dataesr/scientific-paragraphs-categorization huggingface dataset:
5°grid cell, using a newly derived forest management dataset for 2015  66. The boundary of ecological project was used to determine whether disturbance patches were located in the ecological project regions (i. e. , 1-within the ecological project region, 0-outside ecological project region, detailed descriptions of ecological projects were provided below). Population density (persons/km 2 ), road density (km/km 2 ), and distance to the nearest road (km 2 ) were summarized at 0. 5°grid cell. Trends for Landsat observations were calculated using annual availability of all Landsat observations (Supplementary Fig. 1 ). Biophysical factors included topography (e. g. , elevation, slope), vegetation (e. g. , NDVI as a proxy for rate of growth), and soil (e. g. , soil organic content, soil bulk density).
Data availability The Landsat data are freely available via either USGS Earth Explorer (https:// earthexplorer. usgs. gov) or Google Earth Engine (https://earthengine. google. com). The tree cover data was available at https://glad. umd. edu/dataset/long-term-global-landchange and https://earthenginepartners. appspot. com/science-2013-global-forest. The disturbance maps produced in this paper are available at https://liuzh822. users. earthengine. app/view/chinaforestdisturbance.
In the 1970s, thermomechanical models evolved to include time dependence, linking flow laws to strain rate and time. Notable models like the Johnson-Cook  [8] , Zerilli-Armstrong  [9] , and Arrhenius  [10]  flow laws emerged and are commonly used in forming process simulations. The selection of a flow law for simulating material behavior in finite element analysis is crucial, and it should be based on experimental tests conducted under conditions resembling real-world applications. Researchers face a challenge in choosing the appropriate flow law after characterizing experimental behavior. This decision is influenced by the availability of flow laws within the finite element analysis software being used. For instance, users of Abaqus FE code  [1]  may prefer the native implementation of the Johnson-Cook flow law.
Opting for alternative flow laws like Zerilli-Armstrong or Arrhenius requires users to personally compute material yield stress σ through a VUMAT subroutine in Fortran, which is time-consuming, demands expertise in flow law formulations, numerical integration, Fortran programming, and model development and testing  [11] [12] [13]. Developing and implementing a user behavior law on the Abaqus code involves calculating σ, defined by Equation  (1) , and its three derivatives with respect to ε,. ε, and T. This process becomes complex and time-consuming with increasing flow law complexity. The choice of a flow law for simulating thermomechanical forming is influenced by both material behavior and process physics, but primarily by the flow laws available in the FE code. The decision often prioritizes the availability of a flow law over the quality of the model, making the choice of flow law a critical factor in the simulation process.
The approach proposed here is more complete in that we identify a neural network from experimental tests (in our case, cylinder compression tests performed on a Gleeble device), then identify the network parameters. We then implement this ANN in Abaqus as a user subroutine in Fortran, and subsequently validate and use this network in numerical simulations. This can be performed for either Abaqus explicit or Abaqus standard since we can generate the Fortran subroutines for both versions of the code. We therefore work on the entire calculation chain, from experimental data acquisition, through network formulation and learning, to implementation and use. In this study, we will focus only on the use of the explicit version of Abaqus, since the computation of the flow stress in an explicit integration scheme becomes very CPU intensive due to the integration method, and variations in the performance of this implementation have a significant impact on the total CPU time.
The experimental database is composed of all strain/stress data for all 30 couples of strain rate and temperature. Each strain/stress curve contains 701 equidistant strains ε = [0. 0, 0. 7] in ∆ε = 0. 001 increments. The complete database contains 21,030 quadruplets (ε,. ε, T and σ). This dataset will be used here after to identify the ANN flow law parameters depending on the selected hyperparameters of the ANNs. Section 2 is dedicated to the presentation of the ANN based flow law. The first part is dedicated to a reminder of the basic notions on ANNs, with a section on the choice of activation functions to be used in the formulation. The architecture chosen for the formulation of the flow laws based on a two-hidden-layer network will then be presented, together with the formulation of the derivatives of the output with regard to the input variables.
