dataesr/scientific-paragraphs-categorization huggingface dataset:
A natural event was used to simulate acceleration values read by the LSM9DS1 IMU sensors. One thousand acceleration values generated by the natural event were used in the simulations. Two scenarios were then simulated using the linear WSN consisting of two sensor nodes. In the first case, we simulated a scenario where the data measured by both nodes (S1 and S2) were erroneous (noisy) while in the second case, only the data measured by one of the nodes (S1) were erroneous and the data measured by the other node (S2) were error free. For each case, we implemented both the local Kalman filter (without data fusion) and the DKF (with data fusion) and compared the results. The simulation results will be discussed alongside the experimental results in Section 6 so as to validate the approach.
The IMU sensors provide vibration measurements through acceleration monitoring. To measure the performance of our leak detection solution using DKF algorithm, we emulated a leak at a single location along the pipeline, as shown in Figure  5. In this deployment, the leak location is fixed, but variations in the sensors can be made to evaluate the effectiveness of our solution. We carried out measurements for two scenarios: a local Kalman filter implementation and a DKF implementation. For these experiments, we stored traces of data collected from the two sensors, and compared the effectiveness of the approach, i. e. , the effect on leak detection when the sensor nodes implemented the DKF algorithm and in the case where only a local Kalman filter was implemented.
The node only wakes up when it receives an external interrupt from the nRF24L01+ (i. e. , when it receives data from a neighboring node) or when it receives an external interrupt from the LSM9DS1 (i. e. , signifying the threshold acceleration value has been exceeded and the possibility of a leakage). A table showing the energy consumption of the sensor node at different states, obtained from physical measurements, is provided in Table  4  below for analysis and for the validation of the energy consumption of the simulation model presented in Table  3. The ESP32, when operating in modem sleep mode at a speed of either 80 MHz or 240 MHz, is not energy efficient and not suitable for a WSN application that is to be battery powered. However, as shown in the results in Figure  15 , proper optimization and harnessing of the ULP coprocessor of the ESP32 while putting the ESP32 core to sleep can drastically reduce the power consumption, thus enabling it to be used for a battery-powered applications.
For the power consumption, we display the results of the state of the battery for each sensor node for both the cases where our proposed DKF algorithm and the benchmark centralized Kalman filter (CKF) were implemented. For the bandwidth consumption, we provide results for the number of messages transmitted for both the proposed DKF algorithm and the benchmark CKF. In the distributed implementation, the sink node is not involved, whereas in the centralized implementation, the sink node is involved in the fusion of the data obtained from all the sensor nodes. This requires numerous multi-hop communications and the sensor node (S10) directly connected to the sink node is involved in relaying the data from all the other sensor nodes to the sink. This makes S10 a critical node in the centralized implementation since it has a higher probability of developing an energy hole, which will affect the lifespan of the WSN.
Figures  17  and 18  display the simulation results of the energy profile for distributed and centralized solutions, respectively, for a simulation time of 1 day (86,400 s). In this section, we compare the power consumption and bandwidth consumption for the proposed distributed Kalman algorithm (that implements distributed data fusion) and the benchmark centralized Kalman filter algorithm (that implements centralized data fusion), in a sensor network consisting of 10 sensor nodes (S1-S10) connected in a linear topology and simulated in CupCarbon as shown in Figure  16. For the power consumption, we display the results of the state of the battery for each sensor node for both the cases where our proposed DKF algorithm and the benchmark centralized Kalman filter (CKF) were implemented. For the bandwidth consumption, we provide results for the number of messages transmitted for both the proposed DKF algorithm and the benchmark CKF.
