dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The sample consisted of 4364 French participants, 3436 women and 928 men (Mean age = 41. 5, SD = 12. 81, Min = 16, Maxi = 89, N 16-17 years = 11). The participants completed the questionnaire at home (72. 5%) or at work (27. 5%). The study was reviewed and approved by the human ethics committees Sud Est VI, France (clinicaltrials. gov NCT 04308187). All participants were volunteers and were informed of the objective of the survey and that their data would be processed anonymously and be used for research purposes. The ethics committee waived the need for written consent considering that if people respond to the questionnaires by going to the website, they are giving their consent. Furthermore, they can withdraw it at any time. The few minors who completed the questionnaire did so with the consent of their parents who sent them the survey.
Procedure We implemented an open epidemiological, observational, descriptive study by administering a self-reported questionnaire proposed to volunteers using REDCap 1 software available through the COVISTRESS. ORG website. The REDCap 1 questionnaire was hosted by the University Hospital of Clermont-Ferrand. The questions analyzed in this manuscript were therefore specific questions included in a large questionnaire composed of different thematic sections of questions (S1 Questions). The thematic sections were presented in random order after the demographic questions. The online questionnaire was distributed several times through mailing lists held by institutions and French social groups. There were no exclusion criteria. The data that we analyzed were obtained for the period of lockdown from March 31 th to April 12 th , 2020, whereas the French lockdown was ordered on March 17 th at 12:00 noon.
Statistical analyses We performed analyses of variance on the subjective experience of time. We also examined correlations and ran a linear regression model on all the measures of interest by using the standardized data. We used the variance inflation factor (VIF) to examine the multicollinearity in the regression analysis  [31]. Finally, to examine the results of the linear regression model in more detail, we also performed an analysis of mediation. The analyses were performed with SPSS and the Bonferroni correction was systematically applied when necessary. that time passed faster when a longer period of time was considered, i. e. , a week compared to a day or the present moment (Bonferroni comparisons, p < 0. 01).
Context. The presence of a massive close-in planet with an orbital period of a few days or less around a low-mass star can possibly result in a strong variation in the properties of the central star. Indeed, star-planet tidal interactions generate exchanges of angular momentum that can result in tidal spin-up. This effect could then lead to gyrochronological ages biased towards younger ages. Aims. This article provides the community with TATOO, a standalone tool based on tidal-chronology, with which to estimate the age of a massive close-in planetary system using only its observed properties: mass of the planet and the star, stellar rotation, and planetary orbital periods. Methods. I used a star-planet tidal evolution numerical code to create a large multi-parametric grid of the evolution of synthetic starplanet systems.
052 M jup mass planet for which we explored the initial conditions of the stellar rotation rate and orbital period. This tailored gird is thus only valid for planetary systems that are identical to the WASP-43 ones. The age of the system is then extracted from this tailored grid by using the observed pair P rot,obs ; P orb,obs. However, this procedure is extremely time consuming and the resulting tailored grid is only valid for the considered planetary system corresponding to the observed couple M ; M p. A generic numerical tool is thus required to provide us with a faster estimate of the age of a given observed system. In this section, I present such a tool named TATOO (https://github. com/GalletFlorian/TATOO) and based on the work of  Gallet & Delorme (2019). To allow a clear reading of the paper, the nomenclature used in this work is explained below: tailored grid, initial grid, pre-compiled age exploration grid: These are three different grids that will be introduced in the text.
Stellar model The evolution of the internal structure and the main physical properties of the star are provided by the stellar evolution code STAREVOL (see  Lagarde et al. 2012; Amard et al. 2016, 2019, and references therein). A detailed comparison between STAREVOL and similar stellar evolution codes (including MESA, YREC, and PARSEC, see  Choi et al. 2016; Spada et al. 2011; Tognelli et al. 2011 , respectively) can be found in  Amard et al. (2019). However, in this work I did not explore the possible impact of the choice of stellar model on the outcome of this standalone tool. Finally, it is worth noting that as in gyrochronology, the tidalchronology technique will provide degenerated solutions for a rotational period below ten days, which corresponds to a system younger than about 100 Myr.
