dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, delta ratio is to be considered as indicative. In the first version of the script we inserted the criteria; (1) if delta ratio is < 0. 4 AND [Na + -Cl -] < 40 mmol/L a diagnosis of hyperchloremic acidosis is decided, and (2) if delta ratio > 2 a diagnosis of a pre-existing high-level bicarbonate state is decided. Such high-level bicarbonate state can originate from a true pre-existing high level or from a longtime compensation of a chronic respiratory insufficiency. The fourth and last layer compares values to each other and is possible to extend for future versions of the script with machine learning. It can accept the diagnosis from the 1 to 3 levels, or alter key values and require a new calculation from the initial start. Also, the fourth level can be indefinitely extended. When meeting an acidosis it is likely more deviated and less compensated, compared to an alkalosis.
Stewart approach has three players, pCO 2 , SID and A TOT , of which only pCO 2 is directly available. We used the formula [Na + + K + -Cl -lactate -] to estimate SID; however, magnesium, phosphate, sulphate for instance also contribute to SID, but are not readily and frequently available. Our estimation of SID did not correlate well with acidity or alkalinity  (12, 13). A TOT is difficult to estimate and is built up by numerous contributors, such as albumin, globulins, urea, different other proteins. Therefore, a smart clinical usage of Stewart is in our opinion not easy, although there are situations, where it can in bring understanding, such as in hyperchloremic acidosis. The script seems to work in its initial form on MATLAB. The path to a final userexperience version; either on a handheld device or as an integrated software on a device or system used in healthcare is long and includes coding the script and building a smart input module, building a user-friendly design and testing and certifying it clinically.
Our next step will be to build a prototype and plan a prospective multicenter study to test it in sharp clinical situation. No script is perfect in its first version. However, we believe that this script will fast outsource manual calculation in higher versions. However, it can never work alone, it will always require that the human clinical experience is involved. How can one ever create a complete wall-to-wall theory for the acid-base status, when it is affected by so many underlying processes? Models are created by calculating on an impaired balance between respiratory and renal functions, which are the main moderators, and further on buffers, which can be seen as temporary rescuers. Also, the accuracy of the blood gas analysator may have an impact and add up for instance in the delta ratio dependent on at least four electrolyte measurements.
Electronic structure method speed-up Long time-scale simulations will require a substantial reduction in the cost of quantum mechanical quantities (excited state energies, forces and state couplings) to keep computational resource requirements under control. We envisage three possible strategies to achieve this cost reduction: (i) use of parameterized and approximated electronic structure methods; (ii) use of parameterized full-dimensional Hamiltonian models; and (iii) use of ML as a surrogate model for quantum mechanical predictions. They are discussed in the following subsections. [17] [18] [19]  or computational data  [20, 21]. There are several parameterized electronic structure methods currently available for MQC dynamics, allowing low-cost excited state calculations. Some of them are based on configuration interaction procedures, like FOMO-CI  [18]  and OM2/CI  [17].
Finally, the accuracy of the ML model is strictly related to the sampling strategy adopted to generate the training dataset, which should include the most representative molecular configurations accessible during the time evolution of the system on different excited-state surfaces. Despite these challenges, the highly efficient computational performance of various ML models has been successfully demonstrated for some prototypical photoreactive molecules  [9, 13, [53] [54] [55]. In some of these previous studies, it was possible to achieve a nanosecond timescale in the nonadiabatic dynamics, although the molecules investigated undergo decay processes towards the ground state in a much shorter timescale, typically hundreds of femtoseconds. To our knowledge, the potentialities of ML to run nonadiabatic dynamics for more complex systems with intrinsically slow photorelaxation processes such as in fluorescence remains to be explored.
