nils-herrmann/clean_scirepeval huggingface dataset:
Social identity mediates the positive effect of globalization on individual cooperation: Results from international experiments
Venue: PloS one
Year: 2018

Globalization is defined for individuals as their connectivity in global networks. Social identity is conceptualized as attachment and identification with a group. We measure individual involvement with global networks and local, national, and global social identity through a questionnaire. Propensity to cooperate is measured in experiments involving local and global others. Firstly, we analyze possible determinants of global social identity. Overall, attachment to global identity is significantly lower than national and local identity, but there is a significant positive correlation between global social identity and an index of individual global connectivity. Secondly, we find a significant mediating effect of global social identity between individual global connectivity and propensity to cooperate at the global level. This is consistent with a cosmopolitan hypothesis of how participation in global networks reshapes social identity: Increased participation in global networks increases global social identity and this in turn increases propensity to cooperate with others. We also show that this model receives more support than alternative models substituting either propensity to associate with others or general generosity for individual global connectivity. We further demonstrate that more globalized individuals do not reduce contributions to local accounts while increasing contributions to global accounts, but rather are overall more generous. Finally, we find that the effect of global social identity on cooperation is significantly stronger in countries at a relatively low stage of globalization, compared to more globalized countries.
Effects of 12 week lumbar stabilization exercise and sling exercise on lumbosacral region angle, lumbar muscle strength, and pain scale of patients with chronic low back pain
Venue: Journal of physical therapy science
Year: 2018

The purpose of this study is to investigate the effects of lumbar stabilization exercise and sling exercise on lumbosacral region angle, lumbar muscle strength, pain scale of patients with chronic low back pain.  The subjects of this study were 29 chronic low back pain patient women who were selected among participants in exercise class at K Region Health Promotion Center in South Korea and were randomly assigned to the lumbar stabilization exercise group (n=10) sling exercise group (n=10) and the control group (n=9) Both lumbar stabilization and sling exercise programs were executed for 60 minutes, three times a week, for 12 weeks. Before and after exercise we measured lumbosacral region angle (lumbar lordosis angle, lumbosacral angle, sacral inclination angle) lumbar muscle strength, and pain scale in all subjects. Two way analysis of variance was conducted to analyze experimental data. In order to analyze the interaction effect, we conducted paired t test before and after treatment.  Lumbar stabilization exercise group and sling exercises group did not affect lumbar lordosis angle, lumbosacral angle and sacral inclination angle. Whereas the lumbar flexion muscle strength and lumbar extension muscle strength significantly increased in the lumbar stabilization exercise group and sling exercise group. The flexibility increased in the lumbar stabilization exercise group and sling exercise group. The pain scale decreased in the lumbar stabilization exercise group and sling exercise group.  Both lumbar stabilization exercise and sling exercises are useful therapeutic approaches to chronic back pain.
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Year: 2020

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Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations
Venue: BMC Bioinformatics
Year: 2006

BackgroundMicroarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non independence between measured expression levels.ResultsAn analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe probeset transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur.ConclusionMultiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data.
Sick leave diagnoses and return to work: a Swedish register study
Venue: Disability and rehabilitation
Year: 2015

To provide a detailed description of return to work (RTW) for different diagnoses.  A sample of 617 611 cases of sick leave with minimum duration of 14 days started in 2009 and 2010 was followed for 450 days. Data on sick leave episodes, diagnosis (ICD 10 codes on 3 digit level) and background factors were retrieved from Swedish National Social Insurance registers. RTW was analysed with Cox proportional hazard regression with separate analyses for women and men.  The diseases with the lowest RTW rates were present within malign neoplasms, severe mental disorders and severe cardiovascular diseases. High RTW rates were present for infectious respiratory diseases, viral infections and less severe cardiovascular diseases. There were distinct differences across diagnoses within all diagnosis chapters. This also holds for mental disorders and musculoskeletal diseases, which are the most common causes of sick leave. The results for women and men across different diseases and disorders were strikingly similar.  Systematic use of standard information such as detailed sick listing diagnosis could, at low additional cost, pinpoint cases at risk for prolonged sick leave. The use of broad diagnosis categories in sick leave research may be questioned since there are distinct differences in RTW within ICD 10 chapters.  Case managers' systematic use of information on sick leave diagnosis could, at low additional cost, pinpoint cases at risk for prolonged sick leave. Low return to work rates (RTW) are, in general, found among neoplasms, cardiovascular diseases and mental disorders. Alongside medical rehabilitation measures, vocational rehabilitation measures should be considered at early stages in the rehabilitation process by case managers, in collaboration with medical service providers, and employers or the employment service. In sick leave research, in general, and RTW research, in particular, the application of broad ranging categories of diagnoses has to be reflected on, since there are distinct differences in RTW across diagnoses within the same diagnosis chapter.
Fecal specimen diagnosis 2019 novel coronavirus infected pneumonia
Venue: Journal of medical virology
Year: 2020

