dataesr/scientific-paragraphs-categorization huggingface dataset:
The remaining cars on J -1 will be stuck at the junction point forever. Then, we tested the next merging junction condition. The other conditions remain unchanged. We only change the initial value on J -2 u(x, 0) =  • By the time that t = 7, the traffic on J -1 had already left. However, the cars on J -2 can still continue to enter J 0 until t = 22. We turn to an M -in-N -out numerical problem in this subsection. In this part, we select M = 2 and N = 3. We combine the above two subsections that in which the nonzero initial data are on J -1 and J -2. Every condition is unchanged. A real M -in-N -out numerical problem is shown in Fig. 21  and Fig. 22 , which show the following: • The merging flow goes into the junction point according to the proportion of the flux. • The traffic flows into J i , i = 1, 2, 3 according to a certain percentage.
F. APPLICATION TO INTERSECTION WITH MEASURED DATA To further verify the validity of the model and calculation method proposed in this paper, we acquired the measured  data from videos recorded near the intersection of Chaoyangmenwai Street and Dongdaqiao Road in Beijing (these data were provided by Kyland Technology Company), and then we performed numerical simulations using these data. The east-west direction is Chaoyangmenwai Street, and the north-south direction is Dongdaqiao Road. There are four left-turn arrow lights, one direct signal light, one right-turn signal light and four round-headed lights (a total of ten lights) in this intersection. The open and close mode of the signal lamp is shown in this section, part B. The traffic lights are controlled by multi-period periodic control.
ACKNOWLEDGMENT This work is a joint work from when the author  Linfeng Zhang  was visiting the  Math Lab at the Instituts Nationaux des Sciences Appliquées (INSA) de Rouen, France. The author Linfeng Zhang extends thanks for the support from the  Math Lab of INSA. The authors would like to thank the editor and referees for their valuable comments and suggestions which helped us to improve the results of this paper.
The work of L. Zhang was supported by the  China Scholarship Council (CSC)   Visiting Ph. D. Student Program  to the  University of Wisconsin-Madison  under Grant  201706170149. The work of  H. Jia  was supported in part by the  NSF of China  under Grant  51278221 , and in part by the  NSF  of the  Jilin Province Science and Technology Development Program  under Grant  20190303124SF  and Grant  20170101155JC. The work of N. Forcadel was supported in part by the  European Union  through the  European Regional Development Fund (ERDF)  under Grant  HN0002137 , and in part by the  Normandie Regional Council  through the  M2NUM  Project.
Sociological methods "Risk culture" refers to the oil contamination risks awareness, behaviours and social practices to cope with it in everyday life, considering another set of risks (social, economic), and the capacity to think the building of future projects at both individual and territorial scale  (Becerra et al. , 2015). The sociological study was centred on two rural parishes of the NEAR, Dayuma (Orellana Province) and Pacayacu (Sucumbíos Province). We studied how people perceived and managed their environment and the associated contamination risks. Water resources were not the only issue considered in the interviews, while it appeared as the main concern. We focused on both mestizo 1 and indigenous communities. Primary sociological data were collected during five field surveys between 2012 and 2016, most of them during the water sampling campaigns.
Statistical analysis Software ©Rstudio (Version 3. 5. 1) was used for statistical analysis and box plots figures. One sample t-test was used when data showed a normal distribution (Shapiro-Wilcoxon test). In other cases, variables were log-transformed to follow a normal distribution. A confidence level of 0. 95 was chosen. Pearson coefficient correlations were calculated to check the significance of the correlations between the parameters.
, 2016). Similarly, a riparian who saw his water resource damaged by an oil spill for example, won't necessary look for other water sources for domestic use. The role of religious beliefs, cultural ideals, experience and emotions were evaluated in order to understand why people resist to change their practices to face supposed polluted water sources. Religious beliefs. Data collected from interviews with mestizo people showed that culture risk is highly influenced by religious beliefs in at least two ways. First, faith can bring a certain form of fatalism towards one's own existence. Some individuals could also stop worrying about the future since God decides for them. Data collected gave numerous references to biblical scriptures. Cultural ideals and practices. Change to prevent sanitary risk depends first on social perceptions of the risks, and secondly on a cultural process, but also depends on the existence of other available alternative ways and on adequate financial resources.
