dataesr/scientific-paragraphs-categorization huggingface dataset:
EDT Bagged has been applied to solve many real world problems namely travel time prediction  [44] , disease prediction  [45] , and bankruptcy prediction  [47]. In this study, we have used the EDT Bagged for prediction of bubble dissolution time when manufacturing solid structures by SLS technique. Suppose that x = (x 1 , x 2 ,. , x n ) is a vector of input variables (n is the number of input variables), the bootstrap aggregation used in EDT Bagged to average the predictions is defined as follows  [46] : f bag (x) = 1 N N i=1 f * i (x) ( 5 ) where N is the number of various training datasets generated by bootstrapping, and f * i (x) are defined as predictors.
Methodology Chart We present here different steps of the present work's methodology, which contains five main steps as followings (as shown in Figure  2 ): Step 1. Numerical tools for collecting data. In this step, all input parameters such as bubble initial radius (I 1 ), bubble external radius or the domain size (I 2 ), the diffusion coefficient (I 3 ), surface tension (I 4 ), viscosity of the melted materials (I 5 ), the initial saturation (I 6 ), and the chamber pressure (I 7 ) were used to compute the prediction output-bubble dissolution time (O), using the developed finite element model  [17]. Fluid dynamics, mass balance and gas diffusion were taken into account in this numerical model. Step 2. Dataset preparation for training AI models. In this step, all input and output parameters were used to create a complete set of data.
A number of 70% data (48,051 training data) were taken from the initial dataset for training the two proposed AI models. The remaining 30% data (20,593 testing data) were used for validating step, after the AI models were trained. Step 3. Training models. In this step, the AI models were trained using the training data set. Two AI algorithms were used: EDT Bagged and EDT Boosted. The algorithm of these two models were introduced in the previous section. This step was repeated until the models were successfully trained (a tolerance error criteria was reached). Step 4. Validating models. In this step, after the two AI models were trained, the validation process was performed using the testing data set (30% of the initial data set). The models were validated using different statistical measures such as: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R 2 ).
Dataset preparation for training AI models. In this step, all input and output parameters were used to create a complete set of data. A number of 70% data (48,051 training data) were taken from the initial dataset for training the two proposed AI models. The remaining 30% data (20,593 testing data) were used for validating step, after the AI models were trained. Step 3. Training models. In this step, the AI models were trained using the training data set. Two AI algorithms were used: EDT Bagged and EDT Boosted. The algorithm of these two models were introduced in the previous section. This step was repeated until the models were successfully trained (a tolerance error criteria was reached). Step 4. Validating models. In this step, after the two AI models were trained, the validation process was performed using the testing data set (30% of the initial data set).
Conclusions In this study, two ensemble AI models namely EDT Bagged and EDT Boosted were proposed and compared for predicting bubble dissolution time of the SLS process. A metadata of 68,644 simulated ones were created with the help of our previously developed numerical tool. They were used for generating datasets, which included input parameters (initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, the SLS chamber pressure) and output parameter (bubble dissolution time). Validation of the models was achieved using several criteria such as MAE, RMSE, and R 2. Monte Carlo approach was finally used to carry out the sensitivity analysis for determination of the most important input parameter for the prediction of bubble dissolution time.
The results showed that both AI models performed well for prediction of the bubble dissolution time of the SLS process but EDT Bagged (MAE = 9. 14, RMSE = 23. 78, and R 2 = 0. 990) outperforms EDT Boosted (MAE = 79. 62, RMSE = 141. 16, and R 2 = 0. 856). The sensitivity analysis results showed that three most important input of the prediction problem were diffusion coefficient, initial concentration and bubble initial size. The present study simplified the mechanical problem of the SLS process in considering the dissolution of a single spherical gas. Therefore, a perspective of the study is to consider 2D and 3D problem to mimic the real bubble shrinkage problem. Other hybrid AI models should also be studied to improve the predictive capability. In the case of simplified approach, the computation time was estimated about 2 min for each dataset.
