dataesr/scientific-paragraphs-categorization huggingface dataset:
For that, data from high school students from two countries (Japan and China) were analyzed to explore cognitive activities in which students engage when taking lecture notes in mathematics classes. Results provided insights into the cognitive activities accompanying lecture note-taking, such as the metacognitive function, which has yet to be explored in previous research. In addition, the authors inferred that the differences in lecture notes might result from the influence of the student's beliefs and teachers' instruction styles. The fifth, Annamalai et al. used a qualitative case study approach to understand the factors that affect students' motivation and satisfaction with online assessments in three different countries (Malaysia, Lithuania, and Spain) during the COVID-19 pandemic. The finding highlighted the importance of effective assessment guidelines and feedback in online learning environments.
In summary, the 14 papers included in this Research Topic focus on six major Research Topics and using diverse methodologies. The first group has four papers, which directly deal with the issue of methodologies in the field, including natural language processing, mixed-methods integration, sample size determination, and behavioral -neurophysiological data integration. The second group (with two papers) focuses on students' academic life, using survey as the major methodology. The two papers in the third group focuses on scale development. The three papers in the fourth group focuses on framework building and further exploration or examination. One paper uses a qualitative, interview method, and two papers using two different literature review methods. This Research Topic collection may mimic the present research fronts and suggest developing more diverse, novel methodologies to study topics in relation to learning, instruction, and assessment in educational psychology.
We investigated the optimality of the two-dimensional representation by quantifying how this representation distinguishes the states compared with the original high-dimensional space of whole-brain FC, as well as with reduced spaces with dimensions higher than two. To do so, we trained a support vector machine (SVM) with polynomial kernel as implemented in the MATLAB function fitcecoc with the objective of distinguishing between eight class labels (each representing a state of consciousness). We subdivided the 300 samples used for each state into training (90%) and validation (10%) sets and assessed model performance using a 10 k-fold scheme with four different sets of features: (1) the lower triangular part of the FC matrix in the original data dimension; (2) z1, standing for the encoding of the 300 matrices in one-dimensional latent space; (3) the z1,z2 pair representing the encoding of the FC matrices in the twodimensional latent space; and (4) three dimensions representing the encoding of the FC matrices in the three-dimensional latent space.
We also noted that the improvement in the performance is considerably higher when the latent space dimension changes from one to two than when the dimension increases from two to three, which is comparatively very A whole-brain model with local dynamics given by Hopf bifurcations was implemented at nodes defined by the AAL parcellation, coupled with the anatomical connectome. We included spatial heterogeneity based on RSN in the model parameters. The model was tuned to reproduce the empirical FC for each condition, and the resulting parameters were used to generate a surrogate database of simulated FC matrices that were represented in a latent space using a VAE. Finally, perturbations were introduced in the model as an external periodic force, resulting in a set of trajectories in latent space (one per pair of homotopic AAL regions) parameterized accordingly the amplitude of the forcing parameter.
Sleep dataset Simultaneous fMRI and EEG was measured for a total of 73 subjects and a subgroup of 55 was considering (by excluding subjects who did not fall asleep) (36 females, mean ± SD age of 23. 4 ± 3. 3 years). EEG via a cap (modified BrainCapMR, Easycap, Herrsching, Germany) was recorded continuously during fMRI acquisition (1505 volumes of T2*-weighted echo planar images, TR/TE = 2080 m/30 m, matrix 64 3 64, voxel size 3 3 3 3 2 mm3, distance factor 50%; FOV 192 mm2) with a 3 T S Trio (Erlangen, Germany). EEG measurements allow the classification of sleep into 4 stages (wakefulness, N1, N2 and N3 sleep) according to the American Academy of Sleep Medicine (AASM) rules. To facilitate the sleep scoring during the fMRI acquisition, pulse oximetry and respiration were recorded via sensors from the Trio [sampling rate 50 Hz]) and MR scanner compatible devices (BrainAmp MR+, BrainAmpExG; Brain Products, Gilching, Germany).
