Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigation of flicker in a distribution-connected wind farm with STATCOM

Renewable Energy is emerging as major Source of power throughout the world. Wind Energy is one of the most popular sources of renewable energy. Distribution-connected wind turbine is accompanied with various power quality issues. Flicker is one of the power quality issues in a distribution-connected wind turbine imposing technical limitations on amount of wind power to be integrated at particular point of common coupling (PCC). This paper details the flicker problems encountered upon the connection of wind power to distribution networks and its effects on the network characteristics commensurate with the flicker severity. It describes the mitigation of flicker in a distribution connected wind-farm with the STATCOM.
A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid

In smart grids, one of the most important objectives is the ability to improve the grid's situational awareness and allow for fast-acting changes in power generation. In such systems, an energy management system should gather all the needed information, solve an optimization problem, and communicate back to each distributed energy resource (DER) its correct allocation of energy. This paper proposes a memory-based genetic algorithm (MGA) that optimally shares the power generation task among a number of DERs. The MGA is utilized for minimization of the energy production cost in the smart grid framework. It shares optimally the power generation in a microgrid including wind plants, photovoltaic plants, and a combined heat and power system. In order to evaluate the performance of the proposed approach, the results obtained by the MGA are compared with the results found by a genetic algorithm and two variants of particle swarm optimization. Simulation results accentuate the superiority of the proposed MGA technique.
Time-varying effects of socio-demographic and economic factors on the use of institutional long-term care before dementia-related death: A Finnish register-based study

Objectives The effects of socio-demographic and economic factors on institutional long-term care (LTC) among people with dementia remain unclear. Inconsistent findings may relate to time-varying effects of these factors as dementia progresses. To clarify the question, we estimated institutional LTC trajectories by age, marital status and household income in the eight years preceding dementia-related and non-dementia-related deaths. Methods We assessed a population-representative sample of Finnish men and women for institutional LTC over an eight-year period before death. Deaths related to dementia and all other causes at the age of 70+ in 2001–2007 were identified from the Death Register. Dates in institutional LTC were obtained from national care registers. We calculated the average and time-varying marginal effects of age, marital status and household income on the estimated probability of institutional LTC use, employing repeated-measures logistic regression models with generalised estimating equations (GEE). Results The effects of age, marital status and household income on institutional LTC varied across the time before death, and the patterns differed between dementia-related and non-dementia-related deaths. Among people who died of dementia, being of older age, non-married and having a lower income predicted a higher probability of institutional LTC only until three to four years before death, after which the differences diminished or disappeared. Among women in particular, the probability of institutional LTC was nearly equal across age, marital status and income groups in the last year before dementia-related death. Among those who died from non-dementia-related causes, in contrast, the differences widened until death. Conclusions We show that individuals with dementia require intensive professional care at the end of life, regardless of their socio-demographic or economic resources. The results imply that the potential for extending community living for people with dementia is likely to be difficult through modification of their socio-demographic and economic environments.
Household energy efficiency in the UK

Over the past three years, policy towards the taxation of energy has been debated vigorously. In 1991 the European Commission proposed a new carbon/energy tax as part of a package of measures intended to reduce energy use and to help the Community meet international targets for reducing emissions of carbon dioxide and other $lsquo;greenhouse gases’. This would have applied to both domestic and industrial users of energy and motor fuels. Also, in the area of UK domestic policy, the Chancellor’s 1993 Budget announced the phased extension of the standard rate of value added tax to domestic energy, which had hitherto been zero-rated in the UK. The extension of standard-rate VAT to domestic energy was primarily motivated by the need for increased tax revenues, but, at the same time, the Government maintained that the measure would have the valuable byproduct of reducing energy consumption, and hence contributing to achievement of targets for reducing greenhouse gas emissions.
Validity and Clinical Utility of DSM and Empirically Derived Prototype Diagnosis for Personality Disorders in Predicting Adaptive Functioning

