Ferrofluid separator for nonferrous scrap separation

Behavior of nonmagnetic objects within separator is essentially function of density, and independent of size or shape of objects. Results show close agreement between density of object and apparent density of ferrofluid required to float it. Results also demonstrate that very high separation rates are achievable by ferrofluid sink-float separation

Reliability in the identification of metaphors in (filmic) multimodal communication

Research on multimodal communication is complex because multimodal analyses require methods and procedures that offer the possibility of disentangling the layers of meaning conveyed through different channels. We hereby propose an empirical evaluation of the Filmic Metaphor Identification Procedure (FILMIP, Bort-Mir, L. (2019). Developing, applying and testing FILMIP: the filmic metaphor identification procedure, Ph.D. dissertation. Universitat Jaume I, Castellón.), a structural method for the identification of metaphorical elements in (filmic) multimodal materials. The paper comprises two studies: (i) A content analysis conducted by independent coders, in which the reliability of FILMIP is assessed. Here, two TV commercials were shown to 21 Spanish participants for later analysis with the use of FILMIP under two questionnaires. (ii) A qualitative analysis based on a percentage agreement index to check agreement among the 21 participants about the metaphorically marked filmic components identified on the basis of FILMIP’s seven steps. The results of the two studies show that FILMIP is a valid and reliable tool for the identification of metaphorical elements in (filmic) multimodal materials. The empirical findings are discussed in relation to multimodal communication open challenges

MIMO Antenna Optimization: From Configuring Structure to Sizing with the aid of Neural Network

In the last decades, multiple input, multiple output (MIMO) antenna designs play important role and this trend will continue in next-generation mobile technologies. Designing high-performance MIMOs is significant since these types of antennas include multiple radiating elements. For these complex configurations, intelligent-based optimization methods can tackle the problem of designing. This paper devotes to designing and optimizing the configuration and design parameters of a MIMO antenna, respectively. Firstly bottom-up optimization (BUO) approach is executed successfully for building the general topology of the MIMO antenna and afterwards, artificial neural network (ANN) is utilized for obtaining the design parameters with the optimal values. The proposed approach results in generating the optimal topology with size values in a reduced effort by designers. The presented approach is applied to designing a MIMO antenna operating from 13.7 GHz to 29 GHz

Unstable Disk Galaxies. I. Modal Properties

I utilize the Petrov-Galerkin formulation and develop a new method for solving the unsteady collisionless Boltzmann equation in both the linear and nonlinear regimes. In the first order approximation, the method reduces to a linear eigenvalue problem which is solved using standard numerical methods. I apply the method to the dynamics of a model stellar disk which is embedded in the field of a soft-centered logarithmic potential. The outcome is the full spectrum of eigenfrequencies and their conjugate normal modes for prescribed azimuthal wavenumbers. The results show that the fundamental bar mode is isolated in the frequency space while spiral modes belong to discrete families that bifurcate from the continuous family of van Kampen modes. The population of spiral modes in the bifurcating family increases by cooling the disk and declines by increasing the fraction of dark to luminous matter. It is shown that the variety of unstable modes is controlled by the shape of the dark matter density profile.Comment: Accepted for publication in The Astrophysical Journa

Automated optimization for broadband flat-gain antenna designs with artificial neural network

An automated optimization process for designing and optimising high-performance single microstrip antennas is presented. It consists of the successive use of two optimization methods, bottom-up optimization (BUO) and Bayesian optimization (BO), which are applied sequentially, resulting in electromagnetic (EM)-based artificial neural network modelling. The BUO method is applied for the initial design of the structure of the antennas whereas the BO approach is successively implemented to predict suitable dimensional parameters, leading to broadband, high flat-gain antennas. The optimization process is performed automatically with the combination of an electronic design automation tool and a numerical analyser. The proposed method is easy to use; it allows one to perform the design with little experience, because both structure modelling and sizing are performed automatically. To verify the power of the proposed EM-based method experimentally, two single microstrip antennas have been designed, optimised, fabricated, and measured. The first antenna has flat-gain performance (6.9–7.2 dB) in a frequency band of 8.8–10 GHz. The second has been designed to perform in the 8.7- to 10-GHz band, where it exhibits flat-gain performance with reduced fluctuation in the range of 6.7–7 dB. The experimental results are in good agreement with the numerical data