Conclusions and Perspectives In this study, we proposed a fully distributed solution for leak detection in WSN-based Water Pipeline Monitoring (WWPM). In our proposed solution, all the processing required for leak detection is performed at the sensor nodes, without needing a centralized station for the processing of leak signals. The aim was to eliminate multi-hop communications, reduce latency, reduce the sensor node's power consumption and extend the lifespan of the WSN. We used a distributed Kalman filter (combination of a local filter and distributed data fusion) for processing the vibration signals read by an accelerometer attached to the pipe surface in order to detect the occurrence of leakages on the pipeline. Our results show the feasibility of applying distributed computing for leak detection in WWPM and establish the importance of distributed data fusion in improving the reliability of the leak detection system.
Logistic regression-based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12-18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression-based model uses an algorithm implemented in R programming language.
Design Linear regression and LR are among the first machine learning (ML) algorithms  5  used to implement predictive models in healthcare. The number of parameters (hereby named features or independent variables) should not be too large but should contain enough information to predict the output. We deal with the Feature Reduction problem in two steps: first, by using Fisher's  6  exact test to identify parameters (i. e. the LR independent variables) associated with the binary dependent variable (e. g. need/not need for gastrostomy) and second, by testing and ranking the predictive performance of each of all possible subsets (hereby named tuples) of the parameters found in the first step. The PredictMed algorithm takes as input a data matrix having patients as rows and parameters (variables) as columns.
The first column contains the values of the binary dependent variable while other columns those of independent variables (Table  1 ). If we have N independent variables, we can form 2 N -1 different tuples of independent variables. For example, if the independent variables were A, B, and C, we would have 2 3 -1 = 7 tuples: ABC, AB, AC, BC, A, B, C. After splitting the data in a training and test set, PredictMed provides the following outputs for each tuple of the 2 N -1 : 1. A classification using LR, predicting the outcome of the dependent variable based on the probability of this outcome (if the probability of positive outcome is above a threshold, then we predict the outcome as positive: we try different thresholds in a range from 0. 1 to 0. 9). 2. Accuracy, sensitivity, and specificity of the prediction (calculated on the test set) 3.
Data sources and study population Data were collected between June 2005 and June 2015 for a multinational, double-blinded, crosssectional descriptive study on the need of gastrostomy placement in children with CP. 3] [4]  Assessments and data collection for the implementation of the model were conducted in the last 6 months of 2014, and data analysis began in December 2015 and lasted 12 months. The fields included patient demographics, the results of diagnostic procedures, and functional and neurological assessments following the "European Society for Pediatric Gastroenterology, Hepatology and Nutrition Guidelines for the Evaluation and Treatment of Gastrointestinal and Nutritional Complications in Children with Neurological Impairment. "  7  The study followed the guidelines of the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement.
Computing time Usually, we deal with data matrices having 100-300 rows (e. g. patients) and 5-15 columns (1 dependent variable + N independent variables). As previously discussed, the number of tuples and thus of LRs we have to perform is 2 N -1. If having, for example, 100 patients and N = 14, we have 21  4  -1 = 16383 tuples, for example, LRs to perform. Using a notebook with an i7 Intel processor, the computing time was around 20 hours and we can expect it to double for every unit increase of N (or if doubling the number of patients). Supposing to increase the number N of independent variables or the number of patients, a Feature Reduction algorithm to reduce N prior to implement LR will be needed. The outcome used to create and evaluate the validity of the PredictMed was gastrostomy placement independently of the time interval; in future studies, we will control for the time of assessment of the predictors and the time of gastrostomy placement.
Conclusion We developed a LR-based predictive model (PredictMed), and we tested it on 5 different data sets of children with CP, each dataset with a different outcome (a binary dependent variable): (1) feeding disorders needing gastrostomy, yes/no (present study); (2) likelihood of scoliosis, 1,2 yes/no; (3) presence of profound intellectual disability, 3 yes/no; and (4) presence of autistic features, 4 yes/no. In each case, we evaluated accuracy, sensitivity, and specificity in predicting the outcome of the dependent variable. Even if PredictMed needs to be fine-tuned and validated on larger data sets, it showed on presently available data sets a predictive performance in terms of accuracy, sensitivity, and specificity between 74% and 90%. In our vision, PredictMed should significantly help doctors' decision-making process regarding patient prognosis, improving care of patients with CP and reducing healthcare costs and suffering.
