nils-herrmann/clean_scirepeval huggingface dataset:
The bilingual mental lexicon beyond Dutch English written words
Venue: Bilingualism: Language and Cognition
Year: 2019

The contribution that Ton Dijkstra has made to the field of bilingualism, with his colleagues over the years, is beyond measure. He has advanced our field with the thoughtful and thought provoking models of the bilingual lexicon he has put forward, and with the vast empirical data he and his colleagues have collected from numerous bilinguals, using a variety of experimental methods.  It comprises a thoughtful and detailed description of a new model, Multilink, and provides relevant information regarding the context in which the model was developed, its assumptions, its successes and challenges. The model is the first to consider aspects of both word production and word recognition, as well as of word translation, and to explicitly address how translation equivalents that share both form and meaning cognates may be processed. Also addressed are word frequency concerns, including differences in word frequency that are dependent on language proficiency and exposure. The assumptions that are made within the model are clearly laid out, and less than ideal decisions that needed to be made are acknowledged. The authors conclude that a model like Multilink could be the basis for a general model of the mental lexicon. In that, they promote the view taken in Libben, Goral, and Libben (2017) that the bilingual not the monolingual lexicon should be considered as the default.
THE ANALYSIS OF RECIPROCATING COMPRESSOR PK 60 150 FAILURE USING FAILURE MODE AND EFFECT ANALYSIS METHOD
Year: 2017

The reciprocating compressor PK 60 150 is the most common type of compressor used in industries. The effect of each failure mode on the reciprocating compressor was analyzed using FMEA, so that the necessary action can be determined. Both increasing the operating time and preventing the damage of compressor are major concerns. This study was aimed to determine the problems that cause risk of safety, failure of compressor function, and shortening of compressor life time, so that maintenance and repair cost can be reduced. The result shows that the breaking of high wires and fuses, over heat and wear of the piston are the most affecting factors of compressor failure with RPN value of 64
Combining Distributed Word Representation and Document Distance for Short Text Document Clustering
Year: 2020

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K means partitioning based clustering technique is applied, where the similarities of documents are measured by word mover's distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1 score, and adjusted Rand index for both real world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.
Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL
Venue: EC TEL
Year: 2016

In recent years, a number of recommendation algorithms have been proposed to help learners find suitable learning resources on line. Next to user centered evaluations, offline datasets have been used to investigate new recommendation algorithms or variations of collaborative filtering approaches. However, a more extensive study comparing a variety of recommendation strategies on multiple TEL datasets is missing. In this work, we contribute with a data driven study of recommendation strategies in TEL to shed light on their suitability for TEL datasets. To that end, we evaluate six state of the art recommendation algorithms for tag and resource recommendations on six empirical datasets: a dataset from European Schoolnets TravelWell, a dataset from the MACE portal, which features access to meta data enriched learning resources from the field of architecture, two datasets from the social bookmarking systems BibSonomy and CiteULike, a MOOC dataset from the KDD challenge 2015, and Aposdle, a small scale workplace learning dataset. We highlight strengths and shortcomings of the discussed recommendation algorithms and their applicability to the TEL datasets. Our results demonstrate that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset. However, we also find a strong correlation between the average number of users per resource and the algorithm performance. A tag recommender evaluation experiment reveals that a hybrid combination of a cognitive inspired and a popularity based approach consistently performs best on all TEL datasets we utilized in our study.
Comparison of the fermentation characteristics, aerobic stability and nutritive value of barley and corn silages ensiled with or without a mixed bacterial inoculant
Year: 2011

Addah, W. Baah, J. Groenewegen, P. Okine, E. K. and McAllister, T. A. 2011. Comparison of the fermentation characteristics, aerobic stability and nutritive value of barley and corn silages ensiled with or without a mixed bacterial inoculant. Can.  (Hordeum vulgare L. and corn (Zea mays) silage are the most commonly used forages in feedlot diets in western Canada, and are often inoculated in an effort to improve their preservation and quality. However, the relative value of these two forages in feedlot diets or their responses to inoculation have not previously been compared. Whole crop barley (B) and corn (C) forage were ensiled for 63 and 77 d, respectively, in both mini silos and Ag Bags(r) without and with inoculation [Lactobacillus plantarum, Enterococcus faecium, Pediococcus acidilactici at 1.0x105 CFU g 1 forage; inoculated barley (IB) inoculated corn (IC) At the end of the ensiling period in mini silos, the pH of C and IC was lower (P<0.001) than B and IB silag.
A hybrid BA VNS algorithm for coordinated serial batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers
Year: 2017

