dataesr/scientific-paragraphs-categorization huggingface dataset:
Petroleum products, electricity, natural gas, hydrogen, and CO 2 captured may all be traded between countries. Energy is supplied to the demand side, which is composed of residential, commercial, agricultural, transportation, and industrial sectors, via various energy carriers (electricity, fossil fuels, e-fuels, heat, hydrogen, bioenergy, and other renewables). A package of measures and constraints such as quality standards for refinery products, the functioning Article https://doi. org/10. 1038/s41467-023-41527-9 Nature Communications | (2023) 14:5756 hours of power plants, global emission constraints or sectoral restrictions, has also been considered in MIRET-EU. More information on the MIRET-EU model and the considered sectors and technologies as well as the cost parameters can be found in Supplementary Information 1.
Acknowledgements This study was funded by  International Association of Oil and Gas Producers (IOGP) , as a part of the  Hydrogen4EU  project. The authors are very grateful to  François-Régis Mouton  and  Axel Scheuer  for the funding and support and to  Malcolm Rice-Jones ,  Rosanna Fusco ,  Hannah Davies ,  Frank Winkelhoff ,  Julia Schmitt  and  Nareg Terzian  for their very useful comments and insights. The authors would also like to thank  Johannes Brauer ,  Clément Cabot ,  Lisa Ronce  and  David Ah-Voun  for their contribution to the study.
C. OUR CONTRIBUTIONS This paper proposes a supervised ML approach for MRMTA using a feed-forward neural network (FNN) with preprocessing and postprocessing techniques. To prepare the dataset, a convex optimization (CO)-based algorithm for power allocation (PA) is proposed, and MRMTA labels are added through a BF approach. During the online phase, the trained FNN is applied to the input data to assign radars to the targets. The proposed ML-based MRMTA eliminates the need for a complex BF approach, which significantly reduces computation time. An adaptive FNN structure was designed using various numerical simulations, and the effectiveness of its adaptive designation was demonstrated for different environmental factors. Succinctly, the contributions and novelties of this paper are highlighted as follows: • We develop a formulation for joint power allocation and radar-target assignment (JPARTA) in multi-radar multitarget scenarios to achieve low probability of intercept (LPI) radar networks.
It is worth noting that optimizing the RTA in the general case, where multiple radars can be assigned to each target in a multi-target environment, is fundamental and essential, as highlighted in previous studies  [11] ,  [12] ,  [13] ,  [14] ,  [15] ,  [16] ,  [17] ,  [18] ,  [19] ,  [20] ,  [23]. In this subsection, we aim to bridge this gap by introducing supervised ML to enable low-complexity MRMTA. This can be achieved by training a FNN with a BF approach database that returns accurate subsets of selected radars. The proposed algorithm is summarized in Algorithm 2, which operates in two phases: the learning phase and the prediction phase. During the learning phase, we employ an offline BF approach to find the optimum RTA as labeled data, while simultaneously collecting the dataset necessary for training the ML-based model.
A. DATASET INFORMATION 1) INPUT DATA GENERATION In the surveillance region, which is a square area with dimensions of L × L km 2 , we deploy N targets uniformly. To adequately cover the region, M radars are distributed throughout the network. In each individual run, the distances of the targets to the radars are determined, resulting in the generation of an input matrix R. This matrix R has dimensions of M ×N and contains the distances (r m,n ) between the m-th radar and the n-th target. A total of 100, 000 samples are generated in this process. 5   2) OUTPUT DATA GENERATION Fusing decisions from all radars can result in excessive data traffic over the data links connected to the fusion center, which can increase the global false alarm probability as more radars are fused. To account for this, we limit the network complexity by assuming that at most l radars can be combined to detect each target.
