nils-herrmann/clean_scirepeval huggingface dataset:
MARGOT: A web server for argumentation mining
Venue: Expert Syst. Appl.

Argumentation mining is a recent challenge concerning the automatic extraction of arguments from unstructured textual corpora. Argumentation mining technologies are rapidly evolving and show a clear potential for application in diverse areas such as recommender systems, policy making and the legal domain. There is a long recognised need for tools that enable users to browse, visualise, search, and manipulate arguments and argument structures. There is, however, a lack of widely accessible tools. In this article we describe the technology behind MARGOT, the first online argumentation mining system designed to reach out to the wider community of potential users of these new technologies. We evaluate its performance and discuss its possible application in the analysis of content from various domains.
A Modeling and Verification Approach to the Design of Distributed IMA Architectures Using TTEthernet
Venue: ANT/SEIT

Integrated Modular Avionics (IMA) architectures complemented with Time Triggered Ethernet (TTEthernet) provides a strong platform to support the design and deployment of distributed avionic software systems. The complexity of the design and continuous integration of such systems can be managed using a model based methodology. In this paper, we build on top of our extension of the AADL modeling language to model TTEthernet based distributed systems and leverage model transformations to enable undertaking the verification of the system models produced with this methodology. In particular, we propose to transform the system models to a model suitable for a simulation with DEVS. We illustrate the proposed approach using an example of a navigation and guidance system and we use this example to show the verification of the contention freedom property of TTEthernet schedule.
System physiology of respiratory control in man

The respiratory control system is an important chemoreflex feedback control system that maintains arterial partial pressures of CO2 (PaCO2) O2 and pH remarkably constant via ventilatory regulation. It can be divided into two subsystems: a controller (controlling element) and a plant (controlled element) The respiratory operating point (ventilatory or PaCO2 response) is determined by the interplay between the controller (arterial PCO2 [PaCO2] minute ventilation [VE] relation) and plant (V E PaCO2 relation) subsystem elements within the respiratory control system. This review outlines the methodology of converting the closed loop of the respiratory control system to an open loop state, then simplifying the controller and plant subsystems, and identifying the input output relationship using a systems physiological technique (equilibrium diagram method) Changes in central hemodynamics, exercise stimulus, and regular exercise training modify VE and/or PaCO2 levels at rest and during exercise. These respiratory changes can be quantitatively explained by changes in two subsystem elements on the respiratory equilibrium diagram. Using this analysis technique that allows an integrated and quantitative description of the whole respiratory control system will greatly advance the elucidation of pathological conditions manifesting breathing disorders and respiratory regulation during exercise. By repeating thought experiments utilizing this kind of mathematic model and physiological experiments that provide evidence, deeper understanding will be achieved concerning prediction of the behavior of biological systems beyond the physiological range and understanding of the pathophysiology of diseases that are difficult to study by clinical research.
Experimental study on the effect of nanoparticle concentration on the lubricating property of nanofluids for MQL grinding of Ni based alloy

The effect of nanoparticle concentration on the lubricating property of nanofluids for minimum quantity lubrication (MQL) grinding was experimentally investigated based on the status of nanofluid MQL. Different concentrations of nanofluids with molybdenum disulfide (MoS 2 carbon nanotubes (CNTs) and their mixtures (MoS 2 CNTs) were prepared. Difficult to cut Ni based alloy was used as the workpiece material to explore the effect of mass fraction of nanofluids on grinding force ratio G and workpiece surface quality. The statistical analysis of grinding force ratio and surface roughness Ra were performed using one way analysis of variance followed by Tukey's post hoc test with confidence intervals (CI) of 95% Results showed that given the same mass fraction, MoS 2 CNTs achieved lower G G Mix (8% 0.274] and surface roughness Ra 0.328 mm) than MoS 2 and CNTs. These characteristics were attributed to the physical collaboration of the mixed nanoparticles. For the same nanoparticle, G decreased initially, reaching the lowest value at 8% G Mix (8% 0.274] and then increased with the increase in mass fraction of nanofluids because of the influence of agglomeration. Ra, increased gradually with the increase in viscosity of nanofluids. Autocorrelation analysis shows that MoS 2 CNT nanofluid MQL achieved the lowest autocorrelation initial value R Mix 0.64) when t 0. This result confirms that MoS 2 CNT nanofluid MQL could improve machining precision and surface quality. Among different concentrations of nanofluids, the high frequency continuous oscillation of the autocorrelation curve of the 8% MoS 2 CNTs indicated improved workpiece surface quality. Combined friction coefficient, Ra and autocorrelation analytical results show that 8% MoS 2 CNTs was the optimum concentration for nanofluid MQL in the experiment. Autocorrelation analysis of the profile curves reveals the microstructural information of the workpiece surface and, combined with Ra analysis, provides a better method of analyzing the surface quality of a workpiece.
Experimental investigation of the effect of nanoparticle size on thermal conductivity of in situ prepared silica ethanol nanofluid

