Kinetic theory of acoustic-like modes in nonextensive pair plasmas

The low-frequency acoustic-like modes in a pair plasma (electron-positron or pair-ion) is studied by employing a kinetic theory model based on the Vlasov and Poisson's equation with emphasizing the Tsallis's nonextensive statistics. The possibility of the acoustic-like modes and their properties in both fully symmetric and temperature-asymmetric cases are examined by studying the dispersion relation, Landau damping and instability of modes. The resultant dispersion relation in this study is compatible with the acoustic branch of the experimental data [W. Oohara, D. Date, and R. Hatakeyama, Phys. Rev. Lett. 95, 175003 (2005)], in which the electrostatic waves have been examined in a pure pair-ion plasma. Particularly, our study reveals that the occurrence of growing or damped acoustic-like modes depends strongly on the nonextensivity of the system as a measure for describing the long-range Coulombic interactions and correlations in the plasma. The mechanism that leads to the unstable modes lies in the heart of the nonextensive formalism yet, the mechanism of damping is the same developed by Landau. Furthermore, the solutions of acoustic-like waves in an equilibrium Maxwellian pair plasma are recovered in the extensive limit ($q\rightarrow1$), where the acoustic modes have only the Landau damping and no growth.Comment: Accepted for publication in Astrophysics and Space Scienc

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

The design of complexity-aware cascaded detectors, combining features of very different complexities, is considered. A new cascade design procedure is introduced, by formulating cascade learning as the Lagrangian optimization of a risk that accounts for both accuracy and complexity. A boosting algorithm, denoted as complexity aware cascade training (CompACT), is then derived to solve this optimization. CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified. This enables the use of features of vastly different complexities in a single detector. In result, the feature pool can be expanded to features previously impractical for cascade design, such as the responses of a deep convolutional neural network (CNN). This is demonstrated through the design of a pedestrian detector with a pool of features whose complexities span orders of magnitude. The resulting cascade generalizes the combination of a CNN with an object proposal mechanism: rather than a pre-processing stage, CompACT cascades seamlessly integrate CNNs in their stages. This enables state of the art performance on the Caltech and KITTI datasets, at fairly fast speeds

The Effect of Slope Geometry and Shoulder on Rutting Depth of Flexible Pavement

The slope and width of the road shoulder are important parameters in geometry of the road pavement. Therefore, it is important to comply with the requirements relating to the slope and width of the shoulders. So that by using the minimum width and slope of the shoulders according to regulations not only stresses and strains transferred to the lower layers will decrease, but also reduces damages in asphalt layers, base, and sub-base. Therefore, it is vital to conduct analyses which can bring good amount of accuracy in assessment of the stress and settlement due to shoulder width and slope. The aim of this study is to investigate the effect of geometry of the shoulder on the performance and behavior of weak or strong pavement. For this purpose, numerical two-dimensional modeling of the road pavement (asphaltØŒbaseØŒsub-base) on which the axel load is placed was done using finite element method, ABAQUS, and the effect of the shoulder width and slope on the stresses and settlements caused by the strong and weak pavement have been studied. Also for verification of the software, several obtained field values are compared to each other. The results indicate that the increase in the width of the shoulders and the decrease in the slope will cause in decrease of the stress and settlements in different layers of the roadways. Thus, creating less steeper shoulder and wider pavement can reduce damages and will contribute to the increased safety and sustained life of the pavement.  &nbsp

Text Mining Of Variant-Genotype-Phenotype Associations From Biomedical Literature

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to i) the complexity of natural language processing, ii) inconsistent use of standard recommendations for variant description, and iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients’ clinical outcomes on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer, and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind, and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature

DEEMD: Drug efficacy estimation against SARS-CoV-2 based on cell morphology with deep multiple instance learning

Background: Drug repurposing can accelerate the identification of effective compounds for clinical use against SARS-CoV-2, with the advantage of pre-existing clinical safety data and an established supply chain. RNA viruses such as SARS-CoV-2 manipulate cellular pathways and induce reorganization of subcellular structures to support their life cycle. These morphological changes can be quantified using bioimaging techniques. Methods: In this work, we developed DEEMD: a computational pipeline using deep neural network models within a multiple instance learning (MIL) framework, to identify putative treatments effective against SARS-CoV-2 based on morphological analysis of the publicly available RxRx19a dataset, This dataset consists of fluorescence microscopy images of SARS-CoV-2 non-infected cells and infected cells, with and without drug treatment. DEEMD first extracts discriminative morphological features to generate cell morphological profiles from the non-infected and infected cells. These morphological profiles are then used in a statistical model to estimate the applied treatment efficacy on infected cells based on similarities to non-infected cells. Results: DEEMD is capable of localizing infected cells via weak supervision without any expensive pixel-level annotations. DEEMD identifies known SARS-CoV-2 inhibitors, such as Remdesivir and Aloxistatin, supporting the validity of our approach. Conclusions: DEEMD is scalable to process and screen thousands of treatments in parallel and can be applied to other emerging viruses and data sets to rapidly identify candidate antiviral treatments in the future

A numerical study on slip flow heat transfer in micro-poiseuille flow

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.In the present study, two-dimensional incompressible momentu and energy equations are solved with slip velocity and temperature jump boundary conditions in a parallel plate micro channel. The computations are performed for micro channels with CHF (Constant heat flux) boudary conditions to obtain heat transfer characteristics of gaseous flow in slip regime. The effects of creep flow and viscous dissipation are neglected in this study. The numerical methodology is based on Semi-Implicit method for pressure-linked equations (SIMPLE) method. The governing equations are developed by using perturbation expansions of velocity, pressure and temperature fields. It was found that Nusselt number and was substantially reduced for slip flow regimes compared with the continuum flows. The obtained solutions are compared with available numerical and analytical results and found that present study has good agreement with that works

Alerts work! Air quality warnings and cycling

Alert programs are central to strategies to reduce pollution exposure and manage its impact. To be effective alerts have to change behavior, but evidence that they do that is sparse. Indeed the majority of published studies fail to find a significant impact of alerts on the outcome behavior that they study. Alerts particularly seek to influence energetic cardio-vascular outdoor pursuits. This study is the first to use administrative data to show that they are effective in reducing participation in such a pursuit (namely cycle use in Sydney, Australia), and to our knowledge the first to show that they are effective in changing any behavior in a non-US setting. We are careful to disentangle possible reactions to realised air quality from the ‘pure’, causal effect of the issuance of an alert. Our results suggest that when an air quality alert is issued, the amount of cycling is reduced by 14–35%, which is a substantial behavioral response. The results are robust to the inclusion of a battery of controls in various combinations, alternative estimation methods and non-linear specifications. We develop various sub-sample results, and also find evidence of alert fatigue