dataesr/scientific-paragraphs-categorization huggingface dataset:
As in the case of the standard Kuramoto model, we performed ten simulations for each proposed model and combination of parameters. The selection of the initial phases and natural frequencies was performed as above and, for each model, we then computed r sim and ρ sim. In addition, we computed the oscillation frequency ω sim g in the group averaged over time and simulated trials. Then, to calibrate the parameters of model m ∈ {1, 2, 3}, we considered the following cost function: We report the F-statistics, p-value, and the mean and standard deviation of J e,l m. For all models m ∈ {1, 2, 3}, we selected the parameter combination ⋆ m yielding the lowest value of J m , that is, J m ( m ) = J s ( m ) + ω exp -ω sim ( m ) ω exp 2 , ( 14 ) ⋆ m = arg min m ∈A m J m ( m ). ( 15 ) In Table  3 , we report the optimal parameter values corresponding to the minimum cost function for each model and dataset.
RESULTS Here, we compare how each of the models captures the experimental data by evaluating the cost function (Equation  14 ) for each pair (e, l) of experimental and simulated trials. Specifically, denoting N t the number of trials of the dataset, we compute  16 )  represent group frequency, level of frequency coordination, and order parameter recorded in the e-th experimental trial, respectively, whereas ω sim ml , ρ sim ml and r sim ml are the corresponding values in the l-th simulated trial. For each model m, we evaluated the cost function (Equation  16 ) for all the 36 possible pairs of experimental and simulated trials, and then ran an ANOVA test on the distribution of J e,l m to compare the effectiveness of each model in reproducing the experimental data. The outcome of the analysis is reported in the boxplot in Figure  3  and Table 4 , which show a significant difference among the model performances (p < 0.
FUNDING CC was supported by the  Italian-French University VINCI  Program 2017 that supported her Ph. D. Scholarship. PD was supported by the program STAR 2018 of the  University of Naples Federico II  and  Compagnia di San Paolo ,  Istituto Banco di Napoli-Fondazione , project  ACROSS. BB was supported by grant  H2020-FETPROACT-2018-01 EnTimeMent (No 824160 ). This work was also supported by the  Italian Ministry of Research and University  through the project  ICOSAF  under Grant  CUP E26C18000180005.
Measurements From the initial pool of items used between 3. 5 and 12 years of age, we selected 30 items based on the following criteria: (1) best construct validity according to expert opinion and (2) highest loadings on the original factor (Table  2 ). For the former, we have conducted several meetings with experts in child development, developmental psychopathology, and psychometrics, including some of the co-authors of this study (CO, MO, MB, and SMC) to select the most informative items. Items were reported by the person most knowledgeable about the child and by the teacher. Items were answered on a three-point Likert-type scale ("never or not true," "sometimes or somewhat true," "often or very true") referring to the past 12 months. The positively phrased items were reverse-coded. At each data point, the subscales scores were obtained calculating the mean of the items.
Data Screening No outlier was found. In addition, the exclusion of participants presenting missing data on all items per wave led to the inclusion of n = 1,950 participants at age 3. 5, n = 1,942 at 4 years, n = 1,759 participants at 5 years, n = 1,492 at 6 years, and n = 1,466 at 8 years, for maternal-reported SBQ. For teacher-reported SBQ, it led to the inclusion of n = 966 participants at age 6 years, n = 1,311 participants at age 7 years, n = 1,288 at age 8 years, n = 991 at age 10 years, and n = 1,008 participants at age 12 years.
Strengths and Weaknesses This study presents several strengths. First, it used data from a large population-based and longitudinal study, which permitted the assessment of the psychometric properties of the SBQ at multiple time points across childhood. Second, we used two complementary approaches, CFA and MMTM matrix methods, to evaluate the questionnaire's internal consistency. Some limitations should be noted when interpreting the findings. First, the SBQ is a brief questionnaire designed for conducting research in community or clinical samples, but it does not provide clinical or diagnostic assessments. Second, we did not validate the SBQ by comparing it with another tool using the same sample (e. g. , SDQ, CBCL). The QLSCD collected intensive information about participants. Including additional questionnaires, similar to the existing one, for the purpose of comparing their results, would have significantly increased the time and cognitive burden on participants and reduced responses rate  (Edwards et al.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Québec Longitudinal Study of Child Development was supported by funding from the ministère de la Santé et des Services sociaux, le ministère de la Famille, le ministère de l'Éducation et de l'Enseignement supérieur, the Lucie and André Chagnon Foundation, the Institut de recherche Robert-Sauvé en santé et en sécurité du travail, the Research Centre of the Sainte-Justine University Hospital, the ministère du Travail, de l'Emploi et de la Solidarité sociale and the Institut de la statistique du Québec.
