dataesr/scientific-paragraphs-categorization huggingface dataset:
It is worth noting that hybrid unicast-multicast schemes have been thoroughly investigated in the context of 4G and 5G systems  [214] -  [217]. The unique characteristics and requirements of 6G networks, such as higher data rates, ultra-low latency, massive connectivity, mobility, and the integration of emerging technologies, introduce new challenges and considerations for hybrid unicast-multicast schemes. Specifically, the design of hybrid strategies in 6G systems necessitates a deeper understanding of their impact on network architecture, the development of advanced routing protocols that can adapt to dynamic transmission modes, efficient resource allocation algorithms tailored for 6G environments, and the establishment of robust QoS guarantees to meet the stringent requirements of future applications.
The source code that generates planar antenna arrays, HPBWs, and gains is available at https://github. com/NadezhdaChukhno/planar-antenna-array This article has been accepted for publication in IEEE Communications Surveys & Tutorials. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/COMST. 2023. 3319354 This work is licensed under a Creative Commons Attribution 4. 0 License. For more information, see https://creativecommons. org/licenses/by/4. 0/.
Of those, 4312 were eligible for follow-up. Maternal, obstetrical, and neonatal data were collected from medical records following a standardized protocol. All survivors were invited to participate in a comprehensive neurodevelopmental assessment at age 5. 5 years, along with a sample of 600 term born children from the French Longitudinal Study since Childhood (ELFE) cohort  32  intended to serve as a reference for developmental tests. 33  Parents completed a self-administered questionnaire, whereas children had a clinical examination by a physician and a cognitive assessment by a neuropsychologist that were both performed in dedicated examination centers with specifically trained professionals to ensure homogeneity in data collection. At 5. 5 years, at least 1 assessment was performed for 3083 (78%) children.
The proportion of missing data for baseline characteristics ranged from 0% to 17. 9% for each covariate but exceeded 5. 0% only for contractions at admission (5. 5%), socioeconomic status (6. 5%), and maternal level of education (17. 9%). Overall, the primary outcome was missing for 43. 3%. Complete case analysis for the composite survival outcomes may undermine the representativeness of the estimates and introduce selection biases. The main results were therefore presented after multiple imputations to account for selective dropouts and missing information at follow-up. Missing baseline and outcome variables were handled by multiple imputations by chained equations with the SAS multiple imputation procedure (SAS Institute, Cary, NC). 36  Considering missing data as missing at random based on the variables included in the imputation model, we generated 50 independent imputed data sets with 20 iterations each by using all the baseline and outcome variables and those potentially predicting attrition and/or outcomes.
A propensity score analysis was performed in each imputed data set to estimate the exposure effect, and estimates were pooled according to Rubin's rules. We performed the following sensitivity analyses. First, we presented the results of the analyses of complete cases with restriction to children with a follow-up at 5. 5 years. Second, we reported a greedy matching on the propensity score, performed using the PSMATCH SAS procedure without replacement within a caliper of 0. 2 SDs of the logit of the propensity score. Each exposed infant was matched to the closest non-exposed infant, leading to matching 202 children exposed to tocolytics with 202 non-exposed children. This procedure estimated the average treatment effect on the treated (ATT). Third, we investigated the association between the initial tocolytic drug administered (oxytocin receptor antagonists, calcium channel blockers, no tocolysis) and the outcomes by applying a 3-group propensity score based on a generalized boosted modeling to calculate the average treatment effect (ATE) weights for optimal balance between the 3 groups using the mnps function in the twang statistical package in R (R Core Team, Vienna, Austria).
Strengths and limitations Our study was based on a large, prospective, population-based sample of participants exposed to PPROM at a national level and detailed high-quality data. Furthermore, obstetrical management was consistent with the current clinical guidelines, particular in terms of antenatal administration of steroids and antibiotics, thereby ensuring that our findings accurately reflect routine clinical practice. The follow-up at 5. 5 years of age included both parental insights and a standardized medical and neuropsychological evaluation conducted by trained professionals, further enhancing the reliability and validity of our results. Given the dearth of evidence-based data regarding school-age outcomes after antenatal exposure to pregnancy complications and treatments, such observational studies provide valuable insights for evaluating routine clinical practices in a real-life setting.
