nils-herrmann/clean_scirepeval huggingface dataset:
A Grid-Interactive Permanent-Magnet Synchronous Motor-Driven Solar Water-Pumping System

This paper deals with an effective power transfer scheme between the solar photovoltaic (PV) array and single-phase grid, feeding a field-oriented-controlled (FOC) permanent-magnet synchronous motor (PMSM) drive applied to a water-pumping system (WPS). Owing to the intermittency associated with solar (PV) system, the requirement of constant water supply is not possible with the standalone system. In order to mitigate this, a grid-intergraded WPS is proposed here. The grid integration enables the consumer an uninterrupted operation of water pump irrespective of solar insolation level. Moreover, the PV power can be fed to the utility grid when water pumping is not required. To make it possible, one voltage-source converter (VSC) and one voltage-source inverter connected to a common dc link are used for utility grid and PMSM control, respectively. The unit vector template theory is utilized to generate switching pulses for VSC to control the bidirectional power flow between the solar PV system and utility grid through the common dc link. A sensorless FOC is used to drive the PMSM coupled to the water pump. An intermediate stage boost converter is used for extracting optimum power from solar PV array under variable insolation. A perturb and observe algorithm is used for generating the duty ratio for the maximum power point (MPP) operation. The applicability of overall system constituting utility grid in conjugation with PV array fed PMSM-coupled water pump ensuring bidirectional power flow control with MPP tracking of PV array and abiding the utility grid, IEEE-519 standard for power factor, and total harmonic distortion is simulated in MATLAB/Simulink environment with SimPowerSystem toolbox and validated on a prototype developed in the laboratory. The system prototype is tested under variable solar insolation and grid abnormalities such as voltage sag and voltage swell.
A Review of State-ofthe-Art on Wireless Body Area Networks

During the last few years, Wireless Body Area Networks (WBANs) have emerged into many application domains, such as medicine, sport, entertainments, military, and monitoring. This emerging networking technology can be used for e-health monitoring. In this paper, we review the literature and investigate the challenges in the development architecture of WBANs. Then, we classified the challenges of WBANs that need to be addressed for their development. Moreover, we investigate the various diseases and healthcare systems and current state-ofthe-art of applications and mainly focus on the remote monitoring for elderly and chronically diseases patients. Finally, relevant research issues and future development are discussed. Wireless body area networks; review; challenges; applications; architecture; radio technologies; telemedicine
Computational challenges for sentiment analysis in life sciences

Data and connectivity between users form the core of social networks. Every status, post, friendship, tweet, re-tweet, tag or image generates a massive amount of structured and unstructured data. Deriving meaning from this data and, in particular, extracting behavior and emotions of individual users, as well as of user communities, is the goal of sentiment analysis and affective computing and represents a significant challenge. Social networks also represent a potentially infinite source of applications for both research and commercial purposes and are adaptable to many different areas, including life science. Nevertheless, collecting, sharing, storing and analyzing social networks data pose several challenges to computer scientists, such as the management of highly unstructured data, big data, and the need for real-time computation. In this paper we give a brief overview of some concrete examples of applying sentiment analysis to social networks for healthcare purposes, we present the current type of tools existing for sentiment analysis, and summarize the challenges involved in this process focusing on the role of high performance computing.
Proteasome Inhibitors Activate Autophagy Involving Inhibition of PI3K-Akt-mTOR Pathway as an Anti-Oxidation Defense in Human RPE Cells

The two major intracellular protein degradation systems, the ubiquitin-proteasome system (UPS) and autophagy, work collaboratively in many biological processes including development, apoptosis, aging, and countering oxidative injuries. We report here that, in human retinal pigment epithelial cells (RPE), ARPE-19 cells, proteasome inhibitors, clasto-lactacystinβ-lactone (LA) or epoxomicin (Epo), at non-lethal doses, increased the protein levels of autophagy-specific genes Atg5 and Atg7 and enhanced the conversion of microtubule-associated protein light chain (LC3) from LC3-I to its lipidative form, LC3-II, which was enhanced by co-addition of the saturated concentration of Bafilomycin A1 (Baf). Detection of co-localization for LC3 staining and labeled-lysosome further confirmed autophagic flux induced by LA or Epo. LA or Epo reduced the phosphorylation of the protein kinase B (Akt), a downstream target of phosphatidylinositol-3-kinases (PI3K), and mammalian target of rapamycin (mTOR) in ARPE-19 cells; by contrast, the induced changes of autophagy substrate, p62, showed biphasic pattern. The autophagy inhibitor, Baf, attenuated the reduction in oxidative injury conferred by treatment with low doses of LA and Epo in ARPE-19 cells exposed to menadione (VK3) or 4-hydroxynonenal (4-HNE). Knockdown of Atg7 with siRNA in ARPE-19 cells reduced the protective effects of LA or Epo against VK3. Overall, our results suggest that treatment with low levels of proteasome inhibitors confers resistance to oxidative injury by a pathway involving inhibition of the PI3K-Akt-mTOR pathway and activation of autophagy.
WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.
Characterisation of Plasmodium falciparum populations selected on the human endothelial receptors P-selectin, E-selectin, CD9 and CD151

