dataesr/scientific-paragraphs-categorization huggingface dataset:
The sources of (observed) inputs are from STEREO A/B, ACE Real Time, DSCOVR and OMNI. Therefore solar wind conditions are computed at a target (planet/probe) for all source-target pairs. This amounts to a rather unique and complex product which can serve as a heliospheric monitor of solar wind conditions over a broad range of time (parameters are available from 1990 onwards). It directly exploits L1 data held in AMDA to produce new datasets. To attract more exposure for this service, Europlanet RI funding was employed to develop a dedicated interface (in the frame of the PSWS work-package 29  ). This interface is available at http://heliopropa. irap. omp. eu/and shown on Fig. 13. To complement the solar wind magnetic field and plasma moments at planets and probes, an heliospheric view in HEE coordinates is available to help figure out what is the planetary configuration at a given time.
Beyond visualization, the RPC imposed some constraints (i. e. the data should remain PI team restricted, download and parameter combination should be switched off, …) which, given the architecture of AMDA, were easily enforced. The list of users with "RPC access right" was kept by AMDA admin and extended upon PI request (when new PhD student, post-doc, engineer joined a given RPC team). Technically the synchronization between IC (CDF archived files) and AMDA was done on a daily basis. After some tests a web meeting was organized during which the AMDA functionalities were exposed to the RPC community. As the main need of RPC members was to access, plot and browse multiple datasets at once, AMDA was perfectly tailored for this task. Regular feedback between the PI team, the AMDA team and the RPC community on data content and quality ensured that AMDA was used extensively during the whole exploitation phase and after.
Use of machine learning to detect flares In a second phase, we analysed the link between patient-reported flares and activity-trackerprovided steps per minute (and not mean steps per day) using AI by machine-learning using selective (multiclass) naive Bayesian statistical methods  [9]. Machine learning allows analyses of huge amounts of data with minimal aggregation of data. It is remarkable that the machine learning model detected correctly both patient-reported flares and absence of flares with a sensitivity (the ability of a test to correctly classify an individual as flaring) of 96% and a specificity (the ability of a test to correctly classify an individual as not flaring) of 97%  [13]. The corresponding positive and negative predictive values were respectively 91% and 99%. This study is one of the first to demonstrate the usefulness of machine-learning applied to large rheumatology datasets  [14].
In osteoporosis, machine learning methods were used to predict the occurrence of jaw osteonecrosis and bone density loss, based on dental panoramic radiographs  [19, 20]. Figure  2 : comparative imaging of a spine MRI in a patient with axial spondyloarthritis (Aizenberg et al  [18] ). AI methods may also be used to identify prognostic factors and predict outcomes in rheumatic diseases. Thus, a Dutch team elaborated a model predicting flares in RA patients, using random forests applied to demographic, clinical, laboratory and medication data recorded in an Electronic Medical Register. This model had good predictive performances, with a mean AUC of 0. 8  [21]. A regression model based on Gaussian processes, and taking into account clinical, demographic and genetic data, was used to predict the response to anti-TNF therapy after methotrexate failure in a dataset of 1892 American RA patients.
Pooling activity monitoring in epidemiological research Wearable activity trackers could represent a valuable source of physical activity data for epidemiological research, especially due to their widespread use and the long-term nature of the recorded data. In order to record and collect physical activity data from different consumer activity tracker vendors, new solutions are emerging. These solutions aim to solve the problems caused by the large heterogeneity between activity tracker models in terms of the types of data available, the accuracy of the recorded data and the sharing of data between different vendors and third-party systems. Using data from the device that patients already have is useful, but it is not clear that measures of activity, sleep and other behaviours are comparable across devices, and it can be difficult to standardise the collection, collation and processing of data across different devices.
However, proprietary algorithms can be mostly opaque  [25]. In order to face the challenges from the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems, innovative systems are emerging. One example is the mSpider system, which is a working prototype currently capable of recording physical activity data from consumer activity tracker vendors. This experimental system automatically records physical activity, energy expenditure, pulse, sleep etc. , from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung and Withings, as well as trackers storing data in Google Fit and Apple Health. Three modules make up this system: (1) the web front-end, (2) the server back-end, and (3) the mobile application.
The web front-end is used when accessing their activity tracker data to manage surveys and facilitate participant authorisation. The server back-end stores participant authorisation access information, manages data transfer between mSpider and supported vendor cloud storages and stores uploaded activity tracker data. The mobile application further facilitates authorisation and data transfer for providers where communication cannot be made directly between the server back-end and the provider's cloud storage (e. g. Samsung and Apple activity trackers). For these providers, the communication is performed by the provider's mobile application and uploaded to the mSpider server back-end via the mSpider mobile application. One of the applications performed was the identification of changes in physical activity levels during COVID-19, showing that the mSpider system can be a valuable tool for collection of long-term data on physical activity, including historical data and detecting changes in physical activity over time  [26].
