nils-herrmann/clean_scirepeval huggingface dataset:
The importance of strategies of social language learning and cooperative learning in the process of teaching Turkish as a foreign language

In order to implement the teaching of a foreign language at a desired level and quality, and to offer some practical arrangements, which stand for to the best use of time, efforts, and cost, there is a need for a road map. The road map in teaching is a learning strategy. This article shows how strategies of social language learning and cooperative learning can be used in teaching of Turkish as a foreign language. The article uses the framework of the research made on learning strategies and is based on qualitative research methods. It evaluates the strategies which are connected to the social language learning and the principles of cooperative learning and puts a particular emphasis on the issues which are connected to the process of teaching Turkish as a foreign language by bringing into a discussion such questions as which principles should the learning activities be prepared and what are the roles of students and a teacher. In addition, the article gives some suggestions on how to detect learning strategies of every student and to accomplish their internalization by the students achieving thus success in teaching Turkish as a foreign language.  Teaching Turkish as a foreign language, strategies of social language learning, cooperative learning.
A Proposal to Combine Cooperative Learning and Peace Education in a Foreign Language Subject

The present article offers a proposal for teaching using cooperative activities within the framework of peace education in a foreign language subject. The said activities imply that students work in groups in order to accomplish a common goal, and therefore they are involved in the teaching learning process. This research thus aims at offering a pedagogical proposal based on peace education by showing that the principles of peace education are useful for students to be successful in cooperative activities because they deal with global issues and social and peace related content. They also promote negotiation, peaceful conflict resolution, dialogue and respect for diversity. The study reveals that the combination of peace education and cooperative learning allows students to acquire not only content but also social competences such as critical thinking, cooperation, empathy, assertiveness, conflict resolution and active listening, among others. The combination of cooperative learning and peace education offers the opportunity to use pedagogy to produce social transformation because it promotes the teaching of values throughout the teachinglearning process. In this sense, peace education can contribute to developing global and human values so that students can make a contribution to improving the world.
Perspectives of family medicine physicians on the importance of adolescent preventive care: a multivariate analysis
Venue: BMC Family Practice

BackgroundThe study objective was to identify commonalities amongst family medicine physicians who endorse annual adolescent visits.MethodsA nationally weighted representative on line survey was used to explore pediatrician (N 204) and family medicine physicians (N 221) beliefs and behaviors surrounding adolescent wellness. Our primary outcome was endorsement that adolescents should receive annual preventive care visits.ResultsPediatricians were significantly more likely (p .01) to endorse annual well visits. Among family medicine physicians, bivariate comparisons were conducted between those who endorsed an annual visit (N 164) compared to those who did not (N 57) with significant predictors combined into two multivariate logistic regression models.Model 1 controlled for: patient race, proportion of 13 17 year olds in provider's practice, discussion beliefs scale and discussion behaviors with parents scale. Model 2 controlled for the same first three variables as well as discussion behaviors with adolescents scale. Model 1 showed for each discussion beliefs scale topic selected, family medicine physicians had 1.14 increased odds of endorsing annual visits (p .001) and had 1.11 greater odds of endorsing annual visits with each one point increase in discussion behaviors with parents scale (p .51) Model 2 showed for each discussion beliefs scale topic selected, family medicine physicians had 1.15 increased odds of also endorsing the importance of annual visits (p .001).ConclusionsFamily medicine physicians that endorse annual visits are significantly more likely to affirm they hold strong beliefs about topics that should be discussed during the annual exam. They also act on these beliefs by talking to parents of teens about these topics. This group appears to focus on quality of care in thought and deed.
Methods of Information Geometry to model complex shapes

In this paper, a new statistical method to model patterns emerging in complex systems is proposed. A framework for shape analysis of 2 dimensional landmark data is introduced, in which each landmark is represented by a bivariate Gaussian distribution. From Information Geometry we know that Fisher Rao metric endows the statistical manifold of parameters of a family of probability distributions with a Riemannian metric. Thus this approach allows to reconstruct the intermediate steps in the evolution between observed shapes by computing the geodesic, with respect to the Fisher Rao metric, between the corresponding distributions. Furthermore, the geodesic path can be used for shape predictions. As application, we study the evolution of the rat skull shape. A future application in Ophthalmology is introduced.
TensorFlow: learning functions at scale
Venue: ICFP 2016

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training) then with applying those functions (during inference) These functions are defined as compositions of simple primitives (as is common in functional programming) with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.
Large Scale Deep Learning For Building Intelligent Computer Systems
Venue: WSDM

