Miracle-dz/UniFAR-FTUs huggingface dataset:
Die Eltern-Kurzversion des Fragebogens zur Erhebung der Emotionsregulation bei Kindern und Jugendlichen (FEEL-KJ)

Zusammenfassung. Theoretischer Hintergrund: Das Konzept der Emotionsregulation (ER) hat grose Bedeutung fur die Klinische Psychologie und Psychotherapie. Gleichzeitig liegen besonders im Kindes- und Jugendalter kaum zuverlassig evaluierte und normierte Verfahren zur Messung von ER vor. Fragestellung: Es soll gepruft werden, ob eine Eltern-Kurzversion des Fragebogens zur Erhebung der Emotionsregulation bei Kindern und Jugendlichen (FEEL-KJ) uber ausreichende psychometrische Qualitat verfugt, um diese Lucke zumindest teilweise zu schliesen. Auserdem sollen erste Hinweise auf die Validitat der Testwerte und deren Abhangigkeit von soziodemographischen Variablen untersucht werden. Es wurde insbesondere eine mit dem Alter zunehmend haufige Verwendung von ER-Strategien erwartet. Ebenso wurden positive Zusammenhange maladaptiver und negative Zusammenhange adaptiver ER mit psychischen Beschwerden angenommen. Methode: Die Eltern-Kurzversion des FEEL-KJ und weitere Fragebogenverfahren wurden bei einer Elternstichpro.
Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.
Neural signatures of experimentally induced flow experiences identified in a typical fMRI block design with BOLD imaging.

Previously, experimentally induced flow experiences have been demonstrated with perfusion imaging during activation blocks of 3 min length to accommodate with the putatively slowly evolving "mood" characteristics of flow. Here, we used functional magnetic resonance imaging (fMRI) in a sample of 23 healthy, male participants to investigate flow in the context of a typical fMRI block design with block lengths as short as 30 s. To induce flow, demands of arithmetic tasks were automatically and continuously adjusted to the individual skill level. Compared against conditions of boredom and overload, experience of flow was evident from individuals' reported subjective experiences and changes in electrodermal activity. Neural activation was relatively increased during flow, particularly in the anterior insula, inferior frontal gyri, basal ganglia and midbrain. Relative activation decreases during flow were observed in medial prefrontal and posterior cingulate cortex, and in the medial temporal lobe including the amygdala. Present findings suggest that even in the context of comparably short activation blocks flow can be reliably experienced and is associated with changes in neural activation of brain regions previously described. Possible mechanisms of interacting brain regions are outlined, awaiting further investigation which should now be possible given the greater temporal resolution compared with previous perfusion imaging.
RNA Interference in Biology and Medicine

First discovered in plants the nematode Caenorhabditis elegans, the production of small interfering RNAs (siRNAs) that bind to and induce the degradation of specific endogenous mRNAs is now recognized as a mechanism that is widely employed by eukaryotic cells to inhibit protein production at a post-transcriptional level. The endogenous siRNAs are typically 19- to 23-base double-stranded RNA oligonucleotides, produced from much larger RNAs that upon binding to target mRNAs recruit RNases to a protein complexthat degrades the targeted mRNA. Methods for expressing siRNAs in cells in culture and in vivo using viral vectors, and for transfecting cells with synthetic siRNAs, have been developed and are being used to establish the functions of specific proteins in various cell types and organisms. RNA interference methods provide several major advantages over prior methods (antisense DNA or antibody-based techniques) for suppressing gene expression. Recent preclinical studies suggest that RNA interference technology holds promise for the treatment of various diseases. Pharmacologists have long dreamed of the ability to selectively antagonize or eliminate the function of individual proteins—RNAi technology may eventually make that dream a reality.
Overflow Oscillation Elimination of 2-D Digital Filters in the Roesser Model with Wiener Process Noise

Recently, Ahn's criteria were proposed for dealing with deterministic external interference with finite energy in two-dimensional (2-D) digital filters. However, these criteria fail for cases where the 2-D filters have stochastic noise. In this letter, we propose a new criterion for overflow oscillation elimination of 2-D digital filters in the Roesser model with Wiener process noise. The criterion guarantees the asymptotic stability as well as a 2-D expected power bound. Thus, it can ascertain the attenuation of the effect of Wiener process noise to a prescribed level. This criterion is also expressed by linear matrix inequality (LMI). A numerical example is provided to show the usefulness of the proposed result.
User assisted text classification and knowledge management

