Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of modal parameters with the aid of predicted analytical mode shapes

The extraction of the modal parameters for closely spaced modes in the frequency domain is a common problem. However, it is made more difficult if the damping for the closely spaced modes is high. Data from a structure with more than three percent viscous damping is presented which exhibits this phenomenon. Traditional experimental techniques failed to identify all the modal parameters of three closely spaced modes. Mode shapes from an analytical model are manipulated to produce a modal filter which is used to calculate enhanced frequency response functions from which the modal parameters can be more readily identified. Discussion of the advantages and disadvantages of this technique as compared with traditional frequency response function enhancement techniques will be presented.
DSGE Models, Solutions, and Approximations, from Methods for Applied Macroeconomic Research

The last twenty years have witnessed tremendous advances in the mathematical, statistical, and computational tools available to applied macroeconomists. This rapidly evolving field has redefined how researchers test models and validate theories. Yet until now there has been no textbook that unites the latest methods and bridges the divide between theoretical and applied work. Fabio Canova brings together dynamic equilibrium theory, data analysis, and advanced econometric and computational methods to provide the first comprehensive set of techniques for use by academic economists as well as professional macroeconomists in banking and finance, industry, and government. This graduate-level textbook is for readers knowledgeable in modern macroeconomic theory, econometrics, and computational programming using RATS, MATLAB, or Gauss. Inevitably a modern treatment of such a complex topic requires a quantitative perspective, a solid dynamic theory background, and the development of empirical and numerical methods--which is where Canova's book differs from typical graduate textbooks in macroeconomics and econometrics. Rather than list a series of estimators and their properties, Canova starts from a class of DSGE models, finds an approximate linear representation for the decision rules, and describes methods needed to estimate their parameters, examining their fit to the data. The book is complete with numerous examples and exercises. Today's economic analysts need a strong foundation in both theory and application. Methods for Applied Macroeconomic Research offers the essential tools for the next generation of macroeconomists.
Analysis of Visual Demands of In-Vehicle Text Displays Reveals an Age-Related Increase in Time Needed to Reallocate Attention to the Road

The purpose of this study was to evaluate age differences in the visual demands imposed by reading in-vehicle text message displays during simulated driving. Visual demand was operationalized in terms of five eye gaze data parameters. Sixteen young (mean age=20) and 16 older (mean age=77) licensed drivers served as participants. They were required to read variable length text messages from a console mounted display while driving on straight segments on a simulated rural highway. Older drivers required much more time to complete the concurrent text reading task — especially for messages of longer lengths. As hypothesized, most of this age-difference resulted from an increase in the time spent reacquiring the road scene between successive glances to the text display. Lane keeping variability increased and driving speed decreased while reading text for older, but not younger, drivers. This pattern of findings is consistent with prior claims that driving performance in older adults is negatively influenced by problems with attention switching mechanisms.
Nitrile Hydratase Genes Are Present in Multiple Eukaryotic Supergroups

Background Nitrile hydratases are enzymes involved in the conversion of nitrile-containing compounds into ammonia and organic acids. Although they are widespread in prokaryotes, nitrile hydratases have only been reported in two eukaryotes: the choanoflagellate Monosiga brevicollis and the stramenopile Aureococcus anophagefferens. The nitrile hydratase gene in M. brevicollis was believed to have arisen by lateral gene transfer from a prokaryote, and is a fusion of beta and alpha nitrile hydratase subunits. Only the alpha subunit has been reported in A. anophagefferens. Methodology/Principal Findings Here we report the detection of nitrile hydratase genes in five eukaryotic supergroups: opisthokonts, amoebozoa, archaeplastids, CCTH and SAR. Beta-alpha subunit fusion genes are found in the choanoflagellates, ichthyosporeans, apusozoans, haptophytes, rhizarians and stramenopiles, and potentially also in the amoebozoans. An individual alpha subunit is found in a dinoflagellate and an individual beta subunit is found in a haptophyte. Phylogenetic analyses recover a clade of eukaryotic-type nitrile hydratases in the Opisthokonta, Amoebozoa, SAR and CCTH; this is supported by analyses of introns and gene architecture. Two nitrile hydratase sequences from an animal and a plant resolve in the prokaryotic nitrile hydratase clade. Conclusions/Significance The evidence presented here demonstrates that nitrile hydratase genes are present in multiple eukaryotic supergroups, suggesting that a subunit fusion gene was present in the last common ancestor of all eukaryotes. The absence of nitrile hydratase from several sequenced species indicates that subunits were lost in multiple eukaryotic taxa. The presence of nitrile hydratases in many other eukaryotic groups is unresolved due to insufficient data and taxon sampling. The retention and expression of the gene in distantly related eukaryotic species suggests that it plays an important metabolic role. The novel family of eukaryotic nitrile hydratases presented in this paper represents a promising candidate for research into their molecular biology and possible biotechnological applications.
A Systems Biology Approach Identifies a R2R3 MYB Gene Subfamily with Distinct and Overlapping Functions in Regulation of Aliphatic Glucosinolates

