Input Data Formats Research Articles

Implementation of the virtual element method for coupled thermo-elasticity in Abaqus

In this paper, we employ the virtual element method for the numerical solution of linear thermo-elastic problems in two dimensions. The framework is implemented within the commercial software Abaqus using its user element feature. The implementation details of the virtual element method in Abaqus-Matlab software framework are described. The corresponding details on the input data format, which forms the core of the analysis, are given. Both linear and quadratic elements are used within the virtual element framework. A few benchmark problems from linear thermo-elasticity are solved to validate the implementation.

Mobile web-based lightweight and real-time roaming algorithm for large-scale WebBIM scenes

With the popularity of mobile terminals, including smart phones, there is a great push for users to share large-scale data on phone webpages. This study considers building information modeling (BIM) as the core smart construction technology and leverages, its classical data format — industry foundation classes (IFC) as the input data format, and presents a mobile & web-based lightweight and real-time roaming algorithm for large-scale WebBIM scenes. To comply with the semantics information in IFC files and with a buildings structure, our method focuses on creating a more lightweight product, by not relying exclusively on geometry faces as ordinary visualization programs do. Our main idea for optimizing mobile, web-based, lightweight and real-time roaming is threefold: reduce redundancies, generate an efficient hybrid data structure and design dynamic load/unload strategies. In the study, a prototype system was integrated and leveraged for experimentation. The final performance analysis shows that we can significantly reduce the redundancy to about 70 percent of the raw BIM scenes. Meanwhile, our method can maintain the FPS at around 30 and memory consumption at about 400 MB with an increasing scene scale.

A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

With the development of network communication, a 1000‐fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory‐based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two‐level index based on the Alluxio key‐value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory‐based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

Challenges in Modeling Flexible Bodies based on Experimental Data with Utilization in Elastic Multibody Simulation

AbstractThe Elastic Multi‐Body Simulation (EMBS) is the state‐of‐the‐art method when dealing with elastic deformations of components in complex mechanical systems. But the required FE‐model representing the elastic structure contains several uncertainties concerning geometry, local and directional stiffness as well as damping phenomena. A mandatory Model Order Reduction (MOR) embodies further approximation errors. Due to the drawbacks of the conventional procedure, a novel approach is suggested, where the data of the elastic body model is directly gained from the results of an Experimental Modal Analysis (EMA). Therefore, the measured data is transferred to the Standard Input Data (SID) format by means of the software MORPACK (Model Order Reduction PACKage). The novel approach yields several challenges for which adequate solutions are presented. The feasibility is demonstrated at the example of an U‐section. The accuracy in the higher frequency range can be increased, which is especially important for vibro‐acoustic simulations. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis

BackgroundIn a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.ResultsHere, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.ConclusionsWith the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.

The Mega2R R package: tools for accessing and processing common genetic data formats in R

The standalone C++ Mega2 program, available from https://watson.hgen.pitt.edu/register/, has been facilitating data-reformatting for linkage and association analysis programs since 2000. Support for more analysis programs have been added over time, though this has required software developers keenly knowledgeable in the Mega2 internals. Currently, Mega2 converts data from several different genetic analysis data formats (including PLINK, VCF, BCF, and IMPUTE2) into the specific data requirements for over 40 commonly used linkage and association analysis programs (including Mendel, Merlin, Morgan, SHAPEIT, ROADTRIPS, MaCH/minimac3, etc.). Recently, Mega2 has been enhanced to use a SQLite database as an intermediate data representation. Additionally, Mega2 now stores bialleleic genotype data in a highly compressed form, much like that of the GenABEL R facility and the PLINK binary format. Concurrently, the R community and Bioconductor community have developed a variety of genetic analysis programs complimentary to the programs supported by Mega2. The Mega2R R package now makes it easy to load SQLite3 Mega2 databases directly into R as data frames to use these R facilities. In addition, we have developed C++ functions for R to decompress needed subsets of the genotype data, on the fly, in a memory efficient manner. We have also created several more functions that illustrate how to use the Mega2R data frames as well as perform useful tasks: these permit one to run the pedgene package to carry out gene-based association tests on family data using selected marker subsets, to run the SKAT package to carry out gene-based association tests using selected marker subsets, to output the Mega2R data as a VCF file and related files (for phenotype and family data), and to convert the data frames into GenABEL R gwaa.data-class objects. The Mega2R package enhances GenABEL since it supports additional input data formats (such as PLINK, VCF, and IMPUTE2) not currently supported by GenABEL. Mega2R is available here: https://CRAN.R-project.org/package=Mega2R. This work was supported by NIH grant R01 GM076667 (PI: Weeks).