Another example is the Remote Assessment of Disease and Relapses (RADAR)-base. RADAR-base is an open-source platform for collecting physical activity data from smartphones, Fitbit and Garmin activity trackers, and some research accelerometers. RADAR-base uses similar technology to mSpider, but data collection is limited to only two consumer activity tracker vendors. In a 2020 study, this system was also used to track daily steps during national lockdowns in chronically ill participants equipped with a Fitbit tracker  [27]. In the future, these systems might solve interface problems with the practice software if their use is made accessible to health professionals.
Challenges and limits of AI in 2021 Although promising, AI methods have currently several limitations. First, most of these methods are based on supervised learning, which implies that large amounts of data are required to train the models properly. Moreover, these data must be accurate, so that the model does not learn from fallacious information and does not provide wrong conclusions. Consequently, a proper quality control of the data must be performed before the analysis, which can be long and laborious. Thus, even if AI methods mentioned previously may be time sparing for the physicians, the implementation of these methods in daily practice may be time consuming and costly. Additionally, unlike the human brain, no AI method can solve a multitude of problems to date. Indeed, a model based on AI methods may only answer to one specific question, on the basis of a specific dataset.
Human expertise and intervention are therefore still needed to choose the settings of the model, to train and validate it. However, evolutionary algorithms may help designing models in the future, in order to find the optimum parameters automatically  [15]. Another issue is that, although promising on specific datasets, most of AI models does not pass external validation; consequently, even if the number of AI publications is still growing, only a few AI models are presently applied in clinical practice. Additional validation studies are therefore needed, but most of these studies mainly focus on "technical" performances of the models, and do not account for clinical relevance. Thus, a closer collaboration between data scientists and clinical researchers is crucial to implement AI tools in rheumatology practice; this point has been raised in the recent EULAR points to consider for the use of big data and AI in rheumatic diseases  [33].
Consequently, the main source of information on actual practices is grey literature (from professional bodies, social media and conferences) and interviews. The methods in use are qualitative  [21]  because the purpose of the research is to gather non-numerical data, to understand what kind of phenomena are occurring, how and why. Moreover, all the results are drawn from the representations of the interviewed actors because the authors of the paper did not perform a survey within the facilities of the companies themselves. Searches were performed via scientific resource databases with contractual access (Web of Science, Scopus, Econlit, Food Science Source, Tair,. ) or open access (HAL, ORBi,. ), as well as Google Scholar. The main keywords tried were "logistics", "fruit and vegetable" and "ICT".
It is used to trace the products and to list the different transactions for each customer. Thanks to the data collected via ERP, some platforms enable companies to compare themselves with others. The digital pallets management system PAKI is a simpler tool. For each transaction, the parties indicate on their online account the variation of their pallet stock. The operations described by e-vouchers indicate a credit or a flow of pallets. The company picks surplus pallets up from customers for redistribution and offers the replacement of damaged pallets. Warehouse management system (WMS) software is designed to optimize pallet storage in a warehouse. It also states which pallets are to be removed first, on a "First In-First Out" basis, for example. Today, with the replacement of humans by mechanization, WCS (warehouse control system) software is developing.
However, the place of the sensor in the container can influence the results. Pelletier noted temperature differences of up to 5 • C in the same container  [42]. At the end of the trip, the data is read by connecting the sensor to the USB port of a computer (live tracking of the claims is possible from computer, tablet or smartphone). The results appear in PDF format, which makes it impossible to falsify the data. An alert message reaches the partner when the file is opened. The system forwards a link to the partner so that they too can access the data. In some countries, lack of GSM or 4G networks makes the use of land tracking in real time impossible  [43]. Other companies offer simpler sensors that change color when the temperature is out of compliance. They can also give an idea of the duration of temperature noncompliance.
The solution might be to integrate these databases in order to design shared databases. However, changes of tools or operating systems may encounter the problems of confidence mentioned above. Paradoxically, collaboration is easier to implement horizontally than vertically. The cases of "coopetitions"  [55]  are numerous and can be found in the various circuits. Longcircuit wholesalers can exchange their goods, and collectors can purchase for their peers  [29]. As explained above, vertical collaboration is more difficult because historically the relations are strained. This is mainly due to long and difficult price negotiations  [53]. Some large groups use questionable bullying strategies to obtain the lowest prices  [56]. It is obvious that one must start by maintaining good relations with partners before thinking about sharing potentially confidential information  [57].
