Input Format Research Articles

Fully automated web-based tool for identifying regulatory hotspots

BackgroundRegulatory hotspots are genetic variations that may regulate the expression levels of many genes. It has been of great interest to find those hotspots utilizing expression quantitative trait locus (eQTL) analysis. However, it has been reported that many of the findings are spurious hotspots induced by various unknown confounding factors. Recently, methods utilizing complicated statistical models have been developed that successfully identify genuine hotspots. Next-generation Intersample Correlation Emended (NICE) is one of the methods that show high sensitivity and low false-discovery rate in finding regulatory hotspots. Even though the methods successfully find genuine hotspots, they have not been widely used due to their non-user-friendly interfaces and complex running processes. Furthermore, most of the methods are impractical due to their prohibitively high computational complexity.ResultsTo overcome the limitations of existing methods, we developed a fully automated web-based tool, referred to as NICER (NICE Renew), which is based on NICE program. First, we dramatically reduced running and installing burden of NICE. Second, we significantly reduced running time by incorporating multi-processing. Third, besides our web-based NICER, users can use NICER on Google Compute Engine and can readily install and run the NICER web service on their local computers. Finally, we provide different input formats and visualizations tools to show results. Utilizing a yeast dataset, we show that NICER can be successfully used in an eQTL analysis to identify many genuine regulatory hotspots, for which more than half of the hotspots were previously reported elsewhere.ConclusionsEven though many hotspot analysis tools have been proposed, they have not been widely used for many practical reasons. NICER is a fully-automated web-based solution for eQTL mapping and regulatory hotspots analysis. NICER provides a user-friendly interface and has made hotspot analysis more viable by reducing the running time significantly. We believe that NICER will become the method of choice for increasing power of eQTL hotspot analysis.

The Design and Automation of Web Based School Management Portal for Primary and Secondary Schools in Nigeria

Many renowned primary and secondary schools in Nigeria are still dwindling in the use of technology as a supportive measure in achieving a high ranking educational breakthrough in the 21 st century especially when they need to manage a wide range of students’ data. This is because they rely on manual ways of creating, managing and displaying students’ data. Computations of such data are so cumbersome and mistakes are unavoidable. There are always complaints of computational errors in students’ terminal reports, class performance, financial records, and so on. Furthermore, record archival and retrieved processes are so slow which in turn decreases the school administration efficiency. Hence the need for the design and automation of web based school management portal (WBSMP) as an integral part in e-government. This research is conducted to design WBSMP that could be used by various schools in Nigeria. The objective of the WBSMP is to provide the following services among others; reduce school administration running cost, increase school administration efficiency, run platform of communication between school and parents, document download and newsletters provided by the teachers and management, online registration, track students’ enrollment, online checking of results, etc. The design of WBSMP was done using Hypertext Preprocessor (PHP) and JAVA programming languages coupled with user interface (UI) designing languages like Bootstrap, and the database was designed using Structure Query Language (MySQL). Several input formats were designed to capture various data needed to generate various output reports. The WBSMP was carefully tested and evaluated and it was found out that the system achieved the stated goals. This research work ‘Design and Automation of WBSMP’ is recommended for all basic schools and post basic schools in the country.

Towards Photo-Realistic Facial Expression Manipulation

We present a method for photo-realistic face manipulation. Given a single RGB face image with an arbitrary expression, our method can synthesize another arbitrary expression of the same person. To achieve this, we first fit a 3D face model and disentangle the face into its texture and shape. We then train separate networks in each of these spaces. In texture space, we use a conditional generative network to change the appearance, and carefully design the input format and loss functions to achieve the best results. In shape space, we use a fully connected network to predict an accurate face shape. When available, the shape branch uses depth data for supervision. Both networks are conditioned on expression coefficients rather than discrete labels, allowing us to generate an unlimited number of expressions. Furthermore, we adopt spatially adaptive denormalization on our texture space representation to improve the quality of the synthesized results. We show the superiority of this disentangling approach through both quantitative and qualitative studies. The proposed method does not require paired data, and is trained using an in-the-wild dataset of videos consisting of talking people. To achieve this, we present a simple yet efficient method to select appropriate key frames from these videos. In a user study, our method is preferred in 83.2% of cases when compared to state-of-the-art alternative approaches.

