dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple studies have demonstrated their interest in initial training and continuing medical education to improve technical and behavioral performance. 9, 10 Their major inconvenience is cost, especially since this very fragile equipment requires technical maintenance. We propose a low-cost (LC) alternative consisting of an inert infant manikin with limited functionalities and a monitor which displays the progression of respiratory and hemodynamic parameters using SimBaby TM software. We aimed to compare whether the quality and the educational efficacy perceived by HF SimBaby TM simulator learners are superior or not to an LC simulator for pediatric difficult airway management simulation training in a continuing medical education course.
In the LC group, the simulator consisted of an inert infant manikin (Stat Baby, Simulaids Ltd, Leicestershire, United Kingdom) with non-movement and limited functionalities. There was no spontaneous ventilation or color change. The stomach could not be insufflated. It was only possible to close the vocal cords or generate labial edema using a pneumatic system by air through a 50-ml syringe in a long tube connected to the manikin. All the airway management devices described above were usable. The manikin was not connected to a computer system. However, the changes in physiological parameters displayed on the monitor were also controlled by an instructor with SimBaby TM software in the same manner as in the HF group.
Statistical analysis Statistical analysis was performed using Statview ® 5 software (Abacus Concept, Inc. Berkeley, CA). The T0 EQS was the primary outcome. The other scores were secondary endpoints. T0 EQS was 46 ± 6 for 107 participants between 2008 and 2011 in similar pediatric difficult airway workshops with a SimBaby TM simulator (unpublished personal data). The number of participants needed to objectify a 4-point score variation with a 0. 05 ␣ risk and a 0. 9 power with a two-tailed test was 33 participants per group. Given the potential of a high number of refusals to participate in the study and participants lost to follow-up, we chose to include all the participants in a pediatric difficult airway management workshop organized in our simulation center between January 2012 and April 2013.
,  Chevrel et al. , 2022; Favalli et al. , 2005 Favalli et al. , , 2012;; Harris and Rowland, 2015). For investigations of inactive/older flows on Earth and other planetary bodies, the goal is typically to determine the eruption conditions and lava rheology necessary to produce the observed flow length or area (e. g. ,  Flynn et al. , 2022; Ganci et al. , 2020; Peters et al. , 2021). No matter how a lava flow model is applied, or the level of complexity included, topography is an essential dataset. Therefore, understanding how the spatial resolution of a topographic dataset affects model results is critical. For modeling recent terrestrial flows, it is common to have access to digital elevation models (DEM) of the pre-flow surface ranging from moderate (~10-50 m/pixel) to high (≤10 m/pixel) resolution that are derived from photogrammetry, LIDAR, or InSAR datasets (Table  1 ) (e.
g. ,  Chevrel et al. , 2021; Kubanek et al. , 2015; Richardson and Karlstrom, 2019). However, pre-flow topography is not available for investigations of older lava flows. Some planetary bodies may have limited coverage of high to moderate resolution DEMs of a study region, whereas others are limited to much lower resolution DEMs (Table  1 ). The resolution of planetary DEMs and lack of pre-flow datasets have resulted in many studies using a constant slope to perform lava flow modeling (e. g. ,  Baloga and Glaze, 2008; Hiesinger et al. , 2007; Peters et al. , 2021). However, several previous studies concluded that variations in local topography have a measurable impact on model results of a lava flow's emplacement  (Glaze and Baloga, 2007; Russo et al. , 2022). There are numerous lava flow emplacement models, and many have associated sensitivity analyses to topography, including DOWNFLOW  (Favalli et al.
, 2011) , MAGFLOW  (Bilotta et al. , 2019) , MULTIFLOW  (Richardson and Karlstrom, 2019) , and Q-LAVHA  (Mossoux et al. , 2016). However, this has not been done for FLOWGO/PyFLOWGO  (Chevrel et al. , 2018; Harris and Rowland, 2001). The focus of this study, therefore, is to investigate the impact of topographic data resolution on the FLOWGO thermorheological model's output with specific focus on commonly available DEM resolutions for Earth and other planetary bodies. We use a terrestrial study case that was presented in  Ramsey et al. (2019) : the 2012-2013 Tolbachik eruption (Fig. 1 ). It was chosen because  Ramsey et al. (2019)  already performed the FLOWGO modeling and provided the input rheological parameters that best fit the lava flow.
