dataesr/scientific-paragraphs-categorization huggingface dataset:
Because nuclear data largely define the accuracy of modeling and simulation codes used for fuel composition calculations, results from depletion simulations can provide important integral information on data performance. Recent and ongoing developments of adjoint-based depletion sensitivity and uncertainty methods can be used to provide additional insight into the complex relationships between nuclear data and nuclide inventories and can help define a quantitative approach to data evaluation. With the expansion of data representing a wide range of enrichments, burnups, fuel types, moderator and coolant types, along with tools to help the evaluator identify nuclear data importance, SFCOMPO 2. 0 can provide valuable information for nuclear data testing for materials, nuclear data and reaction processes that cannot be accomplished using traditional steady-state benchmarks.
Acknowledgments The authors thank the valuable contributions and efforts of the Expert Group ADSNF members who made SFCOMPO 2. 0 a reality. In particular, the  NEA  acknowledges support received from  Spanish Nuclear Safety Council (Consejo de Seguridad Nuclear)  and the valuable contribution of the  Technical University of Madrid Nuclear Engineering  students who helped capture the PWR data. ORNL wishes to acknowledge support from the  US Department of Energy , the  National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development  under contract number  DE-AC05-00OR22725 , and the  Radiation Shielding Information and Computational Center (RSICC).
Introduction A model in Matlab/Simulink was developed. This model allows simulating MPPT algorithms behaviour and power conditioning control made by MC². The hardware models are the solar arrays and their solar flux, the power multiplexors, the boost converters, the battery and loads. The software model is a The power conditioning algorithm is representative of the software implemented inside the microcontroller for the real power conditioning. In order to test the algorithms robustness, noises have been added to converter outputs currents measurements. This simulation is focused on avionic gondola architecture (4 solar panels). All the simulation results presented hereafter are for a MC2 configured for avionic gondola architecture.
Introduction The European Power Plant Physics and Technology (PPPT) programme, organised as activity of the EUROfusion Consortium, aims at developing a conceptual design of a fusion power demonstration plant (DEMO) as a central element of the European roadmap to the realisation of fusion energy  [1]. Various integrated PPPT projects are being conducted to meet this ambitious goal including Breeder Blanket (BB), Safety and Environment (SAE), Magnets (MAG), Materials (MAT), Diagnostic and Control (DC), Divertor (DIV), and Remote Maintenance (RM). Neutronics plays an important role for all of the related activities since it has to provide essential data for the nuclear design of DEMO, assess and verify its performance. This requires, on one hand, the availability of suitable computational tools and data to ensure reliable neutronics simulations of DEMO, and, on the other hand, a consistent approach for the variety of nuclear analyses to be performed within the different PPPT projects.
Accordingly, the PPPT programme builds on a co-ordinated approach for the DEMO neutronics including both development works on advanced computational tools as required for nuclear, activation and shielding analyses, and a variety of activities related to the nuclear design and performance of the DEMO power plant and specific reactor components and issues. In the following, the methodological approach for the PPPT neutronics is presented including the development works on advanced simulation tools and their application to DEMO. The focus is on the approach for DEMO nuclear analyses including blanket design, shielding, activation and radiation dose issues with the discussion of specific examples. In addition, the role of nuclear data for reliable DEMO neutronics design analyses and uncertainty assessments is addressed.
Computational tools and data for neutronics simulations Neutronics simulations form the basis for providing the nuclear responses which are needed for the engineering design and the performance evaluation of DEMO. The related issues include the Tritium breeding capability, shielding performance, nuclear power generation, activation and radiation damage of irradiated materials/components as well as the resultant radiation dose loads to sensitive components, and related biological dose rate distributions. Suitable computational approaches, tools and data need to be available to provide the required response data with sufficient accuracy. This includes a suitable method for the simulation of neutron transport in complex 3D geometries, high quality nuclear cross-section data to describe the nuclear interaction processes, and simulation models which replicate the real geometry without severe restrictions.
