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How many factors are there? It is a critical question that researchers and practitioners deal with when estimating factor models. We proposed a new eigenvalue ratio criterion for the number of factors in static approximate factor models. It considers a pooled squared correlation matrix which is defined as a weighted combination of the main observed squared correlation matrices. Theoretical results are given to justify the expected good properties of the criterion, and a Monte Carlo study shows its good finite sample performance in different scenarios, depending on the idiosyncratic error structure and factor strength. We conclude comparing different criteria in a forecasting exercise with macroeconomic data. [ABSTRACT FROM AUTHOR]

The process of locating and identifying missing persons presents a complex challenge that hinges on the collection and comparison of diverse data sets. This commentary offers an overview of some of the difficulties and considerations associated with data management in the context of large‐scale missing person identifications. Such complexities include the uniqueness of each disaster event, the response time to the event, the variable quality and quantity of data, and the involvement of numerous stakeholders, all of which contribute to the intricacies of data management. In addition, the paramount considerations of privacy and ethical standards further compound these challenges, especially when dealing with sensitive information such as genetic data. This commentary describes the integrated Data Management System (iDMS) developed by the International Commission on Missing Persons (ICMP) as one example of a comprehensive, freely available solution for data collection, storage, protection, and analysis in missing person cases. The various advantages of the system are discussed, including the system's interoperability among the diverse array of stakeholders involved. While the iDMS streamlines data management processes and therefore represents a significant advancement in the field of missing person identification, it is concluded that the pending issue extends beyond the software tools to encompass the lack of political will among stakeholders to collaborate there remains a pressing need for all stakeholders involved in the identification process to commit to a mechanism that facilitates compatibility and interoperability if different tools are used in disaster victim identification (DVI) scenarios. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the features of processing large amounts of data using the Python programming language. Unlike tabular processors or finished software products, programming languages offer the user a flexible toolkit for the implementation of tasks. At the same time, this creates certain risks associated with the effectiveness of using appropriate tools and optimising the operation of the programme. The purpose of the article is to study the features of processing large amounts of data in Python on the examples of immediate research tasks. The relevance of the topic and purpose of the article is due to the existing scientific gap related to a comprehensive consideration of the technical aspects of the use of programming languages and associated tools for socio-economic research. Thus, many authors who use programming languages in their works rarely provide information regarding the advantages of certain algorithms or approaches. Within the framework of the article, the author examines the procedures and algorithm of processing a large array of data on the example of specific research tasks. The conclusions are drawn about the features and advantages of Python when working with large amounts of data as well as about the prospects for the development of the relevant scientific topics.

The article discusses the need for mechanistic explanations in seed ecology and highlights the limitations of broad-scale correlative studies that ignore mechanisms. The authors specifically focus on seed dormancy and argue that the current understanding of seed dormancy as an adaptive response to climate seasonality is flawed. They propose that fire regime, rather than climate seasonality, is a more plausible mechanism for selecting prolonged seed dormancy. The article also mentions similar issues in other areas of ecology, such as species diversity and trait ecology, and emphasizes the importance of a mechanistic understanding and hypothesis-driven research. [Extracted from the article]

The Erasmus+ project, entitled 'Digital Erasmus – a roadmap to using building performance simulation to achieve resilient design' (DesRes), seeks to transform the learning experience of students in built environment disciplines using a continuous digital learning cycle. Three universities play a part in this project: Graz University of Technology (TU Graz), Delft University of Technology (TU Delft) and the University of Strathclyde (UoS), each developing and delivering a module to complete the learning experience in building simulation. This paper describes the aims and learning objectives associated with the workshops taught at TU Graz as part of the module dedicated to energy monitoring. These workshops tackle the complexity of working with large data sets, which commonly arise from energy monitoring research. In particular, the workshops aim to provide a practical understanding of how to identify, handle, reshape, clean up and evaluate important summary statistics from incomplete data sets. These are fundamental skills in building simulation where model validation and calibration are increasingly commonplace. [ABSTRACT FROM AUTHOR]

