dataesr/scientific-paragraphs-categorization huggingface dataset:
Recruitment of attendees c4c has set up a European multidisciplinary advice service that aids in the planning of innovative and feasible pediatric trials. 19 c4c created a database with over 400 clinical and innovative methodology experts, divided over 24 expert groups based on their expertise. Based on a scoping interview between EU-PEARL and c4c, the best suited experts for the requested advice were selected in collaboration with the leads of the relevant expert groups. 20 A list of c4c proposed experts was shared with EU-PEARL, and c4c established agreements with the experts. Patients and their families were recruited by c4c from the European Neurofibromatosis umbrella patient organization and from patients with NF1 of pediatric physicians included in c4c expert database and/or linked to c4c National Hubs.
Abbreviation: PROMs, patient-reported outcome measures. only validated for patients aged 8 years and older. To also measure pain in younger patients, the outcome measure "Face, Legs, Activity, Cry, Consolability scale (FLACC scale)" was added. The timing of performing these measurements was also discussed. It was proposed to not only collect data on pain at a specific day, but also measure the pain experienced over a certain time period. Prior to this study, the responder definition for one of the manifestations was an unresolved issue. EU-PEARL clinicians were unsure whether to use a single outcome measure to define response in a tumor manifestation (MRI scan results), or to use a composite outcome measure (both MRI and a PROM for pain). The use of PROMs as primary endpoints has been increasingly important in clinical trials; however, regulatory agencies will not always accept them.
24 lthough not described in the literature, trained patients will probably assess a trial protocol differently than nontrained patients. In c4c, PPI experts decide what level of expertise is required from participating patients and caregivers, dependent on the research question. The combination of the three types of meetings resulted in effective and practical recommendations from experts and patients, allowing the development of the protocol to advance substantially. Additional to previous studies that have shown when PPI input is meaningful,  12  we now showcase a methodology that could be used to combine this input with input from trial experts. The utilization of the existing c4c expert database allowed for easy recruitment of clinical and innovative methodology experts. We believe that this methodology could be beneficial to other researchers or sponsors who seek advice on their protocol.
(2013)  evaluated the source term by inverse modelling with ambient gamma dose measurements. Finally, inverse problem methods based on a full Bayesian formalism have also been used. Liu et al. (2017)  applied several methods, including Markov chain Monte Carlo (MCMC) algorithms, to estimate the time evolution of the 137 Cs release, and they accompanied this with an estimate of the associated uncertainty. Terada et al. (2020)  refined the 137 Cs source term with an optimization method based on a Bayesian inference from several measurement databases.
In the first instance, we address the issue of sampling in the case of massive and highly fluctuating releases of radionuclides to the atmosphere. In the second instance, we propose several statistical models of the likelihood scale matrix depending on the available set of observations. The outline of the paper is as follows. In Sect. 2, the observational dataset used in this study and the physical model of the problem are described. We then outline the theoretical aspects of this study and first focus on the concepts of transdimensionality and model selection (Sect. 3). The reversiblejump MCMC algorithm (RJ-MCMC) used to shape the release rates vector q is presented in Sect. 3. 1. Other ways to improve the sampling quality and reduce uncertainties are then explored via the addition of information in Sect.
In particular, the RJ-MCMC algorithm is applied to the Fukushima Daiichi case by  Liu et al. (2017)  using variable discretizations but a fixed number of steps (i. e. grid cells). Here, it is applied in a more ambitious transdimensional framework, where the number of steps of the discretization is modified through the progression of the RJ-MCMC, and using a significantly larger dataset. 2 The Fukushima Daiichi NPP 137 Cs release and numerical modelling.