We selected 15 subjects who reached stage N3 sleep (deep sleep) and contiguous time series of least 200 volumes for all sleep stages. Written informed consent and the experimental protocol was approved by the local ethics committee ''Ethik-Kommission des Fachbereichs Medizin der Goethe-Universita ¨t Frankfurt am Main, Germany'' with the ethics application title ''Visualisierung von Gehirnzusta ¨nden in Schlaf und Wachheit zum Versta ¨ndnis der Abnormita ¨ten bei Epilepsie und Narkolepsie'' and the assigned number: 305/07 in Frankfurt (Germany). Previous publications based on this dataset can be consulted for further details. 58 opofol sedation and anesthesia Resting-state fMRI of three different states following propofol injection: wakefulness, sedation and unconsciousness were acquired from 18 healthy right-handed volunteers (4 men and 14 women; age range, 18-31 years; mean age ±SD, 23.
7 ± 3. 7 years). Data acquisition was performed in Liege (Belgium). Subjects fasted for at least 6 h from solids and 2 h from liquids before sedation. During the study and the recovery period, electrocardiogram, blood pressure, pulse oximetry (SpO2), and breathing frequency were continuously monitored (Magnitude 3150M; Invivo Research, Inc. , Orlando, FL). The clinical evaluation of the level of consciousness was performed considering the scale used in. The investigator considered if the subject is fully awake if the response to verbal command (''squeeze my hand'') was clear and strong (Ramsay 2), as sedated if the response to verbal command was clear but slow (Ramsay 3), and as unconscious, if there was no response to verbal command (Ramsay 5-6). This procedure was repeated twice for each consciousness level assessment.
Functional MRI acquisition consisted of resting-state functional MRI volumes repeated in the three states: normal wakefulness (Ramsay 2), sedation (Ramsay 3), unconsciousness (Ramsay 5). The typical scan duration was half an hour for each condition, and the number of scans per session (200 functional volumes) was matched across subjects to obtain a similar number of scans in all states. Functional images were acquired on a 3 T S Allegra scanner (Siemens AG, Munich, Germany; Echo Planar Imaging sequence using 32 slices; repetition time = 2460 ms, echo time = 40 ms, field of view = 220 mm, voxel size = 3. 45 3 3. 45 3 3 mm3, and matrix size = 64 3 64332). Written informed consent, approval by the Ethics Committee of the Medical School of the University of Liege. For further details on acquisition of this dataset see previous publication.
The ethic committee of the University Hospital of Liege (Belgium) approved the study, where all data were collected. Written informed consents were obtained from all healthy subjects and the legal representative for DOC patients in accordance with the Declaration of Helsinki. 3T Siemens TIM Trio MRI scanner (Siemens Medical Solutions, Erlangen, Germany) was used to acquire the data: 300 T2*-weighted images were acquired with a gradient-echo echoplanar imaging (EPI) sequence using axial slice orientation and covering the whole brain (32 slices; slice thickness, 3 mm; repetition time, 2000 ms; echo time, 30 ms; voxel size, 3 3 3 3 3 mm; flip angle, 78 ; field of view, 192 mm by 192 mm). A structural T1 magnetization-prepared rapid gradient echo (MPRAGE) sequence (120 slices; repetition time, 2300 ms; echo time, 2.
METHOD DETAILS fMRI pre-processing We used FSL tools to extract and average the BOLD signals from all voxels for each participant in each brain state. The FSL pre-processing included a 5 mm spatial smoothing (FWHM), bandpass filtering between 0. 01 and 0. 1 Hz, and brain extraction (BET), followed by a transformation to a standard space (2 mm MNI brain) and down sampling for a final representation to a 2 mm voxel space. The next steps were implemented in MATLAB, using in house developed scripts. First, we corrected the data by performing regressions between the displacement parameters, the average signals extracted from the white matter and ventricles, their first derivatives, and the voxel-wise BOLD signals, retaining the residuals for further analysis. In the second step, we applied volume censoring (scrubbing) and discarded subjects who presented significant relative head displacements in more than 20% of the recorded frames, with a criterion for movement significance set as a displacement between consecutive frames exceeding 0.
5 mm. 61 inally, we averaged all voxels within each ROI defined in the automated anatomical labeling (AAL) atlas, considering only the 90 cortical and subcortical non-cerebellar brain regions to obtain one BOLD signal per ROIs. 62 During pre-processing, 4 subjects were removed from the anesthesia dataset, as well as 9 patients in MCS and 3 patients in UWS.
