dataesr/scientific-paragraphs-categorization huggingface dataset:
The fastened cooling rate is missed when the engine stops. This was expected as the fresh air which enters into the combustion chamber to cool it from the inside is not modeled here. These spots are the consequence of the internal boundary conditions. Those are set with a high internal temperature but a very small heat transfer coefficient (HTC). Assuming this low value was not realistic, a new test was submitted with a HTC about 8 times higher. Since this simulation is very costly and the trend could be captured very quickly, this test simulation did not run for long. Its results are plotted on the figure  16. The new Pow-erFLOW data are the X symbols added on the figure. Though they do not match the tests perfectly, they are much closer and strengthen the assumption made. The need for an improved core flow model is highlighted again.
With the advent of Web 2. 0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about what, when, and how to respond to users' actions over social media. Questions that Social Miner allows to answer include what actions were frequently executed and why certain actions were executed more than others.
We present the design and development of Social Miner, a realtime tracking and mining tool on top of Facebook. The rest of this paper is organized as follows. Section 2 gives a short overview of social media mining in favor of business. Section 3 presents a motivating scenario to stress out the gap in examining the interactions between the business and social worlds. Section 4 briefly discusses data mining (with focus on sentiment analysis) and business process modeling. Section 5 details the real-time tracking and mining of users' actions over social media where Facebook is used for illustration purposes. Section 6 implements this tracking and mining through a real marketing campaign on Facebook. Finally, Section 7 discusses the importance of connecting the business and social worlds together.
Related work Mining social networks in favor of business application is not new. Bonchi et al. present an overview of key problems in this domain and the techniques in social network analysis in an infant stage and emphasize, among others, potential business benefits and technical challenges  [5]. These challenges include data preparation, network dynamics, propaga-tion, evaluation, etc, whilst value-added benefits may be summarized in a short sentence: "Social networking may allow increased revenue". In essence, 2 types of social network mining strategies are in use. The first strategy mimics social networks by tracking activities of employees and their relationships inside an organization during business process execution in order to make them more flexible and productive. Examples include, but not limited to, MiSoN (Mining Social Networks) and SUPER (Social-based bUsiness Process managEment fRamework).
Some preliminaries This section is an overview of data mining and business process modeling. Data mining. The phenomenal growth of online social media (e. g. , discussion fora and blogs) is backed by the abundance and richness of user content such as comments, reviews, and feeds that could correspond to opinions on events like visited places, tried restaurants, and consulted books. Opinions can also be associated with sentiments reflecting users' attitudes and feelings towards events like anger and happiness. For a better understanding of opinions, many Sentiment Analysis (SA) techniques and tools are reported in the literature (e. g. , Batrinca and Treleaven  [3]  and Tang et al. [32] ). Generally speaking, Pang and Lee suggest 3 stages that SA goes through: opinion retrieval, sentiment classification, and opinion summarization  [26].
Over the years, Business Process Model and Notation (BPMN) has rapidly become a standard for BP modeling as per the Object Management Group (OMG)  [24]. BPMN provides graphical elements to develop multiple flows like control flow to show the partial order (i. e. , conditional and concurrent) between tasks in a BP and communication flow to show the exchange of messages between a BP's stakeholders. In addition to BPMN flows, other types of flows are reported in the literature. Sadiq et al. use data flow for process specification following a data/artifact-centric perspective and process verification according to 3 properties: correctness, soundness, and variability  [29]. Reichert and Dadam use control flow and data flow to specify BPs following a process-centric and data-driven perspective, and verify a BP's correctness properties like reachability and termination  [28].
Architecture We developed a tool, named Social Miner (SM), for tracking and mining users' actions over social media with Facebook as a targeted Web 2. 0 application. SM's architecture is given in Fig. 4  (a demo video is available at https://youtu. be/crBsEk2pSzo) and consists of 4 modules: dashboard, social-action manager, social-action tracker, and social-flow analyzer. The dashboard is the interface provided to employees and BP engineers to manage campaigns on GreenUtility's Facebook page like launching a new campaign with the assistance of the social-action manager (1. 1) and to perform the necessary analysis (1. 2). The social action tracker "keeps-an-eye" on any change over this Facebook page while the social-flow analyzer obtains insights into the activities over the Facebook page such as, which subscribers are (un)supportive of a campaign and which campaign is most attractive according to our mining analysis.
To this end, the social action tracker uses Webhooks 7  to subscribe to changes in the Facebook page. These changes are stored in the social actions  1. The social-action manager uses Facebook SDK library for PHP so that requests (e. g. , publish campaign and reply to some comment message) are submitted to Facebook Graph API  8  and published on a Facebook page. 2. The social-actions repository is a MySQL database that stores details like time about the social actions executed over GreenUtility's Facebook page. 3. The subscribers repository is another MySQL database that stores details about the subscribers (e. g. , user id, user name, and weight) who take part in the discussions over GreenUtility's Facebook page. 4. The social action tracker includes 2 modules: CoreNLP and Score calculator.
