nils-herrmann/clean_scirepeval huggingface dataset:
Cross country determinants of IFRS for SMEs adoption
Year: 2020

The purpose of this study is to identify the influence of environmental and institutional factors on the adoption of the International Financial Reporting Standard for small and medium sized entities (IFRS for SMEs) This study used the neo institutional theory and the economic theory of networks to explain why countries choose to adopt IFRS for SMEs.  This study is based on logistic regression analysis to investigate 177 countries, including 77 jurisdictions that adopted IFRS for SMEs between 2009 and 2015.  The findings confirm that the adoption of IFRS for SMEs is significantly related to law enforcement quality, culture, trading networks and economic growth. At the institutional level, coercive and normative isomorphism was found to be positively associated with IFRS for SMEs adoption. The results show also that the quality of the audit has no significant effect on the adoption of IFRS for SMEs. However, the joint effect of the quality of audit and quality of law enforcement is significantly related to the adoption of IFRS for SMEs.  The study contributes to a better understanding of the factors influencing the implementation of IFRS for SMEs standard across the globe and could be used to predict a country's decision to adopt this standard.  This study contributes to the literature on international accounting harmonization by examining both environmental and institutional factors that influence the adoption of IFRS for unlisted private companies.
Metal tolerant bacteria enhanced phytoextraction of lead by two accumulator ornamental species.
Venue: Chemosphere
Year: 2019

Microbially enhanced availability and phytoextraction is a promising technique for phytoremediation of lead (Pb) In this study, Pb resistant strains were isolated and investigated for potential effects on plant growth and Pb phytoextraction. Incubation experiments were carried for inoculated and un inoculated soil containing different levels of Pb. Results revealed that 20% of the isolated bacteria could tolerate Pb up to 800 mg L 1. Five Pb resistant strains with plant growth promoting (PGP) abilities were evaluated for possible influence on water soluble Pb through soil incubation experiments and significant increase i.e. 1.85 and 1.49 folds in water soluble Pb was observed for NCCP 1848 and NCCP 1862 strains, respectively. Pot experiments indicated significantly higher uptake by Pelargonium hortorum than that by Mesembryanthemum criniflorrum at all levels of soil Pb concentrations with the highest increase (1.9 folds) in plants inoculated with NCCP 1848 followed by NCCP 1862 (1.8 folds increase) compared to the control (Pb and without bacterial strain) The strains NCCP 1848 and NCCP 1862 were identified by 16S rRNA gene sequencing as Microbacterium sp. and Klebsiella sp. The results of present study suggest that Pb resistant plant growth promoting bacteria can serve as an effective bio inoculant through wide action spectrum for maximizing efficiency of phytoremediation system.
Review: Endothelial myofibroblast transition, a new player in diabetic renal fibrosis
Venue: Nephrology
Year: 2010

Diabetic nephropathy (DN) is the most common cause of chronic kidney failure and end stage renal disease in the Western world. Studies from diabetic animal models and clinical trials have shown that inhibition of the renin angiotensin system delays the progression of advanced DN. However, a recent large scale clinical trial has revealed that inhibition of renin angiotensin system in early phases of DN does not slow the decline of renal function or the development of morphological lesions, suggesting that different mechanism(s) may be involved in the different stages of DN. The role of epithelial mesenchymal transition in renal fibrosis has been intensively investigated. Recently, endothelial mesenchymal transition, or endothelial myofibroblast transition (EndoMT) has emerged as another mechanism involved in both developmental and pathological processes. The essential role of EndoMT in cardiac development has been thoroughly studied. EndoMT also exists and contributes to the development and progression of cardiac fibrosis, lung fibrosis, liver fibrosis and corneal fibrosis. EndoMT is a specific form of epithelial mesenchymal transition. During EndoMT, endothelial cells lose endothelial markers and obtain mesenchymal markers. Recent evidence from our laboratory and others suggests that EndoMT plays an important role in the development of renal fibrosis in several pathological settings, including experimental DN. This review considers the evidence supporting the occurrence of EndoMT in normal development and in pathology, as well as the latest findings suggesting EndoMT contributes to fibrosis in DN. Whether experimental findings of EndoMT will be reproduced in human studies remains to be determined.
Production Inventory Model for Deteriorating Items with On Hand Inventory and Time Dependent Demand
Year: 2010

Production inventory model plays a dominant role in production scheduling and planning. For the determination of optimal downtime, uptime of production and production quantity, it is required to minimize the expected total cost. The total cost of production is dependent on demand, production rate and rate of decay in deteriorating items. In this paper, we develop and analy invent sensi produ as pa
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Venue: Nonlinear Processes in Geophysics
Year: 2019

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space wise local representation of the ODE system is introduced and is key to coping with high dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model.
Simulation of optical fiber amplifier gain using equivalent short fibers
Year: 2020

