dataesr/scientific-paragraphs-categorization huggingface dataset:
to 6 Feb. 2020) Residential visitors The change in the time spent at residential places compared with the baseline (3 Jan. to 6 Feb. 2020) TEM: Temperature Temperature Mean temperature calculated as average of the minimum (TN) and maximum (TX) temperature ( C)3. 3 | Descriptive statistics and correlation analysisThe panel dataset is composed of nine variables observed throughout the 29 European countries (N = 29) over a duration of 538 days (t = 538), which amounts to 15,574 observations (.
Some of them benefit from a rising attention from national authorities, which intend to fuel their development, such as Lao Cai that is expected to become a major trade hub between China and Vietnam and Koh Kong where the Special Economic Zone is expected to take advantage of the proximity with the Thai border. But they all exemplify ordinary secondary cities in the context of rapid economic development that characterizes the Greater Mekong Subregion. Similarly, some have recently suffered from major disasters, such as Bago with a major flood in 2015. But in general, they face average climate risks that spread over the region (specifically flood and drought), and therefore illustrate how these usual risks are governed and managed on a daily basis at the local scale. The main data were gathered through qualitative interviews with various informants: residents, local and regional authorities, and civil society representatives.
All these studies were conducted within the same research project, Urban Climate Resilience in Southeat Asia (UCRSEA). These interviews were conducted using a variety of research perspectives and frameworks. Nonetheless, all the fieldworks directly engaged with environmental governance and the political dimension of climate resilience building, as well as its social and spatial outcomes: this is why all the data is derived from interviews with both officials, planners and other key informants and with local residents from different parts of each city (see Table  1 ). The current paper draws on this interview data to propose an original synthesis on this political dimension of climate resilience: we reconsider the data through the specific focus of governance and discuss it in the wider context of the Greater Mekong Subregion as a whole.
Acknowledgments The research for this paper was part of the project  Urban Climate Resilience in Southeast Asia , funded by the  Social Sciences and Humanities Research Council of Canada (SSHRC)  and the  International Development Research Centre (IDRC)  of  Canada. The authors would like to thank the  Munich Re Foundation  for financially supporting the publication of this paper as well as for the organization of ' World Risk and Adaptation Futures -Urbanization summer academy 2018 ' which inspired this work.
In addition, the data concerning a country's distribution of patents by technical area have proved to be a reliable indicator of a nation's technological strengths as well as its direction of product development. However, compared to R&D, the use of patent data has always been a second-best solution, in spite of the use of superior patenting databases that have become available over the course of time (for example, see  Griliches, 1984; Griliches, 1990; Hall, 2002). Like any other innovation indicator, patents have advantages and disadvantages. The advantages include: (1) Patents are a direct outcome of the innovation process and, more specifically, of those inventions which are expected to have a commercial impact; (2) Because obtaining patent protection is time-consuming and costly, it is likely that applications are filed for those inventions which, on average, are expected to provide benefits that outweigh these costs; (3) Patents are broken down by technical fields and thus provide information not only on the rate of innovative activity, but also on its direction; (4) Patent statistics are available in large numbers and for a very long time series; and, (5) Patents are public documents.
Human capital (HCAP) Human capital enhances a country's capacity to develop new innovations and to benefit from technologies and ideas developed elsewhere  (Benhabib & Spiegel, 1994; Engelbrecht, 1997). Using data on 4000 Danish firms,  Vinding (2006)  finds that firms with a higher share of employees with academic degrees are more likely to introduce innovations. Using data on OECD countries from the 1960s to the 1990s,  Frantzen (2000)  shows that both R&D and human capital have a significant positive effect on TFP. We thus hypothesize a positive relationship between human capital and R&D. We measure human capital as the ratio of total secondary enrollment, regardless of age, to the population of the age group that officially corresponds to the secondary education level.