On top of time-stamped details about the executed social actions (e. g. , user id, page id, post id, and parent id) collected via Facebook Webhooks' notifications, CoreNLP as a sentiment-analysis tool annotates these actions with sentiment scores. The result of CoreNLP analysis is formatted as JavaScript Object Notation (JSON) and then, translated into a relational format (through an in-house script) prior to storing it in the social-actions repository. The relational format has eased the storage of different details in multiple tables and running queries over these tables when building and analyzing the social flows. Any notification from Webhooks (e. g. , newly added/updated social actions) triggers the score calculator that computes new scores (Equa- 3) on a browser showing how GreenUtility promotes its services to customers and seeks their feedback through Facebook (Fig.
Business world Social miner GreenUtility uses the different flows to identify what social actions that (un)known subscribers have executed over its Facebook page. This could lead into reviewing BPs if their feedback were deemed relevant. Finally, the displayer is developed in HTML 5 and JavaScript while the builder and miner are developed as PHP programs and deployed on an Apache Web server. Besides installing Facebook Graph API, companies interested in using Social Miner do not require any additional installation or configuration to track their campaigns on Facebook.
His research interests include Big Data processing and analytics, spatio-temporal and mobility data management, as well as mobile and ubiquitous systems and IoT in Smart Cities. He has published widely in those and related topics. Djamal Benslimane (http://www710. univ-lyon1. fr/ dbenslim/) is currently a Professor at Lyon 1 University in France. He is the head of the computer science department at the IUT Lyon 1. His research interests lie in the areas of Services computing and database interoperability. His recent works were published in IEEE Transaction on Knowledge and Data Engineering, IEEE Internet Computing, IEEE Transactions on Services Computing, IEEE Transactions on Systems, Man, and Cybernetics, Communications of the ACM, ACM Transactions on Internet Computing, ACM Transactions on Software Engineering and Methodology, and WWW Journal.
• Non-causal simulation languages-e. g. , Modelica  [30] -and associated solvers enable a particular type of friction modelling that also reproduces true sticking. As an additional advantage, they also allow inverse simulation, which is welcome for sizing. However, non-causal friction models are implemented with assumptions for inverse simulation that are not always explicit. So far, it is the author's experience that industry sometimes hesitates to switch to these tools because the engineers concerned are not sufficiently experienced and they fear that the tools may be insufficiently mature for use in industrial projects.
g. , pivoting of the EMA in a three-bar mechanism). If necessary, a 2D or 3D lumped-parameter model of the EMAs and their kinematics to load can be developed using the mechanical libraries of the simulation software. In this case, the external load used to modulate the friction model becomes a compound value-e. g. , made up of radial and axial forces for ball bearings. However, such a 2D or 3D mechanical model requires numerous additional parameters, such as the stiffness and damping matrixes at each hinge joint. These parameters are generally unknown. The addition of high stiffness generates high-frequency dynamics that significantly slow down the simulation. This is why these effects are generally not modelled, as long as the transverse forces remain much lower than the axial forces. The presence of various locations of friction sources acting on a single moving assembly can be dealt with in different manners for simulation.
The solution proposed consists of calculating the magnitude of the nutscrew dynamic friction torque separately in . This enables its relative contribution to overall friction torque to be determined. It is then assumed that this relative contribution remains constant at all times. Therefore, this ratio is applied to the actual friction torque computed by the Karnopp approach of the shaft inertia model in 3. This gives the actual nutscrew friction torque that can be summed in  with the frictionless nut torque in order to feed the rod friction calculation module . Of course, the friction model presented in Figure  14  can be implemented in a script version instead of using a block-diagram representation. There is also no specific problem with applying the proposed approach to gear-drive EMAs.
• The reference speed for the Karnopp friction model can be set, e. g. , to 1/10,000th of the rated speed or, e. g. , 50% of the resolution of the speed measurement. As it is generally impossible to identify these parameters from measurements, the pre-set parameters have to be adjusted using a trial-and-error process to produce a realistic simulation (a few rebounds) without excessive computational burden. Using this approach, the model of the EMA mechanical transmission, including friction and elasto-backlash, has shown an excellent robustness with a short execution time. Such models have also been implemented in the Matlab/Simulink environment without difficulty. It has to be noted that setting the contact parameters is more tricky when the deformation is modelled as a power function of the load, in order to reproduce the nonlinear stiffness of Hertzian contacts.