Such requirements are satisfied with the Monte Carlo (MC) particle transport technique which can handle any complex geometry and employ the nuclear cross-section data without any severe approximations. Furthermore, suitable computing schemes for coupled radiation transport and activation calculations are required for safety, maintenance and waste related analyses including the assessment of the activity inventories produced in DEMO over the anticipated lifetime, the decay heat power generation, and the calculation of shut-down dose radiation maps. Key issues for faithful neutronics simulations of DEMO are thus related to (i) the reliability of the employed MC particle transport code and its coupling to nuclide inventory calculations (to be validated with fusion relevant benchmark experiments), (ii) the capability to describe in the simulation the real reactor geometry with high fidelity and sufficient detail, and (iii) the quality of the nuclear cross-section data available for fusion applications (to be checked against integral experiments).
CAD to MC geometry conversion tool The requirement to represent the real reactor geometry within the neutronics simulation with high fidelity and sufficient detail can be satisfied by using a modelling approach which ensures a true one-to-one translation of the CAD geometry model, as produced for the engineering design of the reactor, into the MC geometry representation. Such an approach is enabled with software tools like MCAM, developed by the FDS Team, Hefei/China  [11] , or McCad, developed by KIT as open source project  [12]. The development work on McCad is conducted within the BB project of PPPT with the objective to provide a mature European software tool for the conversion of CAD geometry models into the semi-algebraic geometry representation utilized in MC particle transport simulations with MCNP or TRIPOLI.
The McCad software, originally developed under the Linux operation system, has been ported to the Windows platform. This has been achieved through its implementation on the SALOME simulation platform  [14]. A new Graphical User Interface (GUI) was developed to this end on SALOME under Windows. It provides the users with a higher flexibility and extended interactive features. The McCad code package, including the source code and pre-compiled binaries, is available on the GitHub software development platform [15].
Coupled radiation transport and activation calculation schemes The calculation of the radiation fields after shut-down ("post irradiation") requires a suitable coupling scheme of codes, data and interfaces capable of simulating both the neutron induced material activation during operation and the decay photon transport in the real 3D geometry of DEMO. Two different computational schemes have been developed, pre-dominantly aimed for shut-down dose rate analyses of the ITER tokamak: the Rigorous 2-Step ("R2S")  [16] , and the Direct 1-Step ("D1S") method  [17, 18]. Both of them rely on the MC technique for the transport simulation and are under further development within the SAE project of PPPT for application to DEMO. The D1S approximation method is based on the assumption that a radioactive nuclide generated during irradiation spontaneously emits the associated decay photons.
Neutron and decay photon transport can be treated in one single MC calculation run using a modified version of the MCNP code together with special purpose activation data libraries. The Advanced D1S method ("AD1S")  [19]  includes the capability to provide shut-down dose rate (SDR) distributions on spatial meshes superimposed to the real geometry by utilizing MCNP5's mesh tally feature. The further development of the AD1S method aims at its adaptation for SDR calculations of DEMO. This requires the extension of the data libraries for nuclides and reactions important to DEMO and taking into account sequential two step activation reactions which are neglected so far. The R2S approach reproduces, in a rigorous sense and sequential order, all computational steps which are required for the estimation of SDR distributions.
The practical implementation of the cR2S approach is conducted on the GitHub software development platform to enable the joint development by the participating institutions using a versioning control system. Validation of the computational approaches is an essential precondition for the application to SDR analyses of a fusion power plant such as DEMO. A series of related benchmark analyses has been previously conducted on the SDR experiments conducted on the JET tokamak and the 14 MeV neutron generator at ENEA Frascati (FNG)  [25]. Within the EUROfusion programme, dedicated validation activities on shut-down dose rate predictions are conducted in the JET3 project ("Technological Exploitation of DT operation"), sub-project NEXP ("Neutronics Experiments") with measurements and analyses of SDR inside and outside the JET vessel.
