Flexible Identification Research Articles

Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling.

Electronic Health Records, Machine Learning, Lupus Erythematosus, Phenotype, Algorithms.

Parameter identification in elastoplastic material models by Small Punch Tests and inverse analysis with model reduction

Small Punch Tests (SPT) are, at present, frequently employed for diagnostic analyses of metallic structural components and are considered in codes of practice because the damage generated by miniature specimen extraction is small (“quasi-non-destructive” tests). This paper contains a description of the following contributions for improvement in the state-of-the-art of SPT practice: assessment of material parameters through inverse analysis, made faster and more economical by employing model reduction through a Proper Orthogonal Decomposition (POD) procedure. The methodology is developed to assess material parameters entering into diverse constitutive models, thus resulting in a more flexible identification framework, capable of addressing different kinds of material behaviour. Within the paper, real experimental data are used and comparisons of computed stress–strain curves with experimentally measured ones, through typical tensile tests, show an excellent agreement.

The impact of common native language and immigration on imports

AbstractThis paper sheds light on the impact of common native language and immigration—both jointly and separately—on imports. Common native language and immigrant networks stimulate bilateral trade relationships by reducing transaction costs through both market creation and the removal of information asymmetries. Whereas evidence on the importance of weak links for trade provides support for the first channel, the need for additional immigrants does not decline, if the second mechanism proves to be a driver of trade relationships. For estimation of a causal and nonlinear effect of the two—common native language and immigration—on imports, we employ a flexible identification strategy, which allows common native language and migration to be endogenous. The results suggest that both common native language and immigration raise imports, but the impact of common native language is ever increasing with its level, whereas that of immigration is of declining importance as its level increases.

Blind Density-Peak-Based Modulation Format Identification for Elastic Optical Networks

Optical modulation format identification is critical in the next generation of heterogeneous and reconfigurable optical networks. Here, we present a blind modulation format identification method by applying fast density-peak-based pattern recognition in the autonomous receiver of elastic optical networks. In this paper, we find that the different modulation format types show different energy level features which can be used as a metric to identify these modulation formats in two-dimensional Stokes plane. The proposed method does not require training symbols, and is insensitive to carrier phase noise, frequency offset as well as polarization mixing. The effectiveness is verified via numerical simulations and experiments with PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16PSK, PDM-8QAM, and PDM-16QAM. The results show that high identification accuracy can be realized using our proposed method over wide optical signal-to-noise ratio ranges. Meanwhile, we also discuss the influence of the residual chromatic dispersion, polarization mode dispersion, and polarization dependent loss impairments to our proposed method. We believe that the simple and flexible identification method would certainly bring a great convenience to the future optical networks.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BackgroundLarge-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.ResultsThis paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.ConclusionsData heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation

Terahertz (THz) spectroscopy technique has been researched and developed for rapid and non-destructive detection of food safety and quality due to its low-energy and non-ionizing characteristics. The objective of this study was to develop a flexible identification model to discriminate transgenic and non-transgenic rice seeds based on terahertz (THz) spectroscopy. To extract THz spectral features and reduce the feature dimension, sparse representation (SR) is employed in this work. A sufficient sparsity level is selected to train the sparse coding of the THz data, and the random forest (RF) method is then applied to obtain a discrimination model. The results show that there exist differences between transgenic and non-transgenic rice seeds in THz spectral band and, comparing with Least squares support vector machines (LS-SVM) method, SR-RF is a better model for discrimination (accuracy is 95% in prediction set, 100% in calibration set, respectively). The conclusion is that SR may be more useful in the application of THz spectroscopy to reduce dimension and the SR-RF provides a new, effective, and flexible method for detection and identification of transgenic and non-transgenic rice seeds with THz spectral system.

Riborex: fast and flexible identification of differential translation from Ribo-seq data.

Global analysis of translation regulation has recently been enabled by the development of Ribosome Profiling, or Ribo-seq, technology. This approach provides maps of ribosome activity for each expressed gene in a given biological sample. Measurements of translation efficiency are generated when Ribo-seq data is analyzed in combination with matched RNA-seq gene expression profiles. Existing computational methods for identifying genes with differential translation across samples are based on sound principles, but require users to choose between accuracy and speed. We present Riborex, a computational tool for mapping genome-wide differences in translation efficiency. Riborex shares a similar mathematical structure with existing methods, but has a simplified implementation. Riborex directly leverages established RNA-seq analysis frameworks for all parameter estimation, providing users with a choice among robust engines for these computations. The result is a method that is dramatically faster than available methods without sacrificing accuracy. https://github.com/smithlabcode/riborex. andrewds@usc.edu. Supplementary data are available at Bioinformatics online.

Monetary Policy and Mispricing in Stock Markets

This paper investigates whether central banks can attenuate excessive mispricing in stocks as suggested by the proponents of a leaning against the wind (LATW) monetary policy. For this, we decompose stock prices into a fundamental component, a risk premium, and a mispricing component. We argue that mispricing can arise for two reasons: (i) from false subjective expectations of investors about future fundamentals and equity premia; and (ii) from the inherent indeterminacy in asset pricing in line with rational bubbles. We show that the response of the excessive stock price component to a monetary policy shock is ambiguous in both the short- and long-run, and depends on the nature of the mispricing. Subsequently, we evaluate the scope for a LATW policy empirically by employing a time-varying coefficient VAR with a flexible identification scheme based on impact and long-run restrictions using data for the S&P500 index from 1962Q1 to 2014Q4. We find that a contractionary monetary policy shock in fact lowers stock prices beyond what is implied by the response of their underlying fundamentals.

DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments.

DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: tassos.bezerianos@nus.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.

