dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations The current study has several limitations. First, the data of this study was obtained from an online survey and therefore our results should be interpreted with caution. Second, our findings for partners were limited by our small sample size. For this reason, our study population might not be representative. Overall, these limitations reveal some concerns about the generalizability of our data. The correlation analysis gave an important overview to understand the positive and negative associations between several variables, however it did not provide a causal relationship. It is therefore important to complete this study with further researches. The CBTS is a promising questionnaire to assess the PTSD-FC; however, the French versions of the CBTS were not yet validated, therefore it is important to interpret our results cautiously although reliability analysis in this study was promising.
Acknowledgements We would like to thank the many people who have helped us disseminating the survey link, namely  Alison Ly Passieux  (Alliance francophone pour l'accouchement respecté, AFAR),  Antje Horsch ,  Lamyae Benzakour ,  Marie-Gabrielle Evanno  (Docctissimo),  Vania Sandoz , … the  TV program  ' La maison des maternelles ', and different radio stations.
is technique allows sensor nodes to avoid transmitting its sensed data to medical server if the predictions match the sensing data. Meanwhile, the medical server always presumes that its prediction reflects the real observation, unless it receives the data from the sensor node. e data is transmitted if it is different from the data predicted, where normal health data is forwarded to the Aggregator through deployed relay nodes. e data is considered critical if it is beyond the upper/lower limits of previously defined thresholds, where emergency data can be sent directly to the Aggregator. e proposed system was developed and tested using MATLAB software simulation, as well as being tested experimentally using MySignals HW V2 hardware platform. Both simulation and experimental results are compared to the E-HARP  [12]  and PCRP  [13]  protocols.
A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of path-cost estimation process, the proposed mechanism achieves efficient and effective multihop routing of data and improves the reliability and efficiency of data transmission over the network. Researchers in  [19]  proposed a protocol named Optimum Path Optimum Temperature Routing Protocol (OPOT). e proposed protocol maintains the temperature of node and communicates the sensed information to remote server with minimum delay and energy, thereby increasing the lifetime of sensor networks. It also considers the critical data signals to be sent when the temperature of node exceeds the admissible threshold limit. e obtained simulation results are compared with conventional routing protocols and analyzed that the proposed protocol has decreased delay, minimum energy, reduced power, uniform temperature distribution, and maximum lifetime of sensor node.
e most essential benefit from the DPM is the ability to shrink traffic volume exchanged in the networks quite significantly. Besides, transmitting less data certainly saves sensor energy and, therefore, prolongs the lifetime of the entire network  [12, [25] [26] [27]. To sum up, the proposed EERP-DPM system was developed and tested using MATLAB software simulation, besides hardware implementation using MySignals HW V2 platform, which is a noticeable and comprehensive contribution from the existing work in the literature. Moreover, the proposed solution runs a prediction model at both the sensing nodes and the base station to allow sensor nodes to avoid transmitting their sensed data to the base station, as long as the predictions match the readings. e medical server always presumes that its prediction reflects the real observation unless it receives the data from the sensor node.
Performance Analyses and Discussion is section assesses the performance of the proposed EERP-DPM scheme from two main perspectives: first, simulation using MATLAB software; second, experimentally using the MySignals HW V2 hardware platform, which is a noticeable and comprehensive contribution from the existing work in the literature. e performance analysis of the proposed EERP-DPM scheme takes place in five indicators, namely, Network Lifetime, Residual Energy, roughput, Path-Loss, and End-to-End Delay. is section is concluded with Table 3 that compares the proposed protocol from simulation and experimental perspectives against existing routing protocols for healthcare using the IoT.
Furthermore, the deployment of relay nodes plays a significant role to balance the Performance in EERP-DPM. Using NN fitting tool in MATLAB, the mean squared error (MSE) is measured between the proposed EERP-DPM simulated (y i ) against experimented (d i ) results, as per MSE � 1 2 􏽘 N j�1 y i -d i 􏼁 2. ( 3 ) Figure  14  shows the MSE regression plot of training, test, and validation steps, where the process determines the best number of iterations, during which validation produces a minimal value. After initial training, the process continues for 82 more iterations, during which error rates do not drop lower. During the 83 iterations, however, training stops as the error rate increases. MSE result seems reasonable since the final MSE is small; besides, there is no significant overfitting that has occurred by iteration 83, before which the best validation performance occurs.