B. PREPROCESSING Preprocessing is crucial in enhancing the training efficiency of ML algorithms. The radar-target distances, denoted by Rs = [r m,n (s)] m∈M,n∈N , have a large input span in the range of (0, ∞), which creates significant challenges in ML convergence. To address this issue, we propose a two-step preprocessing procedure. In the first step, we perform dimensionality reduction by representing the rearranged radar-target distance matrix, i. e. , R s , as an MN × 1 vector c s. In the second step, we normalize the vector c s using a simple per-dataset scaling method, resulting in the input vector v s. This normalization method ensures a stable and meaningful learning process. Specifically, all samples are normalized using one constant value over the entire input data, and we obtain v s = c s max s ||c s || ∞ for s = 1,.
, S. After applying these two preprocessing steps, the input span is restricted to (0, 1]. To improve the training efficiency further, we also normalize the labels. We use a similar per-dataset normalization method, where every optimal radar-target assignment vector, denoted as x s , is normalized using the maximum value of the dataset, i. e. , (1-2 -l )2 M. The output of the normalization process over the label of the s-th sample of the dataset is denoted by q s. By normalizing both the input and output, we ensure that the target-radar distance information is preserved while addressing the convergence issue.
V. ML-BASED APPROACH IMPLEMENTATION The implementation of FNNs codes were carried out in Python 3. 9. 7 using TensorFlow 2. 8. 0. The batch size of 432 and a learning rate of 0. 01 were selected to ensure the lowest possible MSE with a relatively high convergence speed. To compare the performance of back-propagation, three classic optimizers, namely adaptive moment (Adam), stochastic gradient descent (SGD), and root mean squared propagation (RMSProp) were adopted. 9 For simplicity, all neural biases were initialized with zero, while all link weights were initialized randomly. In our experiments, each training round is configured with a maximum of 100 epochs, i. e. , E = 100. In this study, we aimed to design an ML structure that ensures high accuracy, fast convergence speed, and scalability.
VI. SIMULATION RESULTS To assess the effectiveness of the proposed ML-based JPARTA algorithm (Algorithm 2), we conducted numerical simulations under various scenarios. The surveillance region considered in our simulations has dimensions of 180 × 180 km 2. We set M = 4 and N = 2 in Figures  3 4 5 6 7. However, to comprehensively evaluate the performance of our proposed method, we extended the simulation of this study to more complex scenarios involving N = M = 2, 4, 8 as shown in Table  3. The RCS of all targets is assumed to be 1 m 2. 10  We generated the training dataset by solving subproblem 2 for different instantaneous target positions, which were uniformly and randomly placed in the surveillance region. All radars had the same parameters and were assumed to be of airport surveillance radar (ASR)-9 type  [27] , with specifications given in Table  2.
The main idea is to confront the biologist with as less code as possible by using visual programming approaches to simplify the intercomprehension and then generate the code on-the-fly from co-constructed diagrams. Subsequently, it also aims at rendering the qualitative evaluation and validation by the biologists of prototypes accessible through simulation visualization. This simulation visualization tool provides a dynamic step-by-step view of the cells and enables to dynamically plot data from the simulation. ISiCell aims at becoming a participatory tool for developing Agent-Based model in the cellular biology field in the same way as CORMAS (COmmon-pool Resources and Multi-Agent Simulations)  [18]  in the environment and resources management area. The methodology and the platform are evolving in parallel to improve each other.
To this end, we propose a web-based modeling platform which allows (1) to draw diagrams from the proposed methodology, (2) to automatically build executable simulation from the methodology schema and (3) to explore and analyze the created model. To this end, the platform disposes of three main tools: • ISiCell Builder, a schema-to-code tool which generates C++ code from drawn diagrams and compile it to build an executable simulation, • ISiCell Viewer, a 2D/3D visualization tool in which parameters and experimental protocols can be set up, the corresponding simulation can be explored both via a graphical representation of the cells and environment and via customizable plots and statistics, • ISiCell Explorer, an innovative parameter exploration tool that allows to visually explore various sets of parameters and their impacts on the simulation kinetics.
