dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolutionarily related RNAs with divergent sequences often retain the ancestral tertiary folds, very much like proteins do  [140]. However, with the increasing evolutionary distance (and sequence dissimilarity) between the target and the template, the likelihood of obtaining an accurate model decreases, due to the structural divergence and mounting difficulties in the generation of accurate sequence alignments. Technically, it is possible to carry out comparative modeling using templates that are evolutionarily unrelated to the target, but this requires the unrelated template to be structurally very similar to the target. Comparative models based on wrong templates, or on correct templates with wrong alignments, are bound to be wrong. In the absence of closely related molecules in the database of experimentally determined structures, the detection of templates becomes challenging.
Typically, secondary structure information is used in the form of constraints in the process of fragment selection. Additional spatial restraints may also be used. Both in comparative modeling based on restraints and in fragment assembly, additional information may also be encoded: pairwise distance restraints are relatively straightforward to encode and are enabled by most methods, and some methods (e. g. , MMB) also allow 3D shape restraints in the form of density maps. There exist methods for manual (semi-automated) modeling with the use of fragment assembly such as RNA2D3D  [145]  and Assemble  [146, 147]. There are a few fully automated methods based on a computational assembly of RNA fragments, such as RNAComposer  [148]  and Rosetta/FARNA/FARFAR  [149, 150].
PyRy3D has been used to model RNA and RNA-protein complexes such as the complex between the 2 ,5 -oligoadenylate synthetase OAS1 and the 3 -terminal region of the WNV RNA genome  [171]. M3 is yet another computational method that incorporates sparse and hybrid experimental data to develop molecular models, which is applicable to RNA  [172]. The M3 protocol uses the HADDOCK framework  [173] , and accepts input in the form of interatomic distances and/or molecular shapes. Modeling with M3 can account for extensive structural rearrangements both at the domain and atomic levels. There also exist other programs and software packages that support integrative modeling, and their utility for RNA 3D structure modeling remains to be verified. Table  2  summarizes the main features of modeling approaches, with emphasis on data requirements and limitations of applicability.
The most obvious way to assess structural models is to compare them with data that were not used in the process of structure prediction and can be considered independent. In crystallography, for instance the model is assessed with the R-work and R-free values. The R-work measures the agreement between the model and the actual experimental data, by calculating a simulated diffraction pattern from the model. The R-work value can be, however, biased as the model can be refined to better agree with the experimental data. A less biased measure is the R-free, which measures how well the model can predict a test set of reflections (5-10% of the dataset) that were not used during refinement  [175]. For other types of experimental data, there exist specialized methods dedicated to scoring features of 3D models.
Many RNA 3D structure modeling methods, such as MC-Sym  [176] , NAST  [157] , RACER  [39, 177] , and SimRNA  [159]  can use SAXS raw data as a part of the model scoring procedure, to 'filter out' conformations that disagree with the experimental shape measurements. In this approach, the theoretical scattering curve of the generated models is calculated and then compared with the experimental SAXS data. MC-Sym generated models obtained with any method can also be scored/filtered with standalone methods, such as Fast-SAXS-RNA  [178]. RS3D is another program that uses SAXS data, together with the secondary structure and tertiary contact information, to score RNA structures obtained in a Monte-Carlo simulation  [179]. A general purpose method FILTREST3D can score structural models (including RNA 3D structures and RNP complexes) based on a combination of distance restraints with other factors such as local or global structure or molecule shape, and it implements logical operators to enable sets of alternative restraints  [180].
However, some methods for computational modeling of RNA 3D structure are capable of handling ambiguous restraints or could be modified to enable such functionality. Thus, even if the XL-MS experiment yields a highly unambiguous dataset in which every cross-link can be assigned to a whole list of pairs of oligonucleotide fragments from a given sequence, this could be used as an element of the scoring function, e. g. , to eliminate models that are largely incompatible with any of the XL-MS assignments. Last, but not the least, computational methods for RNA 3D structure prediction could be improved in the way how the footprinting data are used. Reactivity profiles, e. g. , from SHAPE and related experiments, are usually first used as an input for specialized secondary structure prediction methods, and only then the predicted secondary structures are used as constraints or restraints in the RNA 3D structure modeling process.
However, for some RNA modeling methods, it should be possible, and potentially advantageous, to use the probing data directly at the stage of model building. For example, probing data that indicate the likelihood of individual residues to be paired compared with unpaired could be used as an element of the scoring function without the necessity to specify the binding partners. This could be particularly advantageous for predicting complex RNA structures with pseudoknots that are notoriously difficult to infer for secondary structure prediction tools. On the other hand, modeling tools such as our own program SimRNA, which make and break base pairs and entire secondary structures as a part of the conformational sampling process, do not require any special treatment for pseudoknots, and could be guided to maximize the agreement of the 3D model directly with the probing pattern  [159].