We study a coordinated serial batching scheduling problem that features deteriorating jobs, financial budget, resource constraints, resource dependent processing times, setup times, and multiple manufacturers simultaneously. A unique feature but also a significant challenge in this problem is the dual constraints on resources, i.e. financial budget and resource quantity. Some key structural properties are first identified for the setting where the jobs and resources are already assigned to each manufacturer, which enables us to develop the optimal resource allocation scheme. Then, a polynomial time scheduling rule is proposed to search for the optimal solution for each manufacturer in this setting. Then, a hybrid BA VNS algorithm combining Bat algorithm (BA) and variable neighborhood search (VNS) is proposed to tackle the studied problem, and the optimal scheduling rule is implemented in its encoding procedure. Finally, computational experiments are conducted to test the performance of the proposed algorithm, and the efficiency and improvements are compared with those of BA, VNS, and Particle Swarm Optimization (PSO) with respect to convergence speed as well as computational stability.
STRING v11: protein protein association networks with increased coverage, supporting functional discovery in genome wide experimental datasets
Venue: Nucleic Acids Res.
Year: 2019

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high throughput text mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https:/string db.org/
Students' Performance Prediction Using Machine Learning Approach
Year: 2020

This report aims to cut back the manual procedures concerned within the performance analysis and analysis of scholars, by automating the method right from retrieval of results to pre processing, segregating, and storing them into information. We additionally expect to perform examination on immense measures of information viably and encourage simple recovery of different sorts of data identified with understudies' execution. We give a degree to build up to information stockroom wherein, we can apply information mining methods to perform different sorts of examinations, making a learning base and use it further, for forecast purposes.
Ce que la pandemie fait a l'ingenierie pedagogique et ce que la rubrique peut en conter
Year: 2020

La rubrique Debat discussion s'est propose de questionner les facteurs de transformation des modeles et des pratiques de l'ingenierie ainsi que du design pedagogiques, de mettre en discussion l'importance relative de differents facteurs susceptibles d'en inflechir l'evolution. Dans un article recent, France Henri (2019) posait deux questions a l'origine de notre reflexion et du projet de la rubrique pour cette annee 2020 Quel changement a l'ere du numerique et Quelle ingenierie pour.
Supply Chain Disruption in Nigeria Oil and Gas Sector: Some Mitigation Strategies
Venue: International Journal of Scientific and Research Publications (IJSRP)
Year: 2019

Understanding the internal drivers of petroleum supply chain disruptions could provide a clue to finding lasting solution to the perennial incidence of petroleum product shortages in Nigeria. This study takes another look at petroleum supply chain disruption in Nigeria oil and gas industry by identifying the drivers of disruption and determining mitigation strategies. The study adopted survey method to randomly draw 284 supply chain executives from 38 oil and gas marketing and logistics firms in Nigeria downstream petroleum industry. Data for the study was gathered through structured questionnaire and analysed with descriptive statistics. Results show the top five drivers/causes of supply chain disruption relates to: poor quality of supply chain information, inaccurate product demand forecast, third party logistics outsourcing firms, inadequacy of critical storage infrastructure and components, and poor visibility of inventory position. From the study, major strategies for handling disruption challenges in the downstream petroleum supply chain includes: flexible supply chain, collaborative outsourcing, efficient management of petroleum products inventory, and supply chain relationship coordination. These findings suggest the need for Nigerian National Petroleum Cooperation (NNPC) to advance its supply chain management practices and possibly hands off the refining of petroleum products to private firms.
Towards 5G Health for Medical Video Streaming over Small Cells
Year: 2016

