gradient theory
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2022 ◽  
Vol 8 ◽  
Author(s):  
Diana Mădălina Mocanu

What I propose in the present article are some theoretical adjustments for a more coherent answer to the legal “status question” of artificial intelligence (AI) systems. I arrive at those by using the new “bundle theory” of legal personhood, together with its accompanying conceptual and methodological apparatus as a lens through which to look at a recent such answer inspired from German civil law and named Teilrechtsfähigkeit or partial legal capacity. I argue that partial legal capacity is a possible solution to the status question only if we understand legal personhood according to this new theory. Conversely, I argue that if indeed Teilrechtsfähigkeit lends itself to being applied to AI systems, then such flexibility further confirms the bundle theory paradigm shift. I then go on to further analyze and exploit the particularities of Teilrechtsfähigkeit to inform a reflection on the appropriate conceptual shape of legal personhood and suggest a slightly different answer from the bundle theory framework in what I term a “gradient theory” of legal personhood.


2021 ◽  
Vol 26 (4) ◽  
pp. 296-305
Author(s):  
Li Yun-dong ◽  
Cheng Feng ◽  
Wen Huabin

Size-dependent effects of a cantilevered piezoelectrically actuated micropipe conveying fluid are investigated. Based on the modified strain gradient beam theory, the model of system is obtained using Hamilton's principle. The motion equation is discretized into ordinary differential equations by Generalized Differential Quadrature Method (GDQM). A stability analysis of the system is completed through eigenvalue analysis. Numerical results show the effect of geometrical shape size, and length scale parameters on critical flow velocity, and critical voltage. Results prove that the modified strain gradient theory (MSGT) has a higher critical flow velocity and critical voltage than predicted by modified couple stress theory (MCST) and classical theory (CT).


2021 ◽  
Vol 11 (24) ◽  
pp. 11787
Author(s):  
Shan Zeng ◽  
Zhangtao Peng ◽  
Kaifa Wang ◽  
Baolin Wang ◽  
Jinwu Wu ◽  
...  

In this study, a sandwich piezoelectric nano-energy harvester model under compressive axial loading with a core layer fabricated of functionally graded (FG) porous material is presented based on the nonlocal strain gradient theory (NSGT). The von Karman type geometric nonlinearity and the axial loading were considered. The electromechanical governing equations were obtained using Hamilton’s principle. The nonlinear vibration frequencies, root mean square (RMS) voltage output and static buckling were obtained using the Galerkin method. The effects of different types of porous distribution, porosity coefficients, length scale parameters, nonlocal parameters, flexoelectricity, excitation frequencies, lumped mass and axial loads on the natural frequency and voltage output of nanobeams were investigated. Results show that the porous distributions, porosity coefficient of porous materials, the excitation frequencies and the axial load have a large effect on the natural frequency and voltage output of the sandwiched piezoelectric nanobeams. When the NSGT is considered, the critical buckling load depends on the values of the nonlocal parameters and strain gradient constants. In addition, the electromechanical conversion efficiency of the post-buckling process is significantly higher than that of the pre-buckling process. The flexoelectric effect can significantly increase the RMS voltage output of the energy harvester.


2021 ◽  
Author(s):  
Simon Stephan ◽  
Kai Langenbach ◽  
Hans Hasse

In separation processes not only thermodynamic bulk but also interfacial properties play a crucial role. Inclassical theory, a vapour-liquid interface is a two-dimensional object. In reality it is a region in whichproperties change over a few nanometres and the density changes continuously from its liquid bulk to its gasbulk value. Many mixtures show unexpected effects in that transition region. While the total density changesmonotonously from the bulk vapour to the bulk liquid, this does not hold for the molarities of the components.The molarities of the light boiling component can have a distinct maximum at the interface. That maximumwould be an insurmountable obstacle to mass transfer according to Fickian theory. Even if that argument isnot adopted, it shows that there is good reason to believe that the maximum may affect mass transfer and,hence, fluid separation processes like absorption or distillation. Unfortunately, there are currently noexperimental methods that can be used for direct studies of density profiles in such interfacial regions. Butsuch data can be obtained with theoretical methods, namely with molecular dynamics simulations (MD) aswell as with density gradient theory (DGT) or with density functional theory (DFT) combined with an equationof state (EOS).Studies from our group on the vapour-liquid interface of several real mixtures and a model fluid using thesemethods yield consistent results and reveal an important enrichment in some cases. Strong enrichment isfound at vapour-liquid interfaces in the systems in which one of the components is supercritical. These resultsindicate that mixtures, which are typical for absorption processes usually show an important enrichment,whereas this is not the case for mixtures that are typically separated by distillation. Possible consequences ofthis finding for the modelling of these separation processes are discussed.


2021 ◽  
Author(s):  
Ivan Priezzhev ◽  
Dmitry Danko ◽  
Uwe Strecker

Abstract Instead of relying on analytical functions to approximate property relationships, this innovative hybrid neural network technique offers highly adaptive, full-function (!) predictions that can be applied to different subsurface data types ranging from (1.) core-to-log prediction (permeability), (2.) multivariate property maps (oil-saturated thickness maps), and, (3.) petrophysical properties from 3D seismic data (i.e., hydrocarbon pore volume, instantaneous velocity). For each scenario a separate example is shown. In case study 1, core measurements are used as the target array and well log data serve training. To analyze the uncertainty of predicted estimates, a second oilfield case study applies 100 iterations of log data from 350 wells to obtain P10-P50-P90 probabilities by randomly removing 40% (140 wells) for validation purposes. In a third case study elastic logs and a low-frequency model are used to predict seismic properties. KNN generates a high level of freedom operator with only one (or more) hidden layer(s). Iterative parameterization precludes that high correlation coefficients arise from overtraining. Because the key advantage of the Kolmogorov neural network (KNN) is to permit non-linear, full-function approximations of reservoir properties, the KNN approach provides a higher-fidelity solution in comparison to other linear or non-linear neural net regressions. KNN offers a fast-track alternative to classic reservoir property predictions from model-based seismic inversions by combining (a) Kolmogorov's Superposition Theorem and (b) principles of genetic inversion (Darwin's "Survival of the fittest") together with Tikhonov regularization and gradient theory. In practice, this is accomplished by minimizing an objective function on multiple and simultaneous outputs from full-function (via look-up table) Kolmogorov neural network runs. All case studies produce high correlations between actual and predicted properties when compared to other stochastic or deterministic inversions. For instance, in the log to seismic prediction better (simulated) resolution of neural network results can be discerned compared to traditional inversion results. Moreover, all blind tests match the overall shape of prominent log curve deflections with a higher degree of fidelity than from inversion. An important fringe benefit of KNN application is the observed increase in seismic resolution that by comparison falls between the seismic resolution of a model-based inversion and the simulated resolution from seismic stochastic inversion.


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