In this paper we represent a new framework for integrated distributed systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyse integrated systems and their evolution process and also ERPSD and ERPDRT framework briefly then we explain the new FDIRS framework. Finally we compare the results of simulation of the new framework with presented frameworks. Result showed In FIDRS framework, the technique of heterogeneous distributed data base is used to improve Performance and speed in responding to users. Finally by using FDIRS framework we succeeded to increase Efficiency, Performance and reliability of integrated systems and remove some of previous frameworks problems.

High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone. Mapping HFO-generating regions can improve the precision of resection sites in patients with refractory epilepsy. However, detecting HFOs remains challenging, and their clinical features are not yet fully defined. Visual identification of HFOs is time-consuming, labor-intensive, and subjective. As a result, developing automated methods to detect HFOs is critical for research and clinical use. In this study, we developed a novel method for detecting HFOs in the ripple and fast ripple frequency bands (80-500 Hz). We validated it using both controlled datasets and data from epilepsy patients. Our method employs an unsupervised clustering technique to categorize events extracted from the time-frequency domain using the S-transform. The proposed detector differentiates HFOs events from spikes, background activity, and artifacts. Compared to existing detectors, our method achieved a sensitivity of 97.67%, a precision of 98.57%, and an F-score of 97.78% on the controlled dataset. In epilepsy patients, our results showed a stronger correlation with surgical outcomes, with a ratio of 0.73 between HFOs rates in resected versus non-resected contacts. The study confirmed previous findings that HFOs are promising biomarkers of epileptogenicity in epileptic patients. Removing HFOs, especially fast ripple, leads to seizure freedom, while remaining HFOs lead to seizure recurrence.

We consider interactions of exact (i.e., solutions of full nonlinear field equations) gravitational waves with matter by using the Einstein-Boltzmann equation. For a gravitational wave interacting with a system of massless particles, we compute the perturbed energy-momentum tensor and obtain explicit form of a set of Einstein-Boltzmann equations. We find solution to this system of equations to obtain the gravitational wave profile. The interaction superposes a static term on the gravitational wave profile which depends on the difference between square of the temperatures of the system in the absence and in the presence of the wave. We compute this perturbed term when the states of the system obey Bose-Einstein, Fermi-Dirac, and Maxwell-Boltzmann distributions, respectively. The relative strength of this term is roughly half for the Fermi-Dirac, and one-third for the Maxwell-Boltzmann distributions compared with that of the Bose-Einstein distribution. We consider both Minkowski and flat Friedmann-Robertson-Walker backgrounds.

In this study, focusing on organizations in a rapid-response component model (FRO), the relative importance of each one, from the point of view of customers and their impact on the purchase of Shahrvand chain stores determined to directors and managers of the shops, according to customer needs and their priorities in order to satisfy the customers and take steps to strengthen their competitiveness. For this purpose, all shahrvand chain stores in Tehran currently have 10 stores in different parts of Tehran that have been studied are that of the 10 branches; Five branches were selected. The sampling method is used in this study population with a confidence level of 95% and 8% error; 150 are more specifically typically 30 were studied in each branch. In this study, a standard questionnaire of 26 questions which is used FRO validity using Cronbach's alpha values of "0/95" is obtained. The results showed that each of the six factors on customer loyalty model FRO effective Shahrvand chain stores. The effect of each of the six Foundation FRO customer loyalty model shahrvand is different chain stores.

We present a rigorous generalization of the classical Ginzburg--Landau model to smooth, compact Finsler manifolds without boundary. This framework provides a natural analytic setting for describing anisotropic superconductivity within Finsler geometry. The model is constructed via the Finsler--Laplacian, defined through the Legendre transform associated with the fundamental function F, and by employing canonical Finsler measures such as the Busemann--Hausdorff and Holmes--Thompson volume forms. We introduce an anisotropic Ginzburg--Landau functional for complex scalar fields coupled to gauge potentials and establish the existence of minimizers in the appropriate Finsler--Sobolev spaces by the direct method in the calculus of variations. Furthermore, we analyze the asymptotic regime as the Ginzburg--Landau parameter epsilon to 0 and prove a precise Gamma--convergence result: the rescaled energies converge to the Finslerian length functional associated with the limiting vortex filaments. In particular, the limiting vortex energy is shown to equal $\pi$ times the Finslerian length of the corresponding current, thereby extending the classical Bethuel--Brezis--He'lein result to anisotropic settings. These findings demonstrate that Finsler geometry unifies metric anisotropy and variational principles in gauge-field models, broadening the geometric scope of the Ginzburg--Landau theory beyond the Riemannian framework.