The next generation of "m health technologies" is an evolving topic that is very much related to two drivers; the first is the evolution of Information and Communications technologies (ICT) in particular mobile technologies and their capabilities. Second are the challenges facing the health and social care sector in terms of demographic change and the need for the provision of a more secure quality integrated patient centered care. 4G Health has addressed the long term evolution of m health and the inclusion of personalized care in the model of m health. 4G health is developed around the concept of Fourth generation (4G) mobile communication systems. M Health is still evolving to beyond 4G Health to cater for the above challenges and benefit from the future technologies of fifth generation (5G) which means that 5G networks will revolutionize m health systems by supporting scenarios not supported by existing 4G networks (e.g. high resolution m health data/image/video from high speed vehicles)
Unmet needs for contraception: A comparative study among Somali immigrant women in Oslo and their original population in Mogadishu, Somalia
Venue: PloS one
Year: 2019

Unmet need for contraception is defined as the proportion of fertile individuals who do not use contraceptives despite wanting to space or limit their childbearing. Studies show that immigrant women in Europe, have higher rates of unintended pregnancies and abortion than native born women. Somali women, have the highest fertility rate in Norway which is much higher than the total fertility rate in Norway (4.0 vs. 1.7) This study investigates the unmet need for contraception among Somali immigrant women in Oslo, Norway, compared to their original population in Mogadishu,   A community based, cross sectional study was carried out among Somali women in Oslo (N 228) and Mogadishu (N 229) from May to December 2018. Pre structured questionnaires were given to women who were recruited through snow ball sampling. Data was analyzed using SPSS version 25. We performed a chi square test for the analyses of categorical variables, a t test for continuous variables and multivariate logistic analysis to determine the association between exposure and outcome variable.  The unmet needs for contraception among Somali women in Oslo was 20.2% which is similar to unmet needs for contraception of women in many sub Saharan African countries. The unmet needs for Somali immigrant women in Oslo (20.2) is two times lower than that of their original population in Somalia (48.5) The odds of having unmet needs for contraception was nearly, three times higher among Somali women in Mogadishu compared to those in Oslo (OR: 2.6, CI: 2.56 7.68) The mean intended fertility was 4 among the women in Oslo and 10.8 in Mogadishu. About 13.4% of study participants in Oslo and 86.6% of those in Mogadishu consider modern contraception irrelevant for women's health. Nearly 50% of women in both places had unintended childbirth on one or more occasion.  The study results show the prevalence of unmet needs for contraception among Somali immigrant women Oslo, is 4 fold higher than that of Norway (20.2 vs 5.5) Information Education Communication to both men and women, may reduce the high unmet need for contraception and also improve partner communication on family planning among Somali immigrants in Oslo. Training primary health providers for provision of tailored information about the modern contraception to immigrant women, which includes an individualized counselling may improve partners' knowledge, demand and uptake of modern contraception.
Effect of Product Innovation and cost leadership Strategy on the Financial Performance of Small and Medium Enterprises
Year: 2017

The success and sustainability of any organization in a competitive environment is determined by its choice of strategy. Most strategies are replicated by competitors and thus a company must position itself in the minds of the consumer in order to remain competitive. The significance of small and medium sized enterprises to world economies and Kenya in particular cannot be over emphasized. SMEs make up the largest business sector in every economy. The lack of proactivity, innovativeness, risk taking and overall poor entrepreneurial orientation have negatively impacted on the SMEs. The study sought to determine whether product innovation and cost leadership strategy were positively associated with business performance of Small and Medium Enterprises in Nairobi County Kenya. Explanatory research design was used in the study. The target population of the study was made up of 7384 SMEs and a sample of 95 SMEs was picked using systematic random. Primary data was collected using a questionnaire with both structured and unstructured questions. Descriptive statistics such as mean and frequencies and inferential statistics such as regression and correlation analysis was used to perform data analysis. The study found out that product innovation and cost leadership strategy were positively and significant related to and financial performance. The study concluded that that product innovation and cost leadership strategy have a positive and significant effect on the financial performance of SMEs. The study recommended that to accomplish the goals of SMEs in the light of growth and profitability, management need to have a positive rethink towards the use of strategic management.  Product innovation, Cost leadership strategy, Financial performance and Small and Medium Enterprises
Global regulation of reactive oxygen species scavenging genes in alfalfa root and shoot under gradual drought stress and recovery
Venue: Plant signaling behavior
Year: 2012