In this study the effects of particle size, temperature and volume fraction of SiO2 nanoparticles on thermal conductivity of nanofluid were investigated. Silica nanoparticles were prepared by the Stober method. The results of experiments showed that with the increase of particle size, temperature and volume fraction the thermal conductivity of silica ethanol nanofluid increased. Effect of particle size on thermal conductivity of nanofluid was attributed to high surface hydrophilicity of silica nanoparticles resulting decrease in interfacial thermal resistance with the increase of particle size. Also an empirical equation incorporating particle size, volume fraction and temperature was proposed for estimation of thermal conductivity of nanofluid. Comparison between this correlation and measurements showed that the deviation of calculated data from experimental results is within 9.5% to 5.4% The literature results agree well with the predictions by correlation proposed.
A mobile system for a comprehensive online characterization of nanoparticle aggregates based on wide angle light scattering and laser induced incandescence.
Venue: The Review of scientific instruments

A mobile demonstrator for the comprehensive online characterization of gas borne nanoparticle aggregates is presented. Two optical measurement techniques are combined, both utilizing a pulsed Nd:YAG laser as light source. Aggregate size and fractal dimension are measured by Wide Angle Light Scattering (WALS) An ellipsoidal mirror images elastically scattered light from scattering angles between 10deg and 165deg onto a CCD camera chip resulting in an almost complete scattering diagram with high angular resolution. Primary particle size and volume fraction are measured by time resolved Laser Induced Incandescence (TiRe LII) Here, particles are heated up to about 3000 K by the short laser pulse, the enhanced thermal radiation signal is detected with gated photomultiplier tubes. Analysis of the signal decay time and maximum LII signal allows for the determination of primary particle diameter and volume fraction. The performance of the system is demonstrated by combined measurements on soot nanoparticle aggregates from a soot aerosol generator. Particle and aggregate sizes are varied by using different equivalence ratios of the combustion in the generator. Soot volume fraction can be adjusted by different levels of dilution with air. Online measurements were carried out demonstrating the favorable performance of the system and the potential for industrial applications such as process control and product development. The particle properties obtained are confirmed through transmission electron microscopy analysis on representative samples.
Synthesis of a three layered SiO2@Au nanoparticle@polyaniline nanocomposite and its application in simultaneous electrochemical detection of uric acid and ascorbic acid.
Venue: Journal of materials chemistry. B

Novel nanocomposites fabricated by incorporating different types of nanomaterials, especially inorganic and organic nanomaterials, have attracted much attention, and shown promising prospects in the applications in biological research fields. Herein, we propose a facile method to synthesize a novel three layered SiO2@Au nanoparticle@polyaniline nanocomposite, which exhibited excellent electrical conductivity and good dispersibility in water. The as proposed nanocomposite has been adopted to construct the modified electrode SiO2@AuNP@PANI/CS/GCE, which was characterized by electrochemical impedance spectroscopy and cyclic voltammetry, and demonstrated excellent electrochemical behaviors in neutral and even alkaline media, making it suitable for sensing species in biological fluids under physiological conditions. Moreover, the SiO2@AuNP@PANI/CS/GCE exhibited excellent catalytic activity towards the electro oxidation of uric acid (UA) and ascorbic acid (AA) and a difference of 384 mV between the oxidation potentials for UA and AA was obtained, which is large enough for the selective and simultaneous determination of UA and AA in a binary mixture. Thus, the modified electrode has been successfully adopted for the simultaneous electrochemical detection of UA and AA, and the detection limits of 2 mM and 6 mM were obtained, respectively.
Atlanta, revised Atlanta, and Determinant based classification application in a cohort of Portuguese patients with acute pancreatitis
Venue: European journal of gastroenterology hepatology