The ability of the parasite Plasmodium falciparum to evade the immune system and be sequestered within human small blood vessels is responsible for severe forms of malaria. The sequestration depends on the interaction between human endothelial receptors and P. falciparum erythrocyte membrane protein 1 (PfEMP1) exposed on the surface of the infected erythrocytes (IEs). In this study, the transcriptomes of parasite populations enriched for parasites that bind to human P-selectin, E-selectin, CD9 and CD151 receptors were analysed. IT4_var02 and IT4_var07 were specifically expressed in IT4 parasite populations enriched for P-selectin-binding parasites; eight var genes (IT4_var02/07/09/13/17/41/44/64) were specifically expressed in isolate populations enriched for CD9-binding parasites. Interestingly, IT4 parasite populations enriched for E-selectin- and CD151-binding parasites showed identical expression profiles to those of a parasite population exposed to wild-type CHO-745 cells. The same phenomenon was observed for the 3D7 isolate population enriched for binding to P-selectin, E-selectin, CD9 and CD151. This implies that the corresponding ligands for these receptors have either weak binding capacity or do not exist on the IE surface. Conclusively, this work expanded our understanding of P. falciparum adhesive interactions, through the identification of var transcripts that are enriched within the selected parasite populations.
How are substance use disorders addressed in VA psychiatric and primary care settings? Results of a national survey.

This study examined interventions for substance use disorders within the Department of Veterans Affairs (VA) psychiatric and primary care settings.



National random samples of 83 VA psychiatry program directors and 102 primary care practitioners were surveyed by telephone. The survey assessed screening practices to detect substance use disorders, protocols for treating patients with substance use disorders, and available treatments for substance use disorders.



Respondents reported extensive contact with patients with substance use problems. However, a majority reported being ill equipped to treat substance use disorders themselves; they usually referred such patients to specialty substance use disorder treatment programs.



Offering fewer specialty substance use disorder services within the VA may be problematic: providers can refer patients to specialty programs only if such programs exist. Caring for veterans with substance use disorders may require increasing the capacity of and establishing new specialty programs or expanding the ability of psychiatric programs and primary care practitioners to provide such care.
A novel context-based implicit feature extracting method

One of the major steps for opinion mining is to extract product features. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews, however; people tend to express their opinions with simple structures and brachylogies, which lead to more implicit features in reviews. By analyzing the characteristics of product reviews in Chinese on the Internet, this paper proposes a novel context-based implicit feature extracting method. We extract the implicit features according to the opinion words and the similarity between the product features in the implicit features' context. We also build a matrix to show the relationship between opinion words and product features, then use a new algorithm to filter the noises in the matrix. Experiments show that our method provides higher accuracy in extracting the implicit features.
Disk repositioning surgery of the temporomandibular joint with bioabsorbable anchor.

The most common temporomandibular joint (TMJ) pathologic disease is anterior-medial displacement of the articular disk, which can lead to TMJ-related symptoms.The indication for disk repositioning surgery is irreversible TMJ damage associated with temporomandibular pain. We describe a surgical technique using a preauricular approach with a high condylectomy to reshape the condylar head. The disk is anchored with a bioabsorbable microanchor (Mitek Microfix QuickAnchor Plus 1.3) to the lateral aspect of the condylar head. The anchor is linked with a 3.0 Ethibond absorbable suture to fix the posterolateral side of the disk above the condyle.The aims of this surgery were to alleviate temporomandibular pain, headaches, and neck pain and to restore good jaw mobility. In the long term, we achieved these objectives through restoration of the physiological position and function of the disk and the lower articular compartment.In our opinion, the bioabsorbable anchor is the best choice for this type of surgery because it ensures the stability of the restored disk position and leaves no artifacts in the long term that might impede follow-up with magnetic resonance imaging.
A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.
A preliminary framework for description, analysis and comparison of creative systems

I summarise and attempt to clarify some concepts presented in and arising from Margaret Boden’s (1990) descriptive hierarchy of creativity, by beginning to formalise the ideas she proposes. The aim is to move towards a model which allows detailed comparison, and hence better understanding, of systems which exhibit behaviour which would be called ‘‘creative’’ in humans. The work paves the way for the description of naturalistic, multi-agent creative AI systems, which create in a societal context. I demonstrate some simple reasoning about creative behaviour based on the new framework, to show how it might be useful for the analysis and study of creative systems. In particular, I identify some crucial properties of creative systems, in terms of the framework components, some of which may usefully be proven a priori of a given system. I suggest that Boden’s descriptive framework, once elaborated in detail, is more uniform and more powerful than it first appears.
Similarities and differences of e-commerce and e-government: Insights from a pilot study