) and diffusion models that can be selected and added in one-click. Thanks to a plugin-oriented architecture, new modules can be added to the platform for specific needs to simulate new type of environment, specific behaviors or high-level statistics. The main panel of ISiCell Builder allows to build the behavior of the cell by drawing the state-transitions diagram of the global cell behavior and the activity diagram of the specific behavior of each state. To implement these specific behavior, a C++ editor is also available for the modeler. Pre-implemented functions such as divide, migrate, grow are also available and can be added as is or modified in the activity diagram. Once designed, the model can be automatically translated in code in one click. The simulated environment is set up by plugging in the selected modules to the program.
ISiCell Explorer ISiCell Explorer enables to launch several simulations with different parameters in parallel and draw personalized plots for each of them. The goal is to offer a primary sensitivity analysis tool to have a qualitative insight on the impact of chosen parameters on the model. This tool is based on editable Python scripts to generate plot and launch simulations. So that the script has access to the data of the simulation at each time step, the code is specifically wrapped and recompiled. This wrapping allows to directly and simply interact with simulations with a programming language, Python, adequate for data analysis. As it is written in Python, the tool also offers to easily load biological data in order to plot them with the simulation for comparison. ISiCell Explorer allows for exploring one or more parameters at a time between selected range.
A. This first study uses the preimplemented hertzian contact physics  [34, 40]  module which allows to maintain the crypt shape and to facilitate cell migration. All in all, our methodology associated to the ISiCell platform allowed to simply  From left to right, the type distribution in the crypt, followed by the state distribution and the local pressure in each cell. The visualization helped to qualitatively validate the model by comparing with the expected results. This model is available here: https://isicell. irit. fr/app. html?demo=Intestine_crypt.
When attaining the carrying capacity for one position in the grid, bacteria start multiplying on the direct neighboring positions if there is a damaged or healthy (only for the virulent type) IMC. Their number also decrease depending on the ROS quantity ; virulent bacteria also have a better resistance to ROS than regular ones. In order to reproduce the diffusion of molecules in the grid, the platform proposes a module to manage 2D diffusion which is here coupled with a 2D discrete environment for the cells. In order to confirm the coherence of the cells behaviors, we use ISiCell Viewer which enables to qualitatively reproduce the dynamics of the original article (as shown in  Fig 13). To calibrate the model we used the ISiCell Explorer tool to qualitatively match the population dynamics graphs of the original paper (as shown in Fig 14.
Discussion / Conclusion Agent-based models offer the possibility to study cellular biology at the individual cell scale to explore the basic behavior of cells which are responsible of the emergence of more complex phenomena at the tissue scale. To facilitate and improve the interaction and intercomprehension between modelers and biologists, we proposed a participatory methodology based on software engineering diagrams. In parallel, we also developed a web platform to support and apply our methodology more efficiently. The platform and the methodology were both tested with the implementation of models from the literature and from workshops with biologists. The code of the platform is available here: https://gitlab. com/CogoniXkill/isicell. Compared to reproducing models from the literature, as computer scientists, developing models with the enlightenment of an expert is far easier.
However, the difficulty to evaluate and quantify the efficiency and relevance of a methodology compared to another makes nearly impossible to prove that a paradigm is better than another for a specific model  [48]. Nevertheless, in a goal to facilitate interactions, ABMs offer a great foot ground for a better intercomprehension between biologists and modelers. Moreover, the platform is currently biased by the modelers that are working with it but the opening of the platform to a wide public might resolve this problem by providing more user feedbacks. Currently, using the ISiCell platform for a modeler requires a small amount of training mainly due to the few C++ code to be written to implement compartmental blocks. AI-based Pair programming such as github copilot  [49, 50]  would be interesting tools to facilitate the handling of the platform for new modelers especially for the creation of new modules.
More generally, the increasingly widespread use of Large Language Models (LLMs)  [51, 52]  such as OpenAI's GPT-3  [53, 54]  which are able to generate coherent text depending on a natural language request, could lead to the development of a myriad of coding tools making programming even more efficient. These AIs already show interesting results for pair-programming or generating code from prompt  [55, 56]  and could be in a mid-term future interesting tool to improve the usability of ISiCell Builder for modelers. Although the platform does not need a lot of coding to obtain a functional model, it still requires a minimum of C++ knowledge. A way to make this platform more accessible and usable for biologist could be to develop a metalanguage in the same way as the gamma platform and its GAML  [57]  to simplify the coding for non computer scientists.