Nuclear data for fusion applications Neutronics simulations need to describe the interactions of neutrons and atomic nuclei including the formation of (stable or radio-active) product nuclei and the emission of secondary particles such as neutrons, photons and charged particles. The interactions are governed by quantum mechanical probabilities described by means of neutron cross-section data ("nuclear data") which depend on the nucleus species, the reaction types and on the neutron energy. The availability of high quality nuclear data is thus a pre-requisite for reliable design calculations to ensure sufficient prediction accuracy for the nuclear responses to be provided. The data quality thus affects significantly the nuclear design and performance of DEMO including safety, licensing, waste management and decommissioning issues.
Work on the development and qualification of nuclear data for fusion so far has been conducted by the European "Consortium on Nuclear Data Development and Analysis" within a framework partnership agreement with F4E, Barcelona  [27]. The related programme, conducted through specific grants, addresses the nuclear data needs of ITER, the IFMIF neutron source and DEMO. The activities include the evaluation and validation of relevant nuclear cross-section data, the development/extension of codes and software tools required for nuclear model calculations and sensitivity/uncertainty assessments. After passing a thorough benchmarking and validation process, the cross-section data evaluations are fed into the Joint Evaluated Fusion File (JEFF), maintained and disseminated by the NEA Data Bank of the OECD, Paris, France  [28].
Generation of neutronics DEMO models The general approach is to generate first a generic CAD neutronics model from the CAD Configuration Management Model (CMM) of DEMO as provided by PPPT's PMU. This model includes the Toroidal Field Coil (TFC), vacuum vessel (VV), divertor, blanket segment box, vessel ports, and plasma chamber, represented in a single torus sector with envelopes. All components are thus described by their bounding surfaces ("envelopes") without any internal structure specified, as shown in Fig. 1  for the latest DEMO baseline model called "EU DEMO1 2015". This model is converted to analysis models for the MCNP and TRIPOLI MC codes using the McCad conversion software and serves as basis for the adaptation to specific tasks as demonstrated in the following section.
It is then is repeatedly filled into the  The model development and the calculations for the considered DEMO variants are performed by the KIT (HCPB), CEA (HCLL), CIEMAT (DCLL) and ENEA (WCLL) expert teams using MCNP or TRIPOLI-4 and nuclear cross-sections from the JEFF data library  [28]. The calculations of the Tritium Breeding Ratio (TBR) showed that the design target of TBR ≥ 1. 1  [30]  can be safely achieved for all blanket variants. For the DEMO 2015 baseline there is actually a large safety margin due to the assumed compact and small divertor. Significant progress has been also achieved in improving the engineering blanket design for an enhanced Tritium breeding as compared to the DEMO 2014 design  [31]. The nuclear power generated in the blanket and the other reactor components exceeds the primary fusion neutron power due to the release of binding energy in the various nuclear reactions.
In the current review, the most pessimistic events of the globe in history are addressed when we present severe impacts caused by storm surges. During previous decades, great progresses in storm surge modeling have been made. As a result, people have developed a number of numerical software such as SPLASH, SLOSH etc. and implemented routine operational forecast by virtue of powerful supercomputers with the help of meteorological satellites and sensors as verification tools. However, storm surge as a killer from the sea is still threatening human being and exerting enormous impacts on human society due to economic growth, population increase and fast urbanization. To mitigate the effects of storm surge hazards, integrated research on disaster risk (IRDR) as an ICSU program is put on agenda.
Alternative splicing generates multiple transcript and protein isoforms from the same gene and thus is important in gene expression regulation. To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes in alternative splicing on a genome-wide scale. Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of high quality, comprehensive transcriptomes for most species, including model organisms such as Arabidopsis, is a major constraint in accurate quantification of transcript isoforms. To address this, we designed a novel pipeline with stringent filters and assembled a comprehensive Reference Transcript Dataset for Arabidopsis (AtRTD2) containing 82,190 non-redundant transcripts from 34 212 genes. Extensive experimental validation showed that AtRTD2 and its modified version, AtRTD2-QUASI, for use in Quantification of Alternatively Spliced Isoforms, outperform other available transcriptomes in RNA-seq analysis.
