Small-scale Factors Research Articles

Research on Identification and Location of Mining Landslide in Mining Area Based on Improved YOLO Algorithm

The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the mAP@0.5 of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area.

Analytical solutions for thermo-elastic damping of rotational ring resonators incorporating thermal relaxations and elastic small scales

At the micro and nanoscales, thermo-elastic damping is regarded as one of the most crucial factors for promoting energy dissipation in vibrating structures. The thermo-elastic damping of a thin rotational ring resonator is explored in this paper based on three phase lag heat conduction model for thermal relaxations and nonclassical elasticity for small scale effects. Solving the heat conduction equation for radial direction heat flow and zero heat flux boundary conditions yields the temperature spread across ring cross-section. The equation takes into account only the rotating narrow ring’s in-plane vibrations. For the frequency dependent thermo-elastic damping, the combined nonlocal thermo-elasto-dynamic equations are solved. The present model is validated with the experimental and finite element data. The thermo-elastic damping of rotating rings has been found to be over predicted by classical elasticity. For a particular small scale factor, as rotational speed rises by 80%, the equivalent thermo-elastic damping of the ring resonator reduces by 43%. In addition, the effects of cross-sectional shape, nonlocal scale factor, rotating speed, modenumber, ambient temperature, and substance on thermo-elastic damping are thoroughly explored in this work. The results presented here are useful to build and construct future nanoscale resonant sensors and radio-frequency devices.

Anisotropic Segre [(11)(1,1)] dark energy following a particular equation of state

A generally anisotropic equation of state originally derived in the context of Newman-Janis rotating systems allows for vacuum energy at a specific density. In this paper we examine the possibility of using that equation of state for cosmological dark energy. We treat the case of large scale ordering of the directions of the energy-momentum tensor eigenvectors with a Bianchi cosmological model, and treat the case where the ordering is random on small scales with an effectively isotropic FLRW system. We find particular spacetimes which evolve towards a vacuum energy/ de Sitter like configuration in either case. In the anisotropic Bianchi case, the system can have behavior reminiscent of big bounce cosmologies, in which the matter content approaches vacuum energy at large scale factor and can behave in a variety of ways at small scale factor. For particular conditions in the effectively isotropic case, we can evolve between true and false vacuum configurations, or between radiation like and vacuum energy configurations. We also show how some simpler equations of state behave under the same assumptions to elucidate the methods for analysis.

Tunneling wave function proposal with loop quantum geometry effects

In Vilenkin's tunneling wave function proposal our expanding Universe is born via a tunneling through a barrier from nothing at the zero scale factor. We explore the viability of this proposal for the spatially-closed Friedmann-Lema\^{\i}tre-Robertson-Walker model with a positive cosmological constant including quantum gravity modifications in the Planck regime. Our setting is the effective spacetime description of loop quantum cosmology (LQC) which is known to replace the big bang singularity with a bounce due to the holonomy modifications. Due to the bounce, the barrier potential of the Wheeler-DeWitt theory is replaced by a steplike potential which makes the tunneling proposal incompatible. But for a complete picture of singularity resolution, inverse scale-factor modifications from quantum geometry must be included which play an important role at very small scale factors in the spatially closed models. We show that with inclusion of inverse scale-factor modifications the resulting potential is again a barrier potential. The Universe at the vanishing scale factor is dynamically nonsingular and in an Einstein staticlike phase. We show that quantum geometric effects in LQC provide a nonsingular completion of Vilenkin's tunneling proposal. We also find that quantum geometric effects result in a possibility of a tunneling to a quantum cyclic universe albeit for a very large value of cosmological constant determined by the quantum geometry.

