nils-herrmann/clean_scirepeval huggingface dataset:
A transparent superhydrophobic coating with mechanochemical robustness for anti icing, photocatalysis and self cleaning
Venue: Chemical Engineering Journal
Year: 2020

Transparent and conformal superhydrophobic coating with mechanochemical robustness is of great importance for various applications, such as waterproof, self cleaning, and anti icing for windows of cars and buildings. However, it remains a daunting challenge to endow the functional surface with programmable wettability. Herein, we propose a hierarchical coating consisting of polydimethylsiloxane (PDMS) nanoparticles (NPs) and PDMS microparticles (MPs) functional NPs through a combination of thermal treatment and spray treatment. The evaporation of PDMS under heat treatment is firstly applied to form a uniform nanoarchitectures, followed by spraying a mixed P25 PDMS dispersion to construct a protective coating with enhanced surface roughness. This resulted coating is conformal and possess reversible wettability on various substrates. In virtue of the light response property of P25, the reversible wettability between superhydrophobicity and hydrophobicity can be achieved by repetitive UV light irradiation and dark environment storage. Besides, the coating can maintain superhydrophobicity with high transmittance (76% optical transmittance) after immersion in acid/base solution for 24 h and continuously sand impingement. Furthermore, the coating exhibits mechanochemical robustness against high speed water jet and Elcometer 99 tape peeling test for 30 cycles due to the high adhesion between the robust coating and the glass. For practical application, the coating was demonstrated to have excellent performance in anti icing, photocatalytic degradation of pollutants and self cleaning.
Attitudes of patients and surgeons towards sham surgery trials: a protocol for a scoping review of attributes to inform a discrete choice experiment
Venue: BMJ Open
Year: 2020

In order to properly evaluate the efficacy of orthopaedic procedures, rigorous, randomised controlled sham surgery trial designs are necessary. However, randomised controlled trials (RCTs) for surgery involving a placebo are ethically debated and difficult to conduct with many failing to reach their desired sample size and power. A review of the literature on barriers and enablers to recruitment, and patient and surgeon attitudes and preferences towards sham surgery trials, will help to determine the characteristics necessary for successful recruitment.  This review will scope the diverse literature surrounding sham surgery trials with the aim of informing a discrete choice experiment to empirically test patient and surgeon preferences for different sham surgery trial designs. The scoping review will be conducted in accordance with the methodological framework described in Arksey and O'Malley (2005) and reported using the Preferred Reporting Items for Systematic Reviews and Meta analyses Protocols extension for Scoping Reviews. The review will be informed by a systematic search of Medline, Embase, PsycInfo, CINAHL and EconLit databases (from database inception to 21 June 2019) a Google Scholar search, and hand searching of reference lists of relevant studies or reviews. Studies or opinion pieces that involve patient, surgeon or trial characteristics, which influence the decision to participate in a trial, will be included. Study selection will be carried out independently by two authors with discrepancies resolved by consensus among three authors. Data will be charted using a standardised form, and results tabulated and narratively summarised with reference to the research questions of the review. Ethics and dissemination The findings from this review will inform the design of a discrete choice experiment around willingness to participate in surgical trials, the outcomes of which can inform decision and cost effectiveness models of sham surgery RCTs. The qualitative information from this review will also inform patient centred outcomes research. The review will be published in a peer reviewed journal.
An evaluation of methods used to teach quality improvement to undergraduate healthcare students to inform curriculum development within preregistration nurse education: a protocol for systematic review and narrative synthesis
Venue: Systematic Reviews
Year: 2015