Optimization for wideband linear array antenna through bottom-up method

This paper presents an automated design methodology for electromagnetic- based (EM-based) optimization of an array antenna by applying bottom-up approach. Firstly, one single antenna is optimized then bottom-up optimization (BUO) method has been implemented by increasing the number of single antennas, sequentially. The proposed method leads to automatically find an optimal array by setting the distance between single antennas. The optimization method is performed in an automated environment with the help of an electronic design automation (EDA) tool and a numerical analyzer. The results of the final design have been compared by means of two EDA tools such as ADS and HFSS. The optimized array antenna works in the frequency band from 12.9 GHz to 14.3 GHz. It offers a linear gain performance higher than 7.5 dB. The simulations in both ADS and HFSS tools illustrate a good match in S-parameter and gain simulation output results

Scale-free equilibria of self-gravitating gaseous disks with flat rotation curves

We introduce exact analytical solutions of the steady-state hydrodynamic equations of scale-free, self-gravitating gaseous disks with flat rotation curves. We express the velocity field in terms of a stream function and obtain a third-order ordinary differential equation (ODE) for the angular part of the stream function. We present the closed-form solutions of the obtained ODE and construct hydrodynamical counterparts of the power-law and elliptic disks, for which self-consistent stellar dynamical models are known. We show that the kinematics of the Large Magellanic Cloud can well be explained by our findings for scale-free elliptic disks.Comment: AAS preprint format, 21 pages, 8 figures, accepted for publication in The Astrophysical Journa

Current Clinical Applications of Testicular Cancer Biomarkers

Current use of testicular biomarkers for screening, diagnosis, and follow-up is reviewed in the context of potential clinical utility of these tests. This information will be of value to clinicians to determine patient suitability for certain treatments and will also assist in reviewing current literature regarding potential biomarkers that may be used for testicular cancer

Yeast Features: Identifying Significant Features Shared Among Yeast Proteins for Functional Genomics

Background
High throughput yeast functional genomics experiments are revealing associations among tens to hundreds of genes using numerous experimental conditions. To fully understand how the identified genes might be involved in the observed system, it is essential to consider the widest range of biological annotation possible. Biologists often start their search by collating the annotation provided for each protein within databases such as the Saccharomyces Genome Database, manually comparing them for similar features, and empirically assessing their significance. Such tasks can be automated, and more precise calculations of the significance can be determined using established probability measures. 
Results
We developed Yeast Features, an intuitive online tool to help establish the significance of finding a diverse set of shared features among a collection of yeast proteins. A total of 18,786 features from the Saccharomyces Genome Database are considered, including annotation based on the Gene Ontology’s molecular function, biological process and cellular compartment, as well as conserved domains, protein-protein and genetic interactions, complexes, metabolic pathways, phenotypes and publications. The significance of shared features is estimated using a hypergeometric probability, but novel options exist to improve the significance by adding background knowledge of the experimental system. For instance, increased statistical significance is achieved in gene deletion experiments because interactions with essential genes will never be observed. We further demonstrate the utility by suggesting the functional roles of the indirect targets of an aminoglycoside with a known mechanism of action, and also the targets of an herbal extract with a previously unknown mode of action. The identification of shared functional features may also be used to propose novel roles for proteins of unknown function, including a role in protein synthesis for YKL075C.
Conclusions
Yeast Features (YF) is an easy to use web-based application (http://software.dumontierlab.com/yeastfeatures/) which can identify and prioritize features that are shared among a set of yeast proteins. This approach is shown to be valuable in the analysis of complex data sets, in which the extracted associations revealed significant functional relationships among the gene products.
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Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made