Local Perturbations Research Articles

Forced convection analysis of Williamson-based magnetized hybrid nanofluid flow through a porous medium: Nonsimilar modeling

The purpose of this work is to analyze the convective horizontal flow of Williamson-based magnetized hybrid nanofluid. The flow of Williamson fluid with heat transfer is stimulated in a porous media over a stretching surface. Two different type of nanoparticles such as, molybdenum disulfide ( Mo S 2 ) and copper (Cu) with engine oil (Eo) as base fluid are considered in this work. The convective flow of Williamson fluid is inspected based on Khanefer model. The governing system is transformed into nondimensionalized partial differential equations (PDE’s) by utilizing nonsimilar transformation. The transformed system is analytically approximated using local nonsimilarity (LNS) and perturbation approach which approximate the nondimensional PDE’s by nondimensional ordinary differential equations (ODEs). The LNS and perturbation ODEs are simulated utilizing finite difference-based algorithm bvp4c. The outcomes of simulation for heat transfer and convective flow over stretching surface incorporating the evolvement with local Nusselt number (Nu) for distinct values of prandtl number are compared with previous published numerical results. The appropriate range of governing nondimensionalized parameters is evaluated to detect the variance of physical quantities namely, velocity ( f ′ ( η ) ) of the fluid, temperature ( θ ′ ( η ) ) of the fluid, co-efficient of local Nusselt number (Nu) and co-efficient of local skin friction (Cf). The percentage difference between single nano-fluid and hybrid nano-fluids is made which are given in tabular form. To the best of author knowledge, nonosimilar study of hybrid-based magnetized Williamson nanofluid is not yet investigated. This work is anticipated to offer crucial information for the implementation of innovative heat transfer devices in the future and serve as an invaluable resource for researchers looking at the flow of nanofluids under various assumptions.

Normalized ground states to the nonlinear Choquard equations with local perturbations

<abstract><p>In this paper, we considered the existence of ground state solutions to the following Choquard equation</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{eqnarray*} \left\{ \begin{aligned} &-\Delta u = \lambda u + (I_{\alpha}\ast F(u))f(u) + \mu|u|^{q-2}u \hskip0.5cm \mbox{in} \hskip0.2cm\mathbb{R}^{N}, \\ & \int\limits_{\mathbb{R}^{N}}|u|^{2}dx = a >0, \end{aligned} \right. \end{eqnarray*} $\end{document} </tex-math></disp-formula></p> <p>where $ N \geq 3 $, $ I_{\alpha} $ is the Riesz potential of order $ \alpha \in (0, N) $, $ 2 < q \leq 2+ \frac{4}{N} $, $ \mu > 0 $ and $ \lambda \in \mathbb{R} $ is a Lagrange multiplier. Under general assumptions on $ F\in \mathcal{C}^{1}(\mathbb{R}, \mathbb{R}) $, for a $ L^{2} $-subcritical and $ L^{2} $-critical of perturbation $ \mu|u|^{q-2}u $, we established several existence or nonexistence results about the normalized ground state solutions.</p></abstract>

Toward Answering Federated Spatial Range Queries Under Local Differential Privacy

Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client’s location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients’ locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients’ reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries’ utility.

The missing hallmark of health: psychosocial adaptation.

The eight biological hallmarks of health that we initially postulated (Cell. 2021 Jan 7;184(1):33-63) include features of spatial compartmentalization (integrity of barriers, containment of local perturbations), maintenance of homeostasis over time (recycling & turnover, integration of circuitries, rhythmic oscillations) and an array of adequate responses to stress (homeostatic resilience, hormetic regulation, repair & regeneration). These hallmarks affect all eight somatic strata of the human body (molecules, organelles, cells, supracellular units, organs, organ systems, systemic circuitries and meta-organism). Here we postulate that mental and socioeconomic factors must be added to this 8×8 matrix as an additional hallmark of health ("psychosocial adaptation") and as an additional stratum ("psychosocial interactions"), hence building a 9×9 matrix. Potentially, perturbation of each of the somatic hallmarks and strata affects psychosocial factors and vice versa. Finally, we discuss the (patho)physiological bases of these interactions and their implications for mental health improvement.