Observational dataset The dataset used in this study consists of 137 Cs air concentration and deposition measurements over the Japanese territory. The vector of 137 Cs air concentration measurements contains 14 248 observations  (Furuta et al. , 2011; Yamada et al. , 2013; Oura et al. , 2015; Nagakawa et al. , 2015; Tanaka et al. , 2013; Tsuruta et al. , 2018; Takehisa et al. , 2012)  from 105 stations, whose locations are shown in Fig. 1. This dataset is significantly larger than the one used by  Liu et al. (2017). This allows us to better evaluate the relevance of our methods and to accurately estimate the uncertainties in the problem. Moreover, the majority of these observations are hourly, which allows for fine sampling of the 137 Cs source term. Additionally, 1507 deposition measurements are used.
This dataset is described and made available in MEXT (2012). The observations are located within 300 km of the Fukushima Daiichi NPP; however, measurements too close to the Fukushima Daiichi plant (typically less than 5 times the spatial resolution of the meteorological fields described in Table  1 ) are not used to consider the short-range dilution effects of the transport model. These measurements represent the averages of each grid cell of a field of deposition observations (in Bq m -2 ) measured by aerial radiation monitoring.
4. 2. 1. A posteriori marginals of these control variables are displayed in Fig. 5. In Fig. 5a , the medians of the two sampling datasets are similar, except for the release rates between 11 and 14 March. Indeed, when using air concentration and deposition measurements, the release rate peaks on 12 March instead of on 13 March, the latter of which is the case when using air concentrations alone. In Fig. 5b , the main mode of the total retrieved released activity (TRRA) distribution retrieved using the air concentration and deposition measurements is 5 PBq larger than the TRRA reconstructed with the air concentration measurements alone. Moreover, the reconstructed density with both types of measurements has a large variance and leads to the possibility of a larger release (up to 75 PBq). The density of the number of boundaries with both types of measurements is shifted to higher values compared with the density with air concentrations alone, which indicates a reconstructed release of greater complexity (Fig.
(2013)  source term was recovered from dose rate measurements. However, the source term found with the RJ-MCMC on air concentration measurements can be fairly evaluated using deposition measurements. It is observed that the fits with the deposition measurements of the three source terms are similar. Furthermore, as expected, Table  2  shows that there is a significant difference in the fit with the deposition measurements when they are added to the observation dataset used to reconstruct the source term. However, the addition of the deposition measurements has no impact on the quality of the fit to the air concentration measurements. This suggests that an RJ-MCMC optimized on dose rate measurements, in addition to the deposition and air concentrations, may obtain a good agreement with the dose rate measurements, without degrading its results on deposition or air concentrations.
Impact of the observation error matrix R representation In this section, we investigate the spatial and spatio-temporal representation of the R likelihood scale matrix. Figure  6  presents violin plots of the FAC 2 and FAC 5 scores for air concentration measurements against the number of scale parameters chosen to describe R (i. e. the number of observations clusters). Violin plots are created out of the best scores of 44 RJ-MCMC sampling datasets with diverse likelihoods chosen after Dumont Le Brazidec et al. (  2021 ) in order to make the conclusions less dependent on statistical modelling. The chosen numbers of spatial clusters (1, 2, and 9) and spatio-temporal clusters (3 and 7) of the violins are a selection for the sake of clarity. For both the FAC 2 and FAC 5 air concentration scores, it can be observed that the spatio-temporal labelling of the observations does not increase the quality of the scores, whereas the number of spatial clusters does.
For example, we can observe a difference of 0. 02 between the second quartile of the FAC 2 score for sampling datasets with two spatial groups and the second quartile of the FAC 2 score for sampling datasets with nine spatial groups. One hypothesis is that the error is mainly related to the distance to the source and remains relatively homogeneous in time. However, the use of an accurate estimate of the effective wind speed as a function of time and depending on the position of the observations is necessary to draw conclusions; such a study is outside the scope of this paper.
Acknowledgements. The authors would like to thank the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for making available the meteorological fields used in this study. They would also like to thank  Sophie Ricci ,  Lionel Soulhac ,  Paola Cinnella ,  Didier Lucor ,  Yann Richet , and  Anne Mathieu  for their suggestions and advice on this work. CEREA is a member of the  Institut Pierre-Simon Laplace (IPSL). Review statement. This paper was edited by  Dan Lu  and reviewed by two anonymous referees.