Citrus canker type A is a serious disease caused by Xanthomonas citri subsp. citri (X. citri), which is responsible for severe losses to growers and to the citrus industry worldwide. To date, no canker-resistant citrus genotypes are available, and there is limited information regarding the molecular and genetic mechanisms involved in the early stages of the citrus canker development. Here, we present the knowledge base for transcriptome of in vivo citrus interactome, the CitrusKB. This is the first in vivo interactome database for different citrus cultivars, and it was produced to provide a valuable source of information on citrus and their interaction with the citrus canker bacterium X. citri. The database provides tools for a user-friendly web interface to search and analyze a large amount of information regarding eight citrus cultivars with distinct levels of susceptibility to the disease and their interaction, at different stages of infection, with the citrus canker bacterium X.
citri at early stages of the infection by the bacterium X. citri. All sequenced data were analyzed to detect the differentially expressed genes between compared genotypes. Thus, we have developed a transcriptome knowledge base of distinct citrus cultivars, the CitrusKB, to facilitate further research on the biology of citrus. Despite other citrus databases, such as Citrus sinensis annotation project  [12]  and Citrus Genome Database (CGD, https://www. citrusgenomedb. org), CitrusKB is the only web tool that comes to integrate a wide range of citrus species and cultivars exhibiting different levels of resistance to citrus canker. The CitrusKB is a userfriendly web-based interface that provides tools to allow researchers to visualize, search, analyze, recover and browse information on citrus and X.
Simple Sequence Repeats (SSR) The search for Simple Sequence Repeats was performed using the tool SSRHTS software (version v1. 0, unpublished). This approach performs custom SSR search using a reference sequence combined with high-throughput sequencing alignment data. This data is then used to validate detected SSRs and providing normalized occurrence values in a RPKM-based calculation for each SSR marker. Higher RPKM-based values denote higher expressed markers. The detected markers through all citrus genotypes, stored in a PostgreSQL database, describes the following information: sample name for each SSR occurred, sequence ID, sequence motif, start and end coordinates for SSR in reference sequence, motif length, length of repeat and RPKM-based value.
CitrusKB implementation CitrusKB was developed on a web-based environment and runs on a Linux operating system (Debian v7. 7), which includes several common software packages: Apache HTTP server, PostgreSQL database, PHP, Java and Perl. The website (Figure  3 ) was developed using PHP, Java and HTML languages, and Twitter Bootstrap was used to achieve an enhanced user interaction. A Basic Local Alignment Search Tool (BLAST)  [29]  server is available in CitrusKB environment. As genome browser for transcriptome visualization, JBrowse  [30]  was implemented, showing all details of the transcripts and their alignments at nucleotide level.
Local Blast To provide an intuitive web-based graphical user interface (GUI) and a rapidly search in a large volume of data available in the CitrusKB, we used the standalone BLAST server (v2. 2. 26)  [31]. To create the BLAST alignment database, we used the makeblastdb program of the NCBI BLAST (v2. 2. 30) software package  [32]. Using web forms, users can perform nucleotide searches using the BLASTN tool and protein searches, using BLASTP tool.
Download page CitrusKB provides a user-friendly interface for the download of entire data sets. The HTTP links provide the download of all RNA-Seq raw sequencing reads from the forty-eight libraries, available in FASTA format and compressed as Gziped files. Users can also download the following data: reference genome, assembled reference transcriptome sequences and gene structure in GFF format, all the gene expression analysis, and other details of SSRs and SNPs genetic markers. The raw data is also available under BioProject number PRJNA470961.
