dataesr/scientific-paragraphs-categorization huggingface dataset:
Main methods currently used when calculating profitability of a given project are chiefly dynamic, by considering for instance the depreciation of capital over time, since they become more accurate. NPV and IRR are the most extended ones along with the Payback Period. When it comes to software, Excel is probably the most popular tool for investment analysis. This spreadsheet includes a wide variety of financial functions that allow calculating the abovementioned profitability indicators and suchlike. Nevertheless, the biggest disadvantage of this tool is that results obtained may be unrealistic, since static calculations cannot consider process variability inherent to manufacturing. DES arises as an alternative capable of taking these influential factors into account. In the context of the fourth industrial revolution, the so-called Industry 4.
Robotised workshop simulation model The software chosen to develop the jacket nodes robotised welding workshop DES model is FlexSim. This selection was made based on the grounds that the personnel had experienced with FlexSim, as well as of the Process Flow tool provided by this software, which significantly improves both the modelling and debugging task. The first objective of the study was to obtain a design of the layout and the requirements of the robotised workshop so that it meets the takt-time of 1 jacket (9 X-nodes and 3 Y-nodes) per week. Different scenarios were examined, and further optimisation was carried out regarding the number of resources used in them while fulfilling the takt-time. In the best scenario, the workshop is able to produce all Ynodes and X-nodes of a jacket with an average time of 4.
Using DES and Excel As previously stated, in the first case study the investment analysis was based on savings and carried out according to the company's methodology, where the DES model provides Excel with the input data. In this sense, before anything else, it was necessary to estimate the cost of subcontracting the manufacture of nodes, manually done so far. To accomplish this, several offers received by the company in previous projects were collected and examined, with the respective transportation costs in addition. On the other hand, the cost of robotised manufacturing was exported directly from the DES model. To work out this value, it was necessary to introduce in the model the hourly cost of an operator as well as implement a code in charge of doing the calculation on a monthly basis, in concert with the hours worked, and by taking into account both shifts and the number of active operators.
The accuracy of ANNIM has a great increasing potential, which is related to the discrepancy between the measurements obtained from different observational carriers. Many previous studies have revealed a discrepancy of the peak parameters between the COSMIC and ionosonde observations over the European region  [18] , Australia  [19] , Brazilian region  [20]  and China  [21]. The discrepancy is dependent on local time, latitude and solar activity, especially in the equator and polar region, the correlation coefficient between COSMIC measurement and ionosonde observations is smaller than 0. 8 sometimes. Therefore, the discrepancy between different source data should be corrected before establishing the ionospheric model for higher accuracy. This paper is structured as follows. The discrepancy between COSMIC data and ionosonde measurements was first corrected, and the quality of electron density profiles and ionosonde observational points were evaluated for quality control.
The global ionospheric data during 2007-2016 (same as in Figure  1 ) observed by COSMIC/ionosonde was utilized to develop the ionospheric models for foF2 and hmF2 based on machine learning, respectively, namely COSMIC peak frequency (COSPF), COSMIC peak height (COSPH), ionosonde peak frequency (IONOPF) and ionosonde peak height (IONOPH). The corresponding averaged accuracies of the four models were about 0. 58 MHz, 19. 59 km, 0. 92 MHz and 23. 40 km, respectively. Then, the strategy was proposed to correct the discrepancies between different ionospheric observational systems, which took the parameters of the "corrected point" as the input information of COSMIC and ionosonde models based on the ANN algorithm, respectively, including time, geographic position and solar-geomagnetic indices. The discrepancy was computed by the difference between the outputs of COSMIC and ionosonde models, then, the final corrected value can be estimated by the sum of pre-corrected value and discrepancy.
For example, in this study, the ionosonde foF2 values would be corrected to the standard of COSMIC technique, then the detail processes were should be conducted as the following steps. (1). Ten input parameters (same as the input layer in Figure  2 ) of ionosonde foF2 points IONO foF2 were taken as the input information for the COSPF and IONOPF models, respectively. (2). The foF2 values were simulated by the COSPF and IONOPF models, respectively, namely, COSPF foF2 and IONOPF foF2 ; then, the difference was computed between COSPF foF2 and IONOPF foF2. (3). The sum of the pre-corrected ionosonde foF2 value IONO foF2 and the above difference was regarded as the final corrected value COSMIC-foF2corr. For more details about the correction process, please refer to the following equation:                COSMIC -f oF2 corr = Iono f oF2 + COSPF f oF2 -IONOPF f oF2 COSMIC -hmF2 corr = Iono hmF2 + COSPH hmF2 -IONOPH hmF2 Iono -f oF2 corr = COSMIC f oF2 -COSPF f oF2 + IONOPF f oF2 Iono -hmF2 corr = COSMIC hmF2 -COSPH hmF2 + IONOPH hmF2 (2) The results showed that the RMSEs of ionospheric foF2 model developed by uncorrected "mixed" data ranged from 1.
