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University libraries are currently facing their greatest challenge. For more than a decade, niversity libraries ave been under the pressure to change its way of operation and management due to the tight of budget nd the appearance of online resources. These situations cause the ibraries to value their development of esources and the application of business marketing to better service. How to retain and grow users through nhancement of service becomes the great concern by library managers. Customer Relationship Management CRM) is not a tactical or functional approach but a key strategic process. A comprehensive CRM will ighlight the preference and learning needs of readers individually for cost saving and enhance the retention nd loyalty. We aim to propose an integrated conceptual frameframework implying with business marketing lan and CRM perspective through literature finding.

In this paper, application of SLMM to a case was described. Because SLMM consultants considered the arget of SLMM as a virtual laboratory in commercial company, it may be difficult to say this result can e applied to real one in same way. But this case study will give enough information about the real pplication of SLMM to real company, and many people can understand how to modify and use SLMM according to heir own environment.

This paper presents a data gathering for Advanced Process Control (APC) in FA (Factory Automation). It s important that the gathered data in FA to control all the processing, to be a predictive maintenance of quipments and manage products. We gathered the entire data of all in FA through Equipment Engineering ystem (EES) and analyzed that to control a processing, predict the next processing and manage the various hings for the enhancement of productivity, overall equipment efficiency, product yield and quality. And we nabled APC to operate micro process control by gathering data from the process control system not only in he current process in real time but also in past process through back-tracking of products [1, 5]. So, we ontrol micro process based on the acquired data using simulating functionality using APC. This simulation roduces the outcome of the data analysis and the operation of micro process control that leads to reduce rrors. The proposed the data gathering system based on agent brings an efficient management of micro rocess control in automated processing. It is optimized in the semi-conductor and the flat panel display FPD) industry.

Power consumption is becoming a primary concern as a result of tremendous increasing in computer power usage. Innumerable methods and techniques have been exploited to address this problem but few concentrate on collaborative approaches. This paper presents coordination mechanisms that integrate operating systems with compilers under power reduction techniques such as DPM and DVS. By remaining the information generated at compile stage about an application’s structure and performance characteristics as much as possible and committing it to system software at run stage, the system software, especially the operation system and compiler, are collaborating toward file-grain power optimizations. The proposed coordination mechanisms also make it possible to integrate the power optimization approaches in our prior work into a whole system. Thus, the optimizations working at distinct levels can be overlaid at run time, and the power reduction effect can be enhanced.

How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage by stage, and established the question classification model based on the incremental modified Bayes. A question classification experiment was done in the domain of Yunnan tourism, the experimental results showed that the presented method evidently excelled than the modified Bayes method in the accuracy rate and the training time, the average accuracy rate was improved 3.3 percentage points than the accuracy rate of the modified Bayes method; the average training time was improved 39.1 percentage points than the training time efficiency of the modified Bayes method.

Many applications in vehicular networks need the data to be disseminated from a source vehicle to a large umber of vehicles in the network. Although many solutions to this problem have been previously proposed y the research community, some challenges and failure scenarios still remain unsolved particularly when the orwarding vehicle is located at an intersection and it wants to disseminate the data to all the intersecting oad segments. In this paper, we first evaluate some of the previously proposed directional mode (along the traight roads) data dissemination mechanisms and then integrate the one with the best performance with our ovel reliable robust intersection mode data dissemination mechanism in order to come up with a united echanism for disseminating data both along the straight roads and at intersections. The effectiveness of our roposed mechanism is verified by performance evaluations.

The proposed broadcast authentication protocols for wireless sensor networks can be divided into two ategories: protocols based on digital signature and protocols based on improved message authentication code. his paper implements and evaluates the performance of two broadcast authentication: TinyECC, which is ased on ECDSA, and GBA, which is based on improved MAC. Through analysis of the performance ifference of the two protocols, this paper proposes a hybrid broadcast authentication protocol (HBA). HBA chieves an appropriate compromise of security and performance according to packet value. The analysis and xperiments show that HBA is efficient and practical.

A new distributed algorithm of data ccompression based on hierarchical cluster model for sensor networks s proposed, the basic ideas of which are as follows, firstly the whole sensor network is mapped into a kind f hierarchical clusters model, and then different wavelet transform models are used to commit data ompression in inner and super clusters respectively, according to the relative regularity of sensor nodes eployed in the inner clusters, and the relative irregularity of sensor nodes deployed in super cluster. heoretical analyses and simulation results show that, the above new methods have good performance of pproximation, and can compress data and reduce the amount of data efficiently. So, it can prolong the ifetime of the whole sensor network to a greater degree.