In addition, the TSV file can be also imported in the CitrusKB HeatMap Tool ('Tools' tab) and interactively inspected in the form of heat maps. The annotation products, Gene Ontology terms and IDs, predicted NOGs and enzyme code of each DEG transcript can be retrieved through the gene expression tables or exported in the TSV file that can be further explored for other biological interpretations and/or downstream analysis. It is worth to mention that the following study consist in a general example of the application of the CitrusKB which was not intended to show a comprehensive transcriptome analysis, but to illustrate the database use, the built-in tools to facilitate the transcriptome data integration, visualization and comparison. The objective to include this example was to present the power of the CitrusKB to revel the plant-pathogen related gene targets for further and more advanced studies.
Conclusions The basic knowledge for transcriptome of Citrus interactome, CitrusKB, was created to provide and facilitate researches on citrus biology, in particular to the genes involved on plant-pathogen interaction. The CitrusKB also provides tools on a user-friendly WEB interface that allows researchers to visualize, search, analyze, and browse information regarding citrus and their interaction with the citrus canker pathogen X. citri. To the best of our knowledge, this is the first in vivo database for analysis of citrus infected by a pathogen, and we expect that it will bring a substantial contribution for researches in the area, particularly in the field of molecular plant-pathogen interactions. We will continue updating CitrusKB by incorporating novel literature data as they get available over time.
individual transcripts with an average transcript length of 1. 5 Kbp, achieved a BUSCO completeness of ~95% with [S: 77. 9%, D: 17. 0%], F: 4. 7%, M:0. 4%, n: 430, according to the viridiplantae OrthoDB release 10 [19], and thus validating the CitrusKB as a comprehensive citrus transcriptome resource. These data were used in the gene expression analysis. In contrast, the C. sinensis and C. clementina transcriptomes available at Phytozome database [20] achieved a similar BUSCO completeness values, i. e. C: 94. 2% [S: 52. 1%, D: 42. 1%], F: 4. 9%, M: 0. 9%, n: 430, and C: 98. 6% [S: 72. 6%, D: 26. 0%], F: 1. 2%, M: 0. 2%, n: 430, respectively.
An interpolation program coded in Fortran for irregular N-dimensional cases is presented and freely available. Needs of interpolation procedure over irregular meshes or matrixes with interdependent input data dimensions is frequent in geophysical models. Also, these models often embed look-up tables of physics/chemistry modules. Fortran is a powerful and fast language, highly portable and easy to interface with other existing Fortran models. Our program does not need any libraries and can be compiled with any Fortran compiler. The program is fast and competitive compared to current Python libraries. A novel optional parameter (normalisation option) is provided when considering different types of units on each dimension. For the general program, the inverse distance is used for the weight calculation with a distance defined as a p-distance.
Introduction Interpolation is commonly used in geophysical sciences for post-treatment operations to evaluate model performances at point observations. The NCO library  (Zender, 2008)  is commonly used in its recent version V4. 9. 2 to propose horizontal and vertical interpolations to manage climate models outputs. The most frequent need is to interpolate in 3D spatial dimension and time therefore in 4 dimensions. Fortran is used extensively for weather and climate-related software  (Sun and Grimmond (2019) , e. g. WRF -Skamarock et al. (2008) ; GFDL AM3 -  Donner et al. (2011) ). Geophysical models can use look-up tables of complex modules instead of a full coupling strategy which is the case of the CHIMERE model  (Mailler et al. , 2017)  with the embedded ISORROPIA  (Nenes et al. , 1998 (Nenes et al.
, , 1999) )  module dealing with chemistry and thermodynamics. In this case the look-up table can easily exceed 5 dimensions to approximate the model. In parallel, Artificial Intelligence methods are developed and can explore the behaviour of complex model outputs that requires fast interpolation methods. While more recent modern languages like Python are used in the scientific community, Fortran remains widely in the geophysics / engineering community and is known as one of the faster language in time execution, very good on array handling, parallelisa-interpolation code was tested with gfortran (GNU Fortran project) and ifort (Intel). As it includes not specific options or function, version of a compiler, there is no reason to have limitations or errors with other compilers. The top shell calling script in the package provide two sets of options for "production" and "debugging" modes.