We use data from the 2011-2016 National Health Interview Survey to examine how the Patient Protection and Affordable Care Act (ACA) has influenced disparities in health care-related financial strain, access to care, and utilization of services by categories of the Federal Poverty Level (FPL). We use multivariable regression analyses to determine the ACA's effects on these outcome measures, as well as to determine how changes in these measures varied across different FPL levels. We find that the national implementation of the ACA's insurance expansion provisions in 2014 was associated with improvements in health care-related financial strain, access, and utilization. Relative to adults earning more than 400% of the FPL, the largest effects were observed among those earning between 0% to 124% and 125% to 199% of the FPL after the implementation of the ACA. Both groups experienced reductions in disparities in financial strain and uninsurance relative to the highest FPL group. Overall, the ACA has attenuated health care-related financial strain and improved access to and the utilization of health services for low- and middle-income adults who have traditionally not met income eligibility requirements for public insurance programs. Policy changes that would replace the ACA with less generous age-based tax subsidies and reductions in Medicaid funding could reverse these gains.

This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.

The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson's pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. The syntax is simple, and the tools show the exploration progress in real-time. In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment. In addition, any user can perform further selections based on the results printed in the console. The paper contains examples of using these tools for large datasets such as the one belonging to the World Values Survey and based on a simple variable naming practice. This article includes many recorded simulations and presents performance results. They depend on different resources and hardware configurations used, including cloud vs. on-premises, large vs. small amounts of RAM and processing cores, and in-memory vs. traditional storage. [ABSTRACT FROM AUTHOR]

U.S. import and export price indexes replaced unit value indexes forty years ago, given quality concerns of mismeasurement due to unit value bias. The administrative trade data underlying the unit values have greatly improved since that time. The transaction records are now more detailed, available electronically, and compiled monthly with little delay. The data are used by academic researchers to calculate price measures, and unit value indexes based on trade data are used by other national statistical offices (NSOs). The U.S. Bureau of Labor Statistics is now evaluating whether replacing price indexes with unit value indexes for homogeneous products calculated from administrative trade data could expand the number of published official import and export price indexes. Using export transactions, the research calculates detailed unit value indexes from 200 + million trade records from 2012–2017 for 123 export product categories. Results show that 27 of the 123 unit value indexes are homogeneous and closely comparable to published official price indexes. This article presents the concepts and methods considered to calculate and evaluate the unit value indexes and to select the product categories that are homogeneous. Compared to official price indexes, export unit value indexes for the 27 5-digit BEA (U.S. Bureau of Economic Analysis) end-use product categories would deflate real exports of these goods by 13 percentage points less over the period. Incorporating these 27 indexes into the top-level XPI would increase the value of real exports of all merchandise goods by 2.6 percentage points at the end of 2017. [ABSTRACT FROM AUTHOR]

The ever-increasing computational power has shifted direct numerical simulations towards higher Reynolds numbers and large eddy simulations towards industrially-relevant flow scales. However, this increase in both temporal and spatial resolution has severely increased the computational cost of model order reduction techniques. Reducing the full data set to a smaller subset in order to perform reduced-order modeling (ROM) may be an interesting method to keep the computational effort reasonable. Moreover, non-tomographic particle image velocimetry measurements obtain a 2D data set of a 3D flow field and an interesting research question would be to quantify the difference between this 2D ROM compared to the 3D ROM of the full flow field. To provide an answer to both issues, the aim of this study was to test a new method for obtaining POD basis functions from a small subset of data initially and using them afterwards in the ROM of either the complete data set or the reduced data set. Hence, no new method of ROM is presented, but we demonstrate a procedure to significantly reduce the computational effort required for the ROM of very large data sets and a quantification of the error introduced by reducing the size of those data sets. The method applies eigenvalue decomposition on a small subset of data extracted from a full 3D simulation and the obtained temporal coefficients are projected back on the 3D velocity fields to obtain the 3D spatial modes. To test the method, an annular jet was chosen as a flow topology due to its simple geometry and the rich dynamical content of its flow field. First, a smaller data set is extracted from the 2D cross-sectional planes and ROM is performed on that data set. Secondly, the full 3D spatial structures are reconstructed by projecting the temporal coefficients back on the 3D velocity fields and the 2D spatial structures by projecting the temporal coefficients back on the 2D velocity fields. It is shown that two perpendicular lateral planes are sufficient to capture the relevant large-scale structures. As such, the total processing time can be reduced by a factor of 136 and up to 22 times less RAM is needed to complete the ROM processing. [ABSTRACT FROM AUTHOR]