Artificial Neural Network Flow Law As previously proposed by Pantalé et al. [15, 16] , the employed methodology involves embedding the flow law, defined by a trained ANN, into the Abaqus code as a Fortran subroutine. The ANN is trained using the experiments, as introduced in Section 1, to compute the flow stress σ as a function of ε,. ε and T. Following the training phase, the ANN's weights and biases are transcribed into a Fortran subroutine, which is then compiled and linked with Abaqus libraries. This integration enables Abaqus to incorporate the thermomechanical behavior by computing the flow stress and its derivatives (∂σ/∂ε, ∂σ/∂. ε, and ∂σ/∂T) essential for the radial-return algorithm within the FE code.
[28]  or Jagtap et al. [29]. The activation function is essential for introducing non-linearity into a neural network, allowing it to capture non-linear features. Without this non-linearity, the network would behave like a linear regression model, as emphasized by Hornik et al. [18]. A number of activation functions can be used in neural networks. In our previous published work  [15, 16] , we have mostly used the Sigmoid activation function for the ANN flow laws. In the present paper, we are going to explore other activation functions and their influence on the final results, up-to the implementation into a FE software. Among the number of activation functions available in the literature, we have selected the six ones reported in Figure  3. The Sigmoid activation function  [30] , also known as the logistic activation function, is widely used in ANNs.
The idea here is to use the property so that the derivative expression is defined only by the function itself, as well as for the Sigmoid and Tanh, but with the simplest formulation. This will reduce the CPU cost since we need to compute both the function and its derivative for our implementation in the FE code into a very CPU intensive subroutine, due to the explicit integration. Of course, there is no limitation to the use of alternative activation functions in ANNs, and there exist some much more complicated, such as the one proposed by Shen et al. [33] , which is a combination of a floor, an exponential and a step function. Those authors have proven that a three hidden layer with this activation function can approximate any Hölder continuous function with an exponential approximation rate.
Because of the pre and post processing of the values introduced in Section 2. 1. 3, the derivative of the flow stress σ with respect to the inputs ε,. ε and T is then given by:      ∂σ/∂ε = s 1 • ∆σ/∆ε ∂σ/∂. ε = s 2 • ∆σ/(. ε • ∆. ε) ∂σ/∂T = s 3 • ∆σ/∆T , ( 14 ) where s i is one of the three components of the vector -→ s defined by Equation  (13). Finally, Equations (  2 )-(  4 ), (  11 )-(  14 ) and the requested activation function defined by one of Equations (  5 )-  (10)  will be used to implement the ANN as a Fortran subroutine for the Abaqus FE software, as it will be presented in Section 3. 1.
Training of the ANN on Experimental Data The Python program, developed with the dedicated library Tensorflow  [34] , utilized the Adaptive Moment Estimation (ADAM) optimizer  [35]  and the Mean Square Error for assessing the loss function during the training phase. With regard to our previous publications about ANN constitutive flow law  [15] , we have made the choice to arbitrarily fix some hyper-parameters of the ANNs, so to use a two hidden layers ANN with 15 neurons for the first hidden layer and 7 neurons for the second hidden layer. There is a total number of 180 trainable parameters to optimize. As we have three inputs and one output, we reference each of the ANNs using the notation 3-15-7-1-act, where act refers the activation function used for the model. All six models underwent parallel training for a consistent number of iterations (6000 iterations, lasting around 1 h), on a Dell PowerEdge R730 server running Ubuntu 22.
04 LTS 64-bit, equipped with 96 GB of RAM and 2 Intel Xeon CPU E5-2650 2. 20 GHz processors. Concerning the dataset used for the training of the ANN, as already introduced in the previous sections, this dataset is composed of 21,030 quadruplets (ε,. ε, T and σ) acquired during the Gleeble experiments described in Section 1. 2. A chunk of 75% of the dataset is used for training while the rest is used for the test during the training of the ANN. All details about this procedure can be found in Pantalé  [36]  where the interested reader can download the source, data and results of this training program. Regarding the training procedure, specifically the starting point, all models are initialized with precisely identical weights and biases. However, due to different activation functions, the initial solution varies from one model to another.