To illustrate some of the difficulties in learning excited state energies, we computed a dataset of 50 surface hopping trajectories for protonated 7-azaindole at the ADC(2)/cc-pVDZ  [57]  level (electronic supplementary material, SM-3) to train a kernel-ridge regression model with the RE descriptor, the so-called KREG model  [58]  (electronic supplementary material, SM-4). The discussion of the physical-chemical properties of this system will be reported elsewhere. Here, we focus exclusively on how well ML can predict its energies. By plotting the difference between the ML-predicted and the ADC(2) energies as a function of time for each state (figure  1 , top panel), one can see that the errors vary approximately in the range of -5 to 5 kcal mol -1 for all states along the first half of the testing trajectory.
The boxplots of figure  2 , representing the RMSE distribution for each state of the protonated 7-azaindole dataset, sheds light on this problem. This statistical distribution is obtained using a 'leave-one-out' cross-validation scheme in the trajectory space, meaning that one trajectory is selected as the test set for each round of training and evaluation (electronic supplementary material, SM-4). As shown in figure  2 , the RMSE values are spread in a wide range, and they increase when moving to high-order excited states (approximately 1. 3 kcal mol -1 in S 0 to 2. 9 kcal mol -1 in S 3 ). A few trajectories appear as outliers in the error distribution (see the black dots in figure  2 ). These findings indicate that geometries belonging to atypical trajectories are   [59, 60]  have been proposed to remedy this issue and improve the quality of the training set, with active learning  [61] [62] [63]  being the most used in nonadiabatic dynamics applications.
Software optimization Originally, Newton-X, our software program for dynamics simulation on excited states, was developed in the frame of ultrafast processes  [96, 97]. For this reason, in the choice between computational efficiency and flexibility (modularity to communicate with different electronic structure methods), most of the time, the latter was favoured. Moreover, it is not fit for processing and storing big data. Simulations in the long timescale invert the priorities. Therefore, efficiency and big data become mandatory issues to be addressed. We have recently started to write a completely new Newton-X version to deal with these issues. The legacy program is now referred to as Newton-X CS (for classical series), while the new program is referred to as Newton-X NS (for novel series).
With the development of fast electronic structure calculations highlighted in §2, this relation does not hold anymore, and the dynamics approach must be adapted to limit the number of I/O operations. This algorithmic restructuring is especially crucial for long time-scale dynamics, where we must tightly optimize the speed of each step. Replacing a set of scripts with a more integrated program can solve this issue by keeping most data in memory during the dynamics and only writing on disk at specific points when unavoidable, like when communicating with an external program. To deal with this problem, we have entirely restructured Newton-X. While Newton-X CS is a collection of Perl scripts with Fortran programs, Newton-X NS is a complete Fortran program using Perl only for dealing with QM input files.
This problem is even more pronounced in FSSH, as the algorithm's stochastic nature requires an ensemble of many trajectories. We can make a simple estimate of what these data requirements would represent in the long timescale. Let us assume we performed simulations for a molecule with 20 atoms for a 1 ns trajectory with 0. 5 fs time step. The storage of the geometries alone (in double precision) would require around 1 GB of space. Standard FSSH studies routinely need around 100 trajectories to achieve satisfying statistical accuracy, so the geometries alone could take up to 100 GB of space. To this amount, we should still add electronic populations, energies, nonadiabatic couplings and surface hopping-related data (like the transition probabilities). This dataset must be further post-processed to deliver physical-chemical information and made publicly available to comply with open data requirements.
It is thus of crucial importance that the data files are uniquely identified and that the set-up for producing the data (software version, operating system, computer used) is also available. The current state in Newton-X CS is to output these data as a series of text files. Although convenient to write and read, text files have some serious drawbacks. For each new dataset, a new file must be created and documented. This can quickly lead to a multiplication of outputs, making data integrity harder to ensure and data sharing more difficult. Therefore, obtaining the output as a single file containing the entire dataset produced, as well as the condition of production as metadata, would be a desirable feature. This can be achieved with a structured file format. However, the context of MQC dynamics makes the use of structured text formats cumbersome.