BackgroundDespite criticism, quality improvement (QI) continues to drive political and educational priorities within health care. Until recently, QI educational interventions have varied, targeting mainly postgraduates, middle management and the medical profession. However, there is now consensus within the UK, USA and beyond to integrate QI explicitly into nurse education, and faculties may require redesign of their QI curriculum to achieve this. Whilst growth in QI preregistration nurse education is emerging, little empirical evidence exists to determine such effects. Furthermore, previous healthcare studies evaluating QI educational interventions lend little in the way of support and have instead been subject to criticism. They reveal methodological weakness such as no reporting of theoretical underpinnings, insufficient intervention description, poor evaluation methods, little clinical or patient impact and lack of sustainability. This study aims therefore to identify, evaluate and synthesise teaching methods used within the undergraduate population to aid development of QI curriculum within preregistration nurse education.designA systematic review of the literature will be conducted. Electronic databases, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Psychological Information (PsychINFO) Education Resources Information Centre (ERIC) Medical Literature Analysis and Retrieval System Online (MEDLINE) and Applied Social Sciences Index and Abstracts (ASSIA) will be searched alongside reference list scanning and a grey literature search. Peer reviewed studies from 2000 2014 will be identified using key terms quality improvement, education, curriculum, training, undergraduate, teaching methods, students and evaluation. Studies describing a QI themed educational intervention aimed at undergraduate healthcare students will be included and data extracted using a modified version of the Reporting of Primary Studies in Education (REPOSE) Guidelines. Studies will be judged for quality and relevance using the Evidence for Policy and Practice Information and Co ordinating Centre's (EPPI) Weight of Evidence framework and a narrative synthesis of the findings provided.DiscussionThis study aims to identify, evaluate and synthesise the teaching methods used in quality improvement education for undergraduate healthcare students where currently this is lacking. This will enable nursing faculty to adopt the most effective methods when developing QI education within their curriculum.
Investigating Entity Knowledge in BERT with Simple Neural End To End Entity Linking
Venue: CoNLL
Year: 2019

A typical architecture for end to end entity linking systems consists of three steps: mention detection, candidate generation and entity disambiguation. In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text representations, i.e. BERT? (b) How much entity knowledge is already contained in pretrained BERT? (c) Does additional entity knowledge improve BERT's performance in downstream tasks? To this end we propose an extreme simplification of the entity linking setup that works surprisingly well: simply cast it as a per token classification over the entire entity vocabulary (over 700K classes in our case) We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state of the art that does mention detection and entity disambiguation jointly. Additionally, we investigate the usefulness of entity aware token representations in the text understanding benchmark GLUE, as well as the question answering benchmarks SQUAD~V2 and SWAG and also the EN DE WMT14 machine translation benchmark. To our surprise, we find that most of those benchmarks do not benefit from additional entity knowledge, except for a task with very small training data, the RTE task in GLUE, which improves by 2%
Graph Attention Networks
Venue: ICLR
Year: 2018

We present graph attention networks (GATs) novel neural network architectures that operate on graph structured data, leveraging masked self attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state of the art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein protein interaction dataset (wherein test graphs remain unseen during training)
Biomarkers to Predict Antidepressant Response
Venue: Current psychiatry reports
Year: 2010

During the past several years, we have achieved a deeper understanding of the etiology/pathophysiology of major depressive disorder (MDD) However, this improved understanding has not translated to improved treatment outcome. Treatment often results in symptomatic improvement, but not full recovery. Clinical approaches are largely trial and error, and when the first treatment does not result in recovery for the patient, there is little proven scientific basis for choosing the next. One approach to enhancing treatment outcomes in MDD has been the use of standardized sequential treatment algorithms and measurement based care. Such treatment algorithms stand in contrast to the personalized medicine approach, in which biomarkers would guide decision making. Incorporation of biomarker measurements into treatment algorithms could speed recovery from MDD by shortening or eliminating lengthy and ineffective trials. Recent research results suggest several classes of physiologic biomarkers may be useful for predicting response. These include brain structural or functional findings, as well as genomic, proteomic, and metabolomic measures. Recent data indicate that such measures, at baseline or early in the course of treatment, may constitute useful predictors of treatment outcome. Once such biomarkers are validated, they could form the basis of new paradigms for antidepressant treatment selection.
Outlier detection in interval data
Venue: Adv. Data Anal. Classif.
Year: 2018

A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.
Pemanfaatan Program Edmodo di Jurusan Pendidikan Bahasa Arab: Sebuah Model Pembelajaran Berbasis Website
Year: 2012