The highest level in the Endsley situation awareness model is called projection when the status of elements in the environment in the near future is predicted. In cybersecurity situation awareness, the projection for an Advanced Persistent Threat (APT) requires predicting the next step of the APT. The threats are constantly changing and becoming more complex. As supervised and unsupervised learning methods require APT datasets for projecting the next step of APTs, they are unable to identify unknown APT threats. In reinforcement learning methods, the agent interacts with the environment, and so it might project the next step of known and unknown APTs. So far, reinforcement learning has not been used to project the next step for APTs. In reinforcement learning, the agent uses the previous states and actions to approximate the best action of the current state. When the number of states and actions is abundant, the agent employs a neural network which is called deep learning to approximate the best action of each state. In this paper, we present a deep reinforcement learning system to project the next step of APTs. As there exists some relation between attack steps, we employ the Long- Short-Term Memory (LSTM) method to approximate the best action of each state. In our proposed system, based on the current situation, we project the next steps of APT threats.

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods.

Let $\theta$ be an isomorphism on $L_0^{\infty} (w)^*$. In this paper, we investigate $\theta$-generalized derivations on $L_0^{\infty} (w)^*$. We show that every $\theta$-centralizing $\theta$-generalized derivation on $L_0^{\infty} (w)^*$ is a $\theta$-right centralizer. We also prove that this result is true for $\theta$-skew centralizing $\theta$-generalized derivations.

The generic monic polynomial of sixth degree features 6 a priori arbitrary coefficients. We show that if these 6 coefficients are appropriately defined in two different ways|in terms of 5 arbitrary parameters, then the 6 roots of the corresponding polynomial can be explicitly computed in terms of radicals of these parameters. We also report the 2 constraints on the 6 coefficients of the polynomial implied by the fact that they are so defined in terms of 5 arbitrary parameters; as well as the explicit determination of these 5 parameters in terms of the 6 coefficients of the sextic polynomial.

In this paper we represent a new framework for integrated distributed and reliable systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyze previous frameworks related to integrated systems, then represent new proposed framework in order to improving previous framework, and we discuss its different phases. Finally we compare the results of simulation of the new framework with previous ones. In FIDRS framework, the technique of heterogeneous distributed data base is used to improve Performance and speed in responding to users and in this way we can improve dependability and reliability of framework simultaneously. In extraction phase of the new framework we have used RMSD algorithm that decreases responding time in big database. Finally by using FDIRS framework we succeeded to increase Efficiency, Performance and reliability of integrated systems and remove some of previous frameworks problems.

The Renyi entropy plays an essential role in quantum information theory. We study the continuity estimation of the Renyi entropy. An inequality relating the Renyi entropy difference of two quantum states to their trace norm distance is derived. This inequality is shown to be tight in the sense that equality can be attained for every prescribed value of the trace norm distance. It includes the sharp Fannes inequality for von Neumann entropy as a special case.

Ultrafast optical control of ferroelectricity based on short and intense light can be utilized to achieve accurate manipulations of ferroelectric materials, which may pave a basis for future breakthrough in nonvolatile memories. Here, we demonstrate that phase manipulation of electric field in the strong field sub-cycle regime induces a nonlinear injection current, efficiently coupling with the topology of band structure and enabling dynamic reversal of both current and polarization. Our time-dependent first-principles calculations reveal that tuning the phase of linearly or circularly polarized light through time-varying chirp, or constant carrier envelop phases within sub-laser-cycle dynamics effectively breaks the time-reversal symmetry, allowing the control over current and electronic polarization reversal over multi-ferroelectric states. Our time- and momentum-resolved transverse current analysis reveal the significance of Berry curvature higher order poles in the apparent association between the odd (even) orders of Berry curvature multipoles to odd (even) pseudo-harmonics in driving polarization dynamics reversal. We suggest that these phase manipulations of short pulse waveform may lead to unprecedented accurate control of nonlinear photocurrents and polarization states, which facilitate the development of precise ultrafast opto-ferroelectric devices.

