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We consider the effect that an upstream cylinder has on the effective inertial collection efficiency of a downstream cylinder. We consider an initially uniform distribution of particles moving past a cylindrical fiber in a potential crossflow and numerically calculate the resulting particle distribution far downstream of the fiber using finite differences. We then investigate the effect that the downstream distribution of particles has on the effective inertial collision efficiency of a downstream test cylinder oriented parallel to the first. We show that the effective collision efficiency of the test cylinder depends on its relative offset in the transverse direction (normal to the flow) with respect to the upstream cylinder. For relatively small offsets there is complete shielding, and no particles collide with the test cylinder. As the offset increases, a growing effective collision efficiency is observed leading to a significant enhancement relative to the collision efficiency of an isolated cylinder for intermediate offsets and Stokes numbers close to unity. Importantly, we observe relative gains in effective collision efficiency above 100% for a range of Stokes number and offset values. For larger offsets and Stokes numbers, the effect of the upstream cylinder on the effective collision efficiency of the test cylinder decreases and eventually becomes negligible. Detailed results are provided for Stokes drag acting on the particles and we also show that a non-linear drag has negligible effect on the observed trends for the conditions relevant to filtration systems. These results suggest that the arrangement of fibers has a substantial effect on inertial particle impaction even when neighboring fibers do not alter the flow field around each individual collector. Moreover, periodic arrangements with specific orientations with respect of the flow could lead to a significant enhancement of the inertial collision of particles on an array of collectors, thus contributing towards a better filtration performance.

In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the SV can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the skewness and SV in a Bayesian framework. We subsequently demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation study results also show that neglecting skewness leads to inaccurate SV estimates and conditional expected returns. Our empirical applications to daily stock return data also show strong evidence of negative skewness.

We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions and security profiles. Our model generalizes much of contagion models in the literature; in particular the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite variance degree distributions. The results allow us to derive a resilience condition of the network to the infection of a large group of agents and quantify how contagion amplifies small shocks to the network. We show that when the degree distribution has infinite variance and, highly correlated in- and out-degrees, then even when agents have high thresholds, a sub-linear fraction of initially infected agents is enough to trigger the infection of a positive fraction. We also show how these results are sensitive to vertex and edge percolation (immunization). We then study the asymptotic Nash equilibrium and socially optimal security investment. In the asymptotic limit, agents' risk depends on all other agents' investment through an aggregate quantity, that we call network vulnerability. The limit theorems allow us to capture the impact of one class of agents' decision on the overall network vulnerability. Based on our results, the vulnerability is semi-analytic allowing for a tractable Nash equilibrium. We give sufficient conditions for investment in equilibrium to be monotone in the network vulnerability. When investment is monotone, we show that the (asymptotic) Nash equilibrium is unique. In the particular example of two types core-periphery agents, we exhibit a strong effect of the cost heterogeneity and in particular non-monotonous investment as a function of costs.

This paper investigates the elliptic localization for moving object problem from time delay (TD) and Doppler frequency shift (DFS) measurements, where the transmitter position and velocity are unknown. The transmitter is not perfectly time syncronized such that unknown offsets exist in the TD and DFS measurements. We propose to jointly estimate the object and transmitter positions and velocities and the offsets. Using the TD and DFS measurements from both the indirect and direct paths between the transmitter and the receivers, we formulate a non-convex weighted least squares (WLS) problem. Local convergence may occur when solving the non-convex WLS problem, implying that good estimate is not guaranteed. Thus, we relax the non-convex WLS problem into a convex semidefinite program by applying semidefinite relaxation (SDR). Moreover, we theoretically show that the performance can be improved by using multiple transmitters as compared to that using single transmitter, although more unknown parameters are introduced. We then extend the proposed SDR method to handle the multiple transmitters case. Finally, the mean square error analysis is provided to show that the proposed WLS method reaches the Cramer-Rao lower bound accuracy under small Gaussian noise condition. Simulation results validate the theoretical analysis and show the superior performance over the existing methods.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Real neural networks are characterized by large-scale and complex topology. However, the current dynamical analysis is limited to low-dimensional models with simplified topology. Therefore, there is still a huge gap between neural network theory and its application. This article proposes a class of large-scale neural networks with a ring-hub structure, where a hub node is connected to n peripheral nodes and these peripheral nodes are linked by a ring. In particular, there exists a hybrid coupling mode in the network topology. The mathematical model of such systems is described by fractional-order delayed differential equations. The aim of this article is to investigate the local stability and Hopf bifurcation of this high-dimensional neural network. First, the Coates flow graph is employed to obtain the characteristic equation of the linearized high-dimensional neural network model, which is a transcendental equation including multiple exponential items. Then, the sufficient conditions ensuring the stability of equilibrium and the existence of Hopf bifurcation are achieved by taking time delay as a bifurcation parameter. Finally, some numerical examples are given to support the theoretical results. It is revealed that the increasing time delay can effectively induce the occurrence of periodic oscillation. Moreover, the fractional order, the self-feedback coefficient, and the number of neurons also have effects on the onset of Hopf bifurcation.