the most frequent polymorphic event found at the intraspecific level in fungi  (Doniger et al. , 2008) , human  (Sachidanandam et al. , 2001) , and plants  (Ching et al. , 2002). Depending on the organism and the genomic position, the SNP/InDel frequency ranges from 1×10 -2 to 1×10 -3 per base and constitutes a vast pool of genetic variants  (Stucki et al. , 2012; Almeida et al. , 2014; Scozzari et al. , 2014; Peter et al. , 2018). With the relative ease of obtaining genome-wide SNPdata, their impact on complex trait can be tracked by either genome-wide association studies (GWAS) or quantitative trait loci mapping (QTL mapping) in medicine  (Beck et al. , 2014)  or agronomy  (Brachi et al. , 2011; Sharma et al. , 2015). When they are statistically linked to a phenotype, these SNPs become QTNs (quantitative trait nucleotides) and could be listed in large databases for research communities  (Grant et al.
, 2010; Youens-Clark et al. , 2011). In contrast to multicellular eukaryotes, large SNP-database regrouping several studies for fungi and yeasts are not really developed. For Saccharomyces cerevisiae species, the first attempts to set up SNP databases have been made 10 years ago  (Doniger et al. , 2008; Schacherer et al. , 2009)  but without establishing a link between genotypic data and phenotypes. With the emergence of high-throughput sequencing in the last 10 years, the number of available complete genomes rose impressively providing a quite complete landscape of genetic polymorphism for this species  (Peter et al. , 2018). A particular focus was done on strains belonging to food fermentation including wine  (Borneman et al. , 2016) , beer  (Gallone et al. , 2016) , distillery  (Barbosa et al.
The functional impact of a subset of 251 QTNs, corresponding to missense mutations, was estimated by using the predictive program SIFT (sorting tolerant from intolerant)  (Ng and Henikoff, 2003). It turns out that 168 mutations are predicted to be tolerated, whereas only 83 are predicted to be deleterious, suggesting that most of the QTNs do not lead to a loss of function (Table  S2 ). By contrast, 22 QTNs (approximately 8%) correspond to nonsense mutations, meaning that genetic variant results in a premature stop codon generating in most of the cases to a loss of function. In addition, one and two QTNs lead to the loss of start and stop codons, respectively. One main feature of the S. cerevisiae species is the bias toward low-frequency variant. Among the 1,011 recently sequenced genomes, much of the detected genetic polymorphisms are very low-frequency variants with 93% of them having a minor allele frequency lower than 0.
Results were highly in correspondence with experimental data followed by standard ASTM tests. To authors knowledge, rare multiaxial experimental data for woven ply laminate is available for model validation in the open literature. The design of an advanced fixture for applying biaxial loading is still highly demanded. In this paper, at first the modification for Arcan fixture was presented as well as the optimal dimension of specimen. Secondly, the entire fixture including the specimen was examined in FEM within commercial software ABAQUS®. The stresses were evaluated for the specimen under preload and tension steps. Von Mises stresses against yield stresses of each components of fixture were investigated. The stress distribution between notch roots affected by notch radius was studied under biaxial loading.
Finite element model Simulations were performed by using the finite element software ABAQUS/Explicit, which is established to analyze complex non-linear contact problems. To ensure the solving of quasi static problems, some special considerations are required to keep inertial forces insignificant in a state of dynamic equilibrium in an accelerated process. In fast loading cases, loading rates are increased with remaining the quality of the quasi-static solution. Smooth amplitude curves were applied to ensure that changes in velocity and displacement are smooth. It also helps to avoid the noise and inaccurate solutions introduced by sudden stress shock waves. The time required for a quasi-static solution with explicit solver is estimated with the lowest mode frequency of corresponding structure.
Influence of notch radius on specimen stress distribution The normalized stress profiles along and across the specimen and along the specimen notch for shear test have been studied by Ref. [13]. In this paper, biaxial loading boundary conditions with loading angle 45 � were applied on the simplified model (zone between clamping jaws) of Arcan specimen with stacking sequence of [0/90] 3s. Python script was used to generate Arcan specimens with different geometry dimensions. The butterfly-shaped specimens with six different radii namely 0, 1. 27, 2. 38, 3. 54, 4. 78 and 7 mm and a fixed notch angle 90 � were considered in the FEM. Elastic properties shown in Table  4  obtained via ASTM standard tests were implemented into the implicit simulation. Shear stresses normalized with the average shear stress calculated by dividing the applied load F s with cross section area on the corresponding path were examined.
