A schema and ontology-aided intelligent information integration

The research issues of intelligent information integration have become ubiquitous and critically important in e-business (EB) with the increasing dependence on Internet/Intranet and information technology (IT). Accessing the intelligent information sources separately without integration may lead to the chaos of information requested. It is also not cost-effective in EB settings. A common general way to deal with heterogeneity problems in traditional III is to create a common data model. The eXtensible Markup Language (XML) has been the standard data document format for exchanging information on the Web. XML only deals with the structural heterogeneity; it can barely handle the semantic heterogeneity. Ontologies are regarded as an important and natural means to represent the implicit semantics and relationships in the real world. And they are used to assist to reach semantic interoperability in III in this research. In this paper, we provide a generic construct orientation no ad hoc method to generate the global schema to enable the web-based alternative to traditional III. We provide a wiser query method over multiple intelligent information sources by applying global-as-view (GAV) and local-as-view (LAV) approach with the use of ontology to enhance both structural and semantic interoperability of the underlying intelligent information sources. We construct a prototype implementing the method to provide a proof on the validity and feasibility.     

Selection of the implant thread pitch for optimal biomechanical properties: A three-dimensional finite element analysis

In this paper, effects of the implant thread pitch on the maximum Von Mises stresses were evaluated in jaw bones and implant–abutment complex by a finite element method. The thread pitch ranged from 0.5 mm to 1.6 mm. Results suggested that under axial load, the maximum equivalent stresses in cortical bone, cancellous bone and implant–abutment complex decreased by 6.7%, 55.2% and 22.3%, respectively with the variation of thread pitch; and under buccolingual load, 2.7%, 22.4% and 13.0%, respectively. When thread pitch exceeded 0.8 mm, minimum stresses were obtained. Data indicated that cancellous bone was more sensitive to thread pitch than cortical bone did; thread pitch played a great role in protecting dental implant under axial load than under buccolingual load; and thread pitch exceeding 0.8 mm were optimal selection for a screwed implant by biomechanical consideration.     

JPEG隐密分析中的重压缩检测

重压缩检测是多级隐密分析中关键的预处理部分,高准确率的重压缩检测是隐密分析获得更高性能的重要前提条件.深入研究了重压缩对于JPEG图像各种特征的影响,基于此提出了一种融合直方图分布特征、Benford特征、DFT特征的重压缩检测算法.仿真实验表明,该算法具有更高的检测率,能够适用于JPEG多级隐密分析中的重压缩检测.     

Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

Individual finger contribution in submaximal voluntary contraction of gripping

The objective of this study was to evaluate individual finger force and contribution to a gripping force, the difference between actual and expected finger forces and subjective discomfort rating at 10 different submaximal voluntary contraction (%MVC) levels (10-100% in 10 increments). Seventy-two participants randomly exerted gripping force with a multi-finger force measurement system. The individual finger force, gripping force and discomfort increased as %MVC levels increased. The middle and ring fingers exerted more force and contributed to a gripping force more than the index and little fingers due to their larger mass fractions of the digit flexor muscles. It was apparent at <50% MVC; however, the index finger increased its contribution and exerted even more force than expected at more than 50% MVC. Subjective discomfort supported the results of the objective measures. This could explain the conflicting findings between index and ring fingers in previous finger contribution studies. STATEMENT OF RELEVANCE: Hand tool design is of special interest in ergonomics due to its association with musculoskeletal disorders in the hand. This study reveals a different contribution pattern of the fingers in submaximal voluntary contraction of gripping exertion.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.