国有企业改革的分类与分流研究

我们国家在对国有企业改革探索的过程当中,因为在理论指导方面的缺失,一直没有获得较为理想的效果。目前,对那些商业类的国有企业加以竞争性和非竞争性的分类应该说是其分类的重要内容,继而逐步推动分流改革,对非竞争性国有企业在其制度上转变为公营企业已势在必行。本文主要探讨了国有企业的分类和分流改革。

综合医院交通精细化治理的实际应用——以东莞市人民医院为例

大型综合医院就医“出行难”和“停车难”的问题普遍存在,如何通过精细化的治理手段解决医院日益凸显的交通问题受到各方的广泛关注。以东莞市人民医院为例,介绍医院的基本情况与出行特征,深入剖析大型综合医院普遍存在的交通问题与成因,按照“以人为本、分类分流、精准供给、规范秩序”的思路,从医院设施布局、内外交通组织和停车管理等方面提出精细化的交通改善措施,为其他大型综合医院的交通优化提供参考。

秉承陶行知教育精神 深入推进专业综合改革

举办本科专业教育十七年来,南京晓庄学院以陶行知精神办学育人,确立＂地方应用型品牌大学＂的发展定位,构建生活教育理论指导下的＂大课程与大教学体系＂,试行＂分类招生,分流培养＂的招生制度与人才培养模式,推行整体规划和分步实施的专业综合改革计划。

Accelerating Packet Classification with Counting Bloom Filters for Virtual Open Flow Switching

The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in Open Flow-based software defined networks. This paper first takes an insight into packet classification in virtual Open Flow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open v Switch in terms of average search length, and contributes to promote virtual Open Flow switching performance.

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams

The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Building a Tree Adjusted Logistic Classification Model in Biomarker Data Analyses

Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification. In this paper, a new method of using a tree regression to improve logistic classification model is introduced in biomarker data analysis. The numerical results show that the linear logistic model can be significantly improved by a tree regression on the residuals. Although the classification problem of binary responses is discussed in this research, the idea is easy to extend to the classification of multinomial responses.

Experimental study of piperlongumine inducing apoptosis of human breast adenoma MDA-MB-231 cells

Objective： The aim of the study was to investigate the apoptosis induced by piperlongumine on human breast adenoma MDA-MB-231 cells and the mechanism involved. Methods： Human breast adenoma MDA-MB-231 cells line was cultured in vitro. The inhibitory effect of piperlongumine on the proliferation of human breast adenoma MDA-MB-231 cells was measured by CCK-8 assay. Distribution of cell cycle was analyzed by flow cytometry. The apoptosis rates of MDA-MB- 231 cells were measured using Annexin V/PI staining. The flow cytometry with the probe of DCFH-DA was used to detect the intracellular reactive oxygen species levels. Western blot was used to explore the protein expression of Bcl-2 and Bax. Results： The CCK-8 assay showed that piperlongumine had an inhibiting effect on the proliferation of MDA-MB-231 cells in a concentrationand time-dependent manner. MDA-MB-231 cells were markedly arrested at G0/G1 phase after treatment of piperlongumine. Piperlongumine induced apoptosis of MDA-MB-231 cells obviously. The level of intracellular reactive oxygen species was increased in a dose-dependent manner. The antioxidant N-acetyI-L-cystein inhibited the apoptosis of cells and the level of intracellular reactive oxygen species was also decreased. By Western blot analysis, we found the expression of Bax was up-regulated whereas that of Bcl-2 was down-regulated in a concentration-dependent manner. Conclusion： PiperIongumine possesses a significant function for inhibiting proliferation, arresting cells at G0/G1 phase and inducing apoptosis of MDA-MB-231 cells, which seems to be associated with the increased generation of intracellular reactive oxygen species as well as the down-regulation of Bcl-2 and up-regulation of Bax.

Video learning based image classification method for object recognition

Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.

An eco-functional classification for environmental flow assessment in the Pearl River Basin in Guangdong, China

River classification has emerged as a major application of environmental science, which can overcome the deficcts of tradition- al methods in focusing on the single objective of maintaining specified, valued features of ecosystems. However, current ef- forts to classify rivers by hydrologic processes may result in a growing temptation to ignore ecological variability across basins Thus, an eco-functional classification is proposed for river management in the Pearl River Basin. This method riews ecological functions as fundamental characteristics of riverine systems and provides a framework for dividing a basin iato eco-specific categories according to the heterogeneity of the primary ecological functions. In addition, we proposed specific environmental flow methodologies corresponding to three typical river reaches of the basin by perceiving the key attributes of flow variability In the upstream region of the West River, flow velocity and wetted perimeter are considered as the key attributes of maintain- ing fish habitat; in a small-sized mountainous tributary of the North river, we choose water surface area to maintain the conti- nuity and biodiversity of the river; while for the aspect of river landscape in the midstream reach of the East R vet, water level is crucial for maintaining the aesthetic value. This research highlights the ecologically relevant heterogeneity that occurs within and among regions of a basin, and is expected to contribute to a simpler and more comprehensive river manage：nent.

Study of Substrate and Physico-Chemical Base Classification of the Rivers of Nepal

The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of the Koshi system, Aandhikhola, Arungkhola, East Rapti, Karrakhola, Seti and main channel Narayani of the Gandaki system, and two independent systems within Nepal, Bagmati and Tinau. Among the morphologies, river bed or the substratum was taken as the main variable for the analysis which was categorized into 7 types as rocks, boulders, cobbles, pebbles, gravels, sand and silt. There were 23 sampling sites each with 2 stretches of around 100m in those rivers. The data were taken as a percentage, and to avoid biases it was observed visually by the same person for a complete year in every season. With 23 sites each with 2 stretches and 4 replicates corresponding to 4 seasons, there are altogether 184 observations, each termed as a case, that constitute this work. Canonical Discrimination Analysis (CDA) which is most suitable when the data pool is huge was applied to see if the rivers studied distinguish themselves in terms of its morphology. The result was remarkably successful and was close to the established regional classification of the rivers. This kind of river classification has great application in the utilization, conservation and restoration of the most important natural re-source of the country.