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

FIRST EXPERIENCES WITH A NOVEL FARMER CITIZEN SCIENCE APPROACH: CROWDSOURCING PARTICIPATORY VARIETY SELECTION THROUGH ON-FARM TRIADIC COMPARISONS OF TECHNOLOGIES (TRICOT)

SUMMARYRapid climatic and socio-economic changes challenge current agricultural R&D capacity. The necessary quantum leap in knowledge generation should build on the innovation capacity of farmers themselves. A novel citizen science methodology, triadic comparisons of technologies or tricot, was implemented in pilot studies in India, East Africa, and Central America. The methodology involves distributing a pool of agricultural technologies in different combinations of three to individual farmers who observe these technologies under farm conditions and compare their performance. Since the combinations of three technologies overlap, statistical methods can piece together the overall performance ranking of the complete pool of technologies. The tricot approach affords wide scaling, as the distribution of trial packages and instruction sessions is relatively easy to execute, farmers do not need to be organized in collaborative groups, and feedback is easy to collect, even by phone. The tricot approach provides interpretable, meaningful results and was widely accepted by farmers. The methodology underwent improvement in data input formats. A number of methodological issues remain: integrating environmental analysis, capturing gender-specific differences, stimulating farmers' motivation, and supporting implementation with an integrated digital platform. Future studies should apply the tricot approach to a wider range of technologies, quantify its potential contribution to climate adaptation, and embed the approach in appropriate institutions and business models, empowering participants and democratizing science.

Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization technique

Advances in computing technology have been instrumental in creating an assortment of powerful information visualization techniques. However, the selection of a suitable and effective visualization technique for a specific dataset and a data mining task is not trivial. This work automatically selects an appropriate visualization technique based on the given metadata and the task that a user intends to perform. The appropriate visualization is predicted based on an artificial neural network (ANN)-based model which classifies the input data into one of the eight predefined classes. A purpose built dataset extracted from the existing knowledge in the discipline is utilized to train the neural network. The dataset covers eight visualization techniques, including: histogram, line chart, pie chart, scatter plot, parallel coordinates, map, treemap, and linked graph. Various architectures using different numbers of hidden units, hidden layers, and input and output data formats have been evaluated to find the optimal neural network architecture. The performance of neural networks is measured using: confusion matrix, accuracy, precision, and sensitivity of the classification. Optimal neural network architecture is determined by convergence time and number of iterations. The results obtained from the best ANN architecture are compared with five other classifiers, k-nearest neighbor, naïve Bayes, decision tree, random forest, and support vector machine. The proposed system outperforms four classifiers in terms of accuracy and all five classifiers based on execution time. The trained neural network is also tested on twenty real-world benchmark datasets, where the proposed approach also provides two alternate visualizations, in addition to the most suitable one, for a particular dataset. A qualitative comparison with the state-of-the-art approaches is also presented. The results show that the proposed technique assists in selecting an appropriate visualization technique for a given dataset with high accuracy.

