dataesr/scientific-paragraphs-categorization huggingface dataset:
Analytical framework These methods mentioned above yielded a hypothesis that the new rice and new irrigation technologies may be to blame for both the crumbling of paddies and the reduced output of the springs observed by the elders in recent years. Once the life-science data collected from the interviews and field study were confirmed by triangulation with the literature, a direct functional comparison of the major factors of rice cultivation was performed on the old (inbred) and new (hybrid) types. A multi-dimensional comparative impact map was then built along the data points of the two types. Furthermore, a direct comparison of old and new irrigation technologies then focused simply on observable percolation rates. Finally, a comprehensive hydro-ecological system before-and-after model was constructed from the ethnological survey.
Acknowledgements The author would like to thank  Caleb Chu ,  Érine Gagnon  and  Marilyn McComb Kahn  for their language assistance,  Drs Geng Yi ,  Shizhou Yang  and  Xiaoqin Dao  at the  Yunnan Nationalities University  for their assistance coordinating data collection, and  Prof. Xiaoyun Zheng  at the  Yunnan Think Tank  for his orientation to the ethnopolitical context of water history and management practices in the region.
Background The World Health Organization's definition of health highlights the importance of mental and physical wellbeing and not only disease state. However, lack of awareness on the burden of impaired vitality and its impact on the quality of life of the general healthy population prevents healthcare providers from delivering appropriate solutions and advice. This study aims to better characterize this population in Europe and identify the profile and the health reported outcomes associated with impaired vitality. Methods This retrospective observational study included National Health and Wellness Survey (NHWS) data collected in healthy participants aged 18-65 years from five European Union countries in 2018. Socio-demographic and lifestyle characteristics, comorbidities, attitudes towards healthcare systems, Patient Activation Measure, health-related quality of life outcomes (EQ-5D), and work productivity and activity impairment were analysed according to SF-12 vitality score subgroups (≥ 60, 50-< 60, 40-< 50, < 40).
Study design and data source This study was a population-based, retrospective observational study, which included NHWS data collected from participants in five European countries (France, Germany, Italy, Spain, and the United Kingdom [UK]) from April to July 2018. Due to the potential impact of the coronavirus disease-19 (COVID 19) pandemic on fatigue and impaired vitality, the data obtained in 2019 and 2020 were not considered for our study. The NHWS aims to reflect the general population of the country surveyed using known population incidences for key demographic characteristics. Potential participants are recruited through an existing, general-purpose web-based consumer panel that is maintained by Lightspeed Research and its partners, with quota sampling within the survey panel to ensure representativeness in terms of age and gender.
Outcomes To define participant profile according to vitality score Data on demographics, lifestyle characteristics, medical conditions, attitudes towards doctors, past healthcare resource use and patient activation measure (PAM ® ) levels were gathered at the time of the survey. All variables were reported for the total population and according to the four previously established vitality groups (< 40, 40-< 50, 50-< 60, ≥ 60). Demographic variables collected included age (years), gender, marital status, education, employment status and household income in the past year. For lifestyle characteristics, data on smoking status, alcohol use, exercise in the past month and body mass index (BMI) were reported. Medical conditions that occurred at any time, or in the past 12 months from a predefined list of around 130 were considered (Additional file 1).
To evaluate the burden of impaired vitality on participant daily life Data regarding quality of life (SF-12 and EuroQol 5-Dimension Health Questionnaire [EQ-5D-5L]) and productivity (Work Productivity and Activity Impairment [WPAI]) were collected by questionnaire at the time of the survey. All variables were reported for the total population and according to the four previously established vitality groups (< 40, 40-< 50, 50-< 60, ≥ 60). The seven remaining sub-scores of the SF-12 (physical functioning, physical role functioning, bodily pain, general health, social functioning, emotional role functioning and mental health), along with the physical component summary (PCS) and the mental component summary (MCS) are standardized on a 0 to 100 metric, with higher scores representing better health status.