ACKNOWLEDGMENTS The authors thank participants and their families for their invaluable time and commitment to our study. The authors thank the whole staff from the  Radiodiagnostic and Nuclear Medicine departments, University Hospital of Liege , especially  Roland Hustinx ,  Claire Bernard ,  Jean-Flory Tshibanda , and  Nathalie Maquet. Y. S. P. is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant   896354. E. T. is supported by  PICT-2019-02294  ( Agencia I+D+i, Argentina ) and  ANID/FONDECYT Regular 1220995  (Chile). A. I. is supported by grants from  Takeda   CW2680521 ;  CONICET ;  FONCYT-PICT (2017-1818 ,  2017-1820 );  ANID/FONDECYT Regular  ( 1210195 ,  1210176 , and  1220995 );  ANID/FONDAP (15150012 );  ANID/PIA/ANILLOS ACT210096 ; and the  Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) , funded by the  National Institutes of Aging of the National Institutes of Health  under award number  R01AG057234 , an  Alzheimer's Association grant  ( SG-20-725707-ReDLat ), the  Rainwater Foundation , and the  Global Brain Health Institute (GBHI).
R. P. is post-doctoral fellow, O. G. is research associate, and S. L. is research director at  FRS-FNRS. The study was further supported by the  University and University Hospital of Lie  `ge; the  Belgian National Funds for Scientific Research (FRS-FNRS); the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ); the  FNRS   PDR  project ( T. 0134. 21 ); the  ERA-Net FLAG-ERA   JTC2021  project  ModelDXConsciousness (Human Brain  Project  Partnering  Project); the fund Generet; the  King Baudouin Foundation ; the  Televie Foundation ; the  European Space Agency (ESA) ; and the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Programme , the  Public Utility Foundation ''UniversiteEuropeenne du Travail,'' ''Fondazione Europea Ricerca Biomedica ,'' the  Bial Foundation , the  Mind Science Foundation , the  European Commission , the  Fondation Leon Fredericq , the  Mind-Care foundation , the  DOCMA  project ( EU-H2020-MSCA-RISE-778234 ), the  National Natural Science Foundation of China  (Joint Research Project  81471100 ), and the  European Foundation of Biomedical Research FERB Onlus.
J. D. S. and E. T. are funded by a grant from  STIC-AmSud  (project  SILIDOC -21-STIC-11 ), ECOS-Sud  A20M02 , and  ECOS-Sud   U20S01. J. D. S. and G. D. are supported by the ModelDXConsciousness in the framework of the  FLAG-ERA JTC 2021-HBP-2021 programme. E. T. is supported by  PICT-2019-02294  ( Agencia I+D+i, Argentina ) and  ANID/FONDECYT Regular 1220995  (Chile). The content of the article is solely the responsibility of the authors and does not represent the official views of these institutions.
Context. Early-type galaxies (ETGs) are divided into slow and fast rotators (FRs and SRs) according to the degree of ordered rotation of their stellar populations. Cosmological hydrodynamical simulations indicate that galaxies form as FRs before their rotational support decreases, usually because of mergers. Aims. We aimed to investigate this process observationally for galaxies outside of clusters. Methods. We made use of the fact that different merger types leave different traces that have different lifetimes. We statistically analyzed multiple characteristics of galaxies that are expected to be influenced by mergers, such as tidal features, kinematically distinct cores, and stellar ages. They were taken from the MATLAS and ATLAS 3D databases. Through multilinear regression we identified the quantities that, at a fixed mass and environmental density of the galaxy, significantly correlate with a measure of the ordered rotation of the galaxy, λ N Re.
EZ10 Here, we describe the EZ10-type vehicle used to validate the framework. The EZ10 shuttle (see Figure  7 ) is an electric vehicle designed by the Ligier Company© for automatic passenger transport. It is the result of several collaborative projects aimed at facilitating the introduction of autonomous vehicles in industrial and urban areas. Table  1  shows the technical specifications of the EZ10. The vehicle is equipped with a number of sensors used in our applications. Proprioception is ensured by three odometric sensors which return the front and rear steering angles at 20 Hz as well as the forward speed measured on the engine shaft. The EZ10 features two cameras (front and rear) used for visual odometry  [30]  (only used here as a ground truth when combined with GNSS). A Kalman filter is used to merge these odometric data with data from an RTK-GPS (Real Time Kinematic Global Positioning System), which provides centimeter localization reference truth.