The post treatment of experimental data for MAF was done by considering the fiber rotation detected via DIC. The fiber rotation effect during test was examined in shear and data were corrected according to the fiber rotate critical point. Results are agreed with the standard test. Biaxial response of woven ply composite was obtained. Failure modes show that fiber breakage is the main cause for all specimens with various loading angles. The new designed MAF has several advantages:  f) Loading angles can be changed easily thanks to the clamping system and a series of tests can be conducted efficiently and effortless. g) Equipped with guide units to insure the in-plane stress states during quasi-static loading or fatigue loading and anti-buckling under compression.
The Strategic Water Storage & Recovery (SWSR) Project in Liwa, Abu Dhabi is a leading and unique hydrogeology project in the world because of both its financial and scientific dimensions. The objective of the project is to store desalinized water in the local Liwa aquifer, to be able to supply water to Abu Dhabi in case of emergency. A total of 315 recovery wells have been drilled in pursuance of the scope of the SWSR project. Out of the total 315 wells, 25 wells met construction problems and were removed from the study. The remaining 290 wells have been analyzed using step drawdown tests (SDTs) and the model of Rorabaugh. This provided a large and unique database regarding the parameters of this model: linear aquifer-loss coefficient (B), non-linear well-loss coefficient (C) and the exponent p.
Introduction The Strategic Water Storage & Recovery Project (SWSR) aims to supply enough water to Abu Dhabi for 3 months. The main objective is to pump water from desalination plants and recharge it into the Liwa aquifer (Figure  1 ). The water is planned to be used in the case of an emergency. The water that is produced in desalination plants is pumped to Liwa and recharged to the aquifer in 3 locations called infiltration basins. 315 pumping wells are located around these infiltration basins (105 in each basin). The step drawdown test (SDT) data focus in this paper were taken from pumping test evaluation reports for each of the 315 wells. Out of the total 315 wells, 25 wells met construction problems and were removed from the study. The remaining 290 wells have been analyzed using SDTs and the model of Rorabaugh  [1].
Lennox (1966)   [6]  argued that p is greater than 2 and can be up to 3. 5 in ideal flow conditions. Well-known computer software such as Aqtesolv  [7] , AquiferWin32  [8] , Aquifertest Pro  [9]  evaluate well efficiency only when exponential parameter (p) equals 2. A lack of information exists to evaluate well efficiency for values of p different from 2. The literature survey shows that the exponential parameter p may quite often be different from 2 and may also be difficult to estimate. In this paper, we present the development of a novel set of criteria to estimate well efficiency whatever the value of the exponent p. The development of this criteria follows the analysis of 290 SDTs conducted within the SWSR Project in Liwa, Abu Dhabi.
Data and Well Design SDTs were conducted for each well with incremented discharge rates of 30 m 3 /h, 60 m 3 /h, 90 m 3 /h, 120 m 3 /h, and 150 m 3 /h, respectively. Time duration of the steps was 3 h per 1 step. The data from each pumping test were obtained from multi-parameter probes at 1-min intervals with 10 -3 m sensitivity. Each well was developed with one production pipe (DN300) and one monitoring pipe (DN65) (Figure  3 ). Water 2019, 11, x FOR PEER REVIEW 4 of 14.
Data and Well Design SDTs were conducted for each well with incremented discharge rates of 30 m 3 /h, 60 m 3 /h, 90 m 3 /h, 120 m 3 /h, and 150 m 3 /h, respectively. Time duration of the steps was 3 h per 1 step. The data from each pumping test were obtained from multi-parameter probes at 1-min intervals with 10 -3 m sensitivity. Each well was developed with one production pipe (DN300) and one monitoring pipe (DN65) (Figure  3 ). Top hole of each well was drilled down to 11. 4 m with 36" drill bit and the 30" conductor casing installed. After that, the wells were drilled down to an average of 80 m with 28" drill bit and a 11 4/5" diameter production pipe (DN300) string was installed. Monitoring pipe (DN65) was installed beside the production pipe (DN300). Before the installation, the lower 28 m of the production pipe were screened for each recovery well.