Discussion It is our understanding that the present study, which involved 24,295 healthy adults across five European countries, is the largest performed in this under-investigated area of the association between mental and physical wellness, vitality, and fatigue. Data related to patient profile, healthcare habits, empowerment, and health economic burden of fatigue in the same sample of participants was assessed. As a result, this study reinforces previous data obtained from smaller, monocentric studies regarding patient socio-demographic attributes, lifestyle and health condition characteristics and healthcare consumption. As previously reported, we contribute to existing evidence that being female, younger and having a lower income was associated with an increased risk of feeling fatigued, even in the population in good health  [3-7, 28, 29].
For participants in the lowest vitality group, odds of mobility problems increased by 3. 4, impairment of usual activity by 5. 8, pain and discomfort by 5. 6 and depression and anxiety by 10. 3, compared with participants in the highest vitality group. Odds of presenteeism also increased by 3. 7, overall work impairment by 3. 4 and daily activity losses by 7. 1. These results are consistent with similar data obtained in a general population of workers who missed an average of 4. 1 (95% CI: 3. 8-4. 4) productive work hours per week due to fatigue  [6]. The meaningful impact of vitality impairment on the population in good health provides evidence that public health initiatives are required to better identify and consider this population as impaired. A strength of the NHWS database is that it allows for the selection of a population in good health.
Finally, a lower number of participants in the highest vitality score group responded to the PAM survey, which is consistent with a population in better health. The present study confirms that even low to moderate sleep disorders, stress and anxiety had a high negative impact on the vitality level of a healthy population. In the meantime, there is increasing evidence of the psychological impact of the COVID-19 pandemic  [62, 63] , particularly on sleep disorders  [64, 65]  and on the level of stress and anxiety  [66, 67]. Therefore, determining the impact of the COVID-19 pandemic on the population in good health would be insightful, and could be achieved by analysing the 2020 NHWS data in the European Union. This analysis would, for instance, assess the prevalence of impaired vitality level, patient engagement in their health and burden during the pandemic, and the subsequent impact on work productivity and quality of life compared with pre-pandemic data.
92 minutes  [30]. [41]  and  [33]  imply F r = 8%, C r = 3. 1 CNY/L, γ = 1 L/order. The average passenger's wait and walk time cost C p and expected subsidies S 2 provided by carpooling platforms is 2. 37 minutes and 2. 88 CNY in our questionnaire 2 , respectively. Additionally, we assume subsidies S 1 provided by carpooling platforms for drivers is 1 CNY. Simulation of the dynamic evolutionary trajectory of the evolutionary system is performed in Matlab 2020a software to verify the accuracy of model consequences and render dynamic evolution trends more clearly and concisely.
Meanwhile, the combined income and hesitant sunk costs of drivers fall short of their excessive fuel  E 1 (0, 0, 0) n -C p + C z < 0, m -C d -C f + C z < 0 1 E 3 (0, 1, 0) R + P -S 2 -C g < 0 2 E 8 (1, 1, 1) C p -S 2 < 0, S 2 + S 1 + C g -R < 0 3 2 https://github. com/tj2032994/passenger-preference-survey-data. consumption and time cost when opting for the PUDO strategy. Under these circumstances, the evolutionary trajectory will ultimately converge toward the ESS of (0, 0, 0), aligning with the model analysis. In Case 2, the incremental benefit and drivers' remuneration deduction from carpooling platforms are outweighed by the subsidies provided to passengers and the operational costs incurred by the platforms. In such circumstances, the evolutionary trajectory will ultimately converge toward the ESS of (0, 1, 0), consistent with the model analysis.
Available Data Sources Agent-based approaches require huge amounts of data to allow the reproduction of the studied population. In the following, we present the data sources available in France, which are useful for ABM mobility demand. They can be grouped into two categories based on the acquisition method. Thus, we distinguish the active approach, where data from surveys are gathered through questionnaires addressed directly to individuals (Section 2. 1. 1). The second method, called the passive approach, consists of collecting data from sensors or other data acquisition devices (Section 2. 1. 2).