3. For each of these couples, linear interpolation on the age as a function of planetary mass is performed, which ends up with four numerical relations of the form: Age = θ 1 ×M p +θ 0 (with F. Gallet: TATOO: Tidal-chronology standalone tool to estimate the age of massive close-in planetary systems θ 1 and θ 0 two numerical constants) and four evaluations of the linearity via the Pearson coefficient Pe c. 4. TATOO then finds the age of these four couples at the observed planetary mass, which creates a 3D map composed of age; P rot,i ; P orb,i for the four nearest points. To assess the robustness of the final age estimation process, I introduced a quality number into the rest of this paper that depends on the value Pe c. 5. Finally, TATOO uses a 3D interpolation method (Python-SciPy griddata routine) to provide the age of the requested system with the observed P rot,obs ; P orb,obs ; M p ; M quadruplet.
missions, since for now only a few asteroseismic age estimations exist the host stars of massive close-in planets. Indeed, most of these estimates are for red giant branch stars or for Kepler targets that are composed of either a planet that is not sufficiently massive or one whose semi-major axis is too large. Moreover, most of the targets listed in Table  2  are located in the southern hemisphere, while the Kepler observations and the asteroseismic data are for targets in the northern hemisphere. Hence, PLATO and the future TESS observations are fundamental to constrain and test the tidal-chronology technique. It is also worth noting that sometimes only the minimum mass of the planet can be estimated, this is the case for instance in a radial velocity survey. In those cases, only a lower age limit could be estimated via TATOO.
0 and 0. 98, respectively. A ratio of R co /a > 1. 0 refers to a planet inside the corotation radius, which may indicate that this latter has had an impact on the surface rotation rate of its host's star. In all of the explored cases, P orb,p is smaller than P rot,. Figure  4  shows the deviation between the age estimated with tidal-chronology and the one using gyrochronology as a function of the ratio P rot, /P orb,p. The Fig. 4 : Deviation between tidal-chronology age and gyrochronology age as a function of the ratio P rot, /P orb,p. The dashed line is a linear fit considering the full dataset. The dashdotted line is also a linear fit but omitting the WASP-4 and Qatar-1 systems from the dataset. The red dots are the systems for which tidal-spin up is suspected while the blue dots are systems without tidal spin-up.
Acknowledgements. F. G. thanks the anonymous referee for the constructive comments about this work that increased the quality of the paper. F. G. thanks  Dr. Marta González García  for her precious help with statistical analysis and her comments on the article. F. G. is grateful to  Dr. Philippe Delorme ,  Dr. Jacques Kluska , and  Dr. Julien Rameau  for helpful discussions and corrections regarding the content of the article. F. G acknowledges financial support from the  CNES  fellowship. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  742095 ;  SPIDI: Star-Planets-Inner Disk-Interactions ).
Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged -therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area.
I. INTRODUCTION The advent of small, inexpensive, and battery-powered computing units such as microprocessors and micro-controllers have paved the way for developing a wide variety of small form-factor devices that can be connected between each other and to the Internet. Such small devices form the basis of the Internet of Things (IoT) concept  [1]. Millions of objects The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi. obtain a possibility to communicate over and to the Internet for seamless on-the-fly access and better control. However, more recently, the concept of smart devices worn and carried near the body, namely -wearables, has emerged forming the novel Internet of Wearable Things (IoWT) paradigm  [2]. The IoWT or, similarly, the Wearable Internet of Things (WIoT)  [3]  is a merge of various smart wearable devices, as depicted in Fig.
II. RESEARCH METHODOLOGY This section discusses the research methodology adopted to carry out this systematic literature review, which is based on the PRISMA guidelines, proposed in  [31]. The initial step was to identify the appropriate keywords and associated synonyms to form a search expression. After brief analysis of the literature, the following search expression was formed: (''energy efficien*'' OR ''energy conserv*'' OR ''low power'') AND (wearable*) AND (edge OR cloud OR fog OR approximate OR IoT OR ''Internet of Things'' OR performance) A search was performed with the selected keywords for the 2010 -2020 period in the two most widely accepted research databases in Information and Communications Technology (ICT) domain, namely Scopus [32] and Web of Science  [33]. We gathered a set of 2370 potentially relevant publications (as of July 2020), excluding grey literature, pre-prints, and duplicates.
Several wearable devices have been developed to continuously sense and measure various physiological parameters of humans and animals, including heart rate, blood pressure, body temperature, stress hormones among others  [52]. The IoWT concept enables remote patient monitoring systems where a patient carries one or more wearable devices that continuously monitor the patient's health and record the measurements in online databases to be assessed by the patient's doctor. Automated help-seeking solutions are also proposed for an emergency case, e. g. , a call could be initiated to the caretaker/medical staff  [70]. From Table  2 , it can be observed that the most commonly monitored performance parameter is energy consumption, followed by accuracy, latency, and throughput. The reliability factor was found to be the least analyzed in the context of wearables for healthcare.
Furthermore, since a significant share of the scientific community relies on simulation-based studies, researchers need to know the existing trend in the choice of simulators used in the field. For this purpose, we collected statistics on the different types of network simulators utilized in the surveyed literature and their percentages of use. The collected statistics are presented in Fig. 7. It has been observed that MATLAB  [115]  has the highest share (around 50%). Several other network simulators were also used, namely Network Simulator 2 [116], OPNET [117], OMNeT++  [118] , and Castalia  [119]. All of the listed network simulators were equally popular among the scientific community, with an equal share of about 5%. Some researchers used custom-built simulators, including C++ and Java-based, with a share of 10%.
