dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The arti cial neural network (ANN) algorithm was used in this study to develop a model that allows predicting the plasticity index of sand-treated clayey soils. In addition, the effect of sand content on the plasticity index value of the mixture was also investigated. The ndings of the study allowed drawing the following conclusions : This study con rms the good performance of the ANN model for predicting the plasticity index of clay soils treated with various contents of sand, with a high coe cient of determination (R²). Indeed, the values R² found for the training, testing, and validation datasets were quite high; these values were respectively equal to 0. 99, 0. 97 and 0. 99. In addition, the mean absolute error (MAE) had the lowest possible value (2. 48%), which means that the ANN model is the appropriate choice for estimating the plasticity index.
2. 3. Figure 4 34 Figure 4 gives an explicit comparison between the sand-treated clay soils plasticity index values obtained with the ANN model and the experimental data used in the training and test sets. This same gure clearly indicates that the correlation coe cient R² (0. 99) obtained is signi cantly high for the training sets, and it is a high (0. 97) for the testing sets. The value R² = 0. 99 is used for the validation of the model. The ANN model was validated using the four experimental results of Gokalp (2009), Jjuuko et al. (2011), Öncü and Bilsel (2018), and Jyothi et al. (2019) ; these results are not included in the training database. This was done for the purpose of predicting the plasticity index from the input parameters such as the liquid limit,.
3D printing by robocasting The robocasting equipment consists of a computer with the installed design software, RoboCAD (version 4. 0. 0. 2; "3D Inks, LLC", Stillwater, OK, USA) which communicates thanks to the motion control software, Aerotech A3200, with the Robocaster itself, Aerotech AGS1000-ES16232 (both "Aerotech, Inc. ", Pittsburgh, PA, USA). Thanks to magnetic linear motors is the ink-filled syringe ("Nordson EFD", East Providence, RI, USA) movable in X-and Y-direction. A total of three displacement controlled plungers, vertically displaceable in Z-direction, are available. A temperature and humidity control package was used to ensure no change of these parameters during printing in the humidity chamber. Before printing, the ink-loaded syringes were cooled down to 4 C in order to benefit from the low viscosity.
Acknowledgement The first author thanks  AUF  ( Agence universitaire de la francophonie ),  PAUSE (Programme national d'aide a l'Accueil en Urgence des Scientifiques en Exil) program ,  Scholar Rescue Fund (SRF)  and the personnels of  INSA Lyon and MATEIS-METAL  for the scholar rescue supports. L. Gremillard and  E. Camposilvan  are acknowledged for their help for operating the robocaster machine. T. Douillard  is acknowledged for the experiment on the FIB/SEM. r e f e r e n c e s.
Constant Growth Rates Let us consider system  (5)  where the growth rate functions follow Assumption 4 (constant values) with values given in Table  1. Table  1. Numerical values of the different parameters: case of constant growth rates. a 1 a 2 r 1 r 2 Y 1 Y 2 T 2 1/2 1 0. 1-0. 5 1 1 2 In this case, the switching time τ introduced by Equation (  17 ) is given by τ = log(4) and the optimal control is given by: α (t) = 1 if t ≥ min(0, 2 -log(4)) 0 otherwise. As underlined in Remark 2, the optimal control α does not depend on the migration rate constant r 2. On Figure  2 , we plot (with Matlab) the optimal control given by Proposition 3 together with the optimal trajectories in the two cases r 2 = 0. 1 (bottom left) and r 2 = 0. 5 (bottom right). As a verification, these plots are also compared with the ones obtained with Bocop  [23] , which is a numerical optimization software dedicated to optimal control problems (using direct method) on Figure  2.
Experimental setting Computing platform. The proposed MNSA algorithm was implemented in C++ and compiled using the g++ compiler with the -O3 option. The experiments were carried on an Intel Xeon E5-2670 processor with 2. 5 GHz and 200 MB RAM under the Linux operating system. We also tested the mathematical programming approach with a 0/1 programming model (see the Appendix) with the CPLEX MIP solver (version 12. 8), run on an Intel Core i7-8750H 2. 20 GHz processor with 32 GB RAM under the Linux operating system with a time limit of 2 h. Parameter settings. We used the automatic configuration tool ''irace''  [28]  to calibrate the parameters of the MNSA algorithm. This was achieved by feeding irace with six randomly selected instances with a tuning budget of 1000. We adopted the best parameter configuration given by irace and shown in Table  2.