Data availability. Each dataset used in the paper is available via a DOI on a compliant repository or directly in the cited papers. More precisely, the air concentration observation datasets used in the inversions are available in Oura et al. (2015)  from http: //www. radiochem. org/en/j-online152. html (last access: 3 February 2023) and in  Tsuruta et al. (2018) , Tanaka et al. (2013),  Takehisa et al. (2012) , and  Yamada et al. (2013). In addition, the deposition observation dataset used in the inversions is available via https://doi. org/10. 5281/zenodo. 7016491 (Dumont Le Brazidec and  Saunier, 2022). The IBISA (inverse Bayesian inference for source assessment) algorithm, which specifically describes the reversiblejump Markov chain Monte Carlo algorithm used in this article, is available from https://doi.
Statistical analysis As shown in Fig. 6 , after the roughness measurements, the dataset was uploaded to statistical software R (v4. 0. 3) for further analysis. Paired hypothesis testing was employed to understand if there was a noteworthy difference in the surface finish (Ra, Rt, Sa) of EBM and SLS samples obtained through turning and milling operations. The samples were paired in four ways, (1) milling samples of EBM and SLS, (2) turning samples of EBM and SLS, (3) EBM samples processed by turning and milling and (4) SLS samples that underwent turning and milling. In addition, the following null hypotheses were considered: "Is there no significant difference in the roughness of (1) milled surfaces attained with EBM and SLS? (2) turned surfaces attained with EBM and SLS? (3) EBM samples attained with turning and milling? (4) SLS samples attained with turning and milling?".
Acknowledgements The authors acknowledge "Project No. 031556-FCT/SAICT/2017 ";  FAMASI -Sustainable  and intelligent manufacturing by machining financed by the  Foundation for Science and Technology (FCT), POCI, Portugal , in the scope of  TEMA ,  Centre for Mechanical Technology and Automation - UID/EMS/00481/2013. The authors would also like to thank  Palbit  for all the support given, both supplying the cutting tools and the availability to use their equipment. They would also like to acknowledge the support given by the companies S3D in digitizing the test samples and DimLaser in providing the SLS test samples.
This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.
A novel chemometric strategy was used and was based on a systematic optimization of two factors influencing the discrimination ability: the set of experimental conditions (laser energy, gate delay, and atmosphere) employed for the LIBS analysis and the set of spectral variables used as a basis for the polymer discrimination  [12]. LIBS is a powerful tool for plastic investigations in the laboratory with the capacity of simultaneous qualitative and quantitative analysis  [13]. Classification of harmful substances needs to be contributed to literature and human health. In the literature, ML applications are newly contribute in LIBS to determine the hazardous substances and classify the ingredient. However, these studies are not related to the appropriate ML method to classify LIBS dataset and make a system but mostly about just training some of ML algorithm and the importance of ML in substances classification.
Classification of hazardous chemical substances studies are developed and contribute to literature but dataset are not related with LIBS  [5, 14]. Therefore, This paper aims to satisfy the requirement for discussion and determination of appropriate ML algorithm for dataset obtained from LIBS. Classification of different polymer composite materials has been performed based on their hardness. Eight different substances' intensity-wavelength values are obtained and dataset is occurred. Seven different models is used for train the dataset and results are compared. Fine Gaussian SVM, Fine tree, linear discriminant, Gaussian Naive Bayes, Kernel Naïve Bayes, Quadratic SVM, and KNN are the most valid and high accuracy ML methods in variety of field which are used for training in this paper  [15] [16] [17] [18].
Experimental Procedure Interior mechanism view of LIBS whole system (Applied Spectra, J200 LA) we used is shown in Figure  1. Laser (Quantel) with wavelength of 213 nm, output energy of 20 mJ, pulse width of 10 ns and repetition rate of 100 Hz has been used. Laser beam focused by 10 cm focal length of plano convex lens. Focused beam spot size was 50 μm. A total of 3 different target regions were determined on the same sample surface and these regions were used for spectral measurements. Spectral data were collected via 10 shots to 4 different locations on target-zone surfaces.