This project was funded by the  French Institute of Public Health Research  ( IRESP TGIR 2009e01   program ), the  Institute of Public Health , and its partners. The partners include the  French Health Ministry , the  National Institute of Health and Medical Research (Inserm) , the  National Institute of Cancer , the  National Solidarity Fund for Autonomy (CNSA) , the  National Research Agency  through the  French EQUIPEX program of investments  in the future (grant no. ANR-11-EQPX-0038 ), the  PREMUP Foundation , the  Foundation of France  ( 00050329  and  R18202KK ), the  Foundation for Medical Research  ( SPF20160936356 ), and the hospital  clinical research program  Epinutri ( DGOS13-040 ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Moreover, several tasks have been performed to integrate the code in real-time simulator  [9]  and  [10] : -Optimization of CPU time performance with integration of OpenMP parallelization directives. -Development of integration procedure through an external library (Plug & Play approach  [11]  and  [12] ). This integration through an external has been successfully used for several simulator networks. Today, it is EDF reference procedure to integrate reference code in simulator. Along the development of the SiRENE project, EDF has launched a rehost FSS project, with the integration of CATHARE_2 model in FSS  [13]. A strong synergy was established between the two projects. Moreover, the very positive feedbacks from training teams strengthened our strategical initial choice. Today at EDF, CATHARE_2 is the reference thermal-hydraulics system code for real-time applications for both engineering and training.
For core physics, the COCCINELLE code was chosen  [14]. COCCI-NELLE is the EDF reference neutronics code widely used for the safety studies and in support of the plant operations since the 90s. In simulator, the code solves 3D neutron diffusion equations with 2 energy groups in kinetics mode with a 3D model. The code includes simplified thermal-hydraulics and rod thermic models to take into account water moderator and fuel feedback on neutronic reactions. The code predicts a full 3D power distribution, the neutronic instrumentation signals and the Residual Heat. The optimizations and works of CATHARE_2 code for real-time simulator applications have also been considered in COCCI-NELLE code (adding OpenMP directives and integration through an external library). The benefits of using our EDF safety codes can be summarized as follow: -Using our expertise acquired in safety analysis, -Increase simulator prediction's reliability, -Reduce economical costs with one maintenance team for both applications (safety analysis and simulator).
The Eurasian Holocene (beginning c. 12 thousand years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using an imputed dataset of >1600 complete ancient genome sequences, and new computational methods for locating selection in time and space, we reconstructed the selection landscape of the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, possibly associated with the dietary shift occurring in this period. We show that the selection on loci such as the FADS cluster, associated with fatty acid metabolism, and the lactase persistence locus, began earlier than previously thought.
In the complex mosaic of ancestries that constitute a modern human genome, any putative signatures of selection may therefore misrepresent the timing and magnitude of the selective process. Similarly, episodes of admixture between ancestral populations can result in present-day haplotypes which contain no evidence of selective processes occurring further back in time. Ancient DNA (aDNA) provides the potential to resolve these issues, by directly observing changes in trait associated allele frequencies over time. Whilst numerous prior studies have used ancient DNA to infer patterns of selection in Eurasia during the Holocene (e. g. ,  [8] [9] [10]  ), many key questions remain unanswered. To what extent are present-day differences in human phenotypes due to natural selection or to differing proportions of ancient ancestry? What are the genetic legacies of Stone Age hunter-gatherer groups in present-day complex traits? How has the complicated admixture history of Holocene Eurasia affected our ability to detect natural selection in genetic data? To investigate these questions, and the selective landscape of Eurasian prehistory, we conducted the largest ancient DNA study to date of human Stone Age skeletal material, generating a phased and imputed dataset of >1,600 ancient genomes  11.