Bias and Refinement of Multiscale Mean Field Models

Mean field approximation is a powerful technique which has been used in many settings to study large-scale stochastic systems. In the case of two-timescale systems, the approximation is obtained by a combination of scaling arguments and the use of the averaging principle. This paper analyzes the approximation error of this 'average' mean field model for a two-timescale model (X, Y), where the slow component X describes a population of interacting particles which is fully coupled with a rapidly changing environment Y. The model is parametrized by a scaling factor N, e.g. the population size, which as N gets large decreases the jump size of the slow component in contrast to the unchanged dynamics of the fast component. We show that under relatively mild conditions, the 'average' mean field approximation has a bias of order O(1/N) compared to E[X]. This holds true under any continuous performance metric in the transient regime, as well as for the steady-state if the model is exponentially stable. To go one step further, we derive a bias correction term for the steady-state, from which we define a new approximation called the refined 'average' mean field approximation whose bias is of order O(1/N2). This refined 'average' mean field approximation allows computing an accurate approximation even for small scaling factors, i.e., N ~10 -50. We illustrate the developed framework and accuracy results through an application to a random access CSMA model.

Investigating Quantum Mechanics in 5th Dimensional Embedding via Deterministic Structure, Small Scale Factor, and Initial Inflaton Field

Investigating Quantum Mechanics in 5th Dimensional Embedding via Deterministic Structure, Small Scale Factor, and Initial Inflaton Field

Role of air pollution on COVID-19 in Istanbul

There are many types of research on strong relations between air pollution and the respiration system. This paper is related to the effect of air pollution on pandemic case numbers. By analyses of the number of patients and mean air pollution (µg/m³) data by using data mining, it is concluded that there was evidence of this relationship with a significant level alpha = 0,10. This paper estimates "the number of patients with coronavirus" as a function of daily air pollution and corona case numbers data using single ANN, LSTM, and W-ANN, W-LSTM hybrid methods. This paper explains small, meso, and large scale factors and their role on COVID-19 patients. This finding does not demonstrate a direct cause-effect relationship between air pollution and COVID-19 patients. This study shows the importance of pollution on the number of patients with COVID -19 infection. Although our results have significant uncertainties, we can clearly distinguish the contribution of air pollution to COVID-19 patients. When we used wavelet transformation of air pollution data to estimate COVID-19 patient’s numbers, R2 score is increased in both ANN and LSTM between [0.01- 0.10]. Nevertheless, the actual number of patients is influenced by many additional factors such as the country's health system. After error analysis, sMAPE [3.6-5.9] changed, there is sufficient evidence of model results (M3, M4 Hybrid LSTM) and observation. (0.91

Structure functions for optical waves in a complex medium of turbulent biological tissues.

Although optical wave propagation is investigated based on the absorption and scattering in biological tissues, the turbulence effect can also not be overlooked. Here, the closed-form expressions of the wave structure function (WSF) and phase structure function (PSF) of plane and spherical waves propagating in biological tissue are obtained to help with future research on imaging, intensity, and coherency in turbulent biological tissues. This paper presents the effect of turbulent biological tissue on optical wave propagation to give a perception of the performance of biomedical systems that use optical technologies. The behavior of optical waves in different types of turbulent biological tissues such as a liver parenchyma (mouse), an intestinal epithelium (mouse), a deep dermis (mouse), and an upper dermis (human) are investigated and compared. It is observed that turbulence becomes more effective with an increase in the characteristic length of heterogeneity, propagation distance, and the strength of the refractive index fluctuations. However, an increase in the fractal dimension, wavelength, and small length scale factor has a smaller turbulence effect on the propagating optical wave. We envision that our results may be used to interpret the performance of optical medical systems operating in turbulent biological tissues.

A common framework for modified Regula Falsi methods and new methods of this kind

We show that the modified Regula Falsi methods based on a scaling factor (Scaling Factor Regula Falsi, SFRF) have a common framework, and that the scaling factor depends only on two dimensionless parameters. This common framework allows for the comparison of different methods and their possible combination, without hybridization for treating some difficult cases, such as multiple roots. With this framework we prove, for all SFRF methods, the global convergence of the successive approximations to the root, and also the convergence of the bracketing interval radius to zero, for both simple and multiple roots. We show that SFRF methods only occasionally need a small scaling factor to make the radius of the interval go to zero. This way, the accumulation of the approximations to the root near an interval limit that some improvements of Regula Falsi methods suffer from is cured. As an example, new SFRF methods are exposed. One is specific for multiple roots of known multiplicity, and greatly outperforms pure bisection. Another, developed for simple roots, compares well with other methods belonging to numerical libraries that are widely used by the scientific community, and for multiple roots the new method outperforms them. The new framework could also allow the development of other SFRF methods overperforming the previously known ones.