Inclusion and exclusion criteria For the article identification step, when the databases allowed, we automatically excluded results related to topics not relevant for our search, such as studies involving animals, molecular biology, immunology studies or clinical case reports. Then, a first round of selective screening was carried out based on titles and abstracts (step 1). Only original articles were included; conference reports, literature reviews, and editorials were excluded. At this stage, only articles that reported MSD or MSD proxy outcomes while mentioning the notion of temporal trends were included. In the full-text assessment (step 2), articles defining MSD as a group or set of diseases localized at or around the joints (wrists, elbows, shoulders, spine, or knees) were selected. The pathologies considered here concerned the muscles, tendons and tendon sheaths, nerves, bursae, joints, ligaments, at the periphery of the joints of the upper limbs, the spine, and the lower limbs.
Analysis of the temporal trends of the occurrence of MSD The following data were extracted from each article: the raw temporal trends of MSD prevalence and/or incidence (if reported), information concerning methods used to control for age, and MSD prevalence and/or incidence over time after controlling for age. For each article, the temporal trends in MSD prevalence and/or incidence were analyzed according to the location and severity of MSD. If an article investigated multiple types of MSD and/or addressed both temporal trends in the prevalence and incidence of MSD, we considered these results independently; therefore, the total number of results could possibly be higher than the number of studies included. We also distinguished two groups of results based on the MSD sites and criteria used for MSD definition, either based on pain or on repercussion on work and/or social life (hereafter called disability).
Studies selected A total of 2680 study records were identified through our systematic search, of which 1977 were excluded, 335 were duplicates, and 1642 were deemed irrelevant as per automatic categorization tools provided by some databases (figure  1 ). A further 658 records were excluded after title and abstract screening because they did not present original results and/or did not report results based on MSD and/or did not address temporal trends in their occurrence. Of the 45 fulltext articles assessed for eligibility, 29 were classified as ineligible, 13 of which because they did not control for age in reported indicators. A total of 16 studies fulfilled all eligibility criteria and were thus included in the review.
General study characteristics The 16 articles included in the present review were published between 2003 and 2020 (table  1 ), 12 of which were published after 2010. Overall, the studies were conducted in three geographic areas: 12 in Europe (among which 5 were in Scandinavian countries), 3 in the USA, and 1 in Australia. The duration of the study period ranged from 10-55 years across studies. In 8 studies, sampled populations were used (3 cohort designs, 5 repeated cross-sectional studies), representing a total of 1 387 930 individual working-age adults. Among these, 2 focused on the male population only. The other 8 studies relied on a time-series design based on surveillance data collected within five countries and one subnational administrative area. The recruitment of individuals for repeated crosssectional studies was carried out from household-based sampling designs.
The main limitation of this review results from the fact that we exclusively searched electronic bibliometric databases of scientific literature. This means that we did not consult the gray literature or governmental reports on MSD that were not peer-reviewed by external readers. Another limitation is that, since we used the generic term musculoskeletal disorders/disease as a keyword, it is possible that we missed articles on specific MSD that did not mention the term MSD in the abstract or key terms. Lastly, variability in MSD definitions and body sites among our study sample prevented us from conducting a meta-analysis and computing pooled estimates of time trends. Finally, we do not have studies capturing MSD data during the health crisis linked to the COVID-19 pandemic. This sanitary situation could possibly be at the origin of the evolution and emergence of certain professions which can potentially be at the origin of changes in the occurrence of MSD (telework and bad postures, sedentary habits, intensification of work, work on task linked to a digital platform, increased deliveries carrying heavy loads at reduced times, stress, etc.
Acknowledgments The authors are responsible for the opinions expressed in this article, and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated. We also would like to thank  David R M Smith  for his precious help and careful proofreading of our work. The authors declare no competing interests and acknowledge funding from the  Cnam-Malakoff Humanis "Corporations and Health" Chair. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
FFigure 1. 1 Figure 1. Flow chart diagram of study selection using PRISMA Flow Diagram recommendations. * Automatically excluded when the classification tools of the database allowed. Inspired by Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10. 1136/ bmj. n71. For more information, visit: http://www. prismastatement. org.
