dataesr/scientific-paragraphs-categorization huggingface dataset:
Fragmentation mechanisms We now analyze in details reaction mechanisms for the formation of the most relevant reaction products. We discuss the mechanisms observed in biotin together with the equivalent ones for the other systems, leading to isolobal analog fragments. In some biotin and oxybiotin simulations, the neutral further breaks in two molecules Concluding, this work shows that it is possible to use the isolobal analogy to guess with a certain confidence the presence many fragmentation products, but for this the detailed mechanisms should be known for at least one system. This result paves the way of using in the future chemical dynamics simulations on one species and use simple mechanistic considerations to propose fragments of isolobal analogs. In the future, it will be thus possible to predict mass spectrum of unknown molecules based on mass spectrum of the known ones with the help of computer simulations also with the aim of building a theoretical MS/MS database which can support experiments and complement existent databases.
In order to calculate the fluorescence yield, the measurement geometry was modeled with MCNP6 Monte-Carlo transport code  [4] , see Fig. 3. Efficiencies at 92 keV, 98 keV and 1001 keV are those calculated with ISOCS software  [6]  for a typical FALCON 5000 geometry. The uncertainty associated to calculated efficiencies is 10 % for 92 keV and 98 keV peaks and 5 % for 1001 keV peak. The following graph represents the  � (92 ) and  � (98 ) concentrations calculated with the net area of the 92 keV and 98 keV peaks, respectively, as a function of the concentration calculated with the 1001 keV peak, which is considered as the reference. The uncertainty calculated for each sample (considered as homogeneous) includes: -detector calibration and detection efficiency calculation by ISOCS  [6] , -the statistical uncertainty on the net area of the 1001 keV, 92 keV and 98 keV peaks, -the relative uncertainties on the intensities of the gamma or X-rays of interest are given in the nuclear databases  [5] : 0.
Description of the surveillance A reporting form was developed which included age of the patient, results of either RT-PCR or serology for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), main clinical features (including seritis, attributes of macrophage activation syndrome (MAS), myocarditis or KLD), type of wards (conventional paediatric unit or intermediate/intensive care unit (ICU)) and, for children admitted to ICU, type of care required (including vasopressor, mechanical ventilation and extracorporeal membrane oxygenation) and if relevant, occurrence of death. No follow-up of the child's condition was planned in this initial data collection. Whenever either the PCR or the serology was noted as pending, clinicians were subsequently asked by email to update the questionnaire a few days after initial notification.
Conclusion French surveillance data confirm the signal of the emergence of an inflammatory multisystem syndrome associated with SARS-CoV-2 infection in children. In the short term, the risk of new cases of COVID-PIMS is likely to be very low in France, given the low circulation of the virus in France in the past few weeks. More data on this new syndrome will be collected through a research protocol that is currently being implemented. Countries with current high incidence of COVID-19 in the general population should consider this rare but severe delayed syndrome in children.
Acknowledgements We thank the following  Santé publique France  staff who support us in the setting up and management of the surveillance:  Mireille Allemand ,  Scarlett Georges ,  Valerie Olie ,  Nolween Regnault  and  Jerome Naud. We also thank all French pediatricians that have contributed to the national surveillance ( Drs Afanetti ,  Bajolle, Baker, Barrey, Bassil, Baur, Beaucourt, Beaujour, Beyer, Blanc, Brouard, Buisson, Caujolle, Charron, Chomton, Chosidow, Claude, Cour-Andlauer, Debray, Deho, De Guillebon, Delion, Bost-Bru, Chenichene, Dumortier, El Nabhani, Fedorczuk, Escoda Gaga, Garrec, Gaschignard, Guerder, Guiddir, Guillot, Hau, Haupt, Helms,Hoeusler, Houmam, Huet, Idier, Ingrao, Jeusset, Kahn, Molimard, Lachaume, Lajus, Languepin, Le Roux Pascal, Lety, Levy, Loeile, Loron, Mahi, Mazeghrane, Mertes, Mezgueldi, Mokraoui, Morand, Morelle, Mortamet, Mutar, Naji, Nattes, Nblot, Pinhas, Pons, Pouyau, Ovignaud, Rey, Richard, Roche, Romain, Romefort, Selegny, Semama, Terzic, Vanel, Villedieu, Vinit, Wiedemann, Zoccarato ).