Reactive oxygen species (ROS) production and scavenging in plants under drought stress have been studied intensively in recent years. Here we report a global analysis of gene expression for the major ROS generating and scavenging proteins in alfalfa root and shoot under gradual drought stress followed by one day recovery. Data from two alfalfa varieties, one drought tolerant and one drought sensitive, were compared and no qualitative differences in ROS gene regulation between the two were found. Conserved, tissue specific patterns of gene expression in response to drought were observed for several ROS scavenging gene families, including ascorbate peroxidase, monodehydroascorbate reductase, and peroxiredoxin. In addition, differential gene expression within families was observed. Genes for the ROS generating enzyme, NADPH oxidase were generally induced under drought, while those for glycolate oxidase were repressed. Among the ROS scavenging protein genes, Ferritin, Cu/Zn superoxide dismutase (SOD) and the majority of the glutathione peroxidase family members were induced under drought in both roots and shoots of both alfalfa varieties. In contrast, Fe SOD, CC type glutaredoxins, and thoiredoxins were downregulated.
Aprendizaje basado en proyectos: Primera experiencia en la asignatura de Fisica del Grado en Ingenieria de Telecomunicacion, Sonido e Imagen
Year: 2015

Esta comunicacion trata sobre la prueba de aplicacion del aprendizaje basado en proyectos en la asignatura de Fisica del Grado en Ingenieria de Telecomunicacion, Sonido e Imagen. Se realiza esta primera experiencia donde el ultimo bloque tematico dedicado al campo magnetico y a la induccion magnetica se articula en forma de proyectos que los alumnos eligen de forma voluntaria. En la comunicacion se resumen como se ha articulo esta metodologia activa, desde el contexto, objetivos, preguntas motrices, enunciado, planificacion, tipos de actividades, interdependencia, valoracion y conclusiones.
The identification of corn leaf diseases based on transfer learning and data augmentation
Venue: CSSE
Year: 2020

Corn is one of the most important food crops in the world, but there are many kinds of corn diseases, and it is difficult to diagnose by planting personnel based on experience. However, the misdiagnosis reduces production efficiency. With the development of computer technology, the use of deep learning and image recognition technology for plant disease detection has become an important research direction. We propose a convolutional neural network based on data augmentation combined with transfer learning to identify corn leaf disease models. The algorithm first increases the data by means of data augmentation to improve the generalization and accuracy of the model, and builds a convolutional neural network model based on transfer learning. Then, it uses the model for training, accelerates the training process of the convolutional neural network, and uses test dataset feedback network training results. In this study, the corn leaf images in PlantVillage were used as the dataset of our experiment to classify the four categories which consists of Corn Gray leaf spot, Corn Common rust, Corn Northern Leaf Blight and healthy leaves. We first obtained our optimization model by fine tuning the GoogLeNet pre training network, adjusting parameters such as optimizer and learning rate. Then we trained the optimization model, like the original GoogLeNet network, ResNet18, Vgg16, and Vgg19 networks based on transfer learning and compare the results. The results show that by using our optimized model, the average recognition accuracy of corn disease which consists of corn common leaf rust, Corn Common rust, Corn Northern Leaf Blight and healthy leaves reached 97.6% and the recognition accuracy of each category was greater than 95% Compared with the original GoogLeNet model, the highest accuracy rate is improved by 5.9% Also, the effect is better when compared with other networks based on transfer learning. Our model provides new ideas for the identification of diseases, insect pests of corn and other crops.
Utilization of economical substrate derived carbohydrates by solventogenic clostridia: pathway dissection, regulation and engineering.
Venue: Current opinion in biotechnology
Year: 2014

Solventogenic clostridia can produce acetone, butanol and ethanol (ABE) by using different carbohydrates. For economical reasons, the utilization of cheap and renewable biomass in clostridia based ABE fermentation has recently attracted increasing interests. With the study of molecular microbiology and development of genetic tools, the understanding of carbohydrate metabolism in clostridia has increased in recent years. Here, we review the pioneering work in this field, with a focus on dissecting the pathways and describing the regulation of the metabolism of economical substrate derived carbohydrates by clostridia. Recent progress in the metabolic engineering of carbohydrate utilization pathways is also described.
Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems
Venue: J. Comput. Appl. Math.
Year: 2011

Heuristic optimization provides a robust and efficient approach for solving complex real world problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA) Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution.