dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection Instrument For screening the musculoskeletal disorders among caregivers in intensive care, an adapted version of previous instruments [18]  [19] [20]  [21]  [12]  was used to collect data. The adopted instrument consisted of a questionnaire, a subjects' assessment gure and a video recording system. The questionnaire consists of demographic characteristics of the sample, the prevalence of WMSD, nurses training in patients' handling tasks, and subjects' rating of pain/discomfort in different body parts. Each subject was video recorded for a mean time of six minutes while performing two patient handling tasks (transferring and repositioning of patients). VSDC Video Software was used to analyze videos (https://www. videosoftdev. com/).
Sampling and data collection MICS is developed and funded by The United Nations Children's Fund (UNICEF). It is a national population-based survey for supporting countries in the collection of wide range and representative indicators in maternal and child health, including unmet needs for modern contraceptive  (30). The study population was selected using a two-stage stratified sampling. The primary sampling units (PSUs) included clusters. They were selected using the sampling frame of the 2010 General Census of Population and Housing (RGPH4) of Togo  (34). A total of 420 PSUs (253 in rural settings, and 167 in urban settings) were selected for the survey. The household were the secondary sampling units. Twenty of them were selected in each PSU, giving 8,400 households. All the women living in the household or who stayed there last night, and aged between 15 and 49 years old, were eligible and invited to participate in the survey  (3).
Participant identification A total of 964,870 participants were identified through the Danish Civil Registration System prior to the 2022 to 2023 influenza season using pre-specified inclusion and exclusion cr iter ia. A database containing data on all eligible subjects was created. The database includes the subject's social security number and registry data on demographics, comorbidities, medication use, prior influenza vaccinations etc. The inclusion cr iter ia for entry into the study were: (1) Age ≥65 years or turning 65 years by January 15, 2023 at the latest. (2) Access to the official, mandatory Danish electronic mailbox system (persons with exemptions were excluded) The only exclusion cr iter ion was: (1) Persons living in nursing homes (approximated as any person living at an address with ≥6 inhabitants aged 80 years and above 32 ).
Randomization A cluster-randomized design was utilized, where households were the unit of randomization. Clustering at the household level was designed to avoid potential contamination of the intervention in which a single household could otherwise have received different versions of the behaviorally designed letters. In our clusterrandomized design, individual participants still each received a separate electronic letter. Due to the large sample size, the trial utilized simple randomization without stratification. Randomization was performed instantaneously in the complete database after all eligible participants were identified. Randomization was performed at the secure database server using SAS software.
The end points of laboratory-confirmed influenza and laboratory-confirmed COVID-19 are retrieved from the Danish Microbiology Database, which is a database containing all microbiology samples analyzed in the Danish public health system. Data on hospitalizations, outpatient visits, and surgical procedures are retrieved from the Danish National Patient Registry. Data on mortality and causes of death are retrieved from the Danish Civil Registration System and the Danish Cause of Death Registry. The cardiovascular composite end point definitions were based on those used in prior large-scale cardiovascular outcome trials. Any statistically significant exploratory end point findings will be considered hypothesis-generating only. F00-F03, G10-G14, G20-G23, G30-G32, G35-G37, G40-G41, G70-G73, G80-G83, G91-G95.
Funding The trial is funded by Sanofi. The trial design and protocol were developed in collaboration with the funder. The funder will be involved in the critical review of this and future manuscripts. The funder will not be involved in data collection, data analysis, data interpretation, or first drafting of any NUDGE-FLU-related manuscript. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of this paper and its final contents.
Deep learning approach to forward modelling The general concept of the proposed method is presented in Fig. 1. It consists of three main blocks: dataset computation, supervised learning and topology optimization. A large dataset of phononic crystal examples is computed by using the finite element method (FEM). The dataset is used for the supervised training of a surrogate DL model. It learns the mapping between inputs (cavity shapes) and outputs (dispersion diagrams). The trained DL model is then used for ultrafast prediction of dispersion diagrams in the topology Fig. 1. Flowchart of the proposed DL-aided topology optimization approach. P. Kudela et al. optimization framework based on a genetic algorithm. An initial population of cavity shapes represented by NURBS curves is evaluated in parallel leading to initial fitness scores.