Electromagnetic wave propagation in optical fiber amplifiers obeys Maxwell equations. Using coupled mode theory, the full Maxwell system within an optical fiber amplifier is reduced to a simpler model. The simpler model is made more efficient through a new scale model, referred to as an equivalent short fiber, which captures some of the essential characteristics of a longer fiber. The equivalent short fiber can be viewed as a fiber made using artificial (unphysical) material properties that in some sense compensates for its reduced length. The computations can be accelerated by a factor approximately equal to the ratio of the original length to the reduced length of the equivalent fiber. Computations using models of two commercially available fibers one doped with ytterbium, and the other with thulium show the practical utility of the concept. Extensive numerical studies are conducted to assess when the equivalent short fiber model is useful and when it is not.
Tecnicas de interpolacion de alto grado en ingenieria estructural
Year: 2016

espanolEl principal objetivo de la Tesis es el desarrollo de metodos numericos de calculo, basados en tecnicas de interpolacion metodos de los elementos finitos MEF y de la integral de contorno BEM fundamentalmente, con funciones polinomicas de alto grado y su aplicacion al campo de la ingenieria y en especial a las estructuras. Para ello se desarrollaran algoritmos de alto rendimiento para resolver de manera eficaz los problemas numericos que presentan las funciones polinomicas de tan alto grado. A partir de la generacion automatica de las funciones de forma interpolantes, se desarrollaran tambien las matrices de rigidez elementales y los vectores de fuerzas consistentes. Las tecnicas asi desarrolladas, se aplicaran a diversos problemas de la ingenieria estructural y de la Mecanica de los Medios Continuos en general. Se comprobara, asimismo, la bondad de los resultados obtenidos y la idoneidad de los elementos que se desarrollan. EnglishThe main aim of the thesis is the development of numerical calculation methods based on interpolation techniques methods of finite element FEM and BEM boundary integral mainly with higher order polynomial functions and their application to engineering field, especially to structural engineering. We create high performance algorithms to effectively solve numerical problems presented by higher order polynomial functions. It has created an automatic generation of shape functions, and from them it will be developed elementary stiffness matrices and vectors of forces consistent. The developed techniques will be applied to various problems of structural engineering and mechanics of continuous media in general. The accuracy of the results and the suitability of the developed elements has been checked.
Recent advances and open challenges in hybrid brain computer interfacing: a technological review of non invasive human research
Year: 2016

In recent years, hybrid brain computer interfaces (hBCIs) have proven to be a promising path towards practical brain computer interfacing. These hybrid interfaces capitalize on the concurrent recording of various physiological signals, or of the elicitation of more than one mental process, to increase the number of possible input commands and achieve more flexible and robust systems. Although hBCIs have previously been reviewed in some articles, a more recent, and complete survey of the literature is missing to lay the foundations for further research.  This work aims at systematically reviewing recent articles on the topic of non invasive hBCIs, to comprehensively identify the current trends, limitations and challenges that these studies report.  Three major databases covering the fields of science and engineering were queried. From these and others sources, 55 journal articles from 2008 to November 2014 were selected and analyzed.  Twenty two items were investigated to o.
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X Ray Security Imagery
Venue: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Year: 2019

X ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives within complex and cluttered X ray security imagery. Here, we address this problem by exploring various end to end object detection Convolutional Neural Network (CNN) architectures. We evaluate several leading variants spanning the Faster R CNN, Mask R CNN, and RetinaNet architectures to explore the transferability of such models between varying X ray scanners with differing imaging geometries, image resolutions and material colour profiles. Whilst the limited availability of X ray threat imagery can pose a challenge, we employ a transfer learning approach to evaluate whether such inter scanner generalisation may exist over a multiple class detection problem. Overall, we achieve maximal detection performance using a Faster R CNN architecture with a ResNet101 classification network, obtaining 0.88 and 0.86 of mean Average Precision (mAP) for a three class and two class item from varying X ray imaging sources. Our results exhibit a remarkable degree of generalisability in terms of cross scanner performance (mAP: 0.87, firearm detection: 0.94 AP) In addition, we examine the inherent adversarial discriminative capability of such networks using a specifically generated adversarial dataset for firearms detection with a variable low false positive, as low as 5% this shows both the challenge and promise of such threat detection within X ray security imagery.
ASPEN Consensus Recommendations for Refeeding Syndrome.
Venue: Nutrition in clinical practice official publication of the American Society for Parenteral and Enteral Nutrition
Year: 2020