FLOWGO model Lava flow modeling is a powerful tool for hazard mitigation and interpreting emplacement processes as well as understanding the evolution of thermorheological properties of flowing lava. One of the most extensive and widely utilized lava flow models is FLOWGO/PyFLOWGO (Table  2 ). The original FLOWGO model was recoded and updated in the Python scripting language to PyFLOWGO, which allows for improved iteration and customization  (Chevrel et al. , 2018). For all modeling discussed throughout the rest of the manuscript, PyFLOWGO is used. As of 2023, twenty-two peer-reviewed publications have used FLOWGO or PyFLOWGO to investigate both active and inactive lava flows on Earth at  a variety of locations. It has also been utilized to investigate volcanism on four other planetary bodies (Table  2 ).
As the modeled velocity reaches zero the simulated channel width will increase substantially. The main goal of this work is to investigate the effects of DEM resolution on PyFLOWGO's results. The rheologic input parameters are the same as provided by  Ramsey et al. (2019)  for the TFE-50 Leningradskoye channelized lava flow (Table  3 ), with the exception of the lava's thermal emissivity module. As shown by  Ramsey et al. (2019)  and later by  Thompson and Ramsey (2021) , PyFLOWGO is also sensitive to the assumed emissivity value chosen, with lava flow runout distance varying by ~6%. As part of this work, we also explore the effect of the three different emissivity modules (where emissivity is considered constant, bimodal, and a function of surface temperature) within PyFLOWGO and their effectiveness to reproduce the observed length of a lava channel for the TFE-50 eruption.
Discussion The resolution of the DEM used for PyFLOWGO modeling has a measurable impact on the resulting channel length. These findings differ from  Harris et al. (2016)  who used two different topographic datasets, a 30 m DEM from the SRTM and a 10 m DEM produced from LIDAR, to compare the effect of different DEM resolutions on the FLOWGO model for a channelized lava flow from the December 2010 eruption of Piton de la Fournaise  (Harris et al. , 2016). Their study found that there is little difference between the two resolutions but did not elaborate on the details, since the focus of the study was on comparing field measurements to model results and not a detailed topographic sensitivity analysis. Comparing the results here for only the 30 m and 10 m DEMs, the length difference is 1. 16 km or 9.
, 2021). Our results indicate that using the highest resolution DEM available, with an effusion rate from early in the eruption, produces the best fit to the observed channel length (provided the model is calibrated to that DEM resolution and chosen model step size as we show here). In an ideal situation, a high-resolution topographic dataset (i. e. , 10 m DEM or better) is available coupled with effusion rate measurements made as soon as possible at the start of the eruption. However, if a study uses a moderate resolution DEM in combination with PyFLOWGO, an overestimate of the final channel length is possible depending on the pre-flow topography. For some volcanoes (e. g. , Etna, Kīlauea, Piton de la Fournaise), a 5 m (or better) DEM may be available. However, for many other volcanoes, especially those in remote locations, high resolution DEMs are not readily available or feasible to acquire.
Fig. 6  shows that the change in measured slope value relates to the resolution of the topographic dataset using data commonly available on Mars (i. e. , a CTX and MOLA/HRSC DEM). Previous modeling of Martian lava flows has used a constant slope (i. e. , the standard rheologic approach described in  Hiesinger et al. , 2007). This value is usually derived from HRSC or MOLA data (e. g. ,  Hiesinger et al. , 2007; Pasckert et al. , 2012; Peters et al. , 2021). As shown in Table  4 , Figs. 2, and Fig. 6 , the higher resolution DEMs can provide a large range of slope values dependent on the resolution of the topographic dataset. This range in slope values can have a quantifiable effect on modeling results for effusion rate, viscosity, and yield strength  (Russo et al. , 2022). However, using a constant slope derived from the regional topography may be the best option if no pre-flow topography is available.
To assess the effects of the constant slope values we perform an additional model run using the average slope value derived from the 200 m DEM (5. 6 • ) (Table  4 ). We compare the 5. 6 • constant slope model run to those from the 11. 3 • constant slope results. Our results show that the differences between a simulation on a constant slope of 5. 6 • or 11. 3 • is only 960 m (5%). Given the large (nearly two times) difference in average slope value, this is a relatively minor difference in distance. Although using a constant slope for lava flow modeling is not optimal, given the limitations of planetary datasets, errors related to this approximation are acceptable for a first order estimation of the lava flow emplacement dynamics.
Acknowledgments and Data Availability Statement This research was supported through the  NASA   Solar System Workings (SSW) program  (grant number:  80NSSC19K0547 ). The authors would like to thank  Dave Hyman  and an anonymous reviewer for their insightful reviews. The authors would also like to thank  Andy Harris  for constructive input regarding FLOWGO. Thank you to  Viktor Dvigalo  and  Alexander Belousov  for providing the Tolbachik DEM and to  Dan Berman  for generating the CTX DEM. All Planet data were supplied through their  Education and Research program. The PyFLOWGO model is available for download at https://github. com/pyflowgo. PyFLOWGO results can be found at Flynn (2023). This is a Laboratory of Excellence ClerVolc contribution no •  594.
MAS focuses on intelligent agent behaviors and interactions to solve problems and provide an improvement on technologies  [7]. For example, MAS was applied in multiple research areas, such as transportation, behavior modeling  [13]  and smart grids  [2]. Internet of Things (IoT) objects are a typical example of a technology in a smart building, in which devices could be considered as agents communicating in real time. Indeed, according to Feng and Hu  [14]  and Fredj et al. [15] , the IoT can be modeled as a network of things or devices with embedded sensors, actuators, and software with the purpose of connecting to other devices to exchange data through the Internet. What can be gleaned from the article published by the Association of Computing Machinery  [16] , To make IoT smart, one solution is to interconnect it with artificial intelligence (AI) and MAS.
To make the system more distributed and mitigate data vulnerability, the deployment of AI and agent-based modeling on IoT devices makes the system safe and greatly improves latency  [21]. Due to the decentralized property of MAS, it is a suitable paradigm for modeling and deploying AIoT in DBOS. In addition, MAS allows the safety and resilience of the system components, as well as its scalability. MAS  [10, 11]  is a type of AI that can model and simulate complex systems that are intrinsically distributed and composed of autonomous entities, called agents, that can interact or communicate to achieve the goals of the global system. Therefore, DBOS becomes a system composed of AIoT "agents" that are deployed on IoT devices  [15, [22] [23] [24]. The current study aims to review the state of the art in scientific publications, exploring the use of MAS in the context of AIoT and DBOS.
The effectiveness and relevance of MAS for modeling and simulating the complex processes involved in smart building management, and more specifically BEMS, are evaluated, as well as the identification of recent research works that led to significant advances in understanding the mechanisms BEMS and the underlying BOS. In this review of the literature, inspired by the systematic review of the literature (SLR) method  [25, 26] , recent studies that employ MAS to represent AIoT are highlighted, as well as work that explores the application of MAS, for the development of energy and daily activity management strategies. Methodologies, algorithms, and frameworks are also extracted from the literature, as well as from the knowledge bases and databases used in these studies. By synthesizing the results of this work, our SLR aims to highlight the strengths and weaknesses of MAS-based approaches in smart buildings, discuss the challenges and opportunities associated with their use, and suggest avenues for future research.