In MQC dynamics, we save data as time series. In practice, this means that the effective rank of each dataset is increased by one. Although conventional formats exist for saving two-dimensional data like molecular geometries as a time series (xyz format, for instance), keeping track of data with higher rank, such as energy gradients when several electronic states are involved, quickly becomes cumbersome and decreases the readability associated with text files. Another problem lies in the analysis of trajectories. Each time step will produce heterogeneous data, which must be saved when they are still available in memory, but for convenience of analysis, it is desirable that the data corresponding to the different sets (energies, populations,. ) are kept together. Structured text formats exist, like JSON or YAML, but they are not suited for a sequential update of the datasets.
One solution would be to save the time steps as a parent field, containing the heterogeneous data as children, post-process the file at the end of the simulation to gather all datasets and produce a new structured file with consistent datasets. These issues can be solved using the hierarchical data format (HDF) model, which allows the creation of portable, self-described and open binary file formats. The model has clear advantages  [99] , with complete C, C++ and Fortran APIs, and officially supported bindings with Python, Julia and R. The files created with the underlying library can be easily explored and shared and are self-documented, with the inclusion of metadata directly along with the datasets. Binary files also occupy less disk space than text files, and HDF5 can add compression to the data to lower the footprint even more.
We have chosen to adopt the H5MD format  [100] -specifically designed to handle molecular dynamics-to Newton-X NS. Using this file format allows us to benefit from all the environments already developed. For the same simulation as the one showed earlier in this section, set to write the maximum amount of output and using Newton-X NS, the program spends around 3-4 s to write output files, showing no difference between text and H5MD files. However, the files obtained differ in size: the text files together occupy 58 MB of disk space, while the H5MD file occupies only 28 MB. It is also worth noticing that HDF5 files can be read in chunks, thus facilitating the treatment of large datasets, and can be opened only partially just to read a header, creating exciting opportunities for automated archiving and research in databases.
Land use and land cover change (LULCC) The unutilised agricultural area (NUAA) of landscapes (non productive infrastructures, urban areas, forests) is documented by geographical databases (Corine Land Cover, shapes of forested areas). Simulating LULCC in utilised agricultural areas a is somewhat complex because of the diversity of farming systems and associated practices  (Thenail et al. , 2009; Benoît et al. , 2012). Several models working at the plot level (a plot corresponds here to an agricultural parcel: a continuous area of land declared by a farmer, on which a single group of crops is cultivated) concern the organization of annual crop rotations  (Dury et al. , 2012) , but those that enable dealing with livestock or both livestock and crop farms are scarce. One of the difficulties lies in their ability to manage the distribution of technical rules among plots, and their respective contribution to the overall fodder balance for livestock and to commercial benefits for annual crop production.
), topographical (slope) or cadastral conditions. In the event that one or several plots cannot be used for annual crops, their dominant use must be indicated in the geographical database describing the NUAA. The maximum flow was computed using the classic Edmonds-Karp max flow algorithm  (Edmonds and Karp, 1972). As this algorithm is stochastic, different solutions of spatial crop allocation can be obtained between two executions depending on random choices for the construction of graph-edges. Unlike  Taillandier et al. (2012) , labor tasks were not explicitly evaluated, but plowing, sowing and hoeing dates were stochastically distributed in the computed crop successions to generate "noise" in calendars. For livestock farms (Fig. 2 ), we implemented stepwise computations controlling the adequacy between plot supply and the priorities for pasture and forage demands, as proposed by  Marie et al.
DMEIs are events representative of typical soil moisture conditions and biomass development, e. g. , low-water periods or fall high-flow recovery. During the transfer period, the SSMT module counts the number of particles that reach the watershed outlet as a function of water trajectories computed by the WMWL model and sink-source determinism. Water trajectories and particle displacements can be interrupted on cells where the soil moisture balance does not exceed saturation thresholds. During the transfer period, we restricted water exchanges with the atmosphere to evapotranspiration flows (rainfall was set to 0) to only account for particle displacements resulting from the landscape draining and water redistribution dynamics produced by DMEIs. Particle tracking was based on previous schemes accounting for pollutant build-up, wash-off and transport processes  (Jiang et al.