Results and Discussion Dataset obtained from LIBS includes aluminum, copper, bismuth, iron, magnesium, nickel, tin, and zinc wavelength-intensity values. Therefore, we have eight classes for classification. In artificial intelligence (AI) applications, dataset preparation is of crucial importance. If the dataset is not properly prepared and adjusted, it is not important whether the AI model works well or not. Eight different substances in polymer were obtained from the LIBS measurement, which are aluminum (Al), bismuth (Bi), copper (Cu), iron (Fe), magnesium (Mn), nickel (Ni), tin (Sn), zinc (Zn). Forty data were obtained from each substance in the three different locations, so each substance has 120 data in total. Also, wavelengths start from 186. 015 to 1048. 107 so in the dataset a total of 12286 wavelength values are used.
The dimension of the data is 12286x 960 for eight classes. The substance in the polymer has been detected by showing the related wavelength for each substance which is demonstrated below in Figure  4. The original dataset includes negative values which are assumed as noise and eliminated before plotting. Plottings include non-negative values in Figure  4. Also, this big data should not be used directly for obtaining good accuracy because the values of intensity of different classes can be matched or overlapped, therefore mismatch and misclassification inevitably occur. To see the effect of the unadjusted dataset result, this 12,285 × 960-dimension dataset was applied directly to the decision fine tree, which is a supervised machine learning model. The eight classes of substances are orderly determined as aluminum (Al), bismuth (Bi), copper (Cu), iron (Fe), magnesium (Mn), nickel (Ni), tin (Sn), and zinc (Zn); in the algorithm response of the substances determined with numbers, one to eight are orderly.
Each substance has 120 data, so 120 responses for each substance were determined; 960 data and responses were used in total. The reason for selecting the fine tree model for classification is that there are so many values, and decision tree accuracy results show whether this dataset is logical or not for training. This fine tree model is used for the unadjusted dataset and the result is obtained and demonstrated, as in Figure  5. Detection with unadjusted dataset is insufficient. This can be interpreted from Figure  5. Confusion matrices show the accuracy of training, the blue diagonal is interpreted as a true-positive detection of the related class, and the orange boxes belong to the false-negative detection according to the giving response. Total of the true positive over eight gives us the total accuracy of training, so the total accuracy of the unadjusted dataset with the fine tree model is 84.
7%. A total of 3. 3% of the Ni is detected as Bismuth and 10% of the Ni is obtained as Sn, so the total accuracy of the Ni is 86. 7%. 6. 7% of the Sn is founded as Cu and 16. 7% of the Sn is identified as Ni, therefore the total trueness of the Sn is 76. 7% while Zn is detected as completely true-positive with 100%. The overall accuracy of the model with this non-adjusted dataset is obtained with the mean of true-positive percentages of the classes which have been founded being 84. 6%. Other confusion matrices are interpreted as mentioned above, and the most accurate ML algorithm for this dataset is determined for using in automatic classification with LIBS. As it is mentioned above, 120 responses were determined for each category separately, so Y is defined as 960 responses for eight classes.
This gives us the accuracy and errors for the total and for each class. During the model-making classification, these defined response data are taken as a basis for true classification. The classification graph of the response data is shown in Figure  6. A data adjustment was made to eliminate false detection caused by using the nonadjusted dataset. Every substance has its own intensity value in a specific wavelength interval, as it is shown in Figure  4. For instance, Al has its peak value at 309. 291 nm and 396. 29 nm, while Bi has it at 306. 82 nm. Cu has been detected at 324. 806 nm and 327. 445 nm, Fe at 324. 806 nm and 327. 447 nm, while Mn has three different intensity values at 257. 619 nm, 279. 585 nm, and 294. 985 nm. Ni has a peak intensity at 300. 248 nm. Sn is detected at 284.
