dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The analyzed time series are the El Niño34 index (El Niño34), the S&P food and beverage select industry index (SPSIFBN), the MSCI agricultural & FC index (M2WO0AGF), the Nasdaq US Smart Food & Beverage Index (NQSSFB), and the Bloomberg world agricultural index (BWAGRI). All time series were collected in the period from March 04, 2015 until November 27, 2021 and downloaded from the Bloomberg Terminal. A detailed definition of our variables is provided in Table  1. We select the observations from the same days in the studied period, then we have a time series of 1659 observations. All studied time series are shown in Fig. 1  (black continued curves) and some of their descriptive statistics are given in Table  2. From Table  2 , we see that El Niño34 time series have a negative minimum value and the lowest values of maximum, mean, and standard deviation (sd) compared to other time series.
Acknowledgements We gratefully thank  Professor Thomas D. Pollard  for his invaluable insights and great help for manuscript preparation, and  Professor Laurent Blanchoin  for help with the experiments. We also thank  Professors Xin Liang ,  Congying Wu ,  Bo Li  and  Yue Shao  for valuable discussions. X. D. C. , X. Q. F, and B. W. X. acknowledge the support from the  National Natural Science Foundation of China  (Grant no. 11921002  and  12032014 ).
We developed an open-source chemical reaction equilibrium solver in Python (CASpy, https://github. com/ omoultosEthTuDelft/CASpy) to compute the concentration of species in any reactive liquid-phase absorption system. We derived an expression for a mole fraction-based equilibrium constant as a function of excess chemical potential, standard ideal gas chemical potential, temperature, and volume. As a case study, we computed the CO 2 absorption isotherm and speciation in a 23 wt % Nmethyldiethanolamine (MDEA)/water solution at 313. 15 K, and compared the results with available data from the literature. The results show that the computed CO 2 isotherms and speciations are in excellent agreement with experimental data, demonstrating the accuracy and the precision of our solver. The binary absorptions of CO 2 and H 2 S in 50 wt % MDEA/water solutions at 323.
Therefore, it is important to investigate the limitations of the method. In this study, we develop a chemical reaction equilibrium solver in Python called CASpy and use it to solve binary (and single-component) CO 2 and H 2 S absorption isotherms in aqueous methyldiethanolamine (MDEA) solutions (see the Supporting Information for source code for CASpy version 0. 1. 6). ] [34] [35] [36]  Molecular simulation is a natural choice for this application as simulations allow studies without the difficulty of working with H 2 S (due to safety and environmental concerns),  37  and eliminate the low accuracy of experiments at low partial pressures of acid gases. 38, 39 For this purpose, we first derive an expression for the equilibrium constant as a function of μ i ex and μ i 0 of species i, temperature, and volume using a mole fractionbased equilibrium constant and develop software to solve chemical reaction equilibria in combination with absorption.
Journal of Chemical Theory and Computation CH 4 in aqueous MDEA solutions) will not influence the outcome of our model. In this study, we use the "least_squares" function as implemented in the Scipy 46 library in Python to solve for the speciation (the number of molecules or concentration of each species in the solution) of the liquid phase at equilibrium. The "least_squares" is a function for solving nonlinear equations with the least-squares method. This requires an objective function to be defined. The objective function is computed by summing the squares of the values of individual equations (residuals). CASpy runs until the value of each residual is lower than 10 -10 to ensure that the global minimum of the objective function is obtained. CASpy can be used to compute the speciation in any reactive liquid phase.