So far, empirically grounded studies, which compare the phenomena of e-commerce and e-government, have been in short supply. However, such studies it has been argued would most likely deepen the understanding of the sector-specific similarities and differences leading to potential cross-fertilization between the two sectors as well as to the establishment of performance measures and success criteria. This paper reports on the findings of an empirical research pilot, which is the first in a series of planned exploratory and theory-testing studies on the subject
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data

We address statistical classifier design given a mixed training set consisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is required to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to improve performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data subsets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data. The classifier, based on a joint probability model for features and labels, is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training. The scope of application for the new method is greatly extended by the observation that test data, or any new data to classify, is in fact additional, unlabelled data thus, a combined learning/classification operation much akin to what is done in image segmentation can be invoked whenever there is new data to classify. Experiments with data sets from the UC Irvine database demonstrate that the new learning algorithms and structure achieve substantial performance gains over alternative approaches.
Polarity Related Influence Maximization in Signed Social Networks

Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
Dual-Matrix Composite Wideband Antenna Structures for CubeSats

A concept for a deployable high-performance antenna structure for CubeSats is presented. The detailed design of a conical log-spiral antenna with wideband operation between 250 500 MHz, a gain of above 5 dB, and circular polarization is performed using electromagnetic simulations. A structural concept using dual-matrix composites with soft hinge regions, allowing the antenna to be packaged into small volumes, is developed. An antenna prototype is fabricated using Astroquartz fibers, an epoxy matrix, and a high-strain silicone matrix for the hinge regions. Structural and electromagnetic analysis is conducted and shows good agreement with predicted performance thus demonstrating the validity of the proposed antenna concept.
Robust Observer Design for a Class of Nonlinear Systems

A Luenberger-based observer is proposed to the state estimation of a class of nonlinear systems subject to parameter uncertainty and bounded disturbance signals. A nonlinear observer gain is designed in order to minimize the effects of the uncertainty, error estimation and exogenous signals in an 7-L, sense by means of a set of state- and parameterdependent linear matrix inequalities that are solved using standard software packages. A numerical example illustrates the approach.
Textual Modelling Embedded into Graphical Modelling

Although labelled graphical, many modelling languages represent important model parts as structured text. We benefit from sophisticated text editors when we use programming languages, but we neglect the same technology when we edit the textual parts of graphical models. Recent advances in generative engineering of textual model editors make the development of such sophisticated text editors practical, even for the smallest textual constructs of graphical languages. In this paper, we present techniques to embed textual model editors into graphical model editors and prove our approach for EMF-based textual editors and graphical editors created with GMF.
Model-driven architecture in practice - a software production environment based on conceptual modeling

The best ebooks about Model Driven Architecture In Practice A Software Production Environment Based On Conceptual Modeling that you can get for free here by download this Model Driven Architecture In Practice A Software Production Environment Based On Conceptual Modeling and save to your desktop. This ebooks is under topic such as model driven architecture in practice a software free download model-driven architecture in practice: a model-driven architecture in practice springer full model-driven practice: from requirements to code return of the antichristand the new world order ebook welfare reform and pensions bill1st sitting tuesday 2 dermatology skills for primary carean illustrated guide labour law and worker protection in developing countries patient communication for pharmacya case study approach on the tools of argumenthow the best lawyers think argue and systems and meaningconsulting in organisations systematic prayer and meditations by bahaulah ebook | thesustainablecorp informe tÃƒÂ©cnico / technical report arxiv mdd4ms: a model driven development framework for modeling model driven architecture nanjing university
CORRIGENDUM: ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing

00Month2017 CORRIGENDUM: ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing Robert C. Green MD, MPH, Jonathan S. Berg MD, PhD, Wayne W. Grody MD, PhD, Sarah S. Kalia ScM, CGC, Bruce R. Korf MD, PhD, Christa L. Martin PhD, FACMG, Amy L. McGuire JD, PhD, Robert L. Nussbaum MD, Julianne M. O’Daniel MS, CGC, Kelly E. Ormond MS, CGC, Heidi L. Rehm PhD, FACMG, Michael S. Watson PhD, FACMG, Marc S. Williams MD, FACMG & Leslie G. Biesecker MD Genet Med (2013) 15, 565–574 doi:10.1038/gim.2013.73 In the published version of this paper, on page 567, on the 16th line in the last paragraph of the left column, the abbreviation of Expected Pathogenic is incorrect. The correct sentence should read, “For the purposes of these recommendations, variants fitting these descriptions were labeled as Known Pathogenic (KP) and Expected Pathogenic (EP), respectively.”
The habenula

The habenula is a tiny brain region the size of a pea in humans. This region is highly conserved across vertebrates and has been traditionally overlooked by neuroscientists. The name habenula is derived from the Latin word habena, meaning "little rein", because of its elongated shape. Originally its function was thought to be related to the regulation of the nearby pineal gland (which Rene Descartes described as the "principal seat of the soul"). More recent evidence, however, demonstrates that the habenula acts as a critical neuroanatomical hub that connects and regulates brain regions important for divergent motivational states and cognition. In this Primer, we will discuss the recent and converging evidence that points to the habenula as a key brain region for motivation and decision-making.