nils-herrmann/clean_scirepeval huggingface dataset:
Controlled retting of hemp fibres: Effect of hydrothermal pre treatment and enzymatic retting on the mechanical properties of unidirectional hemp/epoxy composites

The objective of this work was to investigate the use of hydrothermal pre treatment and enzymatic retting to remove non cellulosic compounds and thus improve the mechanical properties of hemp fibre/epoxy composites. Hydrothermal pre treatment at 100 kPa and 121 degC combined with enzymatic retting produced fibres with the highest ultimate tensile strength (UTS) of 780 MPa. Compared to untreated fibres, this combined treatment exhibited a positive effect on the mechanical properties of hemp fibre/epoxy composites, resulting in high quality composites with low porosity factor (apf) of 0.08. Traditional field retting produced composites with the poorest mechanical properties and the highest apf of 0.16. Hydrothermal pretreatment at 100 kPa and subsequent enzymatic retting resulted in hemp fibre composites with the highest UTS of 325 MPa, and stiffness of 38 GPa with 50% fibre volume content, which was 31% and 41% higher, respectively, compared to field retted fibres.
Empirical Investigation of Threats to Loyalty Programs by Using Models Inspired by the Gordon Loeb Formulation of Security Investment

Loyalty program (LP) is a popular marketing activity of enterprises. As a result of firms' effort to increase customers' loyalty, point exchange or redemption services are now available worldwide. These services attract not only customers but also attackers. In pioneering research, which first focused on this LP security problem, an empirical analysis based on Japanese data is shown to see the effects of LP point liquidity on damages caused by security incidents. We revisit the empirical models in which the choice of variables is inspired by the Gordon Loeb formulation of security investment: damage, investment, vulnerability, and threat. The liquidity of LP points corresponds to the threat in the formulation and plays an important role in the empirical study because it particularly captures the feature of LP networks. However, the actual proxy used in the former study is artificial. In this paper, we reconsider the liquidity definition based on a further observation of LP security incidents. By using newly defined proxies corresponding to the threat as well as other refined proxies, we test hypotheses to derive more implications that help LP operators to manage partnerships; the implications are consistent with recent changes in the LP network. Thus we can see the impacts of security investment models include a wider range of empirical studies.
Internet of Things: Strategic research agenda evolution
Venue: CISTI 2016

The aim of European Research Cluster on the Internet of Things, established in 2007, is to address the large potential for Internet of Things based capabilities in Europe and to coordinate the convergence of ongoing activities. This organization regularly publishes Strategic Research Agendas with short, medium and long term forecasts of development and research topics. The aim of this paper is to study these agendas and make an identification of topics, in order to understand its evolution over time, that in terms of areas that have already been researched (or the research has already started) as well as those not yet explored.
Internet of Things: Strategic research agenda evolution
Venue: 2016 11th Iberian Conference on Information Systems and Technologies (CISTI)

The aim of European Research Cluster on the Internet of Things, established in 2007, is to address the large potential for Internet of Things based capabilities in Europe and to coordinate the convergence of ongoing activities. This organization regularly publishes Strategic Research Agendas with short, medium and long term forecasts of development and research topics. The aim of this paper is to study these agendas and make an identification of topics, in order to understand its evolution over time, that in terms of areas that have already been researched (or the research has already started) as well as those not yet explored.
Motivation and Improvement in Employee Performance in The Restaurant Industry in Ghana

This study examined the impact of motivation on the performance of restaurant employees within the Cape Coast Metropolis of the Central Region of Ghana. The study sought to identify motivational strategies used by the selected restaurants and determine their effectiveness in influencing employee performance. An ex post facto research survey was adopting for the study using Maslow's hierarchy of needs as the theoretical framework. Primary data were collected from 5 restaurant managers and 55 of their employees using simple random sampling to elicit data on motivational strategies enjoyed by the staff. The findings showed that motivational strategies used included the payment of bonuses, free communication, monetary rewards and the provision of free meals, recognition and rewards. Most of the employees were however dissatisfied with the motivational packages resulting in the low performance of the respondents as obtained from performance indicators on profitability, customer satisfaction and increased customer base, showing the ineffectiveness of the motivational strategies employed. Lack of effective implementation of some of the motivational strategies and inconsistency in strategic decisions were the major constraints affecting employees' performance. It was thus recommended that more motivational incentives such as supported study leave should be planned for the employees in order to help reduce absenteeism and the high labour turnover in the restaurant industry in Ghana.
Neuroprotective Activity of Epigallocatechin Gallate against Lipopolysaccharide Mediated Cytotoxicity
Venue: Journal of immunology research