The role of soil structure interaction (SSI) on seismic response of tall buildings with variable embedded depths by experimental and numerical approaches

The soil-structure interaction is an integral part of the seismic evaluation, especially for structures with variable embedment depths. The purpose of this work is to ensure the adequacy of the small scaling factor in the dynamic analysis and study the seismic behavior of tall building with considering variable embedment depths and soil structure interaction effects. Therefore, scaled model tests of a 15-story steel structure were performed using shaking table tests and numerical simulations. Three scaled earthquake records, namely: Chi-Chi (1999), Northridge (1994), and Kobe (1995), were selected. The numerical scaled coupled models were implemented with Plaxis 3D software and validated with the experimental observations. Afterward, the experimental and numerical results of scaled models were verified with prototype models to ensure the adequacy of the small scaling coefficient. The results generated from small-scale models derived from experimental and numerical simulations were in good agreement. In addition, these results achieved good accuracy with full-scale field conditions in the seismic analysis. Therefore, a small geometric scaling coefficient of 1:50 achieved good accuracy for buildings with embedded parts to represent the dynamic behavior of structures. Within the frame results, it is noted that the SSI has an essential role in amplifying the lateral deflection of structures compared with a fixed base and thus the embedded depths significantly affect the lateral seismic response of the considered structures.

Investigation of Small-Scaled Soil Structure Model under Earthquake Loads via Small Shaking Table Tests

This paper aims to determine the appropriate scaling coefficient rigorously in the dynamic analysis of structures via small shaking table tests to represent the full real case while considering the soil-structure interaction problem. In addition, we investigate the seismic effects of the superstructure with flexible and fixed bases. To achieve this purpose, seven stories of concrete moment-resisting frames supported on silty clay soil were scaled. According to the shaking table specifications, a small-scaled soil-structure model was executed with a scaled factor of 1 : 50. Consequently, the scale steel skeleton model was built to represent the real superstructure. In addition, the laminar soil container for the soil block was constructed to reduce undesirable boundary effects. Three earthquakes have been applied at the superstructure base as a fixed base and at the bottom of the soil block in the soil structure system as a flexible base. The numerical simulations are implemented for scaled and real models. According to obtained results from experimental and numerical investigations, the numerical model achieved good results with experimental observation. In addition, the small scaling factor of 1 : 50 can represent the seismic response of full construction conditions with acceptable precision. It is observed that the flexible base has overestimated in lateral displacement of the real superstructure compared with a fixed base, in which the maximum amplification percentage at the roof floor level reaches up to 98% under seismic load. Otherwise, the shear force distribution along the height and base shear of the superstructure with a flexible base decreases compared with a fixed base. The maximum reduction percentage is 38% under seismic load. Consequently, the safety and cost of the superstructure are affected.

Similarity characteristics of geometric scaling matrix cooling channels in turbine blade

• Geometric similarity characteristics of matrix cooling structure are investigated. • The prediction of cooling performance is performed by machine learning. • The cooling performance increases exponentially with the increasing scaling factor. Gas turbine blades require high-performance cooling structures to meet the increasing turbine inlet temperature requirements. The matrix cooling structure has high heat transfer performance, but at the same time brings a large resistance loss. In basic research, the cooling structure is usually modeled according to the similarity criterion, but there is doubt about the generalization of the results obtained from the scaled model to the real-size cooling structure in engineering applications. Therefore, numerical simulation was applied to study the similarity characteristics of matrix cooling performance and the effects of geometric scaling factor in this paper. Through data fitting, it was found that the heat transfer performance and resistance performance increased exponentially with the increase of the scaling factor. As geometric scaling factor shrank 10 times, the heat transfer and resistance loss decreased by 21.9% and 34.7% respectively. The heat transfer and resistance loss of each sub-duct were also markedly affected by the geometric scaling. In small scaling factor cases, the heat transfer was weak in the downstream areas of the cooling channel, and there was an obvious gap in resistance loss for each sub-duct. Due to the same geometric characteristics, the flow characteristics of the channels were less affected by geometric scaling factor, but the relative velocity increased with the decrease of the scaling factor. The exponential regression equations of the heat transfer and resistance with respect to the scaling factor were established by machine learning, providing an accurate and appropriate prediction for engineering application.