Conclusions e recent developments in IoT promise for providing solutions for healthcare. e medical sensors are typically equipped with batteries, which may have limited resources such as storage capacity, battery life, computational power, and channel bandwidth. erefore, the energy-efficiency can be achieved through the development of an effective routing mechanism to prolong the network lifetime. In this paper, we propose EERP-DPM for healthcare using the IoT in order to reduce transmissions between the sensor nodes and the medical server. is technique allows the sensor nodes to avoid transmitting its sensed data to the Medical Server, as long as the predictions match the readings. e proposed system was developed and tested using a MATLAB software, and MySignals HW V2 hardware platform. We analyze the efficiency of the proposed EERP-DPM scheme in terms of five performance indicators, namely, Network Lifetime, Residual Energy, roughput, Path-Loss, and End-to-End Delay.
Species occurrence data We compiled occurrence records of Trachemys dorbigni in its native range and invaded areas from the global biodiversity information facility (GBIF;  [36] ), the VertNet data portal (www. vertnet. org), Horus Institute  [37] , and peer-reviewed published articles (S1 Appendix). Our data set included the largest number of occurrence records with a minimal pairwise distance of 10 Km to prevent sampling bias  [38] , using the randomisation function from 'spThin' R package  [39]. The final dataset contained 56 occurrence records of T. dorbigni within its native range, plus 15 invasive population records. Because the species is freshwater-dependent, presence records are geographically biased towards rivers and lakes.
To prevent multicollinearity, we selected the two sets of variables with the lowest correlation values based on the variance inflation factor (VIF) using the 'fmsb' R package  [43]. VIF is an effective approach for multicollinearity assessment for more than two independent variables at a time; the higher the VIF value, the higher the collinearity between the related variables  [44]. We only selected the bioclimatic variables below the critical threshold of five: isothermality (bio 3), temperature annual range (bio 7), mean temperature of the wettest quarter (bio 8), mean temperature of the warmest quarter (bio 10), and precipitation seasonality (bio 15). Therefore, one predictor dataset comprised only the climatic variables selected by VIF, and other combined the climatic variables and the human influence index.
We projected the mean resulting models (CD and CHD models) into wider-ranged layers (the Americas) to identify the most suitable areas for population establishment. We assumed that higher-valued suitability areas were more prone to successful invasions. We identified the areas with higher invasive potential for the turtle using the ensemble model from five different algorithms (GAM, MAXENT, BRT, RF and GBM) through the 'biomod2' R package  [47]. Ensemble models can provide more robust predictions for potential distributions of invasive species, where species-environment relationships are difficult to determine  [48, 49]. Ensemble distribution maps highlight areas of agreement among different models predictions, reducing areas of uncertainty for individual models  [50, 51]. Because these algorithms require presence and absence records, and absence records are usually either unavailable or unreliable, we replaced them by pseudo-absence occurrence sets  [52].
The occurrence of T. dorbigni can be also limited by other factors besides thermal tolerance  [80]. As freshwater invaders, these organisms are more prone to occupy large areas by using water body networks for dispersal, together with the known pet introduction pathways. The accessibility to other lakes, ponds, and rivers may be key for the spread of this species, and difficulties for dispersal process include habitats split by dams and roads, for example. In this sense, alternative models run with the freshwater environmental database and other variables associated with the presence of water can refine future models and improve predictions by including due inclusion of likely dispersal routes for aquatic invaders  [45]. Furthermore, it is likely that its occurrence is also limited by density-dependent factors (e.
Therefore, we believe the most imperative actions to prevent T. dorbigni from becoming a highly impacting non-native species within prone-invasive areas are: 1) limit or even ban commercial trade. Alternatively, all breeders and shops must implant a subcutaneous microchip for identification of the turtle, and the commercial establishment should register the owner in a country and/or worldwide database of non-native species. 2) Authorities and governments to set high fines for those who release individuals, and establish inspection activity on pet breeders and shops. 3) To promote citizen science programs to increase invasive species monitoring areas, including a user-friendly database to report findings and data. The threat of invasive species can only be mitigated by the close cooperation of the triad of scientists, civil society, and policymakers.
Drones for Studying Sharks A standard drone operation consists of an unmanned aircraft, ground control station, and communications link between the two. Typically, the ground control station serves as the communication gateway with a live-feed visualization screen from the camera. For shark research, often the drone will be launched from a beach or vessel serving as the take-off point and operated along a manual or automated flight path over a target area  [25]. A pilot who is responsible for controlling the flight operates the drone, and there may be additional personnel who are operating or analysing the camera feed for sharks and at times other relevant wildlife and/or people and infrastructure of interest. Once a shark is spotted, data are collected, mostly in the form of pictures or videos, and recorded either in internal storage on the drone, as part of the ground station, or by recording the feed via a stream at an auxiliary location.