Lipopolysaccharide (LPS mediated systemic inflammation plays a critical role in neurodegenerative diseases. The present study was conducted to evaluate the protective effects of epigallocatechin gallate (EGCG) the major component in green tea, on LPS mediated inflammation and neurotoxicity. LPS treatment of macrophages induced expression of proinflammatory cytokines (TNF a, IL 1b, and IL 6) However, EGCG pretreatment of macrophages significantly inhibited LPS mediated induction of these cytokines. In addition, EGCG significantly diminished LPS induced inflammatory cytokines in the peripheral mononuclear blood cells (PBMCs) Supernatant from EGCG pretreated and LPS activated macrophage cultures was found to be less cytotoxic to neurons than that from non EGCG pretreated and LPS activated macrophage cultures. Furthermore, EGCG treatment of neurons could inhibit LPS induced production of reactive oxygen species (ROS) Thus EGCG represents a potent and useful neuroprotective agent for inflammation mediated neurological disorders.
Gut microbiota community adaption during young children fecal microbiota transplantation by 16s rDNA sequencing
Venue: Neurocomputing

Fecal microbiota transplantation (FMT) is to restore the intestinal environment of a diseased individual by using of intestinal microbiota from a healthy donor. In recent years, FMT has been developed into a useful treatment method for various chronic gastrointestinal disease. There are already some works attempt to explain the mechanism of this treatment for gastrointestinal diseases in adult patients. However, much less effort has been focused on pediatric gastrointestinal disorders. In this work, we have invited 3 young children with chronic immune mediated gastrointestinal disorders treated by FMT surgery, and systematically investigated their temporal changes of fecal microbiota after transplantation by 16s rDNA sequencing technology. According to our observations, the fecal microbiota composition of these patients appears obviously interindividual variability and the fecal transplantation significantly increased the species richness in these young patients. The species abundance of Pasteurellaceae was remarkably increased during the FMT treatment in all three patients.
High temperature quantum Hall effect in finite gapped HgTe quantum wells

We report on the observation of the quantum Hall effect at high temperatures in HgTe quantum wells with a finite band gap and a thickness below and above the critical thickness {d}_{c} that separates a conventional semiconductor from a two dimensional topological insulator. At high carrier concentrations, we observe a quantized Hall conductivity up to 60 K with energy gaps between Landau levels of the order of 25 meV, in good agreement with the Landau level spectrum obtained from \mathbf{k}\ifmmode\cdot\else\textperiodcentered\fi{\mathbf{p} calculations. Using the scaling approach for the plateau plateau transition at \ensuremath{\nu}=2\ensuremath{\rightarrow}1$ we find the scaling coefficient \ensuremath{\kappa}=0.45\ifmmode\pm\else\textpm\fi{}0.04$ to be consistent with the universality of scaling theory, and we do not find signs of increased electron phonon interaction to alter the scaling even at these elevated temperatures. Comparing the high temperature limit of the quantized Hall resistance in HgTe quantum wells with a finite band gap with room temperature experiment in graphene, we find that the energy gaps at the breakdown of the quantization exceed the thermal energy by the same order of magnitude.
Radio Frequency Identification Usage in Food Traceability

Challenges and opportunities of Radio Frequency Identification (RFID) adoption for food traceability are treated and evaluated considering, in food supply chains, the different areas (production process, logistics, and distribution) and applications (process, warehouse and retail management, cold chain monitoring, logistic, intelligent packaging, and anticounterfeit) The options offered by RFID systems at different frequencies are presented, compared, and discussed, considering the difficulties and constraints which limit the large scale adoption in the food sector. Aspects which should be carefully evaluated when designing traceability frameworks considering traceable unit size, reading system configuration in static and dynamic conditions, orientation, tag size, and tag to tag collision problem are covered. Examples of RFID implementations in a particular food supply chain, such as meat, cheese, fish, food and vegetables, and plants are reported.
The effect of bariatric surgery on gastrointestinal and pancreatic peptide hormones
Venue: Peptides

