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A well-known class of nonstationary self-similar time series is the fractional Brownian motion (fBm) considered to model ubiquitous stochastic processes in nature. Due to noise and trends superimposed on data and even sample size and irregularity impacts, the well-known computational algorithm to compute the Hurst exponent (H) has encountered superior results. Motivated by this discrepancy, we examine the homology groups of high-dimensional point cloud data (PCD), a subset of the unit D-dimensional cube, constructed from synthetic fBm data as a pipeline to compute the H exponent. We compute topological measures for embedded PCD as a function of the associated Hurst exponent for different embedding dimensions, time delays, and amount of irregularity existing in the dataset in various scales. Our results show that for a regular synthetic fBm, the higher value of the embedding dimension leads to increasing the H dependency of topological measures based on zeroth and first homology groups. To achieve a reliable classification of fBm, we should consider the small value of time delay irrespective of the irregularity presented in the data. More interestingly, the value of the scale for which the PCD to be path connected and the postloopless regime scale are more robust concerning irregularity for distinguishing the fBm signal. Such robustness becomes less for the higher value of the embedding dimension. Finally, the associated Hurst exponents for our topological feature vector for the S&P500 were computed, and the results are consistent with the detrended fluctuation analysis method.

In this paper, we employ the persistent homology (PH) technique to examine the topological properties of fractional Gaussian noise (fGn). We develop the weighted natural visibility graph algorithm, and the associated simplicial complexes through the filtration process are quantified by PH. The evolution of the homology group dimension represented by Betti numbers demonstrates a strong dependency on the Hurst exponent (H). The coefficients of the birth and death curves of the k-dimensional topological holes (k-holes) at a given threshold depend on H which is almost not affected by finite sample size. We show that the distribution function of a lifetime for k-holes decays exponentially and the corresponding slope is an increasing function versus H and, more interestingly, the sample size effect completely disappears in this quantity. The persistence entropy logarithmically grows with the size of the visibility graph of a system with almost H-dependent prefactors. On the contrary, the local statistical features are not able to determine the corresponding Hurst exponent of fGn data, while the moments of eigenvalue distribution (M_{n}) for n≥1 reveal a dependency on H, containing the sample size effect. Finally, the PH shows the correlated behavior of electroencephalography for both healthy and schizophrenic samples.

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework's diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona's assistance., Competing Interests: The authors have declared that no competing interests exist.

In this paper, we examine the light scattering by the flow of levitated flakes in a micro-channel to characterize the tunable functionality of the graphene oxide liquid crystal in the nematic phase. Light interaction with the mentioned material is decomposed to the scattered and transmitted parts and they can determine the orientation of the flakes. Our results demonstrate that, pumping the graphene oxide sample through the micro-channel leads to increase the amplitude of scattered light. The time averaged of scattered light intensity grows by increasing volume fraction. We also find that, the higher volume fraction, the sooner reaching to saturated normalized scattered intensity is. To get deep insight about our experimental results, we rely on the general theoretical properties of the light scattering cross-section incorporating the fluctuation of director vector and dielectric tensor. Our proposal is a promising approach to carry out the mechanical-hydrodynamical approach for controlling the orientation of a typical liquid crystal.

The speech auditory brainstem response (sABR) is an objective clinical tool to diagnose particular impairments along the auditory brainstem pathways. We explore the scaling behavior of the brainstem in response to synthetic /da/ stimuli using a proposed pipeline including Multifractal Detrended Moving Average Analysis (MFDMA) modified by Singular Value Decomposition. The scaling exponent confirms that all normal sABR are classified into the non-stationary process. The average Hurst exponent is H = 0:77 ± 0:12 at 68% confidence interval indicating long-range correlation which shows the first universality behavior of sABR. Our findings exhibit that fluctuations in the sABR series are dictated by a mechanism associated with long-term memory of the dynamic of the auditory system in the brainstem level. The q-dependency of h(q) demonstrates that underlying data sets have multifractal nature revealing the second universality behavior of the normal sABR samples. Comparing Hurst exponent of original sABR with the results of the corresponding shuffled and surrogate series, we conclude that its multifractality is almost due to the long-range temporal correlations which are devoted to the third universality. Finally, the presence of long-range correlation which is related to the slow timescales in the subcortical level and integration of information in the brainstem network is confirmed.