However, this general methodology can be applied to any dataset. Another interesting conclusion that can be drawn from the results is that data collection and data pre-processing have proved crucial to obtaining more accurate models. Moreover, data cleaning and analysis help select the most effective machine learning tool to improve the performance of the models and ensure the reliability of such models to make realistic predictions. Finally, to the best of our knowledge, our proposed models for wind-wave misalignment forecasting are robust compared to the existing related literature (see Figure  1  that summarizes the work phases followed in the research). The structure of the rest of the paper is as follows. Section 2 presents a summary of relevant related works. In Sections 3 and 4, the materials and methods, respectively, are described.
Statistical models, such as ARMA or ARIMA, do not need a physical model but statistical distributions and parametric algorithms. They are obtained by curve fitting. These models are adjusted by comparing the actual data and the very last and following predicted values. They are effective for short-term forecasting  [19]. Numerous models have been developed using artificial intelligence (AI) and machine learning (ML) techniques  [20, 21]. Recent examples of wind speed models can be found in the literature, primarily for short-term forecasting. To mention some examples, in  [22] , a hybrid nonlinear estimation approach combining a Gaussian process (GP) and an unscented Kalman filter (UKF) is proposed to predict dynamic changes in wind speed. In  [23] , the authors propose an ANN model to predict daily wind speed with meteorological measurements ATMP, WDIR, GHI, relative humidity and PRES as input features selected among the 13 attributes available in the dataset.
Shahid et al. [25]  explored some machine learning techniques for short-term wind speed prediction, specifically random forest (RF), SVR, RBFNN and LSTM, on various wind farm datasets located in Pakistan. For long-term predictions, there are some works, such as the one by Paula et al. [26] , where RF, NN and GB were applied to perform regression on wind data for long periods in three wind farms. Wave height forecasting has always had many applications in coastal and marine engineering design and, more recently, in renewable energies related to ocean resources, such as wave converters and floating wind turbines. Different ML have been applied to obtain models of the significant wave height (WVHT). In  [27] , the authors present a novel approach to simultaneously tackle short-and long-term energy flux prediction of waves.
Materials: Data and Pre-Processing The actual data used in this paper correspond to standard meteorological and descriptive wave measures obtained from the National Oceanic and Atmospheric Administration (NOAA, www. ndbc. noaa. gov) in the USA. Data measurements were taken by sensors equipped with floating offshore buoys distributed through the U. S. and international waters maintained by the National Data Buoy Center (NDBC). The Santa Maria (CA) station database was selected. This offshore buoy is located in the Pacific Ocean in the northwest of California (34 • 57 22 N 121 • 1 7 W). Historical files with metocean variables from 2010 to 2020 and real-time files of the last 45 days are available and have been downloaded (indeed from January to April 2020). One of the main reasons for choosing this station was the stability of the weather and sea state, without the strong disturbances that make it suitable for offshore wind turbines.
We used the data obtained at 4-5 m height by measuring devices installed on floating buoys. In similar research, it is considered that these data can be extrapolated up to a height of 90 m, that is, about the height of the rotor of the offshore wind turbines. In addition, NDBC buoys are installed about 20-40 km off the coast. Standard meteorological data files have hourly atmospheric, wind-and wave-related variables averaged values, measured every 8 min. Historical data files are classified by year, while real data files contain the last 45-day measures. The same meteorological features are available in both types of files. The most significant differences between them are the representation of the missing value; in historical files, they are represented by 999 and by 'MM' in real-time data files.