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is a widely applied statistical technique aimed at summarising a multidimensional set of input (partly redundant) variables into a restricted number of independent components that are linear combinations of the inputs. PCA transforms the original data matrix by performing a spectral decomposition of the related variance/covariance (or correlation) matrix. When decomposing a correlation matrix, Pearson product-moment correlation coefficients are traditionally used in the correlation matrix. The statistical properties of Pearson correlation coefficients (being insensitive to non-linear, high-order correlations) represent an intrinsic limitation of PCA, restricting its applicability to linear relationships among inputs. However, working with variables displaying (more or less intense) deviations from linearity is common in both socioeconomic research and environmental studies. Following the theoretical assumptions of earlier studies, a generalisation of PCA aimed at exploring non-linear multivariate relationships among inputs is illustrated in the present article by using non-parametric Spearman and Kendall coefficients to replace linear Pearson coefficients in the correlation matrix. The per cent share of 19 land-use classes in the total landscape in a given study area (the Athens metropolitan region, Greece), obtained from a high-resolution map at the local scale, were used as inputs. The results of the standard PCA (via decomposition of a Pearson linear correlation matrix) and a generalised approach (via decomposition of a nonparametric correlation matrix based on Spearman or Kendall rank coefficients) were compared using traditional diagnostics. The PCA performed by decomposing a Spearman correlation matrix exhibited the highest variance extracted by the principal components, giving refined loadings and scores that allow recognition of latent landuse patterns. Contributing to a recent debate on the use of multidimensional techniques in regional studies, non-parametric approaches are promising tools for analysis of large datasets displaying complex, almost nonlinear relationships among inputs. [ABSTRACT FROM AUTHOR]

In order to solve the problems that the traditional landmark - based spectral clustering algorithm is susceptible to unevenly distributed landmark points and outlier landmark points, and its sampling methods such as K - means consume a large time and space when face large - scale data. This study proposes an accelerated spectral clustering based on improved landmark selection. The algorithm uses the standard deviation of the pairwise similarity matrix between landmark points lo measure the uniformity of the distribution of landmark points. L selects the landmark points set uniformly distributed from landmark points sets generated randomly, and then removes outlier landmark points with low local density. The sparse similarity matrix is constructed by the obtained landmark points set and the original data set. K - means clustering is performed on data points generated by the first k right singular feature vectors of the landmark points set to obtain the final clustering result. This study theoretically analyzes the time complexity and space complexity of The algorithm and performed experimental verification. Experimental results show thal the algorithm is 3% -10% higher than that of The random sampling method, and the Lime - consuming is 50% - 60% less than that of the K - means sampling method. [ABSTRACT FROM AUTHOR]

Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various applications, computational requirements have limited applications to small data sets of a few thousand points. We present Dory, an efficient and scalable algorithm that can compute the persistent homology of sparse Vietoris-Rips complexes on larger data sets, up to and including dimension two and over the field Z 2 . As an application, we compute the PH of the human genome at high resolution as revealed by a genome-wide Hi-C data set containing approximately three million points. Extant algorithms were unable to process it, whereas Dory processed it within five minutes, using less than five GB of memory. Results show that the topology of the human genome changes significantly upon treatment with auxin, a molecule that degrades cohesin, corroborating the hypothesis that cohesin plays a crucial role in loop formation in DNA., Competing Interests: On behalf of all authors, the corresponding author states that there is no conflict of interest.

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m(m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standard linear kernel Genomic Best Linear Unbiased Predictor (GBLUP) and also using eigen value decomposition. In both data sets, we compared the prediction performance and computing time for FGSE versus APSE; we also compared FGGE versus APGE. Results showed a competitive prediction performance of the approximated methods with a significant reduction in computing time. Genomic prediction accuracy depends on the decay of the eigenvalues (amount of variance information loss) of the original kernel as well as on the size of the selected lines m.