For the past five years, the Google Brain team has focused on conducting research in difficult problems in artificial intelligence, on building large scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has recently open sourced the TensorFlow system (tensorflow.org) a system designed to easily express machine ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group, and describe ways in which these ideas have been applied to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
From Deportation to Prison: The Politics of Immigration Enforcement in Post Civil Rights America

From Deportation to Prison unpacks how the incarceration of over two million people in the United States gave impetus to a federal immigration initiative The Criminal Alien Program (CAP) designed to purge non citizens from dangerously overcrowded jails and prisons. Drawing on over a decade of ethnographic and archival research, the findings in this book reveal how the Criminal Alien Program quietly set off a punitive turn in immigration enforcement that has fundamentally altered detention, deportation, and criminal prosecutions for immigration offenses.
Multiobjective Optimisation and Active Control of Bogie Suspension

Railways provide fast, safe, clean, and cheap transportation service. The cost efficiency in railway operations can be scrutinized from different perspectives. Here, passenger ride comfort, wheel/rail contact wear, and safety (in particular running stability, track shift force, and risk of derailment) are considered as objective functions introduced to evaluate the dynamics behaviour of railway vehicles. Running speed also plays a key role in cost efficiency of railway operations. Higher speeds shorten journey time and make railways more competitive with other types of transportation systems. However, this might increase wear and deteriorate ride comfort and safety. To improve the performance in railway operations advanced designs and technologies are developed during the past decades. Bogie primary and secondary suspension systems of high speed trains can significantly affect the dynamics behaviour of the vehicle. Such components might have conflicting effects on different objective functions. It is important to have the optimum performance of suspension components. In this regard, one of the ultimate goals of this thesis is to improve the vehicle performance from different points of views by studying passive and active suspension systems and using multiobjective optimisation techniques to meet conflicting design requirements. Computational cost is one of the main challenges in multidisciplinary design optimisation. The computational efforts for optimisation can be significantly mitigated by narrowing down the number of input design parameters. Here, an efficient global sensitivity analysis is carried out to identify those suspension components that have prominent influences on different objective functions. Based on the global sensitivity analysis results obtained two multiobjective optimisation problems are formulated and solved. First, multiobjective optimisation of bogie suspension components with respect to safety to improve running speed on curves. Second problem is to reduce wear and improve ride comfort when the vehicle is operating with the enhanced speeds. Consequently, the vehicle runs secure and faster with higher ride comfort and less wear by means of the two optimisation problems solved. The optimisations are carried out using the genetic algorithm. In the case of safety optimisation problem, semi active control strategies are also applied using magnetorheological dampers and the effects on the dynamics behaviour are explored. The robustness of the bogie suspension Pareto optimised solutions against uncertainties in the design parameters is also studied. Active control technology is one of the main targets of this thesis. In this regard, a robust controller is designed using the H control technique to stabilize the wheel set motion and improve curving performance. The controller is robust against track irregularities. Finally, the actuator dynamics is considered and a compensation technique is applied to reduce the actuator's time delay and improve the performance.
Prediction of wheel and rail wear using artificial neural networks

The prediction of wheel wear is a significant issue in railway vehicles. It is correlated with safety against derailment, economy, ride comfort, and planning of maintenance interventions, and it can result in delay, and costs if it is not predicted and controlled in an effective way. However, the prediction of wheel and rail wear is still a great challenge for railway systems. Therefore, the main aim of this thesis is to develop a method for predicting wheel wear using artificial neural networks. Initial tests were carried out using a pin on disc machine and this data was used to establish how wear can be measured using an Alicona profilometer. A new method has been developed for detailed wheel wear and rail wear measurements using 'Replica' material which was applied to the wheel and rail surfaces of the test rig to make a copy of both surfaces. The replica samples were scanned using an optical profilometer and the results were processed to establish wheel wear and rail wear. The effect of load, and yaw angle on wheel wear and rail wear were examined. The effect of dry, wet, lubricated, and sanded conditions on wheel wear and rail wear were also investigated. A Nonlinear Autoregressive model with eXogenous input neural network (NARXNN) was developed to predict the wheel and rail wear for the twin disc rig experiments. The NARXNN was used to predict wheel wear and rail wear under deferent surface conditions such as dry, wet, lubricated, and sanded conditions. The neural network model was developed to predict wheel wear in case of changing parameters such as speed and suspension parameters. VAMPIRE vehicle dynamic software was used to produce the vehicle performance data to train, validate, and test the neural network. Three types of neural network were developed to predict the wheel wear: NARXNN, backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) The wheel wear was calculated using an energy dissipation approach and contact position on straight track. The work is focused on wheel wear and the neural network prediction of rail wear was only carried out in connection with the twin disk wear tests. This thesis examines the effect of neural network parameters such as spread, goal, maximum number of neurons, and number of neurons to add between displays on wheel wear prediction. The neural network simulation results were implemented using the Matlab program. The percentage error for wheel and rail wear prediction was calculated. Also, the accuracy of wheel and rail wear prediction using the neural network was investigated and assessed in terms of mean absolute percentage error (MAPE) The results reveal that the neural network can be used efficiently to predict wheel and rail wear. Further work could include rail wear and prediction on a curved track.
Gibbs Curie Wulff Theorem in Organic Materials: A Case Study on the Relationship between Surface Energy and Crystal Growth.
Venue: Advanced materials