Error evaluation of the models uses the Mean Square Error (E MS ), the Root Mean Square Error (E RMS ) and the Mean Absolute Relative Error (E MAR ) given by the following equations:        E MS = 1 N ∑ N i=1 e i -i 2 E RMS (MPa) = √ E MS E MAR (%) = 1 N ∑ N i=1 i -e i e i × 100 , ( 15 ) where N is the total number of points for the computation of those errors, i is the ith output value of the ANN, and e i is the corresponding experimental value. Figure  4  shows the evolution of the common logarithm of the Mean Square Error, i. e. , log 10 (E MS ), of the output s of the ANN during the training, evaluated using only the test data (25% of the dataset). By examining this figure, we can assess and compare the convergence rates of various ANNs, concluding that a stable state was more-or-less achieved for all analyzed ANNs after 6000 iterations.
As expected, the ReLU activation function gives the worst results with a final value of E MS = 32 × 10 -6 , mainly due to the low number of neurons and the fact that this function is a piecewise linear function and not able to efficiently approximate the nonlinear behavior of the material. The other five activation functions enhance more or less the same behavior. The final value of the E MS is pretty much the same for all of them, and around E MS = 12 × 10 -6. Table  2  reports the results of the training phase, the errors reported in this Table are computed using the whole dataset (both train and test parts). From the data, it is evident that all models require approximately the same training time to complete the specified number of iterations, with the complexity of activation functions influencing this duration; notably, the training time is a bit greater for Swish and Softplus functions compared to ReLU.
We also reported in this table the real values of the E RMS and E MAR concerning the flow stress σ using the whole experimental database. From the latter, we can see that the E RMS is about 0. 6 MPa for all activation functions except the ReLU one, where the value is above 0. 8 MPa. Concerning the E MAR , the value of all models is around 1%, while it is more than 2% for the ReLU function. Of the six activation functions, the Exponential function gives the best results in terms of solution quality, while the ReLU function gives the worst, as reported by computing the global error using the following expression ∆E = E 2 RMS + E 2 MAR. But we must note that the results of all models, except the ReLU one are very close at the end of the training stage, as illustrated in Figure  4  and Table  2.
As an illustration the corresponding implementation using Python of those equations is proposed in Appendix A. A dedicated Python program is used to translate those equations into a Fortran 77 subroutine. During the translation phase, all functions corresponding to array operators, as matrix-matrix multiplications or element-wise operations, are converted into unrolled loops (explicitly written), all values of the ANNs parameters are explicitly written as data at the beginning of the subroutine, so that the a 3-15-7-1-exp Fortran routine consist of more than 400 lines of code. A small extract of the corresponding VUHARD subroutine is presented in Figure  A2  in Appendix B. All full source files of the six VUHARD subroutines is available in the Software Heritage Archive  [36]. The Fortran subroutine is compiled with double precision directive using the Intel Fortran 14.
From these two, we can calculate the number of increments performed per second and propose a classification from the fastest to the slowest ANN, where ReLU is the fastest (with 2353 iteration per second) and Softplus is the slowest (with 1200 iteration per second) of the proposed models (two times slower than the ReLU one). Those results are directly linked to the complexity of the expression of the activation function and its derivative as introduced in Section 2. 1. 2. For example, we can note that a simulation using the Sigmoid activation function requires 1,092,001 increments and the model contains 400 under integrated CAX4RT elements; therefore, we will have 436,800,400 computations of the code presented in Figure  A3. From those results, we can note that, as expected, the Exp activation function is very efficient in terms of CPU computation time (with 1996 iteration per second) as it is just second after the very light ReLU function, but gives quite good results as reported in Table  3 , which is not the case for the ReLU function.