In data-driven evolutionary optimization, most existing Gaussian processes (GPs)-assisted evolutionary algorithms (EAs) adopt stationary GPs (SGPs) as surrogate models, which might be insufficient for solving most optimization problems. This article finds that GPs in the optimization problems are nonstationary with great probability. We propose to employ a nonstationary GP (NSGP) surrogate model for data-driven evolutionary optimization, where the mean of the NSGP is allowed to vary with the decision variables, while its residue variance follows an SGP. In this article, the nonstationarity of GPs in the tested functions is theoretically analyzed. In addition, this article constructs an NSGP where the SGP is a degenerate case. Performance comparisons of the NSGP with the SGP and the NSGP-assisted EA (NSGP-MAEA) with the SGP-assisted EA (SGP-MAEA) are carried out on a set of benchmark problems and an antenna design problem. These comparison results demonstrate the competitiveness of the NSGP model.

In this article, the local stabilization problem is investigated for a class of memristive neural networks (MNNs) with communication bandwidth constraints and actuator saturation. To overcome these challenges, a discontinuous event-trigger (DET) scheme, consisting of the rest interval and work interval, is proposed to cut down the triggering times and save the limited communication resources. Then, a novel relaxed piecewise functional is constructed for closed-loop MNNs. The main advantage of the designed functional consists in that it is positive definite only in the work intervals and the sampling instants but not necessarily inside the rest intervals. With the aid of extended reciprocally convex combination lemma, generalized sector condition, and some inequality techniques, two local stabilization criteria are established on the basis of both the discrete- and continuous-time Lyapunov methods. The proposed analysis technique fully takes advantage of the looped-functional and the event-trigger mechanism. Moreover, two optimization schemes are, respectively, established to design the control gain and enlarge the estimates of the admissible initial conditions (AICs) and the upper bound of rest intervals. Finally, some comparison results are given to validate the superiority of the proposed method.

A counter-current jet (CCJ) is significantly different from a classical jump – it is like a classical jump with a reverse surface jet layer above it. A series of experiments, with the jet's Froude number in the range 4–10, was conducted to investigate the velocity distribution characteristics of CCJs. It was found, compared with a classical jump, the CCJ velocity distribution was more even and the decay of the maximum velocity was more efficient. It was beneficial to the energy dissipation and the deceleration of a high-speed flow. The variations of velocity with depth were opposite in the upper and lower parts of the zero-velocity line, resulting in relatively large fluid internal friction near the zero-velocity line. The internal friction between the surface jet layer and the lower fluid also increased the energy dissipation rate. A counter-current energy dissipator was found to achieve a good energy dissipation effect without auxiliary energy dissipators, which could be beneficial to engineering applications.

Biogas, landfill gas, associated petroleum gas, and other tail gases accompanying various industrial processes are potential sources of hydrogen and carbon monoxide for solid oxide fuel cells via the reforming process. As these gases contain heavy hydrocarbons, fine-tuning of steam and carbon dioxide addition and specific temperature control are necessary to avoid carbon deposition during the reforming process. Numerical simulation plays a crucial role in designing miniaturized steam reforming reactors and optimal working conditions. All simulations of reforming processes must account for carbon deposition. The methods commonly seen in the open literature include Gibbs free energy minimization or parametric equations formalism. This paper utilizes Gaussian process regression as a tool for making predictions about which reforming parameters are suitable for carrying out this process without the danger of damaging the catalyst due to a carbon formation. Parametric equations formalism and Gibbs free energy minimization involve either the minimization of objective function or a search for roots of nonlinear functions. These tasks are sensitive to a choice of the starting points of the algorithm — wrong choice of starting points could lead to numerical instability. Unlike conventional methods, the Gaussian process regression approach bypasses the computation of equilibrium composition. It predicts carbon formation tendencies directly from the initial conditions which ensure stability.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed -Nash equilibrium. Performance results show that our algorithm has significant superiority on average profits, utilities and execution time compared with other representative algorithms.