The standard procedure of selection, crossover and mutation is performed to produce offspring of the next generation (new cavity shapes). Again, the trained DL model is used for the prediction of dispersion diagrams. Based on it the objective function value is calculated. The termination criterion is usually set by the maximum number of generations and the number of stall generations. The process of dataset preparation, the development of a surrogate DL model and its performance are described in this section whereas the topology optimization of the unit cell is described in section 3.
The mass and stiffness matrices can be derived by using the finite element method (FEM) formalism. A detailed description of the finite element procedure can be found in Ref. [30]. The efficient solution of the eigenvalue problem of Eq. (  3 ) represents one of the most challenging aspects of the topology optimization problem of phononic crystals. COMSOL Multiphysics is a very popular commercial software with an embedded function calculating the dispersion diagrams k(ω) of periodic structures. Although an excellent tool for the calculation of dispersion curves, their computation for the case of 3D unit cells may take some time depending on the mesh density (for instance, in the case of a mesh consisting of 14,072 tetrahedral elements and 134,040 degrees of freedom, the computation of 24 eigenmodes on a computer equipped with AMD Ryzen 7 1800X Eight-Core 3.
Assumptions It is assumed that the unit cell is made of a solid isotropic material (aluminium alloy) in which a cavity of a certain shape is formed. It is assumed that the cavity extends through the whole thickness of the unit cell. Moreover, the thickness-to-unit cell size ratio is fixed. In our case, the dimensions of the unit cell were as follows: • thickness 3 mm • unit cell width 8 mm. Because we aim to make a proof of the concept, we have made further simplifications which however do not influence the generality of the proposed framework for topology optimization. The simplifications are related to the geometry of the cavity which reduces the computation time needed to generate a relatively large dataset of examples for supervised learning. Since the input to the DL model is encoded into a pixel-wise image, the DL model can take more complex geometry shapes than the simplified one presented in the next section.
Dataset description Cavity geometry is designed by using NURBS. A NURBS curve is defined by its order, a set of weighted control points, and a knot vector. NURBS curves are generalizations of both B-splines and Bézier curves, the primary difference being the weighting of the control points, which makes NURBS curves rational. In our implementation, we used a third-order curve which is represented by a quadratic polynomial. The scheme of construction of cavity shapes within a unit cell by using NURBS and mirroring is presented in Fig. 3. The NURBS curve is spanned over four segments, and its shape is determined by the position of 9 control points P, 9 weights w and knot vector t. But some constraints regarding positions of control points are enforced so that the NURBS curve is within the design space.
In this way, the computation time by using COMSOL Multiphysics can be significantly reduced. On the other hand, the flexibility of geometry refinement is kept within NURBS representation and can be used during DL model evaluation or for the final manufacturing. A 2D mesh of the unit cell generated by using GMSH software  [31]  is shown in Fig. 4. The GMSH software was used for mesh generation instead of COMSOL Multiphysics due to the ease of automation and slightly better quality of resulting meshes. The mesh was generated by using NURBS points defined in Fig. 3. In the next step, the mesh is imported to the COMSOL Multiphysics and extruded to the thickness direction so that the full 3D model is created. Then periodic boundary conditions are applied, and dispersion diagrams are computed. The COMSOL Multiphysics runs through the MATLAB Live-Link so that the consecutive meshes can be imported and the computation process can be automated.