Designation of representative watersheds (RWs) as a reference area representing key behavior of the whole region is an essential tool to provide a time and cost-effective basis for monitoring watershed performance against different driving forces. It is more important in developing countries facing lack of necessary investments in one hand and ever-increasing human interventions and need to assess the outcome behavior of the system in another hand. However, this serious affair has been less considered worldwide, in general, and in developing countries, in particular. Therefore, in the present study, a quantitative-based method of Representative Watershed Index (RWI) with potential range from 0 to 100 has been formulated using four important criteria and available national-wide raster data of elevation (meter), slope (%), rainfall erosivity factor (t m ha -1  cm h -1 ), and land use. The approach was then applied to the data prepared for the unique and invaluable global water ecosystem of the Urmia Lake Basin (ULB), north-western Iran, as a case study. The input raster was overlaid via matrices programming in the MATrix LABoratory (MATLAB) 2016 and Geographic Information System (GIS) 9.3 software environments. The RWIs were accordingly computed for 61 sub-watersheds of the ULB. The RWIs resulted from quadri-partite dimensional matrices that varied from 5.54 to 53.46 with respective maximum dissimilarity and resemblance with the entire 61 study sub-watersheds in the region. However, the sub-watershed with RWI of 40.65 (No. 57) was proposed as the final RW for the whole ULB due to hydrological independency, appropriate locality, and existence of functioning meteorological and hydrometric stations. The identified RW would be suggested to be considered as the basis for future insight monitoring and assessing environmental issues for the region eventually leading to an appropriate adaptive watershed management. Graphical abstract ᅟ.

The spin Hall effect (SHE) has shown promising impact in the field of spintronics and magnonics from fundamental and practical points of view. This effect originates from several mechanisms of spin scatterers based on spin-orbit coupling (SOC) and also can be manipulated through the surface roughness. Here, the effect of correlated surface roughness on the SHE in metallic thin films with small SOC is investigated theoretically. Toward this, the self-affine fractal surface in the framework of the Born approximation is exploited. The surface roughness is described by the k-correlation model and is characterized by the roughness exponent H [Formula: see text], the in-plane correlation length ξ and the rms roughness amplitude δ. It is found that the spin Hall angle in metallic thin film increases by two orders of magnitude when H decreases from H  =  1 to H  =  0. In addition, the source of SHE for surface roughness with Gaussian profile distribution function is found to be mainly the side jump scattering while that with a non-Gaussian profile suggests both of the side jump and skew scatterings are present. Our achievements address how details of the surface roughness profile can adjust the SHE in non-heavy metals.

In this paper, we investigate the crossing statistics of speckle patterns formed in the Fresnel diffraction region by a laser beam scattering through a nanofluid. We extend zero-crossing statistics to assess the dynamical properties of the nanofluid. According to the joint probability density function of laser beam fluctuation and its time derivative, the theoretical frameworks for Gaussian and non-Gaussian regimes are revisited. We count the number of crossings not only at zero level but also for all available thresholds to determine the average speed of moving particles. Using a probabilistic framework in determining crossing statistics, a priori Gaussianity is not essentially considered; therefore, even in the presence of deviation from Gaussian fluctuation, this modified approach is capable of computing relevant quantities, such as mean value of speed, more precisely. Generalized total crossing, which represents the weighted summation of crossings for all thresholds to quantify small deviation from Gaussian statistics, is introduced. This criterion can also manipulate the contribution of noises and trends to infer reliable physical quantities. The characteristic time scale for having successive crossings at a given threshold is defined. In our experimental setup, we find that increasing sample temperature leads to more consistency between Gaussian and perturbative non-Gaussian predictions. The maximum number of crossings does not necessarily occur at mean level, indicating that we should take into account other levels in addition to zero level to achieve more accurate assessments.

The scaling behavior of friction between rough surfaces is a well-known phenomenon. It might be asked whether such a scaling feature also exists for friction at an atomic scale despite the absence of roughness on atomically flat surfaces. Indeed, other types of fluctuations, e.g., thermal and instrumental fluctuations, become appreciable at this length scale and can lead to scaling behavior of the measured atomic-scale friction. We investigate this using the lateral force exerted on the tip of an atomic force microscope (AFM) when the tip is dragged over the clean NaCl (001) surface in ultra-high vacuum at room temperature. Here the focus is on the fluctuations of the lateral force profile rather than its saw-tooth trend; we first eliminate the trend using the singular value decomposition technique and then explore the scaling behavior of the detrended data, which contains only fluctuations, using the multifractal detrended fluctuation analysis. The results demonstrate a scaling behavior for the friction data ranging from 0.2 to 2 nm with the Hurst exponent H=0.61±0.02 at a 1σ confidence interval. Moreover, the dependence of the generalized Hurst exponent, h(q), on the index variable q confirms the multifractal or multiscaling behavior of the nanofriction data. These results prove that fluctuation of nanofriction empirical data has a multifractal behavior which deviates from white noise.