As illustrated here, the interface between the physical devices and their digital twins is represented by future electronic LED datasheets. The first step towards achieving widespread use of such datasheets is the LED suppliers' and LED users' consensus on LED test data reporting, both in terms of the type and number of tests data included and in terms of machine readable file formats such as XML. Work regarding recommendations on LED test data reporting is in progress at the International Commission on Illumination (CIE)  [6]. Figure  5  provides the major steps of creating the multi-domain compact models of LED packages. The LED package (multi-domain) compact model is comprised of; (1) a boundary condition-independent compact thermal model of the physical LED package and (2) a multi-domain, so called Spice-like, model that describes the LED operation at the chip level  [7].
First a detailed thermal model of the LED package structure is set up and "calibrated" with the help of structure functions derived from the measured thermal impedance,  () of the package and then, in a subsequent step, thermal capacitance and thermal resistance values of a thermal RC network model of the package are optimized to best represent the thermal properties of the LED package under any boundary conditions at the package footprints and the LED's dome (lens)  [8]. Figure  5  provides the major steps of creating the multi-domain compact models of LED packages. The LED package (multi-domain) compact model is comprised of; (1) a boundary condition-independent compact thermal model of the physical LED package and (2) a multi-domain, so called Spice-like, model that describes the LED operation at the chip level  [7].
Measurements and Characterisation of LEDs, Test Data Reporting To describe the multi-domain feature of LED operation, parameters including light output, thermal, and electrical characteristics are measured in a consistent way. New testing protocols and modelling methodologies were developed to allow easy creation of compact models based on measurement data  [16] [17] [18] [19]. Measurements take into account the latest LED testing methods, such as JEDEC's thermal testing standards for power semiconductors  [9]  and power LEDs  [10, 11]  as well as CIE's LED optical From LED to luminaire (Figure  5 ), the Delphi4LED approach consists of the following key steps: 1. Standardized measurements and characterisation of LEDs combining the testing standards JEDEC JESD51-14, JESD 51-51, JESD 51-52, and CIE 225-2017  [9] [10] [11] [12] ; for a summary on LED testing see  [13].
Measurements and Characterisation of LEDs, Test Data Reporting To describe the multi-domain feature of LED operation, parameters including light output, thermal, and electrical characteristics are measured in a consistent way. New testing protocols and modelling methodologies were developed to allow easy creation of compact models based on measurement data  [16] [17] [18] [19]. Measurements take into account the latest LED testing methods, such as JEDEC's thermal testing standards for power semiconductors  [9]  and power LEDs  [10, 11]  as well as CIE's LED optical testing recommendations for high power LEDs  [12]. Applying these recommendations, so called isothermal current-voltage-flux characteristics (IVL characteristics) are measured in combination with the real thermal impedance, Z th (t) of the package.
To account for the thermal effect of the phosphor, According to the original DELPHI methodology standardized in the electronics cooling simulation industry  [23]  and also in our present approach, compact thermal models are identified through an optimization process  [8]  using calibrated detailed thermal models  [22]. These are later used in luminaire level analysis (following the hierarchical approach proposed in  [20]  and  [21] ) as the actual digital twins of the LED packages representing their true thermal behavior. A package DCTM connected to a chip level multi-domain LED model is the ultimate digital twin of a packaged LED, provided e. g. as a Spice netlist (see Figure  3 ). For the detailed description of the methodology of creating LED package dynamic compact thermal models refer to the paper of R.
Using the LED Digital Twin for Real Design Tasks The advantage of the compact models is that they no longer carry proprietary information of the LED vendors, thus, LED vendors can share them with end-users without sharing their sensitive IP. A package DCTM connected to a chip level multi-domain LED model is the ultimate digital twin of a packaged LED, provided e. g. as a Spice netlist or implemented in a simple Excel spreadsheet based "luminaire design calculation tool"  [5]. In our prior publication  [5]  we presented a simple application use-case, the design of a "10 W outdoor LED luminaire". In the following we briefly describe a design task where the LED digital twin embedded in a "calculation tool" was used to perform a more complex optimization task of a new light engine aimed as an LED based replacement of "70 W high-pressure sodium (HpS) lamps".
Using the LED Digital Twin for Real Design Tasks The advantage of the compact models is that they no longer carry proprietary information of the LED vendors, thus, LED vendors can share them with end-users without sharing their sensitive IP. A package DCTM connected to a chip level multi-domain LED model is the ultimate digital twin of a packaged LED, provided e. g. , as a Spice netlist or implemented in a simple Excel spreadsheet based "luminaire design calculation tool"  [5]. In our prior publication  [5]  we presented a simple application use-case, the design of a "10 W outdoor LED luminaire". In the following we briefly describe a design task where the LED digital twin embedded in a "calculation tool" was used to perform a more complex optimization task of a new light engine aimed as an LED based replacement of "70 W high-pressure sodium (HpS) lamps".