Software architecture WMWL and OPALE GUIs (Fig. 4 ) were implemented in the MATLAB environment, while the LULCC and SSMT programs (Fig. 5 ) were implemented with the GAMA platform  (Taillandier et al. , 2019). The OPALE GUI allows users to select libraries, implement IOF writing/reading and parameterize commands in the two environments and SAGA geographical treatment (http://www. saga-gis. org). OPALE executable for the Windows-64 bit environment, source codes and documentation are available at github. 1  All programs were run on a computer with a CPU IntelCore(R) i7 and 32 Go RAM. At the center of the OPALE GUI a workflow scheme organizes the workflow steps for landscape assessment. The workflow scheme differs depending on the selected production systems (from the listbox items -Livestocks/Crops & Livestock or Crop/vegetables-).
At step 1, the 'New project' button and a subsequent 'Exec'-clic allows reading input files. Then, it opens the GAMA GUI where users can launch an initialization program, linking together geographical information (soils, hydrological network, fields, farm systems, etc. , as described in supplementary material S3). The computational mesh is also defined here, with the sizes of the square grids based on the resolution of the digital elevation model. Then, the LULCC program is opened, and performs maximum flow or step computation schemes depending on the selected production systems. Configuring decision rules for crop successions and distance priorities between farms and practices is allowed. LULCC outputs are agricultural landscape structures (i. e. an assemblage of different agricultural plots) and landscape infrastructures (e.
g. hedges, filter strips, non-productive areas), as referenced by grids where their proportion area in the cells of the computational mesh are calculated to limit topological representation biases for the following WMWL calculations. The 'G = > M' button (Step 2) builds matrices for the Matlab environment and performs plant development and topography calculations to build the cascade scheme for sequential water flow balances along slopes. The Step 3 top buttons open the WMWL model for optimization. WMWL optimization can be done contextually based on soil and hydrosystem properties, or automatically with the lsqcurvefit tool in Matlab. All calculations up to this stage provide a baseline on the current landscape. The Step 3 bottom buttons open simulation purposes, and build new matrices that can be compared to the current landscape.
If the user needs to analyze production system changes, the workflow differs depending on farming conditions. For crop/vegetable systems (crop distribution determined by random selections in the LULCC model), forward planning of farming systems is implemented by modifying the crop areas of the first year of the crop succession in order to follow the initial patterns of crop distributions. The aim is to avoid repeated simulations to obtain converging agent-based model simulations outputs that are commonly done to compare present and future territories  (Grillot et al. , 2018). The price to pay is that only the first year of the crop succession of current and future landscapes can be compared. We implemented such modifications within the Matlab environment, where the first year of the current landscape matrices of crop allocation are modified according to the expected proportions of crop groups indicated by the user in the agricultural-system-landscape panel.
, 2013)  is computed to compare observed or calculated river flow outputs) or to select parameters to be optimized by the lsqcurvefit program. Optimization processes can be paused for plot-zooming and inspection (from the upper tool bar) or to analyze the contributions of water components to river outflows or select DMEI dates. The 'Agricultural system = > Landscape panel' allows configuring the proportions of the farming systems, as well as climatic conditions, farming practices or infrastructure distribution (several choices are possible, with filter strips or hedgerows of deciduous of perennial tree species). With the 'Interface' button, the program interacts with SAGA libraries to re-calculate the proportions of the infrastructures in the computational meshes. Finally, with step 4, the 'Landscapes = > Eco-functionality' panel allows evaluating the transfer functions SSTF and the nature of the particle matter to be treated in SSMT.