RDF-powered semantic video annotation tools with concept mapping to Linked Data for next-generation video indexing: a comprehensive review

Video annotation tools are often compared in the literature, however, most reviews mix unstructured, semi-structured, and the very few structured annotation software. This paper is a comprehensive review of video annotations tools generating structured data output for video clips, regions of interest, frames, and media fragments, with a focus on Linked Data support. The tools are compared in terms of supported input and output data formats, expressivity, annotation specificity, spatial and temporal fragmentation, the concept mapping sources used for Linked Open Data (LOD) interlinking, provenance data support, and standards alignment. Practicality and usability aspects of the user interface of these tools are highlighted. Moreover, this review distinguishes extensively researched yet discontinued semantic video annotation software from promising state-of-the-art tools that show new directions in this increasingly important field.

Lightweighting for Web3D visualization of large-scale BIM scenes in real-time

As a result of informatization in construction, Building Information Modeling (BIM) has now become a core technology for smart construction. We present a Web3D-based lightweighting solution for real-time visualization of large-scale BIM scenes, considering the redundancy, semantics, and the parameterization of BIM data under the limited resources of network bandwidth and web browsers. Taking the Industry Foundation Classes (IFC) as the input data format, we firstly conduct a semantics-guided lightweighting operation on the raw BIM scenes by removing the repetitive objects and parameterizing the swept surfaces. Secondly, we extract the exterior products from the raw BIM buildings for visibility culling and construct a Double-Layered Sparse Voxel (DLSV) index based on sparse voxelization. Thirdly, we integrate the above two together into a new data structure named Incremental Frustum of Interest (I-FOI) to manage the scene data in real-time. Our experiments demonstrate that: (1) with the semantics information, our method is able to significantly reduce the redundancy of raw large-scale BIM scenes; (2) the DLSV structure supports progressive data loading and facilitates the indoor/outdoor visibility culling efficiently; and (3) the I-FOI introduces a frustum incremental-driven mechanism into progressive data loading or unloading to improve the efficiency of resource consumption.

Multimodal spatial mapping and visualisation of Dinaledi Chamber and Rising Star Cave

The Dinaledi Chamber of the Rising Star Cave has yielded 1550 identifiable fossil elements – representing the largest single collection of fossil hominin material found on the African continent to date. The fossil chamber in which Homo naledi was found was accessible only through a near-vertical chute that presented immense practical and methodological limitations on the excavation and recording methods that could be used within the Cave. In response to practical challenges, a multimodal set of recording and survey methods was thus developed and employed: (1) recording of fossils and the excavation process was achieved through the use of white-light photogrammetry and laser scanning; (2) mapping of the Dinaledi Chamber was accomplished by means of high-resolution laser scanning, with scans running from the excavation site to the ground surface and the cave entrance; (3) at ground surface, the integration of conventional surveying techniques as well as photogrammetry with the use of an unmanned aerial vehicle was applied. Point cloud data were used to provide a centralised and common data structure for conversion and to corroborate the influx of different data collection methods and input formats. Data collected with these methods were applied to the excavations, mapping and surveying of the Dinaledi Chamber and the Rising Star Cave. This multimodal approach provides a comprehensive spatial framework from individual bones to landscape level.

Estimating Detectability and Biological Parameters of Interest with the Use of the R Environment

Abstract Imperfect detectability in animal studies has been an acknowledged problem for several decades. A number of analytical approaches (e.g., capture–mark–recapture, distance sampling, occupancy analyses), have been developed to deal with the problem and estimate biological parameters of interest. These different analytical approaches can be implemented through various software programs, each with its own graphical user interface (GUI), data input format, and graphing capabilities. Although GUIs and mouse clicks might be convenient to conduct analyses, mouse clicks are difficult to replicate and archive in the long term. Using programming environments such as the R open source project along with a text editor yields flexibility and leaves a paper trail after the analyses are conducted. Such environments also provide statistical and graphical tools for further treatment following the analyses. In this article, I use R to conduct classical analyses to estimate demographic parameters in four case studies...