Therefore, it will be possible to determine the advantages and limitations of each approach. • RQ3: What are the advantages and challenges related to the deployment of AI model or algorithm on IoT devices? It allows one to highlight the contribution of AI from the point of view of the authors of the reviewed articles. • RQ4: What are the technical characteristics or properties that have been considered for implementing and deploying AI, and more specifically MAS, on IoT devices? Since most of the technology was deployed in the cloud or on the edge, there is still an open question related to the deployment of AI on the devices themselves. This RQ will help to understand the constraints and limitations of AI software to be deployed in the IoT. The question is split into the three following subquestions: a) What are the sensing and actuation characteristics to be considered as a priority? b) What are the run-time constraints and properties that MAS must enforce for being deployed on IoT? c) How to specify an MAS with run-time and deployment properties?.
Search Engine Selection The selection of article sources is a key point in the SLR methodology. Indeed, it conditions all the rest of our study insofar as only the publications present on the selected search engines are studied. The guidelines proposed by  [31]  have been used to select search engines. Consequently, the following engines are selected: ACM Digital Library, IEEE Xplore, and Google Scholar. The first two databases are the most used in computer science. The third was selected because of its large number of documents that are not indexed in the first two databases, for example articles from some conference proceedings, masters, and Ph. D. theses. Although the indexation criteria used for the publication of articles in Google Scholar are not the same as those used in the first two databases, the topics discussed here are current and very useful.
-ExC4: Impossible to access the article text. Studies that cannot be accessed, such as unpublished works or those with restricted access behind paywalls, may be excluded from the review due to challenges in obtaining the complete text or necessary data. Sometimes, it happens that the topic of a certain scientific article and its summary are found in the database of a certain scientific journal, but unfortunately, it is impossible to have access to the complete content of the article. It is impossible to evaluate an article when its text cannot be accessed (PDF download, online text, etc. ). -ExC5: Duplicate study or extended article. The article is extended by another article by the same authors. The contributions in the extended article have been enclosed from those of the original article so that the latter is excluded.
Methodology Application Summary Because the number of articles provided by search engines is huge, especially by Google Scholar, a stop criterion is applied  [32, 36] : "Stop collecting articles only after a sequence of 10 titles completely incoherent with the query that appeared in the list. " It is the subjective responsibility of reviewers to determine the coherence/consistency of an article when they believe that there is no link between the query performed in the database and the title or abstract of the article that appears in the results. After applying the stop criterion described above, the total number of articles selected directly from the search engines is 427 articles (see Table  2 ). The coarsed and fine-grained selection steps allow to exclude unrelevant articles. 109 articles (25.
The map in Fig. 3  shows the number of articles published per continent. Asia and Europe are the continents with the highest number of articles published (48 and 44), followed by North America  (21). This suggests that there is a strong research focus on the topic in these regions. The lower number of articles from Africa, Oceania and South America may indicate that there is less research activity in these regions, or that authors from these regions are less likely to be published in the particular journals or databases included in the analysis. In general, this geographical distribution provides a useful snapshot of research activity related to the topics of this study in different countries and continents. Fig. 4  shows the number of articles excluded based on the different exclusion criteria that are explained in Section 2.
5. It indicates that ExC7, which is used to exclude articles that are unrelated to AI, has resulted in the exclusion of the highest number of articles, i. e. , 171 (51. 4%). This suggests that a significant number of articles did not contribute to the topics of AI. 138 articles (41. 4%) are excluded because they are duplicated in the results from the initial database query (ExC5). ExC3, which is used to exclude survey articles, has resulted in the exclusion of 19 articles (5. 7%). ExC2, which excludes posters or demos that cannot provide enough details on the contributions, has resulted in the exclusion of 3 articles. ExC5, which excludes articles that are extended by another article by the same authors, has resulted in the exclusion of 4 articles. One article is inaccessible (ExC4). One article is excluded (ExC6) because it does not contain any contribution related to IoT.