"Hot lumens" is a sloppy expression widely used in the English technical language of solid-state lighting, to refer to the emitted total luminous flux of an LED or LED based light source at the operating junction temperature of the device under application conditions. A fully parametrized model with arbitrary dimensions was created including a 3-layer (or less) PCB substrate with arbitrary dimensions and thermal conductivity, dummy LED footprints placed on top to provide the interface area for the compact models of the LED packages, through which LEDs' heat dissipation is conducted towards the ambient. The number of dummy LEDs can vary, potentially filling both x-and y-directions. The 3D model of the physical structure of the PCB substrate is shown in Figure  10a. Figure  10b  illustrates the compact thermal model of such a substrate that has been used in the "design calculator tool" implemented as an Excel based application already used with success for a simpler design  [5].
For each LED module (a PCB populated with the chosen type and number of LED packages, considered as an elementary light source in the final light engine), following the method presented in  [20]  and  [21]  first the so called thermal characterisation matrix is created which is transformed into a Spice compatible thermal resistance network, as follows: 1. Run individual finite element model (FEM)-calculations for each LED-position assuming a unit heat dissipation at the footprint position and fixed temperature at PCB bottom interface towards the ambient (see Figure  10 ). 2. Generate a matrix of response temperatures at each footprint position for all cases. The resulting matrix is the so called  * steady-state thermal characterisation matrix, that is specific to the given layout arrangement of the LED package footprints and represents the strength of the thermal coupling among the footprints and between each footprint and the ambient.
3. Using the algorithm described in  [19]  convert this  * thermal characterisation matrix to a matrix of real thermal resistances that can also be exported as a real, Spice netlist of resistances. Complete this thermal resistance model with the junction to footprint thermal resistance of the chosen LED package type. 4. Convert the thermal resistance matrix corresponding to the above thermal resistance network to a solvable heat transfer coefficient (HTC) matrix. This matrix will represent the LED chips' 3D thermal environment. The luminaire is represented in the overall system model as a single substrate-to-ambient thermal resistance, LED package thermal models are connected to the substrate model at the nodes corresponding to the LED footprint areas (coloured rectangles in Figure  6b ). For each LED module (a PCB populated with the chosen type and number of LED packages, considered as an elementary light source in the final light engine), following the method presented in  [20]  and  [21]  first the so called thermal characterisation matrix is created which is transformed into a Spice compatible thermal resistance network, as follows: 1.
This way their compact thermal models (thermal N-port models for the substrates, equivalent substrate-to-ambient thermal resistances for heat-sinks, see also Figure  10b ) are also created and included in model libraries. In the proof of concept implementation of the above workflow an Excel a spreadsheet application was created  [5]  into which both chip level multi-domain LED model parameter sets, LED package thermal resistances (as simplified LED package compact thermal models) as LED package model libraries and substrate/heatsink thermal models also as library items were included. The user interface of this application allows setting major design goals (total luminous flux to achieve in this demo, see the blue field in Figure  11 ) and design constraints (foreseen ambient temperature, maximum allowed junction, and solder point temperatures; maximum allowed total forward voltage -the orange fields in Figure  11 ).
From LED Physical Device to Multi-Domain Compact Model LED vendors or certified expert labs play a key role in the characterisation and measurements of LEDs aimed as source of input for LED multi-domain modelling. The general vision is that these test date will be reported through electronic LED datasheets. Such e-datasheets, as vendor neutral ways of data exchange can be processed by appropriate software tools by these entities in order to extract the LED MDCM and store the models in a standard, LED vendor and software tool independent data exchange format (e. g. , xml). Such LED models will be stored in a library of LED digital twins. Figure  12  shows the design process associating a set of libraries to a Luminaire Design Tool (LDT). If a certain LED multi-domain digital twin cannot be obtained from an LED vendor or from an independent, certified/accredited LED testing laboratory, the luminaire designers/manufacturers will have to extract the necessary models themselves, taking the LED e-datasheet as input.
Experiment (B) This experiment was performed to demonstrate how the Delphi4LED workflow is applicable to a real product development in a Major company. The chosen example was a high-intensity-discharge (HID) replacement lamp for street lighting application. The Delphi4LED virtual prototyping was used optimise the product by assessing different numbers and types of LEDs with which the total luminous flux target could be met. The purpose of this experiment was to demonstrate the ease of use when scaling the number of LEDs/applied forward current to meet the design specifications and to demonstrate the software tool independence of the proposed design flow. In this experiment the models were created using the commercial finite element simulation tool ANSYS Version 17. 2 (while in Experiment A the Simcenter Flotherm tool from Mentor was used).
