Theoretical and Numerical Analysis of Soil-Pipe Pile Horizontal Vibration Based on the Fractional Derivative Viscoelastic Model

To describe the mechanical properties of the system of pipe pile-soil reasonably and accurately, the constitutive relations of the soil around pile and pile core soil are characterized by the fractional derivative viscoelastic model. We assume that the radial and circumferential displacements of the soil around the pile and pile core soil are the functions of r, θ, and z. The horizontal dynamic control equations of soil layers are derived by using the fractional derivative viscoelastic model. Considering the fractional derivative properties, soil layer boundary condition, and contact condition of pile and soil, the potential function decomposition method is used to solve the radial and circumferential displacements of the soil layer. Then, the force of unit thickness soil layer on the pipe pile and the impedance factor of the soil layer are obtained. The horizontal dynamic equations of pipe pile are established considering the effect of soil layers. The horizontal dynamic impedance and horizontal-swaying dynamic resistance at the pile top are obtained by combining the pipe pile-soil boundary conditions and the orthogonal operation of trigonometric function. Numerical solutions are used to analyze the influence of pile and soil parameters on the soil impedance factor and horizontal dynamic impedance at pile top. The results show that the horizontal impedance factors of the soil layer and horizontal dynamic impedance of pipe pile by using the fractional derivative viscoelastic model can be degraded to those of the classical viscoelastic model and the elastic model. For the fractional derivative viscoelastic model of soil layer, the influence of soil around pile on the dynamic impedance is greater than that of pile core soil. The model parameter TOa, the inner radius of pipe pile, and the pile length have obvious effects on the horizontal impedance of the soil layer and pipe pile, while the influence of the pile core soil on the pile impedance is smaller.

Spatio-Temporal Characteristics of Dry-Wet Conditions and Boundaries in Five Provinces of Northwest China from 1960 to 2020

The study of dry-wet climate boundaries in the context of climate warming is of great practical significance for improving the environment of ecologically fragile zones and promoting economic and natural sustainable development. In this study, based on the daily meteorological data of 110 stations, using the wetness index, empirical orthogonal function decomposition, regime shift detection test, Fourier power spectrum, and Kriging interpolation, the researchers analyzed the spatiotemporal characteristics of dry-wet conditions and boundaries in five provinces of Northwest China from 1960 to 2020. The results showed that the overall wetness index increased in the past 61 years, but with significant internal differences, among which the western and central climate tended to be warm and wet, and the eastern tended to be warm and dry. The annual wetness index changed abruptly in 1986 with cycles of 3.61 a, 7.11 a and 8.83 a. The mutations occurred correspondingly in spring, summer, autumn, and winter in 1972, 1976, 1983, and 1988, with periods of 3.88 a and 4.92 a, 2.18 a and 2.81 a, 2.15 a, and 2.10 a, respectively. The dry-wet climate boundary has fluctuated markedly since 1960. The extreme arid and arid regions boundary shifted southward and shrank in size until the extreme arid region disappeared in the 2010s. The arid along with semi-arid regions and semi-arid in addition to semi-humid regions boundaries both have two boundary lines, and show the shift of the northwestern boundary to the southeast and the southeastern boundary to the northwest, with the area of the arid together with semi-arid regions shrinking significantly by 5.64%, simultaneously, the area of the semi-humid region area expanding significantly by 84.11%. The boundary of semi-humid and relatively humid regions, and the boundary of relatively humid and humid regions all shifted to the southeast, moreover, the area of the relatively humid region and humid region shrank significantly by 12.08%. The expansion of semi-humid region and the contraction of other climate regions are characteristics of the dry-wet climate variability in five provinces of Northwest China. The area of the three arid climate zones dwindled by 9.61%, and the area of the three humid zones extended by 39.01%. Obviously, the climate inclined to be warm and humid in general.