A physics-constrained neural network is presented for predicting the optical response of metasurfaces. Our approach incorporates physical laws directly into the neural network architecture and loss function, addressing critical challenges in the modeling of metasurfaces. Unlike methods that require specialized weighting strategies or separate architectural branches to handle different data regimes and phase wrapping discontinuities, this unified approach effectively addresses phase discontinuities, energy conservation constraints, and complex gap-dependent behavior. We implement sine-cosine phase representation with Euclidean normalization as a non-trainable layer within the network, enabling the model to account for the periodic nature of phase while enforcing the mathematical constraint $\sin^2 \phi + \cos^2 \phi = 1$. A Euclidean distance-based loss function in the sine-cosine space ensures a physically meaningful error metric while preventing discontinuity issues. The model achieves good, consistent performance with small, imbalanced datasets of 580 and 1075 data points, compared to several thousand typically required by alternative approaches. This physics-informed approach preserves physical interpretability while reducing reliance on large datasets and could be extended to other photonic structures by incorporating additional physical constraints tailored to specific applications.

Business research is facing the challenge of scattered knowledge, particularly in the realm of brand loyalty (BL). Although literature reviews on BL exist, they predominantly concentrate on the pre-sent state, neglecting future trends. Therefore, a comprehensive review is imperative to ascertain emerging trends in BL This study employs a bibliometric approach, analyzing 1,468 papers from the Scopus database. Various tools including R software, VOS viewer software, and Publish or Perish are utilized. The aim is to portray the knowledge map, explore the publication years, identify the top authors and their co-occurrence, reliable documents, institutions, subjects, research hotspots, and pioneering countries and universities in the study of BL. The qualitative section of this research identifies gaps and emerging trends in BL through Word Cloud charts, word growth analysis, and a review of highly cited articles from the past four years. Results showed that highly cited articles mention topics such as brand love, consumer-brand identification, and social networks and the U.S. had the most productions in this field. Besides, most citations were related to Keller with 1,173 citations. Furthermore, in the qualitative section, social networks and brand experiences were found to be of interest to researchers in the field. Finally, by introducing the antecedents and consequences of BL, the gaps and emerging trends in BL were identified, so as to present the di-rection of future research in this area.

In this paper, we introduce the notion of conditional $h$-convex functions and we prove an operator version of the Jensen inequality for conditional $h$-convex functions. Using this type of functions, we give some refinements for Ky-Fan's inequality, arithmetic-geometric mean inequality, Chrystal inequality, and H$\ddot{o}$lder-McCarthy inequality. Many of the other inequalities can be refined by applying this new notion.

This paper investigates the impacts of the different surface energy coefficients on the compound nucleus decay modes during heavy ion fusion reactions, with focus given to the superheavy nuclei (SHN) in the range of $Z=112-118$. The evaporation-residue (ER) cross sections were calculated for different surface asymmetric constants, $k_{s}$ and surface energy constants, $\gamma_{0}$. In these calculations, the di-nuclear system model and proximity potential, along with considering deformed nuclei, were employed. Comparing the experimental data and this theoretical approach, the best values of $k_{s}$ and $\gamma_{0}$ are $0.7546$ and $0.9180~\mathrm{MeV~fm^{-2}}$, respectively. Furthermore, this new model was used to investigate the probability of synthesis of experimentally unknown heavier systems with $Z=119$ and $120$. There exist five promising combinations to synthesize SHN with $Z=119$: a) ${^{249}}\mathrm{Cf}({^{45}}\mathrm{Sc},3n){^{291}}119$, b) ${^{249}}\mathrm{Cf}({^{45}}\mathrm{Sc},4n){^{290}}119$, c) ${^{247}}\mathrm {Bk}({^{50}}\mathrm{Ti},3n){^{294}}119$, d) ${^{254}}\mathrm{Es}({^{48}}\mathrm{Ca},3n){^{299}}119$, and e) ${^{254}}\mathrm{Es}({^{48}}\mathrm{Ca},4n){^{298}}119$. In addition, it is found that the best combinations to synthesize SHN with $Z=120$ are ${^{249}}\mathrm{Cf}({^{50}}\mathrm{Ti},3n){^{296}}120$, and ${^{251}}\mathrm{Cf}({^{50}}\mathrm{Ti},3n){^{298}}120$.