The dataset consisting of 9000 unit cell geometries was generated in which the inputs are binary images and the outputs are dispersion diagrams stored as (k*, f) vector pairs in which k* is the reduced wave vector with wavenumber components k * = [k x a/π, k y a/π] with a being the lattice parameter (unit cell size) and f is the vector of frequencies in Hz. The dispersion diagrams are represented by 1464 points corresponding to frequency-wavenumber pairs. It covers about 24 modes. Three types of cavity shapes examined are shown in Fig. 5  and were constructed by random NURBS manipulation (see Appendix A for more details). The cavities in the first row of Fig. 5  are spanned over horizontal and vertical directions, the cavities in the second row of Fig. 5  are spanned over diagonals and the last row has blot-like cavity shapes.
The same number of examples (3000) was generated for each type of cavity shape so that the dataset is well-balanced. The computation of the dataset in COMSOL Multiphysics took about one month on a standard desktop computer. Because the computation time needed to create the large dataset is very long, it would be worth exploring alternative methods which require fewer data for supervised training. Good candidates are physics-informed neural networks  [32]  or other similar approaches  [33]  in which partial differential equations along with boundary conditions describing the underlying physics are incorporated in the loss function used for the neural network training by using a stochastic gradient descent optimization algorithm. The dataset is available in an open repository  [34].
Dataset pre-processing The synthetically generated dataset contains 9000 samples of unit cells that are classified into three equal subsets of 3000 samples each. The shape of a unit cell is represented by a binary image of the size of 512 × 512 pixels. As mentioned earlier, the shape of the unit cell is diagonally symmetrical. To reduce the complexity of the computation, we used only the upper left quarter of the unit cell shape with a size of 256 × 256 pixels for developing inputs to the DL model. Consequently, the total shape of the dataset is (3, 3000, 256, 256, 1). For training purposes, 2900 samples of unit cells were randomly selected from each subset, resulting in a training set with a shape of (8700, 256, 256, 1). The labels (ground truths) of the dataset are the dispersion diagrams (frequency versus wavenumber) values with a range exceeding 500 kHz (the maximum frequency in our dataset was equal to 668.
Deep learning model In general, DL techniques such as artificial neural networks (ANNs) and convolution neural networks (CNNs) are considered universal approximation functions. In essence, the developed model is an approximation function, which can map the input (the shape of the unit cell) to an output of 1464 frequency values. All dispersion diagrams in the synthetically generated dataset have fixed wavenumber values, therefore only the frequency vector was used as an output (prediction). Accordingly, our developed DL model is a multi-output regressor. Fig. 6  presents the general architecture of the developed DL model, which is composed of two main parts: (1) the encoder (CNN) and (2) the dense layers (ANN). The encoder is a CNN model that performs convolution operations followed by subsampling (pooling).
For each example, the BG is properly predicted. The lowest frequency errors were obtained for the cross-like cavity shape (first column of Fig. 7 ) where ε max is 9. 1 kHz and the highest errors are for blot-like cavity shapes (third column of Fig. 7 ) where ε max is 15. 4 kHz. The results of error analysis for all training and testing examples related to three types of cavity shapes are given in Table  2. The training dataset consisted of 2900 samples whereas the testing dataset consisted of 100 samples per cavity shape (cross-like, diagonallike and blot-like). It can be noticed that the errors are on a similar level irrespective of cavity shapes. Also, errors on the testing dataset are higher than on the training dataset, as expected. In conclusion, the results are satisfactory, and the trained surrogate DL model can be used for BG size prediction.
Topology optimization of the unit cell A genetic algorithm (GA) was employed for the problem of the topology optimization of the unit cell of phononic crystals. In particular, the implementation of GA in the Global Optimization Toolbox for MATLAB was used with default parameters such as crossover rate, mutation rate, elitism, etc. with the only changes applied to stopping criteria: • maximum no of stall generations: 40, • maximum no of generations: 50. We also used 400 individuals in the initial population instead of the default 200 because the evaluation of the DL model can be realized more efficiently for the batch of inputs at once and leads to faster convergence. The DL model implemented in Keras was imported to MATLAB by using the Deep Learning Toolbox so that the optimization process was performed in one environment.