The emergence and spread of 2019 novel coronavirus infected pneumonia (COVID 19) from Wuhan, China, it has spread globally. We extracted the data on 14 patients with laboratory confirmed COVID 19 from Jinhua Municipal Central hospital through 27 January 2020. We found that compared to pharyngeal swab specimens, nucleic acid detection of COVID 19 in fecal specimens was equally accurate. And we found that patients with a positive stool test did not experience gastrointestinal symptoms and had nothing to do with the severity of the lung infection. These results may help to understand the clinical diagnosis and the changes in clinical parameters of COVID 19.
A Spitzer survey of Deep Drilling Fields to be targeted by the Vera C. Rubin Observatory Legacy Survey of Space and Time
Year: 2020

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe several Deep Drilling Fields (DDFs) to a greater depth and with a more rapid cadence than the main survey. In this paper, we describe the `DeepDrill' survey, which used the Spitzer Space Telescope Infrared Array Camera (IRAC) to observe three of the four currently defined DDFs in two bands, centered on 3.6 \mu$m and 4.5 \mu$m. These observations expand the area which was covered by an earlier set of observations in these three fields by the Spitzer Extragalactic Representative Volume Survey (SERVS) The combined DeepDrill and SERVS data cover the footprints of the LSST DDFs in the Extended Chandra Deep Field South field (ECDFS) the ELAIS S1 field (ES1) and the XMM Large Scale Structure Survey field (XMM LSS) The observations reach an approximate $5\sigma$ point source depth of 2 \mu$Jy (corresponding to an AB magnitude of 23.1; sufficient to detect a 10${11} M_{\odot} galaxy out to $z\approx 5$ in each of the two bands over a total area of \approx 29\$deg$^2$ The dual band catalogues contain a total of 2.35 million sources. In this paper we describe the observations and data products from the survey, and an overview of the properties of galaxies in the survey. We compare the source counts to predictions from the SHARK semi analytic model of galaxy formation. We also identify a population of sources with extremely red [3.6] [4.5] >1.2$ colours which we show mostly consists of highly obscured active galactic nuclei.
Internal constituent variability and semantic transparency in N Prep N constructions in Romance languages
Year: 2020

Constructions of the type N Prep N represent one of the most controversial issues in Romance word formation. In particular, their lexical status and their degree of productivity are still crucial points of discussion. Hence, it remains unclear whether these constructions fall within the category of morphological word formation or of syntax. Furthermore, the possibilities for internal prepositional variation remain uncertain. This article takes a constructionist approach within the framework of construction morphology in order to describe the internal constituent variability and transparency of the prepositional element in N Prep N constructions in Spanish, Portuguese, and French, as in Sp. juego de ninos, juego para ninos 'kid's game' or in Sp. cabana de arbol and cabana en arbol 'tree house' A qualitative analysis of large scale corpus data from the TenTen corpus family indicates that Romance N Prep N constructions may undergo internal prepositional variation. The analysis focuses on the semantic relations of the internal nominal constituents and the semantic transparency of the constructions in the three Romance languages under investigation. The results indicate that semantic relations and semantic transparency play a role in the internal constituent variability of the prepositional element.
The impact of ICT on coronavirus crisis management case study: National center for security and crises management in Jordan
Year: 2020