Data from Sensors Obtaining mobility data from the sensors can be defined as a passive method and classified into two groups: massive data from GPS and smartphones and data from cameras and induction loops. Massive mobility data are mainly derived from floating mobile data (FMD) and floating car data (FCD). FMD are obtained either by triangulation or by the inter-cellular transfers and the relay antennas of the operators during travel. FCD are generally obtained from vehicle location data (GPS). They can also be obtained via the locations of phones when they are switched on in a vehicle. These data provide better knowledge of the O/D flows than data from standard surveys, as the information is more detailed and more recent for users' travel routes due to the frequently updated location system.
Our Choice for Inter-Modal Simulations Based on MATSim This work is based on the Multi-Agent Transport Simulation (MATSim) software  [14]  coupled with an add-on module for the modal choice (called eqasim)  [15]. Figure  2  describes the general simulation loop for MATSim. Each individual (agent) has a daily plan of trips and activities. Agents' initial daily plans are then represented by the initial demand with the transportation supply (e. g. , the road network and the PT network). The plans of all agents are defined through the mobility micro-simulation module QSim, generating the associated flows and also different phenomena such as traffic congestions and emissions. The plans are then evaluated by a score based on the performed activities and the utility provided by the transportation mode.
At each iteration, a subset of the agents can modify their plans to improve their score by changing either the transportation mode, the route, or the departure time (Re-planning). Each agent, in turn, will have the possibility to optimize his or her daily mobility plans. A co-evolutionary algorithm is used to reach an equilibrium state where no improvement is possible. In eqasim, the initial decision-making module is replaced by a discrete choice module (DMC). Therefore, eqasim allows better behaviors by focusing on the modal choices using a discrete choice model and does not enable other replanning strategies. The simulation produces new trips captured by a Variables component (e. g. , travel times and distances) for the modal choice module. These new values, resulting from interactions between agents, may vary from one iteration to another (e.
This task can be realized with software for geographical data processing, such as QGIS and ArcGIS. In addition, the O/D coordinates of trips are necessary to estimate the discrete choice model and also for the creation of the activity plans for each agent. Thus, we aim to redefine consistent coordinates from the O/D drawing sectors and Euclidean distances provided in the survey. We propose a method which can be defined by two steps: (1) generating random points (N ∈ N) in the O/D drawing sectors and (2) searching for a point from the origin area corresponding to a point in the destination area so that the (Euclidean) distance between these two points is the closest possible to that provided in the survey. Note that we keep the point coordinates corresponding to the home and ensure the consistency of trips between the home and other activities.
Description of the European Metropolis of Lille (MEL) The MEL is composed of 95 municipalities with 1. 1 million inhabitants over an area of 672 km 2. As in most European metropolises, we observe that the population is gathered in Lille (the main city), while the rest of the metropolis is weakly populated. Note that the number of vehicles per household is higher in these peripheral areas, whereas around 70% of households in the center own a private vehicle. The public transport network includes two metro lines (43. 6 km), two tram lines (22 km), and around 90 bus lines. There are currently 12 PR facilities, corresponding to 5005 parking spaces. For our study case, we use the EMD (https://opendata. lillemetropole. fr/explore/dataset/enquete-deplacement-2016/information/?location=10,50. 65641,3.
Discrete Choice Model (SI-MNL) Parameter Estimation Table  1  presents the estimated parameters of the model using one-third of the data from EMD 2016 of MEL. The model was estimated with PandasBiogeme (the last version of the open source package Biogeme for discrete choice model estimation, and commonly used in transportation modes)  [41]. The estimated model (SI-MNL) was statistically significant, with an initial log likelihood (LL) equal to -8668. 555, while the estimated LL was -5086. 908. The initial LL was estimated only with constants. The improvement in the LL values for our SI-MNL model shows that the explanatory variables introduced were significant. The significance of the estimated model was also confirmed by the rho square equal to 0. 412, which is a high value. Moreover, the estimated parameters were statistically significant, except those related to the duration of time spent in the car (β inVehicleTime,PV ) and PT (β inVehicleTime,PT ) and the time to reach the PT (β accessEgressTime,PT , β numberTrans f ers , and β trans f erTime,PT ), with a p-value greater than 0.