Feature Selection Feature selection, also known as variable subset extraction, consists of selecting a subset of relevant predictors from all existing features for modeling. To carry out feature selection, we first applied the F-test, which calculates univariate feature ranking (based on the covariance measure). Then, all predictors were ordered by importance in the dataset and had an associated weight. Correlation analyses, Pearson and Spearman correlation coefficients for pair-wise features, were also performed, obtaining similar values with both approaches. Annual linear correlation matrixes for 2016, 2017 and 2018 were calculated to demonstrate that the annual correlation remains between variables regardless of the particular year. We highlighted some pairs of highly correlated variables, such as WVHT-WDIR or WSPD-GST, that were, therefore, discarded to avoid including redundant information.
Linear Regression (LR) The hypothesis function established a linear relationship between input variables and output, as follows: h ϑ (x) = θ 0 + θ 1 x 1 + θ 2 x 2 +. + θ n x n (1) where θ 0 , θ 1 ,. , θ n are model parameters and x 1 ,. , x n are the components of an input example. θ 0 represents the bias or offset. The cost function used to measure the accuracy of the hypothesis function was 1 2 multiplied by the "squared error function" or "mean squared error" (MSE), which is the mean of the differences between the predicted value and the actual output value for every example in the dataset. J(θ 0, θ 1 ,. , θ n ) = 1 2m m ∑ i=1 ( ŷi -y i ) 2 = 1 2m m ∑ i=1 (h ϑ (x i ) -y i ) 2 (2) The algorithm updates the θ parameters vector for every input training example (x (i) , y i ) by minimizing the cost function.
ML-Based Models and Discussion of the Results The five ML techniques described above were applied to real data of the metocean variables of the buoy. After the training phase, if the model obtained gave an unacceptable classification ratio, we still evaluated the models using the learning curves plot tool. In this way, it was possible to determine whether the problem was due to high bias or high variance. This information may help us optimize the model better, choosing more examples to be included in the training dataset or fewer features, for instance. In the regression learner tool, the hold-out validation scheme was configured with a held-out data subset of 20% for the regression algorithm application. This means that 20% of the 8581 rows were used for validating the models with unseen observations.
In the case of artificial neural networks, the dataset was split into training (70%), validation (15%) and testing (15%) sets. Finally, the best models were tested with data from 2019 and 2020 ("Real-Time") to evaluate their performance. The performance was measured with the root mean square error (RMSE), in m/s for wind speed, in m for wave height, and in degrees for misalignment. The RMSE was calculated as follows: RMSE = 1 m m ∑ i=1 h(θ) i -y i 2 (11) The success rate, S r (%), which is the percentage of correct predictions made by the model, is: S r = 1 - RMSE max % ( 12 ) The best models obtained for each variable were selected. They are presented here regarding the variables that predict wind speed, significant wave height, and wind-wave misalignment.
Wind Speed Models Models were trained to forecast the next hour's wind speed by trying different combinations of features of the dataset for the year 2018. If necessary, more data from other years were added to the training set during the optimization phase. Linear regression was the first algorithm we applied because of its simplicity and ease of understanding. Model variants, namely linear, interactions linear, robust linear and stepwise linear, were also tested in next hour wind speed forecasting. All regressions gave the same results (RSME = 1. 0546, MAE = 0. 7837) except for the robust variant, which gave RMSE = 1. 0558 and MAE = 0. 7819, and the best success rate, 93. 14%, although the rest gave 93. 15%. We also obtained the results of linear regression models trained with 1-h to 6-h previous wind speed data.
• Dataset: data from 2018; • Predictors: WSPD, ATMP, PRES, WDIR; • N • neurons in the hidden layer: 5; • N • input delays: 3 h before forecasting moment (t). Figure  7  shows the predicted wind speed against the actual output values for 2020. eralizes worst for 2019 and 2020 data forecasting, as observed in the RMSE values for those years. The algorithm that performed the best regarding the validation RMSE (0. 9988, the second smallest) and the smallest RMSE for 2019 (1. 1124) and 2020 (1. 1340) was the nonlinear autoregressive with external input neural network (NARX) algorithm, with a success rate of 93. 51%. The best configuration found for this ML algorithm was: • Dataset: data from 2018; • Predictors: WSPD, ATMP, PRES, WDIR; • Nº neurons in the hidden layer: 5; • Nº input delays: 3 h before forecasting moment (t).
