Wookey Lee

Treatment of intertrochanteric fracture with the Gamma AP locking nail or by a compression hip screw – a randomised prospective trial

Abstract.The Gamma AP (Asia-Pacific) locking nail (GAPN) is a modification of the standard Gamma locking nail made especially for use in Oriental patients. We made a randomised prospective comparison of the compression hip screw (CHS) and the Gamma AP locking nail for the internal fixation of 60 intertrochanteric fractures of the hip in elderly patients by comparing perioperative details and analysing the radiographic and clinical results. The operation time for the GAPN group was shorter than for the CHS group and the intraoperative blood loss was lower. The Gamma AP nail enabled earlier mobilisation. We found no significant difference in the time to union and the length of sliding of the lag screw between the two groups. The decrease in the neck shaft angle in the Gamma nail group was significantly smaller than in the CHS patients. There were no significant mechanical complications, such as fracture of the femoral shaft or failure of fixation in the Gamma nail group. On the basis of our observations we conclude that the Gamma AP locking nail is more efficient than the CHS in the treatment of intertrochanteric fractures in geriatric patients.Résumé.Le clou verrovillé Gamma AP (Asie Pacifique) est la modification du clou Gamma standard pour le morphotype Oriental. Nous avons compare au hasard la compression du col du fémur vissé au clou verrouillé Gamma AP pour la fixation de 60 fractures inter-trochantériennes du col de fémur, en comparant, les details périopératoires, en analysant la radiographie et les résultats cliniques chez des patients agés. La durèe d’opération pour le groupe GAPN était plus courte que pour le groupe CHS, et les pertes sanguines étaient plus faibles dans le groupe GAPN. Le clou Gamma AP favorise l’ambulation plus rapide. Nous n’avons trouvé aucune différence notable de temps de consolidation ni de glissement de la vis entre les deux groupes. La diminution d’angle cervico-diaphysaire dans le groupe clou Gamma était nettement moindre que celle du groupe CHS. Il n’y avait, pas de probléme mécanique grave dans le groupe clou Gamma comme la fracture de diaphyse femorale ou la fixation defailiante. Nous concluons que dans le traitement de la fracture intertrochanterienne de patients agés, le clou verrorillé Gamma AP serait plus efficace que le CHS.

Mobile Web Navigation in Digital Ecosystems Using Rooted Directed Trees

Due to advances in techniques for digital ecosystems and cyber engineering, users can now submit queries to search for inclusive information from different Web objects using mobile handheld devices such as smartphones in many real-life situations. As these mobile devices are not necessarily connected to the Internet all the time, users usually want to be able to get information such as e-mail, news, weather, and Web feed as soon as their mobile devices are connected to the Internet. However, these mobile devices have relatively limited resources. In this paper, we focus on mobile web navigation. Specifically, we use rooted directed trees, which enable users to access valuable information from the highly ranked and relevant Web sites, for answering user-inclusive queries.

Optimized combinatorial clustering for stochastic processes

As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

A Network-Flow Based Influence Propagation Model for Social Networks

Due to its popularity, influence propagation model has been recently exploited in several social network applications. However, there are some limitations in applying the model to the social network, in which negative information is propagated. In this paper, we present an effective information propagation model to overcome these limitations. Our minimum cost flow model effectively propagates influences to neighbouring nodes with minimum costs in each path of the social network. The model removes noise associated with social network marketing information and propagates influences without overlapping in information nodes.

Categorical data skyline using classification tree

Skyline query is an effective method to process large-sized multidimensional data sets as it can pinpoint the target data so that dominated data (say, 95% of data) can be efficiently excluded as unnecessary data objects. However, most of the conventional skyline algorithms were developed to handle numerical data. Thus, most of the text data were excluded from being processed by the algorithms. In this paper, we pioneer an entirely new domain for skyline query--namely, the categorical data--with which the corresponding ranking measures for the skyline queries are developed. We tested our proposed algorithm using the ACM Computing Classification System.

Efficient distributed parallel top-down computation of ROLAP data cube using mapreduce

The computation of multidimensional OLAP(On-Line Analytical Processing) data cube takes much time, because a data cube with D dimensions consists of 2D cuboids. To build ROLAP(Relational OLAP) data cubes efficiently, existing algorithms (e.g., GBLP, PipeSort, PipeHash, BUC, etc) use several strategies sharing sort cost and input data scan, reducing data computation, and utilizing parallel processing techniques. On the other hand, MapReduce is recently emerging for the framework processing a huge volume of data like web-scale data in a distributed/parallel manner by using a large number of computers (e.g., several hundred or thousands). In the MapReduce framework, the degree of parallel processing is more important to reduce total execution time than elaborate strategies. In this paper, we propose a distributed parallel processing algorithm, called MRPipeLevel, which takes advantage of the MapReduce framework. It is based on the existing PipeSort algorithm which is one of the most efficient ones for top-down cube computation. The proposed MRPipeLevel algorithm parallelizes cube computation and reduces the number of data scan by pipelining at the same time. We implemented and evaluated the proposed algorithm under the MapReduce framework. Through the experiments, we also identify factors for performance enhancement in MapReduce to process very huge data.

Providing ranked cooperative query answers using the metricized knowledge abstraction hierarchy

Abstract Cooperative query answering supports query relaxation and provides approximate answers as well as exact answers. To facilitate the query relaxation, a knowledge representation framework has been widely adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. In this paper, we propose a metricized knowledge abstraction hierarchy (MKAH) that supports multi-level data abstraction hierarchy and distance metric among data values. We show that the abstraction hierarchy is useful in representing the semantic relationship, and the abstraction hierarchy can provide data values with different scope according to their abstraction levels. The distance metric expresses the semantic similarity among data values with quantitative measure, and thus it enables query results to be ranked. To verify the practicality and effectiveness of the MKAH, we have implemented a prototype system in the area of career job search. Through various experiments, we show that the MKAH provides rich semantic representation and high quality distance measure. Furthermore, the experiments confirm that the domain adopting the MKAH can be compatible with other numeric domains, and that is advantageous in building up large scaled systems.

AnchorWoman: top-k structured mobile web search engine

With advances in technology, mobile handheld devices-such as PDAs-have become very popular. In many real-life situations, users want to find structuring information using these mobile devices, which are convenient to use but have relatively limited resources. In this paper, we present a top-k structured mobile Web search engine. It uses a top-k adaptable search-tree method that utilizes hierarchical structure of hypermedia objects to effectively look for structuring information from the mobile Web model. The engine, which is implemented in the mobile environment, provides users with top-k adaptive Web search recommendations for mobile handheld devices.

Adaptive Hierarchical Surrogate for Searching Web with Mobile Devices

This paper proposes a new web-page search mechanism suitable for mobile devices, called an adaptive hypermedia search. It utilizes hierarchical structure of hypermedia objects for handheld devices, such as cellular phones and PDAs, which have usually limited resources. We developed a tree-filtering algorithm and a Top_K_algorithm that can be used to provide search recommendations for mobile devices. In the experimental section, we implement our system in Windows Mobile 5.0 SDK environment and show that our method can save mobile resource in terms of web- page search time. Also, we show the resource savings according to different wireless technologies such as WiBro, HSDPA, and Wi-Fi.

An innovative majority voting mechanism in interactive social network clustering

We describe a new method of voting system in social networks environment1. We suggest a sequence of continuous support via a social network after electing representatives or exemplars in the network that is different from the typical majority voting. In other words, this paper suggests the method of elected representatives using network clustering approach to counts voting. On the network structure, sending messages from each node reflects the influence or importance to the representative and that can be readjusted and send back to each node. Where the representatives can be clustered within which the selectivity can be decided through the graph edges. In the experiment our algorithm outperformed conventional approaches in social network synthetic dataset as well as real dataset.