이미지 검색을 이용한 사진입력 게임 인터페이스 구현

The paradigm of game development changes with technological trends. If the system can analyze and determine undefined inputs, users' input choices are not restricted. Therefore, game scenarios can have multifarious flows depending upon the user's input data. In this paper, we propose a method of including an output plan in the game system that is based on the user's input but is not restricted to predefined choices. We have implemented an experimental game on the Android platform by combining network communication and APIs. The game interface works as follows: first, the user's input data is transmitted to the server using HTTP protocol; then, the server carries out an analysis on the input data; and finally, the server returns the decision result to the game device. The game can provide users a scenario that corresponds to the decision results. In this paper, we used an image file for the user's input data format. The server calculates similarities between the user's image file and reference images obtained from the Naver Image Search API and then returns determination results. We have confirmed the value of integrating the game development framework with other computing technologies demonstrating the potential of the proposed methods for application to various future game interfaces.

Calculation of the isotope composition and induced activity in irradiated materials of innovative accelerator-driven systems

The paper describes the SNT.1 system for calculating the isotopic composition and induced activity of irradiated materials in innovative accelerator-driven systems (ADS). The SNT.1 algorithm, a list and the format of input data, a list of the evaluated nuclear data libraries to be used in calculations, and the potential calculation options are presented. The activity calculation for the water cooling of the Ta target in the TRADE project is presented, and the calculation results are compared with those obtained using other codes. The concentration and the activity of the nuclides formed during irradiation of a lead-bismuth target have been calculated with due regard for the variations in the accelerator current during irradiation. The possibility for neural networks to be used for the approximation and restoration of data, such as the reaction cross-sections, has been studied. The RBF and GRNN neural network structures were selected to approximate data on the 209Bi (n, 3n) 207Bi and 54Fe (n, p) 54Mn reaction cross-sections. Neural networks were used to restore data for the total cross-section of reactions of interaction with 209Bi nuclei. The approximation quality was assessed using agreement factors.

Classification Similarity Learning Using Feature-Based and Distance-Based Representations: A Comparative Study

Automatically measuring the similarity between a pair of objects is a common and important task in the machine learning and pattern recognition fields. Being an object of study for decades, it has lately received an increasing interest from the scientific community. Usually, the proposed solutions have used either a feature-based or a distance-based representation to perform learning and classification tasks. This article presents the results of a comparative experimental study between these two approaches for computing similarity scores using a classification-based method. In particular, we use the Support Vector Machine as a flexible combiner both for a high dimensional feature space and for a family of distance measures, to finally learn similarity scores. The approaches have been tested in a content-based image retrieval context, using three different repositories. We analyze both the influence of the different input data formats and the training size on the performance of the classifier. Then, we found that a low-dimensional, multidistance-based representation can be convenient for small to medium-size training sets, whereas it is detrimental as the training size grows.

ВИКОРИСТАННЯ СУЧАСНОГО ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ В СИСТЕМАТИЗАЦІЇ ЛІТЕРАТУРНИХ ДАНИХ ЗА ІНТРАНАЗАЛЬНИМИ ЛІКАРСЬКИМИ ЗАСОБАМИ

