dataesr/scientific-paragraphs-categorization huggingface dataset:
For the quantitative data collection, the consent form will be completed for all participants or those legally allowed to consent on their behalf. Because being underaged, the consent forms will be asked to the adolescents and their caregivers. Questions about children will be asked to their caregivers. We will interview participants in an adequate setting allowing confidentiality. We will replace the respondents' names by ID codes that will not appear in the public databases. Once all data are imputed and ready to use, the key, associating names and ID codes, will be destroyed. For the other data collection, depending on the people interviewed and the local issues at stake, written or verbal consent will be systematically requested from participants. The usual ethical criteria for qualitative health research will be respected.
Acknowledgements The co-authors of this manuscript would like to thank  Anthony Cu  for his review of the literature,  Shabab Haider ,  Suhi Hanif  and  Rafee Tamjid  for their support in developing the successive versions of this protocol and  Emilie Dubois , from the  Impakt Scientifik agency (Agence IMPAKT Scientifik , https:// www. impak tsci. co) for the graphic design of the conceptual framework. Finally, we thank the reviewers for their questions and comments.
Medical staff collected clinical data at each quarterly follow-up visit (scheduled or not), including PrEP regimen and quarterly HIV and STI testing results. Trained research assistants administered standardized face-toface questionnaires at enrollment and every three months thereafter, which collected data on individual characteristics, sexual behaviors, psychosocial factors, substance use, and PrEP and condom use. During these same quarterly follow-up visits, participants were provided free clinical examinations, PrEP (event-driven or daily), HIV testing, screening and treatment for other STI, condoms and lubricants, and tailored prevention counseling. The latter was provided by PE and focused on PrEP adherence, risk reduction strategies (including condom use, switching between regimens, abstaining from certain high-risk activities, and other risk-reduction strategies), encouraging testing, and program retention.
Study population The analyses for the present study included CohMSM-PrEP participants enrolled between November 2017 and November 2020 who had at least one available questionnaire from M3 to M36, since there was no pill intake at baseline. We excluded data for participants who declared no male partners in the previous three months (stable or casual). As the study focused on PrEP-based HIV prevention, participants who seroconverted during follow-up were censored at their seroconversion date.
Second, the study population comprised a convenience sample of MSM already attending community-based clinics or recruited via peer networks; accordingly, it was not necessarily representative of the local MSM population. However, the differences found in a previous analysis comparing ex-CohMSM participants with newly enrolled participants, suggest that the addition of PrEP to the original project's (i. e. , CohMSM) sexual health prevention offer, and the continued use of peerbased outreach over time helped reach a new profile of MSM less connected to these clinics  [64]. Third, the fact that we used only self-reported data to assess PrEP adherence may be considered a less reliable strategy than objective biological measures (plasma, urine, hair, etc. ). Nevertheless, multiple studies have shown that drug intake can be accurately predicted by selfreported PrEP outcomes  [93] [94] [95].
Tools and Procedures Data collection was carried out using a standardized structured questionnaire prepared in Arabic, the native language in Lebanon. A forward and backward translation was conducted for the Mini-AQLQ scale. One translator was in charge of translating the scales from English to Arabic, and another one was involved in the translation from Arabic back to English. Discrepancies between the original and translated English versions were resolved by consensus. It was administered via face-to-face interviews by trained researchers to ensure a higher quality of data collection. The first part of the questionnaire included patient demographics, such as gender, age, marital status, educational level (illiterate/primary, secondary, and higher education) and socioeconomic status. The second part assessed the QoL of asthmatic patients using the Mini AQLQ, a validated tool with good reliability since it is discriminates patients with different levels of impairment and validity as it measures asthma-specific quality of life  (28).
Data Entry and Statistical Analysis Using Statistical Package for Social Sciences version 21. 0, statistical analysis was performed. Descriptive statistics were used for patients' characteristics, with frequencies and percentages for categorical variables and means ± standard deviations for continuous variables. Bivariate associations assessed through the Pearson correlation analysis for the continuous variables along with the HRQol score, and the Student t-test and ANOVA F tests were used for categorical variables with two or more levels, respectively. Multivariable linear regression using the Forward method was done for patient's QoL score assessment as the dependent variable, using variables that showed a p < 0. 2 in the bivariate analysis. All reported p-values are two-sided, with alpha set at a significance level of 0.
Socio-Demographic Characteristics Data was collected from 200 participants out of whom 172 were enrolled in the study as the others had mental disorders and thus were excluded. The mean (±SD) age was 31. 79 ± 20. 92 years, with the majority being females 96/172 (55. 8%), single 117/172 (68. 0%), and with secondary level of education 86/172 (50. 0%). 69/172 (46. 3%) of families had a medium monthly income.