Development of the MyPredi platform The MyPredi (formerly E-CARE) platform integrates the monitoring of chronic diseases including diabetes, hypertension, and COPD  (20). The MyPredi platform, created from Ecare, is a generic platform with original architecture with proven functionalities to manage patients with chronic pathologies who require long-term follow up. E-care consists of a patient module, with a tablet and sensors, and a server module which receives and processes the data collected from various patient modules. The server module combines semantic web and artificial intelligence technologies. Generic ontologies are used to adjust to different pathologies and different types of sensors and data. An inference engine is used to monitor the patient's health and to detect any abnormal situations  (21).
All alerts detected by the system are forwarded to the nursing staff responsible for monitoring the patient. Ontologies are particularly suitable for understanding, sharing, and integrating information  (20) (21) (22) (23). However, various issues on ontology remain unanswered. For example, the ideal method of designing, representing, reasoning, automation, evolution, and alignment of ontologies are in early stages. Furthermore, the persistence of ontological data, systems of integration on an ontological basis, design of databases accessible from ontologies, and integration of vagueness in ontologies need to be addressed  (24). In addition to the problems encountered by the heterogeneity of the available data, the sequence of algorithmic processing capable of exploiting these data represents a scientific and technical challenge.
Main developments and functions The platform helps caregivers by automating the processing of sensor information, questions, and questionnaires in order to detect and report medical risk situations early. MyPredi remote monitoring platform will provide personalized care for the main geriatric risks to avoid the occurrence of acute factors leading to decompensation in elderly patients. The information collected will be supplemented by codified therapeutic care, according to the international recommendations in nursing homes. This platform provides all paramedical and medical health professionals with information on the resident's geriatric data. This information will be updated regularly, including anthropometric, nutritional, cognitive, and iatrogenic data. Together, the data will provide a real-time picture integrated into an electronic platform, creating a standardized gerontological assessment based on simple and rapid measures.
Methodology Using a tablet and connected objects (Figure  2, Table 2 ), the patient's vital signs are measured daily: blood pressure, heart rate, weight, oxygen saturation, capillary glucose, temperature, and physical activity. The entire device is ISO13485 medical certified with CE marking  (12). With the GER-e-TEC project, we can now offer medical teams and paramedics a remote monitoring device that optimizes the management of patients suffering from geriatric syndromes and their accompanying conditions. This remote monitoring system will allow monitored patients to record their daily physiological data (weight, oxygen saturation, blood pressure, heart rate, blood sugar) and complete questionnaires on the state of their health. The data will be automatically sent to our MyPredi platform, which is designed to predict situations that are at risk of deteriorating.
Data collection FGs were perceived as most appropriate for this research, because, compared to individual interviews, people's views become clearer through their interaction with other participants  [21]. The duration was approximately 2 h per FG. Prior to the FG, participants were asked to fill out a brief questionnaire including demographic characteristics such as age and occupation, adult vaccines received (including for travel and leisure) and the types and frequency of HCPs consulted with. The focus group guide consisted of a semi-structured open-ended topic list and included the following diseases and their corresponding vaccines: influenza, pneumococcal disease, herpes zoster and tetanus. These vaccines are offered in vaccination programs or are candidate vaccines for programs in either one of the study countries.
Data analysis The data of the demographic questionnaires were entered in MS Excel, uploaded to IBM SPSS statistics (version 24. 001), and analysed on cross-country level. All FGs were recorded and transcribed verbatim in the native language, leaving out any information that could identify participants (e. g. names). The resulting transcripts were translated into English to perform a cross-country analysis. One researcher (MW) analysed all transcripts by applying thematic coding, whereas another researcher (RE) independently analysed 25% of the transcripts. Differences in coding were discussed until consensus was reached. The analysis was conducted using MAXQDA 2020 and consisted of open coding, axial coding and selective coding. In the stage of open coding, codes were assigned to all relevant passages.
Acknowledgements The research leading to these results was conducted as part of the VITAL consortium. For further information please refer to www. vital-imi. eu. The purpose of this study is to provide input for an education and trainings platform on vaccines for older adults, targeting HCPs. We are grateful to  Filippo Bragiotto ,  Dávid Nagy , and  Ivett Jakab  for their contribution to the data collection. Finally, we would like to thank the participants of the focus groups, without whom this study would not have been possible. Part of this research has been presented in the form of a poster at the  WCPH 2020 in Rome, Italy.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS. 2022. 3199445Steps Description Step 1. Input database: a. Population distribution: the number of people that should be evacuated from each node. b. Network map: the city map represented as a graph via a network file. c. Risky zone: the set of all origins that will be considered. d. Destination nodes set: the set of shelters. nodes. Step 2.