In the spring of 2017, the American Society for Parenteral and Enteral Nutrition (ASPEN) Parenteral Nutrition Safety Committee and the Clinical Practice Committee convened an interprofessional task force to develop consensus recommendations for identifying patients with or at risk for refeeding syndrome (RS) and for avoiding and managing the condition. This report provides narrative review and consensus recommendations in hospitalized adult and pediatric populations.  Because of the variation in definitions and methods reported in the literature, a consensus process was developed. Subgroups of authors investigated specific issues through literature review. Summaries were presented to the entire group for discussion via email and teleconferences. Each section was then compiled into a master document, several revisions of which were reviewed by the committee.  This group proposes a new clinical definition, and criteria for stratifying risk with treatment and screening strategies. The authors propose that RS diagnostic criteria be stratified as follows: a decrease in any 1, 2, or 3 of serum phosphorus, potassium, and/or magnesium levels by 10% 20% (mild) 20% 30% (moderate) or >30% and/or organ dysfunction resulting from a decrease in any of these and/or due to thiamin deficiency (severe) occurring within 5 days of reintroduction of calories.  These consensus recommendations are intended to provide guidance regarding recognizing risk and identifying, stratifying, avoiding and managing RS. This consensus definition is additionally intended to be used as a basis for further research into the incidence, consequences, pathophysiology, avoidance, and treatment of RS.
Physiological Signals based Emotion Recognition via High order Correlation Learning
Year: 2019

Emotion recognition by physiological signals is an effective way to discern the inner state of human beings and therefore has been widely adopted in many user centered applications. The majority of current state of the art methods focus on exploring relationship among emotion and physiological signals. Given some particular features of the natural process of emotional expression, it is still a challenging and urgent issue to efficiently combine such high order correlations among multimodal physiological signals and subjects. To tackle the problem, a novel multi hypergraph neural networks is proposed, in which one hypergraph is established with one type of physiological signals to formulate inter subject correlations. Each one of the vertices in a hypergraph stands for one subject with a description of its related stimuli, and the complex correlations among the vertices can be formulated through hyperedges. With the multi hypergraph structure of the subjects, emotion recognition is translated into classification of vertices in the multi hypergraph structure. Experimental results with the DEAP dataset and ASCERTAIN dataset demonstrate that the proposed method outperforms the current state of the art methods.
The epidemiology of chronic pain in children and adolescents revisited: A systematic review
Venue: PAIN
Year: 2011

Chronic pain is prevalent in children and adolescents, with headaches being most common. Quality assessment indicated that most epidemiological studies do not meet quality criteria.  Chronic and recurrent pain not associated with a disease is very common in childhood and adolescence, but studies of pain prevalence have yielded inconsistent findings. This systematic review examined studies of chronic and recurrent pain prevalence to provide updated aggregated prevalence rates. The review also examined correlates of chronic and recurrent pain such as age, sex, and psychosocial functioning. Studies of pain prevalence rates in children and adolescents published in English or French between 1991 and 2009 were identified using EMBASE, Medline, CINAHL, and PsycINFO databases. Of 185 published papers yielded by the search, 58 met inclusion criteria and were reviewed, and 41 were included in the review. Two independent reviewers screened papers for inclusion, extracted data, and assessed the quality of studies. Prevalence rates ranged substantially, and were as follows: headache: 8 83% abdominal pain: 4 53% back pain: 14 24% musculoskeletal pain: 4 40% multiple pains: 4 49% other pains: 5 88% Pain prevalence rates were generally higher in girls and increased with age for most pain types. Lower socioeconomic status was associated with higher pain prevalence especially for headache. Most studies did not meet quality criteria.
Climate influences the response of community functional traits to local conditions
Year: 2020

Functional traits determine an organism's performance in a given environment and as such determine which organisms will be found where. Species respond to local conditions, but also to larger scale gradients, such as climate. Trait ecology links these responses of species to community composition and species distributions. Yet, we often do not know which environmental gradients are most important in determining community trait composition at either local or biogeographical scales, or their interaction. Here we quantify the relative contribution of local and climatic conditions to the structure and composition of functional traits found within bromeliad invertebrate communities. We conclude that climate explains more variation in invertebrate trait composition within bromeliads than does local conditions. Importantly, climate mediated the response of traits to local conditions; for example, invertebrates with benthic life history traits increased with bromeliad water volume only under certain precipitation regimes. Our ability to detect this and other patterns hinged on the compilation of multiple fine grained datasets, allowing us to contrast the effect of climate vs. local conditions. We suggest that, in addition to sampling communities at local scales, we need to aggregate studies that span large ranges in climate variation in order to fully understand trait filtering at local, regional and global scales.
Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review
Venue: BMJ
Year: 2020

To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission.   Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019.  day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models.  Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval 0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77) Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.
Knowledge aware Graph Neural Networks with Label Smoothness Regularization for Recommendation
Year: 2019

Knowledge graphs capture structured information and relations between a set of entities or items. As such they represent an attractive source of information that could help improve recommender systems. However existing approaches in this domain rely on manual feature engineering and do not allow for end to end training. Here we propose knowledge aware graph neural networks with label smoothness regularization to provide better recommendations. Conceptually, our approach computes user specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user specific weighted graph and then applies a graph neural network to compute personalized item embeddings. To provide better inductive bias, we use label smoothness, which assumes that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over edge weights and we prove that it is equivalent to a label propagation scheme on a graph. Finally, we combine knowledge aware graph neural networks and label smoothness and present the unifiedmodel. Experiment results show that our method outperforms strong baselines in four datasets. It also achieves strong performance in the scenario where user item interactions are sparse.