Anonymized Data from Surveys Our methodological approach aims to simulate realistic transportation scenarios from anonymized open data. The good results obtained by our algorithm in building the O/D coordinates depended on the area size and the number of points to be generated randomly. The larger the size of the drawing sector, the more random points should be generated to have a better chance of approximating the Euclidean distances. Therefore, the calculation times for searching for pairs of synthetic O/D points becomes very high, preventing other possibilities from being explored. One solution to this problem would be to use geographic databases (e. g. , BD TOPO, BPE, or OSM) to generate a fixed set of O/D coordinates corresponding to potential activity locations (e. g. , home, workplace, and school).
Our model was also applied to an urban context (MEL). After the calibration and validation of the simulation to reproduce the inter-modality practices (modal shares, trip duration, and distance), we evaluated the use of the park-and-ride facilities (PR) by evaluating the number of entries and their capacities. We showed a consistency between the simulation outputs and the actual use of different PRs, particularly during peak hours. Due to the absence of data for the PRs, it was difficult to compare the simulation results with a reference situation. We also studied the user profiles and their motivations for inter-modal trips. The simulations reproduced the shares of individuals practicing inter-modality, such as for persons aged from 18 to 54 years. As part of future work, we will plan to study the traffic congestion for the city center of Lille.
During these 3-monthly visits, participants completed questionnaires, and clinical data were recorded. The results demonstrated that PrEP use was feasible and acceptable and led to a decrease in HIV incidence (2. 3 per 100 person-years, compared to 10. 0 before 2017)  (24). At the time of this present study, PrEP was only available through the CohMSM-PrEP study in Burkina Faso, Côte d'Ivoire, Mali, and Togo  (24). Currently, PrEP is being implemented in these countries. Understanding how PrEP uptake can be optimized among those at the highest risk is a crucial next step. The objective of this study was to explore the perceptions of West African MSM toward PrEP and their proposed strategies to overcome barriers to PrEP uptake within their communities.
They were selected based on their answers in the questionnaires or the medical records of the CohMSM-PrEP study  (24). They were recruited by the research community assistants. The social science researcher in each country conducted 16 interviews, resulting in 64 interviews in total. They were conducted in the researcher's offices or in a safe space. For this analysis, we focused primarily on data pertaining to the process that resulted in uptake, i. e. , their reactions the first time they heard of PrEP and the reasons why they started taking it. Interviews were either in French or local languages. Participants provided informed consent, completed an additional short questionnaire, and received a transportation fee of ∼e10.
Acknowledgments We thank all the participants for their participation in the study, the research assistants ( Honoré Comsiambo ,  Cissé Adama ,  Bréhima Abdrahamane Ouary ,  Julien Bimba ,  Niamkey Thomas Aka , and  Kpassou Julien Lokrou) , and the healthcare providers ( Alou Coulibaly and Issa Traoré ) for their help and guidance in this study. We would like to thank the  CohMSM-PrEP Study Group  for providing their help in recruiting participants based on the questionnaire data and medical records.
Policy evolution impacts the focus of the research (as mentioned in Box 1, Section 2. 1. 1) and, in turn, is influenced by the research, e. g. , the assessment of territorial imbalance and the conception of social innovation mentioned above. Nowadays, huge amounts of data, reports, videos, etc. , of the European Commission and the EU-funded research projects, e. g. , Framework Programs, ESPON, and RURAGRI ERA-NET, are available on the websites. Thus, research can be closer to operational actors and provide them with case studies, development models, action points, databases, and networks. In France, research has successfully brought a territorial dimension to rural development policy by revealing the impacts of the territorial context on the performance of governance mechanisms. Researchers have also drawn the attention of policymakers to stakeholder diversity and the development of consultation tools promoting collaboration and learning dynamics.