<p>One of the stages of development of new drugs is a generalization and systematization of existing information flows in the form of scientific abstracts, articles, patents, abstracts, theses, monographs. Often this process for researchers is quite painstaking and consuming, while specialist must possess special skills to find publications, both foreign and domestic origin. It should be noted that the search complicated also a great variety of input data formats which can vary depending on the search engine, database type, nature magazines.</p><p>Scientific information on medicines intranasal topical and systemic use in a variety of distributed databases and libraries, some fragments of it were published in scientific journals in many languages in the form of abstracts, articles, short communications and patents. Systematics and processing of such information in diverse research causes some complications.</p><p>Objective. To analyze the art software that can be used to systematize the published data on intranasal drugs.</p><p>Methods. The material used available software used for organizing research. Served as the Information theses, articles, patents, theses and dissertations on nasal medications. In analyzing the resulting software functionality based on convenience, intuitive and availability.</p><p>Results and discussion. Initially performed literature search intranasal medicines remotely through internet network in such well-known databases as the US National Library of Medicine (http://www.ncbi.nlm.nih.gov/pubmed/), full-text database ScienceDirect (http : //www.sciencedirect.com/), full-text database Springerlink (http://www.SpringerLink.com), a specialized database "Inventions (Utility models) in Ukraine" (http://sips.gov.ua), specialized DB "Russian patent documents» (http://www1.fips.ru/), scientific digital library eLIBRARY.RU (http://elibrary.ru), national Library of Ukraine Vernadsky (http: // www.nbuv.gov.ua/). As a result of the search were found literary reference with full access to publications and prints without it.</p><p>As software for working with bibliographic information examined tools such as EndNote ©, Reference Manager ©, ProCite ©, Bible-Master ©, Mendeley ©, Zotero ©. We know that these programs can automate some functions of management science and information retrieval, and can be used by one person or group of people [6]. Having tested the possibility of the above software we stopped at Mendeley © Zotero © and having a similar interface, functionality and freely with each other by exporting it as a universal format RIS and BibTeX. These programs can be used in environments Windows ©, Linux ©, Mac OS ©, are free and easy to use. One advantage of such tools is the ability to synchronize a local base of literature lists and their full texts on selected problem with external servers located in the network internet, which in turn protects against unexpected losses accumulated information and allows you to access your information from anywhere any place. For this Mendeley © Zotero © and can be used free accounts that have a certain amount of disk space in the exhaustion which it is available at an additional cost. In Zotero © along with this is to use your own or free server operating protocol WebDAV (eg "Yandex Disk") that allows you to save free full text publications in the library's own online internet.</p><p>Through tight integration Mendeley © Zotero © and with a word processor «Microsoft Word» and other applications in the design of scientific publications given the opportunity to conveniently cite evidence from the literature by selecting them from the database, and automatically generate lists of references. Making reference list can change quickly due to the built-in templates or styles (Citation Style Language) which is for many magazines and publishing houses. Currently we conducted the adaptation of literary styles of lists as required GOST 7.1: 2006.</p><p>Conclusions. As a result, research was conducted search of published data on the development, manufacturing technology and standardization of drugs intranasal administration. It is established that to optimize storage, analysis and processing of data in the literature appropriate to use modern software.</p>

Development and application of a weather data service client for preparation of weather input files to a crop model

Crop yield prediction has been made using a crop growth model that relies on four categories of input data including soil, crop, management, and weather. Most crop models are single column models, which require individual weather inputs for each site of interest. The objectives of this study were to develop a weather data service client that prepares weather input files for a crop growth model and to examine its application to yield prediction at a national scale. The weather data service client downloads daily weather data from the web-based weather data service portal operated by the Korea Meteorological Administration (KMA). The client also prepares weather input files for the ORYZA 2000 model at minimum effort. In total, 4950 input files were prepared to predict rice yield in 2011 and 2012 using the weather data service client. To prepare nearly 5000 weather input files, it would take more than a month for a skilled person to download weather data from the KMA database and to reorganize those data to the input data format for the ORYZA 2000 model manually. Using the weather data service client, several hours were enough to prepare all the input files without error associated with manual preparation as well as with minimum effort and labor.

Magnitude Modelling of HRTF Using Principal Component Analysis Applied to Complex Values

Abstract Principal components analysis (PCA) is frequently used for modelling the magnitude of the head-related transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between -45°. and +9°.

A Hadoop-Based Performance Optimization of Network Stream Input Format

Network stream analysis is one of the essential applications of industrial research in the era of big data. As the input format of the major massive data application platform--Hadoop, cannot support network stream sufficiently. This paper proposes a feasible optimization design. Firstly, the HDFS block-storage structure and the particular libpcap file format of network stream are considered. Then input files were pre-processed as large as HDFS block-size, and a new data input format called blockPcapInputFormat is achieved by expanding the fileInputFormat of Hadoop. Furthermore, experiments are performed for verifying the proposed design’ effectiveness. Results have shown that the optimization scheme is not only able to accelerate the processing performance of libpcap files effectively, but also suitable for applications where Hadoop parses network stream.