Prototype matching, which involves comparing a patient clinical presentation with a prototype description of the disorder addresses some of the clinical limitations of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases symptom-count approach. Here, we investigated the validity and clinical utility of three diagnostic systems in predicting patient adaptive functioning using a multimethod multi-informant approach. Specifically, we compared a prototype matching approach based on DSM criteria, an empirically derived prototype matching approach, and DSM symptom count diagnostic systems. A convenience sample of clinicians (N = 80) and patients (N = 170) participated in the study. We imposed minimal exclusion criteria for patient participation to maximize generalizability. Clinicians completed assessment of their active patients using two prototype matching diagnses, one based on DSM and another that was empirically derived. Independent interviewers completed the Structured Clinical Interview to provide DSM symptom count. Patient global composite assessment of adaptive functioning, rated across the clinician, patient self-report, and independent interviewer, served as outcome variable. Prototype diagnosis for personality disorders, both one that is based on DSM criteria and one that was empirically derived, demonstrates some incremental validity over and above the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition symptom count, in predicting patient’s adaptive functioning. Specifically, avoidant personality disorder prototype diagnosis significantly contributed to prediction of adaptive functioning. Furthermore, clinicians rated the prototype-matching approach as more useful in clinical practice compared with the current DSM–IV categorical approach. Using a dimensional approach, which is based on prototype matching that also preserves the advantages of categorical system offers a valid and efficient approach to psychiatric assessment for personality disorders.
Bringing the Brain Into Personality Assessment: Is There a Place for Event-Related Potentials?

Advances in technology have provided opportunities to assess physiological correlates and further our understanding of a number of constructs, including personality traits. Event-related potentials (ERPs), scalp-recorded measures of brain activity with millisecond temporal resolution, show properties that make them potential candidates for integrating neurophysiological methods into personality research. Several commonly used ERPs have trait-like properties including test–retest stability approaching . 8 over two weeks. Additionally, ERP methods are relatively inexpensive and tolerable compared to other neurophysiological methods (e.g., functional magnetic resonance imaging [fMRI]) making them easier to obtain sample sizes required for individual differences research. Finally, the tasks that elicit ERPs are flexible enough to allow researchers to customize the tasks to the psychological constructs of interest. These factors suggest that ERPs could potentially be useful in the study of personality and individual differences. A baseline approach to this line of inquiry is to examine the properties of ERPs as neurophysiological individual differences markers and probe their links to personality traits as assessed by self-report questionnaires. This article does this for three well-studied ERPs. Techniques commonly used in personality assessment research—but rarely in ERP research—were applied to these candidate ERPs to examine their psychometric properties and personality correlates. Overall, although ERPs show promising properties as neurophysiological indicators of individual differences, they were only marginally related with existing personality traits. Further research clarifying the ERPs measurement properties and potential links with known personality processes is needed. Finally, we list some strategies to further integrate these two areas of research.
Investigating the Relatedness of Five Different Operationalizations of Explicit Knowledge of English Morphosyntax: A Levels of Processing Approach

This study aimed to investigate the relatedness of five different operationalizations of explicit knowledge of English morphosyntax within the framework of levels of processing. Two groups of university students, majoring in English translation at BA level, participated in the study. For the purpose of the study, five different operationalizations of the explicit knowledge of English morphosyntax were drawn from the literature and two metalinguistic knowledge tests were constructed. The two measures were piloted; the one with 52 items showed strong overall internal consistency (α=0.98) and the other one with 30 items showed an acceptable overall internal consistency (α=.70). The data analyses using Pearson correlation revealed that there is no high correlation between the five variables, and therefore no serious covariation problem was detected. Moreover, the results of repeated-measures ANOVA showed that there was a significant within-group difference between the performances of each group on the metalinguistic test. The results of the study are discussed within the level of processing (LoP) framework and implications are offered.
Lifetime Imaging on Silicon Bricks Using the Ratio of Photoluminescence Images With Different Excitation Wavelengths

The efficiency of silicon solar cells is eventually limited by the bulk lifetime of minority carriers in the wafer. The characterization of the bulk quality, measured on silicon bricks early in the production process, provides an immediate indication of the efficiency potential of the material, the feedstock purity, and the quality of the crystallization process. Previously, the intensity ratio of two photoluminescence images that are acquired with different spectral filters (i.e., the two-filter method) has been used to measure the bulk lifetime of silicon bricks. In this paper, we report on a similar approach, which uses the ratio of two photoluminescence images each acquired with a different illumination wavelength (915 and 1064 nm); one of which is within the photoluminescence emission band of silicon. We demonstrate with modeling that this two-laser method is more robust against uncertainties that affect the spectrum of photoluminescence emission and its detection, while the two-filter method is more robust against uncertainties in the carrier density profile and the illumination system properties. Experimentally, both the two-laser and two-filter methods show good agreement across a monocrystalline and multicrystalline silicon brick. However, challenges using the two-laser method arose when a brick with strongly injection-dependent lifetime was measured.
Oxytocin-Related Single-Nucleotide Polymorphisms, Family Environment, and Psychopathic Traits