Design and testing of a model-scale yaw mechanism for an experimental wind turbine model

In this study a new yaw mechanism for a small existing wind turbine model is designed and tested. The special requirement for this yaw device is its compact design due to the small length scaling factor of the model turbine. Such a dense design is crucial in order for the wake of the model turbine not to be influenced by the yaw mechanism. Furthermore, it has to have a fast system response and high accuracy to take into account the time scaling. Different concepts are investigated and a design located at the tower base is realized. To limit its drag, the structure is being concealed in an aerodynamically shaped cover. Wind tunnel measurements confirmed that the wake of the turbine is not influenced by the structure. A system analysis, using a Simulink model of the turbine helps finding the right motor and shows that the device has a fast system response with high accuracy. Consequently, the designed yaw mechanism is suited for the small wind turbine model, which can be used for experimental wake and wind farm control studies in the future.

The Effect on The Volume and Semi Axes of a Conducting Spheroid Due to The Scaling on Its First Order Polarization Tensor

In order to enhance identification of objects in electrical imaging or metal detection, the polarization tensor is used to characterize the perturbation in electric or electromagnetic field due to the presence of the conducting objects. This is similar as describing the uniform fluid flow that is disturbed after a solid is immersed in the fluid during the study of fluid mechanics. Moreover, in some applications, it is beneficial to determine a spheroid based on the first order polarization tensor in order to understand what is actually represented by the tensor. The spheroid could share similar physical properties with the actual object represented by the polarization tensor. The purpose of this paper is to present how scaling on the matrix for the first order polarization tensor will affect the original spheroid represented by that first order polarization tensor. In the beginning, we revise the mathematical property regarding how scaling the semi axes of a conducting spheroid has an effect to its first order polarization tensor. After that, we give theoretical results with proofs on how scaling the matrix for the first order polarization tensor affects the volume and semi axes of the spheroid. Following that, some numerical examples are provided to further justify the theory. Here, different scalar factors will be used on the given first order polarization tensor before the new volume and semi axes of the spheroid are computed. In addition, we also investigate how the size of the scale on the first order polarization tensor influence the accuracy of computing the related volume and semi axes. In this case, it is found that a large error could occur to the volume and the semi axes when finding them by solving the first order PT with that has being scaled by a very large scaling factor or a too small scaling factor. A suggestion is then given on how to reduce the errors.

Lorentzian quantum cosmology goes simplicial

We employ the methods of discrete (Lorentzian) Regge calculus for analysing Lorentzian quantum cosmology models with a special focus on discrete analogues of the no-boundary proposal for the early universe. We use a simple four-polytope, a subdivided four-polytope and shells of discrete three-spheres as triangulations to model a closed universe with cosmological constant, and examine the semiclassical path integral for these different choices. We find that the shells give good agreement with continuum results for small values of the scale factor and in particular for finer discretisations of the boundary three-sphere, while the simple and subdivided four-polytopes can only be compared with the continuum in certain regimes, and in particular are not able to capture a transition from Euclidean geometry with small scale factor to a large Lorentzian one. Finally, we consider a closed universe filled with dust particles and discretised by shells of three-spheres. This model can approximate the continuum case quite well. Our results embed the no-boundary proposal in a discrete setting where it is possibly more naturally defined, and prepare for its discussion within the realm of spin foams.

MSAR-Net: Multi-scale attention based light-weight image super-resolution

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different features, by treating all the pixels contributing equally to the performance of the network. In this paper, we take into consideration both the performance and the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSAR-Net) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive fashion, for substituting the large scale factors (× 4) combinations, with small scale factor (×2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale features in global and local manners, channel and spatial attention in MSAR block is being used. Visual results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual quality.