Acknowledgements This work was supported by the  French National Research Agency  ( ANR-13-SENV-0003-01 ) and realized in the frame of the  French-Ecuadorian MONOIL Program  " Monitoring of social and environmental impacts and vulnerabilities in an oil country, Ecuador ". We are very grateful to the technical teams of the two laboratories where chemical analyses were performed (OMP-GET and EPOC in France). We would like to sincerely thank the local populations from the  Amazon  and Pacific coast regions, especially from  Dayuma ,  Joya de los Sachas  and Pacayacu villages and in Esmeraldas for their kind cooperation during the.
Machine mapping. Salesses, Schechtner, & Hidalgo (2013) produced the first large-scale machine mapping study using GSV. They mapped the perception of safety in select US cities. They used crowd-sourced data from over 7000 individuals from 91 countries who responded to a website with a game that showed pairwise comparisons of GSV images from Boston, New York, Linz and Salzburg and asked the question "Which looks safer?", the left or right image. Given that each image was compared to numerous others in the online game, the authors were able to compute a 'Q-score' for each image that provided a relative ranking of a each of the images along the perceptual dimension considered. The crowd-sourced dataset they produced was made available and called Place Pulse 1. 0  [47]. Naik et al. [48, 49]  subsequently used the Place Pulse 1.
0 dataset to compute a 'Streetscore' for 21 US cities. They used the Trueskill ranking algorithm to rank the images in the Place Pulse 1. 0 dataset. Using those rankings together with corresponding state-of-the-art feature vectors (GIST, HOG2x2, SIFT etc. ), they employed support vector regression to predict the perceived Streetscore for over one million GSV images. Their study produced the first high resolution street-level maps derived from computer vision and machine learning techniques. Naik et al. [50]  used the Streetscore to predict the 'Streetchange' of over one and a half million GSV images in several US cities. The change in Streetscore values for a given pair of images at successive time intervals at the same location was used to compute the 'Streetchange'. They only included the years of 2007 and 2014.
Dubey et al  [57]  developed the Place Pulse 2. 0 dataset containing over one million pairwise comparisons of GSV images, from several countries, along eight perceptual dimensions. In contrast to  Naik et al. (2014 Naik et al. ( , 2017)) , Dubey et al. [57]  used a deep mapping approach and trained a Siamese fully-convolutional neural network to both predict pairwise comparisons for perceptual attributes as well as a second CNN with fully connected top layers to rank images according to a given perceptual attribute. The prediction accuracy of Dubey et al. [57]  was ~73%, largely because Place Pulse 2. 0 data is heterogenous, derived from pairwise image comparisons among 28 different countries. People from different cultural milieus will introduce ontological and semantic inconsistencies in the dataset and this ultimately affects the generalization potential of a CNN model.
However, the biases in the dataset may be relatively minor. [47] ), found little bias due to age, gender or location (US vs. non-US) in the Place Pulse 1. 0 dataset but that was relatively small compared to Plase Puse 2. 0. To improve predictability of deep mapping models, Blečić et al. [58]  collected over 17,000 GSV images in Italy and, with each being rated on a perceived 'walkability' scale of 1-5, they trained a CNN to rate GSV images and map their walkability scores for several Italian cities. The authors achieved accuracy of 78% and 99% (for '1-class-off'). The results of  Blečić et al. (2018) , suggest that deep mapping applications may achieve improved prediction performance when trained with regionally consistent GSV datasets. The aforementioned studies demonstrate that machine learning techniques applied to GSV imagery provide a monumental leap in our ability to classify and map urban space for several kinds of perceptual/semantic attributes.