Explaining Gold-Mining and Non-Gold Mining Areas' Inequalities in Learning Achievements in Burkina Faso's Primary Education: A Decomposition Analysis

Research shows that learning achievements inequalities exist between students from gold mining areas and those from non-gold mining ones. However, there is no evidence on factors that explain this "new" geographic educational inequality. Exploiting the gold mining boom in Burkina Faso, this study employed re-centered influence function decomposition to explore students' background and school factors which explain these learning achievements inequalities and also estimate the proportion of inequalities explained by unmeasured factors. Findings suggest that, relative to student background factors, most of the learning achievements inequalities between the two types of areas are explained by school factors. Moreover, unmeasured educational factors explain a non-negligible proportion of the inequalities, higher for students on the lower and upper tails of the learning achievements distribution. Suggestions for policymakers are discussed based on the findings of the present study.

Logic Synthesis Strategy Oriented to Low Power Optimization

The article presents a synthesis strategy focused on low power implementations of combinatorial circuits in an array-type FPGA structure. Logic functions are described by means of BDD. A new form of the SWitch activity BDD diagram (SWBDD) is proposed, which enables a function decomposition to minimize the switching activity of circuits. The essence of the proposed idea lies in the proper ordering of the variables and cutting the diagram, ensuring the minimization of switching in the combination circuit. This article contains the results of experiments confirming the effectiveness of the developed concept of decomposition. They were performed on popular benchmarks using academic and commercial synthesis systems.

Winter Cyclone Regimes Over The North Atlantic

To study regimes of winter cyclones in the North Atlantic, empirical orthogonal function decomposition was applied separately to the frequency, depth and area of cyclones obtained using 6-hourly NCEP/NCAR reanalysis data in 1952–2017 and the developed methodology. The first mode represented the opposite changes of cyclone anomalies in the northern and southern/central North Atlantic. The second mode was characterized by the greatest regional anomalies between its phases over Europe, off its coast and over the Mediterranean. The greatest changes of anomalies for the third modes were in temperate latitudes, both over the ocean and Europe. Linear trends were significant only for the first modes of cyclone parameters. The largest part of variability (74–90% of dispersion) of all cyclone modes corresponded to the periods up to 15 years and was used for spectral analysis, which identified significant spectral peaks: 2.5–3, 4.5, 6 and 8.5 years. These periods coincided with spectral peaks of the main interannual climate signals. Regression analysis allowed to identify the sets of teleconnection patterns responsible jointly for 60–85% of dispersion of the first cyclone modes. The North Atlantic Oscillation and Arctic Oscillation were the main patterns for the first modes of the cyclone parameters. For the second and third frequency modes, the East Atlantic (EA) pattern and a combination of the East Atlantic/West Russia (EA/WR) and Scandinavia patterns played the major role, respectively. As for the third depth and area modes, the association with the EA and EA/WR patterns was shown, respectively.

MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks

Value function decomposition (VFD) methods under the popular paradigm of centralized training and decentralized execution (CTDE) have promoted multi-agent reinforcement learning progress. However, existing VFD methods proceed from a group's value function decomposition to only solve cooperative tasks. With the individual value function decomposition, we propose MFVFD, a novel multi-agent Q-learning approach for solving cooperative and non-cooperative tasks based on mean-field theory. Our analysis on the Hawk-Dove and Nonmonotonic Cooperation matrix games evaluate MFVFD's convergent solution. Empirical studies on the challenging mixed cooperative-competitive tasks where hundreds of agents coexist demonstrate that MFVFD significantly outperforms existing baselines.

Indices of the Hadley circulation strength and associated circulation trends

This study compares the trends of Hadley cell (HC) strength using different HC measures applied to the ECMWF ERA5 and ERA-Interim reanalyses in the period 1979–2018. The HC strength is commonly evaluated by indices derived from the mass-weighted zonal-mean stream function. Other measures include the velocity potential and the vertical velocity. Six known measures of the HC strength are complemented by a measure of the average HC strength, obtained by averaging the stream function in the latitude-pressure (φ-p) plane, and by the total energy of unbalanced zonal-mean circulation in the normal-mode function decomposition. It is shown that measures of the HC strength, which rely on point values in the φ-p plane, produce unreliable long-term trends of both the northern and southern HCs, especially in ERA-Interim; magnitudes and even the signs of trends depend on the choice of HC strength measure. The two new measures alleviate the vertical and meridional inhomogeneities of the trends in the HC strength. In both reanalyses, there is a positive trend in the total energy of zonal-mean unbalanced circulation. The average HC strength measure also shows a positive trend in ERA5 in both hemispheres, while the trend in ERA-Interim is insignificant.