2. 4. Quantum Chemistry Calculations. In this study, we perform quantum chemistry calculations using the Gaussian09 software  56  to compute the values of μ i 0 for the MDEAH + ion, the MDEA molecule, the H 3 O + ion, and water. As the MDEAH + ion and the MDEA molecule have many different conformers (molecules with different spatial arrangements), we first conducted a conformer search for these molecules. 57 We optimized the structure of 5 different conformers for both MDEAH + and MDEA with the Gaussian-4 (G4) composite method  58  and chose the conformers with the minimum free energy. The molecular partition function computed in these calculations were used to compute μ i 0. Details on computing μ i 0 using quantum chemistry calculations are explained in the Supporting Information. We also compute the electrostatic potential energy grid of the conformers at the minimum free energy using the Merz-Kollman scheme  59  at the Hartree-Fock (HF)  27  level of theory with a 6-31G* basis set.
CONCLUSIONS We derived an expression for a mole fraction-based equilibrium constant as a function of μ i ex , μ i 0 , and T, and developed an opensource chemical reaction equilibrium solver in Python called CASpy for absorption of gases to reactive solutions, assuming that the values of μ i ex and the liquid phase volume V are constant. CASpy can be used to compute the concentrations of the species in any reactive liquid phase, for example, aqueous alkanolamine solutions for CO 2 and H 2 S capture, and CO 2 capture in an aqueous solution for the electrochemical conversion of CO 2. We first validated that CASpy yields the correct numerical solution at chemical equilibrium. Our results showed that the computed solutions are at chemical equilibrium since the sum of all residuals was 0 within machine precision.
The binding model used for the 2H→2A variant fits the titration dataset reasonably well, and produces the same binding constant (Table  1 ). However, a model where the protein binds three chromophores results in a comparable fit quality (with pK values of 7. 2 for the first two chromophores and 6. 2 for the third). Thus, although these fittings are not conclusive, it is likely that BT6 binds two chromophores relatively strongly, and then a third one more loosely (i. e. , with a dissociation constant comparable to the concentrations employed here, in the micromolar range), obscuring the observation of  3 species. Analysis of circular dichroism (CD) spectra (Figure  1 ) leads to similar conclusions. For the 4H→4A variant, the formation of an excitonically-coupled species (i. e. , chromophore aggregates) is clear at all [C]:[P] ratios.
Both the absorption and CD spectra can be readily compared to those for  4 extracted from SVD, and indeed the main features coincide (Figure  4 c  and f ). An important difference is the appearance of a positive feature at ~662 nm in the CD spectrum at 100 µM protein concentration, that, by comparison with the 4H→4A variant (Figure  1 ), can be attributed to minor aggregate formation. Regardless of that, the experimental data taken at 100 µM protein concentration corroborates the accuracy of the spectra extracted from the titrations. To analyse the properties of the excitonic excited states we performed steady-state fluorescence. From the SVD analyses, we chose two different excitation wavelengths: while 437 nm preferentially excites bound chromophores, 405 nm is more suitable to excite both free and aggregated chromophore molecules (Figure  5a ).
On the other hand, protein structure prediction software, spearheaded by the AlphaFold  35  and RoseTTAFold 36 methods, have recently achieved remarkable accuracy, thus offering an attractive alternative to experimental determination. 54 though these are still limited to apo-structures, we complemented them with an MD-based protocol that mimics the natural binding process. From this discussion, we conclude that our MD-based approach is not at odds with protein design and prediction software; instead, they form a synergistic combination. Finally, it is noteworthy that the excitonic signals observed in CD spectroscopy strongly depend on the relative orientation of the involved chromophores. 55 Therefore, the excellent match we obtained between calculated and experimental spectra can be taken as a confirmation that the structural model we present is accurate.
Acknowledgements M. C. acknowledges the funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  801474. M. C. and E. R. acknowledge funding from the  State Research Agency/Spanish Ministry of Science and Innovation (AEI/MICINN)  through the  Severo Ochoa Excellence Accreditation   CEX2019-000925-S. L. G. T. A. D. , S. S. , L. X. H, and E. R. thank the  European Research Council  under the  ERC starting  grant agreement No. 805524  ( BioInspired_SolarH2 ). V. M. thanks the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 754510. C. C. thanks the  State Research Agency  ( AEI/10. 13039/501100011033 )through grants  MDM-2017-0767  and  PID2020-115812GB-I00.