VOLUME X, 2022 5 2022 This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS. 2022. 3199445Steps Description Step 1. Input database: a. Population distribution: the number of people that should be evacuated from each node. b. Network map: the city map represented as a graph via a network file. c. Risky zone: the set of all origins that will be considered. d. Destination nodes set: the set of shelters. nodes. Step 2.
Study population We used data from a national registry of single mothers who had their first child during adolescence, involved in the RENATA (Réseau National des Tantines) project. RENATA is the French acronym for the National Network of Aunties Associations, an aunty being a teenage mother trained in sexual and reproductive health and rights of adolescents. This network consists of more than 20,000 single mothers trained as "Aunties" in about 305 community-based associations in Cameroon. RENATA is supported by a multidisciplinary team of psychologists, anthropologists and communication specialists. The recording of the profile of teenage mothers began in 2003 and this data was collected before each training session of a new Aunties' association in the community and continued systematically until 2012, nationwide.
Sampling technique and sample size This is a retrospective review of the RENATA's network database. Every girl who gave birth to one or more children between 10 and 19 years and was not married at the time of birth of the child was considered a teenage mother. The inclusion criteria were: having given birth to at least one child between 10 and 19 years, being single and younger than 30 years at the time of the survey, 30-yearold was the age limit to be eligible to be part of the network. Ten surveys were conducted between 2003 and 2012 and 23,723 adolescent mothers included. For this review, we excluded data collected before the year 2005 which did not include information on the participants' reported sexual behavior like: the number of sexual partners during the last 12 months, history of rape, use of condom during the last sexual intercourse and use of a modern contraceptive method.
All Teams data are thus protected within the network. Meanwhile, Azure Active Directory (Azure AD) provides a single trust ed back-end repository for user accounts. User profile information is stored in Azure AD through Microsoft Graph actions. Transport layer security (TLS) and mutual TLS (MTLS) encrypt instant messaging traffic and enable endpoint authentication. Point-to-point au dio, video, and application sharing sequences are encrypted and checked for integrity with the Secure Real-Time Transport Protocol (SRTP). OAuth traffic can also be used for monitoring, especially with reference to nego tiable permissions while switching between tabs in Teams, for example, to move from posts to files. Teams also uses industry standard protocols for user authentication whenever possible, and features the standards ISO 27001, ISO 27018, SSAE16 SOC 1 and SOC 2, HIPAA and the EU model clauses (EUMC).
translating, document presentation, etc. , provides a wide landscape for cyber-security threats 21. The consequences of these cyber-attacks may be very serious for the parties involved in the arbitrations and lead to loss of personal/commercial data, money, intellectual property and reputation; their market value may fall and regulatory actions ensue. What is more, as the literature has rightly noted, after a cyber-security incident, the participants may find it difficult to trust the arbitration process (and the arbitrators) and may also question any data that is presented for its authenticity  22. Similarly, the ICCA-NYC Bar-CPR Protocol on Cybersecurity in International Arbitration (2020) identifies several consequences that may result from inadequate attention to information security: -Economic loss to individuals whose commercial information or person al data is compromised, -Loss of integrity of data, or questions about the reliability and accuracy of data, -Unavailability of data, networks, platforms, or websites due to disrup tion caused by a security incident, -Potential liability under applicable law and other regulatory frame works, -Reputational damage to parties, arbitrators, arbitral institutions, and third parties, as well as to the system of arbitration in general  23.
Also, arbitral institutions now commonly publish awards (with or with out redactions to conceal the identity of the parties involved) and there are several databases available for this purpose. Interest in arbitration -or at least in certain high-profile cases -has been increasing over the years. With the pandemic, the use of online arbitration platforms -with largescale transfers of documents and the holding of remote hearings -has grown exponentially. The vast number of arbitrators, parties, lawyers and witnesses working online and attending remote hearings from their home/ business networks, which may offer little protection against intrusion by, has also exponentiality increased the risk of a cyberattack. Consequently, the issue of confidentiality and privacy of the arbitration has become more difficult to manage with the increased use of all this technology.
Parties involved in Disputes submitted to ARBITRARE The disputes submitted to ARBITRARE may be those between private parties 3 or between private parties and the Portuguese registration bodies legally empowered to grant or refuse registrations. These bodies are the National Institute of Industrial Property (INPI), competent for granting industrial property rights, the association DNS. PT (DNS. PT), responsible for the management, registration and maintenance of the. PT country code top-level domain (ccTLD), and lastly the Institute of Registration and Notary Affairs (IRN), competent, inter alia, for granting trade names and corporate names to legal persons. It is mandatory for those three Portuguese registration bodies to refer disputes to ARBITRARE. INPI and IRN are bound by law 4 and DNS.