This research identifies the impact of the ICT dimensions (readiness of ICT infrastructure, human resource skills and knowledge, and financial capabilities) in managing the coronavirus crisis A quantitative Likert type questionnaire was administered to all workers in the upper and middle administrative levels who are involved in coronavirus crisis management at the National Center for Security and Crises Management in Jordan The researchers adopted the descriptive analytical approach using SPSS version 19 for data analysis The research results show a presence of a statistically significant effect of ICT in the management of the coronavirus crisis in addition to other interesting results about areas that need improvements and attention in crises management centers Furthermore, the research suggested a number of recommendations that all crisis management centers can benefit from (c) 2005 ongoing JATIT LLS
Optimized graphene transfer: Influence of polymethylmethacrylate (PMMA) layer concentration and baking time on graphene final performance
Year: 2015

The influence of transfer parameters on the final structure, morphology and electrical properties of graphene were investigated in this work. Optical microscopy and atomic force microscopy (AFM) images showed that a double layer of PMMA can enhance or degrade graphene quality depending on its concentration. When properly diluted (15% in anisole, resulting in a PMMA layer of 1.35% the transfer technique using double layer PMMA produces high quality graphene with fewer PMMA residues, non cracked surface and sheet resistance around 247 ohm/square. We also investigated the influence of different baking times and temperature, and observed that the increase in baking time can degrade graphene quality thus leaving higher amounts of PMMA residues. Several works regarding graphene transfer are reported in the literature, but PMMA based transfer processes still present challenges in yielding a clean and high quality graphene.
Immune modulation by mesenchymal stem cells.
Venue: Experimental cell research
Year: 2006

Mesenchymal stem cells (MSCs) have been shown to suppress activation of T cells both in vivo and in vitro. In vivo, this may be a way for the body to maintain homeostasis and inhibit immune activation in distinct compartments, such as the bone marrow and the interface between mother and fetus. MSCs modulate the immune function of the major cell populations involved in alloantigen recognition and elimination, including antigen presenting cells, T cells, and natural killer cells. The molecular mechanism that mediates the immunosuppressive effect of MSCs is not completely understood.
On the Integration of IT System with the Budgetary Control System: Insights from the Case of Wanhua Chemical
Venue: Wirel. Pers. Commun.
Year: 2018

With the advances of IT technology, all businesses are disrupted by digitalization and have to adapt and transform. The role of IT has gone beyond computing. The capability of Information management is critical to the organizational strategy. At the same time, the finance function is undergoing massive transformation, with the IT function building systems and solutions for the new environment. Budget, as one of the key management tool of the finance function, has been pushed by digitalization to provide real time information and enhance decision making and value to the business. Based on a case study, the authors discuss the integration of the IT system with the budgetary control system in support of the strategy of the company, and a dynamic model of the process was built. The underlying mechanism linking IT to strategy is also discussed. In this way, the authors prove that when properly integrated with budget of the finance function, IT can move from computation technology to become capabilities that create value for business.
Effectiveness of meropenem for the treatment of aspiration pneumonia in elderly patients.
Venue: Internal medicine
Year: 2009

In Japan, an increase in the elderly population is associated with an increased incidence of aspiration pneumonia. Treatment guidelines for aspiration pneumonia recommend the use of antibiotics effective against anaerobic bacteria, such as carbapenems. However, the role of anaerobic bacteria in aspiration pneumonia and the clinical efficacy of meropenem in elderly aspiration pneumonia patients have only begun to be investigated.  A prospective study of 62 elderly hospitalized patients with aspiration pneumonia (34 males, 28 females; mean age 86.6 years) was conducted. The causative organisms of aspiration pneumonia, including anaerobic bacteria, were investigated using fiberoptic bronchoscopy. In addition, the efficacy and safety of intravenous meropenem for this treatment of this condition were evaluated.  When disease severity was classified according to the Japanese Respiratory Society (JRS) guidelines, 80.7% of the cases in this study were graded as "most severe" The overall detection rate of bacteria was 87.1% (monomicrobial, 32.3% polymicrobial, 54.8% Of the 111 pathogens detected (14 anaerobic pathogens remained unidentified) anaerobic bacteria accounted for 19.8% and gram negative enteric bacilli made up 19.8% The overall clinical efficacy rate of meropenem therapy (1.0 g/day) was 61.3% The mortality rate was 9.7% and anaerobic bacteria coexisted with aerobic bacteria in 66.7% of the patients who died.  The use of antibiotics effective against anaerobic bacteria may be necessary for patients with potentially fatal aspiration pneumonia. Meropenem therapy for aspiration pneumonia is clinically effective and tolerable in elderly patients.
Double Coated VGG16 Architecture: An Enhanced Approach for Genre Classification of Spectrographic Representation of Musical Pieces
Venue: 2019 22nd International Conference on Computer and Information Technology (ICCIT)
Year: 2019