Acute pancreatitis (AP) represents a complex and potentially fatal disease with a highly variable clinical course. Three classification systems for assessing the severity in AP have been validated for clinical use.  The aim of the present study was to evaluate the performance of the Atlanta and Determinant based classifications in predicting severe clinical outcomes in patients with AP.  In this retrospective study we reviewed the treatment and follow up records of 525 patients with AP admitted to our unit between the years of 2003 and 2014. Outcomes included mortality, admission to the ICU, need for interventional procedures or nutritional support, and duration of hospital and ICU stay.  The prevalence of organ failure and persistent organ failure in our cohort was 23.0 and 10.7% respectively, and the mortality rate was 5.9% Higher grades of severity were associated with worse outcomes in all classification systems. The revised Atlanta and Determinant based classifications performed similarly in predicting outcomes, and both proved to be superior to the former classic Atlanta classification.  Recent classifications proved to be more accurate in predicting important clinical outcomes in patients with AP.
Exploring hydro meteorological drought patterns over the Greater Horn of Africa (1979 2014) using remote sensing and reanalysis products

Spatio temporal patterns of hydrological droughts over the Greater Horn of Africa (GHA) are explored based on total water storage (TWS) changes derived from time variable gravity field solutions of Gravity Recovery And Climate Experiment (GRACE, 2002 2014) together with those simulated by Modern Retrospective Analysis for Research Application (MERRA, 1980 2014) These hydrological extremes are then related to meteorological drought events estimated from observed monthly precipitation products of Global Precipitation Climatology Center (GPCC, 1979 2010) and Tropical Rainfall Measuring Mission (TRMM, 1998 2014) The major focus of this contribution lies on the application of spatial Independent Component Analysis (sICA) to extract distinguished regions with similar rainfall and TWS with similar overall trend and seasonality. Rainfall and TWS are used to estimate Standard Precipitation Indices (SPIs) and Total Storage Deficit Indices (TSDIs) respectively that are employed to characterize frequency and intensity of hydro meteorological droughts over GHA. Significant positive (negative) changes in monthly rainfall over Ethiopia (Sudan) between 2002 and 2010 leading to a significant increase in TWS over the central GHA region were noted in both MERRA and GRACE TWS (2002 2014) However, these trends were completely reversed in the long term (1980 2010) records of rainfall (GPCC) and TWS (MERRA) The four independent hydrological sub regions extracted based on the sICA (i.e. Lake Victoria Basin, Ethiopia Sudanese border, South Sudan, and Tanzania) indicated fairly distinct temporal patterns that matched reasonably well between precipitation and TWS changes. While meteorological droughts were found to be consistent with most previous studies in all sub regions, their impacts are clearly observed in the TWS changes resulting in multiple years of extreme hydrological droughts. Correlations between SPI and TSDI were found to be significant over Lake Victoria Basin, South Sudan, and Tanzania. The low correlations between SPI and TSDI over Ethiopia are likely related to inconsistency between TWS and precipitation signals. Further, we found that hydrological droughts in these regions were significantly associated with Indian Ocean Dipole (IOD) events while El Nino Southern Oscillation (ENSO) plays a secondary role.
Studying customers' resource integration by service employees in interactional value co creation

Noting that resource integration is a pivotal dimension of value co creation in Service Dominant logic, this paper aims to explore how service employees engaged in co creation processes with customers integrate the latter's resources.  To address the limitations of previous research on customer resources and their integration by service employees, this study turns to the concept of customer participation to identify the nature of customers' resources. A conceptual framework of their integration by service employees underpins nine key propositions. This foundation leads to the development of theoretical contributions, managerial implications and avenues for research.  Customers can use 12 types of resources in value co creation. Contrasting with earlier findings, the conceptual framework reveals that service employees may not only integrate these customers' resources but also either misintegrate or not integrate them. Non integration and misintegration may be intentional or accidental. Accordingly, value co creation or co destruction may result from interactions.  This conceptual and exploratory text requires complementary theoretical and empirical investigations. It also does not adopt an ecosystems view of co creation.  Knowing the different steps of resource integration and what influences them should increase the chances of value co creation and limit the risks of value co destruction.  Scant research has examined the nature of customer resources and how service employees integrate them. This paper also is the first to distinguish among resource integration, misintegration and non integration.
Sustainable Hazard Mitigation: Exploring the Importance of Green Infrastructure in Building Disaster Resilient Communities

Natural disasters continue to plague the United States, undermining the nation's ability to build disaster resilient communities. Although structural and non structural mitigation measures are currently in place to lessen the impact natural disasters have on society, little attention has been given to the construction of green infrastructure as a sustainable hazard mitigation strategy. The purpose of this article is to explore the benefits of green infrastructure as a sustainable hazard mitigation strategy and offer recommendations to public sector entities to build disaster resilient communities.
When things matter: A survey on data centric internet of things
Venue: J. Netw. Comput. Appl.