Multirotor drones are a comparatively new technology and have advantages of rapid vertical take-off and landing capabilities on coastal beaches and vessels. They can also hover and are more dynamic and responsive in movement positioning, however, are more aerodynamically unstable and have shorter flight durations of typically 12 to 40 min  [2]. Increasing the energy density of the batteries, using alternative fuel systems, or dual or tethered power systems will provide greater flight times and allow for longer periods of flight, but may add complexity and cost to the drone platform. The type of data to be collected will influence the variant of sensor, the type of payload to be attached to the drone, and ultimately the choice of drone for use. Image and The type of data to be collected will influence the variant of sensor, the type of payload to be attached to the drone, and ultimately the choice of drone for use.
Therefore, behaviours such as approach angle/direction and intra and interspecific interactions can be observed. Sharks feeding underneath a whale can also be observed (depending on water clarity) which has not been possible in the past without the use of a shark cage that may influence behaviour. This gives researchers an additional view of an event and behaviours displayed, including some subtle communication behaviours sharks are known for  [54]. Researchers are also able to pilot a drone from a distance that does not influence shark behaviour unlike traditional, handheld cameras which require an operator be close to a carcass. Dicken  [55]  recounts white sharks biting the propellers and pontoons of their boat while filming a scavenging event with traditional cameras, suggesting that these methods do affect behaviour and that drones can be considered a more effective tool for collecting data without altering shark behaviour.
The only limits to numbers of active tracks of sharks using drones are the number of available batteries, pilot fatigue and weather, meaning that in ideal conditions with a battery recharge station and multiple pilots/drones it is possible to obtain large datasets rapidly. Since the drone-based tracks are shorter than active surface tracking, which can last for 24+ h  [99] , obtaining datasets that are sufficiently large to infer habitat use and behaviour from drones thus require more individual tracks than tagging approaches. However, the trade-off from using drones is obtaining higher resolution data than tagging approaches, and the ability to directly examine behaviours. Drones can also be used to assess the abundance and diversity of sharks and rays in reef habitats. For example, by conducting line transects with drones, Kiszka, et al.
In these shallow reef environments, drones currently provide one of the only means to study shark behaviour, distribution and abundance. The use of drones in these environments, however, is relatively novel and there are few long-term studies that have relied on this approach to obtain conservation-relevant data. Satellite and acoustic tagging programs initially encountered similar difficulties as sample sizes were low, yet global datasets are now available that have allowed inferences to be drawn at scales never before possible. As drones become more commonly used tools for shark research in shallow reefs, larger drone-based datasets will allow broader examinations of ecological patterns of shark movement, habitat use, and behaviour not obtainable with other approaches. For example, since drone movement data are georeferenced at high resolutions, large drone-based datasets could examine questions around the use of landmarks or 'highways' in shallow reefs to move across reef lagoons, and whether movement of sharks in these areas has effects on other species of shark and fishes that can be visible concurrently.
They demonstrated that the methods could be made to work well on previously unseen data and on novel data from completely different sites. A key innovation here was the development of an integrated active-learning software platform called FishID (https://globalwetlandsproject. org/tools/fishid/) that allows new data to be rapidly tagged and assimilated into an improved model. In another example, Fernandes et al. [135]  tackled a related challenge to segment the anatomy of fish species in order to estimate body volume. This was largely accurate when applied in a controlled setting (fish out of water on a uniformly lit background) but performance degraded when the system was applied to other species, or in situ. These exemplar studies and similar research showcase the possibilities of AI in a marine setting.
Although accurate, VGG16 is a resource-intensive network and required desktop-class computing hardware to achieve high frame rates (Sharma et al. [31]  report a NVIDIA Quadro P6000 GPU delivered an inference time of 0. 130 s). Other organisations have further expanded on these initial results, although these have not yet been published as peer-reviewed papers. In the USA a team from UC Santa Barbara's Benioff Ocean Initiative, in collaboration with Salesforce AI Research and computer scientists at San Diego State University, have developed a system they call SharkEye. According to their web page (https://www. sharkeye. org), data collection is done via RGB cameras on drones flown over the beaches. The live video stream is processed on remote servers, feeding a system that broadcasts real-time alerts to beach users.
High-quality annotated datasets are the key to building high-performing models and these are extremely time-intensive to create. The data must reflect the full range of conditions encountered in the field and (at least initially) human experts are required to manually label each object of interest. However, the labelling process can also be assisted by AI tools-once reasonable object detection models are available. We note that the studies reported above assess performance by testing on a small sub-sample split from their training data. Such tests can be misleading for the reasons given above and reported accuracies are unlikely to hold when deployed in the field. The tests essentially answer the question 'How well does the AI know the current data?' rather than 'How well does the AI solve the problem?'.