Objective functions The Global Optimization Toolbox for MATLAB is implemented in such a way that the minimization problem is solved in which a custom objective function is delivered. We tested several versions of objective functions which are given in Appendix A. For further analysis, we selected three of them which were the most interesting and robust. The first objective function is built based on the standard gap to mid-gap ratio: F 1 = 1 - f top -f bot f mid , f mid = f top + f bot 2 (6) where f top is the minimum frequency at the top of the BG and f bot is the maximum frequency at the bottom of the BG. The next objective function was proposed for designing a unit cell with a middle of the BG which is around the selected frequency f sel : F 4 = 1 - [ ŵ1 ( f top -f bot f mid ) + ŵ2 ( 1 - 2|f sel -f mid | f sel + f mid ) ] , ŵ1 = 0.
2, ŵ2 = 0. 8 (7) It is composed of two weighted parts: (i) gap to mid-gap ratio and (ii) relative difference between selected frequency and frequency corresponding to the middle of the BG. The weights were selected so that the preference is set towards the BG centered at the selected frequency. Such an objective function can be useful if we want to design a filtering device around a certain frequency for already fixed material properties and thickness to unit cell size ratio. Objective functions F 1 and F 4 take values in a range of 0-1. The last objective function represents the cumulative BG of two successive BG separated by a narrow branch: F 9 = 1 - ( f 1 top -f 1 bot ) + ( f 2 top -f 2 bot ) f max , f max = 668. 6kHz (8) where f max is the maximum frequency in our dataset. Often two successive BGs are separated by a non-propagating mode so that the effective BG obtained by using the objective function given by Eq.
The interviews were conducted to the point of data saturation and supplemented by examining the medical files for clinical elements (weight, height, pathologies, treatment regimen, and viral load measurement). The observations were conducted using a guide that addressed how the health facilities functioned and interactions between health professionals, the CALHIV, and their parents during consultations. Observations written up in the form of field notes made it possible to place the statements in context and to analyze the different stages of care. The data were systematically analyzed using two approaches: • An inductive content analysis, inspired by grounded theory, was performed by manually coding the data as the surveys progressed, allowing new themes to emerge in addition to those previously identified.
Acknowledgements The authors acknowledge  Sharon Calandra  who translated the article and edited the final version. The  ETEA-Study group :  Alioune Diagne ,  Halimatou Diallo ,  Karim Diop ,  Maimouna Diop ,  Seynabou Diop ,  Fatoumata Hane ,  Ndeye Ngone Have ,  Oumou Kantom Fall ,  Ndeye Bineta Ndiaye Coulibaly ,  Gabrièle Laborde-Balen ,  Khoudia Sow ,  Bernard Taverne.
Stimuli and Procedure The study used 20 pictures depicting feet and hands in painful or non-painful situations, randomly selected from a database validated by previous studies  [19]. The presentation of these stimuli was controlled by a computer running the E-Prime software (Version 2, Psychology Software Tools, Inc. , Pittsburgh, PA, USA). Participants stood barefoot in the middle of the force plate (AMTI, Watertown, MA, USA), feet inside the marking. Footprints were marked on the force platform to ensure the maximum reproducibility of participants' foot positions from one trial to another one. From one trial to the next, the participant was asked to keep the same position on the force platform, i. e. , they were asked to maintain a comfortable stance with their arms alongside their body and to stay still.
Measures For each experimental condition, posturographic data were recorded at a sample rate of 100 Hz using the AMTI NetForce software (https://www. amti. biz/product/netforce/) and a AMTI force plate (AMTI, Watertown, MA, USA) constituted of four force gauges measuring postural reaction forces and moments exerted by the feet. The calculation of COP coordinates was carried out using the equations provided within the AMTI Biomechanics Force Platform Installation Manual (version 4. 4, April 2017), COPx = -My Fz and COPy = Mx Fz , where Mx and My are the output moments and Fz is the z force component of the resultant applied force. The time series of COPx (ML direction) and COPy (AP direction) measurements corresponding to 20 s of presentation for each image were combined into a single time series separately for the AP and ML directions for each experimental condition (passivepainful, active-painful, passive-painful, active-painful).