Computational methodology Based on DFT the academic Vienna Ab initio Simulation Package (VASP)  [8, 9]  was used with the projector augmented wave (PAW) method  [9, 10]  for the initial atomic potentials built within the gradient exchange-correlation functional (GGA)  [11]. For relaxing the atoms onto ground state structure, Blöchl tetrahedron method  [12]  was used with Methfessel-Paxton corrections  [13]. Reciprocal space Brillouin-zone (BZ) integrals were approximated with a special k-points sampling  [14]. Properties pertaining to the mechanical stability and hardness were derived from the sets of elastic constants. Besides, we inferred dynamic stability from the phonons computed using the harmonic approximation. The phonon band structures along the direction of the tetragonal Brillouin zone were subsequently obtained using the "Phonopy" interface code  [15].
Simulation Results and Discussion In this paper, the performances of the proposed scheme, compared with the conventional scheme SB_DSICED3_W/OF, are illustrated in terms of the MSE, BER, and processing time by using Monte Carlo simulations in MATLAB. For the 5G QC-LDPC codes, the base graph matrix BG2 was chosen for all simulations. The index of the iterations for the joint iterative decoding and the 5G QC-LDPC decoding is denoted as i and j, respectively. Many simulations with various channel models and the number of channel taps (ranging from 3-8 taps) were implemented. However, the varying of the channel models did not significantly change the performance results. Due to the limitation and not to overload the paper, all of the configurations and figures are not provided for reasons of readability of the paper and better understanding.
Comparison of the Processing Time and Computational Complexity In this section, we compare the processing time and computational complexity of the proposed semi-blind scheme JISB_DSICED3 and the conventional without feedback scheme SB_DSICED3_W/OF. The processing time is an important factor for performance evaluation since it quantifies the effectiveness of the algorithm, especially in 5G short-packet transmissions and IoT applications. In this case, a computer with the hardware configuration of Intel (R) Core (TM) I5-10500 CPU @ 3. 10 GHz (12 CPUs) and a memory of 16 GB of RAM was used with MATLAB Version 2020b. For the simulation parameters, we set p SI /σ 2 w = 30 dB, 10 6 transmission frames, E = 128 symbols, β = 1/32 (βE = 4 symbols), i max = 4, and j max = 1 for the semi-blind scheme JISB_DSICED3 and j max = 20 for the semi-blind without feedback scheme SB_DSICED3_W/OF.
Acknowledgments: The authors would like to thank the  University of Brest (UBO) , the  Direction Europe and International (DEI) , the  Brest Institute of Computer Science and Mathematics (IBNM) CyberIoT Chair of Excellence , and the  Brittany region-Pôle d'Excellence Cyber  for their funding. We also want to give a warm thank you to all staff and faculties at the  School of Electrical Engineering in International University, Vietnam National University (HCMIU), Ho Chi Minh City, Vietnam , for their support regarding the documents and facilities during the  Ph. D. Student International Mobility  for three months. We also thank the  Consulate of Vietnam in France  and the  Vietnam Airlines Joint Stock Company  for their support regarding the airline ticket and entering process for Vietnam.
The white pixels represent memristors in HRS, while the colored ones correspond to LRS pixels. The yellow neuron did not learn any feature (random weights and minimum threshold), while the red neuron clearly learned a feature present in several letters. The green and blue neurons showed different intermediate learned features. Although stochastic binary STDP was proposed originally in 2013  [20] , for realistic size systems a number of regularization techniques are required  [21]. In the rest of this paper we will use acronym SB-STDP to refer to this regularized version. To first illustrate SB-STDP learning we simulated it in Matlab using behavioral models, with 64 preand 64 post-synaptic neurons and 4,096 binary synapses with initial random values. Every time a post-synaptic neuron j reached its threshold, a post-synaptic spike was produced, and the algorithm would check the list of its pre-synaptic spikes with n < N p , identifying the pre-synaptic neurons i 1 , i 2 , etc.