Fixed Measurements The fixed monitoring was carried out by a fixed weather station and some fixed sensors, all recording data continuously. The fixed weather station was put on the roof of the school entrance building (Figure  3 ) through a "pole" fixed to the railing of the ladder. This allows the station to be one meter above the roof and 10 m above the ground level, as previously performed in the literature  [39]. The average daily temperatures during the year do not drop below 4 • C in the cold months and are between 25 and 30 • C for most of July and August. All around the year, temperatures evolve from -3. 3 • C (the 4th of January) to 44. 5 • C (the 16th of August). From May to October, most of the daily maximum temperatures are up to 30 • C, which is detrimental to the quality of life in open spaces.
Fixed Measurements The fixed monitoring was carried out by a fixed weather station and some fixed sensors, all recording data continuously. The fixed weather station was put on the roof of the school entrance building (Figure  3 ) through a "pole" fixed to the railing of the ladder. This allows the station to be one meter above the roof and 10 m above the ground level, as previously performed in the literature  [39]. The meteorological station selected was a Watchdog 2700 Weather Station, as previously used in the literature  [39]. The station measures each hour and has an internal memory that allows measurements to be recorded for a period of approximately one year. Variables measured are presented in Table  1 , as well as the manufacturer has given their specifications and accuracy. The meteorological station selected was a Watchdog 2700 Weather Station, as previously used in the literature  [39].
Semi-Mobile Monitoring A PCE-FWS20N climatic station carried out the semi-mobile monitoring. Table  3  shows the measured variables and their measurement range, resolution, and accuracy given by the manufacturer. The temperature measured by each sensor is shown in Figure  7  for two weeks of October. It can be seen that data recorded by each sensor is quite similar all the time. Thus, measurements of both four can be accepted. Temperatures reached over 30 °C in the middle hours of half of the days, highlighting the severe climate problem faced by the School's pupils. All the fixed devices used allowed us to conduct long-term monitoring and characterize the study area's entire climatology. In order to also better capture the spatial distribution of the space and the peculiarities experienced by the users, mobile monitoring and semi-mobile monitoring were used during the survey periods.
Mobile Monitoring For the mobile monitoring, a temperature sensor was used, for which a cover was designed. This consists of a hollow cylinder on both sides, 16. 4 cm high and 6 cm in diameter. It was firstly covered by an insulating layer and secondly by a reflective layer. This ensures that the sensor is not affected by incident radiation and can accurately measure the temperature of the air flowing through it. At the top was attached a small extractor fan powered by an external battery via USB to avoid air stagnation (Figure  9 ). The investigator always carried this device during the surveys. It does measurements each minute, and data were extracted after each use for a survey campaign. fan powered by an external battery via USB to avoid air stagnation (Figure  9 ). The investigator always carried this device during the surveys.
Very cold Secondly, 9 "questions" were filled in directly by the interviewer without needing to ask the person in approximately 2 min. These correspond to personal details (age and gender), clothing (color and seasonality), activity, and humor  [29]. Then, the solar exposure (either in the sun or in the shade) and a basic estimation of the person's time exposure was reported. Finally, the location (latitude, longitude, and altitude) was also put. Since the one given by the web page is relatively unprecise, additionally free apps were used. GPS coordinates were recorded automatically every minute with Geo Tracker-GPS Tracker on Android or Maps 3D-Outdoor GPS on iOS. Each one was registered with the date and time that allowed to make it to correspond with the right questionnaire. For this purpose, a database was generated where the results of the surveys were stored daily, together with their geographical position.
In this way, all the planned days were covered. A team of two surveyors assumed each survey session. They carried with them the mobile monitoring equipment (air temperature sensor), the semi-mobile device (tripod), and their own mobile from which they conducted the surveys and registered the geolocation (Figure  10 ). Data from the mobile and semi-mobile monitoring and survey responses with their geolocation were downloaded by the survey team after each survey day and stored on an internal database. Surveys were performed during the Autumn and Spring of the year 2021. This month was chosen for its intermediate weather. Moreover, the summer months correspond to the summer holidays and so to a period when the School is not used. A survey session lasted approximately one hour, from 12 h 30 min to 13 h 30 min, which corresponds to the period where there is the largest number of pupils in the study area and the hours when the sun is the hottest.
The team of surveyors was made up of 10 people, both researchers from the research group and students from the Higher Technical School of Engineering of Seville interested in the study. Surveys were carried out over 22 days, corresponding to the working days of the month of October. An online schedule was created where people in the survey team had to register weekly on which days, they could carry out the surveys. In this way, all the planned days were covered. A team of two surveyors assumed each survey session. They carried with them the mobile monitoring equipment (air temperature sensor), the semi-mobile device (tripod), and their own mobile from which they conducted the surveys and registered the geolocation (Figure  10 ). Data from the mobile and semi-mobile monitoring and survey responses with their geolocation were downloaded by the survey team after each survey day and stored on an internal database.