The following methodology was used to build ILSS: a mesh sphere was created in the software, which allows manual point selection on the internal aspect of the MD. Its size (radius ILSS) and its position were adjusted manually in the 3D model, as close to the desired position as possible (Fig  3 ). Then the least square methodology was used: the software eliminates all the points for which the normal vectors are not in the same direction as the sphere's normal vector. If the scalar product between the two directions: "normal" x "dir (node-center)" is over 0, the point is eliminated. Then, the software's "distance" function eliminates 10% (with each click) of the points most distant from the surface of the sphere, using the following equation: dist 2 = ABS ((node-cent)2 -r 2 )). The radius of this sphere was identified as ILSS radius and recorded.
Discussion The aim of this study was to assess the effect of lateralizing the glenosphere on the DMA and coaptation role of MD. To this end, the reliability of the methodology needed to be evaluated. The rater's proficiency in identifying and selecting anatomical structures on MRI is key. The results of the inter-and intra-rater agreement assessment of the 3D models and ILSS positioning (Table  2 ) gave strength to our methods. They were also consistent with other studies describing the SliceOmatic™ software as a reliable 3D reconstruction tool  [23, 24]. Therefore, using the model developed by Billuart et al. [20]  and then by Hereter Gregori et al. [21] , we compared four RSA lateralization options: 0, 6, 9, 12 mm. Our hypothesis that lateralization increases the coaptation role of the MD was confirmed.
[18] ). The algorithm is described in Ref. [18] , and the codes implemented are available for download (http s://softmat. mat. ethz. ch/opensource. html). However, for the biconical geometry, the correction for the contributions of the sub-phases is integrated into the RheoCompass software using the equations of Oh and Slattery  [7, 19]. There is also an algorithm described in reference  [11] , and the codes implemented are available for download (https://data. mendeley. com/datasets/4tmy9k4ys3/1). At the time of their preparation, processed polymer systems have the capacity to organize and form multi-scale structures. In the past, such preparation procedures were often conducted in the molten state, under high temperatures, high shear rate or under radiation (laser, UV, etc. ). Nowadays, the 2D rheology measurements are conducted at ambient temperature bellow a maximum of 70 • C.
Characterization methods 2. 1. 2. 1. Bulk rheological measurements. In order to determine the rheological properties of different bulk subphases and their effect on the interfacial rheological measurements, dynamic rheological measurements were performed. The first rheological tests on the model fluids were carried out on a DHR-2 machine from TA Instruments. i. e. a combined motor-transducer (CMT) rotational rheometer  [22]. A cone-plate geometry was chosen (diameter 40 mm, angle 1. 994. • ) and the heating system used was a Peltier Plate temperature system. Frequency sweeps were performed at angular frequencies decreasing from 100 to 0. 1 rad s -1. Complex viscosity (|η*|) were measured as a function of the angular frequency. In order to obtain more rheological data at high frequency, the timetemperature superposition principle was applied.
Numerical assessment of the compatibility of the studied polymer systems. To predict the compatibility of the systems studied, their Hansen' solubility parameters (HSP) were evaluated  [26]. In the present work, we used HSPiP software (Louisville, Kentucky, USA) to evaluate the HSP. For the calculations, the software used a genetic algorithm optimization approach  [27]. According to Hansen  [28] , the global solubility parameter is a combination of three components reflecting dispersive (London) (δ D ), polar (δ P ) and hydrogen bond (δ H ) interactions. δ 2 = δ 2 TOT = δ 2 D + δ 2 P + δ 2 H ( 3 ) The determination of the Hansen solubility parameters of a polymer consists in testing the solubility of the polymer in different solvents with known solubility parameters using a sphere that encompasses the good solvents of the polymer in a 3D solubility diagram (δ D , δ P , δ H ), which defines as a solubility sphere with a radius R 0  [28, 29].
The algorithm is described in Ref. [11] , and the codes implemented are available for download (https://data. mendeley. com/ datasets/4tmy9k4ys3/1). Without making these corrections, Oh and Slattery  [9]  deduced an exact solution to the velocity distribution in the two sub-phases and the interface. For both sub-phases, the authors assumed that mass and momentum were conserved  [33]. In addition, the interface is specified to have the following features: A jump mass balance at the interface: the dynamics of each of the two  fluids is driven by the continuity and the Navier-Stokes equations in another words the velocity is continuous at the two-fluid interface. div v θ ¼ 0 with v θ ¼ v s (in the above Equation (  7 )) (Equation (  8 )). And a jump momentum balance at the interface: the balance between the pressure and bulk viscous stress jumps through the interface, the stress generated by surface intrinsic viscosity and elasticity and the stress resulting from dynamic surface tension, which includes equilibrium interfacial tension, Gibbs elasticity, and the Marangoni effect.