Bariatric surgery for obesity has proved to be an extremely effective method of promoting long term weight reduction with additional beneficial metabolic effects, such as improved glucose tolerance and remission of type 2 diabetes. A range of bariatric procedures are in common use, including gastric banding, sleeve gastrectomy and the Roux en Y gastric bypass. Although the mechanisms underlying the efficacy of bariatric surgery are unclear, gastrointestinal and pancreatic peptides are thought to play an important role. The aim of this review is to summarise the effects of different bariatric surgery procedures upon gastrointestinal and pancreatic peptides, including ghrelin, gastrin, cholecystokinin (CCK) glucose dependent insulinotropic hormone (GIP) glucagon like peptide 1 (GLP 1) peptide YY (PYY) oxyntomodulin, insulin, glucagon and somatostatin.
Structural Insight into Recognition of Plant Peptide Hormones by Receptors.
Venue: Molecular plant

Secreted signaling peptides or peptide hormones play crucial roles in plant growth and development through coordination of cell cell communication. Perception of peptide hormones in plants generally relies on membrane localized receptor kinases (RKs) Progress has recently been made in structural elucidation of interactions between posttranslationally modified peptide hormones and RKs. The structural studies suggest conserved receptor binding and activation mechanisms of this type of peptide hormones involving their conserved C termini. Here, we review these structural data and discuss how the conserved mechanisms can be used to match peptide RK pairs.
Land cover monitoring using time series hyperspectral data via fractional order darwinian particle swarm optimization segmentation
Venue: 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

This paper presents a new method for unsupervised detection of multiple changes using time serires hyperspectral data. The proposed method is based on fractional order Darwinian particle swarm optimization (FODPSO) segmentation. The proposed method is applied to monitor land cover changes following the Fukushima Daiichi nuclear disaster using multitemporal Hyperion images. Experimental results indicate that the integration of segmentation and a time series of hyperspectral images has great potential for unsupervised detection of multiple changes.
LncRNAs expression profiling in normal ovary, benign ovarian cyst and malignant epithelial ovarian cancer
Venue: Scientific reports

Long noncoding RNA (lncRNA) has been recognized as a regulator of gene expression, and the dysregulation of lncRNAs is involved in the progression of many types of cancer, including epithelial ovarian cancer (EOC) To explore the potential roles of lncRNAs in EOC, we performed lncRNA and mRNA microarray profiling in malignant EOC, benign ovarian cyst and healthy control tissues. In this study, 663 transcripts of lncRNAs were found to be differentially expressed in malignant EOC compared with benign and normal control tissues. We also selected 18 altered lncRNAs to confirm the validity of the microarray analysis using quantitative real time PCR (qPCR) Pathway and Gene Ontology (GO) analyses demonstrated that these altered transcripts were involved in multiple biological processes, especially the cell cycle. Furthermore, Series Test of Cluster (STC) and lncRNA mRNA co expression network analyses were conducted to predict lncRNA expression trends and the potential target genes of lncRNAs. We also determined that two antisense lncRNAs (RP11 597D13.9 and ADAMTS9 AS1) were associated with their nearby coding genes (FAM198B, ADAMTS9) which participated in cancer progression. This study offers helpful information to understand the initiation and development mechanisms of EOC.
Using Classifiers for Software Defect Detection

Classifier models have successfully been applied to the task of detecting and predicting defects in software projects. These models work by learning patterns between attributes of the software and a corresponding binary label that indicates whether or not a defect exists. In this study, we evaluate the performance of five different classifiers in the context of software defect detection naive Bayes, neural networks, support vector machines, logistic regression, and k nearest neighbor. We measure performance using both accuracy and F1 score, and use ANOVA to test for significance. Each classifier was run on five datasets from NASA's metrics data program repository. Results show that all models can detect software defects using static software features with a relatively high degree of certainty, although naive Bayes and support vector machines were outperformed by the remaining algorithms for some datasets.
Assessment of software defect detection efficiency and cost through an intelligent collaborative virtual environment
Venue: IEEE Latin America Transactions

The use of collaborative virtual environments facilitates communication, coordination and cooperation within a group of people. In the field of information and communications technology (ICT) collaborative virtual environments have been developed to support the learning of programming. In this work, we report an empirical study (controlled experiment) which assess the efficiency and cost of detecting defects in instrumented programs with and without the support of an intelligent collaborative virtual environment (ICVE) The ICVE was used as part of a programming course at undergraduate level. The results suggest an equivalent efficiency in defect detection for both types of participants, those who used the ICVE (3.76 observed defects per hour) and those who worked in a traditional manner (4.07 observed defects per hour) With regard to cost, it was observed that participants who used the ICVE completed the activities within a slightly shorter time frame (49.25 minutes) than those who worked without the environment (52.90 minutes) however, the difference was not significant. It was observed that with regard to efficiency and cost, for this type of task (the detection of defects) working virtually through an ICVE versus working ad hoc (in a tradition manner, at the same time and in the same place) yields equivalent results. When collaborative work cannot be done in a traditional way, the use an ICVE is an alternative approach equally effective.
Discrete Choice Methods with Simulation

