dataesr/scientific-paragraphs-categorization huggingface dataset:
A. F. J. M. is grateful for financial aid from NSERC (Canada) and FQRNT (Quebec). J. S. B. acknowledges the support received from the UNAM PAPIIT project IA 101220 and from the CONACyT project 263975. N. D. R. acknowledges some support from HST-GO programs 15611 and 15992. C. M. P. R. acknowledges support from NASA grants with Chandra award number GO0-21006A and NICER award numbers 80NSSC19K1451 and 80NSSC19K1459. A. D. thanks support from FAPESP #2019/02029-2. F. N. acknowledges FAPESP for support through proc. 2017/18191-8. This research has made use of the services of the ESO Science Archive Facility. This publication makes use of the SIMBAD database operated at CDS, Strasbourg, France. We thank the referee for helpful suggestions.
In order to preserve or limit the physical and mental health declines (i. e. anxiety; stress), we implemented an unsupervised validated physical activity (PA) program (MATCH;  (4, 5) ) in a short-stay Covid-19 geriatric unit, during the covid-19 European 1st wave (March to April 2020). Our hypothesis was that MATCH program will be acceptable and may counteract physical and mental decline among older adults hospitalized in covid-19 unit. This program, carried out independently by the patients themselves, seemed to be particularly suited to the period of over-work of the health care professional and the containment conditions of infected patients. This was a pilot phase of a larger study ethically approved (#20. 04. 08. 48526), designed as a prospective, monocentric, proof-of-concept study. Short Physical Performances Battery (SPPB), Hospitalized Anxiety and Depression (HAD) scale, and Activities of Daily Living score (ADL) at baseline and at the end of the unit hospitalization were assessed by trained medical team.
At baseline, the MATCH decisional tree (3 tests: 30 second chair test, balance with joint-feet and semi-tandem stance and 4 meters comfortable walking test) was also performed in order to prescribe and explain one of the five MATCH standardized program  (3). This specific MATCH program should be done every day by patients themselves unsupervised. At the end of the intervention, self-satisfaction of the program was recorded. During the period, 48 COVID-19 patients were hospitalized. Eleven patients (7 women, 4 men; age: 86. 6years (SD=6. 3)) were included in the MATCH intervention (see Table  1  for clinical characteristics). The others (n=37) were excluded due to age lower than 75 years (n=4), inability to pass the decisional tree tests (n=23), severe dementia (n=4), or severe disease complication (n=3) or not proposed (n=3).
Implementation of the MATCH intervention was feasible and took only 15-20 minutes for all tests required to complete the decisional tree. The exercise intervention length was 9. 3 days on average. Moreover, the medical staff found it easy to learn and to teach the program. In addition, the program did not require specific materials and the ones used were very easy to clean (plastic sets). Feasibility (our main outcome), evaluated by the adherence of the intervention (number of completed exercise days /total intervention days) showed that the PA was done 53% of the time (from 26 to 80%) and 82% of patient were satisfied. Importantly, 36% of the participants presented some medical issues that could limited the adherence. Moreover, another potential limitation on adherence reliability was that collecting data on program adherence was difficult due to the overload of the staff.
Acknowledgments:  Laurine Andre ,  Yves Rolland ,  Stéphane Gerard ,  Marie-Jeanne Kergoat ,  Eva Peyrusqué ,  Mylène Aubertin-Leheudre  contributed significantly to the study conception, the writing of the manuscript and approved the final version. The authors would like to thank  Dr Sara Vienne-Noyes  for her clinical investigation as investigator.