The ever-increasing computational power has shifted direct numerical simulations towards higher Reynolds numbers and large eddy simulations towards industrially-relevant flow scales. However, this increase in both temporal and spatial resolution has severely increased the computational cost of model order reduction techniques. Reducing the full data set to a smaller subset in order to perform reduced-order modeling (ROM) may be an interesting method to keep the computational effort reasonable. Moreover, non-tomographic particle image velocimetry measurements obtain a 2D data set of a 3D flow field and an interesting research question would be to quantify the difference between this 2D ROM compared to the 3D ROM of the full flow field. To provide an answer to both issues, the aim of this study was to test a new method for obtaining POD basis functions from a small subset of data initially and using them afterwards in the ROM of either the complete data set or the reduced data set. Hence, no new method of ROM is presented, but we demonstrate a procedure to significantly reduce the computational effort required for the ROM of very large data sets and a quantification of the error introduced by reducing the size of those data sets. The method applies eigenvalue decomposition on a small subset of data extracted from a full 3D simulation and the obtained temporal coefficients are projected back on the 3D velocity fields to obtain the 3D spatial modes. To test the method, an annular jet was chosen as a flow topology due to its simple geometry and the rich dynamical content of its flow field. First, a smaller data set is extracted from the 2D cross-sectional planes and ROM is performed on that data set. Secondly, the full 3D spatial structures are reconstructed by projecting the temporal coefficients back on the 3D velocity fields and the 2D spatial structures by projecting the temporal coefficients back on the 2D velocity fields. It is shown that two perpendicular lateral planes are sufficient to capture the relevant large-scale structures. As such, the total processing time can be reduced by a factor of 136 and up to 22 times less RAM is needed to complete the ROM processing.

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m (m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standard linear kernel Genomic Best Linear Unbiased Predictor (GBLUP) and also using eigen value decomposition. In both data sets, we compared the prediction performance and computing time for FGSE versus APSE; we also compared FGGE versus APGE. Results showed a competitive prediction performance of the approximated methods with a significant reduction in computing time. Genomic prediction accuracy depends on the decay of the eigenvalues (amount of variance information loss) of the original kernel as well as on the size of the selected lines m. [ABSTRACT FROM AUTHOR]

Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a scalable compute platform for processing large data sets in the scope of the EU H2020 project PROCESS. We are presenting requirements of the platform, related works, infrastructure with focus on the compute components and finally results of our work. [ABSTRACT FROM AUTHOR]

The author reviews the history of early efforts by libraries to build digital collections, the development of CONTENTdm software at the University of Washington, and OCLC's current directions in helping libraries build and manage digital collections. [ABSTRACT FROM AUTHOR]

Abstract Community Colleges provide a great opportunity to diversify the space sciences or other science, technology, engineering, and math (STEM) fields due to their large population of underrepresented students. However, since the primary mission of community colleges is teaching, there is a challenge to support undergraduate research training and promote undergraduate research opportunities. Undergraduate research experiences are one of the leading high‐impact practices (HIPs) to encourage students to pursue STEM majors, graduate degrees, and careers. Space weather provides the opportunity to engage undergraduate students in real‐world research due to the accessibility of space mission data sets within open access data repositories. Space weather's impact on Earth's geospace environment, life, and society also provides a compelling real‐world hook to engage students in activities that have a number of impacts including (1) long‐term integration of space weather into the undergraduate curricula; (2) experience in analyzing large geospace data sets, which increases students' computational, critical thinking, and analytical skills, useful for any career; and (3) increase in students' interest in and motivation to study STEM, as well as preparing them for choosing a career path in space science and related fields. The model presented below has the potential to increase the retention, graduation, and transfer rate of community college students to four‐year STEM programs, as well as increase representation and inclusion in space sciences and other related STEM fields.