While there are many aspects to managing corporate knowledge, one key issue is how to organize corporate documents into categories of interest. In this paper, we focus on using user assisted text classification in conjunction with a web portal, multiple document management systems and an ontology, to provide a powerful solution for organizing information about a company's technology. We propose a system that interacts with an author using an automatic text classifier to suggest controlled keywords to be used as metadata. The proposed approach does not require professional librarians or that the end users have extensive training. The use of a controlled vocabulary allows for a more consistent description of corporate documents, and promotes easier access by people across the company. It is easier to find similar documents which use different nomenclature. Finally, the interactive nature of the system results in a more correct and precise description of each document than a fully automatic system would.
Impact of SSSC Control Parameters on Available Transfer Capability Enhancement

In this paper, the impact of static synchronous compensator on the ATC enhancement has been studied. The SSSC has been modeled in N-R load flow algorithm to obtain power transfer distribution factors. The ATC enhancement is obtained considering the variations in the control parameters of SSSC. The ATC is obtained for bilateral and simultaneous transactions considering the impact of SSSC series voltage and angle control parameters. The results obtained are compared with the independent change and the simultaneous change in the control parameters. The results have been determined for IEEE 24 bus Reliability Test System for both the bilateral and multilateral transaction cases.
PDBSiteScan: a program for searching for active, binding and posttranslational modification sites in the 3D structures of proteins

PDBSiteScan is a web-accessible program designed for searching three-dimensional (3D) protein fragments similar in structure to known active, binding and posttranslational modification sites. A collection of known sites we designated as PDBSite was set up by automated processing of the PDB database using the data on site localization in the SITE field. Additionally, protein-protein interaction sites were generated by analysis of atom coordinates in heterocomplexes. The total number of collected sites was more than 8100; they were assigned to more than 80 functional groups. PDBSiteScan provides automated search of the 3D protein fragments whose maximum distance mismatch (MDM) between N, Calpha and C atoms in a fragment and a functional site is not larger than the MDM threshold defined by the user. PDBSiteScan requires perfect matching of amino acids. PDBSiteScan enables recognition of functional sites in tertiary structures of proteins and allows proteins with functional information to be annotated. The program PDBSiteScan is available at http://wwwmgs.bionet. nsc.ru/mgs/systems/fastprot/pdbsitescan.html.
Determination of Genetic Structure and Signatures of Selection in Three Strains of Tanzania Shorthorn Zebu, Boran and Friesian Cattle by Genome-Wide SNP Analyses

Background More than 90 percent of cattle in Tanzania belong to the indigenous Tanzania Short Horn Zebu (TSZ) population which has been classified into 12 strains based on historical evidence, morphological characteristics, and geographic distribution. However, specific genetic information of each TSZ population has been lacking and has caused difficulties in designing programs such as selection, crossbreeding, breed improvement or conservation. This study was designed to evaluate the genetic structure, assess genetic relationships, and to identify signatures of selection among cattle of Tanzania with the main goal of understanding genetic relationship, variation and uniqueness among them. Methodology/Principal findings The Illumina Bos indicus SNP 80K BeadChip was used to genotype genome wide SNPs in 168 DNA samples obtained from three strains of TSZ cattle namely Maasai, Tarime and Sukuma as well as two comparative breeds; Boran and Friesian. Population structure and signatures of selection were examined using principal component analysis (PCA), admixture analysis, pairwise distances (FST), integrated haplotype score (iHS), identical by state (IBS) and runs of homozygosity (ROH). There was a low level of inbreeding (F~0.01) in the TSZ population compared to the Boran and Friesian breeds. The analyses of FST, IBS and admixture identified no considerable differentiation between TSZ trains. Importantly, common ancestry in Boran and TSZ were revealed based on admixture and IBD, implying gene flow between two populations. In addition, Friesian ancestry was found in Boran. A few common significant iHS were detected, which may reflect influence of recent selection in each breed or strain. Conclusions Population admixture and selection signatures could be applied to develop conservation plan of TSZ cattle as well as future breeding programs in East African cattle.
NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud

We have often heard that data is the new oil. In particular, extracting information from semi-structured textual documents on the Web is key to realize the Linked Data vision. Several attempts have been proposed to extract knowledge from textual documents, extracting named entities, classifying them according to pre-dened taxonomies and disambiguating them through URIs identifying real world entities. As a step towards interconnecting the Web of documents via those entities, dierent extractors have been proposed. Although they share the same main purpose (extracting named entity), they dier from numerous aspects such as their underlying dictionary or ability to disambiguate entities. We have developed NERD, an API and a front-end user interface powered by an ontology to unify various named entity extractors. The unied result output is serialized in RDF according to the NIF specication and published back on the Linked Data cloud. We evaluated NERD with a dataset composed of ve TED talk transcripts, a dataset composed of 1000 New York Times articles and a dataset composed of the 217 abstracts of the papers published at WWW 2011.
Cost-Effective and Scalable Image Matching Across Heterogeneous Online Social Networks

In this modern era, social media facilitates us in communicating with the people across the world. Sharing of photos on social networks is due to their addictive interest in receiving the likes from other users in the network to gain popularity. Users on social network upload nearly 1.8 billion photos every day. Malicious users try to gather the publicly available photos on social networks and use it for creating bogus accounts. To identify those anomalous users, the photos shared are collected and processed by the social network manager to classify the original person from the fake one. As there are billions of users in each social network, there are an enormous amount of photo uploads which leads to the problem of scalability, slower processing performance and execution speed. The primary objective of this paper is to identify the similar image for a given a query image on a large set of image datasets crawled from online social networks through the Internet. For handling the scalability incurred from large image data sets, the image matching computation is implemented in distributed computing Map Reduce framework. The face recognition involves supervised machine learning approach employing Computer vision algorithms, namely Fisher face, Eigenface and Local Binary Pattern Histogram (LBPH). The similarity functions are used to calculate the distance between the query image and image sequence in the Hadoop file system. Experiments use the trained data sets to find the least similarity measure. The results obtained show that LBPH provides better and accurate matching results compared to other two face recognition approaches.
The Appropriation of Western Management Concepts and Methods in Omani Higher Education

The government of Oman continues to invest heavily in Western management methods and experts. While there is increased interest in exploring ways to maximize and align such Western importations in the developing state of Oman, there is a lack of research on how Omanis actually perceive and negotiate Western philosophies. Scant attention has been given both to the imported nature and the impact of Western methods in education. This study employs a mixed methods research strategy, comprising an online questionnaire and semi-structured interviews to examine the specific ways people at different levels of the Omani education system understand and appropriate Western methods. The study proceeds progressively through three levels of penetrating analysis: a quantitative-qualitative analysis of the questionnaire (n= 168) that identifies the perceptions of end users; a content analysis of the interview data (n= 17) that identifies the perspectives of an elite group of top officials, decision makers and senior managers in higher education institutions; and an additional Foucauldian discourse analysis of the same interview data designed to identify the specific narratives and rhetorical strategies adopted by the interviewees as they reflect and are influenced by their level of power and positions. The study reveals that end users of the system use a variety of arguments and narratives: identity defensiveness, scepticism, pro-Western pragmatism, the adoption of a middle position, or topic avoidance. However, the elite interviewees approach the practice with narratives of defence, disapproval, compliance or tension, which are influenced by their level of authority within the higher education landscape. This wide disparity of views reflects the continued division in Muslim-Arab thought towards the West and the continued dilemma of how to reconcile the demand for modernisation whilst preserving traditional Islamic culture. The study findings contribute to the general field of international education and the specific issue of the extension of Western management methods in Omani education. Finally, the study adds to the understanding of Omani society as part of the broader Arab Middle East.
Application of iTRAQ Reagents to Relatively Quantify the Reversible Redox State of Cysteine Residues

