dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ± 0. 9%, when dust content is low (10 ppm of dust) and by 10. 9 ± 2. 6%, when dust content is high (100 ppm of dust) (Fig. 3b ). In the combined presence of BC and dust, annual melting can increase from 5. 0 ± 1. 0% to 11. 7 ± 2. 3% (Fig. 3b ). For comparison, this value is comparable to that for glaciers in Kyrgyzstan (central Asia), which have been documented to have an increase in snow melt rates of 6. 3% due to the BC-albedo effect  39. comparing biomass burning "forcing" with water discharge at Zongo glacier. A database of in situ interannual water discharge was used for direct comparison with the estimated mass balance. Water discharge behaved similarly to precipitation, displaying a major increase between October and February in 2009/2010 and following radiation flux seasonal variability for the tropics (Fig.
Methods Determination of BC emission source regions and the calculation of trajectory density. We used the fire database from the Brazilian Institute for Space Research (INPE)  26  (which uses the collection 6 MODIS active fire detection algorithm  47  ) to locate regions with the highest density of fire events spanning the period from 2000 to 2016. To determine fire event densities, we applied two spatial analyses: the kernel density analysis  48  and the spatial statistic GI* 49 using ArcGIS 10. 2 software. The results of the two analyses supported one another, that is, regions with the highest density of fire events defined by the kernel analysis coincided with regions delimited by the GI* analysis as "hot spots". The highest density of fire events occurred in Brazil, in the region known as the deforestation arc, and in the southwestern Amazon basin, which comprises the Bolivian Amazon.
The aerosol types, including "polluted continental", "biomass burning", "desert dust", and "polluted dust", are derived from a comprehensive cluster analysis applied to AERONET data gathered from numerous sites around the globe. "Clean continental" and "marine" sources are synthesized using measurements of long-range continental transport and sea salt acquired using a backscatter nephalometer  52. Products from the CALIOP sensor were obtained from the NASA website: https:// www-calipso. larc. nasa. gov/search/index. php. The dates analyzed were 24 August 2010 and 11 September 2010. These dates were chosen by taking into account the availability of data during extreme fire events and the presence of smoke plumes near the Andes Mountain range. Modeling the emission, transport, deposition, and impact of BC on the Zongo Glacier.
Our work presents a methodological framework, that is, a set of steps and methods, for modeling the impact of BC aerosol on the Glacier. The methodological framework is presented in the Supplementary Methods (Supplementary Methods, Fig. 2 ). A brief explanation of the framework is as follows: (1) The first stage of modeling includes determining the emission regions to be inserted into the HYSPLIT model. For this step, the locations of fire events were determined, and the spatial density statistics were performed. (2) For each delimited emission source, total BC emissions were calculated using BC emission data produced by the Global Fire Emission Database -GFEDv4 53 (available at http://www. globalfiredata. org/). We constructed a time series of BC emissions for each region from 2000 to 2011.
This step was important in determining the most critical years of BC emissions and the emission rates to be used within the HYSPLIT model. Since the data are available monthly and since the HYSPLIT model takes into account the emission rate per hour, we assumed that the BC emissions were constant each month. In other words, we assumed that the total monthly emissions were equal to the number of hours in each month. The years 2007 and 2010 displayed higher BC emissions and numbers of fire events. Therefore, they were chosen for the modeling procedure. (3) Source emission regions and emission data were used as input data for the HYSPLIT model. We also used NCEP/NCAR reanalysis data  54  as input meteorological data. The meteorological data employed were as follows: temperature, u-velocity, v-velocity, pressure vertical velocity, geopotential height, relative humidity (upper air data), 2 m temperature, 10 m u-and v-velocity components and precipitation (surface data), and the surface geopotential field (terrain height).
Modeling water discharge using meteorological and aerosol index data. To assess the relationship between water discharge on the Zongo Glacier and meteorological parameters/the Amazonian aerosol index, we applied a backward stepwise multiple regression analysis using SigmaPlot ® software. The parameters used to predict water discharge were air temperature, humidity, precipitation, incident shortwave radiation, net longwave radiation (all meteorological parameters were measured by the automatic weather station located on the Zongo glacier), and the Amazonian aerosol index (with a 3-day lag) (calculated for the Amazon Basin from MODIS satellite observations). The period analyzed ranged from 5 August 2010 to 18 September 2010.