Aim/Purpose The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes. [ABSTRACT FROM AUTHOR]

Background Plant membrane transporters are involved in diverse cellular processes underpinning plant physiology, such as nutrient acquisition, hormone movement, resource allocation, exclusion or sequestration of various solutes from cells and tissues, and environmental and developmental signalling. A comprehensive characterization of transporter function is therefore key to understanding and improving plant performance. Scope and Conclusions In this review, we focus on the complexities involved in characterizing transporter function and the impact that this has on current genomic annotations. Specific examples are provided that demonstrate why sequence homology alone cannot be relied upon to annotate and classify transporter function, and to show how even single amino acid residue variations can influence transporter activity and specificity. Misleading nomenclature of transporters is often a source of confusion in transporter characterization, especially for people new to or outside the field. Here, to aid researchers dealing with interpretation of large data sets that include transporter proteins, we provide examples of transporters that have been assigned names that misrepresent their cellular functions. Finally, we discuss the challenges in connecting transporter function at the molecular level with physiological data, and propose a solution through the creation of new databases. Further fundamental in-depth research on specific transport (and other) proteins is still required; without it, significant deficiencies in large-scale data sets and systems biology approaches will persist. Reliable characterization of transporter function requires integration of data at multiple levels, from amino acid residue sequence annotation to more in-depth biochemical, structural and physiological studies. [ABSTRACT FROM AUTHOR]

Ｉｎ ｏｒｄｅｒ ｔｏ ｓｏｌｖｅ ｔｈｅ ｐｒｏｂｌｅｍ ｏｆ ｈｉｇｈ ｋｅｙｗｏｒｄｓ-ｓｅａｒｃｈ ｔｉｍｅ-ｃｏｍｐｌｅｘｉｔｙ ｃａｕｓｅｄ ｂｙ ｅｘｃｅｓｓｉｖｅ ｓｅｃｕｒｉｔｙ ｉｎｄｅｘｅｓ ｉｎ ｓｙｍｍｅｔｒｉｃ ｓｅａｒｃｈａｂｌｅ ｅｎｃｒｙｐｔｉｏｎ ｓｃｈｅｍｅꎬ ｃｏｍｂｉｎｅｄ ｗｉｔｈ ｔｈｅ ｃｌｏｕｄ ｓｔｏｒａｇｅ ａｐｐｌｉｃａｔｉｏｎ ｅｎｖｉｒｏｎｍｅｎｔꎬ ａ ｎｅｗ ｓｅａｒｃｈａｂｌｅ ｅｎｃｒｙｐｔｉｏｎ (ＳＥ) ｓｃｈｅｍｅ ｆｏｒ ｅｎｃｒｙｐｔｅｄ ｌａｒｇｅ ｄａｔａ ｓｅｔｓ ｉｎ ｄｉｓｔｒｉｂｕｔｅｄ ｅｎｖｉｒｏｎｍｅｎｔｓ ｗａｓ ｐｒｅｓｅｎｔｅｄ. Ｔｈｅ ｓｃｈｅｍｅ ｗａｓ ａｉｍｅｄ ａｔ ｄａｔａ ｏｗｎｅｒｓ ｗｈｏ ｈａｖｅ ｌａｒｇｅ ｓｉｚｅｓ ｏｆ ｆｉｌｅｓ ｉｎ ｔｈｅ ｃｌｏｕｄ ｓｔｏｒａｇｅ ａｐｐｌｉｃａｔｉｏｎ ｅｎｖｉｒｏｎｍｅｎｔ. Ｂｙ ｕｓｉｎｇ ｂｌｏｃｋ ｓｔｏｒａｇｅ ｓｔｒｕｃｔｕｒｅ ｔｏ ｏｐｔｉｍｉｚｅ ｔｈｅ ｓｔｒｕｃｔｕｒｅ ｏｆ ｓｅｃｕｒｉｔｙ ｉｎｄｅｘꎬ ｔｈｅ ｓｅｃｕｒｉｔｙ ｉｎｄｅｘｅｓ ｗｅｒｅ ｄｉｖｉｄｅｄ ｉｎｔｏ ｔｈｒｅｅ ｃａｔｅｇｏｒｉｅｓ: Ｓｍａｌｌꎬ Ｍｅｄｉｕｍꎬ ａｎｄ Ｌａｒｇｅꎬ ａｃｃｏｒｄｉｎｇ ｔｏ ｔｈｅ ｂｌｏｃｋ ｐａｒａｍｅｔｅｒｓ. Ａｎｄ ｗｉｔｈ ｔｈｅ ｍｅｔｈｏｄ ｏｆ ｒｅｇｉｓｔｅｒ ｉｎｄｉｒｅｃｔ ａｄｄｒｅｓｓｉｎｇꎬ ｔｈｅ ｓｃｈｅｍｅ ｃａｎ ｍａｉｎｔａｉｎ ｇｏｏｄ ｓｅａｒｃｈ ｔｉｍｅ ｃｏｍｐｌｅｘｉｔｙ ｉｎ ｔｈｅ ｃａｓｅ ｏｆ ｌａｒｇｅ ｓｅｃｕｒｉｔｙ ｉｎｄｅｘꎬ ａｎｄ ｂｅ ａｃｃｅｐｔａｂｌｅ ｆｏｒ ｕｓｅｒｓ. Ｔｈｅ ｅｘｐｅｒｉｍｅｎｔａｌ ｒｅｓｕｌｔ ｓｈｏｗｓ ｔｈａｔ ｔｈｅ ｋｅｙｗｏｒｄ ｓｅａｒｃｈ ｔｉｍｅ ｒｅａｃｈｅｓ ｓｕｂｌｉｎｅａｒｉｔｙ ａｓ ｔｈｅ ｓｅｃｕｒｉｔｙ ｉｎｄｅｘ ｉｎｃｒｅａｓｅｓ. [ABSTRACT FROM AUTHOR]