A Bivariate Chebyshev Polynomials Method for Nonlinear Dynamic Systems With Interval Uncertainties

A bivariate Chebyshev polynomials approach is proposed to estimate the dynamic response bounds of nonlinear systems with interval uncertainties. The existing collocation method directly searches the maximum and minimum values of the surrogate model in the entire interval space by the scanning method (SM). The presence of too many uncertain parameters will lead to expansive computational cost. To overcome this shortcoming, the dynamic response is decomposed by a bivariate function decomposition (BFD), established based on high-order Taylor expansion, into the sum of multiple univariate and bivariate response functions. The above univariate and bivariate functions are fitted using Chebyshev polynomials, and polynomial coefficients are obtained through one-dimensional (1D) and two-dimensional (2D) interpolation points. Thus, the solution of the nonlinear dynamic systems with uncertain parameters can be transformed into that of univariate and bivariate Chebyshev interval functions. The extremum values of the low-dimensional Chebyshev interval functions can be found by SM, and then the bounds of dynamic response are acquired by interval arithmetic. Since SM searches for extreme values only in 1D and 2D uncertain domains, the amount of calculation is reduced compared to searching the whole uncertain space. The efficiency, practicability and effectiveness of the proposed interval uncertainty analysis method are proved by three dynamic examples.

Application of linear dynamical mode decomposition to ensembles of climate simulations

<p>An analysis of the climate system is usually complicated by its very high dimensionality and its nonlinearity which impedes spatial and time scale separation. An even more difficult problem is to obtain separate estimates of the climate system’s response to external forcing (e.g. anthropogenic emissions of greenhouse gases and aerosols) and the contribution of the climate system’s internal variability into recent climate trends. Identification of spatiotemporal climatic patterns representing forced signals and internal variability in global climate models (GCMs) would make it possible to characterize these patterns in the observed data and to analyze dynamical relationships between these two types of climate variability.</p><p>In contrast with real climate observations, many GCMs are able to provide ensembles of many climate realizations under the same external forcing, with relatively independent initial conditions (e.g. LENS [1], MPI-GE [2], CMIP ensembles of 20th century climate). In this report, a recently developed method of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs) [3] based on Bayesian approach, is modified to address the problem of self-consistent separation of the climate system internal variability modes and the forced response signals in such ensembles. The LDM method provides the time series of principal components and corresponding spatial patterns; in application to an ensemble of realizations, it determines both time series of the internal variability modes of current realization and the time series of forced response (defined as signal shared by all realizations). The advantage of LDMs is the ability to take into account the time scales of the system evolution better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Furthermore, the modified ensemble LDM (E-LDM) method is designed to determine the optimal number of principal components and to distinguish their time scales for both internal variability modes and forced response signals.</p><p>The technique and results of applying LDM method to different GCM ensemble realizations will be presented and discussed. This research was supported by the Russian Science Foundation (Grant No. 18-12-00231).</p><p>[1] Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S., Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and M. Vertenstein (2015), The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-13-00255.1, 96, 1333-1349 </p><p>[2] Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li, H., Manzini, E., Notz, N., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B. and Marotzke, J. (2019). The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability. Journal of Advances in Modeling Earth Systems, 11, 1-21. https://doi.org/10.1029/2019MS001639</p><p>[3] Gavrilov, A., Kravtsov, S., Mukhin, D. (2020). Analysis of 20th century surface air temperature using linear dynamical modes. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(12), 123110. https://doi.org/10.1063/5.0028246</p>