Cysteines are one of the most rarely used amino acids, but when conserved in proteins they often play critical roles in structure, function, or regulation. Reversible cysteine modifications allow for potential redox regulation of proteins. Traditional measurement of the relative absolute quantity of a protein between two samples is not always necessarily proportional to the activity of the protein. We propose application of iTRAQ reagents in combination with a previous thiol selection method to relatively quantify the redox state of cysteines both within and between samples in a single analysis. Our method allows for the identification of the proteins, identification of redox-sensitive cysteines within proteins, and quantification of the redox status of individual cysteine-containing peptides. As a proof of principle, we applied this technique to yeast alcohol dehydrogenase-1 exposed in vitro to H2O2 and also in vivo to the complex proteome of the Gram-negative bacterium Bacillus subtilis.
Hybrid model combining empirical mode decomposition, singular spectrum analysis, and least squares for satellite-derived sea-level anomaly prediction

ABSTRACT In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods.
Language-modeling kernel based approach for information retrieval

this presentation, we propose a novel integrated information retrieval approach that provides a unified solution for two challenging problems in the field of information retrieval. The first problem is how to build an optimal vector space corresponding to users' different information needs when applying the vector space model. The second one is how to smoothly incorporate the advantages of machine learning techniques into the language model-ing approach. To solve these problems, we designed the language-modeling kernel function, which has all the modeling powers provided by language modeling techniques. In addition, for each information need, this kernel function automatically determines an optimal vector space, for which a discriminative learning machine, such as the support vector machine, can be applied to find an optimal decision boundary between relevant and nonrel-evant documents. Large-scale experiments on standard test-beds show that our approach makes significant improvements over other state-of-the-art information retrieval methods.
Architecture Design and Evaluation of Ldpc Decoder on Tta Based Codesign Environment

High quality digital video transmission requires efficient and reliable data communication over broadcasting channels as there is a risk of data corruption associated during transmission. The near channel performance of Low Density Parity Check Codes (LDPC) has motivated its use in second generation Digital Video Broadcasting (DVB) standards for mobile, cable, satellite and terrestrial channels as an error correction code. But iterative decoding of LDPC codes provides significant implementation challenges as the complexity grows with the code size. This problem can be mitigated by exploiting the modular nature of the iterative decoding scheme for efficient parallel implementation. Transport Triggered Architecture (TTA) provides a processor template that exploits operation style parallelism and parallelism at data transport level. TTA-Based Codesign Environment (TCE) provides the necessary toolset to design a TTA processor. In addition, the toolset also provides the means to design application specific processors to accelerate the execution of the application and implement the processor on reconfigurable logic platform such as Field Programmable Gate Arrays (FPGAs) with ease. This work leverages TCE toolset to implement iterative decoding scheme such as reduced minimum sum algorithm on field programmable gate arrays. It also presents the throughput gains and evaluates the capabilities of the TTA architecture and the TCE toolset for the design of application specific instruction set processors.
Searching the hypermedia Web: improved topic distillation through network analytic relevance ranking

Abstract The Web is a large hypermedia space that is generally explored using search engines. These search engines are evolving to make more effective use of the hypermedia structure of the Web. This paper contributes to this evolution by proposing new methods of topic distillation in structured search based on co-citation and network analysis. We describe a set of 21 network analysis measures of relevance in Web search output. These measures are then compared with human judgments in two studies. In the first study, we compare the average judged relevance of the top 20 search results selected by Google vs. the top 20 results as selected by each of the 21 network analysis measures. All but one of the network analysis measures (“inlink”) showed significantly (p<.05) better (as compared to Google) average judged relevance amongst their top 20 selections. Stepwise regression analysis was then used to identify a linear model with three network analysis measures as predictors, which accounted for roughly 17% of the variance in relevance judgments. In the second study the human judges compared ranked output from Google with the ranked output from the best fitting one- and three-predictor regression models. There was a tendency for people to prefer the ranked output from the three-predictor regression model. Only four of the 21 subjects made the Google output their first choice (out of the three options given to them). The output as ranked by the three-predictor model was also rated as having (within the top 20 ranked results) significantly more highly relevant results, and significantly fewer irrelevant results, than the corresponding ratings for Google. While these results need to be extended with more detailed analysis of a wide range of queries and topics, they suggest that network analysis of search output adjacency matrices (where adjacency/proximity is based on Web-wide co-citations) may significantly improve topic distillation by search engines.