive.

With the development of computer technology, computer applications in a distributed environment have become the trend of development. However, the even higher requirement of the data consistency and integrality under the distributed environment makes the traditional transaction process technology incompetent. To meet the new challenge, the object transaction service model for the distributed application environment was proposed. It combines both the traditional transaction process technology and the distributed object technology. The object transaction management system (OTMS) we implemented was based on the OTS model, which provides full capability of transaction service and management. Both the theory and the concrete implementation technology of the object transaction management system are presented in this paper.