Non-Linear Mesaures The study employed 3 non-linear measures to assess COP dynamics: SampEn, LyE, and FD. These non-linear coefficients were computed separately for the AP and ML directions using MatLab software (V2021a, MathWorks, Natick, MA, USA). SampEn calculates the probability that a sequence of N-data points, having repeated itself within a tolerance r for m points, will also repeat itself for m+1 points without allowing self-matches: SampEn(m, r, N) = -ln A m (r) B m (r). B represents the total number of matches of length m, while A represents the subset of B that also matches for m+1. Thus, a low SampEn value arises from a high probability of repeated template sequence in the data, hence greater regularity. For calculating the SampEn, we used the MatLab codes obtained from the Physionet tool  [12]  with "default" parameters: m = 2, r = 0.
Statistical Analysis The analysis was performed using the JASP software (V 0. 18. 1, JASP Team, Amsterdam, The Netherlands). Statistical significance was set at p < 0. 05. Two-way repeated-measures ANOVAs with Bonferroni post hoc tests were used to determine if there was a statistically significant interaction effect between stimulation (active or passive) and valence (painful or non-painful stimuli). Statistical tests were conducted independently for all 3 measures (SampEn, LyE, and FD) and separately for COP displacement in the AP and ML directions. The same analyses were then performed to look at the effect of direction on the behavior of non-linear parameters, looking for the presence of interactions between the stimulation factors, valence, and direction.
Background: Hard-to-reach, vulnerable and cross-border populations are often disproportionately affected by communicable diseases. Epidemiological data on viral hepatitis in French Guiana and Suriname are available for urban areas, but not for remote communities. The Maroni River, which separates FG and Suriname, is home to Tribal and Indigenous communities. Reaching these populations is challenging due to logistical constraints, cultural and language barriers, and mistrust of outsiders. Objectives: We aimed to conduct an epidemiological study of viral hepatitis [Maroni Hepatites Virales (MaHeVi)] in this remote and complex area. Here, we describe the operational hurdles and solutions required to achieve this. Methods: We undertook a preliminary assessment of the area with local community leaders and health workers to gain approval of MaHeVi, acceptance of blood sampling, and suggestions for adapting the study to cultural and logistical constraints.
Study design The MaHeVi study is a population-based, cross-sectional, non-interventional epidemiological study, whose main objective was to estimate the prevalence of HBV, HCV, Hepatitis D and HIV infection in the general adult population of the Maroni River basin upstream Apatou. The inclusion criteria were being 18 years or older, planning to stay on the Maroni within 2 months of inclusion, and signing informed consent to participate. The MaHeVi study (ClinicalTrials. gov, Identifier: NCT05002907) received ethics approval from the Ethics Committee of the Ministry of Health of Suriname (VG 023-16) and from the Institutional Review Board (IRB00003888) of the French Institute of Medical Research And Health. In order to improve the feasibility and acceptability of the MaHeVi study, preliminary field missions were carried out to meet with local health actors (health centre staff and associations) and community leaders and authorities, to gather the support of community agents in communicating about VH and the study.
Polygamous marriage is traditional and widespread in the Maroon community, but not among Amerindians. Two independent teams of anthropologists worked on each side of the river. The study sites were selected based on the ethnic groups residing in the villages/settlements. Triangulation, by involving multiple researchers and the combination of various data collection methods, was used to reduce bias and ensure the validity of the study. In Suriname, two anthropologists and two assistants collected data in 3 villages and one Brazilian gold miners' settlement (Antonio do Brinco), each with populations belonging to a different ethnic community (Figure  1 ). They performed 4 focus group discussions (FGD) per location, with a total of 120 participants, to obtain insights on common general behavioral patterns that people see around them: girls aged 15-20, women aged >20, boys aged 15-20 and men aged >20 years old, as well as 2 FGDs with SWs in Antonio do Brinco.