Expanding its community is one of the future goal of ISiCell. Involving more modelers in this project will lead to a virtuous and continuous improvement of the platform while leading to the development of new models. These models will bring new modules specifically created to answer new problematics. If the community is active enough, it could lead to the development of ISiCell Hub, a web platform to more easily share models and modules. In the same way as ISiCell Builder, ISiCell Viewer could become modular to accept new visualization and analysis tools. June 12, 2023 22/28.
Acknowledgments This work is supported by grants from the  Occitanie Region and University Toulouse Capitole (OnkoOptim  project), from  Bristol-Myers-Squibb  (no. CA184-575 ) and from the  AI Interdisciplinary Institute ANITI  (funded by the  French program "Investing for the Future -PIA3 " under Grant agreement no. ANR-19-PI3A-0004 ). The research has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 Research and Innovation Programme  (Grant agreement No. Syn-951329 ).
Bistable VEH behaviors The dynamics of a bistable VEH may exhibit multiple behaviors for a given driving frequency, including low-power intra-well orbits, high-power inter-well orbits, and chaotic orbits. In this study, we define an orbit as robust if it is less sensitive to perturbations and easily attainable. In order to detect all possible behaviors in the frequency range of [20 Hz, 100 Hz] and A = 4 m/s 2 , the nonlinear Ordinary Differential Equations (ODEs) system (1) was solved for a large number of initial conditions using the Dormand-Prince method  [31]. Since the nonlinear ODEs (1) can be solved independently across multiple resolutions, this problem is well-suited to parallel computing, that can greatly enhance computational performance. For this task, a custom Python CUDA code was executed on an NVIDIA RTX A5000 GPU featuring 8 192 CUDA cores, enabling the resolution of (1) with 80 000 distinct initial conditions for each driving frequency.
The criterion (C 1 ) allows to select orbit jump parameters that maximize the harvested energy while minimizing the invested energy. This criterion allows to evaluate effectiveness of the orbit jump strategy. While the criterion (C 2 ) makes it possible to anticipate potential experimental deviations in the characteristics of the VEH or in the parameters of the orbit jump strategy. This criterion allows to evaluate robustness of the orbit jump strategy. Then, the optimization of the orbit jump parameters according both (C 1 ) and (C 2 ) criteria is performed by means of an evolutionary strategy algorithm  [35]  implemented in our in-house Python CUDA code. Evolutionary strategy algorithm has been selected due to its robustness in handling multi-extremal and discontinuous fitness functions, as well as its ability to benefit from GPU parallel computing.
Specifically, we take 7 values in [0. 85×τ opt 0 , 1. 15×τ opt 0 ] for τ 0 , 7 values in [0. 85×∆τ opt , 1. 15×∆τ opt ] for ∆τ and kw = k opt w. The VEH starts in an intra-well orbit at each driving frequency. Then, the optimal jump is applied, and the power is measured in order to evaluate the inter-well orbit power. Through optimization of the orbit jump strategy, the highest orbit was achieved at each driving frequency both experimentally and numerically, as illustrated in Fig. 10. Differences between experimental and numerical data may result from the mismatch between the numerical model and our vibration harvester prototype. It is worth mentioning that applying an orbit jump strategy always yields a significant increase in power in this frequency range. As a matter of example, for f d = 55 Hz the experimental mean harvested power of the intra-well orbit is 0.
Conclusion Due to the existence of low-power orbits in nonlinear VEHs dynamics, robust and effective orbit jump strategies are essential to ensure good energy harvesting per-formance by enabling transition from low-power to high-power orbits. To achieve this, orbit jump parameters can be optimized. This paper presents the optimization of an existing orbit jump strategy using an evolutionary strategy algorithm. As a result of the development of an in-house Python CUDA code compatible with GPU computations, the optimization results are accelerated. The experimental results consistently demonstrate that the optimized orbit jump parameters generated high-power inter-well orbits, while maintaining their performance even under potential fluctuations in the VEH bistable environment. By considering the experimental amplification factor at its optimal value and times within a ±15% variation of the optimal times, the optimized strategy's robustness was demonstrated with an average success rate of 48% for the orbit jump.