Acknowledgements We acknowledge the support provided by the  French Institute of Research for Development (IRD)  and the Bolivian partners (UMSA IGEMA and IHH) for the glacier dataset provided through GLACIOCLIM and the  GREAT ICE program. We also thank the  Brazilian National Institute for Space Research (INPE)  for the fire dataset provided through the  fire monitoring program  (http://www. inpe. br/queimadas/bdqueimadas/)and the  NASA  Worldview application (https://worldview. earthdata. nasa. gov/)operated by the  NASA/Goddard Space Flight Center Earth Science Data and Information System (ESDIS)  project for providing the MODIS images. The authors gratefully acknowledge the  NOAA Air Resource Laboratory (ARK)  for the provision of the HYSPLIT transport and dispersion model used in this publication.
MATERIALS AND METHODS Cells and viruses. Mammalian Vero, 293T, and BHK-21 cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM; Thermo Fisher) supplemented with 10% fetal bovine serum (FBS; Thermo Fisher) and 1% penicillin-streptomycin (P-S; Thermo Fisher). Cells were maintained at 37°C in 5% CO 2. Mosquito cell lines C6/36, U4. 4, and Aag2 were grown in Leibovitz medium (L-15; Thermo Fisher), supplemented with 10% FBS, 1% P-S, 2% tryptose phosphate broth, and 1% nonessential amino acids (NEAA) at 28°C. Viruses were generated from a CHIKV infectious clone (CHIKV La Réunion strain, 06-049; GenBank accession number AM258994)  (48). An AvrII site (CCTAGG) was engineered into the 06-049 clone in position 7612 to facilitate insertion/cloning back of the de novo synthetic constructs Stop and SuperStop (Eurogentec) by ligation with T4 DNA ligase.
Virus titers were determined by plaque assay. Plaque size measurement. Subconfluent monolayers of Vero cells (10 7 ) in 10-cm petri dishes were infected as described previously with 100 PFU of viral supernatants obtained from the endpoint of the antiviral treatment experiment. After incubation for 1 h at 37°C, virus was removed, and cells were washed twice with PBS and covered with an overlay of DMEM containing 3% FBS and 0. 8% agarose. At 66 h after infection, 4% formalin was added to fix the cells, agarose plugs were removed, and cells were stained with crystal violet. Plaque size was quantified using ImageJ software (http://rsbweb. nih. gov/ij). Mosquito infections. Aedes albopictus Providence (ALPROV) mosquitoes (F8 generation) from La Reunion were used for standard mosquito infections.
Quantification of NLuc was performed using Living Image software (IVIS Imaging software) by defining specific areas of interest for infected and negative-control mice. NLuc measures were normalized by the value of the negative control for each group and time point. Protection studies. Five-week-old C57BL/6 mice were injected in the footpad as described previously with 10 4 PFU in 50 l of wild-type or mutant viruses or PBS (n ϭ 5 per condition). Mice were monitored daily. Four weeks after infection, mice were anesthetized as described above and bled for up to 600 l from a facial vein into a BD Microtainer tube. Immediately, mice were euthanized by cervical dislocation. Serum containing neutralizing antibodies was separated from whole blood by centrifugation for 5 min at 5,000 rpm and 4°C. Supernatants were inactivated for virus and complement system for 1 h at 56°C.
The reaction was performed at 98°C for 30 s, followed by 35 cycles at 98°C for 10 s, 60°C of annealing for 10 s, and extension at 72°C for 90 s. PCR products were purified with a Macherey-Nagel PCR purification kit and quantified with Quant-it Picogreen (ThermoFisher) solution in a Tecan machine. DNA was then fragmented (Covaris), ligated to adapters (NEBNext Ultra DNA; New England Biolabs), sequenced in an Illumina NextSeq 500 system, and analyzed with in-house scripts. Determination of stop codon frequencies. Sequenced reads for each sample were trimmed using Trimmomatic  (51)  in order to remove low-quality bases and reads shorter than 36 nucleotides and aligned to their respective reference genomes using Burrows-Wheeler Aligner (BWA)  (52). Codon frequencies were estimated using quality scores in the aligned data as described in Moratorio et al.