Dataset used for SWAT implementation Relief and land use maps were produced from remote sensing images and field surveys (see Table  1  for description and Fig. 1  for illustration). A detailed soil description of the study area made for agricultural purposes was composed of a soil map with a complete description of the representative profile for each soil type, including the maximum rooting depth of the soil profile and standard soil parameters for each soil layer (e. g. , texture, total and organic carbon content, soil bulk density, and available soil water content). Additional soil descriptions and analyses were performed between 2010 and 2017 to cross validate the existing source of data and quantify hydrological soil properties such as water infiltration rates. Considering climatic data (see Table  1  for description and Fig.
3  for location), time series were available at eight stations for daily rainfall and at four gauge stations for daily temperatures and relative humidity. Existing gaps in the daily rainfall and temperature time series for a given station were filled using multiple linear correlations between stations to obtain continuous series from 1980 to 2010. The daily temperatures (minimum and maximum) and precipitation amounts at the different gauge locations were finally interpolated using an inverse distance weighting method (IDW), which allows the production of synthetic daily time series on a regular grid with a resolution of 1 km. Datasets from all the climatic stations were considered in the interpolation process because they can be captured by the IDW interpolation function and improve interpolation at the edge of the watershed.
Because relative humidity, solar radiation and wind were only available at the Tangier station, these three climatic datasets were assumed to be uniform over the whole watershed. Daily streamflow was derived from the hydrological budget of the reservoir, as explained in  Raclot and Albergel (2006). This assessment is based on the knowledge of the height-surface and height-volume curves established by bathymetric surveys. A differentiation between direct and delayed runoff was determined using the graphic method on some individual events and automatic filtering methods (local minimum method, one parameter digital filter and recursive digital filter included in the Web-based Hydrograph Analysis Tool available at https://engineering. purdue. edu/mapserve/WHAT/) on the whole daily runoff dataset.
Sediment volumes entering the reservoir between two bathymetries were estimated by differences between the bathymetric surveys and a correction using a trapping efficiency coefficient of 93% evaluated from Heinemann's formula  (Heinemann, 1981). The volume of sediment arriving at the dam between two measures was then deconvoluted at a daily time step, assuming that the daily solid flows (Sed) are proportional to the maximum flow (Qmax) and the daily total runoff volume (Vtot) according to Equation (1) proposed by  Williams (1975) : Sed $ ðVtot Â QmaxÞ 0:56 Â K Â LS Â C Â P (1) The main datasets used as input for SWAT implementation are summarized in Table  1.
Implementation of the SWAT model under current conditions The SWAT model was implemented over a 31-year period (1980e2010) using the Hargreaves method for PET estimation and the dataset listed in Table  1 , knowing that temperature and rainfall time series were interpolated on the 1 km resolution grid before being used as input data. Thus, each subbasin in SWAT was assigned with the closest bias-corrected climatic time series from the regular grid. For each land use type, the most similar land use type from the SWAT land use database was selected, and only minor adjustments for some plant growth/management parameters were made. In a very traditional split-sample approach (Kleme s, 1986;  Refsgaard, Henriksen, Harrar, Scholten, & Kassahun, 2005) , this period was divided into an initialization or warm-up phase (1980e1982), a calibration phase (1983e1996) and a model validation phase (1997e2010).