Samples and data Our analyses are undertaken on an unprecedented sample of shotgun-sequenced ancient genomes, presented in the accompanying study 'Population Genomics of Stone Age Eurasia'  11. This dataset comprises 1664 imputed diploid ancient genomes and more than 8. 5 million SNPs. These represent a considerable transect of Eurasia, ranging longitudinally from the Atlantic coast to Lake Baikal, and latitudinally from Scandinavia to the Middle East. Included are many of the key Mesolithic and Neolithic cultures of Western Eurasia, Ukraine, western Russia, and the Trans-Urals, constituting a thorough temporal sequence of human populations from 11,000 cal. BP to 3,000 cal. BP. Additionally, an especially dense sample set from Denmark represents continuous populations from the Mesolithic to the Bronze Age.
Ancestry-stratified patterns of natural selection in the last 13,000 years To account for population structure in our samples, we developed a novel chromosome painting technique that allows us to accurately assign ancestral population labels to haplotypes found in both ancient and present-day individuals. We built a quantitative admixture graph model (Fig. 1 ; Supplementary Note 1a) that represents the four major ancestry flows contributing to modern European genomes over the last 50,000 years  12. We used this model to simulate genomes at time periods and in sample sizes equivalent to our empirical aDNA dataset, then inferred tree sequences using Relate  13, 14. We then trained a neural network classifier to estimate the path backwards in time through the population structure taken by each simulated individual, at each position in the genome.
We note that the strongest signal of selection observed in this locus is at MAPT (rs4792897; p=4. 65e-10; s=0. 03 (Fig. 3A ; Supplementary Note 2a), which codes for the tau protein  56  and is involved in a number of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease  [57] [58] [59] [60] [61]. However, the region is also enriched for evidence of reference bias in our dataset-especially around the KANSL1 gene-due to complex structural polymorphisms (Supplementary Note 2h).
Acknowledgements We thank all the former and current staff at the  Lundbeck Foundation GeoGenetics Centre  and the  GeoGenetics Sequencing Core , and to colleagues across the many institutions detailed below. We are particularly grateful to  Line Olsen  as project manager for the  Lundbeck Foundation GeoGenetics Centre  project. We thank  UK Biobank Ltd. for access to the  UK Biobank  genomic resource. We are thankful to  Illumina Inc. for collaboration. EW thanks  St. John 's  College, Cambridge , for providing a stimulating environment of discussion and learning. The  Lundbeck Foundation GeoGenetics Centre  is supported by the the  Lundbeck Foundation  ( R302-2018-2155 ,  R155-2013-16338 ), the  Novo Nordisk Foundation  ( NNF18SA0035006 ), the  Wellcome Trust  ( UNS69906 ),  Carlsberg Foundation  ( CF18-0024 ), the  Danish National Research Foundation  ( 44113220 ) and the  University of Copenhagen  ( KU2016   programme ).
Code availability The modified version of CLUES used in this study is available from https://github. com/standardaaron/clues. The pipeline and conda environment necessary to replicate the analysis of allele frequency trajectories of trait-associated variants in Supplementary Note 2a are available on Github at https://github. com/ekirving/mesoneo_paper. The pipeline to replicate the analyses for Supplementary Note 2c-2e can be found at https://github. com/albarema/neo. All other analyses relied upon available software which has been fully referenced in the manuscript and detailed in the relevant supplementary notes.
Self-excited induction generator model The real machines, with their different kinds of windings and geometries, are too complex for analysis taking into account their real configurations, hence it is needed to develop a model, the behavior of which is as close as possible to that of the original one  [21]. In this context, it is essential to use a simple model to facilitate numerical implementation and to minimize the simulation computation time. To study the transient and steady state of the asynchronous generator, the SEIG model using space phasor formalism is implemented in the Matlab Simulink environment. Let us consider a p pole pair rotor cage induction machine with a three-phase equivalent rotor winding. The rotor and stator spatial reference (d r and d s ) are tied to the rotor and stator phase 1 axes.