It has been implemented in the cooling processes of data centers in Mauritius  (Elahee & Jugoo, 2013). SWAC   has also been considered in district cooling in Florida  (Porak, Zwieten, & Rauchenstein, 2012; Porak, Zwieten, & Wiles, 2013) , for a resort community in upper Egypt  (Elsafty & Saeid, 2009) , in San Andres, Colombia (Devis-Morales, Montoya-Sánchez, Osorio, & Otero-Díaz, 2014), in Malta  (Sant, Buhagiar, & Farrugia, 2014) , in California  (Davidson, 2003)  and for cooling in oil production platforms on the Brazilian coast  (Miranda, 2008). The main contribution of this article is to analyze the insertion of Deep Seawater Cooling and Desalination (DSCD), which combines SWAC and RO desalination in one system with the intention to supply both affordable water and cooling services in a one-way district cooling system.
Assessment of solar resource, climate change, cooling demand and possible desalination needs of coastal cities The method employs a selection of coastal cities and an assessment of the energy demand for cooling, of the solar resource and of the possible desalination needs. The major coastal cities (Fig. 7 (a)) were chosen depending on the following criteria: 1) the majority of the coastlines have to be covered; 2) the city has to be less than 1000 km from the ocean; 3) the capitals of major countries with a coastline are chosen; 4) the city has preferably more than 1 million inhabitants; 5) if populated cities are close (less than 1000 km), the city with the major population is chosen; 6) the data must be available in the weather database of Trnsys software  (Klein, 2010). The relative populations are represented in Fig.
The method was described byByrne et al. (2015). It uses the population of agglomerations and their population growth rate  (City Population, 2021). The data on climate change (temperature increase between 1901 and 2012) and the change in annual precipitation from recorded values over the past 60 years is available in the latest IPCC report (weatherbase, 2021). The annual precipitation is retrieved from the website www. weatherbase. com (Stocker et al. , 2013). The solar resource and cooling-degree-days (CDD) were obtained and calculated using Trnsys software  (Klein, 2010). The weather data is read from a typical meteorological year (TMY) file that was built using hourly values of temperatures, humidity ratios, wind velocities and orientations, solar radiation and other meteorological variables recorded over a few decades.
MESSAGEix energy system modelling for the case study of Malé In this case study, the MESSAGEix energy system modeling tool is used to simulate Malé´s cooling and water services demand and optimize the renewable energy supply. The MESSAGEix is a dynamic, linear, least-cost optimization model developed by the IIASA Energy Program in the 1980s  (Huppmann et al. , 2019). MESSAGEix is a free and open-source documentation wherein a Python package links together all components of the framework. The model seeks to satisfy given demand levels by country or region at a minimal cost  (Hall & Buckley, 2016). It determines the optimal configuration of the energy system under various technical engineering, socio-economical, or biophysical constraints (S. Zhang et al. , 2016). It helps the user determine the optimal capacity portfolio and least-cost solution for satisfying a given demand for commodities or energy services.
Water services The customer requires a considerable amount of water and does not require cooling services. In this case, another customer with high cooling requirements should use the cold water first. Greenhouse, vertical farming, agricultural fields, industrial processes, power generation. change in temperature and salinity depending on depth. This data was extracted from the NODC (Levitus) World Ocean Atlas project (National oceanic and atmospheric administration, 2018). It provides annual, seasonal, and monthly temperature and salinity data at multiple depths with 1 arc degree resolution. The present study assumes that the DSCD plants extract water at a depth of 1,000 meters, where the temperature is on average 5 • C. Comparing this temperature with the average air temperature of the island results in an average temperature difference of 23.
2 • C. Given that the continental crust between Malé and the deep ocean is small, this paper proposes the construction of the onshore, open cycle DSCD as shown in Fig. 19. The bathymetric data used in the project was taken from the General Bathymetric Chart of the Oceans (GEBCO) project  (GEBCO, 2021). GEBCO presents bathymetric data with 30 arc second resolution of the world's oceans, seas and lakes and the topography of islands and continents  (Weatherall et al. , 2012). The proposed   deep-sea pipeline has a length of 5 km and depth of 1000 m and transports cold deep seawater to the DSCD plants, while the mix water pipeline with 1 km length and 200 meters depth discharges cold brine back to the sea. The surface water pipeline with 0. 2 km length and 30 meters depth catches warm seawater for desalination.
