nils-herrmann/clean_scirepeval huggingface dataset:
Conceptualizing and measuring consumer social responsibility: a neglected aspect of consumer research

Despite the availability of extensive research on a wide range of consumer related areas, some socially sensitive consumer issues have still remained relatively unexplored. Since literature review revealed a gap in the conceptualization of consumer social responsibilities, this article conceptualized, developed and validated a measurement scale for consumer social responsibility (CnSR) using a rigorous methodology suggested in the scale development literature. The study responded to the scholarly calls for further research in this area. Using a total sample of 491 respondents (53 respondents for pilot, 207 for exploratory survey and 231 for confirmatory survey) the study proposed a six dimensional measurement scale for CnSR, including: (1) social impacts, (2) solidarity, (3) critical appraisal, (4) supporting business growth, (5) environmental impacts and (6) action. It is argued that with ever growing impacts of corporate social responsibility on business practices, the validated measurement scale will contribute to the advancement of research in this nascent consumer area.
Measuring consumer perception of CSR in tourism industry: Scale development and validation

The purpose of this study is to develop a new scale to measure consumer perception of corporate social responsibility (CSR) activities in the tourism industry. A qualitative research has been carried out to generate a pool of items. Based on the scale development procedure suggested by Churchill (1979) various reliability and validity tests have been carried out to confirm scale structure. Results show the three dimensional 18 item scale for measuring consumer perception of CSR activities in the tourism industry. This study contributes to literature by offering a comprehensive framework based on a sustainable development approach for measuring consumer perception of CSR activities.
Compatibility of Arbitrary Speeds with Special Relativity Axioms for Interior Dynamical Problems

In this paper, we outline the rapidly growing literature on arbitrary speeds within physical media and show that, contrary to a widespread belief for one century, arbitrary speeds for interior dynamical problems are compatible with the abstract axioms of special relativity, provided that they are realized with the covering isomathematics specifically developed for the conditions considered. We finally point out a number of intriguing implications in cosmology, particle physics, nuclear physics, chemistry, gravitation, and mathematical models of interstellar travel.
Study of the Parameters of Steam Assisted Gravity Drainage (SAGD) Method for Enhanced Oil Recovery in a Heavy Oil Fractured Carbonate Reservoir

By increasing in the use of nonrenewable energy and decreasing in discovering hydrocarbonic reservoirs, in near future the world will encounter with a new challenge in the field of energy, so increase in recovery factor of the existing oil reservoirs is necessary after the primary production. In one hand the existence of untouched heavy oil reservoirs in Iran and lack of producing from them and maturity of light oil reservoirs to 2nd and 3rd stage of their production age in other hand make the development and production of these heavy oil reservoirs necessary in Iran. The goal of this study is to survey common methods of producing oil reservoirs and appliance in heavy oil reservoirs of Iran especially in Kuh e Mond. For choosing the best EOR method we should consider factors such as the reservoir fluid and rock characteristics, availability of injection material, available equipments and other items. One way to choose an optimized method is the comparison of reservoirs' parameters in successful EOR projects with the considered reservoir. However, it should be consider that each reservoir has its especial characteristics and we can not give certain idea about it. Thermal methods have the most useage in the recovery of the world's heavy oil and between these methods; steam injection with the most amount of oil production has terrific importance. The gained results show that the best way for recovery heavy oil of Sarvak reservoir of Kuh e Mond is thermal way and especially steam injection Steam modeling by activating gravity drainage drive process by using steam injection (SAGD) which is designed in a reservoir model in Sarvak reservoir of Kuh e Mond has been successful.
Non hydrocarbon gas injection followed by steam gas co injection for heavy oil recovery enhancement from fractured carbonate reservoirs

Considerable heavy oil is accumulated in naturally fractured carbonate reservoirs, with very low oil recovery efficiency through costly production techniques. In this experimental study, the effects of different non hydrocarbon gases on the performance of gas injection process followed by steam gas co injection on heavy oil recovery from low permeable full sized carbonate cores were investigated at reservoir conditions in a long fractured laboratory model. Three different gases: pure CO2, pure N2, and their mixtures as synthetic flue gas were used to study the performance of gas injection process. Also, the tests were continued by steam gas co injection process that gas was injected with specific steam/gas ratio at steam saturation temperature condition. In the course of experiments, oil and water productions, pressure and temperature were monitored carefully. It was found that during fracture depletion, the piston like displacement by N2 injection and oil foaming by CO2 injection are the most important mechanisms affecting the oil recovery performance before gas breakthrough. The results showed that after gas breakthrough (GBT) the ultimate oil recovery based on residual oil at GBT for CO2 injection was 58.4% (14.8% by gas injection and 43.6% by steam gas co injection) for flue gas injection was 73.8% (9.8% by gas injection and 64% by steam gas co injection) and for N2 injection was 47% (13.5% by gas injection and 33.5% by steam gas co injection) The results of oil recovery and production rate indicated that the flue gas injection during gas injection and steam gas co injection processes was outperforming for heavy oil recovery enhancement from fractured reservoirs.
A color to gray conversion based on visual feature extraction of JND
Venue: 2016 International Conference on Progress in Informatics and Computing (PIC)