(12) The numerical implementation in the Matlab/Simulink environment of Equation (  12 ) is shown in Fig. 2. The T s, which appears in Fig. 2  is the sample time used in numerical model and less or equal to 5. 10 -5 for this application. The detailed diagram of Fig. 2  is developed in the appendix. Fig. 3  shows the numerical implementation of the equations giving K 1r and K 2r. The electromagnetic torque can be defined as a function of magnetizing inductance by Expression  (13)  and its implementation is shown by Fig. 4 : T e = 3 2 p [ (L m + l s ) ī′s + γL m īr + χL m īa ] × ī′s. (13) Finally, the numerical implementation of the mechanical equation given by Expression (6) will be presented by Fig. 5. The numerical implementation of the already presented blocs allows the simulation of the stator voltage variations (amplitude and frequency) during transient and steady state considering the numerical values given in the appendix.
METHODS KESTREL (NCT03481634; conducted between July 2018 and October 2021) and KITE (NCT03481660; conducted between July 2018 and June 2021) were two prospective, 100week, multicenter, randomized, double-masked, active-controlled Phase 3 studies in subjects with visual impairment due to DME. The primary objective of the studies was to demonstrate the non-inferiority of brolucizumab to aflibercept in visual outcomes at Week 52. Both studies were conducted in accordance with the International Council for Harmonization E6 Good Clinical Practice Guideline, the Declaration of Helsinki, and all applicable local, state, and federal laws. Study protocols were reviewed and approved by an independent ethics committee or an institutional review board of each participating center. All subjects provided written informed consent prior to the study initiation.
Search strategy This step consisted firstly of establishing a search query with the keywords resilienc* AND training 3. In addition to the keywords, the search query integrated the selection level of the publications (title OR keywords) and general criteria: period (must have been published between 2010 and 2022); type of publication (must be a peer reviewed article); language (must be in English). Next, the search query (Supplementary material B) was applied in eight online databases: ERIC, ProQuest, PsycINFO, PubMed, SAGE, ScienceDirect, Scopus, Wiley Online Library. In the end, 1133 potentially relevant articles were identified (Table  2 ). The references retrieved were exported to EndNote version X9.
Resilience training intervention research A classical difficulty in research on training is to demonstrate its impact. Ideally, effects would be measured during real-life events, but this is difficult, if not impossible, in many cases. A part of the effects of the training interventions in this review were thus measured through self-reported data or through bio-indicators. Limitations of self-reported data are over-or underestimation of effects, the risk of social desirability or recall biases  (Althubaiti, 2016). Bio-indicators allow for an objective measurement on specific indicators but are often used in experimental settings or in situations far from the target situation. Thus, both types of data make it hard to judge the actual effects on resilience and performance. Future studies with a stronger phenomenological interest hold promise to deepen our understanding of how people experience training interventions, beyond selfreported or biomedical data.
17 %); 2) the classification results are not interpretable at datasetlevel, namely it is difficult to determine which features are dominant for the classification. We can conclude that: In this context, the proposed wearable system has been developed to overcome the previous drawbacks, as mentioned in Table  1. In particular, X-RCRNet, the AI framework that will be integrated into this wearable system, enables us to efficiently monitor the evolution of COVID-19 symptoms in real-time by using ECG beat signals. X-RCRNet, as an explainable AI, will be use as a cross-validation tool for practitioners. In the next section, we describe the overall system, and the ECG beat classification adopted for patient monitoring architecture.
Data description The ECG dataset is an open-source image dataset of cardiac and COVID-19 patients  [26] , since it has been used in several research  [25, 27, 29]     data were collected using the ECG device EDAN SERIES-3. This device collected 12 lead ECG trace images sampled at 500 Hz. Each lead has a 2. 5 s duration and the total duration length is 10 s on the 12 lead ECG images. The current dataset is not suitable for our wearable system due to data mismatch (Our single lead wearable ECG sensor measure 1d signal, X-RCRNet need to be train with 1d signal from a single lead instead of images). The first contribution of this paper is to generate a new single lead ECG beat signal dataset by converting single lead ECG trace images to 1d signals and extract heartbeat for each signal. Lead II ECG images was chosen for the image-signal conversion for two reasons: 1) The wearable ECG sensor of our system is a single lead ECG sensor; 2) Lead II is the lead that describes the best all ECG beat waves.