It is then assumed that this relative contribution remains constant at all times. Therefore, this ratio is applied to the actual friction torque computed by the Karnopp approach of the shaft inertia model in 3. This gives the actual nutscrew friction torque that can be summed in  with the frictionless nut torque in order to feed the rod friction calculation module . Of course, the friction model presented in Figure  14  can be implemented in a script version instead of using a block-diagram representation. There is also no specific problem with applying the proposed approach to gear-drive EMAs. In this case, each additional moving assembly has to be combined with a transmission compliance/backlash to avoid any causality issues.
The use of Karnopp friction models introduces a specific issue here: the calculation of the friction force generated by the anti-rotation device on the EMA rod uses the torque applied to the nut, including friction. However, the shaft inertia model handles only the overall friction torque, including that of seals and axial thrust bearing. The solution proposed consists of calculating the magnitude of the nutscrew dynamic friction torque separately in . This enables its relative contribution to overall friction torque to be determined. It is then assumed that this relative contribution remains constant at all times. Therefore, this ratio is applied to the actual friction torque computed by the Karnopp approach of the shaft inertia model in 3. This gives the actual nutscrew friction torque that can be summed in  with the frictionless nut torque in order to feed the rod friction calculation module .
However, the shaft inertia model handles only the overall friction torque, at of seals and axial thrust bearing. The solution proposed consists of calculating the of the nutscrew dynamic friction torque separately in . This enables its relative to overall friction torque to be determined. It is then assumed that this relative remains constant at all times. Therefore, this ratio is applied to the actual friction torque y the Karnopp approach of the shaft inertia model in 3. This gives the actual nutscrew ue that can be summed in  with the frictionless nut torque in order to feed the rod culation module . Of course, the friction model presented in Figure  14  can be d in a script version instead of using a block-diagram representation. There is also no blem with applying the proposed approach to gear-drive EMAs.
ference speed for the Karnopp friction model can be set, e. g. , to 1/10,000th of the rated r, e. g. , 50% of the resolution of the speed measurement. s generally impossible to identify these parameters from measurements, the pre-set have to be adjusted using a trial-and-error process to produce a realistic simulation (a ds) without excessive computational burden. Using this approach, the model of the EMA transmission, including friction and elasto-backlash, has shown an excellent robustness rt execution time. Such models have also been implemented in the Matlab/Simulink t without difficulty. It has to be noted that setting the contact parameters is more tricky eformation is modelled as a power function of the load, in order to reproduce the noness of Hertzian contacts. with the frictionless nut torque in order to feed the rod friction calculation module Actuators 2020, 9, x FOR PEER REVIEW The friction model  combin on the rod  The inertia effects (in included.
• The reference speed for the Karnopp friction model can be set, e. g. , to 1/10,000th of the rated speed or, e. g. , 50% of the resolution of the speed measurement. As it is generally impossible to identify these parameters from measurements, the pre-set parameters have to be adjusted using a trial-and-error process to produce a realistic simulation (a few rebounds) without excessive computational burden. Using this approach, the model of the EMA mechanical transmission, including friction and elasto-backlash, has shown an excellent robustness with a short execution time. Such models have also been implemented in the Matlab/Simulink environment without difficulty. It has to be noted that setting the contact parameters is more tricky when the deformation is modelled as a power function of the load, in order to reproduce the non-linear stiffness of Hertzian contacts.
This gives the actual nutscrew friction torque that can be summed in  with the frictionless nut torque in order to feed the rod friction calculation module . Of course, the friction model presented in Figure  14  can be implemented in a script version instead of using a block-diagram representation. There is also no specific problem with applying the proposed approach to gear-drive EMAs. In this case, each additional moving assembly has to be combined with a transmission compliance/backlash to avoid any causality issues.
This gives the actual nutscrew friction torque that can be summed in  with the frictionless nut torque in order to feed the ro friction calculation module . Of course, the friction model presented in Figure  14  can b implemented in a script version instead of using a block-diagram representation. There is also n specific problem with applying the proposed approach to gear-drive EMAs. In this case, eac additional moving assembly has to be combined with a transmission compliance/backlash to avoi any causality issues.
In this article, we study Singapore' catching-up model and its limitations by using a mixedmethod case study methodology. Based on the elements provided by exploratory interviews conducted at the beginning of our investigation, we build on descriptive statistics and network analyses of scientific publications and patent data in order to assess outcomes of research and innovation production and its associated rents. Since the 2000s, Singapore's science, technology and innovation policy explicitly aimed to develop a knowledge (cum innovation) hub, understood as a condition that could contribute to local firms' catching-up (while the country had historically relied on foreign multinationals). The different policies introduced to create such a hub assigned a key role to Singapore's two research universities -the National University of Singapore (NUS) and the Nanyang Technological University (NTU).