Climate change scenarios Two climate scenarios based on weather forecast projections from France's ALADIN-Climat (Aire Limit ee Adaptation dynamique D eveloppement InterNational) regional model have been tested. They correspond to projections based on RCPs (Representative Concentration Pathways) 8. 5 and 4. 5, i. e. , to radiative forcings of 8. 5 and 4. 5 W m À2 by 2100. The time series provided by ALADIN-Climat are at daily time steps and refer to minimum and maximum temperatures (T), relative humidity (Hr), rainfall (R), global radiation (Rg) and wind speed (W) 2 m above the ground. Considering the location of the watershed, the different climatic stations used and the native resolution of 12 km of ALADIN-Climat, the data provided by ALADIN-Climat on 7 grid cells were selected (Fig. 3 ).
Acknowledgements This work benefits from the financial support of JEAI "Vecteurs" funded by  IRD institution ,  ALMIRA  ( ANR-12-TMED-0003  funded by  ANR)  and  MASCC  (through  ARIMNET2 , an  ERA-NET  funded by the  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement no. 618127 ) projects. J. P. Nunes was further supported by a research grant from the  Fundação para a Ciência e a Tecnologia  ( IF/00586/ 2015 ).
This information offers DMs an indication of where they might need further discussions in the presence of several non-dominated location alternatives. We support this contribution by applying our proposal to the UN WFP's real dataset for the 2015 Nepal earthquake response. Third, we add to the existing body of research about the value of Monte Carlo Simulations to address the uncertainties and ambiguities  [21]  regarding the preferences of multiple DMs in disasters response. To validate our proposal and support the second contribution, we conduct a group decision-making experiment based on the described case with participants from multiple HOs. The remainder of the paper is structured as follows. We provide background regarding the criteria for location problems and methods to address DMs' preferences when such criteria are modelled in Section 2.
Moreover, it is interesting that only two of the reviewed papers  [16, 59]  have benchmarked their outcomes with a real operation although several used a case study. Other reviewed research papers confine their research to numerical analyses based on randomly generated datasets. However, benchmarking results with real operations can provide helpful insights for practitioners, and eventually contributes to convincing HOs, as the final users of proposals, to use decision support systems for a more efficient and effective response  [7].
Minimise total uncovered demand ¼ Total demands À Total shipped items We included those parameters in the model that related information could be easily reached in disasters response. In fact, disasters are not any more totally data lacking contexts: HOs normally have access to maps, distances and logistics prices through information sharing platforms such as Relief Web, 2  Logistics Cluster,  3  and Humanitarian Data Exchange 4  in rather early days of disasters response. Our interviewees also informed us that in situations where data is not available or it is incomplete, practitioners use estimates.
Case study To test and validate the proposed approach, we apply it to the 2015 Nepal earthquake data from the UN WFP. The UN WFP is the leading agency of the Logistics Cluster and as such often coordinates the logistics activities in the humanitarian response. The stakeholders for UN WFP's decision can be representatives from other HOs who normally participate in the Logistics Cluster decision-making meetings and may have different aims, mandates and priorities  [18]. Reports published after multiple recent disasters since the Haiti Earthquake in 2010 confirm that majority of responders coordinate under the UN Cluster System. These organisations include UN agencies, IFRC societies, local or international NGOs, and volunteers. For instance, in the 2015 Nepal earthquake response, "as of 20 June [2015], over 250 participants from around 110 organisations attended coordination meetings in Kathmandu, Deurali and Chautara"  [86].
Model results Fig. 7  compares the performance of models' results with the UN WFP's network for the Nepal relief phase. We fix the UN WFP's SAs in the model constraints and solve the model by using Mavrotas & Florios  [90] 's algorithm, which is called augmented epsilon constraint method version 2 (known as AUGMECON2). The algorithm has been demonstrated to be very efficient for providing the set of Pareto optimal solutions in multi-objective mixed-integer problems compared to the alternative methods in the literature. Then, we order alternatives based on the demand coverage, response time and logistics costs. The empty cells in Fig. 6  mean that the solution algorithm could not find any non-dominated alternative for the specific response time. For the Nepal case, the algorithm could solve the model in 115 min using a personal laptop with Intel Core i5-4300U processor at 1.