One person is a surveyor, and the other is responsible for sensorization. The mobile sensorization (see Figures  8  and 9 ) allows for obtaining objective data for the estimation of the COMFA comfort indicator in the conditions of the respondent, according to Section 2. 4). - In parallel to mobile sensorization, the collection of people's opinions in the study area is carried out. People respond about their thermal sensation, state of mind, and if they have eaten recently. The interviewer collects more information about gender, clothing, and physical activity (see Section 3. 1). -Once all the information from points 2, 3, and 4 have been collected, the measured results are analyzed. Sections 3. 1 and 3. 2 show the results of the surveys and the environmental values measured, respectively. -Finally, we compare the objective stations of the COMFA indicator and the recorded values of the opinion of the people surveyed.
Data Recorded Two types of data were recorded: objective measurements provided by the monitoring and subjective responses given by the space users during survey sessions. The first one allowed us to know the area's climatic conditions during the study month, which was necessary for computing the comfort index and thus obtaining the objective evaluation of the level of comfort of an occupant located in a specific area of the space. On the other hand, the second group of data, the subjective responses, allowed us to know the real perception of the person in the same place and with the same climatic conditions and compare it to the previously calculated comfort level.
Analysis After collecting data for the whole month, the analysis consisted of calculating the COMFA comfort indices and producing statistics from the survey responses. For the COMFA model inputs, the climatic variables recorded by the monitoring campaign were used. The sky temperature was extracted from the typical climate file of the city of Seville. A person's characteristics were defined using ASHRAE standards and being adapted to a child  [10]. Thus, an average Dubois area of 1. 1 m 2 was taken, corresponding to a pupil's average height and weight. The clo was taken equal to be 0. 57 to correspond to a person wearing trousers and a short-sleeved shirt which is the typical outfit of a Sevillian in October. All exchanges between the person and his/her environment were calculated using ASHRAE standards  [10].
Survey Results During the survey campaign in 2021, a total of 1537 questionnaires were conducted in the study area and its direct surroundings. The results presented here are based on those carried out in the non-winter and non-summer months, spring and Autumn. Specifically, these measured data are from April, May, and October, and these are some classic months of neither cold nor heat in Seville. This guarantees favorable comfort situations in the subjective evaluations carried out and to be able to contrast them with objective estimates. For example, the location of interviews in the school area (more than 800) can be visualized in Figure  13 , where each color corresponds to a survey day. Figure  14  shows the distribution of respondents by gender for all days of the campaign. Most of the time, there is a higher number of male respondents, although the difference is small.
Conclusions The present investigation was conducted to determine the applicability of the current thermal model COMFA for assessing the outdoor thermal comfort of space in the Mediterranean climate of Seville, Spain. Microclimate parameters monitoring was carried out to gather local data as inputs of the COMFA model. In parallel with its application, field surveys were conducted during 2021 and then compared to the model predictions. The results of this work prove, with respect to the published material, that the band of neutrality in comfort can be considered higher. The surveys carried out to demonstrate that up to values of 70-80 W/m 2 , people accustomed to hot climates think they are fine. Even values up to 90 W/m 2 are acceptable for transit areas when the length of stay in that space is less than 10 min.
Data Collection and Measures The following data were systematically collected via an electronic medical record in each participating center: patient characteristics (age, gender), tobacco consumption, and anthropometric measures (weight, BMI, waist circumferences, percentages of fat mass, muscle mass, and body water). Anthropometric measures were collected during each in-patient follow-up visit with a calibrated bioelectrical impedance scale (BG42, Beurer, Ulm, Germany). Waist circumference was measured to the midpoint between the lower border of the rib cage and the iliac crest. For participants with abdominal adiposity with no visible natural waist, the measurement was taken approximately 2-5 cm above the navel. Main comorbidities and medications for diabetes and hypertension were also assem-bled.
Information about pre-existing diagnosis of sleep apnea syndrome and treatment by continuous positive airway pressure (CPAP) was available. Specifically, for the DietSleep study, the following data were collected: (1) the Berlin score, obtained from a 9-item questionnaire aiming to assess the risk of OSA  [27] , which is defined by a score of 2 points or more. Each sub-score of the Berlin questionnaire was analyzed separately. The sensitivity (Se) and specificity (Sp) of the Berlin questionnaire were assessed to screen severe OSA in the general population and were: Se = 76. 9 and Sp = 72. 7  [23]. (2) Daytime sleepiness was measured using the ESS. The ESS was considered as both a continuous and a categorical variable, with a threshold at 11; patients with ESS ≥11 were considered to have excessive daytime sleepiness.