Introduction Intracranial mechanical thrombectomy (MT) is the first line treatment strategy for selected patients with acute ischemic stroke (AIS) due to proximal vessel occlusion (PVO). 1  Amongst factors associated with treatment benefit over best medical treatment (BMT) alone, is the baseline ischemic core volume, known to be strongly associated with worse outcome. 2, 3 A large initial ischemic core volume being associated with poor clinical outcome, most trials that validated MT as standard of care excluded patients with Alberta Stroke Program Early CT Scores (ASPECTS) below 6, or ischemic cores above 70 mL precluding to draw strong conclusions in patients with a deemed unfavorable imaging profile at presentation. Yet, growing evidence has shown that a subsample of these patients may benefit from revascularization, even if outside currently validated eligibility criteria for MT.
Methods This report was prepared with reference to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRIS-MA)  6  and the Cochrane Handbook for Systematic Reviews of Interventions. No ethical approval was seeked as only data from previously published studies in which informed consent was obtained were retrieved and analysed. The summary protocol was developed in-house in August 2020, and registered through the international database of prospectively registered systematic reviews PROSPERO (registration number, 2020 CRD42020125428). This study was conducted and reported independently of industry or the ongoing trials of MT for patients with LICs.
Statistical analysis Data were pooled in a meta-analysis when at least two studies with relevant data were available. The cumulative rate (per-centage) and 99% confidence interval (CI) for each outcome was computed for each column. Heterogeneity of the data was assessed by the Higgins index (I 2 ), and visually through inspection of the forest-plot. For high levels of heterogeneity (Isquared >50%), the DerSimonian and Laird random-effects model was applied. 14 We conducted the statistical analysis two ways. First, we computed a cumulative meta-analysis of unfavorable outcome, mortality, and sICH rates in patients that received MT. Weighted random effects meta-regression analysis was performed to assess the influence of potential effect modifiers: we tested the relationship between the modality for ischemic core segmentation (CTP or MR-DWI), the study specific definition of a LIC volume (core ≥50 or ≥70 mL) in patients with MT and the rate of 90-day unfavorable outcome.
In a previous meta-analysis, Cagnazzo et al. 4  found MT to be associated with higher odds of favorable 3 months outcome in patients with low ASPECTS (OR, 4. 76; 31/149 vs. 10/184; 99% CI, 1. 3 to 16. 8; I 2 =31%; P=0. 01). Yet, as the clinical-imaging selection framework for MT evolves with imaging software technological advances and the advent of individualized decision making, there's a need for quantitative biomarkers to identify patients with LIC who may benefit from MT. ASPECTS is a universally used semi-quantitative scale to assess for ischemic core extent, and has fair accuracy when compared to CTP or DWI. 24 SPECTS assessed on DWI has further been shown to correlate with DWI segmented ischemic core volume,  25  but with decreasing performances in patients with an ASPECTS of 4 or less.
Furthermore, the optimal ASPECTS cut-off found to discriminate accurately patients with ischemic cores over 70 mL, has been shown to be quite consistently around 4. 20, 25  Yet in patients with ASPECTS of 4 or less (hence with isch- emic cores over 70 mL), the effect of MT remains uncertain. In the work by Cagnazzo et al. ,  4  patients with very low ASPECTS (0-4) had a good outcome rate of 17. 1% (86/461; 99% CI, 12. 3 to 21. 9; I 2 =37. 6%), which is of similar magnitude to that found in our analysis of patients with LICs volume, although there was considerable heterogeneity in this outcome aggregation (I 2 =92. 3). The use of volumetric approaches has been facilitated by the development of multiple dedicated post-processing softwares,  5  prompting the need for a comprehensive analysis of the outcome of these patients after MT, and a comparison to the outcomes of patients with BMT alone.
pro- Identification 623 Records after duplicate removed 622 Records yielded on database review 2 Records yielded from and citation tracking other sources Screening 557 Records excluded on title or abstract Eligibility 60 Records excluded after full-text assessment 10 Records finally included 6 Records with BMT control group Included 2 Records with unsuccessful reperfusion control group 2 Records without control group Figure 1. Flowchart for studies selection. BMT, best medical treatment.