Validation of Accuracy in the Solar Minimum Year To evaluate the accuracies of GIPFM and GIPHM in 2009, the observations of ionosonde stations distributed in six continents, excluding Antarctica, were gathered over the entire year. Then, the data sets were extracted from the total observations randomly; each data set contained 10,000 points. Therefore, the selected points were not equally distributed among all seasons and all local times. It was noted that the data set extraction should be conducted before establishing ionospheric models. After that, the selected points were simulated by the GIPFM/GIPHM and IRI-2016, respectively. The results are shown in Figures  4  and 5. In Figure  4 , the abscissa axis represents the ionosonde measurements, and the vertical axis represents the simulated values.
Conclusions In this study, a new method based on deep learning method was proposed to reduce the remarkable discrepancies between multiple instrument measurements, such as space-borne COSMIC radio occultation and a ground-based ionosonde. The results show that this method is very helpful for improving the performances of ionospheric models. Then, the "mixed" corrected data (COSMIC and ionosonde) were utilized to develop the ionosphere models for the predictions of foF2 and hmF2 using an artificial neural network optimized by the genetic algorithm, namely, GIPFM and GIPHM. The results indicate that the genetic algorithm could improve the learning efficiency of the proposed neural network approach significantly. The learning efficiency of a linear regression method optimized by the genetic algorithm improved by about 29.
Background : Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There is a vast number of people without access to care because of economic, geographic, and cultural barriers as well as limited availability of clinical experts who could help advance our understanding of mental health. Objective : To create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include: an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems, and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care.
The Beth Israel Deaconess Medical Center maintains an extensive database of apps that claim to respond to mental health needs  [42]  , and provides a protocol for their evaluation  [43, 44]. There is an unmet need for a free, open, configurable platform to translate pencil-and-paper assessment instruments, cognitive tasks, and therapies into attractive, engaging, and effective digital, mobile, remote tools that exceed current standards of privacy, security, and accessibility. Here, we (1) review existing digital, mobile, remote tools for configurable data collection and content delivery, (2) summarize the motivation for and development of a new mobile platform called MindLogger, and (3) describe MindLogger applets, including an initial use case as part of a large-scale mental health study.
Review criteria for existing customizable, mobile, experience sampling tools In the present review, we did not consider apps limited to specific assessments, cognitive tasks, or therapies, but rather examined platforms that enable the creation and distribution of such apps. We wanted to find products that have an administrator interface for creating and scheduling recurring, customized questionnaires, where users receive and respond to scheduled notifications on a mobile device. For a detailed outline of the protocol we followed, please see Appendix 1, which is briefly summarized here. We gathered information about products with desired characteristics over the last three years through clinical and research collaborators and colleagues. To extend this search we conducted three queries in Google's search engine (without quotation marks or Boolean operators): (1) "digital electronic data capture systems", to broadly identify any electronic tools for capturing data; (2) "mobile phone software sensor data collection", to identify mobile data collection software that may involve sensors; (3) "alternative to qualtrics", to identify alternatives to one of the most prominent products that enables online customization of surveys (though Qualtrics itself does not currently have a mobile app).
We collected additional information from product websites, teleconferences and online demonstrations with the product creators, and from free trials, to determine the degree to which each is (a) customizable in its information presentation and data capture capabilities, (b) private and secure, and (c) accessible (easy to use, economical, and open source). Customization is important to ensure that content, language, and presentation can be adapted and updated to meet the specific needs of a given population of intended end users, and to expand the scope of possible dimensions to assess, analyses to perform, and inferences to make. Privacy and security is a central concern, especially as we conduct research involving a triply vulnerable population of end users: (i) child and adolescent (ii) patients with (iii) mental health and learning disorders.
Application of MindLogger in the Healthy Brain Network study The Child Mind Institute's Healthy Brain Network study  [48]  is an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing functional MRI, diffusion MRI, morphometric MRI), electroencephalography, eye tracking, voice and video recording, genetics, and actigraphy. In order to obtain more in-depth information on real-time tracking of emotions, behavior, daily activities, and their contextual influences, we have adapted the the combined actigraphy and EMA mobile assessment tools and content from the National Institute of Mental Health (NIMH) Family Study of Affective Spectrum Disorders, a large community-based controlled family study  [49].
