Routledge International Handbook of Rural Studies.:Understanding the association between rural ethnicity and inequalities

Rural areas are facing demographic transformation. Some localities have experienced significant levels of (internal and international) immigration in recent decades. In other rural places, a shifting minority: majority ratio (arising mainly from increased minority fertility and decreases in the majority population) is altering the rural landscape. It is this context of increasingly diverse rural societies that frames this chapter. It begins by examining inequalities arising from ethnicity in a rural context. The review proceeds by identifying how different factors, including recent patterns of international migration and historical legacies of ethnic diversity, intertwine to produce multi-cultural rural areas. First of all an overview of the significance of the ‘ethnic’ label is presented, recognizing its limitations and also its usefulness. Having established this context the chapter proceeds by highlighting the way in which rural ethnic inequalities are measured and also the particular challenges of measuring rural poverty. The processes that produce inequalities among ethnic groups are examined, with particular attention on migration and space and place, but mindful of historical legacies along with economic transformations and associated recent migration patterns. Finally, the conclusion of the chapter highlights gaps and identifies areas for future research agendas.

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

Multi-Device Selection Scheduling in Non-Identically Distributed Fading Channels

Multiuser selection scheduling concept has been recently proposed in the literature in order to increase the multiuser diversity gain and overcome the significant feedback requirements for the opportunistic scheduling schemes. The main idea is that reducing the feedback overhead saves per-user power that could potentially be added for the data transmission. In this work, the authors propose to integrate the principle of multiuser selection and the proportional fair scheduling scheme. This is aimed especially at power-limited, multi-device systems in non-identically distributed fading channels. For the performance analysis, they derive closed-form expressions for the outage probabilities and the average system rate of the delay-sensitive and the delay-tolerant systems, respectively, and compare them with the full feedback multiuser diversity schemes. The discrete rate region is analytically presented, where the maximum average system rate can be obtained by properly choosing the number of partial devices. They optimise jointly the number of partial devices and the per-device power saving in order to maximise the average system rate under the power requirement. Through the authors’ results, they finally demonstrate that the proposed scheme leveraging the saved feedback power to add for the data transmission can outperform the full feedback multiuser diversity, in non-identical Rayleigh fading of devices’ channels.

Determination of multi-mycotoxin occurrence in maize based porridges from selected regions of Tanzania by liquid chromatography tandem mass spectrometry (LC-MS/MS), a longitudinal study

Residents of certain areas of Tanzania are exposed to mycotoxins through the consumption of contaminated maize based foods. In this study, 101 maize based porridge samples were collected from villages of Nyabula, Kikelelwa and Kigwa located in different agro-ecological zones of Tanzania. The samples were collected at three time points (time point 1, during maize harvest; time point 2, 6 months after harvest; time point 3, 12 months after harvest) over a 1-year period. Ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was used to detect and quantify 9 mycotoxins: aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1), aflatoxin G2 (AFG2), fumonisin B1 (FB1), fumonisin B2 (FB2), deoxynivalenol (DON), ochratoxin A (OTA) and zearaleneone (ZEN) in the samples following a QuEChERS extraction method. Eighty two percent of samples were co-contaminated with more than one group of mycotoxins. Fumonisins (FB1 + FB2) had the highest percentage occurrence in all 101 samples (100%) whereas OTA had the lowest (5%). For all three villages the mean concentration of FB1 was lowest in samples taken from time point 2. Conversely, In Kigwa village there was a distinct trend that AFB1 mean concentration was highest in samples taken from time point 2. DON concentration did not differ greatly between time points but the percentage occurrence varied between villages, most notably in Kigwa where 0% of samples tested positive. ZEN occurrence and mean concentration was highest in Kikelelwa. The results suggest that mycotoxin contamination in maize can vary based on season and agro-ecological zones. The high occurrence of multiple mycotoxins found in maize porridge, a common weaning food in Tanzania, presents a potential increase in the risk of exposure and significant health implications in children.

Multi-purpose utility of circulating plasma DNA testing in patients with advanced cancers.

