nils-herrmann/clean_scirepeval huggingface dataset:
Effect of acetylation on the physico chemical properties of Indian Horse Chestnut (Aesculus indica L. starch
Year: 2015

The present work was carried out to study the effect of acetylation (0, 3, 6, and 9 g vinyl acetate/gram starch) on the physico chemical properties of starch, isolated from Indian horse chestnut (Aesculus indica) Work on starch acetylation and characterization from Indian horse chestnut has not been reported so far in the literature. Moisture, protein, fat, and ash content of native starch was found as 5.97% 0.27% 0.22% and 0.01% respectively. Acetyl content, swelling index, solubility index, and water absorption capacity of acetylated starch increased with increase in acetyl whereas, pasting temperature, syneresis, and amylose content reduced with acetylation. Granule morphology under the scanning electron microscopy revealed that granules were round, oval, irregular, or elliptical with smooth surfaces.
Association of early inflammatory parameters after subarachnoid hemorrhage with functional outcome: A prospective cohort study
Venue: Clinical Neurology and Neurosurgery
Year: 2015

Early brain injury after aneurysmal subarachnoid hemorrhage (aSAH) comprises a pronounced neuroinflammatory reaction. Nevertheless, its relevance for functional outcome and its role as outcome predictor remains uncertain. We evaluated the relationship of various early inflammatory parameters regarding functional outcome according to the modified Rankin Scale score (mRS) at discharge (primary objective) and six months after aSAH.  A total of 81 patients (63% female) with a mean age of 53.8 13.2 years were included.  At admission clinical data and various inflammatory parameters in serum and wherever applicable cerebrospinal fluid (CSF) of patients after aSAH were assessed. Outcome was evaluated according to dichotomized mRS at discharge and six months after aSAH (unfavorable outcome: mRS 3 6) Univariate and thereafter multivariate logistic regression analyses were performed using SAS 9.2.  Elevated levels of interleukin 6 (IL 6) and leukemia inhibitory factor (LIF) in serum and CSF were related to unfavorable outcome at discharge (p<0.05; univariate analyses) IL 6 remains the only parameter relevant for outcome applying a multivariate model including the relevant baseline characteristics. Six months after aSAH no significant correlation was found regarding the outcome, most likely due to the high drop out rate (27% A pronounced rise of LIF serum and CSF levels after aSAH was observed.  Higher early IL 6 serum levels after aSAH are associated with poor outcome at discharge. In addition, involvement of LIF in the early inflammatory reaction after aSAH has been demonstrated.
Strategies for increasing coarse particle flotation in conventional flotation cells.
Year: 2013

If the particle size that could be effectively recovered in a flotation cell could be increased, the product size from grinding could be significantly coarsened, resulting in a more eco efficient flowsheet. A number of strategies that could potentially increase coarse particle flotation recovery are investigated in this work by performing tests using a pilot scale 3m Metso RCS flotation cell operated using a copper ore. Turbulence was manipulated by changing the impeller speed, impeller size, feed pulp density and cell aspect ratio. Tests were performed at different froth depth to enable the effect of the froth and the impact of the turbulence on the froth phase to be evaluated independently of effects in the pulp. This work shows that coarse particle recovery is extremely sensitive to froth phase effects with recovery optimal at shallow froth depth and when turbulence at the pulp froth interface is minimised.
FUN LDA: A Latent Dirichlet Allocation Model for Predicting Tissue Specific Functional Effects of Noncoding Variation: Methods and Applications.
Venue: American journal of human genetics
Year: 2018

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell type and/or tissue specific way (FUN LDA) Using this unsupervised approach, we predict tissue specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome wide association studies and (2) estimating a tissue based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN LDA with state of the art functional annotation methods and show that FUN LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue and cell type specific functional prediction methods tend to have substantially better prediction accuracy than organism level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources)
Impact of Thyristor Controlled Series Capacitor on Voltage Profile of Transmission Lines using PSAT
Year: 2018