0  [49, 50] , we created the training dataset using a webpage in which a property (long,lat) was selected and the user was shown two sequential GSV images within the sequence of time-stamped images at that location and, for each pair, asked to respond to the question: 'Is there a property improvement?' (Fig  2 ). The authors of this paper undertook the comparisons, with it being understood that we were concerned with property improvements that are identifiable as gentrification-like visual changes. A gentrification-like visual change to a property is anything that provides evidence of a significant reinvestment in the property being viewed, including such things as the removal of an older house and construction of a new house, significant updates to the facade of an existing structure, or a clear improvement in landscaping and other relevant visual changes that are suggestive of the visual cues of gentrification including those identified by  [29, 35].
Unlike the Place Pulse dataset, our line of investigation was centered on the atomic unit of gentrification: the individual property, which includes the building structure and frontage together rather than a default GSV camera orientation. Thus, using the webpage (Fig 2 ), we were able to record when a GSV image pair exhibited a gentrification-like visual change between each successive image for the same property. The result of each comparison was then saved in a file, recording the unique GSV assigned image identifier (panoid) for the left image, right image and yes or no for property improvement (Section B in S1 Appendix). The training dataset was subsequently randomly shuffled and 40% was held back for model validation (n = 3245 pairs) and testing (n = 3245 pairs). The remaining 9734 pairs were used for model training.
Siamese model We trained a Siamese CNN (SCNN) to detect the visual improvements to a given property between two successive GSV images (Fig  3 ). We programmed the SCNN in python using the Keras library [61] and a tensorflow backend (Section B in S1 Appendix). An excellent introduction to convolutional neural networks can be found in the book 'Deep Learning With Python' by Francois Chollet  [62]. To train the Siamese model (Fig  3 ), a transfer learning approach was employed  [63, 64] , whereby the two VGG19  [65]  convolutional branches (Fig 3 ) were initialized using weights trained on the ImageNet  [66]  challenge database  [67]. These VGG19 model weights are the result of training on 1. 3 million images with 1000 categories  [65]. Because the ImageNet weights were produced on a band order of Blue-Green-Red (BGR) images of size 224x224 that were mean-centered across the entire ImageNet database  [65] , our dataset of RGB images were likewise resized and channel-wise color normalized by converting to BGR order and mean-centered by subtracting the mean of each image band using the same means computed on the ImageNet dataset (B-103.
Because the training dataset was imbalanced in favor of negative cases, class weights were employed to balance model parameter weight penalties during training. The class weights were representative of the overall dataset with 10% importance assigned to negative cases and 90% importance assigned to positive cases. Training time was approximately 33 hours on a single NVIDIA 1080ti GPU. The final test accuracy of the fully trained model SCNN-FC-8 (~235 M trainable parameters) was 95. 6% (Table  1 ), an AUC of 0. 84 and an F1 Score of 0. 72. All subsequent analyses presented herein use that model. To crudely assess the sensitivity of our model to the partitioning of the GSV training dataset, we repeated the entire training process as described above using a different random permutation of the dataset, that was subsequently partitioned into training, validation and test arrays.
There are many types of gentrification-like visual changes that can occur to individual structures that, when in relative spatial isolation, are not associated with a potential gentrification process, despite mimicking the range of visual changes in the training dataset that we associate with gentrification for individual properties. These are visual analogues of gentrification-like visual changes. So, the model will detect gentrification-like visual changes across the spatial domain of study wherever they arise. We stipulate, however, that such changes must be numerous and occur in relatively close spatial proximity in order to define a potential gentrification process. Therefore, we are concerned with identifying the pattern of the local spatially varying intensity, λ, where λ = n/A, the number of locations, n, divided by the area, A.