Nowadays, in many applications, we need to convert images from color to gray scale and preserve both the visual appearance and feature discrimination. This paper presents a maximum entropy algorithm, a new color to gray conversion, based on the Just noticeable difference (JND) Unlike previous approaches, the proposed method extracts the visual feature edges in the image based on the characteristics of human eye recognition. And we propose an entropy based algorithm to choose the optimal line direction so that all the pixel color vectors in an image have the most spread out projections, thus increasing the grayscale image contrast. Then, we make use of histogram specification on all the edge pixels to further increase the image contrast. Experimental results demonstrate that the proposed method is superior to the current methods in terms of both image quality and information presentation.
Theoretical Comparisons of Positive Unlabeled Learning against Positive Negative Learning
Venue: NIPS

In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e. ordinary supervised learning) Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning based on the upper bounds on estimation errors. We find simple conditions when PU and NU learning are likely to outperform PN learning, and we prove that, in terms of the upper bounds, either PU or NU learning (depending on the class prior probability and the sizes of P and N data) given infinite U data will improve on PN learning. Our theoretical findings well agree with the experimental results on artificial and benchmark data even when the experimental setup does not match the theoretical assumptions exactly.
Theoretical Comparisons of Learning from Positive Negative, Positive Unlabeled, and Negative Unlabeled Data
Venue: ArXiv

In PU learning, a binary classifier is trained only from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e. supervised learning) in experiments. In this paper, we theoretically compare PU (and the opposite NU) learning against PN learning, and prove that, one of PU and NU learning given infinite U data will almost always improve on PN learning. Our theoretical finding is also validated experimentally.
Improving career prospects for the low educated the role of guidance and lifelong learning

This report draws both on literature review and an original collection of stories from biographical interviews of individuals in the Czech Republic, Denmark, Germany, France, Italy, Poland and the UK. The narrative accounts describe the wide variety of experiences with initial and further education. The analysis focuses on motivations for learning (or not) and the findings confirm that early negative experiences with schooling have a scarring effect inhibiting workers' willingness to re engage in education later in life. Nevertheless, many low educated adults command a variety of skills, which they have developed in the work context, while the interest of the low educated in education and training may be rekindled by making learning instrumental to improvement in their work situation.
The Biological Activity of Alkaloids from the Amaryllidaceae: From Cholinesterases Inhibition to Anticancer Activity
Venue: Natural product communications

Modern research has shown that Amaryllidaceae alkaloids represent a rich reservoir of potential small chemical molecules exhibiting several medicinal properties through various mechanisms. Among the many Amaryllidaceae compounds, galanthamine has been given a great amount of attention due to the fact that it possesses potent acetylcholinesterase inhibitory activity. In spite of the amount of evidence indicating the potential usefulness of Amaryllidaceae alkaloids in therapy, research groups have focused their attention on the other alkaloids present in this plant family. New investigations have shed light on many aspects of the structure of Amaryllidaceae alkaloids and on their semisynthetic modification, function, and mechanisms underlying in vitro and in vivo activity. In addition, Amaryllidaceae alkaloids have frequently been identified as having promising cytotoxic properties against cancer cell lines. While follow up studies have repeatedly shown that Amaryllidaceae alkaloids and their derivatives demonstrate antiproliferative, cytotoxic and apoptosis inducing activity, the mechanisms remain unclear. This review addresses the most important Amaryllidaceae alkaloids with anticancer potential, particularly those that have been studied for the purpose of gaining a better understanding of the basis of the activity at the cellular and molecular level.
Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas

We study the adaptive estimation of copula correlation matrix \Sigma$ for the semi parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for \Sigma$ is the plug in estimator \hat{\Sigma} with Kendall's tau statistic. We first obtain a sharp bound on the operator norm of \hat{\Sigma} \Sigma$ Then we study a factor model of \Sigma$ for which we propose a refined estimator \widetilde{\Sigma} by fitting a low rank matrix plus a diagonal matrix to \hat{\Sigma} using least squares with a nuclear norm penalty on the low rank matrix. The bound on the operator norm of \hat{\Sigma} \Sigma$ serves to scale the penalty term, and we obtain finite sample oracle inequalities for \widetilde{\Sigma} We also consider an elementary factor copula model of \Sigma$ for which we propose closed form estimators. All of our estimation procedures are entirely data driven.
Dynamic conditional copula correlation and optimal hedge ratios with currency futures