In power system voltage stability is very important in order to maintain the voltage within the defined limits. The demand of electrical power increases in the last decade due to the lack of expansion in the generation and transmission network. The available transmission network is heavily loaded. This loading of transmission network cause the voltage instability. This instability on heavy loaded system creates voltage collapse which causes the power loss. Due to this phenomenon it is necessary to keep monitoring the voltage instability and to reduce voltage collapse. Voltage collapse mostly occurs due to the minimum availability of the reactive power. A power electronic device i.e. Flexible AC Transmission System (FACTS) is used to add the reactive power to the system which improve the voltage profile and minimizes the chances due to which the voltage collapse occur. In this paper FACTS series compensator, i.e. Thyristor Controlled Series Capacitors (TCSC) is injected between the two nodes of the IEEE 6 bus bar test system to check the voltage profile. PSAT (Power System Analysis Tool) tool, which is a new tool in MATLAB for study in power system analysis is used. IEEE 6 bus system is used as a test system for the effectiveness of the proposed method. The voltage profile with and without TCSC device is then compared to conclude the result.
Effectiveness of the Sport Education Fitness Model on Fitness Levels, Knowledge, and Physical Activity
Year: 2015

The purpose of this study was to investigate changes in fitness levels, content knowledge, physical activity levels, and participants' perceptions following the implementation of the sport education fitness model (SEFM) at a high school. Thirty two high school students participated in 20 lessons using the SEFM. Aerobic capacity, muscular strength/endurance, flexibility, and health related fitness knowledge were measured pre and post SEFM. Physical activity levels during each SEFM lesson were measured using Actigraph GT3X triaxial accelerometers. After completion of posttesting, focus groups were conducted to elicit students' perceptions of SEFM. Paired sample t tests were used to compare pre and posttest scores. Significant increases between pre and posttest scores were found for aerobic capacity, t(31) a^ 3.968, p .01; the number of push ups performed on push up test, t(31) a^ 3.329, p .01; and health related fitness knowledge, t(31) a^ 6.355, p .01. Students achieved 60.47% moderate to vigorous physical activity in the SEFM lessons. Students were positive toward the SEFM and provided many suggestions on improving the SEFM. In conclusion, the SEFM was effective in improving fitness levels for aerobic capacity, the number of push ups performed, health related fitness knowledge, and meeting at least 50% moderate to vigorous physical activity levels in SEFM lessons. Based on analysis of qualitative data, the SEFM is an instructional model that can meet the needs of students' diverse learning styles.
Terahertz imaging using quantum cascade lasers: a review of systems and applications
Year: 2014

The terahertz (THz) frequency quantum cascade laser (QCL) is a compact source of THz radiation offering high power, high spectral purity and moderate tunability. As such, these sources are particularly suited to the application of THz frequency imaging across a range of disciplines, and have motivated significant research interest in this area over the past decade. In this paper we review the technological approaches to THz QCL based imaging and the key advancements within this field. We discuss in detail a number of imaging approaches targeted to application areas including multiple frequency transmission and diffuse reflection imaging for the spectral mapping of targets; as well as coherent approaches based on the self mixing phenomenon in THz QCLs for long range imaging, three dimensional imaging, materials analysis, and high resolution inverse synthetic aperture radar imaging.
Visual attention in deep learning: a review
Venue: ICRA 2018
Year: 2018

When facing a complex visual scene, human can efficiently locate region of interest and analyze the scene by selectively processing subsets of visual input. Attention was employed to narrow down the search and speed up the process.1 Visual attention is a hot topic in computer vision, neuroscience and deep learning area. It's widely used in object segmentation, object recognition, image caption generation2,3 and visual question answering (VQA) etc.4 In last few years, deep learning has been growing rapidly. Many Convolutional neural networks and recurrent neural networks have achieved much better performance in various computer vision and natural language processing tasks, compared to previous traditional methods. The visual attention models are mainly categorized into Bottom up models and top down models.
Deep Learning for Military Image Captioning
Venue: 2018 21st International Conference on Information Fusion (FUSION)
Year: 2018