Intangible assets (such as patents, copyright, trademarks and brand names) were early defined by  Veblen (1908, p. 105)  as 'immaterial items of wealth, immaterial facts owned, valued, and capitalized on an appraisement of the gain to be derived from their possession'. In terms of empirical evidence concerning the rise of intangible assets, Clarivate Analytics (2019) uses its centralized patents' database -Derwent Innovation-to define top 100 innovators by considering their total number of granted patents together with indicators of success, globalization and influence. 1 Since 2011, when the ranking was created, only 204 organizations made it at least once to the top 100 innovators, 35 of which appeared every year. This strong core of organizations profiting from intellectual rents is dominated by multinationals (97 of the 100 organizations in 2018-19 ranking) mostly originally from the 1 Success is defined as the ratio of patent applications to granted patents in the last five years.
Data collection In order to study Singapore's knowledge hub, we conducted a mixed-methods case study relying on two types of data: qualitative data derived from in-depth interviews and quantitative data. We complemented this collection with grey literature from the NUS, the NTU and the Singaporean government (including reports where the hub strategy was presented). Regarding qualitative data, we interviewed 15 stakeholders in November 2016: (i) managers of the NUS and the NTU; (ii) managers of overseas Higher Education institutions settled in Singapore; (iii) researchers from the NUS and NTU; and (iv) a responsible for the scientific cooperation from a core country embassy in Singapore. Interviews lasted between 60 and 90 minutes. For confidentiality issues we do not provide the identity of the interviewees.
Our quantitative data relies on the analysis of R&D outcomes in terms of (i) scientific publications retrieved from Web of Science, (ii) patents retrieved from Derwent Innovation database and (iii) innovation or intellectual rents gathered from Singapore's National Survey of Research and Development. The survey also provided information on the distribution of patent ownership per sector (private, government, higher education institutions). We also used OECD data on R&D expenditure at country and sector level to complement our analysis on the place of the private sector as R&D investor in Singapore. Scientific publications' corpuses were defined after looking at the research priorities of the 'Research, Innovation, Enterprise 2020' plan which reoriented research towards more applied topics and economic valorisation (Ministry of  Trade and Industry, 2016).
Discussion By integrating all our findings we may point out that while the NUS and the NTU are actively contributing to knowledge and innovation, they barely collect intellectual rents from that activity. Both our interviews and analysed data provided evidence on multinational companies' appropriation of most of the innovation rents of the Singaporean hub, while local companies remain laggards. These results are in line with the intellectual monopoly capitalism theory  (Durand & Milberg, 2019; Pagano, 2014; Rikap, 2018). They also point to a specific form of unequal exchange  (Emmanuel, 1972)  and call to rethink catching-up policies. In these respects, our results are in line with  Cooke's (2006)  concluding remark on global networks of bioscientific knowledge. According to the author, the formation of knowledge clusters in certain regions (among which he mentions Singapore, although not as one of the most important in bioscience, unlike Cambridge-Boston led by Harvard Medical School) "have begun to exert spatial knowledge monopoly effects that are already bringing 'increasing returns' as multinationals establish R&D facilities to capture 'knowledge spillover' advantages from cluster conventions of both 'open science  ' and 'open innovation.
Population data The data used for this study came from the third Individual and National Study on Food Consumption Survey (INCA3, 2014-2015) and the NutriNet-Santé cohort study (2009, ongoing). The third Individual and National Study on Food Consumption Survey 3 (INCA3) is a French nationwide and representative cross-sectional survey, which has been fully described elsewhere  (18). INCA3 study was carried out in accordance with the Declaration of Helsinki guidelines and was approved by the 'Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé' (Advisory Committee on Information Processing in Health Research). Verbal consent was obtained from, witnessed and formally recorded for all subjects. Briefly, dietary data were collected using three non-consecutive validated 24-h dietary recalls over a 3-wk period.
Professional investigators were responsible for collecting the dietary records by telephone, and participants were not aware of the days of recall. The nutrient contents of food items came from the 2016 database of the French Centre d'Information sur la Qualité des Aliments (CIQUAL). Sociodemographic questionnaires were also completed, by administered face-to-face questionnaires. Under-reporters for energy intake (EI) were excluded using the Black's cut-offs based on the Goldberg method  (19). Basal metabolic rate (BMR) was estimated using Henry's equations  (20). Participants were not considered under-reporters for a ratio EI:BMR > PALxexp(-1. 96xS/100) where PAL represents the physical activity level, and S a factor taking into account the variability of EI and BMR and depends on the number of dietary recalls.