In particular, the visual appearance of beaches in different locations can vary dramatically, meaning that even a well-trained algorithm would need tuning to avoid significant false positive detections. This problem is known as 'domain shift' and can be mitigated by using an active learning system that facilitates the rapid assimilation of new data into the AI model. Tools for active learning have started appearing in the literature recently. These take the form of software that predict labels for new data and allow an 'oracle' user to make corrections via an interactive graphical interface (e. g. , ICON by Gonda et al. [137] , RootPainter by Smith et al. [138] , FishID by Ditria et al. [134]  and the Transfer Sampling method by Kellenberger et al. [139] ). A large trial (involving some of the authors of this manuscript) of AI shark detection algorithms on drones has recently been completed in NSW, Australia to systematically test the ability of ML algorithms to distinguish between shark species, and to determine how well algorithms can generalise across a range of water and environmental conditions.
Over 4 TB of data was gathered at five beaches on the NSW coast during March-June 2020, covering a wide range of environmental conditions. The scientific team developed in-house active learning tools to assist with labelling the dataset and expect to release results of the trial in early 2021. The goal is to deploy a robust and accurate software tool to assist beach managers in confidently identifying potentially dangerous sharks in real time. Further enhancements will aim to mitigate against surface distortions, e. g. ,  [140]  and apply photogrammetric methods, e. g. ,  [141]  to measure shark size, orientation and swim-parameters  [8]. Taken as a whole, these capabilities have the potential to transform any intelligent device into a data-gathering tool. For example, such automated systems could be the foundation of a high-quality citizen science program.
8 m and 27. 1 to 7. 9 • C, respectively  [6]. Video footage and drone sensor data revealed that two of the white sharks being tracked and eight other white sharks in the area approached, bumped, and/or bit the AUV during these tracks  [6]. Not only did this study demonstrate that a drone could be used to effectively track and observe the behaviour of a large shark, but it also produced the first observations of subsurface predatory behaviour for this species. Using the behavioural and environmental datasets generated by these tracks and three additional missions, Gabriel  [7]  conducted more detailed analyses related to underwater drone performance, white shark behaviour, and environmental correlates. This study concluded that the drone was not only able to track sharks more accurately, both horizontally and vertically in the water column, than traditional vessel-based methods, but also provided fine-scale, environmental datasets related to the tracks and direct observations of white shark behaviour  [7].
Acknowledgments: Thank you to the following people for their contributions to the projects encompassed in this review:  Kevin Boswell ,  Craig Brand ,  Jose Carlos Castillo ,  Mike Heithaus ,  Brendan Kelaher ,  Kirk Gastrich ,  Jeremy Kiszka ,  Jessica Meeuwig ,  Guillaume Rieucau ,  Louise Tosetto  and the many pilots and observers that made these shark projects possible.
This was a partnership between multiprofessional clinicians and people living with GLILD. This research prioritisation exercise was based on methodology developed by the James Lind Alliance ( JLA) but was not an official JLA process and did not include the final workshop stage (the final ranking was based on voting as described further below). We created an online survey using SurveyMonkey in nine European languages, open between 12 August and 14 September 2020. Dissemination of the survey was assisted by the ERS, the International Patient Organisation for Primary Immunodeficiencies (IPOPI) and the European Society for Immunodeficiencies (ESID). Clinicians were invited to provide the link to patients. The survey asked participants what questions they had about the diagnosis, treatment, follow-up and scientific understanding of GLILD.
In this final stage, respondents could vote for ⩽10 of the 27 questions they most preferred to see answered. The questions were provided in both lay and technical language, again using SurveyMonkey. The survey was available in Dutch, English, German, Italian and Spanish (the surveys from the first round that generated the most frequent responses). Dissemination of the final survey was facilitated by ERS, ESID and IPOPI, together with a direct e-mail to respondents who had left contact details in the initial survey. The survey was open between 1 December 2020 and 20 January 2020. At closure, each question was ranked separately by the number of votes it received from 1) patients and carers, and 2) clinicians. A final joint list, with equal weight given to clinician and patients responses, was created by taking the average of the two ranks and ordering these.
47 (17%) were people affected by GLILD or carers, and 222 (83%) were clinicians: 53 (24% of clinicians) primarily worked in immunology whilst 169 (76% of clinicians) primarily worked in respiratory or internal medicine specialties. The five commonest countries were Italy (n=34), the UK (n=28), Spain (n=23), the Netherlands (n=12) and Switzerland (n=11), together accounting for 39% of respondents. The results of the research prioritisation are reported in table 1, ordered by overall rank but also indicating rank by patients and clinicians separately. The number of votes cast for questions varied between 36 and 177. Because of the relative preponderance of respiratory clinicians over immunologists, we also analysed the data by first giving equal weight to respiratory and immunology preferences in the clinician group, then combining this with the patient rank.