Differential Radio Link Protocol: An Improvement To Tcp Over Wireless Networks

New generations of wireless cellular networks, including 3G and 4G technologies, are envisaged to support more mobile users and a variety of wireless multimedia services. With an increasing demand for wireless multimedia services, the performance of TCP becomes a bottleneck as it cannot differentiate between the losses due to the nature of air as a medium and high data load on the network that leads to congestion. This misinterpretation by TCP leads to a reduction in the congestion window size thereby resulting in reduced throughput of the system. To overcome this scenario Radio Link Protocols are used at a lower layer which hides from TCP the channel related losses and effectively increases the throughput. This thesis proposes enhancements to the radio link protocol that works underneath TCP by identifying decisive frames and categorizing them as {\em crucial} and {\em non-crucial}. The fact that initial frames from the same upper layer segment can afford a few trials of retransmissions and the later frames cannot, motivates this work. The frames are treated differentially with respect to FEC coding and ARQ schemes. Specific cases of FEC and ARQ strategies are then considered and it is shown qualitatively as how the differential treatment of frames can improve the performance of the RLP and in effect that of TCP over wireless networks

Obeticholic acid for the treatment of non-alcoholic steatohepatitis: interim analysis from a multicentre, randomised, placebo-controlled phase 3 trial

Background Non-alcoholic steatohepatitis (NASH) is a common type of chronic liver disease that can lead to cirrhosis. Obeticholic acid, a farnesoid X receptor agonist, has been shown to improve the histological features of NASH. Here we report results from a planned interim analysis of an ongoing, phase 3 study of obeticholic acid for NASH. Methods In this multicentre, randomised, double-blind, placebo-controlled study, adult patients with definite NASH,non-alcoholic fatty liver disease (NAFLD) activity score of at least 4, and fibrosis stages F2–F3, or F1 with at least oneaccompanying comorbidity, were randomly assigned using an interactive web response system in a 1:1:1 ratio to receive oral placebo, obeticholic acid 10 mg, or obeticholic acid 25 mg daily. Patients were excluded if cirrhosis, other chronic liver disease, elevated alcohol consumption, or confounding conditions were present. The primary endpointsfor the month-18 interim analysis were fibrosis improvement (≥1 stage) with no worsening of NASH, or NASH resolution with no worsening of fibrosis, with the study considered successful if either primary endpoint was met. Primary analyses were done by intention to treat, in patients with fibrosis stage F2–F3 who received at least one dose of treatment and reached, or would have reached, the month 18 visit by the prespecified interim analysis cutoff date. The study also evaluated other histological and biochemical markers of NASH and fibrosis, and safety. This study is ongoing, and registered with ClinicalTrials.gov, NCT02548351, and EudraCT, 20150-025601-6. Findings Between Dec 9, 2015, and Oct 26, 2018, 1968 patients with stage F1–F3 fibrosis were enrolled and received at least one dose of study treatment; 931 patients with stage F2–F3 fibrosis were included in the primary analysis (311 in the placebo group, 312 in the obeticholic acid 10 mg group, and 308 in the obeticholic acid 25 mg group). The fibrosis improvement endpoint was achieved by 37 (12%) patients in the placebo group, 55 (18%) in the obeticholic acid 10 mg group (p=0·045), and 71 (23%) in the obeticholic acid 25 mg group (p=0·0002). The NASH resolution endpoint was not met (25 [8%] patients in the placebo group, 35 [11%] in the obeticholic acid 10 mg group [p=0·18], and 36 [12%] in the obeticholic acid 25 mg group [p=0·13]). In the safety population (1968 patients with fibrosis stages F1–F3), the most common adverse event was pruritus (123 [19%] in the placebo group, 183 [28%] in the obeticholic acid 10 mg group, and 336 [51%] in the obeticholic acid 25 mg group); incidence was generally mild to moderate in severity. The overall safety profile was similar to that in previous studies, and incidence of serious adverse events was similar across treatment groups (75 [11%] patients in the placebo group, 72 [11%] in the obeticholic acid 10 mg group, and 93 [14%] in the obeticholic acid 25 mg group). Interpretation Obeticholic acid 25 mg significantly improved fibrosis and key components of NASH disease activity among patients with NASH. The results from this planned interim analysis show clinically significant histological improvement that is reasonably likely to predict clinical benefit. This study is ongoing to assess clinical outcomes

Clinical Variable Relationship Evaluation using Decision Tree Rule Extraction

Evaluating associations and relationships between variable is a very challenging and important problem in the domain of medicine and clinical data analysis. Not many classification methods have been tried in the literature to tackle this problem. Decision Tree is one of the most important data mining methods that can be used, due to their property of implicitly performing variable screening or feature selection and their requirement of relatively little effort from users for data preparation. However, a major drawback associated with the use of decision tree for decision making is their lack of interpret-able capability specially when using tools like Weka. Though decision trees can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. But this problem can be handled very easily if the decision tree can be utilized by extracting their rules and analyzing these rules. In this paper we present an approach for extracting rules from the decision tree which can be utilized for determining relationship between clinical variables. Furthermore, we also discuss how these rules can be visualized in a compact and intuitive tabular format that facilitates easy analysis. It is concluded that decision tree rule extraction can be considered as powerful analysis tools that allow us to facilitate analysis of clinical variables and its association

Improving Rlp Performance By Differential Treatment Of Frames

In this paper, we propose enhancements to radio link protocols by identifying decisive frames and categorizing them as crucial and non-crucial. The fact that initial frames from the same upper layer segment can afford a few trials of retransmissions and the later frames cannot, motivates our work. We treat the frames differentially with respect to FEC coding and ARQ schemes. We consider specific cases of FEC and ARQ strategies and qualitatively show how the differential treatment of frames can improve the performance of the RLP

A Distributed Security Architecture For Ad Hoc Networks

Secure communication in ad hoc networks is an inherent problem because of the distributiveness of the nodes and the reliance on cooperation between the nodes. All the nodes in such networks rely and trust other nodes for forwarding packets because of their limitation in the range of transmission. Due to the absence of any central administrative node, verification of authenticity of nodes is very difficult. In this paper, we propose a clusterhead-based distributed security mechanism for securing the routes and communication in ad hoc networks. The clusterheads act as certificate agencies and distribute certificates to the communicating nodes, thereby making the communication secure. The clusterheads execute administrative functions and hold shares of network keys that are used for communication by the nodes in respective clusters. Due to the process of authentication, there are signalling and message overheads. Through simulation studies, we show how the presence of clusterheads can substantially reduce these overheads and still maintain secure communication

Correlation based Feature Selection using Rank aggregation for an Improved Prediction of Potentially Preventable Events

This paper presents a methodology for developing a novel feature selection model that will help in a more accurate and robust prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Various clinical factors and patient health conditions can affect a patient's chance of developing the risk of PPE. We propose a robust Correlation based feature selection method using Rank Aggregation (CRA) which helps to identify the key contributing factors for the prediction of PPE. Unlike existing feature selection techniques that causes bias by using distinct statistical properties of data for feature evaluation, CRA uses rank aggregation thus reducing this bias. The result indicates that the proposed technique is more robust across a wide range of classifiers and has higher accuracy than other traditional methods