Asymptotic behavior of a viscous incompressible fluid flow in a fractal network of branching tubes

<p>We considered a viscous incompressible fluid flow in a varying bounded domain consisting of branching thin cylindrical tubes whose axes are line segments that form a network of pre-fractal curves constituting an approximation of the Sierpinski gasket. We supposed that the fluid flow is driven by volumic forces and governed by Stokes equations with boundary conditions for the velocity and the pressure on the wall of the tubes and inner continuity conditions for the normal velocity on the interfaces between the junction zones and the rest of the pipes. We constructed local perturbations, related to boundary layers in the junction zones, from solutions of Leray problems in semi-infinite cylinders representing the rescaled junctions. Using $ \Gamma $-convergence methods, we studied the asymptotic behavior of the fluid as the radius of the tubes tends to zero and the sequence of the pre-fractal curves converges in the Hausdorff metric to the Sierpinski gasket. Based on the constructed local perturbations, we derived, according to a critical parameter related to a typical Reynolds number of the flow in the junction zones, three effective flow models in the Sierpinski gasket, consisting of a singular Brinkman flow, a singular Darcy flow, and a flow with constant velocity.</p>

Stability of the Spectral Gap and Ground State Indistinguishability for a Decorated AKLT Model

We use cluster expansion methods to establish local the indistiguishability of the finite volume ground states for the AKLT model on decorated hexagonal lattices with decoration parameter at least 5. Our estimates imply that the model satisfies local topological quantum order, and so, the spectral gap above the ground state is stable against local perturbations.

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques

ABSTRACT The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.

Future Paths in Cryogenic Single-Molecule Fluorescence Spectroscopy.

In the last three decades, cryogenic single-molecule fluorescence spectroscopy has provided average-free understanding of the photophysics and of fundamental interactions at molecular scales. Furthermore, they propose original pathways and applications in the treatment and storage of quantum information. The ultranarrow lifetime-limited zero-phonon line acts as an excellent sensor to local perturbations caused either by intrinsic dynamical degrees of freedom, or by external perturbations, such as those caused by electric fields, elastic and acoustic deformations, or light-induced dynamics. Single aromatic hydrocarbon molecules, being sensitive to nanoscale probing at nanometer scales, are potential miniaturized platforms for integrated quantum photonics. In this Perspective, we look back at some of the past advances in cryogenic optical microscopy and propose some perspectives for future development.

A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance

The grey wolf optimizer is an effective and well-known meta-heuristic algorithm, but it also has the weaknesses of insufficient population diversity, falling into local optimal solutions easily, and unsatisfactory convergence speed. Therefore, we propose a hybrid grey wolf optimizer (HGWO), based mainly on the exploitation phase of the harris hawk optimization. It also includes population initialization with Latin hypercube sampling, a nonlinear convergence factor with local perturbations, some extended exploration strategies. In HGWO, the grey wolves can have harris hawks-like flight capabilities during position updates, which greatly expands the search range and improves global searchability. By incorporating a greedy algorithm, grey wolves will relocate only if the new location is superior to the current one. This paper assesses the performance of the hybrid grey wolf optimizer (HGWO) by comparing it with other heuristic algorithms and enhanced schemes of the grey wolf optimizer. The evaluation is conducted using 23 classical benchmark test functions and CEC2020. The experimental results reveal that the HGWO algorithm performs well in terms of its global exploration ability, local exploitation ability, convergence speed, and convergence accuracy. Additionally, the enhanced algorithm demonstrates considerable advantages in solving engineering problems, thus substantiating its effectiveness and applicability.

Approximate Gibbsian structure in strongly correlated point fields and generalized Gaussian zero ensembles

AbstractGibbsian structure in random point fields has been a classical tool for studying their spatial properties. However, exact Gibbs property is available only in a relatively limited class of models, and it does not adequately address many random fields with a strongly dependent spatial structure. In this work, we provide a very general framework for approximate Gibbsian structure for strongly correlated random point fields, including those with a highly singular spatial structure. These include processes that exhibit strong spatial rigidity, in particular, a certain one‐parameter family of analytic Gaussian zero point fields, namely the ‐GAFs, that are known to demonstrate a wide range of such spatial behavior. Our framework entails conditions that may be verified via finite particle approximations to the process, a phenomenon that we call an approximate Gibbs property. We show that these enable one to compare the spatial conditional measures in the infinite volume limit with Gibbs‐type densities supported on appropriate singular manifolds, a phenomenon we refer to as a generalized Gibbs property. Our work provides a general mechanism to rigorously understand the limiting behavior of spatial conditioning in strongly correlated point processes with growing system size. We demonstrate the scope and versatility of our approach by showing that a generalized Gibbs property holds with a logarithmic pair potential for the ‐GAFs for any value of . In this vein, we settle in the affirmative an open question regarding the existence of point processes with any specified level of rigidity. In particular, for the ‐GAF zero process, we establish the level of rigidity to be exactly , a fortiori demonstrating the phenomenon of spatial tolerance subject to the local conservation of moments. For such processes involving complex, many‐body interactions, our results imply that the local behavior of the random points still exhibits 2D Coulomb‐type repulsion in the short range. Our techniques can be leveraged to estimate the relative energies of configurations under local perturbations, with possible implications for dynamics and stochastic geometry on strongly correlated random point fields.