Diversification of paths in sport games as a determinant of differentiating competences of trainers and team managers - the paradigm vs. reality

Contemporary sport games are characterised by an increasing diversification of paths and phases one may select when determining one's objective of engaging in a game. As these objectives - and, consequently, conditions of achieving them - change, trainers and other support personnel need to adapt their competences to one specific path and its accompanying objectives. In the light of the above, the purpose of this publication is to:justify the need for diversified identification of the above-mentioned paths and phases,subsequently, define a network-processual model that enables flexible identification of these paths and phases,explore the inadequacy of coaching and managing competences currently favoured in trainers and managers in Poland; finally, present a model of qualitatively diversified sets of competences, including: player's fitness coach, talented player's tutor, team coach and team manager.

A model-based approach to operational event groups ranking

Abstract The operational experience (OE) feedback provides improvements in all industrial activities. Identification of the most important and valuable groups of events within accumulated experience is important in order to focus on a detailed investigation of events. The paper describes the new ranking method and compares it with three others. Methods have been described and applied to OE events utilised by nuclear power plants in France and Germany for twenty years. The results show that different ranking methods only roughly agree on which of the event groups are the most important ones. In the new ranking method the analytical hierarchy process is applied in order to assure consistent and comprehensive weighting determination for ranking indexes. The proposed method allows a transparent and flexible event groups ranking and identification of the most important OE for further more detailed investigation in order to complete the feedback.

Electromagnetic Analysis of Radio Frequency Identification Antennas for Green Electronics

This article demonstrates in-depth electromagnetic analysis of a radio frequency identification tag antenna manufactured by inkjet printing technology on different paper substrates to achieve ultra-low cost flexible radio frequency identification tags using a novel hole-matching technique for reducing the consumption of substrate material, and conductive ink. Nevertheless, the electromagnetic properties of the paper substrate are vulnerable to various environmental effects. Thus, the proposed antenna design is optimized for consistent wideband performance throughout the complete UHF radio frequency identification band (860–960 MHz) while presenting a greater degree of material insensitivity. An advanced antenna design methodological analysis is performed to accomplish an extended read range, while exhibiting benchmarking results when across cardboard cartons filled with metal or water containing objects.

床反力計測に基づく力学同定を用いた柔軟アームを有する産業用ロボットのIDCS制御

床反力計測に基づく力学同定を用いた柔軟アームを有する産業用ロボットのIDCS制御

Identifying the age profile of patent citations: new estimates of knowledge diffusion

AbstractPrevious research studies the age profile of patent citations to learn about knowledge flows over time. However, identification is problematic because of the collinearity between application year, citation year, and patent age. We show empirically that a patent's ‘citation clock’ does not start until it issues, and propose a highly flexible identification strategy that uses the lag between application and grant as a source of exogenous variation. We examine the potential bias if our assumptions are incorrect, and discuss extensions into other research areas. Finally, we use our method to re‐examine prior results on citation age profiles of patents from different technological fields and application year cohorts. Copyright © 2009 John Wiley & Sons, Ltd.

Assembly of flexible RFID tag inlays with anisotropic conductive paste (ACP)

PurposeThe purpose of this paper is to accomplish the low cost mass‐production of flexible radio frequency identification (RFID) tag inlays.Design/methodology/approachAn anisotropic conductive paste (ACP) is prepared by mixing uniform micro‐sized spherical conductive particles, latent curing agent and other additives into a thermoset epoxy resin. RFID tag inlays are assembled with the paste through flip‐chip technology. The microstructural analysis of bonded joints, bond strength testing, and high‐temperature and humidity aging testing are employed to evaluate the performance of the inlays.FindingsIt was found that the chips are hard assembled on the antennae by the ACP. Flexible RFID tag inlays assembled using the presented method have good reliability when working under high frequency (13.56 MHz) conditions.Research limitations/implicationsThe method presented is a promising new way for packaging flexible RFID tag inlays with ACP. Through the use of flip‐chip technology, large‐scale production is possible with low manufacturing costs.Originality/valueThe paper details a simple way to prepare an anisotropic conductive paste and to assemble flexible RFID tag inlays. The technique uses flip‐chip technology with the paste as the electrical and mechanical interconnection material. It presents a simple and fast method of assembly for flexible RFID tag inlays on a large‐scale with low cost.

SubpathwayMiner: a software package for flexible identification of pathways

With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates sub-pathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up-to-date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (∼100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/.

Automatic extension of Gene Ontology with flexible identification of candidate terms

Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. http://autogo.biopathway.org Supplementary materials are available at Bioinformatics online.

MANAGEMENT AND TRANSPORT AUTOMATION IN WAREHOUSES BASED ON AUTO-GUIDED VEHICLES

This paper describes a modular and integral solution for management and transport automation in warehouses developed in the context of a research project, AutoTrans. Management automation includes a warehouse, an optimal location process and a flexible identification system based on RFID and PDAs. Warehouse supervision includes a SCADA system with internet access for remote control. In the context of transport automation, it has been implemented several facilities such as navigation with path planning, line tracking and teleoperation using a force-feedback device.

A laser marking system for flexible circuit identification

A laser marking system for flexible circuit identification

A Flexible Identification Strategy for DABNet Models

Abstract The focus of this paper is to introduce a flexible identification strategy for a general class of models. We use DABNet models, which are composed of a linear state space system whose states, de- coupled by input, are mapped by a neural network. The linear state space matrices can be represented as loosely coupled first and second order sections. A set of conditions will be analyzed for this kind of model that allows an identification process to be as flexible as possible. By flexible we mean a set of features that would be desirable to find in an identification approach: constraint handling and optimal-input design.