Companies are subjected to the difficult tasks of storing, protecting, manipulating, analyzing and classifying large amounts of data - functions mainly performed by the IT sector. But, in addition, the value of data as an intangible asset of a company requires more exploration to take advantage of its use in different sectors. Thus, estimating the value of data involves almost all the functional areas of a company and is context-dependent (Short and Todd, 2017). This paper aims to present some aspects of data valuation and possible issues that companies can face now and in the future. [ABSTRACT FROM AUTHOR]

The Hosmer-Lemeshow (H-L) test is a widely used method when assessing the goodness-of-fit of a logistic regression model. However, the H-L test is sensitive to the sample sizes and the number of groups in H-L test. Cautions need to be taken for interpreting an H-L test with a large sample size. In this paper, we propose a simple test procedure to evaluate the model fit of logistic regression model with a large sample size, in which a bootstrap method is used and the test result is determined by the power of H-L test at the target sample size. Simulation studies show that the proposed method can effectively standardize the power of the H-L test under the pre-specified level of type I error. Application to the two datasets illustrates the usefulness of the proposed model. [ABSTRACT FROM AUTHOR]

In the past one decade there has been significant increase in the growth of digital data. Therefore, good data mining techniques are important for the better decision making. Clustering is one of the key element in the field of data mining. K-means is a very popular algorithm present in the literature which is widely used for the clustering purpose. However k-means algorithm suffers from the problem of stucking into local optimum solution because of it’s dependency on the random initialization of initial cluster center. In this paper a novel variant of Bat algorithm based on dynamic frequency is introduced. Further the proposed variant is hybridized with K-means to present a new approach for clustering in distributed environment. Since evolutionary computation is very computation intensive, traditional sequential algorithms are not able to provide satisfactory results within the reasonable amount of time for the large scale data problems. To mitigate this problem the proposed variant is parallelized using the MapReduce model in the Hadoop framework. The experimental results show that the proposed algorithm has outperformed K-means, PSO and Bat algorithm on eighty percent of the benchmark datasets in terms of intra-cluster distance. Further DBPKBA has also achieved significant speedup for dealing with massive datasets with increase in the number of nodes. [ABSTRACT FROM AUTHOR]

Concrete services are goods and services, such as rent, utilities, healthcare, or other tangible services, that are purchased directly by child welfare agencies to help support the short-term needs of a family. Practice wisdom in social work details the importance of meeting basic needs in order for clients to be able to concentrate on completing the case plan. Some literature has mentioned the importance of concrete services for child welfare practice. However, research has been limited about utilization of these services and their impact on child welfare outcomes. The current mixed methods study examines expenditures for concrete services across four categories over a four-year period in one Midwestern state. The justifications for caseworker concrete service requests and the perceptions of regional and executive managers regarding the effectiveness of the use of concrete services are also examined. Descriptive analysis revealed distributions varied by age and concrete service type. Both caseworker justifications and regional manager interviews revealed the same general themes for children: support of child safety, permanency, and well-being are supported with the use of concrete services. [ABSTRACT FROM AUTHOR]