The same approach can also be used to write a UHARD routine enabling the same flow laws to be used in Abaqus standard. Numerical results obtained from a compression test on a metal cylinder using the Abaqus explicit code have shown that neural network behavior models give very satisfactory results, in line with experimental tests. The Exp activation function, which is rarely used in the formulation of artificial neural networks, showed very good results (in agreement with more complex models such as Tanh), while enabling the code user to benefit from the efficiency and ease of implementation of an exponential function. These results are satisfactory insofar as the inputs remain entirely within the model's learning domain, since the extrapolation capabilities of the network based on the exponential function are very limited.
We then obtain results equivalent in terms of solution quality to sigmoid or tanh-type formulations, while having computation times comparable to a ReLU function. Overall, this study concludes by recommending the use of the sigmoid activation function for the development of flow laws, since it gives very good results in the identification domain, allows us to leave the learning domain with a behavior that is certainly biased, but physically admissible, and offers very good performance in terms of simulation time when implemented in a finite element code. This study emphasizes the valuable impact of neural network-derived flow laws for numerical finite element simulation executed with a commercial FE code like Abaqus.
Images were acquired using a 10× objective lens in phase contrast and in green fluorescence channels (ex: 453-485 nm, Em: 494-533 nm). Nine images were acquired from each well every 6 h. Segmentation analysis was performed using the IncuCyte Cell-by-Cell Analysis Software Module (segmentation adjustment: 1, cell detection sensitivity: 0. 5, cell contrast: 2, cell morphology: 3, surface fit) and the green fluorescence mean intensity of each segmented cell from the last acquisition was used to create a frequency distribution. For each experiment (n = 3), the fluorescence intensity of each cell measured at 78 h (last acquisition) was used to calculate a population median value for each formulation tested. These median values were then used to quantify the inhibition of fluorescence intensity relative to untreated cells.
Finally, the impact of the analytical method used was studied by comparing the data obtained by DLS or NTA for each formulation. A significant difference was observed for the CSL3/Cer-PEG formulation, which can be explained by the very high size measured by DLS, but no significant difference between the methods was observed for the other formulations. A PdI between 0. 037 and 0. 466 was measured for all LNPs, but no correlation with the lipid-PEG length could be made when CSL3/DSPE-PEG LNPs were taken as control (Figure  2B ). Unsurprisingly, neutral zeta potential was measured for all of formulations, as LNPs contained ionizable lipids with a pKa lower than 7. 0. Finally, no impact of the lipid-PEG was observed on the siRNA encapsulation level when CSL3 and SM-102/DSPE-PEG were taken as control and compared with the other formulations of the corresponding group (Figure  2D ).
Impact on Metabolic Activity in Serum-Containing Medium In order to evaluate the safety of the tested LNPs, two different human cancer cell lines were used: HeLa and HepG2 cells. HepG2 hepatic cells were used considering the potential hepatic specificity of LNPs covered by a protein corona. Moreover, this cell line is commonly used for hepatotoxicity studies  [22]. The metabolic activity study was performed without and with serum (10% FBS) after 24 h of transfection. Data are shown in Figure  5  and were analyzed following several parameters. In serum-free medium (filled bars) and as compared to untreated cells (Dunnett test, Figure  5 ), the lipid-PEG has no significant impact on the metabolic activity of Hela cells and HepG2 cells, except for SM-102/Cer-PEG LNPs for HepG2 cells. In serum (patterned bars), only one formulation significantly decreased the metabolic activity of HeLa cells (SM-102/Cer-PEG LNPs), while three formulations significantly decreased the metabolic activity of HepG2 cells (SM-102/Cer-, /DMG-and /DTA-PEG LNPs).
Acknowledgments: The authors would like to thank Jean-Christophe Monbaliu (CiTOS, ULiege) for his helpful advice on microfluidic technique. The authors thank the GIGA Viral Vectors and Genome Editing Platform team (ULiège, Belgium), and Sandra Ormenese and Raafat Stephan from the GIGA Cell Imaging and Flow Cytometry Platforms (ULiège, Belgium) for their help and advice.