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through tremendous progress, which can help reduce costly breakdowns. However, different datasets and hyper-parameters are recommended to be used, and few open source codes are publicly available, resulting in unfair comparisons and ineffective improvement. To address these issues, we perform a comprehensive evaluation of four models, including multi-layer perception (MLP), auto-encoder (AE), convolutional neural network (CNN), and recurrent neural network (RNN), with seven datasets to provide a benchmark study. We first gather nine publicly available datasets and give a comprehensive benchmark study of DL-based models with two data split strategies, five input formats, three normalization methods, and four augmentation methods. Second, we integrate the whole evaluation codes into a code library and release it to the public for better comparisons. Third, we use specific-designed cases to point out the existing issues, including class imbalance, generalization ability, interpretability, few-shot learning, and model selection. Finally, we release a unified code framework for comparing and testing models fairly and quickly, emphasize the importance of open source codes, provide the baseline accuracy (a lower bound), and discuss existing issues in this field. The code library is available at: https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark.

Deep learning of topological phase transitions from entanglement aspects

The one-dimensional $p$-wave superconductor proposed by Kitaev has long been a classic example for understanding topological phase transitions through various methods, such as examining Berry phase, edge states of open chains and, in particular, aspects from quantum entanglement of ground states. In order to understand the amount of information carried in the entanglement-related quantities, here we study topological phase transitions of the model with emphasis of using the deep learning approach. We feed different quantities, including Majorana correlation matrices (MCMs), entanglement spectra (ES) or entanglement eigenvectors (EE) originated from Block correlation matrices (BCMs), into the deep neural networks for training, and investigate which one could be the most useful input format in this approach. We find that ES is indeed too compressed information compared to MCM or EE. MCM and EE can provide us abundant information to recognize not only the topological phase transitions in the model but also phases of matter with different $U$(1) gauges, which is not reachable by using ES only.

Efficient contouring of functionally represented objects for additive manufacturing

Functionally (implicitly) defined 3D objects allow us to quite easily model parts with complex topology such as lattices and organic-like structures with a high level of flexibility. Previous works in this area are based on the direct generation of CNC programs for the 3D printing of these objects and are backed by the growing support for this input format from hardware manufacturers. Efficient contouring of functionally defined models, however, is not an easy task. In this paper, we develop an algorithm for contour extraction of implicitly defined objects for direct additive manufacturing (AM). By comparing various adaptive and exhaustive (non-adaptive) methods of the function representation contouring for AM (FRepCAM), we make a set of recommendations for its usage depending on the specific resolution of the printer. In particular, we use a novel criterion based on affine arithmetic to maintain efficiency while preserving the robustness of the contouring process. The techniques mentioned were evaluated for algebraic and non-algebraic solids and heterogeneous models under a resolution that is comparable with that of current AM technology. The results show that the chosen adaptation criteria allow us to efficiently obtain a contour for complex models and generally outperform those of traditional algorithms based on exhaustive enumeration, especially for high-resolution contouring. In addition, the results present proof of the printability of implicitly defined objects with different 3D printing techniques.

Multiphysics for nuclear energy applications using a cohesive computational framework

With the recent development of advanced numerical algorithms, software design, and low-cost high-performance computer hardware, reliance on coupled multiphysics to predict the behavior of complex physical systems is beginning to become standard practice. This is especially true in nuclear energy applications where strong nonlinear interdependencies exist between reactor physics, radiation transport, multi-scale nuclear fuels performance, thermal fluids, etc. Resolving these nonlinear dependencies requires choices in multiphysics software approaches. Two main multiphysics modeling and simulation approaches have emerged. The first is based upon “code coupling” where disparate physics codes of different software design, code languages, and spatial and temporal integration schemes are coupled together with relatively complex data passing interfaces. The second multiphysics software approach is to employ a “cohesive” framework where all physics applications are developed with a common software design, i.e., data structures, syntax, input format, integrated spatial and temporal discretization schemes, etc. Here we present the Multiphysics Object-Oriented Simulation Environment (MOOSE) development and runtime framework and describe the framework’s cohesive modeling and simulation multiphysics approach. Then, a “cohesive-like” extension of the MOOSE framework is presented where MOOSE-based physics software applications are efficiently coupled to non-MOOSE (external) physics codes to form multiphysics applications using MOOSE’s unique interface capabilities. Finally, several examples of MOOSE’s cohesive and cohesive-like multiphysics applications will be demonstrated. These multiphysics demonstrations will incorporate both MOOSE-based applications and external codes, including Nek5000, RELAP-7, TRACE, BISON, and Pronghorn.