Further, a detailed discussion based on the qualitative and comparative analysis of existing studies in each category was provided, presenting their advantages, limitations, main performance parameters, and major contributions, as well as giving a quick overview of this field's research. Similarly, a statistical analysis was presented to depict the percentage of tools used in the performance evaluation of the proposed solutions, which revealed that the research community is more inclined to develop a prototype to validate the efficiency of their proposed solution. However, some studies presented simulation-based results only where MATLAB was found to be the most widely used simulator besides others. Whereas, some fraction of the studies presented both simulation-based results validated through real-time experiments on prototypes.
Domain-Specific Language A Domain-Specific Language (DSL) is a computer language specialized to a particular application domain that enables domain experts to create a system using concepts they are familiar with. A DSL has to be designed in a way to be understandable for humans while executable by machine. For example, SQL is a well-known DSL specific for manipulation of databases  [8]. Regarding the main objective of LCDPs, i. e. , providing system development facilities for domain experts, DSLs are the underlying theory in the LCDP development. The target application domain of an LCDP, or more specifically, the aspects of a system that are modeled in that LCDP, defines which kind of DSLs are used on its basis. For instance, the Business Process Model and Notation (BPMN) is a well-known DSL for modeling business processes.
e. , editable source code or bytecode) that can be executed at runtime environments  [5]. In both cases, the transformation is implemented once by language engineers, and then is used several times to auto-generate many systems of the same type. Abstraction along with automation resulted in simplicity, reusability, higher accuracy, portability, interoperability, complexity management, lower cost, and faster release time  [6] , and these are the potential advantages offered by LCDPs. LCDPs follow MDE principles. Indeed, they support system design through visual modeling, and automatic generation of the executable final system following two distinct architectural approaches. Some LCDPs, such as OutSystems, use code generation engines to produce executable code, and others, such as Mendix and Lightening, use a model-driven execution platform  [5].
Mendix Mendix LCDP is introduced for application development on the web, mobile, and IoT platforms. It includes two IDEs to support both no-code and low-code. The former is a drag & drop web-based studio providing pre-built reusable components, while the latter is an IDE for experienced developers to integrate models (e. g. , data models, UI models, and microflow models) with manually written code  [34]. Testing in Mendix: Quality assurance in Mendix is performed using several tools and services, some of which are for testing while the others help to enhance the quality of the application. Unit Testing Module is a Mendix-dedicated module for unit testing of the application's logic (i. e. , microflow models). The unit tests can be created using microflows and JUnit operations without writing any code  [33].
For supporting other kinds of testing, Mendix recommends the use of commercial tools such as SoapUI 1  for automated integration and API testing, Selenium IDE 2  for browser-based UI and acceptance testing, and TestNG  [3]  for scripting automated tests in Java language  [33]. Moreover, there are three quality add-ons provided by Mendix which are not testing tools, but their usage improves the quality of the application: 1) Application Test Suite (ATS): ATS is a set of tools built on top of Selenium  [29]  for embedding test into application lifecycle; 2) Application Quality Monitor (AQM): By this service, the application models are analyzed statically and the technical quality of the application is calculated based on a subset of features of software maintainability derived from ISO 25010  [7].
Temenos Quantum Temenos Quantum LCDP owned by Kony is designed to build mission-critical applications. It leverages novel technologies such as intelligent chatbots, conversational applications, augmented reality, and AI to build modern web and mobile applications  [16]. Testing in Temenos Quantum: Automated testing of multichannel applications is supported by Quantum Testing Framework (QTF) that is integrated with TestNG  [3]  and Jasmine  7. TestNG is a Java testing framework inspired by JUnit and NUnit which uses Selenium server for test execution, while Jasmine is an open-source JavaScript testing framework that follows the Behavior-Driven Development (BDD) technique  [23]  and introduces new syntax for writing unit tests. QTF allows test case creation through recording the user activity on the application and also manual coding  [36].
OutSystems OutSystems LCDP is well-known for building enterprise solutions as it provides many services for the context of case management, business process management, legacy modernization, and business operations  [25]. In this platform, data models, business logic, workflow processes, and UIs had to be defined by models to build the final product. Testing in OutSystems: Quality assurance in OutSystems LCDP is regarded as it offers several tools and techniques for it. The most important of which are: 1) OutSystems Test Framework: It is composed of three frameworks to support software testing at different levels (i. e. , unit, integration, system, etc). One of which is a BDD framework that offers a no-code test environment to create unit and API test cases in BDD style  [23]. The other one is the Unit Testing Framework which enables testers to define unit tests on the logic of the system without any dependency on the UI.