Planar lidars are mounted on all four corners of the vehicle to provide a 360 • scan of the entire vicinity over a distance of 50 m (lidars maximum range). Each lidar has an angular range of -135 • to 135 •. Lidar data are provided at 20 Hz. Only the two front lidars were used to build our occupancy grid, as we did not plan to drive in reverse. The scans are merged in a common frame related to the vehicle using the ROS  [31]  ira-lasers tools package  [32]. The on-board computer runs Linux with ROS 1 used as middleware to manage the software bricks. All our experiments were carried out under ROS with Python and C++ nodes. We approximate the vehicle model with an Ackermann model with two control parameters, namely, the steering angle δ and velocity v:      θ k = θ k-1 + v L tan(δ)dt x k = x k-1 + v cos(θ k )dt y k = y k-1 + v sin(θ k )dt (14) Although this model does not accurately reflect actual vehicle behavior, the small available steering angle limits the impact of this approximation.
This paper presents an automatic particle detection algorithm to study the time-resolved resuspension of isolated microparticle mono-layers exposed to airflow acceleration followed by steady-state. The algorithm post-processes movies of the deposit behaviour and returns the particle number, the granulometry, and the homogeneity of each frame. It allows the detection and isolation of the particle clusters to process them separately. The algorithm is validated using both synthetic images, and experimental datasets corresponding to ventilated duct cases. The number of particles remaining on the surface over time is returned, and correlations can be made with instantaneous physical parameters of the flow (e. g. , centre or friction velocity).
Algorithm description The optical tracking acquisitions provide movies that consist of 2560 frames corresponding to the maximum buffer capacity of the computer. Each frame from acquisitions (see an example in Fig. 3(a) ) is post-processed to obtain characteristics of the deposit and the number of particles within the frame. This section describes the process performed for one frame. The routines are then repeated for each frame of the movie. The routines are developed using MATLAB, which offers the Image Processing Toolbox with several reference-standard algorithms. The algorithm as well as the data are accessible (see  Cazes, Fiabane, Heitz, et al. , 2023; Cazes, Fiabane, Theron, et al. , 2023, respectively). Still, similar routines can be created using other programming languages (e. g. , C++ and the OpenCV library, Python and either the OpenCV or the Scikit-Image libraries) or image processing software.
The closer the distribution is to the Gamma distribution, the more random the cell size distribution. Such a random cell size distribution can be linked to the randomly distributed deposition of particles on the whole area of interest, with no significant clusters of particles in some parts and wide empty areas in others. This random distribution is, therefore, what we regard as a homogeneous deposit. Fig. 12 (a) provides an example of a Voronoïsegmentation on an experimental dataset, and the resulting area distribution is compared to a Gamma distribution in Fig. 12(b ). As one can observe, the experimental result is very close to that of a random distribution, meaning that the initial deposit is spatially homogeneous. Combining the size and spatial distribution results, one can conclude that the deposition procedure is valid: it creates a homogeneous mono-layer deposit of a relatively narrow particle diameter range.
Known limits Although we are confident in the results obtained using the presented algorithm, one has to be aware of some limits identified during our different tests. First, the particle detection algorithm relies on the particles being spherical, with a corresponding roundshaped object on the images. Therefore, one can apply the algorithm to other experimental datasets to detect particles as long as the particles are spherical. Otherwise, the cluster detection routine might detect them as clusters, as it is based on the spherical  property of particles (mean size and eccentricity of an ellipse). Nevertheless, this difficulty might be overcome by tuning the criteria using suitable geometric properties for both particle and cluster detection; it would be helpful for other applications, such as fibre resuspension  (Capone et al.
Data availability The reference and link for the algorithm code: Cazes, C. ,  Fiabane, L. , Heitz, D. , Theron, F. , Le Coq, L. , 2023. Automatic particle detection algorithm. URL: https://hal. inrae. fr/hal-03954927 Code repository: https://github. com/LioFia/Automatic-Particle-Detection-algorithm. git -the reference and link for the data: Cazes, C. , Fiabane, L. , Theron, F. , Heitz, D. , Le coq, L. , 2023. Bronze particle resuspension in an accelerated flow. URL: https://doi. org/10. 57745/XHRRXF.