In this article, the exponential synchronization control issue of reaction-diffusion neural networks (RDNNs) is mainly resolved by the sampling-based event-triggered scheme under Dirichlet boundary conditions. Based on the sampled state information, the event-triggered control protocol is updated only when the triggering condition is met, which effectively reduces the communication burden and saves energy. In addition, the proposed control algorithm is combined with sampled-data control, which can effectively avoid the Zeno phenomenon. By thinking of the proper Lyapunov-Krasovskii functional and using some momentous inequalities, a sufficient condition is obtained for RDNNs to achieve exponential synchronization. Finally, some simulation results are shown to demonstrate the validity of the algorithm.

The compatibility problems become main concern in the utilization of biodiesel especially regarding to degradation and corrosion. The present study is conducted to describe the corrosion behaviour that occurs in steel tanks after contact with palm-based biodiesel for a period of 7 months. The storage tank used is respectively made of galvanised steel (GS), carbon steel (CS) and stainless steel (SS). It is performed in vertical cylinders with a floating-roof model. In every 30 days of storage time, the profile changes in the wall, roof and base surface of each tank were observed. The oil fuel properties were monitored in the parameter of viscosity, acid numbers, water content and oil colour. The topographic profiles on the surface of the walls and bottom of each tank wetted by biodiesel were examined over three zones inside the tanks using an endoscope imager. The main results of this study indicate that the tank steel with different protective layers exhibits different types of corrosion. The corrosion was localized in SS tanks but generalized in GS and CS tanks. The oil contamination in SS tanks was relatively lower than that in CS and GS tanks. The type of corrosion performed differently in different zones on the inside of the tank. The potential for leaks is greater at the base than on the walls and roof of the storage tank.

Many nations have adopted comprehensive data privacy laws to protect customers’ data. However, privacy policies of mobile wallet digital payment systems (DPS), and particularly the mobile wallet and remittance services that are part of DPS, are often not compliant with privacy laws. There is a lack of measures to assess how adequate the policies are in addressing data privacy issues. To address this problem, this article develops a compliance index to help DPS organizations assess the compliance of their privacy policies with the general data protection regulation (GDPR). The compliance index is created through a natural language process that includes term frequency-inverse document frequency matrix and topic modeling using latent Dirichlet allocation, to compute 1) an emphasis density score that indicates the level of emphasis a privacy policy places on GDPR dimensions, and 2) a privacy score that identifies the level of compliance of a privacy policy with GDPR. The compliance index is validated by assessing its effectiveness at the country level in comparison with an international publicly available data privacy benchmark.

Background Emerging epidemiological evidence suggests that compared to engaging in 1 activity mode alone, a combination of moderate-to-vigorous physical activity (MVPA: brisk walking/jogging, cycling) and muscle-strengthening exercise (MSE: push-ups/sit-ups, using weight machines) has more favorable associations with optimal weight status. However, few studies have examined the dose-dependent and joint associations of MVPA and MSE with obesity. Methods Based on cross-sectional analyses of the European Health Interview Survey Wave 2 (2013–2014), we examined prevalence ratios (PRs) of joint and stratified associations between MVPA (4 categories: (i) 0 min/week, (ii) 1–149 min/week, (iii) 150–299 min/week, and (iv) ≥300 min/week) and MSE (3 categories: (i) 0 day/week, (ii) 1 day/week, and (iii) ≥2 days/week) with body mass index-defined obesity (body mass index of ≥30.0 kg/m2) using Poisson regression with robust error variance. PRs were examined unadjusted and adjusted for sociodemographic and lifestyle characteristics (e.g., sex, age, education, income, and smoking status). Results Data were available for 280,456 adults (≥18 years), of which 46,166 (15.5%) were obese. The interaction MVPA × MSE guideline adherence was statistically significant for obesity (p ≤ 0.05). The joint MVPA–MSE analysis showed that compared to the reference group (i.e., no MVPA and no MSE), the PRs followed a dose-dependent pattern, with the lowest observed among those reporting ≥150 MVPA min/week and ≥1 MSE days/week (PR: 0.43; 95% confidence interval: 0.41–0.46). When stratified across each MVPA strata, the PRs were mostly lower among those engaging in MSE 1 day/week, as compared to those doing MSE ≥2 days/week. Conclusion There was evidence for a dose-dependent association between joint MVPA–MSE with a reduced prevalence of obesity. Public health strategies for the prevention and management of obesity should recommend both MVPA and MSE.

