Modern Template Design and Biological Evaluation of Cephradine-loaded Magnesium Calcium Silicate Nanocomposites as an Inhibitor for Nosocomial Bacteria in Biomedical Applications

Silicon - Magnesium calcium silicate nanostructures (MCSNS) loaded with (0.0, 0.6, 0.9, and 1.2 wt%) of Cephradine-drug consisting of mesoporous particles were functionally prepared by sol-gel...     

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

Magnetic Resonance Materials in Physics, Biology and Medicine - The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of...     

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

Role-based access to facilities lifecycle information on RFID tags

Radio Frequency Identification (RFID) based systems have been used in different applications in the Architecture, Engineering, Construction, Owner and Operator (AECOO) industry. Applications are mainly designed for specific lifecycle stage of the facility and serve the needs of only one of the stakeholders. This would increase the cost and the labor for adding and removing tags and eliminate the chance of using shared resources. In this paper, the usage of tags permanently attached to components is proposed where the memory of the tags is used during the lifecycle by different stakeholders for data storage and handover. A Building Information Model (BIM) database is used for tackling the interoperability issues allowing different users to access and share the data. To securely and efficiently store data on RFID tags in ifcXML format, multi-level encryption together with role-based access control is applied on the data stored on RFID tags. Each user is assigned a certain role and can only access the part of data for which he has authorization according to a predefined role and the Access Control Policy. To explore the technical feasibility of the proposed approach, a case study considering both facilities management and emergency management has been implemented and tested at Concordia University.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

Analyzing the Effects of Hyperthreading on the Performance of Data Management Systems

As information processing applications take greater roles in our everyday life, database management systems (DBMSs) are growing in importance. DBMSs have traditionally exhibited poor cache performance and large memory footprints, therefore performing only at a fraction of their ideal execution and exhibiting low processor utilization. Previous research has studied the memory system of DBMSs on research-based simultaneous multithreading (SMT) processors. Recently, several differences have been noted between the real hyper-threaded architecture implemented by the Intel Pentium 4 and the earlier SMT research architectures. This paper characterizes the performance of a prototype open-source DBMS running TPC-equivalent benchmark queries on an Intel Pentium 4 Hyper-Threading processor. We use hardware counters provided by the Pentium 4 to evaluate the micro-architecture and study the memory system behavior of each query running on the DBMS. Our results show a performance improvement of up to 1.16 in TPC-C-equivalent and 1.26 in TPC-H-equivalent queries due to hyperthreading.     

Cytotoxic T Cell Expression of Leukocyte-Associated Immunoglobulin-Like Receptor-1 (LAIR-1) in Viral Hepatitis C-Mediated Hepatocellular Carcinoma

Virus-related hepatocellular carcinoma (HCC) pathogenesis involves liver inflammation, therefore, despite successful treatment, hepatitis C virus (HCV) may progress to HCC from initiated liver cirrhosis. Cytotoxic T cells (Tcs) are known to be involved in HCV-related cirrhotic complications and HCC pathogenesis. The inhibitory checkpoint leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is expressed on Tcs. Therefore, we aimed to determine whether the Tc expression level of LAIR-1 is associated with HCC progression and to evaluate LAIR-1 expression as a noninvasive biomarker for HCC progression in the context of liver cirrhosis related to HCV genotype 4 (G4) in Egyptian patients’ peripheral venous blood liquid biopsy. A total of 64 patients with HCC and 37 patients with liver cirrhosis were enrolled in this case-controlled study, and their LAIR-1 expression on Tc related to the progression of liver cirrhosis was examined and compared to that of the apparently healthy control group (n = 20). LAIR-1 expression was analyzed using flow cytometry. Results: The HCC group had significantly higher LAIR-1 expression on Tc and percentage of Tc positive for LAIR-1 (LAIR-1+Tc%) than the HCV G4-related liver cirrhosis group. LAIR-1+Tc% was correlated with the HCC surrogate tumor marker AFP (r = 0.367, p = 0.001) and insulin resistance and inflammation prognostic ratios/indices. A receiver operating characteristic (ROC) curve revealed that adding LAIR-1+Tc% to AFP can distinguish HCC transformation in the Egyptian patients’ cohort. Upregulated LAIR-1 expression on Tc could be a potential screening noninvasive molecular marker for chronic inflammatory HCV G4 related liver cirrhosis. Moreover, LAIR-1 expression on Tc may be one of the players involved in the progression of liver cirrhosis to HCC.     

DEA window analysis and Malmquist index to assess energy efficiency and productivity in Jordanian industrial sector

This research evaluates the energy efficiency and productivity growth in the industrial sector over the period of 1999 till 2013 using data envelopment analysis (DEA). Two cases are analyzed; in the first case (GVA), the output is the gross value added, whereas two outputs are considered in the second case (GCO), CO₂ emission and GVA. Five key input factors are considered in both cases. From DEA window analysis, the technical inefficiency (TIE) values are zeros in windows (2001–2005) till (2003–2007), (2007–2011), and (2008–2012), whereas the pure technical inefficiency (PTIE) values are zeros in windows (1999–2003) till (2003–2007). Finally, the scale inefficiency (SIE) values are zeros in windows (2001–2005) till (2003–2007). These results help policy planners on how to better utilize resources and management efficiency over time and guide operational managers when to increase or decrease the scale. Moreover, the averages of inefficiency values in the GVA case are smaller than their corresponding in the GCO case, which indicates the negative effect of CO₂ emission on efficiency. Further, Malmquist index is estimated for three 5-year energy plans. The productivity index is found less than one for the third plan (2009–2013), which indicates a decrease productivity growth. In conclusions, research results provide valuable support when assessing the progress of energy efficiency and productivity in industrial sector.     

Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.