Model parameterization Table  2  gives parameter values for the WMWL and SSMT models. We evaluated the parameters of the WMWL model (k m , c 1 , c 2 , c 3 , λ, b, i max ), following the lsqcurvefit automatic optimization module or by referencing contextual values from expert estimation. Doing so and considering the respective soil conditions, the bottom permeability ratio k m was high in Miribel and Aiguebelette but low in Autrans. Horton store emptying was considered rapid, and a maximum c 2 value was thus retained. The saturation overflow emptying value c 3 was lower, considering potential retardation effects due to variable extension of source areas. All SSMT parameters were predefined (δ, a k , d k , k d , r k , e k ), referenced from available data given in Table  2 , as well as those proposed in the LULCC model (supplementary material S1 parameters), although a few decision variables can be redefined from the GAMA GUI to account for local organization and decision rules (Fig.
Modeling of matter outflows. At Miribel, turbidity probes operated until late spring, and data were collected to evaluate SSMT outputs (Fig. 14 , NSE = 0. 73). One event (obs = 2. 51 ntu. d -1 ; cal = 1. 40 ntu. d -1 ) situated at the end of the modeled sequence reduced the NSE ratio. It corresponded to stochastic conjunctions of the plowing, sowing or hoeing dates that probably led to lower F2-F3 differences. At Autrans, observed values were filtered according to the proportions of bacteroidales markers of human and bovine origins (the bovine origin was dominant, ranging from 0. 7 to 0. 9%). Observed and calculated EC values were confronted in Fig. 15  (NSE = 0. 61). Even though acceptable, efficiency was moderate, as often when comparing manual samples to daily computations; the spatial variability of fecal populations can be large in river systems, with diurnal variation (often by several orders of magnitude), as noted by EPA, 2010.
It collects raw data from the managed device to transfer it, in a comprehensible or exploitable format, to the network manager. The ''management database" contains information concerning the managed device parameters. The ''messaging protocols" can be used to exchange information between the network manager and the managed devices. This allows the network manager to get parameters from managed devices and accordingly take appropriate decision concerning the reconfiguration of network devices. Typically, a network management system needs to support the following operations: Network configuration management: It refers to the process that helps setting a network in order to meet a desired objective (e. g. level of security, low latency, etc. ). It encompasses different operations related to the configuration and reconfiguration of all the (writable) network device parameters.
It encompasses operations such as software maintenance (e. g. firmware update and bug fixes) and troubleshooting network problems. (Younis et al. , 2014; Paradis and Han, 2007 )  (Alamri et al. , 2013 )  (Ndiaye et al. , 2017; Haque and Abu-Ghazaleh, 2016 )  (Thoma et al. , 2014 )  (Wang et al. , 2017; Mao et al. , 2018)  IoT network management protocols p p p p - - - - Cloud-based frameworks p p p - p - - - SDN-based frameworks p - - - - p - - Semantic-based frameworks p - - - - - p - Machine learning based frameworks p - - - - - - p To manage traditional networks, various network management protocol such as SNMP  (Mauro and Schmidt, 2005) , CMIP  (Hunt, 1997) , NETCONF  (Enns et al. , 2011) , RESTCONF  (Watsen and Protocol, 2016) , CWMP  (Rachidi and Karmouch, 2011)  and OMA-DM  (Alliance, 2010)  have been proposed.
Simple Network Management Protocol (SNMP) SNMP is a network protocol developed by IETF (Internet Engineering Task Force) for remote monitoring of IP devices. It supports a set of operations including monitoring, configuring and/ or reconfiguring network device parameters. SNMP involves the three elements of network devices management (agents, nodes and manager) described above. It relies on Structure of Management Information (SMI) and Management Information Base (MIB). MIB designates the database used for managing network devices while SMI defines the structure and types of objects stored in the MIB. Common Management Information Protocol (CMIP) CMIP is a network protocol responsible for the communication between the network manager and the managed devices. CMIP enables various network management operations such as fault management, security management, performance monitoring and so forth.