Here, we analyzed magnetic elements of the solar active regions (ARs) observed in the line-of-sight magnetograms (the 6173 Å~Fe \small{I} line) recorded with the Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). The Yet Another Feature Tracking Algorithm (\textsf{YAFTA}) was employed to extract the statistical properties of these features (\textit{e.g.} filling factor, magnetic flux, and lifetime) within the areas of $180{\small^{\prime\prime}} \times 180{\small^{\prime\prime}}$ inside the flaring AR (NOAA 12443) and the non-flaring AR (NOAA 12446) for 3 to 5 November 2015 and for 4 to 6 November 2015, respectively. The mean filling factor of polarities was obtained to be about 0.49 for the flaring AR; this value was 0.08 for the non-flaring AR. Time series of the filling factors of the negative and positive polarities for the flaring AR showed anti-correlation (with the Pearson value of -0.80); while for the non-flaring AR, there was the strong positive correlation (with the Pearson value of 0.95). A power-law function was fitted to the frequency distributions of flux ($F$), size ($S$), and lifetime ($T$). Power exponents of the distributions of flux, size, and lifetime for the flaring AR were obtained to be about -2.36, -3.11, and -1.70, respectively; these values of exponents for the non-flaring AR were found to be about -2.53, -3.42, and -1.61, respectively. ...

We show that deep learning algorithms can be deployed to study bifurcations of particle trajectories. We demonstrate this for two physical systems, the unperturbed Duffing equation and charged particles in magnetic reversal by using the AI Poincar\'e algorithm. We solve the equations of motion by using a fourth-order Runge-Kutta method to generate a dataset for each system. We use a deep neural network to train the data. A noise characterized by a noise scale L is added to data during the training. By using a principal component analysis, we compute the explained variance ratios for these systems which depend on the noise scale. By plotting explained ratios against the noise scale, we show that they change at bifurcations. For different values of the Duffing equation parameters, these changes are of the form of different patterns of growth-decline of the explained ratios. For the magnetic reversal, the changes are of the form of a change in the number of principal components. We comment on the use of this technique for other dynamical systems with bifurcations.

Structural Health Monitoring (SHM) evaluates the integrity of a structure by observing its dynamic responses by an array of sensors over time to determine the current health state of the structure. The most important step of SHM is system identification which in civil structures is the identification of modal parameters of structures. Due to numerous limitations of input-output methods, system identification of ambient vibration structures using output-only identification techniques has become a key issue in structural health monitoring and assessment of engineering structures. In this paper, four beams with different boundary conditions and with arbitrary loading have been modeled in finite element software, ANSYS, and the responses (Acceleration of nodes) have been achieved. By using these data and the codes written in MATLAB software, the modal parameters (natural frequencies, mode shapes) of the beams are identified with FDD (frequency Domain Decomposition) and PP (Peak Picking) methods and then justified with the results of input-output method which was determined by frequency relation function (FRF). The results indicate a good agreement between the three methods for determining the dynamic characteristics of beams.

Networks are landmarks of many complex phenomena where interweaving interactions between different agents transform simple local rule-sets into nonlinear emergent behaviors. While some recent studies unveil associations between the network structure and the underlying dynamical process, identifying stochastic nonlinear dynamical processes continues to be an outstanding problem. Here we develop a simple data-driven framework based on operator-theoretic techniques to identify and control stochastic nonlinear dynamics taking place over large-scale networks. The proposed approach requires no prior knowledge of the network structure and identifies the underlying dynamics solely using a collection of two-step snapshots of the states. This data-driven system identification is achieved by using the Koopman operator to find a low dimensional representation of the dynamical patterns that evolve linearly. Further, we use the global linear Koopman model to solve critical control problems by applying to model predictive control (MPC)--typically, a challenging proposition when applied to large networks. We show that our proposed approach tackles this by converting the original nonlinear programming into a more tractable optimization problem that is both convex and with far fewer variables.