This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.

The excellent remote sensing ability of synthetic aperture radar (SAR) will be misled seriously when it encounters deceptive jamming which possesses high fidelity and fraudulence. In this paper, the dynamic synthetic aperture (DSA) scheme is used to extract the difference between the true and false targets. A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing ability. The system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be estimated. An iterative re-weighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework, where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its non-convexity. After dictionary parameter learning, the grid mismatching problem in a fixed grid based method is avoided. Therefore, the proposed method can reap a super resolution result. Besides, a simple yet effective DSA section scheme is developed for the SAR data excerpting, in which only two DSAs are required. Experimental results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

A numerical investigation of transient, two-dimensional natural convection in horizontal isothermal cylindrical annuli is performed to investigate the Prandtl number and diameter ratio effect on flow and heat transfer characteristics. The finite difference method is used to solve the governing equations, in which buoyancy is modeled via the Boussinesq approximation. Both vorticity and energy equations are solved using alternating direction implicit (ADI) method and stream function equation by successive over relaxation (SOR) method. Solutions for laminar case are obtained up to Grashof number of 105 as well as three different diameter ratios, namely 1.2, 1.5, and 2.0 and the Prandtl number varies from 0.7 to 10 are considered. The computed flow patterns and temperature fields are shown by means of streamlines and isotherms, respectively, and the average heat transfer coefficients are also presented. The numerical results are summarized by Nusselt number vs. Grashof number correlations with the Prandtl number and diameter ratio as a parameter. The results of the parametric study show that the diameter ratio and Grashof number have a profound influence on the temperature and flow field and they are almost independent of a low Prandtl number fluid. The average Nusselt number increases by 25% at large of diameter ratio and Prandtl number. Good agreement with earlier available data is obtained.

This research including a study of mechanical ,physical properties (band structure, density of states) and the elasticity constants of the Barium Ortho titanate by using Materials studio software. The calculations were based on the generalized gradient approximation (GGA) by linear method. The calculated equilibrium lattice constants of monoclinic barium orthotitanate were: a = 0.612nm, b = 0.77nm, c = 1.05nm,calculated bulk modulus =42.3021 GPa, young modulus= 40.29 GPa, share modulus = 15.97 GPa, lame constant (λ)= 142.56 GPa ,and energy band gap=3.435eV which is indirect band gap and the composite is insulator and the electronic properties where be calculated in first Brillion zone. According the mechanical stability conditions the composite is stable mechanically. These results were calculated by Materials studio software.

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

Learning automata (LA) with artificially absorbing barriers was a completely new horizon of research in the 1980s (Oommen, 1986). These new machines yielded properties that were previously unknown. More recently, absorbing barriers have been introduced in continuous estimator algorithms so that the proofs could follow a martingale property, as opposed to monotonicity (Zhang et al., 2014), (Zhang et al., 2015). However, the applications of LA with artificial barriers are almost nonexistent. In that regard, this article is pioneering in that it provides effective and accurate solutions to an extremely complex application domain, namely that of solving two-person zero-sum stochastic games that are provided with incomplete information. LA have been previously used (Sastry et al., 1994) to design algorithms capable of converging to the game's Nash equilibrium under limited information. Those algorithms have focused on the case where the saddle point of the game exists in a pure strategy. However, the majority of the LA algorithms used for games are absorbing in the probability simplex space, and thus, they converge to an exclusive choice of a single action. These LA are thus unable to converge to other mixed Nash equilibria when the game possesses no saddle point for a pure strategy. The pioneering contribution of this article is that we propose an LA solution that is able to converge to an optimal mixed Nash equilibrium even though there may be no saddle point when a pure strategy is invoked. The scheme, being of the linear reward-inaction ( $L_{R-I}$ ) paradigm, is in and of itself, absorbing. However, by incorporating artificial barriers, we prevent it from being ``stuck'' or getting absorbed in pure strategies. Unlike the linear reward-εpenalty ( $L_{R-ε P}$ ) scheme proposed by Lakshmivarahan and Narendra almost four decades ago, our new scheme achieves the same goal with much less parameter tuning and in a more elegant manner. This article includes the nontrial proofs of the theoretical results characterizing our scheme and also contains experimental verification that confirms our theoretical findings.