The outlet height (H) of the convergent section is 10 mm, and the width (Lz) is 190 mm. The aspect ratio for the jet is Lz/H = 19. This configuration represents a rectangular jet configuration in its central region  [57]. The jet exiting the convergent section impinges on a slotted aluminum plate with a thickness of 4 mm. The plate has a square shape of 250 mm length for each side. The slot on the plate has the same dimensions as the jet outlet (H = 10 mm, Lz = 190 mm). The slot is beveled at a 45 • angle downstream and aligned with the jet outlet in order to create optimal conditions for acoustic generation  [23, 55]. The impact distance "L" between the jet outlet and the slotted plate can be adjusted using a displacement system (ISEL MS 200 HT2 Direkt, with a precision of 0. 01 mm, controlled by the "Galaad" software) along the Y-axis.
Acoustic measurement A microphone from B&K of type 4189 was placed behind the plate. This microphone has a sensitivity ranging from 20Hz ≤ f ≤ 20kHz and can achieve acoustic level measurements between 15. 2 dB and 146 dB. The sampling frequency was equal to 15 kHz. The acquisitions were performed thanks to a LabVIEW software installed on a National Instruments NI PXI 1036 workstation equipped with a dynamic card NI PXI 4496 having 16 outputs sampled simultaneously with a maximum sampling frequency of 204. 8 kHz and 24-bit resolution. The acoustic signals were acquired for a total number of 1085 spatial positions of the rod for the present Reynolds number with a delay of 12 s between two consecutive acoustic measurements. The choice of the microphones aimed at enabling the acquisition of high-frequency signals and high acoustic levels, such as those found in the studied flow.
Macro lenses were used from "Tokina" with a focal length of 100 mm. The Davis 10. 1. 0 software allowed us to establish the stereoscopic reconstruction of the velocity vector fields of the two fields in order to obtain the three-dimensional component of the flow. The kinematic SPIV fields are obtained with a sampling frequency of 2. 5 kHz. In our configuration, the uncertainty on the values of each component of the velocity is estimated to be less than 5 %  [59].
INTRODUCTION Evaluated nuclear data are informed by either experimental data, nuclear models, or both. The accuracy and precision of these nuclear data strongly rely on the quality of available experimental data and the magnitude of their associated covariances. Experimental covariances not only bound the evaluated uncertainties in many cases, they also provide a weight of individual experimental data points with respect to nuclear model calculations and other data sets if applicable. This weight can only be appropriate if all pertinent uncertainty sources are consistently provided for all experimental data used in the evaluation. However, more often than not, one or more uncertainty sources that are expected based on knowledge of similar measurements are missing for a specific data set in the EXFOR database  [1] -an experimental low-and intermediate-energy nuclear reaction data library and the go-to database for experimental data for nuclear-data evaluations.
1 We focus on standard measurement techniques and do not cover experiments specifically designed to validate them. While these are key in ensuring that our standard measurement techniques yield reliable results, there is insufficient information (i. e. , too few measurements of the same type) to establish templates. We also do not cover integral experiments. For some types of integral experiments (e. g. , criticality experiments in the "International Handbook of Evaluated Criticality Safety Benchmark Experiments" (ICSBEP)  [12] ), stringent uncertainty-quantification rules and template-like tables have been developed  [13]. They are rigorously applied as part of the review process to data entering ICSBEP since the release of Ref. [13]. This process significantly enhances the quality of uncertainties in that database and serves as an example of the benefit of applying templates such as the ones presented here.
