nils-herrmann/clean_scirepeval huggingface dataset:
E Cigarettes and Future Cigarette Use
Venue: Pediatrics

There has been little research examining whether e cigarette use increases the risk of cigarette initiation among adolescents in the transition to adulthood when the sale of cigarettes becomes legal.  The Children's Health Study is a prospectively followed cohort in Southern California. Data on e cigarette use were collected in 11th and 12th grade (mean age 17.4) follow up data on tobacco product use were collected an average of 16 months later from never smoking e cigarette users at initial evaluation (n 146) and from a sample of never smoking, never e cigarette users (n 152) frequency matched to e cigarette users on gender, ethnicity, and grade.  Cigarette initiation during follow up was reported by 40.4% of e cigarette users (n 59) and 10.5% of never users (n 16) E cigarette users had 6.17 times (95% confidence interval: 3.30 11.6) the odds of initiating cigarettes as never e cigarette users. Results were robust to adjustment for potential confounders and in analyses restricted to never users of any combustible tobacco product. Associations were stronger in adolescents with no intention of smoking at initial evaluation. E cigarette users were also more likely to initiate use of any combustible product (odds ratio 4.98; 95% confidence interval: 2.37 10.4) including hookah, cigars, or pipes.  E cigarette use in never smoking youth may increase risk of subsequent initiation of cigarettes and other combustible products during the transition to adulthood when the purchase of tobacco products becomes legal. Stronger associations in participants with no intention of smoking suggests that e cigarette use was not simply a marker for individuals who would have gone on to smoke regardless of e cigarette use.
Nicotine delivery to users from cigarettes and from different types of e cigarettes
Venue: Psychopharmacology

BackgroundDelivering nicotine in the way smokers seek is likely to be the key factor in e cigarette (EC) success in replacing cigarettes. We examined to what degree different types of EC mimic nicotine intake from cigarettes.MethodsTwelve participants 'dual users' of EC and cigarettes) used their own brand cigarette and nine different EC brands. Blood samples were taken at baseline and at 2 min intervals for 10 min and again at 30 min.ResultsEleven smokers provided usable data. None of the EC matched cigarettes in nicotine delivery (Cmax 17.9 ng/ml, Tmax 4 min and AUC0 >30 315 ng/ml/min) The EC with 48 mg/ml nicotine generated the closest PK profile (Cmax 13.6 ng/ml, Tmax 4 min, AUC0 >30 245 ng/ml/min) followed by a third generation EC using 20 mg/ml nicotine (Cmax 11.9 ng/ml, Tmax 6 min, AUC0 >30 232 ng/ml/min) followed by the tank system using 20 mg/ml nicotine (Cmax 9.9 ng/ml, Tmax 6 min, AUC0 >30 201 ng/ml/min) Cig a like PK values were similar, ranging from Cmax 7.5 to 9.7 ng/ml, Tmax 4 6 min, and AUC0 >30 144 to 173 ng/ml/min. Moderate differences in e liquid nicotine concentrations had little effect on nicotine delivery, e.g. the EC with 24 mg/ml cartridge had the same PK profile as ECs with 16 mg/ml cartridges. Using similar strength e liquid, the tank EC provided significantly more nicotine than cig a like ECs.ConclusionsEC brands we tested do not deliver nicotine as efficiently as cigarettes, but newer EC products deliver nicotine more efficiently than cig a like brands. Moderate variations in nicotine content of e liquid have little effect on nicotine delivery. Smokers who are finding cig a like EC unsatisfactory should be advised to try more advanced systems.
Patch Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Convolutional Neural Networks (CNN) are state of theart models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch level classifier on image patches will perform better than or similar to an image level classifier. The challenge becomes how to intelligently combine patch level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch level predictions given by patch level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non small cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non cancer dataset of smaller images that a patch based CNN can outperform an image based CNN.
Impact of Social Media Marketing Strategies used by Micro Small and Medium Enterprises (MSMEs) on Customer acquisition and retention