Background Glucosinolates are natural metabolites in the order Brassicales that defend plants against both herbivores and pathogens and can attract specialized insects. Knowledge about the genes controlling glucosinolate regulation is limited. Here, we identify three R2R3 MYB transcription factors regulating aliphatic glucosinolate biosynthesis in Arabidopsis by combining several systems biology tools. Methodology/Principal Findings MYB28 was identified as a candidate regulator of aliphatic glucosinolates based on its co-localization within a genomic region controlling variation both in aliphatic glucosinolate content (metabolite QTL) and in transcript level for genes involved in the biosynthesis of aliphatic glucosinolates (expression QTL), as well as its co-expression with genes in aliphatic glucosinolate biosynthesis. A phylogenetic analysis with the R2R3 motif of MYB28 showed that it and two homologues, MYB29 and MYB76, were members of an Arabidopsis-specific clade that included three characterized regulators of indole glucosinolates. Over-expression of the individual MYB genes showed that they all had the capacity to increase the production of aliphatic glucosinolates in leaves and seeds and induce gene expression of aliphatic biosynthetic genes within leaves. Analysis of leaves and seeds of single knockout mutants showed that mutants of MYB29 and MYB76 have reductions in only short-chained aliphatic glucosinolates whereas a mutant in MYB28 has reductions in both short- and long-chained aliphatic glucosinolates. Furthermore, analysis of a double knockout in MYB28 and MYB29 identified an emergent property of the system since the absence of aliphatic glucosinolates in these plants could not be predicted by the chemotype of the single knockouts. Conclusions/Significance It seems that these cruciferous-specific MYB regulatory genes have evolved both overlapping and specific regulatory capacities. This provides a unique system within which to study the evolution of MYB regulatory factors and their downstream targets.
Teaching Programming to the Uninitiated Using Mathematica

In October 1997 an experiment was started to use Mathematica 4] in order to teach programming to rst year students of Technical Mathematics. In the rst semester the programming course focused on functional programming. The contents were determined by a parallel computer science course, where the programming language SML 3] was used as the basic notation. In this paper the experiences of using Mathematica to teach programming are outlined. Especially, a survey is given how the basic concepts and features of a standard functional programming language like SML are provided by Mathematica.
ESTIMATING K, C VARIABLE PARAMETERS OF A WEIGHING SYSTEM USING RBF NEURAL NETWORKS

Neural networks are often used as a powerful discriminating estimator for tasks in system identification. This paper describes a neural-network-based method relies on the Radial Basis Function Network (RBF network), for estimating the variable damping factor C (n) and spring constant K (n) of a weighting platform. Firstly, the RBF network learns key properties of the step response of the weighting platform and then predicts the damping factor C (n) and spring constant K (n) of other systems with different step responses before the platform settled to the steady state. In the simulation and the experimental results, with the related applied masses, the correlation rates between the actual C(n), K(n) and estimated C(n) and K(n) are presented that shows the success of this method.
Related-Key Collision Attack of AES_256

This paper utilized the arrangement points of AES_256 key, analyzed the difference features of AES_256, and found a method of collision attack. This paper also made use of connection differential features and collision method of AES_256 to analyze the 72 bytes of the initial key of AES_256, with a complexity of 2128.
A multi-similarity algebra

The need to automatically extract and classify the contents of multimedia data archives such as images, video, and text documents has led to significant work on similarity based retrieval of data. To date, most work in this area has focused on the creation of index structures for similarity based retrieval. There is very little work on developing formalisms for querying multimedia databases that support similarity based computations and optimizing such queries, even though it is well known that feature extraction and identification algorithms in media data are very expensive. We introduce a similarity algebra that brings together relational operators and results of multiple similarity implementations in a uniform language. The algebra can be used to specify complex queries that combine different interpretations of similarity values and multiple algorithms for computing these values. We prove equivalence and containment relationships between similarity algebra expressions and develop query rewriting methods based on these results. We then provide a generic cost model for evaluating cost of query plans in the similarity algebra and query optimization methods based on this model. We supplement the paper with experimental results that illustrate the use of the algebra and the effectiveness of query optimization methods using the Integrated Search Engine (I.SEE) as the testbed.
Performance Evaluation of Feature Extraction Algorithm on GPGPU