This paper here serves as preface to this special issue on templates for various observables; we introduce in Sec. 2 the concept of templates, their history, and discuss potential use cases. Section 3 covers the mathematical basis for establishing templates. A specific example, 235 U(n,f) average prompt fission neutron multiplicities, is given in Sec. 4 to illustrate how templates can be applied to estimate missing experimental uncertainties for evaluation purposes and their impact on evaluated mean values and covariances. Section 5 provides conclusions and an outlook to future work. 2 Introduction to the concept of templates The templates cover many common direct measurement techniques typically employed to provide experimental data for a specific nuclear-data observable. A minimal set of uncertainty sources that are expected to be addressed in the uncertainty quantification of an experiment is then tabulated for each of these measurement types.
Also, one has to consider that some uncertainties reported in the literature might be overly optimistic or very low as considerable effort was spent to reduce them. We choose here to give uncertainties for partial uncertainty sources that were selected to be mutually independent, where possible, following the mathematical framework discussed in Sec. 3. In addition to that, the templates list estimates of correlation coefficients between uncertainties of a specific source within the same and between different experiments. While the former information is rarely found in the EXFOR database or the experimental literature, correlations between uncertainty sources of different experiments are even less likely to be provided by experimenters. Estimating the latter correlations is usually the evaluator's task.
Journal editors and reviewers can also use the guidelines presented here to ask for the information needed by evaluators to be presented in an article. This should increase the usability of the resulting journal article. These templates were designed to help evaluators in making better informed choices to fill in missing uncertainty and correlation information comprehensively across the whole experimental database used for an evaluation. These templates are provided by means of large literature reviews and in discussion with the experimental community. Given that, the estimated values are expected to be better justified for many cases than those evaluators come up on their own. Also, they can help evaluators pinpoint missing uncertainty sources and estimate them. They can also help identify unrealistically low uncertainties.
[4] [5] [6] [7] [8] [9] [10] [11]. In the preparation of this paper, the uncertainty values obtained in Ref. [16]  for the 59 Ni observables (rare isotope) were compared to pertinent template values. Out of these ideas and out of the need for a comprehensive uncertainty quantification for Neutron Data Standards evaluations  [14] , a template of expected measurement uncertainties in fission chamber measurements was developed and published in an early version in Ref. [17]. It was finalized and systematically applied to updating 239 Pu(n,f) cross-section covariances in the database underlying the Neutron Data Standards evaluation. This work presented in Ref. [2]  highlighted clearly that considering all pertinent uncertainties in this database changed the evaluated 239 Pu(n,f) mean values and covariances so significantly that application calculations were impacted.
Example of Applying a Template for Evaluations Templates of expected measurement uncertainties were applied to estimate missing experimental uncertainties for measurements of neutron-induced 235 U average prompt fission neutron multiplicities, ν p , listed in Table  I. This analysis of experimental data was undertaken for an evaluation of the 235 U ν p in the fast range for ENDF/B-VIII. 1β 1  [21]. Most measurements employed the ratio liquid-scintillator method. For the uncertainty analysis, the EXFOR entry and literature of each experiment was studied in detail to extract all possible uncertainty sources. The uncertainty sources found through these analyses were compared to those that should be supplied for ratio liquid-scintillator measurements in Table  1  of Ref. [9]. This procedure aids in identifying missing uncertainty sources  3.
If uncertainty values were found to be missing, they were estimated based on template values. It is obvious from the right-hand side column of Table  I  (tabulating missing uncertainty sources) that uncertainty sources were missing for all of the measurements which, unfortunately, is the rule rather than the exception. Correlation coefficients for specific uncertainty sources were only rarely provided in the experiment literature, and were thus estimated using templates of expected measurement uncertainties in the majority of the cases. The code ARIADNE  [46]  was then used to estimate total covariances for each correlated group of experiments. The code starts out by estimating covariances for each individual uncertainty source following the procedures outlined in Sec. 3. The same code was employed to perform two evaluations: One evaluation was undertaken with the generalized-least squares algorithm  [47].