08 nm and 326. 303 nm, while Zn has a peak value at 213. 959 nm. Dataset adjusted in the direction of these peak values for each class. By focusing on its own peak values for each substance, the new dataset was created based on the circumference of the wavelength that belonged to the peak values. Seven different models have been trained and compared: the fine Gaussian SVM, fine tree, linear discriminant, Gaussian Naïve Bayes, Kernel Naïve Bayes, Quadratic SVM, and KNN on the adjusted dataset. MATLAB is used for applying ML algorithms to the dataset which is also widely used in ML studies. The model which gives the best result is tested and demonstrated also. The fine tree model has been trained again for the new adjusted dataset. This model compares the similarities between classes and focuses on reducing the error while comparing  [26].
A total of 40% of the Mn is detected as Al and 3. 3% of the Mn is detected as Cu, so the total true positive value of the Mn is 56. 7%. A total of 3. 3% of the Ni is detected as Bismuth and 10% of the Ni is obtained as Sn, so the total accuracy of the Ni is 86. 7%. 6. 7% of the Sn is founded as Cu and 16. 7% of the Sn is identified as Ni, therefore the total trueness of the Sn is 76. 7% while Zn is detected as completely true-positive with 100%. The overall accuracy of the model with this non-adjusted dataset is obtained with the mean of true-positive percentages of the classes which have been founded being 84. 6%. Other confusion matrices are interpreted as mentioned above, and the most accurate ML algorithm for this dataset is determined for using in automatic classification with LIBS. As it is mentioned above, 120 responses were determined for each category separately, so Y is defined as 960 responses for eight classes.
The fine-tree-model accuracy is obtained as 97. 9% with adjusted dataset as it is demonstrated in Figure  7. It is clearly evident that data adjustment plays a critical role in the training process. For this dataset, Zn and Ni are completely detected and other classes have 3. 1% error in total; Cu mostly gives the false negative by having percentages in two classes. The fine-tree-model accuracy is obtained as 97. 9% with adjusted dataset as it is demonstrated in Figure  7. It is clearly evident that data adjustment plays a critical role in the training process. For this dataset, Zn and Ni are completely detected and other classes have 3. 1% error in total; Cu mostly gives the false negative by having percentages in two classes. The second trained model is the fine Gaussian SVM. It can map data to an infinite dimensionality, so it is suitable for training high-dimensional datasets  [27].
Formula of the fine Gaussian SVM is shown below. G X J , X k = exp(-X j -|X k | 2 ) (1) it calculates the similarities between X j and X k by measuring the Euclidean distance between X j and X k. According to formula one, the model is trained, and the accuracy of the fine Gaussian SVM model is obtained as 86. 5%, the result being quite worse since the SVM has a linear classifier and such a complex dataset needs to be trained with nonlinear solvers. Confusion matrices are shown below. This model truly detected Cu with 100% and other classes have an error, however, the accuracy is over the 75% and the lowest value also belongs to Al as it is demonstrated in Figure  8. The second trained model is the fine Gaussian SVM. It can map data to an infinite dimensionality, so it is suitable for training high-dimensional datasets  [27].
𝐺 𝑋 , 𝑋 𝑒𝑥𝑝 𝑋 |𝑋 | (1) it calculates the similarities between Xj and Xk by measuring the Euclidean distance between Xj and Xk. According to formula one, the model is trained, and the accuracy of the fine Gaussian SVM model is obtained as 86. 5%, the result being quite worse since the SVM has a linear classifier and such a complex dataset needs to be trained with nonlinear solvers. Confusion matrices are shown below. This model truly detected Cu with 100% and other classes have an error, however, the accuracy is over the 75% and the lowest value also belongs to Al as it is demonstrated in Figure  8. The linear discriminant is another method for classification. It uses dimension reduction to increase the distance between the mean of classes and it is also appropriate for classifying multi-classes.