Meanwhile, in 2017, ARBITRARE was contacted by a -fortunately well-intentioned -hacker, alerting the Centre to the existence of vulnera bilities in the platform, with threats and attempts to intrude on the system. At this point, it became clear that, after nine years of intensive use, this version of the platform had become obsolete (entirely normal for this type of software). Despite the security challenges that ARBITRARE faced with its first online platform, the overall picture was a positive one, considering that it allowed the Centre to offer dematerialized services over a period of ten years (with a few limitations related to the conduct of arbitration proceedings, as mentioned above). In early 2017, ARBITRARE decided to invest in the development and implementation of a new Online Platform for Dispute Resolution, here inafter referred to as 'platform'.
-Security The platform was developed and implemented with extensive security mechanisms in order to avoid different types of vulnerabilities, such as: Injections and common security breaches: -SQL, SO, LDAP, XSS injection prevention; -CSRF attack prevention; -All redirections on the Platform are validated. Communication and configuration of web services -All communication is via HTTPS (HTTP over SSL); -Sensitive data (such as usernames, password, tokens, etc. ) never appear in the URL; -Sensitive data, such as passwords, are never present in the software code; -A user session is associated with an IP address and it is not possible for that session to change address. Authentication -When changing password, the previous password is always required; -Passwords are encrypted, and never directly recorded in a database; -Password recovery features never display either current or new pass words; -No user information is given during login or password recovery.
I. When registering on the platform, users have to read and accept this Privacy Policy, which states: -The personal data collected and processed for user authentication on the platform (name and email address) and for filing of proceed ings (name, nationality, address, post code, locality, country, email address, tax identification number, language, mobile phone number and IBAN); -The purposes and legal grounds for processing personal data, and how long it is stored. ARBITRARE processes the personal data of platform users: -For authentication on the platform as an authorised user, allowing them to file and access dispute resolution proceedings; -For all purposes necessary in managing dispute resolution proceed ings, including contacts via the platform, email and/or telephone, notifications, answering questions, satisfaction surveys and evaluating the services provided; -To fulfil any legal obligation to which ARBITRARE is subject.
The legal grounds for processing are pre-contractual due diligence at the request of the data subject, the provision of alternative dispute resolution services, in the form of mediation and/or arbitration, in matters relating to industrial property, domain names, trade names and corporate names, and the fulfilment of legal obligations. Users' personal data, collected and processed in the course of dispute resolution proceedings, is stored until the data subject requests its deletion, provided five years have elapsed since the end of proceedings. If the data subject requests the deletion of his/her personal data, ARBITRARE must delete the arbitral proceedings from the platform, as well as from any document stored in a physical medium relating to the proceedings. Personal data which has been collected and processed for purposes of user authentication on the platform is stored until the data subject asks for it to be deleted.
Data description and productivity criterion We use firm-level data from two sets of standardised surveys conducted since 2002 by the World Bank and administered to business owners and senior managers of private sector establishments: 4  (i) the Informal Enterprise Surveys (IFS) which collect data on non-registered informal firms in a number of developing countries with a large informal sector and (ii) the Enterprise Surveys (ES) which interview registered formal firms from several countries around the world. In each country, businesses located in the cities or regions of major economic activity are interviewed. The two sets of surveys cover firms with manufacturing or services as their main activity and give further information on firms' general characteristics, infrastructure, finance, labour and assets.
Method Data, Setting, and Participants Data were collected from a sample of 460 French university undergraduates (249 men [54. 13%], 209 women [45. 43%] and two who did not specify their gender), aged between 18 and 48 years (M = 22. 45 years; SD = 3. 14 years). All the participants were at the end of their degree course and were taking an optional 6-month entrepreneurial program (see Supplementary Materials). This program is based on group learning and team competition. The participants were in 298 teams of two to eight members. This entrepreneurship program aims to develop a sense of business, and includes a "taught" component, with different modules (accounting, finance, marketing, management); a "business-planning" component, which includes business plan competitions and advice on developing a specific business idea; and an "interaction with practice" component, which includes talks from practitioners and networking events.
After each scenario, the researchers asked the participating child the post-narration questions, in order to elicit responses about the emotion that the scenario was designed to elicit. The sequence of scenarios and questions was identical for all children. Each session lasted between 10 and 15 minutes. All the sessions were recorded, but no personal data about the participating children were collected, except for their given name and their exact age, expressed in years and months. The recordings were transcribed by the researchers, and the answers that the children provided were transferred on protocol forms.