After the paper prototyping, we asked the participants to present their prototypes to the group. We videotaped the workshops. We collected the recordings and 24 worksheets. Three authors analyzed the recording and the worksheets to identify teachers' needs. The collected data is open online  (Author, 2022). Pedagogical AR activities that teachers want: In our analysis, we identified five types of pedagogical AR activities and two ways of combining AR activities together to create sequences. The most common type of activity is image augmentation. In this activity, teachers would like to add multimodal resources (texts, images, videos, audios, 3D models, and interactive menus) to images, such as posters, books, and exercise sheets. For example, a primary school teacher wanted to augment the pages of a book with audio recordings of her narrating textual vocabulary (e.
Participants The participants were 39 (N=39) teachers [Age: 26-30 = 1, 31-35 = 1, 36-40 = 3, 41-45 = 15, 46-50 = 12, > 50 = 7; Discipline: STEM = 22, Non-STEM = 7, Computer Science = 1, Other = 9]. We divided teachers into two groups: the pilot group (P) with 12 teachers and the non-pilot group (NP) with 27 teachers. The pilot teachers were familiar with the MIXAP. We recruited the non-pilot teachers during a local workshop, and they were not familiar with the MIXAP prior to the study. All teachers voluntarily participated in the study without compensation and signed a consent for using and analyzing their data for research. Figure  3 : An example of the AR activity that the teachers created to associate two cards. We introduced this activity to the teachers as: "This type of activity allows you to visualize augmentations if two images are visible in the scene at the same time (e.
Data Collection and Analysis We collected 39 responses to the questionnaire from the participants; 12 responses from the partner group and 27 responses from the non-partner group. For the usability (H1-2), we used the 10 SUS questions [Q1-10] to assess the total SUS score as well as ease of use [Q1,2,3,5,6,7,8,9] and learnability [Q4,10]  (Brooke et al. , 1996; Lewis and Sauro, 2009). We formulated the question  [Q15]  along with an open-ended question [Q16] to assess the utility (H3). For the acceptability (H4-5), we formulated [Q12,14] to assess emotional experience [Q11,13] to assess the cognitive load. For transfer (H6-7), we analyzed the variance of responses with respect to the factors Group and Demographics (Disciple and Age). Questions were on a 5-point Likert scale, except for [Q16].
ACKNOWLEDGMENT We thank the  Rising Star program of Pays de la Loire, France , for funding the  MIXAP  research project. Special thanks to the teachers, including the pilot:  Annabel Le GOFF ,  Camille POQUET ,  Damien DUMOUSSET ,  Delphine DESHAYES ,  Elisabeth PLANTE ,  Frederic LLANTE ,  Laurent HUET ,  Nicolas JOUDIN ,  Tony NEVEU ,  Vanessa FROC ,  Yannick GOURDIN ,  Régis MOURGUES ,  Morgane ACOU-LE NOAN , and  Adeline JAN.
The computational cost of this approach is doubly exponential in the dimension of the ambient space and polynomial in the number of input polynomials and their maximum degree. A more modern approach, introduced in  [11]  and refined in  [3]  allows one to compute sample points per connected components of basic semi-algebraic sets in time which is singly exponential in the number of variables and polynomial in the number of input polynomials and their maximum degree. We refer the reader to  [4]  for the foundations of such approaches and to  [23]  for more modern algorithms based on the critical point method and to the Maple package RAGlib  [22]  which implements them. The two main functions which are provided by Maple package RAGlib are HasRealSolutions and PointsPerComponents. Both take as input polynomial systems of equations and strict inequalities with coefficients in Q.
Contributions We investigate some structural properties of the polynomial system to solve and in particular the degree pattern of our polynomial constraints w. r. t. the involved variables. By leveraging this degree pattern, we simplify significantly the first steps of the Cylindrical Algebraic Decomposition algorithm and show that on the system we need to solve, the classical complexity growth one faces does not occur. We push forward this investigation and show how to exploit even better the degree patterns of intermediate data to find even stronger simplifications. In the end, after those simplifications, we obtain a polynomial system with less variables which can be solved rather easily (within a few seconds) by the RAGlib Maple package. These simplifications are based on a careful geometric analysis of the solution set to the system we need to solve.