Kebutuhan terhadap model pembelajaran berbasis website di dunia pendidikan dengan memanfaatkan jaringan internet semakin meningkat seiring dengan perkembangan teknologi modern dewasa ini. Karenanya, dengan penelitian ini, diharapkan ada temuan berupa model e learning yang representatif untuk dimanfaatkan dalam proses pembelajaran bahasa Arab. Dengan pendekatan kualitatif, penelitian ini mendeskripsikan Program Edmodo yang pernah diimplementasikan di Jurusan Pendidikan Bahasa pada Fakultas Humaniora dan Budaya Universitas Islam Negeri (UIN) Maulana Malik Ibrahim Malang. Hasil penelitian ini menunjukkan bahwa Program Edmodo adalah sistem manajemen pembelajaran atau Learning Management System (LMS) berupa laman website berbasis situs jejaring sosial (social networking) yang khusus diperuntukkan bagi pendidikan. Edmodo adalah representatif ideal bagi model e learning karena memiliki fitur fitur pendukung yang mampu mempermudah proses pembejaran. Ada 8 fitur utama Edmodo, yakni, library, calender, note, alert, assignment, quiz, grades dan poll. Melalui eksperimen pada mata kuliah "e learning" dan "ilmu terjemah" 94% dari 208 mahasiswa menyatakan setuju bahwa Edmodo adalah model e learning yang tepat untuk membantu proses pembelajaran. Karenanya, penelitian ini merekomendasikan bahwa Edmodo layak dijadikan proyek percontohan bagi lembaga lembaga pendidikan di Indonesia yang hendak menerapkan Learning Management System atau model pembelajaran berbasis website.  Edmodo, Website, Pembelajaran Pendahuluan Seiring dengan perkembangan Teknologi Informasi dan Komunikasi (TIK) yang semakin pesat, kebutuhan terhadap konsep dan mekanisme belajar mengajar berbasis TIK menjadi tidak terelakkan lagi. Konsep yang lalu dikenal dengan e learning ini, menurut Hartley (2001:30) membawa pengaruh terjadinya proses transformasi pendidikan konvensional ke dalam bentuk digital, baik secara isi (content) dan sistem.
Analysis of Tainted Transactions in the Bitcoin Blockchain Transaction Network
Venue: COMPLEX NETWORKS
Year: 2020

Blockchain technology, with its decentralised peer to peer network and cryptographic protocols, has led to a proliferation of cryptocurrencies, with Bitcoin at the forefront. The blockchain publicly records all Bitcoin transactions which can be used to build a dynamic and complex network to give a representation of the transactions in the underlying monetary system. Despite the cryptographic guarantees there exist inconsistencies and suspicious behavior in the chain. We reported on two such anomalies related to block mining in previous work. In this paper, we build a network using bitcoin transactions and apply techniques from network science to analyse its complex structure. We focus our analysis on sub networks induced by the two sets of anomalies, and investigate how inequality in terms of wealth and anomaly fraction evolves from the blockchain's origin. Thereby we present a novel way of using network science to detect and investigate cryptographic anomalies.
Transcriptional changes in right ventricular tissues are enriched in the outflow tract compared with the apex during chronic pulmonary embolism in rats.
Venue: Physiological genomics
Year: 2009

Moderate to severe pulmonary embolism (PE) can cause pulmonary arterial hypertension and right ventricular (RV) heart damage. Previous studies from our laboratory indicate that the basal outflow tract of the RV is injured and has acute inflammation followed by tissue remodeling while the apex appears normal. The present studies examine transcription responses to chronic PE in RV apex and outflow tracts using DNA microarrays to identify transcription responses by region. Changes predominated in the RV outflow tract (8,575 genes showed =1.5 fold expression change) Gene ontology and KEGG analyses indicated a significant decrease in genes involved in cellular respiration and energy metabolism and increases in inflammatory cell adhesion molecules and extracellular matrix proteins. Signal pathways for wound healing such as fibroblast growth factor, collagen synthesis, and CCN proteins (named for the first three members of the family: cysteine rich protein 61, connective tissue growth factor, and nephroblastoma overexpressed gene) were strongly upregulated. In comparison, few genes (422) showed significant change in the RV apex tissue. Apex selective genes included two genes affecting metabolism and a stretch sensitive transcription factor (ankyrin repeat domain 1) We conclude that the RV outflow tract is subject to strong proinflammatory and profibrotic remodeling transcriptional responses in chronic PE. Severe loss of genes involved in cellular respiration is consistent with previous histology indicating a shift in cell types present within the outflow tract tissue away from highly energy dependant cardiomyocytes to less metabolically active cells during remodeling. The apex region of the RV had few compensating adaptations.
Analysis of the competitive gains in the local productive arrangement of milk from Sao Luis dos Montes Belos in Goias, Brazil
Year: 2018

