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Xu L.L.,Zhejiang Textile and Fashion College
Applied Mechanics and Materials | Year: 2014

The development of sportswear is closely related to the development of scientific and technological,the improvement of the community's civilization.As the professional sportswear design and production are close to new material,high-tech sportswear will not only help the athletes improve performance and raise campaign skills,but also more advantageous in protecting athlete's safety. © (2014) Trans Tech Publications, Switzerland.

Qiu W.-R.,Jing de Zhen Ceramic Institute | Xiao X.,Jing de Zhen Ceramic Institute | Xiao X.,ZheJiang Textile and Fashion College | Xiao X.,Gordon Life Science Institute | And 2 more authors.
International Journal of Molecular Sciences | Year: 2014

Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

Ying Y.,Zhejiang Textile and Fashion College
Lecture Notes in Electrical Engineering | Year: 2011

Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems. © 2011 Springer-Verlag Berlin Heidelberg.

Ye H.,Zhejiang Textile and Fashion College
Journal of Software | Year: 2011

With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using itembased collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems. © 2011 ACADEMY PUBLISHER.

Zhang J.F.,Zhejiang Textile and Fashion College
Advanced Materials Research | Year: 2013

Due to fast-paced society and fierce competition, more and more attentions are paid to consumers' way of life and their psychological needs in fashion design. Color, a major element of fashion design, is a certain psychological feeling stimulated from vision, a combination of perception and ration. It should be designed and applied from the aspect of color psychology. Based on the cases and survey, this study aims to explore the application of psychological effect of color in fashion design. Fashion designers should consider the fashion color, focus on consumers' preference for color, shade, style, patterns and the psychological effect, meet psychological needs of the target and potential consumers, and express psychological suggestion effect of color. Designers should also develop products at the level of "matter" and "spirit", select fashion color to consumers' mind, and finally apply color psychological effect in fashion planning and designing, products developing and marketing. The results of this study may play a guiding role in the strategy of modern fashion design. © (2013) Trans Tech Publications, Switzerland.

Xiao X.,Jing de Zhen Ceramic Institute | Xiao X.,ZheJiang Textile and Fashion College | Xiao X.,Gordon Life Science Institute | Min J.-L.,Jing de Zhen Ceramic Institute | And 3 more authors.
PLoS ONE | Year: 2013

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks. © 2013 Xiao et al.

Ju H.,Zhejiang Textile and Fashion College
Applied Mechanics and Materials | Year: 2012

The shortage of transmission link bandwidth limits the application of cloud computing. In this contribution a multilink dynamic aggregation model is suggested. Through effective integration of link flow monitoring units, link aggregation control unit and the gateway, more effective aggregation of transmission multilink and rational utilization of network bandwidth in cloud computing network were realized. In addition, dynamically access load flow problems were solved based on the actual load of different transmission link. The model is costly, stable and highly efficient.

Ju H.,Zhejiang Textile and Fashion College
International Journal of Sensor Networks | Year: 2014

The disaster recovery structure and mechanism for cloud computing networks are the key factors that determine the disaster recovery ability and efficiency of networks. Through studies on technologies, including intelligent monitoring, load scheduling, hybrid gateways, clustering and data mirroring, we construct a multi-level disaster recovery network framework with remote intelligent monitoring mechanism by integrating remote dynamic monitoring, round robin algorithm, address mapping, load balancing and virtual private network technology. The principle of the framework is also elucidated. The proposed framework solves intelligent remote disaster recovery problem of cloud computing networks. Safe inter-regional services and fast response are realised. The structure also increases the disaster recovery ability and the security level of cloud computing networks. Copyright © 2014 Inderscience Enterprises Ltd.

Du L.Y.,Zhejiang Textile and Fashion College
Applied Mechanics and Materials | Year: 2013

With the development of multimedia and information network technology, physical education has broken the traditional teaching mode. Furthermore, teaching method and content have also been innovated. The process of sports teaching has not only been confined to the classroom. The class also implements the multimedia teaching process outside. Under this background, this paper has designed the manufacturing operation of aerobics' multimedia teaching content by taking advantage of the WEB and VRML technology. At the same time, the vc++ programming software has also been used to control program so as to develop the multimedia network and interactive teaching platform for aerobics. In addition, it also gives the basic framework and module of multimedia teaching platform and realizes the process of integrating in-and-out-of-class teaching. Finally, this paper evaluates the feasibility and stability of the experimental teaching platform system. Experimental studies have shown that this kind of teaching is beneficial to exercise the student's multi-dimensional thinking ability, and can develop their creative thinking, thus achieving the teaching effect that gym and learning can be unified. © (2013) Trans Tech Publications, Switzerland.

Hua Q.,Zhejiang Textile and Fashion College
Advances in Information Sciences and Service Sciences | Year: 2011

The recommendation model of textile products used by family, which is based on adaptive resonance theory(ART) of artifical neural network, is researched. The first, data collection of web pages, which is browsed, is introduced. Then data refining procedure is analysed. In order to solve the recommendation model, we study the ART, which analyses clustering items and processes, implements self-adaptive recommendation services. Experimental results show that the system is stable and effective to predict users interest preferences and capture users excursion of interests. All these results demonstrate that the recommendation algorithm performed well.

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