Significant Wave Height Prediction Models to predict significant wave height in the frequency domain have been trained based on wind and other weather known variables and wave direction. Initially, we considered the data from 2018 for the dataset. We started applying the linear regression (LR) algorithm based on the high linear correlation between wind speed and wave height. First, the LR model was trained with wind speed as a predictor; the 5-fold cross-validation schema partition was used for the dataset, giving RMSE = 0. 6747 and MAE = 0. 5149 (success rate of 87. 71%). In the optimization phase, we included wind and wave direction as predictors in addition to wind speed, obtaining a bit smaller RMSE = 0. 6290 and MAE = 0. 4749 errors and a slightly better success rate of 88. 54%. As for the wind, we also applied the support vector machine for regression (SVR) to see if we could obtain better results.
The configuration of SVR with Gaussian kernel function, wind speed (WSPD), wave direction (MWD), and wind direction (WDIR) as predictors, and 20% hold-out cross-validation schema applied to the dataset, gave RMSE = 0. 5889 and MAE = 0. 4312 (success rate = 89. 27 %). The learning curves showed noisy values, which means that the training dataset could be unrepresentative of this problem. Therefore, we selected an additional predictor: sea surface temperature (WTMP) and applied the 5-fold CV instead of the 20% hold-out CV scheme, obtaining a LR model with RMSE = 0. 5203 and MAE = 0. 3709 (success rate of 90. 52%). With the Gaussian process regression (GPR) algorithm and WSPD, MWD and WDIR as predictors, we obtained a model with RMSE = 0. 5820 and MAE = 0. 4373 (success rate of 89. 39%). Then, we tried to improve this model, which suffered a high bias as per the learning curves plot, adding pressure (PRES) and WTMP measures as additional predictors and adding the data from 2017 to the dataset, obtaining a GPR model that gave RMSE = 0.
Misalignmet Model Wind and wave misalignment forecasting is a novel but necessary task; indeed, models for misalignment (MIS) forecasting have not been found in the literature. Due to this, the reference error selected has been defined using some knowledge, considering 90º an acceptable limit MIS for FOWT. The models were trained to forecast the next hour's misalignment. The selected dataset contained data from 2018, and when necessary, more data was added during the optimization phase to improve the convergence. The RMSE of the angle was measured in degrees.
Misalignmet Model Wind and wave misalignment forecasting is a novel but necessary task; indeed, models for misalignment (MIS) forecasting have not been found in the literature. Due to this, the reference error selected has been defined using some knowledge, considering 90 • an acceptable limit MIS for FOWT. The models were trained to forecast the next hour's misalignment. The selected dataset contained data from 2018, and when necessary, more data was added during the optimization phase to improve the convergence. The RMSE of the angle was measured in degrees. First Finally, nonlinear autoregressive with exogenous input networks (NARX) were trained. Initially, the NARX model was configured with one hidden layer of 10 neurons and trained with last two hours values (delay = 2) using MIS and WSPD as predictors, which gave an RMSE(Train.
Data Climate resources were assessed using ERA5 reanalysis data  [64] , which has proven reliable for both solar radiation  [65]  and wind power  [66]  estimations, despite some issues related to underestimating winds in mountainous areas  [67]  and potential improvements that could be made at the local level concerning PV estimates  [68]. This climate dataset is particularly suitable for this study because it presents high temporal and spatial resolutions over a long time period. We use hourly climate data at surface levels from 1999 to 2020 at the native ERA5 resolution of 0. 25 degrees (the bottom panels of Fig. 1  show the grid points over Spain). The input data for the e4clim model are the temperature at 2 m, wind components at 100 m, surface pressure, mean sea level pressure, mean surface downward short-wave radiation flux, and mean surface downward short-wave radiation flux with clear skies.