(18). In brief, the observed codon frequencies were modelled by a multinomial model with noisy observations. Maximum likelihood estimation was used to infer the per-site codon frequencies for each sample based on this model. The computed frequencies were estimated using all the reads overlapping that position, ignoring bases with a Phred score of Յ30. Finally, per-sample stop codon frequencies were computed by summing the per-site stop codon frequencies over all modified codon sites. Bar plots were used to visualize and compare the frequency estimates for wild-type, Stop, and SuperStop samples. Statistical analyses. Statistics were performed in GraphPad Prism software. P values of Ͼ0. 05 were considered nonsignificant (ns). No samples or infected animals were excluded from analysis. For animal studies, mice were randomly allocated to different cages prior to experiments.
ACKNOWLEDGMENTS Lucía Carrau and Daniela Megrian were supported by the Pasteur-Paris University (PPU) International Ph. D. Program. This work was funded by the DARPA PREEMPT program managed by Jim Gimlett, Brad Ringeisen, and Amy Jenkins and administered though DARPA Cooperative Agreement HR001118S0017-PREEMPT-FP001. The content of the information does not necessarily reflect the position or the policy of the U. S. Government, and no official endorsement should be inferred. We acknowledge Ana-Bella Failloux for Aedes albopictus mosquitoes, J. Boussier for advice in statistical analyses, Rasmus Henningsson for advice in designing constructs, and Oxana Vratskikh for experimental support. A patent on this vaccine approach has been awarded to M. V. and G. M. (53).
As CAMEL targets 1 Kubernetes -https://kubernetes. io/ 2 Docker Swarm -https://docs. docker. com/engine/swarm/ 3 Pipeline -https://github. com/banzaicloud/pipeline Fig. 1  Models@run-time architecture DevOps, a user study was conducted in this work, in terms of adaptive provisioning of applications in the Cloud for various domains (e. g. , data farming, flight scheduling). It shows the unique CAMEL benefits, i. e. , a good level of usability, comprehensiveness and suitability. Fourth, to address heterogeneity and interoperability, CAMEL has been also aligned with TOSCA. As expressed in  [8] : "Having the TOSCA standard, it is desirable to align existing and potential new CMLs for providing continuous modeling support, for example, by achieving interoperability among the languages".
Scalarm overview Scalarm is a complete platform for conducting data farming experiments across heterogeneous computing infrastructures. It has been developed by the Akademia Grniczo-Hutnicza (AGH) University of Science and Technology. Data farming represents a methodology via which a simulation model is repeatedly executed according to an extensive parameter space such that sufficient data can be collected with the goal to provide an insight over the correlation between the model properties and behaviour, as well as the simulation's input parameters. Thus, Scalarm supplies to the user a set of well-known experiment design methods to generate the experiment parameter space. Via Scalarm, each data farming experiment can be monitored, while the initial parameter space can be extended at runtime.
By using the PaaSage platform and CAMEL, Scalarm became a fully autonomous data farming platform. This was achieved by using suitable scalability rules that enabled the automatic scaling of Scalarm components when certain conditions are met. These rules are derived by the Reasoner component in the PaaSage platform by considering the user's non-functional requirements. Furthermore, Scalarm initial deployment is handled by PaaSage itself so that there is no need to involve a system administrator or a user to perform scaling/deployment actions, as the PaaSage platform automatically handles all Scalarm services. Moreover, via PaaSage and CAMEL, Scalarm managed to be executed in multi-cloud environments. Multi-Cloud deployments free Scalarm from vendor lock-in and allows for fine-grained optimization of computation cost by selecting the cheapest possible cloud providers for executing large scale data experiments.
The master-worker architecture of Scalarm makes it mostly insusceptible to network latency problems (which may result from highly geographically distributed deployments), and data farming does usually only requires to distribute the simulation binary -the input and output data remain reasonably small to avoid high of data transfers. Finally, by exploiting the Scalarm CAMEL model, which is publicly available, and modifying it according to specific deployments, PaaSage users can conduct data farming experiments without any prior investment in software infrastructure or the development of the right coordination software.