With the recent advances in radio frequency identification (RFID) low cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine to human and machine to machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state of the art research efforts in IoT from data centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.
Composition chimique et activite antibacterienne de l'huile essentielle de Teucrium polium L. du mont de Tessala (Algerie occidentale)
Venue: Phytotherapie

ResumeL'extraction de l'huile essentielle de Teucrium polium L. par hydrodistillation a permis d'obtenir un rendement moyen de 1,66 0,12 Sa caracterisation par chromatographie en phase gazeuse couplee a la spectrometrie de masse montre une dominance des composes hydrocarbones (77,85 avec 43,41 de carbures monoterpeniques et 34,44 de carbures sesquiterpeniques. Les monoterpenes et les sesquiterpenes oxygenes revelent un taux global de 20,31 L'activite antibacterienne montre une importante efficacite vis a vis des souches testees (75 de sensibilite) Staphylococcus aureus enregistre le plus haut taux de sensibilite (100 Escherichia coli, Proteus mirabilis et Pseudomonas aeruginosa exhibent une resistance (33,33 vis a vis de l'huile essentielle. La concentration minimale inhibitrice est de 31,5 ml/ml, notee pour la souche Staphylococcus aureus. Cette inhibition est consideree comme tres forte.AbstractThe extraction of essential oils from Teucrium polium L. growing in Tessala Mountain, using hydrodistillation provided an average yield of 1.66 0.12% The "GC" and "MS" characterization allowed to identify hydrocarbons as the major compounds (77.854% followed by monoterpenes hydrocarbons (43.41% and then sesquiterpene hydrocarbons (34.44% Oxygenated monoterpenes and sesquiterpenes reveal a total rate of 20,31 The antibacterial activity showed effectiveness against the tested strains (75% sensitivity) Staphylococcus aureus strain recorded the highest sensitivity level (100% Escherichia coli, Proteus mirabilis and Pseudomonas aeruginosa exhibits a resistance (33.33% towards the tested essential oil. The minimal inhibitory concentration (MIC) was 31.25 ml/ml for Staphylococcus aureus strain. This inhibition is considered as high.
Deep Convolutional Neural Networks for Computer Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Venue: IEEE Transactions on Medical Imaging

Remarkable progress has been made in image recognition, primarily due to the availability of large scale annotated datasets and deep convolutional neural networks (CNNs) CNNs enable learning data driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off the shelf pre trained CNN features, and conducting unsupervised CNN pre training with supervised fine tuning. Another effective method is transfer learning, i.e. fine tuning CNN models pre trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre trained ImageNet (via fine tuning) can be useful. We study two specific computer aided detection (CADe) problems, namely thoraco abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state of the art performance on the mediastinal LN detection, and report the first five fold cross validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
Youtube Online Video And Participatory Culture

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Recurrent Neural Network Based Language Model Adaptation for Accent Mandarin Speech
Venue: CCPR

In this paper, we propose to adapt the recurrent neural network (RNN) based language model to improve the performance of multi accent Mandarin speech recognition. N gram based language model can be easily applied to speech recognition system, but it is hard to describe the long span information in a sentence and arises a serious phenomenon of data sparsity. Instead, RNN based language model can overcome these two shortcomings, but it will take a long time to decode directly. Taking these into consideration, this paper proposes a method which combines these two types of language model (LM) together and adapts the RNN based language model to rescore lattices for different accents of Mandarin speech. The architecture of the adapted RNN LM is accent specific top layers and shared hidden layer. The accent specific top layers are used to adapt different accents and the shared hidden layer stores history information, which can be seen as a memory layer. Experiments on the RASC863 corpus show that the proposed method can improve the performance of accented Mandarin speech recognition over the baseline system.