The equilibrium crystal shape and shape evolution of organic crystals are found to follow the Gibbs Curie Wulff theorem. Organic crystals are grown by the physical vapor transport technique and exhibit exactly the same shape as predicted by the Gibbs Curie Wulff theorem under optimal conditions. This accordance provides concrete proof for the theorem.
Wulff shape emergence in graphene

Graphene samples are identified as minimizers of configurational energies featuring both two and three body atomic interaction terms. This variational viewpoint allows for a detailed description of ground state geometries as connected subsets of a regular hexagonal lattice. We investigate here how these geometries evolve as the number n of carbon atoms in the graphene sample increases. By means of an equivalent characterization of minimality via a discrete isoperimetric inequality, we prove that ground states converge to the ideal hexagonal Wulff shape as n Precisely, ground states deviate from such hexagonal Wulff shape by at most Kn3/4 o(n3/4) atoms, where both the constant K and the rate n3/4 are sharp.
Knowledge and Attitude of Pregnant Women towards Management of Pregnancy induced Hypertension in Southwest Nigeria

The study explores the knowledge and attitude of pregnant women towards the management of pregnancy induced hypertension (PIH) It was a descriptive study, sampling technique was purposive and data was collected using a self administered structure questionnaire. A sample of two hundred pregnant women was selected and data was analysed using descriptive and inferential statistics. Almost half of the participants were between the ages of 21 to 30 years (49.0% with highest level of education as tertiary (58.0% and 26.5% are currently pregnant for an average of 21 to 29 weeks. Knowledge and attitudes assessment indicated a high proportion of awareness (82.0% hospital's education (65.5% and the majority (58.0% believed that anybody can have pregnancy induced hypertension and (75.5% relate the possible causes to eating too much salt, stress (57.5% and over weight (49.5% Most of the participants prefer to visit Original Research Article Fadare et al. JAMPS, 11(2) xxx xxx, 2016; Article no.JAMPS.29764 2 hospitals/clinics whenever they have headache (63.5% abdominal pain (72.5% swollen face and leg (65.5% reduced foetal movement (77.5% high blood pressure (65.0% nausea and vomiting (60.0% and blurred vision (62.5% The study recommended that consideration be given to richer advocacy beyond creating awareness on PIH but also advocacy for women and girl children to acquire formal education so as to better appreciate modern medical services in Nigeria. Higher formal education and the urban place of residence of the participants' accounts for increased application of knowledge on pregnancy induced hypertension; this makes them visit the clinics in the event of any observed issue in relation to their health as pregnant women.
Background Knowledge and Attitude of Pregnant Women towards Ultrasound Screening at 20 23 Weeks Gestation.
Venue: Zeitschrift fur Geburtshilfe und Neonatologie

Prenatal ultrasound screening in the second trimester is widely used in maternal health care. Prenatal diagnosis and prenatal screening is often exposed to several accusation, such as inducing unnecessary anxiety, carrying out a selection and forcing pregnant women into attending ultrasound screening. The aim of this study was to investigate the knowledge and the intention of pregnant women attending the second trimester ultrasound screening.  This was a prospective study. Women attending the 20 weeks anomaly scan were given an anonymous questionnaire to evaluate the background knowledge about the anomaly scan and their opinion about prenatal screening.  (96.7% of 620 recruited women filled in the questionnaire completely. To the majority >80% of women it is important to exclude severe fetal anomalies and to secure normal fetal growth. The background knowledge of the women was good, but a clear supply of information and a detail counselling would improve the understanding, especially in the group of lower education. The majority of the women stated that the 20 weeks anomaly scan should be offered to every woman.  Prenatal ultrasound examination is not only essential from a medical point of view for example for important information regarding the status of the fetus, but also has a major impact as a positive psychological factor for pregnant women.
All photonic drying and sintering process via flash white light combined with deep UV and near infrared irradiation for highly conductive copper nano ink
Venue: Scientific reports