This study investigates efficiency of the futures hedge implemented through the currency markets. The copula DCC EGARCH model is estimated with the bivariate error correction term to minimize variance of the currency portfolios. The estimation results for the currencies of the Australian dollar, Canadian dollar, euro, British pound and Japanese yen show that the inclusion of the external realized variance estimators into the variance equation of the estimated model improves the model's ability to account for the clustered data variance. In hedging portfolios, the information content of the realized variance estimator effectively reduces the variance of the portfolios.
Absence of Visual Input Results in the Disruption of Grid Cell Firing in the Mouse
Venue: Current Biology

Grid cells are spatially modulated neurons within the medial entorhinal cortex whose firing fields are arranged at the vertices of tessellating equilateral triangles  The exquisite periodicity of their firing has led to the suggestion that they represent a path integration signal, tracking the organism's position by integrating speed and direction of movement [2, 3, 4, 5, 6, 7, 8, 9, 10] External sensory inputs are required to reset any errors that the path integrator would inevitably accumulate. Here we probe the nature of the external sensory inputs required to sustain grid firing, by recording grid cells as mice explore familiar environments in complete darkness. The absence of visual cues results in a significant disruption of grid cell firing patterns, even when the quality of the directional information provided by head direction cells is largely preserved. Darkness alters the expression of velocity signaling within the entorhinal cortex, with changes evident in grid cell firing rate and the local field potential theta frequency. Short term <1.5 s) spike timing relationships between grid cell pairs are preserved in the dark, indicating that network patterns of excitatory and inhibitory coupling between grid cells exist independently of visual input and of spatially periodic firing. However, we find no evidence of preserved hexagonal symmetry in the spatial firing of single grid cells at comparable short timescales. Taken together, these results demonstrate that visual input is required to sustain grid cell periodicity and stability in mice and suggest that grid cells in mice cannot perform accurate path integration in the absence of reliable visual cues.
In Vivo Host Response and Degradation of Copolymer Scaffolds Functionalized with Nanodiamonds and Bone Morphogenetic Protein 2.
Venue: Advanced healthcare materials

The aim is to evaluate the effect of modifying poly[(l lactide) co (e caprolactone) scaffolds (PLCL) with nanodiamonds (nDP) or with nDP+physisorbed BMP 2 (nDP+BMP 2) on in vivo host tissue response and degradation. The scaffolds are implanted subcutaneously in Balb/c mice and retrieved after 1, 8, and 27 weeks. Molecular weight analysis shows that modified scaffolds degrade faster than the unmodified. Gene analysis at week 1 shows highest expression of proinflammatory markers around nDP scaffolds; although the presence of inflammatory cells and foreign body giant cells is more prominent around the PLCL. Tissue regeneration markers are highly expressed in the nDP+BMP 2 scaffolds at week 8. A fibrous capsule is detectable by week 8, thinnest around nDP scaffolds and at week 27 thickest around PLCL scaffolds. mRNA levels of ALP, COL1a2, and ANGPT1 are significantly upregulating in the nDP+BMP 2 scaffolds at week 1 with ectopic bone seen at week 8. Even when almost 90% of the scaffold is degraded at week 27, nDP are observable at implantation areas without adverse effects. In conclusion, modifying PLCL scaffolds with nDP does not aggravate the host response and physisorbed BMP 2 delivery attenuates inflammation while lowering the dose of BMP 2 to a relatively safe and economical level.
A novel mixed mode cohesive formulation for crack growth analysis

Modeling fracture with a cohesive zone model requires an appropriate cohesive law for correlating interfacial tractions and crack face separation, especially in mixed mode loading scenarios. Various approaches have been employed in order to develop such a law which can be characterized into potential based and non potential based formulations. A critical re examination of these methods is presented here, followed by a novel mixed mode formulation which satisfies a physical criterion for crack propagation. Specifically, as the crack propagates, the trailing current crack tip is defined through the vanishing of normal and tangential components of the interfacial tractions simultaneously. A general formulation for mixed mode conditions is proposed in this paper. In particular, given normal and tangential traction separation laws for pure normal and tangential modes as being material properties, the normal and tangential traction separation laws in mixed mode loading are formulated so that all traction components disappear at the same effective separation when the crack advances. The new mixed mode law is used to analyze three standard fracture problems in laminated composites, including double cantilever beam (DCB) end notch flexure (ENF) and mixed mode bending (MMB) A comparison with predictions from some selected mixed mode cohesive laws and experimental data available in the literature is also included to further validate the proposed mixed mode law.
Quantum nonlinear dynamics of optomechanical systems in the strong coupling regime