Figure10compares numerically (points) and experimentally (stars) mean harvested power (3) as a function of the driving frequency. Note that experimental power (stars) plotted in Fig. 10comes from experimental results of the orbit jump. The VEH starts in an intra-well orbit at each driving frequency. Then, the optimal jump is applied, and the power is measured in order to evaluate the inter-well orbit power. Through optimization of the orbit jump strategy, the highest orbit was achieved at each driving frequency both experimentally and numerically, as illustrated in Fig. 10. Differences between experimental and numerical data may result from the mismatch between the numerical model and our vibration harvester prototype. It is worth mentioning that applying an orbit jump strategy always yields a significant increase in power in this frequency range.
This demonstrates MitoCore's ability to accurately model human cardiomyocyte mitochondrial metabolism. In this work, we set out to construct a mouse model of mitochondrial metabolism, based on the human MitoCore model. We first converted the human MitoCore to mitoMouse, a mouse-specific model of mitochondrial metabolism, to allow the modelling of mitochondrial metabolism in this organism using FBA. We also expanded the model to include CoQ reduction from DHODH, introducing the importance of CoQ and the RC in orchestrating metabolic paths in mitochondria. We tested our model on experimental data provided by  16  , who showed that increased import of malate through the Malate Aspartate Shuttle (MAS) leads to increased fluxes associated with oxaloacetate metabolism and results in the prioritisation of Complex I reduction over Complex II.
Mapping MitoCore genes to mouse orthologs Considering the similarity between human and mouse mitochondria, conversion of MitoCore's gene-product-reaction (GPRs) to mouse equivalents would enable the construction of a mouse-specific model of mitochondrial metabolism. Human genes contained within MitoCore were mapped to corresponding mouse orthologs by automatic application programming interface (API) programming complemented with a manual search of Biomart  42  and the ENSEMBL database, as well as with orthology information stored within MitoXplorer2  43. This resulted in 388 mouse orthologs out of the original complement of 391 MitoCore genes. Figure  1  a summarises the construction of mitoMouse from the MitoCore model. The set of mitoCore GPR rules were compiled to their corresponding logical expressions for mitoMouse based on orthology relations between human and mouse genes (Supplementary Table  S1  b).
Constraint-based modelling using FBA and FVA Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) were performed using Python in conjunction with the COBRApy toolbox  45  , using the default 'GLPK' solver. mitoMouse was first tested on its ability to correctly produce accurate ATP levels from glucose oxidation. All nutrient input reactions except glucose and oxygen were constrained to zero to reflect aerobic glycolytic conditions. Maximisation of ATP production was used as the objective function. A robustness analysis was performed to investigate the ability of mitoMouse to import malate through the Malate-Aspartate Shuttle (MAS) and subsequent metabolism into oxaloacetate. The constraints for the malate import reaction from MAS varied from 0 to a maximum of 3 mmol/gDw/h at 0. 1 step intervals.
The resulting flux through complex I and complex II reactions were then observed as a function of imported malate. The same constraints were applied as previously described and the ATPproducing reaction was optimised. The resulting flux distributions for malate import were initially mapped using the Escher package  46  , redrawn for clarity, and compared (Jupyter notebooks containing constraints, simulation parameters and Escher flux visualisations are provided in Supplementary File 2). The resulting fluxes from the two conditions were scored and ranked in decreasing order, based on their absolute difference (Supplementary Table  S1  c).
using different objective functions, or by interfering with different parts of the mitochondrial metabolic network (e. g. in mutant conditions by blocking certain parts of the metabolic network). Moreover, experimental data in the form of gene or protein expression, as well as different metabolic data can be used in the combination with mitoMouse, making the model generally very useful in predicting the effects of different cellular conditions on mitochondrial metabolism. MitoMouse is open source and freely /gitlab. com/habermann_lab/mitomouse.