We developed an ultra high speed photonic sintering method involving flash white light (FWL) combined with near infrared (NIR) and deep UV light irradiation to produce highly conductive copper nano ink film. Flash white light irradiation energy and the power of NIR/deep UV were optimized to obtain high conductivity Cu films. Several microscopic and spectroscopic characterization techniques such as scanning electron microscopy (SEM) a x ray diffraction (XRD) and Fourier transform infrared (FT IR) spectroscopy were employed to characterize the Cu nano films. Optimally sintered Cu nano ink films produced using a deep UV assisted flash white light sintering technique had the lowest resistivity (7.62 m*cm) which was only 4.5 fold higher than that of bulk Cu film (1.68 m*cm)
Functional activity and white matter microstructure reveal the independent effects of age of acquisition and proficiency on second language learning
Venue: NeuroImage

Two key factors govern how bilingual speakers neurally maintain two languages: the speakers' second language age of acquisition (AoA) and their subsequent proficiency. However, the relative roles of these two factors have been difficult to disentangle given that the two can be closely correlated, and most prior studies have examined the two factors in isolation. Here, we combine functional magnetic resonance imaging with diffusion tensor imaging to identify specific brain areas that are independently modulated by AoA and proficiency in second language speakers. First language Mandarin Chinese speakers who are second language speakers of English were scanned as they performed a picture word matching task in either language. In the same session we also acquired diffusion weighted scans to assess white matter microstructure, along with behavioural measures of language proficiency prior to entering the scanner. Results reveal gray and white matter networks involving both the left and right hemisphere that independently vary as a function of a second language speaker's AoA and proficiency, focused on the superior temporal gyrus, middle and inferior frontal gyrus, parahippocampal gyrus, and the basal ganglia. These results indicate that proficiency and AoA explain separate functional and structural networks in the bilingual brain, which we interpret as suggesting distinct types of plasticity for age dependent effects (i.e. AoA) versus experience and/or predisposition (i.e. proficiency)
Wide field TCSPC based fluorescence lifetime imaging (FLIM) microscopy
Venue: SPIE Commercial Scientific Sensing and Imaging

Time correlated single photon counting (TCSPC) is a widely used, sensitive, precise, robust and mature technique to measure photon arrival times in applications such as fluorescence spectroscopy and microscopy, light detection and ranging (lidar) and optical tomography. Wide field TCSPC detection techniques, where the position and the arrival time of the photons are recorded simultaneously, have seen several advances in the last few years, from the microsecond to the picosecond time scale. Here, we summarise some of our recent work in this field with emphasis on microsecond resolution phosphorescence lifetime imaging (PLIM) and nanosecond fluorescence lifetime imaging (FLIM) microscopy.
The complete mitochondrial genome of the fire coral inhabiting barnacle Megabalanus ajax (Sessilia: Balanidae) gene rearrangements and atypical gene content
Venue: Mitochondrial DNA. Part A, DNA mapping, sequencing, and analysis

The complete mitochondrial genome of Megabalanus ajax Darwin, 1854 (Sessilia: Balanidae) is reported. Compared to typical gene content of metazoan mitochondrial genomes, duplication of one tRNA gene (trnL2) and absence of another tRNA gene (trnS1) are identified in M. ajax mitochondrial genome. There is a replacement of one tRNA (trnS1) by another tRNA (trnL2) in M. ajax mitochondrial genome compared to Megabalanus volcano mitochondrial genome. Inversion of a six gene block (trnP nd4L nd4 trnH nd5 trnF) is found between M. ajax/M. volcano and Tetraclita japonica mitochondrial genomes. With reference to the pancrustacean mitochondrial ground pattern, there is an inversion of a large gene block from the light strand to heavy strand in the two Megabalanus mitochondrial genomes, including three PCGs and two tRNAs (nd4L nd4 trnH nd5 trnF) Furthermore, four tRNAs (trnA, trnE, trnQ and trnC) exhibit translocation, while translocation and inversion occur in three tRNAs (trnP, trnY and trnK)