Migration of a Broken Kirschner Wire after Surgical Treatment of Acromioclavicular Joint Dislocation

Kirschner wire (K-wire) is one of the commonly used implants in orthopaedics practice. Migration of the wire is one of the most frequently reported complications after fixation by the K-wire. In particular, it has been reported that a greater range of motion in the shoulder, negative intrathoracic pressure associated with respiration, gravitational force, and muscular activities may cause migration from the upper extremities. In general, thin and long foreign bodies with smooth surfaces that are localized within the tendon sheath and at an upper extremity can migrate more readily and can reach longer distances. Here, we present a patient with long-term migration of a broken K-wire who underwent fixation for acromioclavicular joint dislocation 5 years ago

Icariin Promotes the Early and Late Stages of the Fracture Healing in Rats

Recent research using statistical moments to describe moving shapes through an image sequence has led to an interest in reconstructing moving shapes from their moment description. This paper discusses how the moment description through a series of frames might be used to predict missing or intermediate frames within a sequence. Additionally, this highlights generic aspects of moment reconstruction which rarely receive more than scant attention. The ideas presented use Zernike moments, although the general framework is applicable to all types of moments. We show how a moving human silhouette can be reconstructed with accuracy by interpolation from a moment history.

Markov model based traffic classification with multiple features

Traffic prioritization has recently become more critical and crucial for home Wi-Fi networks due to the increased number of connected devices and applications. While some of these applications are delay sensitive, some have high throughput requirements. Quality of Service (QoS) in Wi-Fi is achieved via differentiation and prioritization of traffic streams, which can be performed successfully as long as the packets can be classified with high precision. As a solution for this problem, this paper presents a new Discrete Time Markov Chain-based traffic classification algorithm, which exploits a multidimensional feature set, named as k-Nearest Markov Component with 3 Dimensions (kNMC-3D). Considering results obtained on two different datasets with current, most popular multimedia applications from different categories, we present the performance of the proposed algorithm, kNMC-3D in comparison to kNMC, two feature extraction based machine learning techniques, Support Vector Machines (SVM) and Random Forest (RF) and a deep learning approach, Auto Encoder with RF (AE+RF). It is shown that kNMC-3D achieves 84.93% and 90.73% accuracy at the application level, 91.13% and 99.17% accuracy at category level on our dataset and a benchmark dataset, respectively. Outperforming the existing methods that focus mainly on feature extraction, kNMC-3D prevents information loss by making use of sequentiality in the traffic, while it improves kNMC by considering multiple features, number of bits, inter-arrival times and number of packets

Multimedia traffic classification with mixture of Markov components

We study multimedia traffic classification into popular applications to assist the quality of service (QoS) support of networking technologies, including but not limited to, WiFi. For this purpose, we propose to model the multimedia traffic flow as a stochastic discrete-time Markov chain in order to take into account the strong sequentiality (i.e. the dependencies across the data instances) in the traffic flow observations. This addresses the shortcoming of the prior techniques that are based on feature extraction which is prone to losing the information of sequentiality. Also, for investigating the best application of our Markov approach to traffic classification, we introduce and test three data driven classification schemes which are all derived from the proposed model and tightly related to each other. Our first classifier has a global perspective of the traffic data via the likelihood function as a mixture of Markov components (MMC). Our second and third classifiers have local perspective based on k-nearest Markov components (kNMC) with the negative loglikelihood as a distance as well as k-nearest Markov parameters (kNMP) with the Euclidean distance. We additionally introduce to the use of researchers a rich multimedia traffic dataset consisting of four application categories, e.g., video on demand, with seven applications, e.g., YouTube. In the presented comprehensive experiments with the introduced dataset, our local Markovian approach kNMC outperforms MMC and kNMP and provides excellent classification performance, 89% accuracy at the category level and 85% accuracy at the application level and particularly over 95% accuracy for live video streaming. Thus, in test time, the nearest Markov components with the largest likelihoods yield the most discrimination power. We also observe that kNMC significantly outperforms the state-of-the-art methods (such as SVM, random forest and autoencoder) on both the introduced dataset and benchmark dataset both at the category and application level