The Ansys Parametric Design Language (ADPL) feature of ANSYS allowed the integration of both the LED package and the multi-domain chip level compact model to be solved in LTspice, in a similar relaxation type electro-thermal co-simulation scheme as depicted in Figure  13b. (Refer to  [7]  for details on the chip level multi-domain LED models aimed at different types of the implementation schemes of the electro-thermal co-simulation). This way the light output of the given LED configuration was obtained. The middle part of the light engine of the HpS lamp replacement solution is composed of six symmetrically assembled LED modules. Due to the symmetry, it is enough to study only one single LED module. Figure  15  shows one of these six identical segments of the light engine of the lamp for two scenarios denoted by (x) and (y).
Luminaire Level Results The first results of Experiment A were reported in  [5]  together with the description of a pilot implementation of the overall Delphi4LED workflow. In this demo implementation, a set of models has been created and combined into an Excel spreadsheet application, aimed as a simplified design/virtual prototyping tool for SMEs. Based on prior characterisation of LEDs foreseen for the project demonstrators consortium members specialised in LED testing and modelling created a Spice-like chip level multi-domain models  [5]  and extracted the model parameters (see Figure  16a ). The DCTM of the package was also created. In both cases variability among the different LED samples was also considered in a simplified way: Instead of creating Monte Carlo models from parameter distributions identified for a larger population of LEDs (as suggested in  [26] [27] [28] [29] ) the model parameter sets obtained for the actual measured LED samples (five different ones, see Figure  16a ) were randomly used.
In the subsequent paragraphs of this subsection we focus Experiment B and the version of the Delphi4LED workflow used by a Major company. Energies 2019,  12, 2389  Figure  19  illustrates the thermal characterisation and compact modelling of the LED module of demonstrator b, according to scenario (x) (five LEDs driven by high current). Figure  19a  is a temperature snapshot from the series of CFD simulations performed as part of the thermal characterisation of the module substrate (as outlined in Section 2. 4 and detailed in papers  [20]  and  [21] ). Figure  19b  presents the complete luminaire level Spice netlist that corresponds to the simplified thermal modelling approach shown in Figure  10b. The substrate model shown corresponds to the thermal boundary condition of fixing the bottom surface of the PCB substrate at 45 • C.
For this specific case study, the XPG3 LEDs deliver in both scenarios (x) and (y) the targeted luminous flux at feasible junction temperatures and providing the highest system efficacy of both scenarios. The final decision between LED types, number of LEDs per module (5 LEDs, 22 LEDs, or any number of LEDs in between) will depend on the trade-off between optical performance, thermal limits, and cost. The approach proposed allows the user, in a very short time frame, to develop very valuable insights in design choices. The case study highlighted the relation between the number of necessary LEDs and type of LED combination with system efficacy. Finally, comparing demonstrations carried out in Experiment A (using Flotherm) with Experiment B (using ANSYS), the approach proved to be fast and accurate, independently of the software tool used: In a nutshell, tool agnostic.
Impact on the Company Digital Footprint Key to the success of this new LED design workflow is the availability of sets of libraries interacting with each other. The LED package libraries eventually should be provided obviously by the LED vendors, while other libraries such as module substrate, and heat-sink. Thermal model libraries, as indicated in Figure  12 , are the new digital assets of a luminaire designer/manufacturer. This forms the future company digital footprint. Figure  20  shows the input to those libraries from LED suppliers (LED e-datasheets or directly the LED digital twins) and customers (luminaire requirements). In case of SMEs, those databases are input for a spreadsheet based luminaire calculator tool. In case of Major companies, the model libraries can be directly linked to commercially available simulation tools (e.
Conclusions In the Delphi4LED project, new methodologies have been developed for compact modelling of LED products. The multi-domain nature of LED operation is treated on the chip level, using multi-domain LED models that can be implemented both in generic Spice circuit simulators and also as Visual Basic macros in a spreadsheet based luminaire calculation tool application. With an approach similar to the standard DELPHI methodology  [25] , boundary condition independent compact models of LED packages are created. These, combined with the chip level multi-domain model and the compact thermal models of module substrates and luminaires, allow a system level multi-domain simulation of an LED application. The combined chip + package + substrate + luminaire model is the "digital twin" or "virtual prototype" of a foreseen luminaire.
What problems arise in evaluating scholarly work in mathematics education? Research evaluation depends largely on assessment of the quality of research outputs. In mathematics education, papers in peer-reviewed journals are typically the most highly regarded form of publication. Evaluation of such outputs can be either quantitative, relying on various forms of bibliometric analysis using citation data, or qualitative, relying on expert peer judgment. A major limitation of citation-based systems for evaluating journal quality is the limited coverage they give to mathematics education journals. Nivens and Otten  (2017) compiled a list of 69 journals that have an explicit focus on mathematics education research, but found that only six appeared in the Web of Science database from which journal impact factors are calculated.