The scalarm cAMEL model The key requirements for the Scalarm use case are the ability to define and modify the deployment model, as well as to specify both appropriate requirements and rules for autonomously conducting different data farming experiments. For these reasons and the need to showcase the Scalarm model definition in a clear and neat way, we present the deployment, requirement, metric and scalability models. All other models are accessible through the PaaSage repository 16. 16. As dictated by its architecture (see Fig. 8 ), Scalarm comprises four internal components, from which two are presented here along with their respective deployment requirements. The ExperimentManager has one provided communication port (443) and two required communication ports  (20001 & 11300). It also requires to be hosted on a core intensive VM (i.
e. , Data Farming, Automotive Simulation, Flight scheduling, ERP, Financial Services and Human Milk Bank, and completed a questionnaire for evaluating the above TAM factors. The studied use cases are summarised in Table  3. For more information on the use cases the interested reader may refer to the PaaSage website  17. In specific, the following steps were used for the evaluation: -The participants were familiarised with different CAMEL versions, reported bugs, requested features, and supplied feedback to developers. -The participants modelled their use cases scenarios with the final version of CAMEL language and editor. -The participants assessed CAMEL features via an online questionnaire 18. Based on the above, the final questionnaire was divided into different section, covering : usability of the CAMEL Textual Editor, CAMEL documentation, CAMEL Requirements, CAMEL Metric Model, CAMEL Deployment Model, CAMEL Scalability Rules and CAMEL Organisation Model, whereas demographic data and prior user knowledge were also collected.
Moreover, the participants high scores for model quality indicates that they are satisfied also with the features provided by the CAMEL editor, e. g. , code completion, syntax highlighting, error reporting. Because of the importance of CAMEL model completeness, which is one of the main contributions of this work, the analysis was performed also at the level of individual models as presented in Fig. 9c. In fact, from the analysis of the results it is evident that the participants rate higher the deployment, scalability and requirements models, while the metric model is evaluated lower. This can be attributed to the fact that in most use cases simple metric models were defined using single metrics, such as CPU utilisation. Nevertheless, more complex models and composite metrics can be defined, but the platform limitation is that it only supports CPU and RAM sensors.
Threats to validity In terms of external validity -i. e. , the extent to which the conclusions can be generalised, the selected use cases cover a wide spectrum of identified aspects of selfadaptive cross-cloud applications. However, extending the evaluation of CAMEL to other scenarios, environments, or even demographics may alter the findings. Internal validity, i. e. , the extent to which the conclusions based on a study are warranted is not affected, since the data are unambiguous. In terms of construct validity, i. e. the degree to which a test measures what it claims, is not affected, since all questions were carefully prepared to cover all capabilities of CAMEL and its textual editor. Finally, the small sample size (N = 23) and the fact that the participants were part of the PaaSage project are perhaps the greatest threat to the validity of the results.
On the other hand, CAMEL undergoes constant evolution and some extensions have been already performed on it, like the aforementioned PaaS/SaaS features, while others are currently in development or planned. As such, CAMEL will be further optimised (e. g. , Melodic EU H2020 Big-Data Cloud project), by also attempting to adopt some interesting modelling features from these languages. As the languages are presented in a chronological order in the comparison table, some interesting time-based patterns can be inferred from this table :.
g. , scaling and service replacement ones). Melodic aims to support big data application management. CAMEL is at the core of Melodic, which attempts to build upon the PaaSage platform to provide support to this application kind. As such, CAMEL is planned to be enhanced to cover the big data aspect. As Melodic is a formal PaaSage successor, also the improvements over the user survey suggestions will be included in the forthcoming CAMEL release. That release is also planned to be enhanced with CAMEL extensions from other projects, like CloudSocket. This will then map in producing an even more complete and extensive DSL, also broadening its application scope. Hence, the CAMEL community will continue its effort in optimising CAMEL and further extending it, possibly via participating in forthcoming projects that guarantee the respective funding needed.