An extreme p-value implies that the observed data would be unlikely to occur in replications of the data if the model was true and so, may represent an outlier. Here, posterior predictive p-values quantify the uncertainty associated with each study in the network by measuring departure of each study from the assumed NMA model. For each study i let y i = {y i,k | k ∈ K i }, then the posterior predictive p-value is as follows: p Di = P D i y * i |θ, τ 2 ≥ D i y i θ, τ 2 y = P D i y * i θ, τ 2 ≥ D i y i θ, τ 2 θ, τ 2 P θ, τ 2 y dθdτ 2 , ( 10 ) where y is the observed data, y * a hypothetical future dataset generated from the model predictive distribution and P (•|y) the joint posterior distribution of (θ, y * ) given y. Plugging-in the discrepancies of (  8 ) and (  9 ) into the p-value in (10) we obtain respectively our proposed posterior predictive p-values.
Down-weighting outliers Statistical detection of outlying effects in a network meta-analysis should always be complemented by an accurate investigation of the causes underlying the observed outlyingness. Before taking any decision, investigators should carefully check the characteristics of all included studies looking for possible explanations, such as systematic differences that modify the observed effect and produce extreme results. In particular, characteristics of the trial design, conduct, participants, interventions and outcomes should be explicitly assessed. Placing more stringent inclusion criteria in the systematic review may not always capture differences when they are subtle, and thus, a thorough assessment of the nature and reliability of the data is always necessary. When no clear causes are identified, it is possible to construct systems to down-weight the effect of outlying studies towards the overall network estimates.
Simulation study We conducted a simulation study to assess the performance of our outlier detection tools for binary outcomes. We constructed four different network geometries and we analysed a number of different scenarios, with varying the amount of heterogeneity and number of outliers included in the network. For each scenario, we simulate r = 1000 data sets for two-and multi-arm trials by drawing study-specific treatment effects θ and covariance matrix Ψ 2 i , as defined in Section 2. In all scenarios we sampled two MCMC chains, with 50000 iterations and a burn-in period of size 10000 each. Non-informative vague normal priors were used for the effect parameters, non-informative uniform distribution for the heterogeneity parameter and a beta prior centred around 0. 5 for the down-weighting factors, i.
Application to real networks of interventions We demonstrate our methods using two real data sets with binary endpoints: first, a real network of 112 RCTs comparing 62 treatments for non-small cell lung cancer (NSCLC)  28  and then a smaller, well-known network of 24 RCTs investigating 4 different interventions for smoking cessation. In lung cancer data, we use objective response (ObR), defined as a complete response or a partial response according to the Response Evaluation Criteria in Solid Tumors 29 (RECIST), while smoking cessation data report the number of individuals who successfully quit smoking after 6 to 12 months. The NSCLC data comprehend 70 unique treatment comparisons, many of which informed by one or very few studies only. This makes it a good candidate to suspect the presence of outliers.
Such priors automatically raise the outlying likelihood to a power α ∈ [0, 1] that would here represent the down-weighting factor. In the case of normal data, this is equivalent to dividing the variance of the observed effect sizes by study-specific weights. Although in Section 5 we followed this approach as we employed the normal approximation, the outliers are not automatically down-weighted in a single modelling process, but rather, the down-weighting factors are specified manually for outlying studies only. Again, expert information could be used for constructing more appropriate down-weighting factors. The source code to implement the proposed methods is freely available at https://github. com/silviametelli/Bayes-NMA-outlier-detection, and we further plan to incorporate this code into an R package for Bayesian network meta-analysis to facilitate a broader usage.
SOD activity was determined based on the photoreduction of the nitroblue tetrazolium (NBT) using the protocol described by  Asada et al. (1974)  at 37°C. Absorbance was recorded at 580 nm. Data were expressed as units/milligram of protein. Catalase (CAT) activity was analyzed by the decomposition of hydrogen peroxide according to the method described by  Aebi (1984)   Non-enzymatic antioxidant activity (glutathione: GSH) GSH reacted with 5,5 dithiodis-nitrobenzene (DTNB) forming thionitrobenzene (TNB) of yellow color measured at 412 nm at 37°C according to the method described by  Ellman (1959). GSH contents were expressed as micromoles/milligram of protein.