Accurate prediction of the lithium-ion battery’s future capacity and remaining useful life (RUL) is of great significance to the battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey-Markov chain model (GMCM) and the improved Gaussian process is proposed in this paper. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multi-output mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multi-step prediction.

The article investigates full information control problem for switched neural networks subject to fault and disturbance. First, the main objective is realizing interval stability and zero tracking error under condition that neither of the neuron states' vectors including the plant and reference models is available. Second, the desired full information controller and neural networks' observer are designed to ensure observer-based dynamic error system mean-square exponentially stable with sufficient condition of strict weight ${H}_{∞} /{H}_-$ performance levels. Finally, we concentrate on stability analyses and fault tolerance for switched neural networks with fault accompanied by disturbance through linear matrix inequalities (LMIs), Lyapunov function, and average dwell time, discussing it according to different values of fault. Finally, simulation examples are listed to account for the availability and effectiveness of the research methodology.

In this paper, an enhanced thermal model is proposed for pouch batteries, which can be used for a battery management system (BMS) at low temperatures and high current rates. This thermal model integrates a virtual resistance model (VRM) and post resistance model at low temperatures. Firstly, based on the Arrhenius equation, the resistance model at low temperatures (from 0 °C to -10 °C) and high discharge rates (from 3.125C to 12.5C) is extrapolated from the resistance model under room temperature conditions. In this context, the resistance model at low temperatures is regarded as the VRM. In comparison to the experimental results, the maximum error of this VRM is 0.851 mΩ. Additionally, the equivalent post resistance model of the pouch battery, including that of the positive electrode and negative electrode, is also given for low temperatures. Finally, the complete thermal model for pouch batteries is validated at low temperatures and high discharge rates. The temperature evolution at different points on the battery, along with the heat generation properties of the battery, are simulated under cyclic pulse current and constant current working conditions. For cyclic pulse current, the maximum mean error in terms of temperature evolution is 0.72 °C, and for constant current working conditions, the maximum mean error is 0.79 °C.

The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems. To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals. To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP). Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems. Then, the end-to-end transmission latency is derived based on the proposed model. Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations. Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.

Due to the unavoidable influence of sparse and Gaussian noise during the process of data acquisition, the quality of hyperspectral images (HSIs) is degraded and their applications are greatly limited. It is therefore necessary to restore clean HSIs. In the traditional methods, low-rank and sparse matrix decomposition methods are usually applied to restore the pure data matrix from the observed data matrix. However, due to the fact that the optimization of the l0-norm for the sparse modeling is a non-convex and NP-hard problem, convex relaxation and regularization parameters are usually introduced. However, convex relaxation often leads to inaccurate sparse modeling results, and the sensitive regularization parameters can lead to unstable results. Thus, in this paper, to address these issues, an accurate multi-objective low-rank and sparse denoising framework is proposed for HSIs to achieve accurate modeling. The l0-norm is directly modeled as the sparse noise and is optimized by an evolutionary algorithm, and the denoising problem is converted into a multi-objective optimization problem through simultaneously optimizing the low-rank term, the sparse term, and the data fidelity term, without sensitive regularization parameters. However, since the low-rank clean image and sparse noise of the HSI are encoded into a solution, the length of the solution is too long to be optimized. In this paper, a subfitness strategy is constructed to achieve effective optimization through comparing the objective function values corresponding to each band for each solution. The experiments undertaken with simulated images in 11 noise cases and four real noisy images confirm the effectiveness of the proposed method.

This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.