Scalarm Model -https://gitlab. ow2. org/paasage/camel/blob/master/examples/The main concepts in the deployment DSL are now exemplified via the Scalarm use case. As such, part of the deployment model is defined in Listing 1 to reduce the model length and complexity. The ". " denotes additional CAMEL elements omitted from readability. 40 hosting The scalarm deployment model. ExperimentManagerToCoreIntensiveUbuntu Germany { 41 from ExperimentManager. CoreIntensiveUbuntuGermanyHostReq to 42 CoreIntensiveUbuntuGermany. CoreIntensiveUbuntuGermanyHost 43 } 44 hosting SimulationManagerToCPUIntensiveUbuntu Listing 1 Scalarm Deployment model (excerpt) 45 46 47 Germany { from SimulationManager. CPUIntensiveUbuntuGermanyHostReq to CPUIntensiveUbuntuGermany. CPUIntensiveUbuntuGermanyHost } 1 deployment model ScalarmDeployment { 2 requirement set CoreIntensiveUbuntuGermanyRS { 3 os: ScalarmRequirement.
After that, Gurobi Solver, Simulated Annealing, Tabu Search, and Variable Neighborhood Search are adapted to solve the model respectively. Finally, a series of experiments have been performed to validate the proposed models and algorithms. Experimental results from Monte Carlo simulation highlight the strength of considering uncertainties when modeling the problem. Additional, the influences of other characters in instances, like the width of time-window, distributed location have also been empirically analyzed. Finally, the comparison performed between the solutions obtained by the stochastic model and the RO model also demonstrates the advantage of the RO model. This work provides a valuable framework for HHC companies to make a robust schedule when arranging the caregivers.
However, both methods have two distinct drawbacks. (1) These methods assume that the distribution of known parameters, however, in real-life, the history data is inferior to estimate the parameter. (2) Neither of these modeling methods considers the robustness of the solution as a goal. So, these do not guarantee the anti-interference of the final solution. To sum up, our review of the abundant literature reveals the recent research related to HHCRSP. A majority of work involved HHCRSP only considers the deterministic model without considering the commonly encountered uncertainties. Consequently, the decision made from these solutions show less robust in practical situations. Even though a few works have been done on uncertainties in HHCRSP, they did not consider the problem from the perspective of Robust Optimization.
Experiments To validate the proposed models and algorithms, we have performed several series of experiments. In this section, firstly, corresponding instances are introduced in section 6. 1, then section 6. 2 reports the experimental results of the deterministic model which is solved by Gurobi Solver, SA, VNS, TS respectively. After that, the detailed experiments and analysis of robust model are presented in section 6. 3. Finally, the comparison between the solutions obtained by the robust optimization model and stochastic programming with recourse is performed. All the heuristic algorithms are implemented in Java.
Comparison for the small instances Table  3  reports the experimental results obtained by Gurobi Solver for the small instances. As shown in the table, "ID" indicates the name of the instance, "NV level1" and "NV level2" represent the number of the used caregivers with the skill of level 1 and level 2 respectively. While "TC upper" and "TC lower" are upper bound and lower bound obtained by Gurobi Solver. Finally, "gap" indicates the difference between the lower bound and upper bound. Even though Gurobi Solver is one of the best Solver in the world, but it still shows powerless to solve the instances even for the size of patients is 10. We also find that the solutions obtained from the Gurobi Solver is quite time-consuming. So, meta-heuristic is a better choice to solve this problem. Table  4  shows the experimental results for solving the small instances with meta-heuristic approaches.
2 * t i. The uncertainty budget coefficient θ t ∈ [0, 1] determines the uncertainty budget of Γ k t. The simulating procedures can be viewed in Figure  5. As discussed in section 6. 1, instances in different datasets have different types of patients' locations. To show the effect of different types of locations, we present the details of deterministic and robust solutions for all instances of datasets RO-R1, RO-RC1 and RO-C1 together in Table  6. In these tables, the columns under "Deterministic" display the details of each deterministic solution. The columns under "Robust" show the details of each robust solution. The columns named "gap" 575 show the increase in the number of vehicles used and the percentage increase in the total distance traveled by the robust solution compared to the deterministic solution in each case.