In the field of Music Information Retrieval(MIR) one of the most demanding task would be music genre classification. The search for automated categorization of music on the basis of genre yielded some diverse techniques, prominent ones of them rely on machine learning, and recently deep learning. In this paper we approached this very problem with Convolutional Neural Network(CNN) which has already been proven very useful in categorizing images in the past few years. In this work, some of the popular CNN architectures including ResNet, VGG etc. are experimented with, of which a version of the latter the VGG16 has outperformed the rest by a handsome degree. On top of the vanilla VGG16 architecture, we coupled that with two layers of additional dense layers tuned for this specific task. We used audio clips of each genre, in this case ten genres, and got the spectrogram images of them and fed them into the network. In this approach we were able to achieve 84% accuracy, thus providing a promising stance in the genre classification problem.
A Multimodal Fusion Approach By Exploiting Concept Interactions for Efficient Multimedia Analysis
Year: 2012

Multimedia data intrinsically contains multimodal information in it. In order to obtain a successful multimedia analysis, all available information should be utilized by following a multimodal approach. In addition, the interaction between concepts is another crucial source of information and helps to increase the fusion performance. The focus of this study is to show that fusing all available modalities along with the concept interactions can yield better results. The experiments conducted on TRECVID 2007 dataset validated the superiority of such combination over best single modality and alternative modality combinations. Combining all available modalities performs 10.5% relatively better than the best single modality in overall. Additionally, utilizing from other concept information provides a 5.6% relative performance gain over the multimodal fusion results.
Software Requirements Classification and Prioritisation Using Machine Learning
Year: 2020

Software Development Lifecycle (SDLC) is a systematic process used to achieve high quality software that meets customer requirements. During SDLC requirements, engineering plays an important role. Prioritisation helps to focus on the most important requirements in terms of importance, cost, penalty, time and risk. Stakeholders (users, developers) of the software product identify requirements. The two major activities of requirement engineering process are requirements classification and requirements prioritisation. Sometimes requirement mentioned by stakeholder can be of both types, i.e. functional and non functional. So it is challenging to classify requirements separately in two different categories. There are many fundamental prioritisation techniques available to prioritise software requirements. In this paper, we have compared existing requirements prioritisation techniques based on ease of use, speed, scalability and accuracy. Our literature study suggests that the appropriate requirements prioritisation technique has to be selected that can help software developer to minimise the risk, penalty. In automating various tasks of software engineering, machine learning (ML) has shown useful positive impact. This paper discusses the various algorithms used to classify and prioritise the software requirements. The results in terms of performance, scalability and accuracy from different studies are contradictory in nature due to variations in research methodologies and the type of dataset used. Based on the literature survey conducted, we propose a new architecture that will use both types of datasets, i.e. Software Requirement Specifications (SRS) and user text reviews to create a generalised model. Our proposed architecture will attempt to extract features which can be used to train the model using ML algorithms. The ML algorithms for classifying and prioritising software requirements will be developed and assessed based on performance, scalability and accuracy.
Distributional Hypernym Generation by Jointly Learning Clusters and Projections
Venue: COLING
Year: 2016

We propose a novel word embedding based hypernym generation model that jointly learns clusters of hyponym hypernym relations, i.e. hypernymy, and projections from hyponym to hypernym embeddings. Most of the recent hypernym detection models focus on a hypernymy classification problem that determines whether a pair of words is in hypernymy or not. These models do not directly deal with a hypernym generation problem in that a model generates hypernyms for a given word. Differently from previous studies, our model jointly learns the clusters and projections with adjusting the number of clusters so that the number of clusters can be determined depending on the learned projections and vice versa. Our model also boosts the performance by incorporating inner product based similarity measures and negative examples, i.e. sampled non hypernyms, into our objectives in learning. We evaluated our joint learning models on the task of Japanese and English hypernym generation and showed a significant improvement over an existing pipeline model. Our model also compared favorably to existing distributed hypernym detection models on the English hypernym classification task.