Pre-processing The pre-processing consists of creating a new dataset from the ECG images datasets. Three steps were followed: • Image segmentation, binarization and filtering: Image cropping near the lead II area has been performed. The binarization has been performed using Otsu method  [41]. The remaining black pixels in the background can be assimilated to "pepper noise". A 3 × 3 median filter was applied to remove noise (median filter preserves image edges). The final image is a black-and-white image with the ECG signal as the foreground. • Image -to-signal conversion: The signal was extracted by detecting the foreground pixel. The resulting signal has been down sampled to 125 Hz (Suitable for both time-domain analysis  [42]  and wearable systems  [40] ). The signal is smoothed by convoluting the signal with a Hamming Windows  [43].
Benchmarking with ECG classification models A benchmarking was performed between our model with other ECG classifications models, using our current dataset. The goal is to assert our hypothesis about the enhancements provided. For this study, we choose CNN, LSTM Network, ResNet, CNN + LSTM Network. The models mentioned are the most used models for ECG classification. The results in Table  6  shows that our model outperforms the existing ECG classification tools in terms of accuracy, sensitivity, precision, and specificity. It also confirms our assertion about the enhancements provided.
Benchmarking with related studies A benchmarking was performed between our model with COVID-19 dedicated models using the original dataset. The results in Table  7  shows that aside of  [27] , our model outperforms the existing ECG classification tools for COVID-19 in terms of accuracy, sensitivity, precision, and specificity. It also confirms the possible use of an ECG beat signal to monitor COVID-19 patients. In the next sub-section, we present the last paper contribution of X-RCRNet: the model explainability.
The saliency map is directly correlates with the importance of a particular spatial location (i, j) for a particular class c and thus functions as a visual explanation of the class predicted by the network. We implement a one dimensional version of Grad-CAM ++  [50] , an enhance version of Grad-CAM for better results. Fig. 9  illustrates some local Grad-CAM++ visualizations for COVID-19, Normal and Normal beat signal. The results shows that for COVID-19 signal, ST interval is the feature with the highest gradient value, and by extension the most prominent key feature for COVID-19 beat signal classification. While useful for local explanation, the main limitation of Grad-CAM++ is its inability to give a dataset-level explanation. By using Grad-CAM++ we propose to compute, for each key feature, its frequency as the most prominent feature for each sample.
The method is described by Algorithm 1. 8  represents each key feature occurrence frequency, as the most important feature across the dataset. The results shows that X-RCRNet identifies the ST interval as the most relevant feature for COVID-19 classification at the dataset level. Other researchers  [2, 4, 5]  confirm that the manifestation of a severe case of COVID-19 patients occurs in the ST interval. To our best knowledge, X-RCRNet is the first Deep Learning model to confirm ST interval of ECG pattern as the feature affected by the COVID-19 at a dataset-level.
Conclusions and perspectives This paper presents X-RCRNet, an explainable deep neural network, based in ResNet18 for COVID-19 patient symptoms monitoring using an ECG beat signal. The first known open database of ECG beat signals for COVID-19 patients has been created to conduct our experiments. The results from the five-class classification demonstrated that X-RCRNet can be efficiently used to identify COVID-19 patients using an ECG beat signal. With an overall accuracy of 96. 48 % (95. 80 % for COVID-19 alone) and a loss value of 0. 1042, the experimental results and the benchmarking confirmed that the model is superior to the state-of-theart models. In addition to the overall performance, X-RCRNet is the first known Deep Neural Network to confirm the ST interval as the most prominent feature affect by the COVID-19.