The original EMA collected data four times per day for two weeks from phones provided to study participants (the Tungsten E2 model of a Palm mobile device and more recently from Android mobile phones). See below for an excerpt from a description of its content: "The EMA assessments included questions concerning a diversity of daily life experiences and behaviors, including data assessed at the moment of the EMA signal (current location, social company, performance of specific behaviors, mood states) and data assessed over the time period between the current and previous assessment or, for the first assessment of the morning, since awakening (experience of daily events and event negativity, food intake, substance use, experience of headache and its specific symptoms). Additional questions were asked at the first EMA assessment of the day concerning sleep duration, quality and sleep problems, and at the end of the day concerning global ratings of the stressfulness of the day, food craving for the day and specific physical symptoms (gastrointestinal symptoms, muscle pain).
The response possibilities included Likert scales for dimensional constructs (such as mood or event negativity) and checklists that allowed for either multiple responses (such as for noting all food types consumed since the last assessment) or single responses (such as current physical location). For daily events, participants were asked at each assessment to identify the one event or experience, good or bad, that had affected them the most since the last questionnaire, and to rate the impact the event had on them on a 7-point Likert scale…" The EMA data from the NIMH Family Study that evaluated the association between daily events and emotional experience yielded important differences in patterns of reactivity among the major subtypes of mood disorders, including bipolar I disorder, bipolar II disorder, major depression, anxiety disorders without a mood disorder, and controls  [50].
MindLogger architecture MindLogger's architecture (Figure  6 ) consists of a set of end-user-facing front-ends (two mobile applications and a web application) and organizer-fac ing front-ends (an admin panel, data dashboard, and applet builder) with a shared RESTful HTTP API using MongoDB  [54]  for data storage. The mobile front-ends, Android and iOS apps, are built using React Native  [55]. This allows us to share a single code base across mobile platforms, resulting in increased speed of development and ease and cost-effectiveness of maintenance. The web application is a ReactJS browser-based counterpart to the mobile applications, and currently provides a subset of their functionality. Organizers (managers, coordinators, editors, and reviewers) have access to different single-page applications built using VueJS  [56].
The admin panel and applet builder are for managing user roles and applets, and the data dashboard is for reviewing user data, with custom charts implemented using d3. js  [57]. The computer security firm Alpine Security  [58]  has conducted extensive cybersecurity black, gray, and white penetration tests to ensure that MindLogger follows best practices for privacy and security. Our mobile app front-end and back-end code base is accessible as online GitHub repositories  [45]  , and is licensed under an extremely permissive, Open Source Initiative-approved open-source license, the Common Public Attribution License (CPAL-1. 0) [59]. The license requires that attribution be given by including (1) the copyright notice: "Copyright (c) 2017 MATTER Lab at the Child Mind Institute", (2) the URL: "https://matter.
childmind. org", (3) the Child Mind Institute's logo, and (4) the attribution phrase: "Child Mind Institute product intended for building applications for good. " We include the attribution phrase to give credit to the developers while also making it clear that while we intend for people to build MindLogger applets that will be benevolent, we have no control over their intent, content, or the data they collect. See  [60]  for an example license file. The back-end API is built in Python using Girder's RESTful API  [61]  with the CherryPy framework  [62]. This software layer provides a set of RESTful endpoints that allow you to manage users, applets, activities, items (such as individual questions), and user responses. All user data is stored in a MongoDB database hosted in an Amazon Web Services [63] cloud instance with password-based encryption.
Thus, the children most in need are the least likely to have access to evidence-based treatment. The EDUCATE study, funded by the National Institute of Child Health and Human Development, is a collaborative effort between researchers at the University of Connecticut and the Child Mind Institute. This clinical trial will examine the effectiveness of an at-home, game-based intervention for reading disorders. Parents of participants in this study will complete daily and weekly assessments in MindLogger, which will allow researchers to assess the home environment and compliance with the intervention protocol throughout the study period. This data will be critical for evaluating the impact of this clinical trial.