1 mg/kg was administered intravenously through a central venous catheter in the right internal jugular vein (Vygon, arterial LeaderCath, 6 French, 15 cm). The ICG-emitted fluorescence signal was analysed using the ER-PERFUSION software (IRCAD, Strasbourg, France), which allowed for a virtual perfusion cartography based on time-to-peak fluorescence intensity (TTP; in seconds). TTP results from the velocity of the fluorescence signal until it reaches its maximum intensity peak in a specific region of interest (ROI) within the first 40 s following ICG injection  23. This is a relatively short period following ICG injection, as a result of ICG administration via the central venous catheter. The virtual perfusion cartography is subsequently overlapped onto the images, providing fluorescence-based enhanced reality (FLER).
In this study, TTP was measured from the time when there was 25% of fluorescence intensity to 75% of fluorescence intensity using the ER-PERFUSION software. In addition, the minimum and maximum absolute fluorescence intensities were measured in four randomly chosen ROIs on the bowel surface using the FLER software. TTP and absolute fluorescence intensity were analysed as mean of the four ROIs, together with the mean difference in maximum and minimum absolute fluorescence intensities.
Statistical analysis. Continuous variables are presented as means with standard deviation (SD). Categorical data were reported as frequency and proportion. Fisher's exact test was used to calculate P values for categorical variables. The paired sample T-test was used to analyse the differences in TTP fluorescence intensity (in seconds) and mean absolute fluorescence intensity in ROIs between the different vasopressor doses. All results were analysed using IBM SPSS Statistics for Windows, version 24 (IBM Corporation, Armonk, NY, USA). A p value < 0. 05 was considered statistically significant.
When data is transmitted, then the node waits for the acknowledgment. Suppose it could not receive within a specified time. In that case, the same frame is transmitted again until it exceeds the maximum frame retries limit, which has been defined in a MaxFrameRetries parameter. Most of the adversaries do not follow the algorithm and do not follow the standard policies, severely disturbing the network quality by transmitting the smaller or longer stream of packets in the medium. A description of different adversary attacks on IEEE 802. 15. 4 standard is given in the following section. 2. 2. 1. Exhaustion Attack. In most WSN applications, legitimate nodes are deployed in an open environment. In the IEEE 802. 15. 4 standard, each member node applies a slotted CSMA/CA algorithm before sending its packet to other nodes or coordinator.
This decrease in BO reduces the inactive period, and consequently, nodes' waiting time is reduced If there is an increased number of collisions during BI, it may reduce the throughput. This increase in the collision may occur due to insufficient active duration, and there is a requirement to increase the active period so that nodes have more time to send their data requests. Following are the criteria for adjusting the parameter values of SO and BO. Input: Collision Ratio CR, Packet Successful Transmission ratio PST, Request Ratio RR Calculate PC = 1/[1 + exp{-E×(CR -F)}] Calculate PS = 1/[1 + exp{-E×(PST -F)}] Calculate PE = 1/[1 + exp{-E×(RR -F)}] Calculate Z 1 = (PS×φ) + (PC×θ) Calculate Z 2 = (PS×φ) + (PE×θ) ifZ 1 > (i) If the difference between BO and SO is greater than 0 and BO is less than 14, then both SO and BO will be increased in the next BI (ii) In case BO approaches its maximum limit of 14, and the difference between BO and SO is 0, then SO will be increased without any change in BO value (iii) If both SO and BO are 14, then the next SO and BO value will remain unchanged as it is already on its maximum limit  6 Wireless Communications and Mobile Computing (iv) If BO is less than 14 with a 100% duty cycle, then we have the flexibility to increase the SO.