Section 4), we first normalised the values of objective functions for each solution. Then, we used MATLAB software to draw random weight vectors. The spread of results for multiplying 1000 samples of random weight vectors to normalised objective function values of every solution is shown in Fig. 8. As this figure shows, the solution with the highest score suggests covering %88 of demands in 52 days. On the other hand, solutions with longer response times (for instance  100-60, 98-59, 96-59, 92-60, 94-59)  got lower overall scores. This trend can be explained by the assigned weight interval to each criterion, although it is not the only reason. To better explain another reason, we prepared Fig. 9  which shows the normalised values for the top-5 and the worst-5 solutions based on the overall scores.
Conclusions This study has combined multi-objective optimisation with a posteriori preference articulation approach to support decision-makers (DMs) in a sudden-onset disaster response. Our research has targeted locating temporary relief distribution centres (RDCs), which has been referred to as one of the first critical decisions for an effective and efficient response  [9]. A location-allocation model has been adapted to minimise uncovered demand, response time, and logistics costs, and this research offers an approach based on Monte Carlo Simulation to facilitate group decision-making by investigating the stability of the non-dominated alternatives. Our approach supports finding the tipping points which can assist DMs to focus group discussions and converge to a consensus quicker. The proposed methodology has been applied to a real dataset from UN WFP's operations after the 2015 Nepal earthquake and then validated by representatives from humanitarian organisations (HOs) through a small experiment.
Self-organizing map (SOM) and quantization error (QE) The Self-Organizing Map (a prototype is graphically represented in Fig. 3 , for illustration) may be described formally as a nonlinear, ordered, smooth mapping of high-dimensional input data onto the elements of a regular, low-dimensional array  [16]. It is assumed that the set of input variables is definable as a real vector x, of n-dimension. A parametric real vector m i of n-dimension is associated with each element in the SOM. Vector m i is a model and the SOM is therefore an array of models. Assuming a general distance measure between x and m i denoted by d(x,m i ), the map of an input vector x on the SOM array is defined as the array element m c that matches best (smallest d(x,m i )) with x. During the learning process, the input vector x is compared with all the m i in order to identify m c.
The difference between x and m c , ||xm c ||, is a measure of how close the final SOM value is to the original input value and is reflected by the quantization error, QE. The average QE of all x (X) in an image is given by: QE = 1 / N ∑ N i=1 ‖X i -m ci ‖ ( 2 ) where N is the number of input vectors x in the image. The final weights of the SOM are defined by a three dimensional output vector space representing each R, G, and B channel. The magnitude as well as the direction of change in any of these from one image to another is reliably reflected by changes in the QE. The code used for implementing the SOM-QE is available online at: https://www. researchgate. net/publication/330500541_Self-organi zing_map-based_quantization_error_from_images. The SOM training process consisted of 1 000 iterations.
IV. CASE STUDY In order to validate the proposed test strategy, this section provides an application example in a practical case study. For this case study we modeled in MATLAB a 10-bit (6×4) SCDAC SAR ADC designed in STMicroelectronics 65nm technology, that will be used as the DUT. A first-order IADC was embedded for MCT-based reduced-code test as described above. Taking into consideration the analysis in  [7] , we choose a resolution of 8-bits for the IADC (i. e. 256 cycles/sample) as a trade-off between test accuracy and design complexity. The reference voltages for the IADC are reused from the SAR ADC. In order to provide a realistic validation of the technique, we generate a set of SC-SAR ADC samples employing the mismatch models provided in the Process Design Kit of the technology. We selected one of the worst-case generated samples (3σ variation in terms of static linearity) and characterized it using behavioral-level simulation.