Despite the genomic resources in A. thaliana, there is limited information on AS transcripts in existing data repositories. Construction of an initial Reference Transcript Dataset (AtRTD) (now referred to as AtRTD1) demonstrated the value of this approach in giving a high correlation of AS using individual transcript abundances from RNA-seq data analysed with Sailfish and Salmon and experimental data from high resolution (HR) RT-PCR  (48). Here, we present a comprehensive high-quality reference transcript dataset (AtRTD2) having an increased number of diverse, high confidence Arabidopsis transcripts with over 82k unique transcripts from around 34k genes. The pipeline for construction includes stringent filtering and quality control measures based on our knowledge of plant intron and splicing characteristics, and extensive experimental validation to reduce the number of false transcripts which could perturb quantification.
Plant material for RNA-seq and datasets Two different extensive RNA-seq datasets were generated for a range of diverse genetic lines and treatments in the A. thaliana Col-0 background (Supplementary Table  S1 ). These two combined datasets included 285 RNA-seq runs obtained from 129 libraries. Dataset 1 was from samples of a time-course of adult Arabidopsis plants (5 weeks old) transferred from 20 • C to 4 • C (unpublished data). Plants were sampled every 3 h for the 24 h period at 20 • C directly before transfer to 4 • C, and for the first and fourth days following transfer (26 time-points in 78 libraries--a total of 234 total biological/sequencing repeats) (Supplementary Table  S1 ). Dataset 2 consisted of RNA-seq data from 51 libraries generated from various samples (Supplementary Table  S1 ).
The genetic lines were over-expression lines and knockout mutants of the serine-arginine-rich (SR) splicing factor genes, At-RS31 (AT3G61860) and At-RS2Z33 (AT2G37340); the mutant of the DNA cytosine methyltransferase MET-1 (AT5G49160), met1-3; and mutants of three MAP kinase genes (AT3G45640, AT4G01370 and AT2G43790), mpk3, mpk4 and mpk6. The latter were treated with flg22 or mock-treated, and wild type Col-0 controls were included for all of the above. The total number of 100 bp paired-end reads generated in the two datasets of RNA-seq was 4. 76 and 3. 73 Bn pairs of reads, respectively, such that a total of ca. 8. 5 Bn pairs of reads (17 Bn pairedend reads) entered the assembly pipeline.
Pipeline for generation of AtRTD2 A detailed description of the transcript assembly and parameters used, merging with the original AtRTD1  (48)  and Araport11 is given in Supplementary Methods and shown schematically in Figure  1. Briefly, RNA-seq reads from Datasets 1 and 2 were mapped to the genome using STAR and TopHat2 respectively, and transcripts for both datasets were assembled with both Cufflinks and StringTie. Transcripts supported by non-canonical junctions or low abundance splice junction reads were removed. Transcripts from unknown genes, antisense transcripts and low abundance transcripts were also filtered out. The resulting Cufflinks and StringTie transcriptome assemblies were merged and redundant transcripts were removed. AtRTD1 was reassessed using the splice junction sequence set generated here and 10 397 transcripts deriving from Marquez et al.
Splice site scores from introns in AtRTD2 transcripts For intron scoring, a total of 3 exonic and 10 intronic nucleotides were extracted from the 5 and 3 splice site sig-Figure  1. Pipeline for construction of AtRTD2. (A) The original AtRTD1 was generated from the merge of transcripts from TAIR10 and from an AS discovery analysis  (8)  and filtered to remove redundancy  (48). Using the splice junction data from the assemblies in (B) and (C), probable mis-assembled transcripts were removed; (B) Dataset 1 RNA-seq reads were mapped with STAR and then assembled using Cufflinks and StringTie. The assemblies were merged and filtered to give a transcriptome; (C) Dataset 2 RNA-seq reads were mapped with TopHat2, assembled, merged and filtered as above; the two datasets were analysed separately in different research groups and used different mapping programs (STAR and TopHat2) but both used the transcriptome assembly functions in Cufflinks and StringTie and similar filtering approaches.