Inputs From Hell:

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Grammars</i> can serve as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">producers</i> for structured test inputs that are syntactically correct by construction. A probabilistic grammar assigns probabilities to individual productions, thus controlling the distribution of input elements. Using the grammars as input parsers, we show how to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn input distributions from input samples,</i> allowing to create inputs that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">similar</i> to the sample; by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverting</i> the probabilities, we can create inputs that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dissimilar</i> to the sample. This allows for three <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test generation strategies</i> : 1) “Common inputs”–by learning from common inputs, we can create inputs that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">similar</i> to the sample; this is useful for regression testing. 2) “Uncommon inputs”–learning from common inputs and inverting probabilities yields inputs that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strongly dissimilar</i> to the sample; this is useful for completing a test suite with “inputs from hell” that test uncommon features, yet are syntactically valid. 3) “Failure-inducing inputs”–learning from inputs that caused failures in the past gives us inputs that share similar features and thus also have a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high chance of triggering bugs</i> ; this is useful for testing the completeness of fixes. Our evaluation on three common input formats (JSON, JavaScript, CSS) shows the effectiveness of these approaches. Results show that “common inputs” reproduced 96 percent of the methods induced by the samples. In contrast, for almost all subjects (95 percent), the “uncommon inputs” covered significantly different methods from the samples. Learning from failure-inducing samples reproduced all exceptions (100 percent) triggered by the failure-inducing samples and discovered new exceptions not found in any of the samples learned from.

Critical fault diagnosis method of small components of power transmission based on optimizational YOLOv3 algorithm

In order to solve the problem that the image recognition algorithm in UAV (Unmanned Aerial Vehicle) inspection of power transmission has low recognition rate for small, indiscoverable and high-risk component failures, this paper proposes a critical fault diagnosis method based on the optimizational YOLOv3 algorithm. We add SPP net under the basic framework of the original YOLOv3 and extract multi-scale depth features in the same convolutional layer which can transform all the input images into a uniform size and solve the problem caused by different input image sizes and formats. An optimizational scale prediction framework of YOLOv3 is proposed, which expands the original three-scale prediction frameworks to four scales and carries out multi-scale integration. This improvement is used to strengthen the feature extraction of small and indiscoverable fault. The optimizational algorithm is verified by using the dataset of electrical components of transmission system in the actual scene which have small and indiscoverable faults. The results show that the optimizational YOLOv3 improves the recognition rate of the selected categories by 3.98% on average, and increases the recognition rate of the small size and indiscoverable fault by 11.95% on average.

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening.

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.

Diving in uncharted waters: An updated genetics toolkit highlights the challenges of polyploidy in landscape genomics analyses.

As molecular ecologists, we have by necessity become adept at working across computational platforms. A diverse community of scientists has developed a broad array of analytical resources spanning command line to graphical user interface across Linux, Mac, and Windows environments and a dizzying array of program-specific input formats. In light of this, we often explore our data like free divers - filling our lungs with air and descending for a short period of time into one part of our data set before resurfacing, reformatting, and preparing for our next analysis. In this issue of Molecular Ecology Resources, Meirmans (2020) presents an updated version of GenoDive, a program with a toolkit that provides users with the opportunity to stay a while and delve deeper into the diverse portfolio of information provided by a genomic data set. The comprehensive nature of GenoDive coupled with its unique capability to handle both diploid and polyploid data also provides an opportunity to reflect on the unevenness of resources available for the analysis of polyploid versus diploid data. Since new updates include the addition of plug-ins for genotype-environment association analyses, we limit the observations presented here to the common tools used for landscape genomics analyses.

Propositionalization and embeddings: two sides of the same coin

Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.

MirnaQC: a webserver for comparative quality control of miRNA-seq data.