Emergency department (ED)  overcrowding is an ongoing problem worldwide. Scoring systems are available for the detection of this problem. This study aims to combine a model that allows the detection and management of overcrowding. Therefore, it is crucial to implement a system that can reason model, rank ED resources and ED performance indicators based on environmental factors. Thus, we propose in this paper a new domain ontology (EDOMO) based on a new overcrowding estimation score (OES) to detect critical situations, specify the level of overcrowding and propose solutions to deal with these situations. Our approach is based on a real database created during more than four years from the Lille University Hospital Center (LUHC) in France. The resulting ontology is capable of modeling complete domain knowledge to enable semantic reasoning based on SWRL rules.
The domain ontology for emergency management is a mechanism to provide a consistent view of the specific domain that can be used by all relevant authorities. Semantic technology has been used to build an extensible data model (the Resource Description Framework, RDF), in which data is represented as meaningful triples (subject, predicate, and object)  [17]. The ontology is used to create the knowledge base that stores facts and procedures about the emergency and expert advice. A major aspect of this paper is to define a knowledge representation structure in the form of an ontology that provides information about emergency situations. These are essential to achieve a successful resolution of an evolving situation and to propose adequate solutions according to the severity of the solution. The ontology thus created will be able to represent and capture enough information about situations such as natural disasters, accidents, pandemics and climate data that can influence the demand flows on emergencies.
To define the domain concepts, we use standard medical Dictionary of ED and Medical Dictionary of Health in collaboration with the staff of LUHC to build a healthcare domain ontology. Emergency physicians have validated the EDOMO ontology and each element defined and the relations between classes, then we describe all its elements with the OWL 2 language in ontology form. Table  2  presents an overview of the properties defined in EDOMO. Each property is responsible for defining the relationships between domain concepts in order to create overcrowding situation case scenarios in the ED. Therefore, we define the different data and knowledge in the field of ED by domain concepts and relationships such as context, evaluation and patients. This ontology presents a combination of exact indicators such as the ED capacity at time t and estimated indicators such as the climate forecast.
The case model: Based on the FOTS ontology  [10] , we developed the EDOMO ontology that represents the ED environment and overcrowding situations. EDOMO provides a case model. The latter presents a detailed description for each ED overcrowding situation scenario containing all data. We describe the different relationships between domain concepts in our EDOMO ontology and set up the case model by defining real ED situation case scenarios. To improve the communication between the case model and the domain model, we integrate these situation cases into the ontology through the data and object properties (Figure  1 ).
Results To implement the EDOMO domain ontology, we used Protégé 5. 5 (Figure  2 ). It is an open source software  [12]  that provides a plug-and-play environment for rapid application development. Protégé is the most familiar tool for developing the ontology. Developers can create the ontology easily through its graphical interface without having to worry about the syntax language. Thus we integrate the SWRLTAB tool into protégé for the creation and exploitation of SWRL rules. Thus, we have selected 100 overcrowding scenarios from a real database of the ED in LUHC hospital and we implemented our ontological base in EDOMO. It is a 4-year database including arrival information between 2016 and 2020. This work is part of the OIILH project (2018-2022): Optimization for inter and intra logistics in hospitals.
Conclusions In this paper, we propose a new complete ED domain ontology (EDOMO) to handle the overcrowding in EDs. This ontology is enriched with several types of data (e. g textual and semantic data) which facilitate the development of a DSS. This latter contains semantic reasoning algorithms through SWRL rules to assess the situations of EDs and propose health care personnel to manage them. Thus, the overcrowding estimation score (OES) corresponds to a semantically ontology-based assessment tool with a high coverage of the ED domain and its environment by defining all the useful knowledge. We prove in this paper that our functional ontology presents the necessary knowledge of the emergency domain and can be integrated into a DSS based on semantic reasoning. This allows us to provide very efficient results and to solve the problem of missing data.
One well-known HIS component is the electronic dental record (EDR), which is used by practitioners to document both patients' medical and dental history and detailed information on consultations. Given the obvious benefits of EDRs, especially in the context of large clinical institutions, EDR overall adoption rate in the US increased from 52% in 2012  [8]  to 77% in 2017  [9]. Indeed, EDRs are not a simple transposition of paper records. Ideally interoperable with other HIS components, they allow to control data capture, facilitate data storage and access, support administrative and management processes, and guide public health policies. They can also be used in research and education  [10] [11] [12] [13]. To take full advantage of EDRs, particularly with regards to the communication, aggregation, and reuse of data, standardized clinical coding systems (SCCSs) are needed that are scalable, shareable, and adapted to the dentistry domain  [13].