Dialectical Behavior Therapy (DBT) applet The DBT Diary Card is a digitized version of the diary card used in evidence-based DBT programs. This tool is a daily tracker of mood, targeting behavioral urges and specific behaviors and using coping skills to manage these emotions, urges, and behaviors. Our Dialectical Behavior Therapy applet (Figure  9 ) will enable therapists to create a digital DBT diary card with a patient to include specific treatment targets for that individual. It will allow patients to set notification reminders to complete the diary card on a daily basis. The patient's device will automatically update with the new targets and schedule, allowing the user to progress in the therapy without having to change the way the data is retrieved. Both patients and therapists will have access to the diary card data to guide treatment planning and sessions.
Products are also adding features over time, and some companies or organizations offer paid services to build in desired features. Our comparison should be seen as a snapshot of the current state of a subset of features provided by software products intended for experience sampling. MindLogger is a new platform that meets most of the stated needs of our collaborators around the world, who desire an open source mobile mental health platform for informing, assessing health, acquiring data, and administering therapies. We prioritized clinicians' and researchers' needs and users' experience during development, aligned technologies to meet these priorities, and are on track to achieve the full feature set we set out to include. One limitation of MindLogger is that it currently does not support passive data collection, or interaction with peripheral devices.
Acknowledgments This project was generously supported by the  Hearst Foundations , the  Intramural Research Program  of the  National Institute of Mental Health  (grants with PI KRM: ZIA MH002954 Motor Activity Research Consortium for Health (mMarch) and  ZIA MH002804-15 Family Study of Affective and Anxiety Spectrum Disorders ), the  Centre for Research and Interdisciplinarity (CRI) -Université de Paris , the  Blanca and Sunil Hirani Foundation ,  MIT  through the laboratory of SSG (SSG and SAA were supported by grant:  NIH P41 EB019936 ), and by  Joseph Healey. We thank the companies and organizations that responded to questions about and provided demonstrations of their products. We thank  Rangle. io  for their help in refactoring MindLogger's mobile app code base and  Alpine Security  for conducting penetration tests of the entire code base.
The third contribution describes the cognitive model Emotion, Personality, Interaction, Knowledge (connaissance), Experience (EPICE) and its different layers to describe the elements of an artificial psyche. The fourth contribution is the artificial thinking model (ATM) presented in the conference paper  [1]  which is based on a double pyramid: the cognitive pyramid that includes two levels, a symbolic level and a subsymbolic level and the linguistic pyramid based on language abilities and a linguistic conception of thought. The results section presents the notion of cognitive dissonance and two examples of the application of the TFVS model-the modelling of emotion during the purchase decision and the application to gambling addiction. The discussion section shows the benefits of the proposed approaches in computer sciences and psychological modelling and the research opportunities to improve them in the future work section that includes the development of a platform to automate the steps of the TFVS design method.
The Experience Layer E The experience allows to enhance the knowledge in a specific area of the science. Case-based reasoning (CBR) implements a kind of analogy. CBR is a model of experience that allows us to index and store cases in an object-oriented database and then, in next consultation, to use a distance to retrieve in the database the more similar cases to the new case in order to apply the most appropriate methods of the previously stored cases to solve the new problem  [35] [36] [37]. CBR involves semantic distances developed by different approaches-algorithms of structural similarity  [55] ; statistical learning as proposed by  [56] ; digital approaches from neural networks and fuzzy logic. The distances are using to implements the different stages of the CBR cycle as depicted on Figure 15  [57].
Future Work We are also using the model in clinical medicine where taking into account the time modeling is mandatory to properly design the process of doing the diagnosis and to choose the treatment of evolving diseases. However, at the moment, the UML diagram ontology, the membership functions, the TFVS are designed by hand and requires a close collaboration between the system designer and the domain expert. Each case is modelled on a spreadsheet with the various fuzzy features needed. A java application has been developed to build from the objects stored in a database, the fuzzy membership functions and the corresponding fuzzy vectors. We are currently developing a design support platform to manage the steps of the FVSOOMM method from the domain ontology class diagram to the EVF model and to test the resultant vectors at each level of the composition hierachy.
The quantity of experimentally recorded point cloud data, such generated in single-molecule experiments, is increasing continuously in both size and dimension. Gaining an intuitive understanding of the data and facilitating multi-dimensional data analysis remains a challenge. It is especially challenging when static distribution properties are not predictive of dynamical properties. Here, we report a new open-source software platform, Genuage, that enables the easy perception, interaction and analysis of complex multidimensional point cloud datasets by leveraging virtual reality. We illustrate the benefit of the Genuage with examples of threedimensional static and dynamic localization microscopy datasets, as well as some synthetic datasets. Genuage has a large breadth of usage modes, due to its compatibility with arbitrary multidimensional data types extending beyond the single-molecule research community.