Tumor genomic instability and selective treatment pressures result in clonal disease evolution; molecular stratification for molecularly targeted drug administration requires repeated access to tumor DNA. We hypothesized that circulating plasma DNA (cpDNA) in advanced cancer patients is largely derived from tumor, has prognostic utility, and can be utilized for multiplex tumor mutation sequencing when repeat biopsy is not feasible. We utilized the Sequenom MassArray System and OncoCarta panel for somatic mutation profiling. Matched samples, acquired from the same patient but at different time points were evaluated; these comprised formalin-fixed paraffin-embedded (FFPE) archival tumor tissue (primary and/or metastatic) and cpDNA. The feasibility, sensitivity, and specificity of this high-throughput, multiplex mutation detection approach was tested utilizing specimens acquired from 105 patients with solid tumors referred for participation in Phase I trials of molecularly targeted drugs. The median cpDNA concentration was 17 ng/ml (range: 0.5-1600); this was 3-fold higher than in healthy volunteers. Moreover, higher cpDNA concentrations associated with worse overall survival; there was an overall survival (OS) hazard ratio of 2.4 (95% CI 1.4, 4.2) for each 10-fold increase in cpDNA concentration and in multivariate analyses, cpDNA concentration, albumin, and performance status remained independent predictors of OS. These data suggest that plasma DNA in these cancer patients is largely derived from tumor. We also observed high detection concordance for critical 'hot-spot' mutations (KRAS, BRAF, PIK3CA) in matched cpDNA and archival tumor tissue, and important differences between archival tumor and cpDNA. This multiplex sequencing assay can be utilized to detect somatic mutations from plasma in advanced cancer patients, when safe repeat tumor biopsy is not feasible and genomic analysis of archival tumor is deemed insufficient. Overall, circulating nucleic acid biomarker studies have clinically important multi-purpose utility in advanced cancer patients and further studies to pursue their incorporation into the standard of care are warranted.

Secrecy Performance of Wirelessly Powered Wiretap Channels

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely maximum ratio transmission (MRT) and transmit antenna selection (TAS). Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Physical Layer Security with Threshold-Based Multiuser Scheduling in Multi-antenna Wireless Networks

In this paper, we consider a multiuser downlink wiretap network consisting of one base station (BS) equipped with AA antennas, NB single-antenna legitimate users, and NE single-antenna eavesdroppers over Nakagami-m fading channels. In particular, we introduce a joint secure transmission scheme that adopts transmit antenna selection (TAS) at the BS and explores threshold-based selection diversity (tSD) scheduling over legitimate users to achieve a good secrecy performance while maintaining low implementation complexity. More specifically, in an effort to quantify the secrecy performance of the considered system, two practical scenarios are investigated, i.e., Scenario I: the eavesdropper’s channel state information (CSI) is unavailable at the BS, and Scenario II: the eavesdropper’s CSI is available at the BS. For Scenario I, novel exact closed-form expressions of the secrecy outage probability are derived, which are valid for general networks with an arbitrary number of legitimate users, antenna configurations, number of eavesdroppers, and the switched threshold. For Scenario II, we take into account the ergodic secrecy rate as the principle performance metric, and derive novel closed-form expressions of the exact ergodic secrecy rate. Additionally, we also provide simple and asymptotic expressions for secrecy outage probability and ergodic secrecy rate under two distinct cases, i.e., Case I: the legitimate user is located close to the BS, and Case II: both the legitimate user and eavesdropper are located close to the BS. Our important findings reveal that the secrecy diversity order is AAmA and the slope of secrecy rate is one under Case I, while the secrecy diversity order and the slope of secrecy rate collapse to zero under Case II, where the secrecy performance floor occurs. Finally, when the switched threshold is carefully selected, the considered scheduling scheme outperforms other well known existing schemes in terms of the secrecy performance and complexity tradeoff

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

A New Technique for Studying Vapour–liquid Equilibria of Multi-Component Systems

A Fourier transform infrared gas-phase method is described herein and capable of deriving the vapour pressure of each pure component of a poorly volatile mixture and determining the relative vapour phase composition for each system. The performance of the present method has been validated using two standards (naphthalene and ferrocene), and a Raoult’s plot surface of a ternary system is reported as proof-of-principle. This technique is ideal for studying solutions comprising two, three, or more organic compounds dissolved in ionic liquids as they have no measurable vapour pressures.