Local Quench within the Keldysh Technique

The problem of quantum scalar field evolution after an instantaneous local perturbation (quench) is considered. A new approach to descriptions of a quench from an arbitrary initial state is developed in the framework of the Keldysh technique. This approach does not require the procedure of the analytical continuation, which can be ambiguous in some cases. The evolution of the energy density after local quench is calculated for a simple case, and its dependence on the interaction region width and the initial conditions is analyzed.

Local Quench within Keldysh Technique

The problem of quantum scalar field evolution after an instantaneous local perturbation (quench) is considered. A new approach to descriptions of a quench from an arbitrary initial state is developed in the framework of the Keldysh technique. This approach does not require the procedure of the analytical continuation, which can be ambiguous in some cases. The evolution of the energy density after local quench is calculated for a simple case, and its dependence on the interaction region width and the initial conditions is analysed.

Analytical Representations of Far Fields of Internal Gravity Waves from Local and Nonlocal Perturbation Sources

Analytical Representations of Far Fields of Internal Gravity Waves from Local and Nonlocal Perturbation Sources

Explaining Deep Graph Networks via Input Perturbation.

Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The privacy and safety-critical nature of such domains motivates the need for developing effective explainability methods for this family of models. So far, progress in this field has been challenged by the combinatorial nature and complexity of graph structures. In this respect, we present a novel local explanation framework specifically tailored to graph data and DGNs. Our approach leverages reinforcement learning to generate meaningful local perturbations of the input graph, whose prediction we seek an interpretation for. These perturbed data points are obtained by optimizing a multiobjective score taking into account similarities both at a structural level as well as at the level of the deep model outputs. By this means, we are able to populate a set of informative neighboring samples for the query graph, which is then used to fit an interpretable model for the predictive behavior of the deep network locally to the query graph prediction. We show the effectiveness of the proposed explainer by a qualitative analysis on two chemistry datasets, TOX21 and Estimated SOLubility (ESOL) and by quantitative results on a benchmark dataset for explanations, CYCLIQ.

Exploring mechanisms of spatial segregation between body size groups within fish populations under environmental change

Ample evidence has indicated shifts in distribution of fish populations in response to environmental stress. However, most studies focused at the whole population scale. This neglects the spatial dynamics between groups of different body size (body size groups), that fundamentally shapes the spatial structure of a population. Here, we explored the mechanisms that modulate spatial dynamics of body size groups, and applied our analyses to three North Sea fish populations which experienced severe declines in biomass from 1977 to 2019: Atlantic cod Gadus morhua, haddock Melanogrammus aeglefinus and whiting Merlangius merlangius. All three populations exhibited strong declines in the overlapped area between body size groups in winter over 43 years, yet their mechanisms differed. These declines were either due to 1) different magnitudes of contraction of the distribution area of body size groups; and/or 2) different speeds and directions of spatial shift among various body size groups, both increasing spatial segregation within populations. These patterns were either associated with ocean warming, and/or declining population biomass, and such associations often varied according to distinct body size groups. Increasing spatial segregation between size groups of a population likely hampers life‐cycle connectivity and stability to local perturbations. Our analytical approach provides a powerful tool for identifying vulnerable populations under environmental stress and can be generalized to study a variety of size/age structured populations at various ecosystem types.