Ghost Inspector 8  automated testing tool is also integrated with the framework to support automated UI testing. Besides the mentioned tools, if further features are required for testing a system in OutSystems LCDP, it is possible to integrate other testing tools within the framework  [24] ; 2) Performance analyzer: By capturing real-time data (e. g. , fast, fair, or slow response time distribution), it provides detailed reports to help application performance monitoring during its usage growth; 3) Automated UI testing with Selenium: By Selenium IDE, UI test steps can be defined by recording user interaction with UI pages, using reusable scripts with some adjustments if needed, or manual scripting, while Selenium WebDriver enables writing complex test scenarios by common OO languages like Java, JS, and so on  [29].
The Status of the Testing Component of Commercial LCDPs Section 3 presented an overview of the testing facilities, provided by the selected LCDPs. Generally, most of the testing activities are supported by their integrated third-party testing tools such as Selenium, SoapUI, AccelQ, TestNG, GhostInspector, Jasmine, and EasyRepro. In this subsection, we organized our analysis result based on the proposed feature list, to specify the status of commercial LCDPs providing testing facilities. ( In conclusion, the results of our evaluation reveales that there are specific features for low-code testing that should always be considered and cannot be neglected in the testing component of LCDPs. They are the role of citizen developer and the side effects of her non-programming knowledge in her involvement in testing, the need for high-level automation, and leveraging the cloud.
Previous Attempts. The mentioned technique i. e. , in detail DSL extension with further properties (e. g. , testing features) is a language engineering issue that is investigated in several papers. In  [20, 21] , a framework, named ProMoBox, is introduced that enables DSL engineers to auto-integrate five sub-languages to a given DSL, to support specification and verification of temporal properties of a system modeled using the given DSL. It uses Linear Temporal Logic (LTL) to specify properties and offers a model checking engine plug-able to DSL environments to run and evaluate them. The ProMoBox framework is restricted to the DSLs whose semantics are described as a rule-based transformation; by this semantics, the system behavior is captured through state changes. Moreover, it is limited to the verification of LTL-based properties.
Totally relying on the model checking technique causes the framework's low performance due to high memory usage. The main issues with the ProMoBox framework were inherited in using model checking. In  [19] , the framework is adapted to test case generation techniques as it is a valuable alternative to model checking. It proposes an automatic approach to augment a given DSL with testing elements derived from a specific test DSL so that modelers can model functional unit tests in the same language as system models. The model instances in Figure  1  are taken from the running example of this paper. The testing support of the ProMoBox framework is also restricted to DSLs with rule-based semantics. In addition, it does not support real-time models, other testing scales such as API testing, and distributed test execution since the testing DSL that is used, involves only basic testing elements while there are other testing DSLs covering those of complex.
Immunoinformatics  [1]  builds and organizes molecular immunogenetics knowledge to be managed and shared in IMGT ®. The accuracy and consistency of IMGT ® data and the coherence between the different IMGT ® components (databases, tools and Web resources), are based on the IMGT Scientific chart rules, generated from the seven axioms and concepts of IMGT-ONTOLOGY, the first ontology for immunogenetics and immunoinformatics  [21] [22] [23] [24] [25] [26] [27] [28] [29]. As examples, IMGT Scientific chart rules comprise the IMGT ® standardized keywords (IDENTIFICATION axiom  [30, 31]  and concepts of identification), the IMGT ® standardized labels  [32] [33] [34]  (DESCRIPTION axiom  [34, 35]  and concepts of description), the IMGT ® gene and allele nomenclature  [1] [2] [3] [4] [5] 36]  (CLASSIFICATION axiom  [37, 38]  and concepts of classification), the IMGT unique numbering  [39] [40] [41] [42] [43] [44] [45] [46]  and IMGT Colliers de Perles  [47] [48] [49] [50] [51] [52]  (NUMEROTATION axiom  [53]  and concepts of numerotation).
The IMGT ® information system comprises seven databases  [54] [55] [56] [57] [58] [59] [60] , 17 tools  [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] [80]  and more than 25,000 pages of Web resources. IMGT ® dababases are specialized in sequences (i. e, IMGT/LIGM-DB  [54, 55] ), genes and alleles (IMGT/GENE-DB  [56] ), two-dimensional (2D) (IMGT/2Dstructure-DB) and three-dimensional (3D) structures (IMGT/3Dstructure-DB)  [57] [58] [59]. IMGT/mAb-DB  [60]  allows querying therapeutic monoclonal antibodies (IG, mAb), fusion proteins for immunological applications (FPIA), composite proteins for clinical applications (CPCA) and related proteins (RPI) of therapeutic interest (with links to amino acid sequences in IMGT/2Dstructure-DB and, if available, to 3D structures in IMGT/3Dstructure-DB).