The screened part of the wells thus taps the sandy formation only. Three multi-parameter probes were used in each well located above the pump, below the pump, and in the monitoring pipe DN65. Probes recorded temperature, electrical conductivity, and head pressure. Discharge rate was recorded using an electromagnetic flow meter for each well. The probe at weir tank recorded the electrical conductivity, temperature, and tank level. An example set of SDT data is given in Figure  4. Water 2019, 11, x FOR PEER REVIEW 5 of 14 Top hole of each well was drilled down to 11. 4 m with 36" drill bit and the 30" conductor casing installed. After that, the wells were drilled down to an average of 80 m with 28" drill bit and a 11 4/5" diameter production pipe (DN300) string was installed. Monitoring pipe (DN65) was installed beside the production pipe (DN300).
Before the installation, the lower 28 m of the production pipe were screened for each recovery well. The screened part of the wells thus taps the sandy formation only. Three multi-parameter probes were used in each well located above the pump, below the pump, and in the monitoring pipe DN65. Probes recorded temperature, electrical conductivity, and head pressure. Discharge rate was recorded using an electromagnetic flow meter for each well. The probe at weir tank recorded the electrical conductivity, temperature, and tank level. An example set of SDT data is given in Figure  4. Figure  4A  shows the sequencing of the 5 steps, the variation of the discharge rate and the drawdown evolution at each step. The plot of drawdown vs. logt (Figure  4B ) indicates a linear evolution of the drawdown in accordance with Jacob's assumption.
8 and 2. 2). Thus, only 45% of the SDT could have been correctly analyzed using Jacob's model. Statistics of the well-loss coefficient C are shown in Table  2. They reveal that the variability of this parameter is extremely high. The standard deviation is higher than the average. Statistics of the well-loss coefficient C are shown in Table  2. They reveal that the variability of this parameter is extremely high. The standard deviation is higher than the average. As in Rorabaugh's model the well-loss coefficient C and the exponent p are related to each other, and taking advantage of the exceptional large database available, an analysis of the relationship between these parameters was performed. Figure  9  shows the plot of p values versus C values in a semi-logarithmic diagram. A significant positive relation of p vs.
logC, with a determination coefficient R 2 of 0. 83, is highlighted. This result shows that p is closely associated with parameter C. It cannot be constant, which calls again into question the model of Jacob. As in Rorabaugh's model the well-loss coefficient C and the exponent p are related to each other, and taking advantage of the exceptional large database available, an analysis of the relationship between these parameters was performed. Figure  9  shows the plot of p values versus C values in a semi-logarithmic diagram. A significant positive relation of p vs. logC, with a determination coefficient R² of 0. 83, is highlighted. This result shows that p is closely associated with parameter C. It cannot be constant, which calls again into question the model of Jacob. Equation linking both parameters is given below: p = 0.
Development of a General Well Efficiency Criteria (GWEC) Using the database of the Rorabaugh's equation parameters (Equation (  4 )), the well efficiency was estimated for each of the 290 wells. Accordingly, the wells were classified in three groups: developed wells, developing wells, non-properly designed (or deteriorating) wells. These classified wells are plotted in a log-log diagram of the exponent p vs. well-loss coefficient C (Figure  10 ). This diagram clearly discriminates the deteriorating non-properly developed wells from the developed or developing wells. A boundary could be delineated on the diagram, differentiating between deteriorating and developed/developing wells. Equation of this deteriorating boundary is: p = 0. 2972 × lnC -0. 1743 (C > 5)  (14)  where units used to express C are m and s.