We used Kernel Density Estimation (KDE) with a Gaussian kernel and bandwidth of 200 m to produce maps that illustrate the spatially varying intensity of gentrification related property improvements within the study area. We used the 'density()' function within the R package Spatstat 1. 56-0  [69]  to produce the KDE estimates. The use of KDE does not imply anything about the continuity/discontinuity of the underlying gentrification process: KDE is used to identify the likely 'hot-spots' of gentrification-like visual changes that are a product of the underlying spatial process of gentrification.
However, in Ottawa during the period of study, there were no large-scale disasters that would have induced local clusters of property reconstruction. Our reliance on KDE to identify hot-spots of gentrification-like visual change assumes that there is a background intensity of physical property improvements that are non-gentrification related and follow an inhomogeneous Poisson process. It would be reasonable to assume that this inhomogeneous process is random through space and time contingent on the distribution of structures in a city and secondly their age. Our current mapping process includes the unknown background intensity estimate within the KDE maps. However, because the full GSV dataset within the study area is already contingent on the spatial distribution of building stock, a spatially random process contingent on that building stock would be unlikely to produce the high-intensities we find on our KDE maps of the model detections.
Similarly, we find moderate intensity in Old Ottawa South, a neighbourhood that contained gentrification largely prior to our study  [81] , but just across the northern boundary we found Ottawa East to be currently undergoing intense gentrification. A distinct advantage of using a deep mapping approach based on CNNs, at least in our case, is the robustness of the model with respect to GSV images containing different weather, exposure and vegetation phenology. However, we worked in a restricted spatial domain and expect that false-positive model detections will be, for the most part, contained in the random background. Still, using a larger GSV dataset for detection could lead to areas of high intensity of gentrification-like visual changes being identified that are due to weather or exposure issues.
While the SCNN-FC-8 model was developed and tested in the context of Ottawa, we believe that similar results can be reproduced in other urban contexts within peer countries. It remains to be seen what or if similar results can be replicated in different contexts and the degree to which we can detect different kinds of gentrification (such as slum gentrification). Definitions and visual indicators of gentrification can differ based on cultural or architectural norms across different countries. With a relatively small but regionally consistent training dataset, other north-American locales would be able to train the SCNN-FC-8 architecture using a transfer learning approach. Given the relative ease with which deep learning models can now be applied, thanks to high level APIs like Keras [61], a relatively modest time investment can produce highly spatially and temporally resolved maps of the gentrification process.
Objective: To examine the impact of study characteristics on the score of the pragmatism/explanatory continuum of randomized-controlled-trials (RCTs) published in nursing journals using the PRECIS-2 tool. Study design: RCTs concerning five themes of nursing care indexed in the PubMed and CINAHL databases published from 2002 to 2005 and 2012 to 2015 were selected by title/abstract. A sample of 400 was randomly selected and evaluated with the PRECIS-2 tool and reading grid. Results: The median PRECIS-score was 32 out of a possible 45 [28; 36]  corresponding to a medium level of pragmatism. Studies with "medication" as an intervention had a more explanatory PRECISscore than studies with other intervention types (p=0. 015). Studies with "placebo" and "no usual care" as comparators had a more explanatory PRECIS-score (p=0.
BACKGROUND The scientific nursing literature is extensive (with 230 nursing journals indexed on Web of Science database). The number of Randomized Controlled Trials (RCTs) in nursing science has doubled since 2000 (from 127 items in 2000 to 331 items in 2017 indexed in PubMed database). The research-to-practice gap is a worldwide challenge to stakeholders, including health actors (health policy experts, caregivers, patients, families, and society). Over 85% of biomedical research investments are wasted because of the poor transferability of research results to practice  [1]. To address this problem of implementation, results must not only be valid and reliable but also conducted in a way that ensures that they are directly applicable to the real-world conditions in which practice is carried out.