The book is published by Cambridge University Press and is also available for download on the author's homepage for private use. The second edition corrects some errors and contains two additional chapters, one on endogenous regressors and one on the expectation maximization (EM) algorithm. As the title suggests, the book has two main topics. One concerns models of discrete (multinomial) choices that deal with the decision between a finite set of mutually exclusive alternatives such as the choice between brands of consumer goods, travel modes, or college majors. These models are important for fields such as marketing, transportation, environmental economics, education, labor, and industrial organization. The second major subject of the book is a simulation based estimation with a focus on maximum simulated likelihood. These two topics are very compatible since multinomial choice models quite naturally lead to likelihood functions and other objective functions that are analytically infeasible to evaluate, requiring approximation methods such as a Monte Carlo simulation. As a consequence, much of the methodological work on simulation based estimation was inspired by discrete choice models and much of the more recent empirical work studying discrete choices uses simulation based estimation.
MLlib: Machine Learning in Apache Spark
Venue: J. Mach. Learn. Res.

Apache Spark is a popular open source platform for large scale data processing that is well suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high level API that leverages Spark's rich ecosystem to simplify the development of end to end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.
Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming
Venue: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Streaming data processing has been gaining attention due to its application into a wide range of scenarios. To serve the booming demands of streaming data processing, many computation engines have been developed. However, there is still a lack of real world benchmarks that would be helpful when choosing the most appropriate platform for serving real time streaming needs. In order to address this problem, we developed a streaming benchmark for three representative computation engines: Flink, Storm and Spark Streaming. Instead of testing speed of light event processing, we construct a full data pipeline using Kafka and Redis in order to more closely mimic the real world production scenarios. Based on our experiments, we provide a performance comparison of the three data engines in terms of 99th percentile latency and throughput for various configurations.
Superregular matrices and applications to convolutional codes
Venue: ArXiv

The main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matriz is superregular if all of its minors that are not trivially zero are nonzero. Given a a times b, a larger than or equal to b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a b+1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.
Convolutional Codes With Maximum Column Sum Rank for Network Streaming
Venue: IEEE Transactions on Information Theory

The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank that parallels the column Hamming distance when streaming over a network with link failures. We prove the rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction involves finding a class of super regular matrices that preserve this property after multiplication with non singular block diagonal matrices in the ground field.
A New Perspective of Cyclicity in Convolutional Codes
Venue: IEEE Transactions on Information Theory

In this paper, we propose a new way of providing cyclic structures to convolutional codes. We define the skew cyclic convolutional codes as left ideals of a quotient ring of a suitable non commutative polynomial ring. In contrast to the previous approaches to cyclicity for convolutional codes, we use Ore polynomials with coefficients in a field (the rational function field over a finite field) so their arithmetic is very well known and we may proceed similarly to cyclic block codes. In particular, we show how to obtain easily skew cyclic convolutional codes of a given dimension, and we compute an idempotent generator of the code and its dual.
The Role of Oil Prices and Monetary Policy in the Norwegian Economy Since the 1980s

We use a TVP VAR model to investigate possible changes in the time series properties of key Norwegian macroeconomic variables since the 1980s. The sample period is characterised by deregulation, globalization, sizable petroleum revenues, a switch from exchange rate to infl?ation targeting and adoption of a policy rule for the use of petroleum revenues.We fi?nd that the long run means of CPI and core in?flation rates declined signifi?cantly until the mid 1990s and have since then remained close to the ifln?ation target of 2.5% from 2001 onwards. The persistence in especially CPI infl?ation has fallen during the infl?ation targeting period while the volatility of both infl?ation rates and the nominal effective exchange rate has increased. We document an increase in the correlations between money market rates and the in?flation rates as well as the output gap during the in?flation targeting period and a steady decline towards zero in the correlations between money market rates and nominal exchange rate changes. There is evidence of an increase in the correlations between oil prices and the other macroeconomic variables over time. Our counterfactual analysis suggests oil shocks to have been important for output gap and in?flation volatility while monetary policy shocks have been important for driving infl?ation persistence and the correlation of money market rates with macroeconomic variables.