which had a direct influence on the spiking post-synaptic neuron. All synapses given by (i 1 , j ), (i 2 , j ), etc. would be activated with a probability P LT P , while all other synapses connected to neuron j would be deactivated with a probability P LT D. At the same time, the threshold of output neuron j would be potentiated to make that neuron less sensitive (e. g. , it would become more difficult for this neuron to fire again). This way, after receiving enough pre-synaptic spikes, several post-synaptic neurons would become more specific to certain input features. Fig. 7  illustrates MATLAB simulations of the proposed SB-STDP rule when the 64 input neurons received the simple binary 8 × 8 visual input stimuli images shown later in Fig. 15(a). Fig. 7(a)  shows the evolution of the neuron thresholds as their respective synapses were updated after firing.
The spike list for each character was repeated several times. This experiment was first simulated in Matlab using a behavioral model of the system. For each input spike, the current flowing through all 64 memristors connected to the corresponding input neuron was calculated using each spikes' physically measured resistance. This way, 64 input currents were obtained for all 64 output neurons. For each output neuron, the input current was compared to a threshold I th (10μA). When the current was larger than this threshold, the capacitor voltage V c representing the membrane potential, was updated by δV c. If the new V c value reached the neuron threshold V th , an output spike was sent and all neurons were reset. This experiment was simulated introducing a random variation to δV c in order to emulate the mismatch between neurons in the chip.
Fig. 14(a)  shows the chosen set of 64 images. The 64 irregular shapes were used as inputs for this second template matching experiment. First, they were encoded in the memristive crossbar by programming LRS if a template pixel was black or HRS if it was white. The resistive values of all the memristors were measured, obtaining the results shown in Fig. 14(b). After all the synaptic values had been programmed in the memristive crossbar, the inference experiment consisted of sequentially presenting all 64 input symbols to the network, as in the previous experiment. This experiment was first simulated using the same Matlab model, obtaining the raster plot shown in Fig. 14(c ) and the confusion matrix shown in Fig. 14(e). The performance of this simulation was computed by calculating the ratio of correct spikes over the total number of output spikes, obtaining 82.
For that purpose, all input letters A, B, C and D were processed by the SB-STDP layer with learning disabled, and the spikes obtained were used to train and validate the classification layer, following the same procedure as that described above and obtaining network performance measurements (R ev and RR) which can be compared with respect to those obtained using SB-STDP learning. This allowed us to assess the benefits of using SB-STDP feature learning for the first layer in helping the classifier layer. To validate the network implemented on-chip, we first simulated its behavior with Matlab and then compared the experimental results with the simulation results. 3) Simulation Results: First, we implemented the complete SB-STDP learning algorithms described above, including the training of both the SB-STDP and classification layers, in Matlab.
In the simulations, we included a simple model of the mismatch in neuron current I c that we had found between post-synaptic neurons on-chip. This mismatch was the dominant non-ideal effect, as it produced a different discharging slope in the capacitor voltage for each neuron. Due to this effect, several neurons needed to integrate different numbers of input spikes before reaching the threshold. We modeled this mismatch in Matlab by adding a random number to the capacitor current. More specifically, we used a membrane capacitor current with a mean value of I c = 10n A and a standard deviation of 2. 5n A, representing a mismatch of 25%. This value produced a simulated behavior consistent with the experimental data. To compensate the mismatch, we modified the threshold of each output neuron when SB-STDP learning was disabled.
This work was supported in part by  EU H2020  under Grant  687299 "NeuRAM3 " and Grant  871371  " MeM-Scales ;" and in part by the  Spain's Ministry of Science and Innovation , with support from the  European Regional Development Fund , under Grant  PID2019-105556GB-C31  ( NANOMIND ). The work of Luis A. Camuñas-Mesa was supported by the  VI PPIT  through the  Universidad de Sevilla. This article was recommended by Guest Editor  R. Tetzlaff.
Rheological behavior of ethylene glycol-based nanofluids containing TiO 2 nanoparticles was examined by many reserchers  [16] [17] [18] [19]. It was presented that nanofluids containing high fraction of particles exhibit non-Newtonian nature  [16, 18] , while a low fraction generally induces a Newtonian one  [17]. Thermal conductivity of TiO 2 -EG nanofluids was also well studied, as shown in Refs. [17, [19] [20] [21]. More information about the thermophysical properties of various nanofluids could be found in the state-of-the-art review paper by Qiu et al. [22]. As illustrated by Fig. 1 , the Web of Science database evidences that only few papers considering nanofluids containing titanium dioxide nanoparticles in various base fluids and their electrical properties were published (data collected at 07-09-2021).