Although miRNA-seq is extensively used in many different fields, its quality control is frequently restricted to a PhredScore-based filter. Other important quality related aspects like microRNA yield, the fraction of putative degradation products (such as rRNA fragments) or the percentage of adapter-dimers are hard to assess using absolute thresholds. Here we present mirnaQC, a webserver that relies on 34 quality parameters to assist in miRNA-seq quality control. To improve their interpretability, quality attributes are ranked using a reference distribution obtained from over 36 000 publicly available miRNA-seq datasets. Accepted input formats include FASTQ and SRA accessions. The results page contains several sections that deal with putative technical artefacts related to library preparation, sequencing, contamination or yield. Different visualisations, including PCA and heatmaps, are available to help users identify underlying issues. Finally, we show the usefulness of this approach by analysing two publicly available datasets and discussing the different quality issues that can be detected using mirnaQC.

Abstract B50: Using pattern recognition neural networks to detect prostate cancer: A new method to analyze flow cytometry-based immunophenotyping using machine learning

Abstract Prostate cancer (PCa) screening and detection relies heavily upon prostate-specific antigen (PSA) testing, but PSA testing has a high rate of false positives, leading to increased risks for overdiagnosis and overtreatment; thus, additional blood-based biomarkers for PCa detection are needed. Flow cytometry-based immunophenotyping of peripheral blood is an accessible and noninvasive technology, but as more parameters are included, new computational methods must be developed for the efficient analysis and utilization of these large datasets for clinical applications. Machine learning algorithms, specifically pattern recognition neural networks (PRNNs), have the potential to assist in these types of analyses, but the flow cytometry data need to be transformed into a usable input format. The goal of this study was to use our newly developed “hypervoxelation of cytometry events” computational technique, or HyperVOX, to transform flow cytometry data into a useable format for input into a series of PRNNs to detect PCa of all Gleason scores (GS) from circulating immune cells. We used standard multiparametric flow cytometry techniques to measure 16 different myeloid and lymphoid cell populations found in the peripheral blood of 156 biopsy-confirmed PCa (GS6 n = 59, GS7 n = 68, GS8 n = 12, GS9 n = 16, and GS10 n = 1; median age = 68 ± 8.7 years) along with 99 male healthy donors (HD) (median age = 53 ± 8.5 years). Flow cytometry data were then transformed using HyperVOX in order to create hypervoxels that can be used as the common feature across all samples. Briefly, each channel was used as an axis in a multidimensional space and divided into four segments, with each event being defined by its location within each segment of each axis. The resulting count of events that fall within each hypervoxel for each sample is then used as the input for the PRNN. With this, a screening-type assay was developed to detect PCa compared against HD. PRNNs were trained using raw flow cytometry data processed using HyperVOX from 97 PCa patients and 67 HD controls. Predictions were evaluated using the performance of the trained PRNNs on 59 PCa patients and 32 HD that were not used for PRNN training (holdout test set). The PRNN classified 28 out of 32 HD and 57 out of 59 PCa samples correctly, resulting in a sensitivity of 96.6% (95% CI, 88.3–99.6), specificity of 87.5% (95% CI, 71.0–96.5), positive predictive value (PPV) of 93.4% (95% CI, 85.1–98.2), negative predictive value (NPV) of 93.3% (95% CI, 78.1–98.2), and an AUC of 0.9656 (95% CI, 0.9202–1). Upon Gleason score stratification, the NN classified 27 out of 28 GS6, 18 out of 19 GS7, and 11 out of 11 &amp;gt;GS7 samples correctly. In a clinical setting, this technology would improve PCa detection and allow clinicians to have a more informed decision when recommending their patients for a prostate biopsy procedure and subsequent medical interventions to help reduce overdiagnosis and overtreatment. Citation Format: George A. Dominguez, John Roop, Alexander Polo, Anthony Campisi, Dmitry I. Gabrilovich, Amit Kumar. Using pattern recognition neural networks to detect prostate cancer: A new method to analyze flow cytometry-based immunophenotyping using machine learning [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B50.

