Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks

This paper addresses the cell association problem in the downlink of a multi-tier heterogeneous network (HetNet), where base stations (BSs) have finite number of resource blocks (RBs) available to distribute among their associated users. Two problems are defined and treated in this paper: sum utility of long term rate maximization with long term rate quality of service (QoS) constraints, and global outage probability minimization with outage QoS constraints. The first problem is well-suited for low mobility environments, while the second problem provides a framework to deal with environments with fast fading. The defined optimization problems in this paper are solved in two phases: cell association phase followed by the optional RB distribution phase. We show that the cell association phase of both problems have the same structure. Based on this similarity, we propose a unified distributed algorithm with low levels of message passing to for the cell association phase. This distributed algorithm is derived by relaxing the association constraints and using Lagrange dual decomposition method. In the RB distribution phase, the remaining RBs after the cell association phase are distributed among the users. Simulation results show the superiority of our distributed cell association scheme compared to schemes that are based on maximum signal to interference plus noise ratio (SINR)

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

Seroprevalence of hepatitis B virus and its co-infection with hepatitis D virus and hepatitis C virus in Iranian adult population

Context: Hepatitis B virus (HBV) infection is one of the most prevalent public health problems worldwide (especially in developing countries). Aims: This study was carried out to determine the seroprevalence of HBV and its co-infection with hepatitis D (HDV) and C (HCV) viruses in the northeastern part of Iran. Setting and Design: A population-based cross-sectional study in Iran. Materials and Methods: As many as 1,850 subjects were explored for HBsAg. Anti-HDV and anti-HCV antibodies were assessed in HBsAg-positive cases. Statistical Analysis Used: Proportions were compared by Chi-square and Fisher's exact tests. Results: The mean age of subjects was 43.86 ± 11.2 years. The age- and sex-standardized prevalence for HBsAg positivity was 9.7%. It was higher in males than in females (OR: 1.28; 95% CI: 0.9-1.7). The risk of infection in singles was significantly higher than in married cases (OR: 2.13). Eight (5.8%) of HBsAg-positive cases were infected with HDV, and 17 (12.3%) were positive for anti-HCV antibody. Conclusion: This study demonstrates that the prevalence of HBsAg seropositivity in Golestan province of Iran is higher than the levels reported by WHO and previous studies from Iran. It is very important, especially for health providers and policy makers, to recognize the risk factors of HBV infection and its co-infection with HDV and HCV in this area and design effective preventive programs

Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction

Organic Thin Film Transistor with Carbon Nanotube Electrodes

The contact resistance between organic semiconductors and metallic electrodesaffectsthe performance of the organic thin film transistor (OTFT) negatively so that it may make thefield effect mobility of charge carrier seem small. In order to reduce the contact resistance weused conducting Carbon Nanotube (CNT) films, which consist of the same element as the basicmaterial of the organic semiconductors, as source or drain electrodes. The measurements oftransistor properties based on pentacene single crystals have been carried out by using both CNTfilm electrodes and metal electrode

Nanoscale axial position and orientation measurement of hexagonal boron nitride quantum emitters using a tunable nanophotonic environment

Color centers in hexagonal boron nitride (hBN) have emerged as promising candidates for single-photon emitters (SPEs) due to their bright emission characteristics and potential for high temperature operation, but precisely resolving emitter location is an important outstanding issue for many applications. While single-molecule super-resolution microscopy schemes can resolve emitter lateral position at the nanometer scale, complete determination of both axial position and three-dimensional dipole orientation (θ, φ) of these quantum emitters is a fundamental challenge. We report a method for determining both the axial position and three-dimensional orientation of SPEs in \textit{h}BN by tuning the photonic local density of states, using a vanadium dioxide (VO₂) phase change material. Using this method, we were able to locate several specific quantum emitters at an axial distance of ~ 20 nm from the hBN/VO₂ interface while also determining their full dipolar orientation (θ, φ). Our approach may serve as a practical method to deterministically couple quantum emitters in hBN and other materials to photonic nanostructures, for applications in integrated quantum photonics

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chemistry Central Journal 10 (2016): 75, doi:10.1186/s13065-016-0211-y.Comprehensive two-dimensional gas chromatography (GC×GC) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the (GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the (GC×GC) topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33–154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining “match” between samples, without necessitating training data sets. We validate our methods across 34 (GC×GC) injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 Deepwater Horizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match (99.23±1.66)% using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the (GC×GC) biomarker ROI image of the MW pre-spill sample (sample #1 in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8. We provide a peak-cognizant informational framework for quantitative interpretation of (GC×GC) topography. Proposed topographic analysis enables (GC×GC) forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.This research was made possible in part by a grant from the Gulf of Mexico Research Initiative (GoMRI-015), and the DEEP-C consortium, and in part by NSF Grants OCE-0969841 and RAPID OCE-1043976 as well as a WHOI interdisciplinary study award