and objectives of the study: The primary objective of the researchers is to study and understand the concept of social media marketing and its role in the Micro Small and Medium Enterprise (MSME) sector. It is also intended to study the correlation between the sales obtained by a successful marketer and the time spent on social media marketing.  Data was collected from 50 Micro Small and Medium Enterprises (MSMEs) using judgement sampling. Exploratory research is used to study the social media marketing strategies used by these MSMEs for customer acquisition and retention.  It is found that social media participation has a strong influence on brand awareness and brand trust, which in turn, have a strong influence on customer acquisition and customer retention. Also, there is a strong positive correlation between sales and amount of time spent on social media. Social media marketing strategies have a positive influence on customer acquisition and retention and to increase in market share.  This research will be helpful for MSME's as they will be able to target customers with appropriate social media marketing strategies. The research will also uncover factors contributing to increase in sales through social media marketing. The MSME's will be able to choose social media channels, social tactics and create an effective social media marketing strategy to acquire and retain customers based on the findings and conclusion of the research.
Geology and Archaeology: Submerged Landscapes of the Continental Shelf

Sea level change has influenced human population globally since prehistoric times. Even in early phases of cultural development human populations were faced with marine regression and transgression as a result of changing climate and corresponding glacio isostatic adjustment. Global marine regression during the last glaciation changed the palaeogeography of the continental shelf, converting former marine environments to attractive terrestrial habitats for prehistoric humans. These areas of the shelf were used as hunting and gathering areas, as migration routes between continents, and most probably witnessed the earliest developments in seafaring and marine exploitation, until the postglacial transgression re submerged these palaeo landscapes. Based on modern marine research technologies and the integration of large databases, proxy data are increasingly available for the reconstruction of Quaternary submerged landscapes. Also, prehistoric archaeological remains from the recent sea bottom are shedding new light on human prehistoric development driven by rapidly changing climate and environment. This publication contributes to the exchange of ideas and new results in this young and challenging field of underwater palaeoenvironmental investigation.
Salience in a social landscape: electrophysiological effects of task irrelevant and infrequent vocal change.
Venue: Social cognitive and affective neuroscience

In a dynamically changing social environment, humans have to face the challenge of prioritizing stimuli that compete for attention. In the context of social communication, the voice is the most important sound category. However, the existing studies do not directly address whether and how the salience of an unexpected vocal change in an auditory sequence influences the orientation of attention. In this study, frequent tones were interspersed with task relevant infrequent tones and task irrelevant infrequent vocal sounds (neutral, happy and angry vocalizations) Eighteen healthy college students were asked to count infrequent tones. A combined event related potential (ERP) and EEG time frequency approach was used, with the focus on the P3 component and on the early auditory evoked gamma band response, respectively. A spatial temporal principal component analysis was used to disentangle potentially overlapping ERP components. Although no condition differences were observed in the 210 310 ms window, larger positive responses were observed for emotional than neutral vocalizations in the 310 410 ms window. Furthermore, the phase synchronization of the early auditory evoked gamma oscillation was enhanced for happy vocalizations. These findings support the idea that the brain prioritizes the processing of emotional stimuli, by devoting more attentional resources to salient social signals even when they are not task relevant.
Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation.
Venue: The Review of scientific instruments

Electrical impedance tomography (EIT) reconstruction is a nonlinear and ill posed problem. Exact reconstruction of an EIT image inverts a high dimensional mathematical model to calculate the conductivity field, which causes significant problems regarding that the computational complexity will reduce the achievable frame rate, which is considered as a major advantage of EIT imaging. The single step method, state estimation method, and projection method were always used to accelerate reconstruction process. The basic principle of these methods is to reduce computational complexity. However, maintaining high resolution in space together with not much cost is still challenging, especially for complex conductivity distribution. This study proposes an idea to accelerate image reconstruction of EIT based on compressive sensing (CS) theory, namely, CSEIT method. The novel CSEIT method reduces the sampling rate through minimizing redundancy in measurements, so that detailed information of reconstruction is not lost. In order to obtain sparse solution, which is the prior condition of signal recovery required by CS theory, a novel image reconstruction algorithm based on patch based sparse representation is proposed. By applying the new framework of CSEIT, the data acquisition time, or the sampling rate, is reduced by more than two times, while the accuracy of reconstruction is significantly improved.
Relationship between Result of Sentiment Analysis and User Satisfaction The case of Korean Meteorological Administration