Information technology application in the construction project life cycle

Development management models of object information base are necessary for definition the volume of resources and construction object cost at each stage of building life cycle taking into account the existing requirements to definition each type of cost at stages from PRE-BIM to RE-BIM. To develop and implement the cost management system of the object in the construction industry, it is necessary not only to determine the main levels and parameters of choice when taking into account decisions from the organizational and technological documentation, but also to build an architecture of application software based on artificial intelligence for the operation of the management system at all subsequent stages of the life cycle of the construction object. It is important to determine the main factors influencing on the process of decision-making, and the parameters of the formation of input and output data for modelling each stage of the life cycle of the object, as well as technical and economic indicators of the information model by stages. The diversity of digital technologies opens new possibilities for every project and the cost model help to connect the processes throughout project lifecycle.

Flagging incorrect nucleotide sequence reagents in biomedical papers: To what extent does the leading publication format impede automatic error detection?

In an idealised vision of science the scientific literature is error-free. Errors reported during peer review are supposed to be corrected prior to publication, as further research establishes new knowledge based on the body of literature. It happens, however, that errors pass through peer review, and a minority of cases errata and retractions follow. Automated screening software can be applied to detect errors in manuscripts and publications. The contribution of this paper is twofold. First, we designed the erroneous reagent checking (ERC) benchmark to assess the accuracy of fact-checkers screening biomedical publications for dubious mentions of nucleotide sequence reagents. It comes with a test collection comprised of 1679 nucleotide sequence reagents that were curated by biomedical experts. Second, we benchmarked our own screening software called Seek&Blastn with three input formats to assess the extent of performance loss when operating on various publication formats. Our findings stress the superiority of markup formats (a 79% detection rate on XML and HTML) over the prominent PDF format (a 69% detection rate at most) regarding an error flagging task. This is the first published baseline on error detection involving reagents reported in biomedical scientific publications. The ERC benchmark is designed to facilitate the development and validation of software bricks to enhance the reliability of the peer review process.

PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data.

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.

TeamTat: a collaborative text annotation tool.

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC(uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

Assessing L2 listening at a Japanese university: Effects of input type and response format

Large-scale and classroom-based tests of L2 listening comprehension typically employ audio-only listening texts and a multiple-choice response format. However, the effect of these test task characteristics on test-taker performance remains elusive, and begs the question as to whether they are the best option in terms of construct validity. Furthermore, the interaction between these test task characteristics and their potential joint effect on performance has not been sufficiently explored. To address this gap, a study was conducted at a Japanese university which investigated the effect of input type (audio-only vs. video) and response format (multiple-choice vs. short answer) on L2 listening test performance. Participants were divided into four groups to take an academic listening test with one of four combinations of input and response format: (1) audio-only with multiple-choice questions; (2) video with multiple-choice questions; (3) audio-only with short answer questions; and (4) video with short answer questions. Results of a 2 x 2 factorial ANOVA revealed a statistically significant effect of response format on test-taker performance. No significant effect for input type was found and no significant interaction among the variables was detected. Results suggest that visual input and audiovisual literacy need to be more clearly articulated within the construct definition of academic listening, if they are to be included at all.

Bioenergy efficiency change and its determinants in EU-28 region: Evidence using Least Square Dummy Variable corrected estimation

Applying the data envelopment approach, this paper computes efficiency change (EFFCH) in terms of its components: pure technical and scale efficiency changes. The least squares dummy variable corrected (LSDVC) method was employed to examine the determinants of efficiency change of bioenergy industry of the EU28 countries covering the period 1990–2013. Results show that the bioenergy industry recorded an average annual rate of progress in efficiency change of 3.8% during the period studied. Interestingly, the region recorded a technical progress with both pure technical efficiency change and scale efficiency change increasing over the study period, however, scale efficiency was the major contributor to efficiency with annual increase of 4.5%. We also found variations in regional performance with EU developing region experiencing a slightly higher increase in efficiency at 3.86% while this figure is at 3.76% for the EU developed countries. Empirical finding show that economic growth, gross fixed capital formation, inflation and size of biomass input affected EFFCH of bioenergy industry of the EU28 region significantly during the period studied. Policy implications of these results are that the bioenergy industry should relentlessly pursue technical progress and aim to improve both technical and scale efficiencies simultaneously through optimal allocation of resources.