Data collection A read grid was created on Microsoft Access Software® 2016 to assess the pragmatism of the RCTs. Extracted data included features of the selected studies (year of publication, impact factor, h-index of first and last authors, funding, field, type of comparator groups, blinding, and sample size). We chose these variables because they are accessible, common, and factual. We used the PRECIS-2 tool to evaluate the pragmatism/explanatory level of RCTs in the nursing literature. The UK National Institute for Health Research Randomized Trials Methods website listed the PRECIS-1 tool as one of eight "useful papers" for trial designers, which has been used by authors prospectively and retrospectively  [13]  to assess the pragmatic level of RCTs. As already mentioned, the PRECIS-2 tool (Figure  2 ) is an updated version of the PRECIS-1 tool, in which the number of domains was reduced from 10 to 9  [9] ,  [17].
Sample size Statistical analysis was performed using R-3. 3. 1 software (http://www. R-project. org). Based on the study results of Palese et al. , the sample size was calculated to detect a difference of five points between the mean PRECIS-score of the two period groups with a standard deviation of 15, an alpha risk of 0. 05, and a power of 0. 8, meaning a minimum of 142 articles for each group were needed. We selected 400 articles (200 for each period) with stratification by intervention type to anticipate poor indexing of the articles in the PubMed and CINAHL database.
Although other tools exist, such as the ASPECT-R tool  [7]  and a binary tool  [8] , the PRECIS-2 tool is the most cited and reliable tool available to retrospectively evaluate the pragmatism/explanatory level of RCTs  [27]. Second, our study used two large databases for biomedical and healthcare practitioners, representing a substantial panel of published international nursing studies. Furthermore, our results came from reading the full-text of 400 articles. Finally, we investigated several intervention types, representing major fields of nursing practice, including therapeutics without drugs, such as hypnosis and meditation. Nevertheless, our study had one main limitation: the full-text of the studies was read by only one reviewer. However, the selection, based on the title and abstract, was performed by two reviewers and a calibration exercise was conducted with three independent reviewers on a set of 10 articles to assess inter-rater reliability.
Acknowledgements: We would like to thank the authors who agreed to share their articles for this study and Loudon et al. who granted permission for our use of the PRECIS-2 tool. We are also grateful to  Assistance Publique -Hôpitaux de Paris  ( AP-HP ) and all members of the  Pharmacology and Drug Evaluation in Children and Pregnant Women EA 7323 group  for their advice and support.
Data The countries, Central and South America) comprise several countries, their GDP and population data were calculated by summing the figures for the major countries in the two regions, and their real CPIs were calculated as the average of their major countries. Data on terrorist attacks were collected from news reports on terrorist attacks in France, with the date, place, fatalities/injuries, and a brief description of each attack incident being documented. These data were then trans-coded as a dummy variable (,   ) or continuous variable ().
Descriptive data analysis A thorough online investigation was conducted to identify the terrorist attacks that occurred in France during the study period ( The changes in the inbound tourism demand (total nights of stay) and market shares of the seven wine tourism regions during the study period are presented in Figures  1  and 2 , respectively, along with the contrasts between the trends before and after adjusting for seasonality. The results show significant seasonal variation before adjustment, with peaks in June and July and troughs in December and January. To exclude the seasonality effects, the data were adjusted using the software X-12-ARIMA, as suggested by the US Census Bureau, before being entered into the regression. The figures show that after adjustment, the seasonality effects are mitigated.
The direct effects of terrorism on the inbound tourism flows to France and the substitution effects between the urban and wine tourism markets were estimated. Based on the results of the data analysis, the following conclusions can be drawn. First, terrorist attacks reduced the inbound tourist flow to France during the study period. The intensity of an attack (i. e. , the number of casualties) was negatively correlated with the inbound tourism demand. However, this effect was not related to the location of the incident. The negative effect of terrorism was found to have no time lag and could last around 3 months. This finding supports studies that have shown that terrorist attacks can scare off tourists and damage local tourism industries (e. g. ,  Abadie & Gardeazabal, 2008)  and that the negative effects are significant in developed countries, although some studies have found that they are minor in developed countries  (Blomberg et al.