US Department of Defense (DoD) big data is extensively multimodal and multiple intelligence (multi INT) where structured sensor and unstructured audio, video and textual ISR (Intelligence, Surveillance, and Reconnaissance) data are generated by numerous air, ground, and space borne sensors along with human intelligence. Data fusion at all levels "remains a challenging task." While there are algorithmic stove piped systems that work well on individual modalities, there is no system to date that is mission and source agnostics and can seamlessly integrate and correlate multi INT data that includes textual, hyperspectral, and video content. The considerable volume and velocity aspects of big data only compound the aforementioned encountered in fusion. We have developed the concept of "deep fusion"1based on deep learning models adapted to process multiple modalities of big data. Rather than reducing each modality independently and fusing at a higher level model (feature level fusion) the deep fusion approach generates a set of multimodal features, thereby maintaining the core properties of the dissimilar signals and resulting in fused models of higher accuracy. We have initiated two deep fusion experiments one is to automatically generate the caption of an image to help analysts tagging and captioning large volumes of images gathered from collection platforms, and the other is an audio visual speech classification with potential applications to lip reading and enhanced object tracking. This paper presents the proof of concept demonstration for caption generation. The generative model is based on a deep recurrent architecture combined with the pre trained image to vector model Inception V3 via a Convolutional Neural Network (CNN) and the word to vectors model word2vec via a skip gram model. We make use of the Flickr8K dataset extended with some military specific images to make the demonstration more relevant to the DoD domain. The detailed results from the image captioning experiment is presented here. The captions are generated from test image are subjectively evaluated and the BLEU (bilingual evaluation understudy) scores are compared and found substantial improvements.
HYBRID MODELING APPROACH TO COMPETITIVENESS THROUGH FAST STRATEGY
Year: 2013

Understanding the sources of sustained competitive advantage is the fundamental task of strategic management research. An interesting concept of fast strategy has emerged, which builds a firm's competitiveness from its ability to react to change more rapidly than its competitors. We approach this concept with a simulation model based on a hybrid modeling technique. The simulation model is used to test the profitability of the fast strategy concept in different business environment conditions. The theoretical finding of our study supports the fast strategy concept and shows that there are some internal and external factors that control the value of agility. The methodological finding of this work is that the hybrid modeling technique offers an interesting platform for understanding the effects of environmental dynamics in strategic and technology management research.
HIV/AIDS stigma and discrimination among nurses in Suriname
Year: 2007

The more civilized a society becomes the more subtle the stigma and discrimination gets. Suriname is a country known for its folklore, hospitality, and social control. In the last decennia a new trend of cultural diffusion has led to greater recognition of formerly classed deviant practices of behavior. Under the influence of this kind of exposure our norms, values, and beliefs now cater for modern day perspectives of liberal thinking that encompass acceptance of one another on the basis of perceived differences of culture and appearance. Yet still, stigma and discrimination play a part in our day to day experiences of religion, poverty, independence, freedom of choice, social strata, disease prevention and even political ferment.
A Study on Business Contract Translation from the Perspective of Three Dimensional Transformation of Eco Translatology
Year: 2017

As the deepening of economic globalization and implementation of China's "one road, one belt" policy, the transaction between Chinese and foreign companies is increasingly stepping up, which requires a much higher quality of the translation of the most important legal text in transaction business contract. To achieve this goal, this paper has selected the new and advanced approach in translation three dimensional transformation of Eco Translatology to analyze the problems in business contracts and give suggestions correspondingly. Having been categorized into three dimensions, the problems mainly lie in the vocabulary and grammar errors from linguistic dimension, the different thinking modes and idiomatic expressions from the cultural dimension, and the misunderstanding of the original texts from the communicative dimension. Finally, the paper suggests that translators should select the words based on their connotations and sentence structures properly, adapt themselves to the transformation of different cultures, and strengthen their translating abilities to resolve the problems, so as to better meet the needs of this new era.
Effectiveness of e learning portal from students' perspective
Venue: Interact. Technol. Smart Educ.
Year: 2019