Development of a General Well Efficiency Criteria (GWEC) Using the database of the Rorabaugh's equation parameters (Equation (  4 )), the well efficiency was estimated for each of the 290 wells. Accordingly, the wells were classified in three groups: developed wells, developing wells, non-properly designed (or deteriorating) wells. These classified wells are plotted in a log-log diagram of the exponent p vs. well-loss coefficient C (Figure  10 ). This diagram clearly discriminates the deteriorating non-properly developed wells from the developed or developing wells. A boundary could be delineated on the diagram, differentiating between deteriorating and developed/developing wells. Equation of this deteriorating boundary is: p = 0. 2972 × lnC -0. 1743 (C > 5) (14) where units used to express C are m and s.
Conclusion SDTs are performed to assess pumping well performance. Interpretation of SDTs is usually done using the Jacob model or the more generalized Rorabaugh model. There is much debate in the literature on the parameters of these models. The large and unique database, assembled within the SWSR Project in Abu Dhabi, allowed a rigorous and thorough analysis of the parameters of Rorabaugh's model. This study resulted in novel findings. First it was shown that the range of exponent p values is much more extensive compared to what is proposed in the literature. In addition, The GWEC (Figure  10 ) shows that the region below the deterioration curve represents the minimum and maximum limits of C values with allowable p ranges. This allowed to defined intervals of the exponent p and the well-loss coefficient, associated with wells categories (developed/developing wells; deteriorating wells).
Conclusion SDTs are performed to assess pumping well performance. Interpretation of SDTs is usually done using the Jacob model or the more generalized Rorabaugh model. There is much debate in the literature on the parameters of these models. The large and unique database, assembled within the SWSR Project in Abu Dhabi, allowed a rigorous and thorough analysis of the parameters of Rorabaugh's model. This study resulted in novel findings. First it was shown that the range of exponent p values is much more extensive compared to what is proposed in the literature. In addition, p behaves like a lognormal variable. The study also revealed that the exponent p and the well-loss coefficient C are closely associated, implying that p is not constant. This calls into question the Jacob model where p is constant and equal to 2.
Conclusions We presented a method to measure transport mean free path l* based on the use of a cheap tunable laser source. The scattered intensity correlation function with the incident wavelength modulation is measured using a multi-speckle setup. The method allows probing samples in non-destructive way, simple and cost-effective in its arrangement and fast in data processing. Interestingly, we demonstrated that it can be readily modified in a way to provide spatially resolved measurements and, thus, can be applied for the measurements in non-homogeneous samples. The statistical noise may be reduced if the size of the matrix of the detecting camera is increased, or by performing another kind of ensemble average. The method has been demonstrated here with a near-IR DFB laser, but may be easily performed with 760 or 780 nm chip which allow the use of visible camera.
Postural Analysis Protocol and Data Acquisitions. Analysis of the posture of each volunteer was done according to a previously described protocol  [20]  providing sagittal intersegmental moments and summarized hereafter. A set of 36 markers was used to divide the body in 10 segments (head, thorax, abdomen, pelvis, thighs, legs, and feet), and then a mass was attributed to each segment according to Dumas et al. [21]  using anthropometric tables and the height/weight of the subject. Once equipped, the two volunteers were asked to adopt 4 times a free-standing position without external constraint or support, which each foot positioned over a force plate (AMTI, Watertown, MA, USA) in order to collect the ground reaction force of the subject. During acquisition, the location of the markers over time were recorded using a Vicon ® (Vicon, Oxford, UK) optoelectronic system with 6 high-resolution infrared cameras and a 100 Hz sampling frequency.
Analysis of the acquired data was conducted on a one-second record sample, with the less body sway, in order to calculate mean intersegmental moments in the sagittal plane at each joint center. Postacquisition calculations were done in order to obtain the sagittal net intersegmental moments (ankles, knees, hips, lumbo-sacral junction, thoraco-lumbar junction, and cervico-thoracic junction) using an ascending manner (i. e. , going upward from the ground reaction forces) between each body segment previously identified except for the cervico-thoracic junction, where the sagittal net articular moment was calculated using a descending manner from the center of mass and mass of the head and neck.