The fifth year of PSDR4 was specifically dedicated to transforming scientific results into practical tools to promote knowledge transfer to local actors and other regions. The PSDR projects created about 1000 operational products, including thematic meetings and workshops, training courses for students, professionals, and the public, videos, e-books, manuals, software, databases, posters, etc. These products include good practice manuals for actors and territorial expertise guides to help local decision-makers select and elaborate public action. Most of them have been shared on social media and are freely available on the web.
Acknowledgments This work was supported by the  PSDR4 Program (On and For Regional Development/Pour et Sur le Développement Régional ), funded by the  French National Institute for Agriculture, Food and Environment (INRAE)  and ten French Regions, namely,  Auvergne-Rhône-Alpes, Bourgogne-Franche-Comté, Bretagne, Centre-Val de Loire ,  Grand Est ,  Île-de-France ,  Normandie ,  Nouvelle-Aquitaine ,  Occitanie and Pays de la Loire. The authors thank all the participants of the projects, the coordinators at different levels, the members of the  Scientific Committee , and other people who have contributed to the program and the activities of the projects. The authors are grateful for the valuable comments from the two anonymous reviewers.
Sample size The estimate of the number of participants needed for the study was based on the effect size of the combined physical and cognitive training practice on global cognition. Gheysen et al  4  conducted a meta-analysis of the effect of combined practice on global cognition according to practice condition (simultaneous or sequential) and found an effect size of d=0. 385 for simultaneous combined practice. Using this effect size, and with the error rate set at α=0. 05 and power set at β=0. 80, the power analysis from the SPSS (IBM SPSS Statistics V. 27. 0) software indicated that a sample size of 132 participants (44 participants per condition) would be sufficient to detect the critical effects of the conditions and their potential interactions. The power analysis was calculated from a power test analysis of variance (ANOVA): repeated measures, within-between interaction with a 4×3 model (four repeated measures and three conditions).
| Data Data were collected in the SAPRIS project from parents through two internet questionnaires. The first questionnaire was available from April 16 to May 4, 2020 (T1) and the second one from May 5 to May 31, 2020 (T2). Information on sleep and light exposure was asked only once at either T1 or T2. We considered the responses collected at T1 and those at T2 when missing at T1.
| Sleep and light exposure Collected sleep data focused on change in both sleep duration and sleep difficulties during the lockdown compared to before. The questions asked to the parents were: "Would you say his or her sleep duration has changed since the lockdown began?" (No, it is as usual; yes, it has increased; yes, it has decreased), and "Would you say that since the lockdown began, your child has had difficulty sleeping (such as difficulty falling asleep, waking up at night frequently or too soon with no opportunity to fall back asleep)?" (That have appeared; that have increased; that have decreased; that have disappeared, that have remained stable; no difficulty). We grouped the categories "that have appeared" and "that have increased" in one category "Appeared/increased" and the categories "that have decreased," "that have disappeared," and "that have remained stable" (1.
Analyses were performed using SAS® version 9. 4 software (SAS Institute Inc. , Cary, North Carolina). Comparison of the characteristics of children included and excluded from the analysis was performed with Chi 2 tests for categorical variables, and Student t-tests for continuous quantitative variables. Wilcoxon-Mann-Whitney rank test was used when frequency distribution was nonnormal. Multiple imputations of missing data were performed by using Fully Conditional Specification (FCS). Binary variables were imputed by logistic regression, nominal or ordinal variables were imputed by multinomial logistic regression, and continuous variables by linear regression. 20 databases were imputed and analyzed. Estimates and confidence intervals were pooled to obtain overall results. Multinomial logistic regression models, with either stable sleep time or no sleep difficulty as reference categories, were used to study the associations between the duration of exposure to light, whether outdoor or via screens, and the different sleep changes, without (M1) and with (M2) adjustment for family, child, and study characteristics.