Nvidia's GPGPU based Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on GPU. It provide several key abstractions- a hierarchy of thread block, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scale transparently to hundreds of cores: many industry and academia are already using CUDA to achieve speedups on production and research codes. This paper analyze distinct feature of CUDA GPU, summarizes general programming mode of CUDA. This paper presents image processing algorithm i.e. Canny Edge detector which is used as pre-processing steps in many computer vision application as an optimal edge detection algorithm. Detailed comparison of parallel and sequential algorithm implementations is also presented.
An approach to nonstandard quantum mechanics

We use nonstandard analysis to formulate quantum mechanics in hyperfinite-dimensional spaces. Self-adjoint operators on hyperfinite-dimensional spaces have complete eigensets, and bound states and continuum states of a Hamiltonian can thus be treated on an equal footing. We show that the formalism extends the standard formulation of quantum mechanics. To this end we develop the Loeb-function calculus in nonstandard hulls. The idea is to perform calculations in a hyperfinite-dimensional space, but to interpret expectation values in the corresponding nonstandard hull. We further apply the framework to nonrelativistic quantum scattering theory. For time-dependent scattering theory, we identify the starting time and the finishing time of a scattering experiment, and we obtain a natural separation of time scales on which the preparation process, the interaction process, and the detection process take place. For time-independent scattering theory, we derive rigorously explicit formulas for the Mo/ller wave oper.
Signalling in Neural Stem Cells

Stem cells in vitro Cell replacement approaches have provided the basis for the development of potentially powerful new therapeutic strategies for a broad spectrum of brain diseases. The rationale of this therapeutic approach relies on transplantation to the central nervous system of cells that are able to replace the lost elements and rewire the disrupted circuitries, thus restoring normal brain function (Rossi and Cattaneo, 2002). Since mature neurons do not survive dissection and grafting procedures, immature or proliferating cells represent the best source of donor cells for transplantation to the Central Nervous System (CNS). Transplantation experiments in animal models of brain lesion or neurodegeneration have shown that foetal cells are able to integrate into the host brain and ameliorate functional defects. Starting from these results, clinical trials have been undertaken on selected neurological patients affected by Parkinson disease, Huntington disease or stroke (Bjˆrklund and Lindvall, 2000). However, despite the encouraging results obtained by this experimentation, the use of foetal cells yields serious ethical and methodological problems that actually hamper the full development of this therapeutic approach. A major breakthrough in cell replacement approaches to brain degenerative disorders came with the demonstration of the existence of cells in the developing and adult nervous system that can be isolated and grown in vitro where they behave as bona-fide Neural Stem Cells (NSC) (reviewed in Conti, 2003). The possibility to grow such cells unlimitedly in a laboratory setup and, subsequently, to provide them with the factors required for their full maturation, sustains the hopes of scientists and patients. However, toward such goal and in spite of the excitement, a great deal of basic research is still needed since the in vitro properties of these cells remain poorly understood. In particular: (i) Research activities based on the use of NSCs suffer from a major lack of efficient and validated procedures for their prospective identification and isolation. In the absence of such protocols, most authors isolate cells from the developing and adult brain and use the ability to grow these cells, in vitro, as neurospheres, as an operative definition for neural stem cells. Neurospheres are, however, cell aggregates that grow in suspension (as many cell types would do given the absence of a coating substrate in most protocols), which are at different maturation stages-from bona-fide stem to progenitors, to mature and immature neurons and glia-and that are capable to differentiate in vitro as glia and …
Exploring effect of preprocessing on classifier ensembles in imbalanced dataset classification

During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance. Also data level approaches are independent of classifier being used. In past few years, combination of data level and classifier ensemble technique has been applied and has proved to be effective. This paper explores the impact of pre-processing algorithm on the performance of classifier ensemble approach for imbalanced data set. The aim of this study is to investigate the effect of pre-processing on two level classifier ensemble approaches. Experimental work and analysis of results shows that use of pre-processing is not beneficial for Random Subspace Method since results reflect performance degradation while AdaBoost has shown improvement due to application of pre-processing.
Introducing RDF Graph Summary with Application to Assisted SPARQL Formulation