E learning has become an increasingly prevalent learning approach in higher educational institutions due to the fast growth of internet technologies in India. This paper aims to mainly focus on evaluating the effectiveness of the e learning experience from students' perceptive. Survey" method has been used to collect the data with the help of a structured questionnaire from the students who have registered on COURSERA (www.coursera.org/ website for e learning. The questionnaire consisted of two sections e learning system and e learning effectiveness. E learning system included items related to system quality, information quality and service quality. E learning effectiveness dimension included user satisfaction and net benefits. The items in this section were measured on a five point Likert scale ranging from strongly disagrees to strongly agree. The data collected have been analyzed using the SPSS version 17.0 and AMOS version 21.0.  Results show that system quality and service quality contribute more in e learning system compared to information quality. Students perception may be that information available on the website may not be very useful as it's a one way mode of communication. The researcher also found that the three dimensions (system quality, service quality and information quality) of e learning system contributes to user satisfaction and net benefits. Students are satisfied with e learning websites and intent to continue to use it in future as well. They also found it beneficial as it helps them in career growth and making them employable.  This paper proposed a second order model of e learning system and a second order model e learning effectiveness. E learning system has been defined by three first order constructs: a system quality, service quality and information quality. E learning effectiveness has been defined by two first order constructs: a user satisfaction and net benefits. The predictability of the proposed model is high to explain the impact of e learning system on e learning effectiveness.
Internet of things forensics: Challenges and Case Study
Venue: IFIP Int. Conf. Digital Forensics
Year: 2018

Today is the era of Internet of Things (IoT) millions of machines such as cars, smoke detectors, watches, glasses, webcams, etc. are being connected to the Internet. The number of machines that possess the ability of remote access to monitor and collect data is continuously increasing. This development makes, on one hand, the human life more comfort able, convenient, but it also raises on other hand issues on security and privacy. However, this development also raises challenges for the digital investigator when IoT devices involve in criminal scenes. Indeed, current research in the literature focuses on security and privacy for IoT environments rather than methods or techniques of forensic acquisition and analysis for IoT devices. Therefore, in this paper, we discuss firstly different aspects related to IoT forensics and then focus on the cur rent challenges. We also describe forensic approaches for a IoT device smartwatch as a case study. We analyze forensic artifacts retrieved from smartwatch devices and discuss on evidence found aligned with challenges in IoT forensics
Early commitment on financial aid and college decision making of poor students: Evidence from a randomized evaluation in rural China
Year: 2011

Many educational systems have struggled with the question about how best to give out financial aid. In particular, if students do not know the amount of financial aid that they can receive before they make a decision about where to go to college and what major to study, it may distort their decision. This study utilizes an experiment (implemented by the authors as a Randomized Control Trial) to analyze whether or not an alternative way of providing financial aid by providing an early commitment on financial aid during the student's senior year of high school instead of after entering college affects the college decision making of poor students in rural China. We find that if early commitments are made early enough; and they are large enough, students will make less distorting college decisions.
An Evolutionary Algorithm for Large Scale Sparse Multiobjective Optimization Problems
Venue: IEEE Transactions on Evolutionary Computation
Year: 2020

In the last two decades, a variety of different types of multiobjective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e. most decision variables of the optimal solutions are zero) especially when the number of decision variables is large. Such large scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large scale sparse MOPs. The experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large scale sparse MOPs.
Self report Response Bias: Learning How to Live with its Diagnosis in Chaplaincy Research
Year: 2010

Chaplaincy research is dominated by self report data collected directly from research subjects or participants. Self report response bias is the research measurement inaccuracy that originates with the respondent. A review of research published in The Journal of Pastoral Care Counseling (1998 2008) found that all but one of thirty eight research articles used self report data. Of this total, less than half acknowledged methodological limitations, and only two acknowledged the potential impact of self report response bias.This article focuses on seven categories of self report response bias that may impact chaplaincy research: social desirability, acquiescence, leniency or harshness, critical event or recency, halo effect, extreme response style and midpoint response style. Although these biases have the potential to impact self report data, the data themselves are not inherently flawed. This discussion offers recommendations for addressing self report response bias during the research process. It also s.