Multiple studies have linked oxytocin to social behavior and affiliation–attachment. This research would suggest that oxytocin function may relate to the absence of loving kindness and empathy in psychopathy. The current study examined the associations between 3 oxytocin-related single-nucleotide polymorphisms (SNPs), participant-reported invalidating childhood environment, and psychopathic traits in community adults, predicting that alleles associated with higher empathy in the literature would relate to lower levels of psychopathic affective traits in particular. Results showed that the rs53576 SNP on the oxytocin receptor and cumulative risk alleles across the 3 SNPs were associated with psychopathic traits, and the interaction between cumulative risk and an emotionally invalidating environment was associated especially with affective deficits of psychopathy. Although this study requires replication in larger samples, results lend support to the role of attachment-related processes in psychopathy.
Fusion d'images multimodales pour l'aide au diagnostic du cancer du sein. (Multimodal image fusion for breast cancer aided diagnosis)

Le cancer du sein est le cancer le plus repandu chez les femmes de plus de 40 ans. En effet, des etudes ont montre qu'une detection precoce et un traitement approprie du cancer du sein augmentent de maniere significative les chances de survie. La mammographie constitue le moyen d'investigation le plus utilise dans le diagnostic des lesions mammaires. Cependant, cette technique peut etre insuffisante pour montrer les structures du sein et faire apparaitre les anomalies presentes et le medecin peut faire appel a d'autres modalites d'imagerie telle que l'imagerie IRM. Ces modalites sont generalement complementaires. Par consequent, le medecin procede a une fusion mentale des differentes informations sur les deux images dans le but d'effectuer le diagnostic adequat. Pour assister le medecin et l'aider dans ce processus, nous proposons une solution permettant de fusionner les deux images. Bien que l'idee de la fusion paraisse simple, sa mise en oeuvre pose de nombreux problemes lies non seulement au probleme de fusion en general mais aussi a la nature des images medicales qui sont generalement des images mal contrastees et presentant des donnees heterogenes, imprecises et ambigues. Notons que les images mammographiques et les images IRM presentent des representations tres differentes des informations, etant donnee qu'elles sont prises dans des conditions distinctes. Ce qui nous amene a poser la question suivante: Comment passer de la representation heterogene des informations dans l'espace image, a un autre espace de representation uniforme. Afin de traiter cette problematique, nous optons pour une approche de traitement multi-niveaux : niveau pixel, niveau primitives, niveau objet et niveau scene. Nous modelisons les objets pathologiques extraits des differentes images par des ontologies locales. La fusion est ensuite effectuee sur ces ontologies locales et resulte en une ontologie globale contenant les differentes connaissances sur les objets pathologiques du cas etudie. Cette ontologie globale sert a instancier une ontologie de reference modelisant les connaissances du diagnostic medical des lesions mammaires. Un raisonnement a base de cas est exploite pour fournir les rapports diagnostic des cas les plus similaires pouvant aider le medecin a prendre la meilleure decision. Dans le but de modeliser l'imperfection des informations traitees, nous utilisons la theorie des possibilites avec les differentes ontologies. Le resultat fourni est presente sous forme de rapports diagnostic comportant les cas les plus similaires au cas etudie avec des degres de similarite exprimes en mesures de possibilite. Un modele virtuel 3D complete le rapport diagnostic par un apercu simplifie de la scene etudiee.
Generating Conformation‐Specific Polyclonal and Monoclonal Anti‐Protein Kinase C Antibodies and Anti–Active State Specific PKC Antibodies

The protein kinase C (PKC) family of serine/ threonine kinases has been shown to play active roles as either suppressors or promoters of carcinogenesis in different types of tumors. Using antibodies that preferentially recognize the active conformation of classical PKCs (cPKCs), we have previously shown that in breast cancer samples the expression levels of cPKCs were similar in estrogen receptor–positive (ER+) as compared to triple‐negative tumors; however, the levels of active cPKCs were different. Determining the activation status of PKCs and other kinases in tumors may thus aid therapeutic decisions. Further, in basic science these tools may be used to understand the spatial and temporal dynamics of PKC signaling under different stimuli and for co‐immunoprecipitation studies to detect binding partners and substrates of active cPKCs. In this article, we describe how monoclonal and polyclonal anti–active state PKC antibodies can be obtained using rational approaches to select bona fide epitopes through inspection of the crystal structure of classical PKCs coupled to molecular modeling studies. We believe that this methodology can be used for other kinases and multi‐domain enzymes that undergo changes in their conformation upon activation. © 2018 by John Wiley & Sons, Inc.
Summarization using mapreduce framework based big data and hybrid algorithm (HMM and DBSCAN)