Experimental validation of the quantification of splicing ratios by high resolution RT-PCR To validate the quantification of splicing ratios from transcript isoforms, HR RT-PCR (52-54) was performed on RNA from two different time-points (each with three biological repeats): dawn at 20 • C (T1) and in the middle of the dark period four days after transfer to low temperature (4 • C) (T2). T1 and T2 are from the same plant material as used for RNA-seq of Dataset 1. A total of 762 data points (127 AS events from 62 genes and three biological replicates of the two time-points) were analysed by HR RT-PCR using gene-specific primers (Supplementary Table  S2 ). Primer pairs covering the AS events in these genes, where the upstream primer was end-labelled with a fluorescent tag, were used in RT-PCR reactions with 24 cycles of PCR and separated on an ABI 3730 automatic DNA sequencing machine as described previously  (5, 8, 22, 52).
Translation of AtRTD2 and new peptide database The main consequences of AS are either to alter the proteincoding sequence to generate protein variants or introduce premature termination codons (PTCs)/long faux 3 untranslated regions (UTRs) which can target transcripts to the NMD pathway. In order to assess accurately the consequences of an AS event(s), translation of the transcript from the authentic translation start site is required  (56). We therefore developed an algorithm which defined the position of the translation start AUG in the transcripts of a gene and used this translation start site as the reference point for translation of all of the transcripts. The AUG was defined by selecting the transcript which generated the longest open reading frame (ORF). The AUG of this ORF was then used to translate all of the other transcripts of that gene.
This method translated ca. 94% of the protein-coding transcripts in AtRTD2. The majority of transcripts which were not translated did not contain the fixed AUG because they were either shorter transcripts or an AS event had removed the AUG. These were then translated by a second part of the programme to identify the longest ORF. The resulting proteins were compared to the current Arabidopsis proteome  (57). In addition, the resultant AtRTD2 protein isoforms were converted to a peptide database by in silico trypsin digestion using an in-house script. The standard rules of trypsin digestion were applied, in which a given protein is cleaved in every arginine or lysine residue with the exception of those followed by a proline. Only resulting peptides longer than or equal to seven amino acids were kept and mapped back to the original proteins to determine if they can be assigned to a single transcript/protein isoform (unique peptides) or to multiple isoforms.
thaliana database (Proteome ID UP000006548, updated October 2016) including further Arabidopsis protein isoform sequences from UniProtKB (updated October 2016). The variable modifications were set as oxidation of methionine and acetylation of the protein N-terminus; fixed modifications were set to carbamidomethylation of cysteines only. The MS tolerance was set to 7 ppm with the MS/MS tolerance set to 0. 5Da. The peptide and protein False Discovery Rate (FDR) were both set to 1%  (58).
Pipeline for generation of AtRTD2 The main objective of generating AtRTD2 was to provide a reference transcript dataset that was as comprehensive and diverse as possible in terms of AS isoforms and contained the highest quality transcripts for gene expression and, in particular, AS analysis  (59). To provide diversity of transcripts, two different extensive datasets of ca. 8. 5 billion pairs of reads, obtained from 285 RNA-seq runs of 129 libraries (Materials and Methods and Supplementary Table  S1 ), were assembled into transcripts and merged with our previous AtRTD1  (48)  and the recently released Araport11 transcript set (50) (Figure  1 ). The high transcript quality was achieved by applying stringent criteria and filters to minimize the number of false, mis-assembled and poorly supported transcripts, and transcript fragments.