This work sought to identify the gains resulting from the cooperation of the local productive arrangement (LPA) of Dairy, in the city of Goias, Brazil. The methodology of the proposed work consisted of a qualitative case study. The results of this research presented the following competitive gains: gains in scale and market power (market strength, bargaining power and legitimacy) provision of solutions [shared marketing, training (full bucket) creation of the school farm and credit guarantee] learning and innovation (creation of the technology course, dissemination of information and increase of added value) reduction of costs and risks [shared activities, creation of school lacy, productivity, cooperative of producers of Palminopolis, confidence in new investments, gain in social relations, (confidence building partially) It was concluded that the studied LPA presents satisfactory results, as they exist in the cooperation networks.
End to end trained encoder decoder convolutional neural network for fetal electrocardiogram signal denoising
Year: 2020

Non invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal to noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision making. In this paper we propose the use of artificial intelligence for the task of one channel fetal ECG enhancement as a post processing step after maternal ECG suppression.  We propose a deep fully convolutional encoder decoder framework, learning end to end mappings from noise contaminated fetal ECGs to clean ones. Symmetric skip layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details.  Experiments on synthetic data show an average improvement of 7.5 dB in the signal to noise ratio (SNR) for input SNRs in the range of 15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise contaminated signals some morphological features might be unreliably reconstructed.  The presented method has the advantage of preserving individual variations in pulse shape and beat to beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.
Comorbilidad entre los factores de ansiedad del SCARED y la sintomatologia depresiva en ninos de 8 12 anos
Year: 2010

Comorbidity between SCARED anxiety factors and depressive symptomatology in 8 to 12 yearold children. The aim of this study was to examine the comorbidity between the SCARED anxiety factors and depressive symptoms in 8 12 year old children. Participants were 792 girls and 715 boys, who completed: the 41 item version of the Screen for Child Anxiety Related Emotional Disorder (SCARED) the Children's Depression Inventory (CDI) and a Socio demographic questionnaire. Of the sample, 47% showed anxiety symptoms and 11.5% showed depressive symptoms. Heterotypic comorbidity was 82% in children at risk of depression and 20% in children at risk of anxiety. Homotypic Comorbidity between anxiety factors was 87% Homotypic comorbidity and heterotypic comorbidity were high; their early detection will prevent the continuity of an anxious disorder and the development of depression.
An analysis of influence factors for academic performance about personality traits and thinking styles of students: Use a C programming language course in college as an example
Venue: 2017 12th International Conference on Computer Science and Education (ICCSE)
Year: 2017

Academic performance is one of the indicators to evaluate the achievement of college students, which plays an important role in individual development. However, it is worth mentioning that personality traits and thinking styles have an influence on academic performance. To further study the correlation among personality traits, thinking styles and academic performance, we carry out a series of experiments using certain C programming language course in certain college as an example. Firstly, we eliminate the overlap of personality traits and thinking styles by principal component analysis (PCA) Then, three groups of regression analysis on C programming language course grade are performed, i.e. the personality traits with course grades, the thinking styles with course grades, the principal components of the personality traits and thinking styles with course grades. Finally, the experimental results show that Agreeableness has a positive influence on C programming language grades, while Local has a negative influence on C programming language grades. Meanwhile, the personality traits and thinking styles and C course grades are generally not related and the irrelevance is not caused of the multiple collinearity between personality traits and thinking styles.
Towards better substitution based word sense induction
Venue: ArXiv
Year: 2019

Word sense induction (WSI) is the task of unsupervised clustering of word usages within a sentence to distinguish senses. Recent work obtain strong results by clustering lexical substitutes derived from pre trained RNN language models (ELMo) Adapting the method to BERT improves the scores even further. We extend the previous method to support a dynamic rather than a fixed number of clusters as supported by other prominent methods, and propose a method for interpreting the resulting clusters by associating them with their most informative substitutes. We then perform extensive error analysis revealing the remaining sources of errors in the WSI task. Our code is available at this https URL.
Review and Prospect of Research on Generative Adversarial Networks
Venue: 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)
Year: 2019

Since it was proposed in 2014, generative adversarial networks (GAN) has been highly concerned and widely studied by industrial circles and artificial intelligence researchers. It provides a new idea for the construction of the generative model. This paper reviews the research progress of GAN and prospects its development trend. Section 2 describes the basic idea and model structure of GAN. Section 3 introduces several typical derivative models of GAN. Section 4 lists GAN's applications in many fields, such as image, vision, voice, language, and etc. Section 5 makes a forward look and thinks on the development trend of GAN, and discusses the application of GAN in the field of communication countermeasures. Finally, section 6 summarizes this paper.