With the adventure of the internet there is vast increase in the storage of information. Almost all the information exists in digital form which reduces lots of the paper work and increases ease of storage. Searching relevant information in collection of documents is a tedious task. The solution comes in picture for this problem is automatic text summarization. In this paper, abstract summary generation of large amount of documents for big data is proposed which will consider user input as topic. The proposed system is designed using DBSCAN algorithm which works with MapReduce framework for clustering and Hidden Markov Model for summarization. The summarization process is performed in three main stages and provides a modular implementation of large numbre of documents for summarization. The proposed method follows preprocessing step in which documents are scanned with similarity and various machine learning technique are applied. The result of applying clustering enhances the summarizer system to collect exact words rather than copying redundant words. Topic based abstract summarization from big data is challenging task particularly when there are multiple documents with same or different content. Hadoop with its programming techniques can provide better ways of generating summary and it also enhances the complexity of summarization process using distributed computing.
Energy Storage Control for Dispatching Photovoltaic Power

The strong growth of the solar power generation industry requires an increasing need to predict the profile of solar power production over a day and develop highly efficient and optimized stand-alone and grid-connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems (BESSs) coupled with photovoltaic (PV) systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy of BESSs for dispatching a photovoltaic generation farm in the 1-h ahead and day-ahead markets. The forecasting of the solar irradiation and load power consumption is performed by developing a predictive model based on a feed-forward neural network trained with the Levenberg–Marquardt back-propagation learning algorithm.
Colon organoid formation and cryptogenesis are stimulated by growth factors secreted from myofibroblasts

Although small intestinal epithelial stem cells form crypts when using intestinal culture conditions, colon stem cells usually form colonospheres. Colon mesenchymal cell feeder layers can stimulate colon crypts to form organoids and produce crypts. We have investigated whether conditioned medium from colon mesenchymal cells can also stimulate colonosphere and organoid cryptogenesis. We prepared conditioned medium (CM) from WEHI-YH2 cells (mouse colon myofibroblasts); the CM stimulated both colonosphere formation and organoid cryptogenesis in vitro. The colon organoid-stimulating factors in WEHI-YH2 CM are inactivated by heating and trypsin digestion and proteins can be concentrated by ultrafiltration. Both the colonosphere- and organoid cryptogenesis- stimulatory effects of the CM are independent of canonical Wnt and Notch signaling. In contrast, bone morphogenetic protein 4 (BMP4) abolishes colonosphere formation and organoid cryptogenesis. The Transforming Growth Factor beta (TGFβ) Type I receptor kinase inhibitor (A83-01) stimulates colonosphere formation, whereas the Epidermal Growth Factor receptor (EGFR) kinase inhibitor (AG1478) reduces the formation of colonospheres, but in the presence of EGF, a “just-right” concentration of AG1478 increases colon organoid cryptogenesis.
How seamless are technology-rich learning environments? the voice of IT educators

Educational environments in higher education Information Technology (IT) courses typically involve the use of multiple technologies. Such technology-rich learning and teaching environments (TRLTEs) can enhance a student’s learning experiences and support learning in different settings (e.g., in-class and out-of-class, individual and group learning) and promote flexibility in learning in terms of time, space and access to learning resources. However, these can be complex teaching environments for educators to manage. A challenge for educators is to ensure that learning across multiple technologies and contexts happens seamlessly. Seamless learning is a notion whereby students transition smoothly from one learning setting to another, typically aided by technology. This paper reports the findings of an exploratory qualitative study of IT educators’ experiences of a TRLTE. Semi-structured interviews were used to elicit IT educators’ views of enablers and barriers to learning seamlessly. Data were analyzed using a thematic analysis technique. The findings revealed several issues in facilitating learning in a TRLTE and IT educators used various technologies to bridge the seams in their students’ learning. We discuss the findings and the implications for providing a seamless learning experience.
Characterizations of critical heart disease in ECG signal features through recurrence plots as for medical imaging diagnostics

this paper presents the realization of recurrence plots as a tool for characterizations of critical heart diseases in electrocardiogram (ECG) signal features. The technique is typically a conversion of a time-domain ECG signal to a colored image in a particular pattern of recurrence plots in order to indicate normal or abnormal heart conditions. The proposed technique is distinctive to other previously suggested approaches since the investigation directly focuses on the recurrence plot of each lead that significantly reveals the cause and type of heart diseases. Fifteen-lead ECG signals provided by MIT-BIH databases were utilized, including of one normal and particularly selected eight critical anomalies, involving Bundle Branch Block, Cardiomyopathy, Dysrhythmia, Myocardial Hypertrophy, Myocardial Infarction, Myocarditis, Valvular Heart Disease, and Unstable Angina. Simulations were performed in MATLAB, and the results reveal apparent difference in recurrence plots even though there is little change in time domain. This paper therefore proposes an alternative tool for characterizing critical heart diseases in ECG signal features for further utilization of medical imaging diagnostics.