Local Fuzzy Fractional Partial Differential Equations in the Realm of Fractal Calculus with Local Fractional Derivatives

In this study, local fuzzy fractional partial differential equations (LFFPDEs) are considered using a hybrid local fuzzy fractional approach. Fractal model behavior can be represented using fuzzy partial differential equations (PDEs) with local fractional derivatives. The current methods are hybrids of the local fuzzy fractional integral transform and the local fuzzy fractional homotopy perturbation method (LFFHPM), the local fuzzy fractional Sumudu decomposition method (LFFSDM) in the sense of local fuzzy fractional derivatives, and the local fuzzy fractional Sumudu variational iteration method (LFFSVIM); these are applied when solving LFFPDEs. The working procedure shows how effective solutions for specific LFFPDEs can be obtained using the applied approaches. Moreover, we present a comparison of the local fuzzy fractional Laplace variational iteration method (LFFLIM), the local fuzzy fractional series expansion method (LFFSEM), the local fuzzy fractional variation iteration method (LFFVIM), and the local fuzzy fractional Adomian decomposition method (LFFADM), which are applied to obtain fuzzy fractional diffusion and wave equations on Cantor sets. To demonstrate the effectiveness of the used techniques, some examples are given. The results demonstrate the major advantages of the approaches, which are equally efficient and simple to use in order to solve fuzzy differential equations with local fractional derivatives.

Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base-pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization of the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base-pairing to minimize the barrier height.

Stability analysis of anisotropic stars in f(R, T) gravity through cracking technique

In this article, cracking technique is developed for spherically symmetric compact sources in the framework of f(R, T) gravity, where R denotes Ricci scalar and T stands for trace of energy momentum tensor. The characteristics of a star with anisotropic pressure stresses are investigated by utilizing the Tolman–Kuchowicz spacetime solutions. Modified field equations are developed for a particular model i.e., f(R,T)=R+2γT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(R,T)=R+2\\gamma T$$\\end{document}, where γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document} is constant, that are further used to develop expressions for matter density, radial and tangential pressures. A generalized form of the Tolman Oppenheimer Volkoff (TOV) equation is developed for the modified field equations. The consequence of the local density perturbation scheme, as presented by Biswas et al. (Eur Phys J C 80:175, 2020) is considered. The mathematical framework for cracking has been tested on five realistic stars namely, Vela X-1, Cen X-3, SMC X-1, PSR J1614-2230 and PSR J1903+327. The graph of forces distribution of these stars have been observed to check the stability regions. The results of cracking/overturning for various values of the parameters involved in this model are observed by checking the instability regions in the form intervals.

Ultrasensitive Photothermal Switching with Resonant Silicon Metasurfaces at Visible Bands.

Dynamic access to quasi-bound states in the continuum (q-BICs) offers a highly desired platform for silicon-based active nanophotonic applications, while the prevailing tuning approaches by free carrier injections via an all-optical stimulus are yet limited to THz and infrared ranges and are less effective in visible bands. In this work, we present the realization of active manipulations on q-BICs for nanoscale optical switching in the visible by introducing a local index perturbation through a photothermal mechanism. The sharp q-BIC resonance exhibits an ultrasensitive susceptibility to the complex index perturbation, which can be flexibly fulfilled by optical heating of silicon. Consequently, a mild pump intensity of 1 MW/cm2 can yield a modification of the imaginary part of the refractive index of less than 0.05, which effectively suppresses the sharp q-BIC resonances and renders an active modulation depth of reflectance exceeding 80%. Our research might open up an enabling platform for ultrasensitive dynamic nanophotonic devices.

Depth-selective method for time-domain diffuse reflectance measurements: validation study of the dual subtraction technique.

Research on the spatial distribution of sensitivity of time-domain near infrared diffuse reflectance measurement is reported in this paper. The main objective of the investigation is to validate theoretically calculated sensitivity profiles for a measurement geometry with two detectors and two sources in which sensitivity profiles of statistical moments of distributions of time of flight of photons (DTOFs) are spatially restricted to a region underneath the detectors. For this dual subtraction method, smaller sensitivities to changes appearing in the superficial layer of the medium were observed compared to the single distance and single subtraction methods. Experimental validation of this approach is based on evaluation of changes in the statistical moments of DTOFs measured on a liquid phantom with local absorption perturbations. The spatial distributions of sensitivities, depth-related sensitivity and depth selectivities were obtained from the dual subtraction method and compared with those from single distance and single subtraction approaches. Also, the contrast to noise ratio (CNR) was calculated for the dual subtraction technique and combined with depth selectivity in order to assess the overall performance (product of CNR and depth selectivity) of the method. Spatial sensitivity profiles from phantom experiments are in a good agreement with the results of theoretical studies and feature more locally restricted sensitivity volume with the point of maximal sensitivity located deeper. The highest value of overall performance was obtained experimentally for the second statistical moment in the dual subtraction method (∼10.8) surpassing that of the single distance method (∼8.7). This confirms the advantage of dual subtraction measurement geometries in the suppression of optical signals originated in the superficial layer of the medium.