With an increasing coupling between light and mechanics, nonlinearities begin to play an important role in optomechanics. We solve the quantum dynamics of an optomechanical system in the multiphoton strong coupling regime retaining nonlinear terms. This is achieved by performing a Schrieffer Wolff transformation on the Hamiltonian including driving terms. The approach is valid away from the red and blue sideband drive. We show that the mechanical resonator displays self sustained oscillations in regimes where the linear model predicts instabilities and that the amplitude of these oscillations is limited by the nonlinear terms. Related oscillations of the photon number are present due to frequency mixing of the shifted mechanical and cavity frequencies. This leads to additional peaks in the cavity's spectral density. Furthermore, we show that it is possible to create phonon states with sub Poissonian statistics when the system is red detuned. This result is valid even with strong driving and with initial coherent states.
Multimode optomechanical system in the quantum regime
Venue: Proceedings of the National Academy of Sciences

Optomechanics is the field of research studying the interaction of light and mechanical motion of mesoscopic objects. Recently, the quantum mechanical character of this interaction has been of particular interest. So far, experimental research, especially in the quantum regime, has focused on canonical systems with only one optical and mechanical degree of freedom or mode respectively. In this work, we introduce a simple and robust optomechanical system featuring many, highly coherent mechanical modes. We evidence and investigate strong quantum correlations in this system, generated by the presence of this multitude of mechanical modes. This represents a key step toward multimode quantum optomechanics, which offers richer dynamics, new quantum phenomena, and a more accurate representation of real world mechanical sensors. We realize a simple and robust optomechanical system with a multitude of long lived (Q 107) mechanical modes in a phononic bandgap shielded membrane resonator. An optical mode of a compact Fabry Perot resonator detects these modes' motion with a measurement rate (96 kHz) that exceeds the mechanical decoherence rates already at moderate cryogenic temperatures (10 K) Reaching this quantum regime entails, inter alia, quantum measurement backaction exceeding thermal forces and thus strong optomechanical quantum correlations. In particular, we observe ponderomotive squeezing of the output light mediated by a multitude of mechanical resonator modes, with quantum noise suppression up to 2.4 dB 3.6 dB if corrected for detection losses) and bandwidths 90 kHz. The multimode nature of the membrane and Fabry Perot resonators will allow multimode entanglement involving electromagnetic, mechanical, and spin degrees of freedom.
Exploring the spectroscopic diversity of Type Ia supernovae with dracula: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate the automatic discovery of sub populations of SNIa; to that end we introduce the DRACULA Python package (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy) Our approach is divided in three steps: (i) Transfer Learning, which takes advantage of all available spectra (even those without an epoch estimate) as an information source, (ii) dimensionality reduction through Deep Learning and (iii) unsupervised learning (clustering) using K Means. Results match a previously suggested classification scheme, showing that the proposed method is able to grasp the main spectral features behind the definition of such subclasses. Moreover, our methodology is capable of automatically identifying a hierarchical structure of spectral features. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa sub classes, followed by 91bg like events. In this context, SNIa spectra are described by a space of 4 dimensions 1 for the time evolution of objects. We interpreted this as evidence that the progenitor system and the explosion mechanism should be described by a small number of initial physical parameters. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of subclasses (and outliers) DRACULA is publicly available within COINtoolbox (https:/github.com/COINtoolbox/DRACULA)
Assessing progression of clinical reasoning through virtual patients: An exploratory study.
Venue: Nurse education in practice

To avoid test driven learning, there have been discussions regarding the use of more formative assessments in health care education to promote students' deep learning. Feedback is important in formative assessment, but many students ignore it; therefore, interventions should be introduced which stimulate them to reflect on the new knowledge. The aim for this study was to explore if Virtual Patient (VP) based formative assessments, in connection with self evaluations, had an impact on postgraduate pediatric nursing students' development of clinical reasoning abilities. Students' self evaluations served as the basis for measuring progress. Data was analysed using deductive content analysis. The findings showed a clear progression of the clinical reasoning ability of the students. After the first assessment, the students described feelings of uncertainty and that their knowledge gaps were exposed. At the mid course assessment the awareness of improved clinical reasoning was obvious and the students were more certain of knowing how to solve the VP cases. In the final assessment, self efficacy was expressed. VP based assessments, in connection with self evaluations, early in the education resulted in a gain of students' own identification of the concept of clinical reasoning, awareness of what to focus on during clinical practice and visualised expected clinical competence.