Experimental methods The X-ray diffraction patterns of nanoparticles were measured in the 20-90°2θ range with a step size of about 0. 015°and collection time of 250 s/step using a monochromatized (Cu Kα1, λ=1. 5406Å) Bruker D8 Advance diffractometer equipped with a fast LynxEye detector. The powder X-ray diffraction patterns were refined by the Rietveld method  [31]  using the FullProf software  [32] , enabling the quantification of the phases in presence. The fitting of the peak broadening using a Thompson-Cox-Hasting function enables the deconvolution of the instrumental (determined from a corundum standard sample) and sample contributions to the broadening  [33]. A Micromeritics Gemini VII 2390 t instrument using a mixture of N2/ He (30%/70%) was used to experimentally evaluate from nitrogen physisorption the (BET) specific surface area of TiO 2 nanoparticles.
The experimental data presented in this paper have also been numerically analysed and the evolution of electrical conductivity of titanium dioxide ethylene glycol nanofluids can be described with good accuracy by the following power function: σ nf ¼ 1 þ Bφ A m σ bf : ð10Þ Consequently, the enhancement in EC can be expressed by following equation: σ nf σ bf ¼ 1 þ Bφ A m , ð11Þ where A and B are numerical factors. The fitting was performed with OriginPro software with R 2 better than 0. 997 for TiO 2 A-EG, TiO 2 M1-EG and 0. 928 for TiO 2 M2-EG nanofluids. The results of the fitting can be seen in Fig. 6  where experimental data and obtained functions were presented. For evidencing the relevance of the power function to describe experimental results of electrical conductivity obtained by other researchers, Eq.
FEM Simulation of a Biocompatible Package Finite element method (FEM) simulation, with appropriate mechanics theories, becomes useful to find a solution of the reliability issues of different package structures. For example, underfill has significant impact on flip chip package reliability due to delamination driven by coefficient of thermal expansion (CTE) mismatch between organic substrate and silicon die. Such a delamination problem can be solved through FEM study of the effects of various design variables, including underfill material properties, fillet dimensions, and die overhang on underfill delamination fracture parameters. Since the delamination can be considered as a bimaterial interfacial crack, the FEM study is based on interfacial fracture mechanics, fundamentally a mixed mode, including both energy release rate and phase angle  [38].
Likewise, the biocompatible package can be simulated through FEM modeling to study its reliability issues concerning delamination of encapsulating polymer. Figure  5a  shows a conceptual drawing of biocompatible package for FEM simulation. The Si chip is attached to PCB carrier with glue and then wire-bonded for electrical connection. Flexible cables are connected with typical flexible cable connector at I/O ports of the Si chip. The conventional package has been encapsulated with a biocompatible polymer. Failure of such biocompatible package could be caused by imperfect encapsulation material with voids or microcracks as indicated in Figure  5b. Therefore, the failure mode, due to the initial crack, is of interest for the FEM simulation. Figure  5c  presents 2D FEM model for fracture analysis due to the initial crack.
5 mm crack length, K II is slightly reduced and K I is still increasing, which is confirmed in phase angle change as function of crack length. Phase angle starts from near 90 • (shear mode) and shows 72 • at 1 mm crack length. Phase angle of 0 • represents opening mode. Therefore, shear mode strength of epoxy is important when crack length is small, while tensile mode strength becomes important when crack length is substantial. SERR in mode 2 (G 2 ) has maximum at 0. 5 mm crack length, while SERR in mode 1 (G 1 ) increases in the crack length of interest. Micromachines 2021, 12, x 9 of 16 nation can be considered as a bimaterial interfacial crack, the FEM study is based on interfacial fracture mechanics, fundamentally a mixed mode, including both energy release rate and phase angle  [38]. Likewise, the biocompatible package can be simulated through FEM modeling to study its reliability issues concerning delamination of encapsulating polymer.