Research Topics A research topic refers to the subject or area of interest that a researcher chooses to investigate and explore. It is the central idea around which the research is conducted and provides a focus for the study. Table  3  provides the list of articles per research topic. Fig. 5  shows the number of articles for each of these topics. The addressed research topics are listed below: -T1: Design, implementation of AI algorithms on the fog architecture: (1%) This research topic focuses on developing and implementing AI algorithms on fog nodes or fog computing architecture. Fog nodes are intermediate devices between the cloud and IoT devices that help reduce latency and improve data processing efficiency. An example of this research topic would on developing AI algorithms that can extract meaningful information and knowledge from large and complex datasets generated in various IoT applications.
T2, which pertains to the design and implementation of AI algorithms on the edge architecture, has the second-largest number of articles, followed by T7, which concerns the design and implementation of IoT to improve processes in the health domain. T4, T5, and T8 each have 8 or 7 articles, which indicates that they are also areas of active research interest. T10, which deals with the services and quality of AI services, and T11, which concerns the extraction of knowledge from large datasets. Each has 8 and 1 articles, respectively, suggesting that they are areas of interest for some researchers but are not as widely studied as some of the other topics. Finally, T1, T3, and T9 have the fewest number of articles, each with only one or two articles, indicating that they are relatively less popular research topics.
But it still has some attention from researchers in the field. In general, the table gives a good indication of current research interests and trends in the field of AI and IoT. 6  shows the distribution of articles between the different research topics for each year from 2014 to 2022, which can help researchers identify areas that require further investigation. Research topics T6 (Design, implementation of IoT for improving processes in cities and smart cities) and T7 (Design, implementation of IoT for improving processes in health domain) have consistently received more attention over the years, with more articles published in these areas. T10 (Services and quality of services of AI) has also gained attention, with an increase in the number of articles published over the years. On the other hand, research topics T1 (Design, implementation of AI algorithms on the fog architecture), T2 (Design, implementation of AI algorithms on the edge architecture), T3 (Design, implementation of AI algorithms on the IoT devices), T8 (Increase efficiency, safety, and security of communications and devices in IoT), T9 (Classification and characterization of concepts and properties in IoT environment), and T11 (Extract knowledge from large datasets) have received less attention, with only a few articles published in these areas.
Application Domains RQ1 addresses the application domains of the articles. They refer to practical problems that scientific and research studies tend to solve. or edge computing involves the use of distributed computing architectures to process data generated by IoT sensors and devices. Typical applications include real-time processing of sensor data at the edge, which allows for faster response times and reduced latency; and cloud-based analytics, which allows for the processing of large datasets and the deployment of machine learning models. Table  4  and Fig. 7  represent the distribution of articles among the different application domains of AI and IoT. Based on the numbers, it appears that D7, which is "Multiple application in smart building", has the highest number of articles  (27) , followed by D4, which is "Mobility or energy management in cities", with 23 articles.
SaaS providers typically offer subscription-based pricing models in which users pay a monthly or yearly fee for access to the software. In the context of the reviewed articles, the microservice-based model or SaaS was used to build and deploy various types of applications, such as healthcare monitoring systems, chatbots, and recommendation systems. The microservices were often deployed on cloud platforms, such as Amazon Web Services or Microsoft Azure, to provide scalability and fault tolerance. -M8: General Object-Oriented Modelling. This family refers to general object-oriented modeling, where the primary modeling tool used is the Unified Modeling Language (UML). UML is a standardized language used in software engineering to create visual models of software systems. Overall, UML provides a powerful modeling tool set to design, document, and communicate the architecture and behavior of software systems.
Controller and Computing Resources Choosing the right type of controller is an important decision when deploying AI on IoT devices. The two most common options for controllers are Raspberry Pi and Arduino. Both have advantages and disadvantages, and the choice will depend on the project's specific needs. Raspberry Pi is a popular choice because of its processing power and flexibility. It can handle complex AI algorithms and can be easily programmed using languages such as Python. Additionally, it has built-in WiFi and Bluetooth connectivity, making it easy to connect to other devices and networks. However, it's high power consumption and cost may not be ideal for certain IoT projects. On the other hand, Arduino is a low-cost and low-power option that is suitable for simple AI applications.