Conclusions and Outlook Templates of expected measurement uncertainties were established by the CSEWG covariance committee and with the help of many other international contributors. These templates list expected uncertainty sources for each distinct measurement type of an observable. They also give ranges of uncertainties for most sources and estimates of correlation coefficients between uncertainties of each source. The uncertainty values were estimated conservatively based on information found for a broad range of experiments, from their literature, respective EXFOR entries, and expert judgment from experimenters. In addition to these templates, we list what information (what data, uncertainty information, data descriptors) evaluators need in order to include experimental data faithfully into the evaluation process.
This information could help referees point out if information is missing in the journal articles that could potentially improve the usability of the data. If this information would be provided by experimenters, the content of the papers could be more easily used by evaluators for evaluation purposes. Also, if all pertinent information is in the paper or supplemental material, it is more likely to be recorded in the EXFOR database. In addition to that, we will provide these templates for EXFOR compilers in the hope that they ask experimenters for missing information compared to the template leading, thus, to more complete new EXFOR entries for evaluation purposes. These templates will also be used as part of Subgroup 50 of the OECD "Working Party on International Nuclear Data Evaluation Co-operation" at the NEA  [52].
This subgroup aims at developing an automatically readable, comprehensive and curated experimental reaction database that starts off from a few selected entries of EXFOR. The structure of this database (especially the uncertainties) will be aligned such that it can contain uncertainty sources expected from the templates and hold information needed by evaluators. The template uncertainties might also be used to provide otherwise missing uncertainties for data sets in the database. It should be emphasized that the templates should not be understood as immutable laws but rather a snapshot in time. Also, they will not apply to all possible measurement types; experimenters and evaluators should use their judgment in how far they are applicable. They should be also understood as a "living document" in as far as our understanding of uncertainty sources evolves over time as we uncover new physics effects with ever improving technology and research.
Acknowledgments DN thanks  P. Schillebeeckx  and  S. Kopecky  for discussions. Funding Work at  LANL  was carried out under the auspices of the  National Nuclear Security Administration (NNSA)  of the  U. S. Department of Energy (DOE)  under contract  89233218CNA000001. We gratefully acknowledge partial support of the  Advanced Simulation and Computing program  at  LANL  and the  DOE Nuclear Criticality Safety Program , funded and managed by  NNSA  for the  DOE. Part of this material is based upon work supported by the  Department of Energy National Nuclear Security Administration  through the  Nuclear Science and Security Consortium  under Award Number(s)  DE-NA0003180  and the  Office of Nuclear Physics  under  DE-20SSC000056  and  DE-SC0021243. This work was performed in part under the auspices of the  U.
I. INTRODUCTION Our society has relied on the discovery and development of functional materials to create new functionality with improved efficiency. For example, a permanent magnet has been improving its energy product, known as BHmax, by moving from binary iron-oxide to more sophisticated Nd-Fe-B magnet  [Coey 2020 ]. In spintronics, a ferromagnetic material with higher spin polarization has been developed  [Elphick 2020 ]. Such research in materials science emerged in the 1990s thanks to the development of vacuum and growth technologies. The collection of these studies created a considerably-sized and reliable database, such as MatNavi [NIMS web] and Springer-Materials [Springer web], which can be accessed and processed via the recent advancement in computational capability. This allows artificial intelligence (AI) and/or machine learning (ML) approaches for the material search, so-called materials informatics (MI)  [Hu 2022 ].
Random forests Random forests reduce overfitting problems in decision trees. They can handle large datasets with higher dimensionality. Gradient-boosted trees (GBT), such as XGBoost, can improve the optimization process via a weak prediction model. They may be seen like a black-box approach for statistical modelers as you have very little control over what the model does. They can be slow to evaluate. Principal component analysis (PCA) PCA can simplify a dataset by reducing a large set of variables to a smaller one that still contains most of the information. The original variables are replaced by a set of principal components, which are not as interpretable as the original data. ------------------------------------perience convolution operation with a mask function. In a GNN, graph nodes and the neighbor nodes perform similar operations.