ANSYS (v. 19. 2, 2018, Ansys, Inc. , Canonsburg, PA, USA) is a program capable of simulating the stress of a material subject to force. Regarding the use of ANSYS in the simulation of ultrasonic bonding, Wang et al. [10]  simulated the stress and strain generated by the bonding of composite polymer materials and Al, and compared the simulated joint shear strength with the value obtained in an experimental demonstration. Arai et al. [11]  simulated horizontal and vertical deformation in bumps subject to ultrasonic bonding and suggested that high deformation is associated with high bonding strength. Sasaki et al. [12]  simulated the effect of different welding heads on ultrasonic bonding and reported a positive correlation between bonding strength and the depth of stress distribution. In ultrasonic bonding, friction exerts a substantial effect on the stress and strain distributions.
Finite Element Analysis To explore the effect of bump shape and size on the effectiveness of ultrasonic bonding, FEA was performed to simulate the stress exerted on the bump surface during the bonding process. Figure  1  presents the setup for simulation. First, 3D software was used to create a model and generate three types of bumps, namely little square bumps (LSB), little round bumps (LRB), and big round bumps (BRB). The bumps were stacked on a chip coated with metal thin films, and the workpiece was placed on a platform with a heating rod. Model meshing was performed based on the bump size. Next, the static structural model was selected in ANSYS. The steady-state thermal model was then selected and linked with the static structural model to configure the properties of the materials, namely Al, Cu, and Ni/Pd/Au.
Since the TMOS may be operated at a subthreshold level, thus consuming very low power, it may be powered by a battery, enabling a wide range of applications related to mobile phones, smart homes, security, and IoT. This low power feature of the TMOS is a great advantage in comparison to the passive bolometers for example, which currently dominate the IR imaging market. In addition, since the TMOS is a transistor, which is an active device exhibiting large internal gain even at subthreshold, it exhibits unprecedented temperature sensitivity TCV[K -1 ] = (dV/dT)/V and very high responsivity in terms of voltage/wattage in comparison to the commercial passive thermal sensors such as pyroelectric sensors, thermopiles, and diodes. This paper presents modeling and simulations based on the LUMERICAL software package  [20]  for the absorption of IR radiation by a vacuum-packaged TMOS sensor.
Since the TMOS is processed in a CMOS-SOI FAB and is comprised of multiple thin layers, the absorbed electromagnetic radiation cannot be modeled accurately and a simulation tool is required. Section 2 discusses the choice of the simulation tool and the advantages and drawbacks of LUMERICAL. Section 3 presents the design of a TMOS pixel, as well as the vacuum-packaged TMOS. Incident IR radiation is first transmitted through the upper silicon wafer, which provides the optical window. The transmitted electromagnetic radiation is absorbed in the MEMS/NEMS released TMOS. Section 4 discusses the absorption, reflection, and transmission of the front optical window cap. Since the optical window has a simple structure, the simulation is compared with analytical numerical modeling with MATLAB. The correspondence between the simulation, modeling and measurements validates the results.
In the FEM method, the domain volume is discretized to a finite number of elements and nodes, usually by tetrahedron cell mesh. The field is approximated for each cell. Among the nodes, a piecewise polynomial solution is assumed and applying the boundary conditions and simulation properties yields a sparse matrix to determine the fields. Unlike the FDTD method, the FEM method solves the equations in the frequency domain, which makes it more suitable for example for resonators or other high-Q circuits. RF engineers and researchers prefer to use CST or HFSS commercial software while electro-optical scientists may appreciate the advantages of LUMERICAL.
LUMERICAL Simulation A FDTD model has been created in LUMERICAL as described in Figure  4  in order to simulate the TMOS optical window. As can be seen in Figure  4 , on the sides of the FDTD region periodic boundary conditions were applied and on the top and bottom of the FDTD region a perfectly matched layer (PML) boundary conditions were applied. A radiation source with the wavelength range of 5-20 µm was set above the TMOS cap. The refractive indices of the materials were set by the software using Palik data  [20, 24]. The simulation results are shown in Figure  5. The simulations and modeling show good agreement, as can be seen in Figure  6. The measured transmission results, compared with the simulation, as shown in Figure  7 , are very different and do not show the resonance lines. A good fit between measurements and simulation or modeling requires partial spectral averaging, and δ (delta) estimation, as explained below.