θ δi i is the bottom (δ i = 0) and top (δ i = 1) edges on each dimension i ∈ [1. N ] so that x i ∈]θ 0 i , θ 1 i ]. Y k is a 1-dimensional array with 2 N elements storing the value Y of the function at the identified neighbours Ψ on each dimension: Y k (δ N. δ i. δ 1 ) = f (Ψ(θ δ N N ,. , θ δi i ,. , θ δ1 1 )) (4) with k ∈ [0, 2 N -1] The tuple (δ N. δ i. δ 1 ) is the binary transformation of integer k defined as N -1 i=0 (δ i ×2 i ). The coefficients Γ k = w δ N N. w δi i. w δ1 1 as a product of weighting factors on each direction can be seen as a binary suite that is convenient to handle in a compacted and optimized Fortran programming strategy for the regular grid version of the code (Appendix B).
Computation strategy for the general program The list of input/output arguments is provided in Appendix C. In the main program calling the subroutine the key point is to transform first the N-dimension matrix in a 1D array. An example of main program is provided in the code package. The computation strategy in the subroutine can be broken down into the sequential steps as follows: (I) Find the nearest neighbour of the input data by minimizing a distance with a simple incremental method stepping every ±1 coordinates on each dimension (detailed later in this section). (II) Scan the surroundings of the nearest point within the matrix on ±1 step on each dimension and store the corresponding block of input data to be tested. The size of the block is therefore (1 + 2 × 1) N but can be extended to (1 + 2 × 2) N if we increase the scanning process to ±2 on each dimension (hard coded option iconf =1 or 2 in the declaration block).
(iii) A test between the target value and the input data grid points coordinates determines the ±1 steps to add on each dimension (see Figure  2  for an example in 2D). (iv) If the grid point falls on the edges or outside the borders the closest coordinates within the matrix is selected. (v) A test on the p-distance computation between the running point and the target is performed so that if the distance calculated at Iteration N it is equal to distance at Iteration N it -2 the closest point is found. (vi) If the distance is too high compared to the characteristic distance of the cell, the point is considered to be outside the borders of the input grid data. Therefore, the code allows a slight extrapolation if the target point is not too far from the borders. (vii) At this stage, the procedure can stop if the distance to the closest neighbour is 0 returning to the main program with the exact value of the input data grid.
6 Comparison with Python for a regular grid The code has been tested against the Python procedure scipy. interpolate. griddata freely available by  (Scipy, 2014) , for the following function: Y = f (X) =x 1 × (1 -x 1 ) × cos(4πx 1 ) × sin(4πx 2 ) × cos(4πx 3 ) (13) with x 1 , x 2 , x 3 ∈ [0, 1]. The input data grid is a regular grid of I i = 35 interval edges on each dimension i ∈ [1, 5] then 35 3 = 42875 grid points. The goal is to find the results on a coarse grid of 9 elements on each dimension then 9 3 = 729 grid points. A case in 3D has been used for this test because the Python library was not able to work with very large datasets (overflow error) while our program could make it. scipy. interpolate. griddata is used with the bilinear interpolation option while our method is configured with p = 2.
Methodology The back-trajectory module called BACKPLUMES detailed in this section can use output files from CHIMERE  (Mailler et al. , 2017)  or WRF  (Skamarock et al. , 2008). This kind of model has mainly one calculation to do several times i. The model is easy to use and light because a small set of meteorological parameters is required. These meteorological parameters are described in Parameters are the three-dimensional wind components, the boundary layer height h, the surface sensible heat flux Q 0 and the altitude of each model layer. The wind components are used for the horizontal and vertical transport. The boundary layer height is used to define the vertical extent of the possible mixing and the surface sensible heat flux is used to know if the current modelled hour corresponds to a stable or unstable surface layer (for when the particle is close to the surface).