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Effect of Al2O3 nanoparticles on the performance of passive double slope solar still

This paper present the enhancement in yield (productivity) of passive double slope solar still (DSSS) using Al2O3 nanoparticles in the basefluid (water) for two different masses 35 kg and 80 kg. The analytical expression of fluid temperature has been derived for passive DSSS. On the basis of developed model, the analysis has been carried out for the basefluid (without nanoparticles) and for nanofluid with three different concentrations (0.04% 0.08% and 0.12% Effect of different concentrations of Al2O3 nanoparticle on fluid temperature, thermal conductivity, internal heat transfer coefficients (HTC) and yield of the fluid has also been analyzed. For 0.12% concentration of Al2O3 nanoparticles, the enhancement of yield for 35 kg and 80 kg basefluid has been found to be 12.2% and 8.4% respectively as compared to that of basefluid.
Excellent energy density of polymer nanocomposites containing BaTiO3@Al2O3 nanofibers induced by moderate interfacial area

Inorganic/polymer nanocomposites, using one dimensional (1D) core shell structure BaTiO3@Al2O3 nanofibers (BT@Al2O3 nfs) as fillers and poly(vinylidene fluoride) (PVDF) as the polymer matrix, have been prepared. The core shell structure BT@Al2O3 nfs have been synthesized via coaxial electrospinning. The breakdown strength (Eb) and discharged energy density of the nanocomposites can be significantly improved by creating an insulating Al2O3 shell layer with moderate dielectric constant on the surfaces of BT nanofibers to form a moderate interfacial area. The Al2O3 shell layer could effectively confine the mobility of charge carriers, which reduces energy loss by reducing the Maxwell Wagner Sillars (MWS) interfacial polarization and space charge polarization between the fillers and the polymer matrix. As a result, the nanocomposite films filled with 5 vol% BT@Al2O3 nfs exhibit a excellent discharge energy density of 12.18 J cm 3 at 400 MV m 1, which is 254% over bare PVDF (4.8 J cm 3 at 350 MV m 1) and 1015% greater than the biaxially oriented polypropylenes (BOPP) (1.2 J cm 3 at 640 MV m 1) The work here indicates that this promising state of the art method of preparing high energy density nanocomposites can be used in the next generation of dielectric capacitors.
Erratum to: Study on thermal conductivity of water based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles
Venue: Journal of Thermal Analysis and Calorimetry

The aim of this work was to investigate the effects of temperature and solid volume fraction on thermal conductivity of CNTs Al2O3/water nanofluids. Both Al2O3 nanoparticles and CNTs are dispersed in the base fluid with equal solid volume. Experiments were conducted with various solid volume fractions of 0.02, 0.04, 0.1, 0.2, 0.4, 0.8 and 1.0 and various fluid temperatures of 303, 314, 323 and 332 K. Measured data reveal that the thermal conductivity of nanofluid highly depends on the solid volume fraction. Also, temperature may play an important role in enhancing thermal conductivity of CNTs Al2O3/water, especially at high solid volume fractions. Based on experimental data, correlations are proposed for different temperatures by nonlinear regression. These correlations are able to predict thermal conductivity of nanofluid with high precision. Besides, a general correlation of thermal conductivity with function of temperature and solid volume fraction was proposed.
Confined aquifer head measurements and storage properties in the San Luis Valley, Colorado, from spaceborne InSAR observations