One of the reasons for the slow adoption of SPARQL is the complexity in query formulation due to data diversity. The principal barrier a user faces when trying to formulate a query is that he generally has no information about the underlying structure and vocabulary of the data. In this paper, we address this problem at the maximum scale we can think of: providing assistance in formulating SPARQL queries over the entire Sindice data collection - 15 billion triples and counting coming from more than 300K datasets. We present a method to help users in formulating complex SPARQL queries across multiple heterogeneous data sources. Even if the structure and vocabulary of the data sources are unknown to the user, the user is able to quickly and easily formulate his queries. Our method is based on a summary of the data graph and assists the user during an interactive query formulation by recommending possible structural query elements.
An Investigation into the Role of Coping in Preventing Depression Associated with Perfectionism in Preadolescent Children

The relationships between self-oriented and socially prescribed perfectionism (SPP) and maladaptive and adaptive coping strategies and their collective impact on depression symptoms were examined in the context of a randomized controlled universal trial of the Aussie Optimism Positive Thinking Skills Program. Five hundred and forty-one children aged 8–12 completed a battery of self-reports, of which responses for measures of depression symptoms, perfectionism, and coping strategies were examined for the purposes of this study. Structural equation modeling tested whether coping mediated the effects of perfectionism on depression. Results indicated that SPP had both a direct and an indirect relationship with depression symptoms through a moderate association with maladaptive coping. Implications for prevention of depression were discussed and recommendations for future research were proposed.
Silver Nanoparticles: Technological Advances, Societal Impacts, and Metrological Challenges

Silver nanoparticles (AgNPs) show different physical and chemical properties compared to their macroscale analogs. This is primarily due to their small size and, consequently, the exceptional surface area of these materials. Presently, advances in the synthesis, stabilization, and production of AgNPs have fostered a new generation of commercial products and intensified scientific investigation within the nanotechnology field. The use of AgNPs in commercial products is increasing and impacts on the environment and human health are largely unknown. This article discusses advances in AgNP production and presents an overview of the commercial, societal, and environmental impacts of this emerging nanoparticle (NP), and nanomaterials in general. Finally, we examine the challenges associated with AgNP characterization, discuss the importance of the development of NP reference materials (RMs) and explore their role as a metrological mechanism to improve the quality and comparability of NP measurements.
Adapting the Perseverative Thinking Questionnaire for Measuring Repetitive Negative Thinking in Clinical Psychology Trainees

Repetitive negative thinking (RNT) focused on clinical practice might play a relevant role in the emotional difficulties clinical psychology trainees may experience during their training. However, the empirical evidence in this regard is very limited. To begin to address this topic, the current study aimed to adapt and preliminarily test the psychometric properties of the Perseverative Thinking Questionnaire–Clinical Psychology Trainees (PTQ-CPT), a measure of RNT focused on clinical practice. The instructions and 15 items of the Perseverative Thinking Questionnaire (PTQ) were modified to resemble RNT occurring in clinical practice. Four experts evaluated the items, and a nine-item version of the PTQ-CPT was obtained. This version was administered to 412 undergraduates in a psychology program in Colombia, all of them Spanish speakers. They were also at the beginning of or near their clinical practice. The PTQ-CPT showed excellent internal consistency (alpha of .93). A cross-validation study was conducted to analyze the factor structure of the PTQ-CPT, yielding a one-factor structure. The PTQ-CPT showed strong positive correlations with the PTQ, experiential avoidance, emotional symptoms, and obstruction in values, as well as medium negative correlations with life satisfaction and progress in values. In conclusion, the PTQ-CPT seems to be a valid and reliable measure of RNT focused on clinical practice, which might be used to explore the role of RNT in the stress and learning difficulties experienced by clinical psychology trainees.
On adaptive observers design for a class of uniformly observable systems

In this paper, one aims at addressing the adaptive observer design for a class of multivariable nonlinear systems with Lipschitz nonlinearities. Two adaptive observers have been derived using two different approaches issued from an overview of the available adaptive observer design literature. The first one is a natural extension of a recently proposed adaptive observer for single output systems, while the second one has been derived bearing in mind the fact that an adaptive observer can be derived using a high gain state observer design approach based on an augmented system model accounting for the unknown parameter dynamics. Both observers are shown to be equivalent in the sens that they are governed by the same equations and the underlying exponential convergences of state observation and parameter estimation errors are insured under the the same persistent excitation condition.