A novel and robust scale-invariant WENO scheme for hyperbolic conservation laws

A novel, simple, robust, and effective modification in the nonlinear weights of the scale-invariant WENO operator is proposed that achieves an optimal order of accuracy with smooth function regardless of the critical point (Cp-property), a scale-invariant with an arbitrary scaling of a function (Si-property), an essentially non-oscillatory approximation of a discontinuous function (ENO-property), and, in some cases, a well-balanced WENO finite difference/volume scheme (WB-property) (up to machine rounding error numerically). The classical WENO-JS/Z/D operators do not satisfy the Si-property intrinsically due to a loss of sub-stencils' adaptivity in the WENO reconstruction of a discontinuous function when scaled by a small scaling factor. By introducing the descaling function, an average of the function values in the weights to build the scale-invariant WENO-JSm/Zm/Dm operators, the operators are independent of both the scaling factor and sensitivity parameter. The Si-property and Cp-property of the WENO operators are validated theoretically and numerically in quadruple-precision with small and large scaling factors and sensitivity parameters. The results show that the WENO-JSm/Zm/Dm operators satisfy the Si-property and the WENO-D/Dm operators satisfy the Cp-property. Furthermore, the ENO-property of the WENO-Zm/Dm schemes is illustrated via several one- and two-dimensional shock-tube problems. In solving the Euler equations under gravitational fields, the well-balanced scale-invariant WENO schemes achieve the WB-property intrinsically without imposing the stringent homogenization condition.

Conductivity Estimates of Fractal Models of Geological Media

AbstractGeological media are inherently fractal over multiple length scales, exhibiting a wide range of fractal dimensions . We study the size dependent conductivity of models of geological media based on so called regular random fractals. Specifically, we consider generalizations of the classic Menger sponges where the impermeable cells can take random locations. This results, for a given random fractal generator and a given construction order, in multiple realizations of the target media, each one maintaining the same . We use direct numerical simulation to estimate the conductivity, over a number of patterns, with orders . The conductivity bounds and estimates for regular fractal media are, in general, not applicable to these random fractal models. The conductivity of a specific realization may differ from the ensemble average by a few percents, which suggests a lack of self‐averaging; however, the self‐averaging is regained if the size of the domain of interest is beyond the maximal fractality length scale. We propose a low computational cost approach for estimating the ensemble average conductivity of a given random fractal at a given order, which matches the order of magnitude predicted from more intensive calculations. Through tabulation of conductivities of generators of random fractals with small scaling factors and fitting models, we explain how these fractals can model geological media in which fractal dimensions and conductivities (or effective diffusion coefficients) are measured.

Certainty Equivalent Pricing Under Sales-Dependent and Inventory-Dependent Demand

When underlying demand is uncertain and follows a complex stochastic process, pricing problems are difficult to solve. In such cases, certainty equivalent (CE) policies, based on solving the deterministic relaxation of a stochastic pricing problem, can be used as practical alternatives. CE policies have lighter computational and informational requirements compared to solving the optimal problem. This is particularly true when the firm does not have complete information about the underlying demand distribution. While the effectiveness of CE pricing policies has been theoretically studied in some settings (e.g, independent demand, continuous price changes), the performance of CE policies are not known in general. This paper analyzes the performance of CE policies in a pricing problem (for a given inventory level) where future demand depends on sales and inventory and the firm has limited opportunities to change price. We show that CE policies are asymptotically optimal: as the problem scale (denoted by m) becomes large, the percentage regret decreases at the rate of O (m^(-1/2)). We also extend the result to the joint pricing and (initial) inventory problem. Our numerical results are even more promising. Even in non-asymptotic settings (small scaling factor and a few price changes), CE policies perform well and often result in revenues that are only a few percentage points lower than optimal. In addition, we compare the benefit of a closed-loop CE policy over an open-loop CE policy and provide a theoretical comparison between the two policies.

Research on Countermeasures and Financing Risk of Small and Medium-Sized Enterprises in China

SMEs have played an important role in deepening of construction of socialist market economy in China. Their contribution rate to our country's economy is rising continuously, they also stimulate technical innovation. However, because of their small scale, credit margin and other factors and imperfect market mechanism, SMEs are facing a severe situation in the process of financing, the huge financing risk has become the core problem restricting the development of SMEs. This paper employed the KMV model to make an empirical analysis. Then, the author puts forward corresponding suggestions for the financing of SMEs in the future.