Interferometric Synthetic Aperture Radar (InSAR) a remote sensing technique for measuring centimeter level surface deformation, is used to estimate hydraulic head in the confined aquifer of the San Luis Valley (SLV) Colorado. Reconstructing head measurements from InSAR in agricultural regions can be difficult, as InSAR phase data are often decorrelated due to vegetation growth. Analysis of 17 L band ALOS PALSAR scenes, acquired between January 2007 and March 2011, demonstrates that comprehensive InSAR deformation measurements can be recovered over the vegetated groundwater basin with an improved processing strategy. Local skeletal storage coefficients and time delays between the head change and deformation are estimated through a joint InSAR well data analysis. InSAR subsidence estimates are transformed to head changes with finer temporal and spatial resolution than is possible using existing well records alone. Both InSAR and well data suggest that little long term water storage loss occurred in the SLV over the study period and that inelastic compaction was negligible. The seasonal head variations derived from InSAR are consistent with the existing well data at most locations where confined aquifer pumping activity dominates. Our results demonstrate the advantages of InSAR measurements for basin wide characterization of aquifer storage properties and groundwater levels over agricultural regions.
Evaluation of the robustness of critical infrastructures by Hierarchical Graph representation, clustering and Monte Carlo simulation
Venue: Reliab. Eng. Syst. Saf.

In this paper, we present a methodological work that adopts a system of systems (SoS) viewpoint for the evaluation of the robustness of interdependent critical infrastructures (CIs) We propose a Hierarchical Graph representation, where the product flow is dispatched to the demand nodes in consideration of different priorities. We use a multi state model to describe different degrees of degradation of the individual components, where the transitions between the different states of degradation occur stochastically. The quantitative evaluation of the CIs robustness is performed by Monte Carlo simulation. The methodological approach proposed is illustrated by way of two case studies: the first one concerns small sized gas and electricity networks and a supervisory control and data acquisition (SCADA) system; the second one considers a moderately large power distribution network, adapted from the IEEE 123 node test feeders. The large size of the second case study requires hierarchical clustering for performing the robustness analysis.
Integrating interpersonal neurobiology with play therapy.

Advancements in neuroscience support play and empathic attunement as contributors to brain development and formation. As such, a natural connection seems to exist between play therapy and current neuroscience concepts. This article assimilated theories from the interpersonal neurobiology model (Siegel, 2012) including the 9 levels of integration, with play therapy processes and practices. Identification of complementary components may bolster efforts to advocate for the play therapy field as well as inform intentional intervention by practitioners.
Handbook Of Play Therapy

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Adlerian play therapy with students with externalizing behaviors and poor social skills.

Government reports emphasize the dire need for counseling services to be accessible and effective for children in need in the United States. We employed a single case exploratory design and used group and individual Adlerian play therapy (AdPT) as the independent variable to examine its effects on children with disruptive classroom behaviors and poor social skills. Results for each participant revealed effective to very effective treatment effects during the intervention and follow up phases of the designs.
Flexible Hybrid Organic Inorganic Perovskite Memory.
Venue: ACS nano

Active research has been done on hybrid organic inorganic perovskite materials for application to solar cells with high power conversion efficiency. However, this material often shows hysteresis, which is undesirable, shift in the current voltage curve. The hysteresis may come from formation of defects and their movement in perovskite materials. Here, we utilize the defects in perovskite materials to be used in memory operations. We demonstrate flexible nonvolatile memory devices based on hybrid organic inorganic perovskite as the resistive switching layer on a plastic substrate. A uniform perovskite layer is formed on a transparent electrode coated plastic substrate by solvent engineering. Flexible nonvolatile memory based on the perovskite layer shows reproducible and reliable memory characteristics in terms of program/erase operations, data retention, and endurance properties. The memory devices also show good mechanical flexibility. It is suggested that resistive switching is done by migration of vacancy defects and formation of conducting filaments under the electric field in the perovskite layer. It is believed that organic inorganic perovskite materials have great potential to be used in high performance, flexible memory devices.
Getting Started with Neural Models for Semantic Matching in Web Search
Venue: ArXiv

The vocabulary mismatch problem is a long standing problem in information retrieval. Semantic matching holds the promise of solving the problem. Recent advances in language technology have given rise to unsupervised neural models for learning representations of words as well as bigger textual units. Such representations enable powerful semantic matching methods. This survey is meant as an introduction to the use of neural models for semantic matching. To remain focused we limit ourselves to web search. We detail the required background and terminology, a taxonomy grouping the rapidly growing body of work in the area, and then survey work on neural models for semantic matching in the context of three tasks: query suggestion, ad retrieval, and document retrieval. We include a section on resources and best practices that we believe will help readers who are new to the area. We conclude with an assessment of the state of the art and suggestions for future work.