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For households not connected to a sewer, we based our calculations on the fact that 90% of N and 65% of P is excreted via urine and the remaining via faeces  [7]. N-DEC and P-DEC were calculated as follows: • The N excretion of people was taken from N diet data  [19]  to which a 20% decrease was applied to take into account non-ingested food. The multiplication by the total population gave the total amount of N contained in excretions. N-DEC was calculated on the basis of total quantity and N content of collected night soil and final products obtained from cesspool management for the 1780s  [20]  and the years 1817,  1852, 1869, 1911 and 1926 [6, 9, 21, 22]. Data were linearly extrapolated for the missing decades. N-DEC was estimated at 0% for the period since the 1980s. • The P content of products obtained from cesspool management was not available in previous studies.
Methods We conducted a prospective, descriptive, single-center study of MS in the geriatric center of a university hospital. This study was outside the field of Jardé's law and Ethics Committee approval was considered unnecessary under French law. Oral consent was obtained from all participants, and the data were processed anonymously. We included all MS who completed a CIHT in our geriatric facility in the 2nd cycle of their studies between 1 May 2018 and 31 October 2018. Our facility includes an ambulatory geriatric department, acute geriatric units, geriatric rehabilitation units, and a nursing home. There was no exclusion criterion. The participants were divided into two groups: those who were considering a CIG before CIHT, group DSSG+ (GDSSG+), and those who were not considering it, group DSSG-(GDSSG-).
The survey had no impact on the outcome of CIHT. Students were assured that their responses would not affect the CIHT validation. The questionnaires were analyzed at the end of inclusions. We described the data collected on the criteria influencing students' choice of a future specialty before CIHT, the strengths and weaknesses of geriatrics before and after CIHT, and the change in student views of geriatrics after CIHT in the total study population and the two groups. Finally, we assessed whether CIHT had an effect on whether MS were considering a CIG. Chi-square and Fisher tests were used to analyze categorical variables. Student's t-test was used for quantitative variables. Statistical significance was set at p < 0. 05. Seventy-four participants (43 women) aged 22 ± 1. 69 years were included.
Why are some African nations moving ahead in innovation? We develop seven hypotheses for what determines national-level innovation in Africa using seven well established lines of argumentation grouped into two distinct policy areas: international and domestic. The international policy areas are trade arrangements (benefits from economic integration), export specialization (resource curse problem) and inward migration (social capital spillovers). The domestic policy areas are education (investment in human capital), control of corruption (institutional quality), financial inclusion ('reaching the unbanked') and internet usage (closing the digital divide). Testing these hypotheses on a unique dataset collated from diverse secondary sources and representing up to 39 African countries, we find that trade arrangements and financial inclusion are the most consistent predictors while the remaining arguments find inconsistent or no support.
Domestic policy robustness tests To represent education, besides adult literacy we used public spending on education, as well as gross enrollment at secondary, tertiary and upper secondary levels  (Fagerberg and Srholec, 2008)   (World Bank Education database, averaged for 2006 -2015). None of these were significant (e. g. , for public spending on education: β=0. 19, p=0. 35, n=33 for IP applications by residents and β=0. 03, p=0. 32, n=38 for the GCR 12th pillar). As an alternative to institutional quality indicators to replace control of corruption, we used political stability and absence of violence from the World Bank's World Governance Indicators database (this correlated with control of corruption in our sample; r=0. 72, p<0. 001, n=39). In regression models, this alternative indicator was also insignificant (e.
g. , β=-0. 45, p=0. 22, n=34 for IP applications by residents and β=0. 07, p=0. 38, n=39 for the GCR 12th pillar). For an alternate measure of financial inclusion we used the Global Findex variable "account" which reflects the percentage of people over 15 years old who own or co-own any kind of a financial account, formal or informal and including mobile money  (Lashitew et al. , 2019)  (sourced from the Global Findex database). There were mixed results with this, although the signs were positive (e. g. , β=2. 43, p=0. 30, n=32 for IP applications by residents and β=2. 52, p<0. 01, n=37 for the GCR 12th pillar). As a robustness test for internet diffusion we used mobile money account (sourced from Findex), also noting mixed results (β=1. 47, p=0. 44, n=30 for IP applications by residents but β=1.