Analysis and Results In this section, the proposed scheme is evaluated by developing a simulation environment in MATLAB, as shown in   2. The proposed scheme detects intruder's attack by exposing collision and exhaustion probabilities as described in Section 3. Results shown in Figure  4  describe the scenario when there is no intruder's attack found. In this case, the probabil-ity of successful transmission is maximum, and the probability of collision is 0. The sum of both probabilities is less than the threshold, and hence, no collision detection is found in this case. However, the probability of collision and successful transmission increases in the presence of an intruder's attack  and is verified by the results shown in Figure  5. It is evident from the results that the probability of successful transmission depends upon the probability of collisions.
In the 3 rd and 5 th beacon intervals, the probability of successful transmission decreases with the increase in collision probability. Figure  6  shows the probabilities of exhaustion attack in the presence of intruders for different beacon intervals. It is evident from the results that whenever exhaustion probabilities increase from the threshold, our proposed scheme detects the presence of intruders. Delay transmission is calculated as the time duration when a node has data to transmit and successfully transmits its data to the coordinator or sink. Network delay is the accumulated delay of all the nodes in a network. Figure  7  shows the amount of delay experienced by the nodes in the presence and absence of an intruder's attack. It is evident from the results that network delay in the presence of an intruder's attack is much more than the delay without an intruder's attack.
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CU RIE, a drift detector relying on cellular automata. Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CU RIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy.
Thus, the most suitable drift detector depends on the characteristics of the DSM problem under study, giving more emphasis to some metrics than others. Regarding the detection metrics, we usually tend to put in value those drift detectors that are able to show a good classification performance while minimizing the distance of the true positive detections. Cellular automata (CA), as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we present a Cellular aUtomaton for concept dRIft dEtection (CU RIE), capable of competitively identifying drifts over data streams. The proposed approach is based on CA, which became popular when Conway's Game of Life appeared in 1970, and thereafter attracted attention when Stephen Wolfram published his CA study in 2002  [41].
Although CA are not very popular in the data mining field, Fawcett showed in  [13]  that they can become simple, low-bias methods. CU RIE, as any other CA-based technique, is computationally complete (able to perform any computation which can be done by digital computers) and can model complex systems from simple structures, which puts it in value to be considered in the DSM field. Moreover, CU RIE is tractable and interpretable  [25] , both ingredients that have lately attracted attention under the eXplainable Artificial Intelligence (XAI) paradigm  [1]. Next, we summarize the main contributions of CU RIE to the drift detection field: -It is capable of competitively detecting abrupt and gradual concept drifts. -It does not required the output (class prediction) of the base learner. Instead, it extracts the required information for drift detection from its internal structure, looking at the changes occurring in the neighborhood of cells.
Besides, CU RIE offers other additional advantage in DSM: -It is also able to act as an incremental learner and adapt to the change  [25] , going one step further by embodying an all-in-one approach (learner and detector). The rest of the manuscript is organized as follows: first, we provide the background of the field in Sect. 2. Next, we introduce the fundamentals of CA and their role in DSM in Sect. 3. Sect. 4 exposes the details of our proposed drift detector CU RIE, whereas Sections 5 and 6 elaborate on experimental setup and analyze results with synthetic and real-world data stream respectively. Finally, Sect. 7 concludes the manuscript with an outlook towards future research derived from this work.
-Neighborhood: a neighborhood and its radius must be specified. Even though a diversity of neighborhood relationships has been proposed in the related literature, the "von Neumann" (see Figure  1 ) or "Moore" are arguably the most resorted definitions of neighborhood for CA. -Initialization: the grid is seeded with the feature values of the instances that belong to the training dataset. In order to decide the state of each cell, we assign the label corresponding to the majority of training data instances with feature values falling within the range covered by the cell. After that, cells of the grid are organized into regions of similar labels (Figure  2 ). -Generations: when the initialization step finishes, some cells may remain unassigned, i. e. not all of them are assigned a state (label).