The latter consists of an assembly of a metallic, glass, thermoplastic polymers and elastomeric ring components. The interfacial cell contains a threaded hole with a sealed screw that allows the introduction of the upper subphase fluid into the cup (Fig. 5 ). A normal force assisted surface detection methodology was used to accurately position the titanium-based biconical geometry at the fluidfluid interface. The rheometer's interfacial analysis software (Rheo-Compass), based on the unique Navier-Stokes solution of the full flow field for a biconical geometry (see details in the introduction of this paper) was used to calculate of the corrected interfacial rheological properties.
Bulk rheological characterization The bulk rheological data of different model fluids were evaluated from the master curves obtained using the time-temperature superposition principle. The main objective was to determine their rheological behavior, over a large frequency domain. The latest data were used to define the viscous properties of PDMS and PIB subphases and their effect on the interfacial rheological measurements. The master curves corresponding to each PDMS and PIB model fluids are presented in the supporting information (Appendix 1). It was observed that PIB and PDMS fluids exhibit Newtonian behavior until an angular frequency of 1000 rad/s is reached. The Newtonian viscosities of the different model fluids are summarized in Table  5. It is useful to note that the zero-shear viscosities will be used to subtract the effects of the sub-phases effects.
Solubility parameters From the HSHiP software analysis (see Fig. 10 ), the Gibbs free energy of mixing is positive regardless of the polymer volume fraction (blue curve). Also, the second derivative of the Gibbs free energy is negative (red curve). Therefore, the systems studied are partially miscible, confirming the optical microscopy and SEM analysis results. Furthermore, the distance R a between the centres of the solubility spheres is equal to 2. 87 MPa 1/2 which is lower than the critical distance R 0min. The theoretical solubility parameters of the PIB & PDMS obtained by HSHiP software are summarized in the supoorting informations (Appendix 4).
Acknowledgments The authors are indebted to  Murat Arli  ( INSA Lyon ) in his help for the design of interfacial rheological cell and thank  Dr. Yves Chevalier  ( Claude Bernard University Lyon 1 ) for the stimulating discussions. The authors are thankful to  David Duval ,  Mr Georg Krennfrom ,  Mr Markus Thiele  and  Dr. Joerg Laeuger  ( Anton Paar Gmbh ) for their recommendations and technical assistance regarding the interfacial rheological measurements with the bicone. The authors also gratefully acknowledge  Thibault Tavernier  and  David Israel  for their helpful discussion and training on the use of the DWR setup. Finally, the authors also gratefully acknowledge the  French Ministry of Superior Education and Research (MESRI)  for the doctoral study grant.
In this work, we fill the gap by presenting the first formulation of the user project planning problem. We also develop solution methods based on mathematical programming and greedy search, which can serve as the basis for building decision-making tools for effectively managing personalized user projects in social and medico-social structures. This work is part of the "MSUsager" software developed by the Company GePI 3  for the social and medico-social sector and concerns specially its decision support system. The rest of the paper is organized as follows. Section 2 provides a literature review of related studies. Section 3 is dedicated to the description of the personalized user project problem, followed by its formulation in Section 3. 2. Section 4 describes two solution approaches using mathematical programming and greedy search.
Computational Results The experiments were carried on a computer with an Intel Core i7-8750H 2. 20GHz processor and 32 GB RAM under Linux. The integer programming model and the 0/1 programming model are solved by running the CPLEX MIP solver (Version 12. 8) with the MemoryEmphasis parameter as true and other default parameters under a time limit of 2 hours. The proposed greedy algorithm was implemented in C++ and compiled using the g++ compiler with the -O3 option. The greedy algorithm has no parameter to tune and stops when no more activities can be scheduled. For the importance coefficient α o , we collected four combinations recommended by professionals in the social and medico-social sector and then distributed them randomly to 20 test instances. Table  3  shows computational results on the 20 instances obtained by CPLEX with the two mathematical models and results from the greedy algorithm.
Conclusions and future research perspectives We presented a real-life user project scheduling problem in the social and medico-social sector. The targeted application requires selection and scheduling of a set of activities for users and resources while satisfying some imperative constraints and optimizing different objectives. To tackle this problem, we introduced a formal description and devised two integer programming models as well as greedy algorithm. The proposed approaches were tested on 20 simulated test instances (that we made publicly available) corresponding to real scenarios identified according to a large-scale survey. The 0/1 programming model (solved by the CPLEX software) showed a high competitive performance compared to the integer programming model and obtained close-to-optimality solutions for some small instances.
Third, the activities in the proposed model are considered to be independent. It would be interesting to study possible relationships within the activities. Such an extension could be useful in structures where such situations can be encountered. Finally, the research outcomes will contribute to enrich the existing "MSUsager" software mentioned in the introduction with a powerful decision-making component for social and medico-social structures. The data collected from the system after its deployment will enable the development of new system features based on machine learning techniques. These new features can provide automatic generation of user preferences for activities by learning useful information from historical data such as activity evaluation and similar user experiences, which will definitively help to define the most suitable personalized user projects of high quality.