The wide deployment of machine learning algorithms has become a severe threat to user data privacy. As the learning data is of high dimensionality and high orders, preserving its privacy is intrinsically hard. Conventional differential privacy mechanisms often incur significant utility decline as they are designed for scalar values from the start. We recognize that it is because conventional approaches do not take the data structural information into account, and fail to provide sufficient privacy or utility. As the main novelty of this work, we propose Matrix Gaussian Mechanism (MGM), a new (, δ)-differential privacy mechanism for preserving learning data privacy. By imposing the unimodal distributions on the noise, we introduce two mechanisms based on MGM with an improved utility. We further show that with the utility space available, the proposed mechanisms can be instantiated with optimized utility, and has a closed-form solution scalable to large-scale problems. We experimentally show that our mechanisms, applied to privacy-preserving federated learning, are superior than the state-of-the-art differential privacy mechanisms in utility.

In this paper, a fault detection method based on chaos is proposed for doubly-fed induction generator (DFIG). Firstly, the nonlinear differential equation of the DFIG is established by the stator flux-oriented vector control method, and then the motion state of the DFIG is proved by the 0-1 test algorithm. Secondly, the motion state of the DFIG is analyzed under normal and fault conditions. Due to some failures that may occur in the normal operation of the DFIG, the motion state of the DFIG system may enter chaos from the cycle, which is mainly manifested by the violent oscillation of the rotor current and speed. At this time, the system is in an extremely unstable operating condition. Thereby, a new sliding mode control rate is mentioned to suppress chaotic behavior. Its stability is analyzed by constructing the Lyapunov function to ensure that the system state can quickly and stably converge to the given value. After that, the simulation model of the system is built. The simulation results show that sliding mode control can make the DFIG get out of the chaotic state quickly. And, the sliding mode control has the characteristics of fast fault elimination and high control accuracy. In addition, the traditional sliding mode approach rate is compared with the proposed new sliding mode approach rate. It proves that the proposed new approach rate can achieve better control effect.

We propose a model that allows for conditional heteroskedasticity in the volatility of asset returns and incorporates current return information into the volatility nowcast and forecast. Our model can capture all stylised facts of asset returns even with Gaussian innovations and is simple to implement. Moreover, we show that our model converges weakly to the GARCH-type diffusion as the length of the discrete time intervals between observations goes to zero. Empirical evidence shows that our model has a better fit, a more efficient parameter estimator as well as more accurate volatility and VaR forecasts than other common GARCH-type models.

This article investigates event-triggered and self-triggered control problems for the Markov jump stochastic nonlinear systems subject to denial-of-service (DoS) attacks. When attacks prevent system devices from obtaining valid information over networks, a new switched model with unstable subsystems is constructed to characterize the effect of DoS attacks. On the basis of the switched model, a multiple Lyapunov function method is utilized and a set of sufficient conditions incorporating the event-triggering scheme (ETS) and restriction of DoS attacks are provided to preserve performance. In particular, considering that ETS based on mathematical expectation is difficult to be implemented on a practical platform, a self-triggering scheme (STS) without mathematical expectation is presented. Meanwhile, to avoid the Zeno behavior resulted from general exogenous disturbance, a positive lower bound is fixed in STS in advance. In addition, the exponent parameters are designed in STS to reduce triggering frequency. Based on the STS, the mean-square asymptotical stability and almost sure exponential stability are both discussed when the system is in the absence of exogenous disturbance. Finally, two examples are given to substantiate the effectiveness of the proposed method.

Applying chaos theory for secure digital communications is promising and it is well acknowledged that in such applications the underlying chaotic systems should be carefully chosen. However, the requirements imposed on the chaotic systems are usually heuristic, without theoretic guarantee for the resultant communication scheme. Among all the primitives for secure communications, it is well-accepted that (pseudo) random numbers are most essential. Taking the well-studied two-dimensional coupled map lattice (2D CML) as an example, this paper performs a theoretical study towards pseudo-random number generation with the 2D CML. In so doing, an analytical expression of the Lyapunov exponent (LE) spectrum of the 2D CML is first derived. Using the LEs, one can configure system parameters to ensure the 2D CML only exhibits complex dynamic behavior, and then collect pseudo-random numbers from the system orbits. Moreover, based on the observation that least significant bit distributes more evenly in the (pseudo) random distribution, an extraction algorithm E is developed with the property that, when applied to the orbits of the 2D CML, it can squeeze uniform bits. In implementation, if fixed-point arithmetic is used in binary format with a precision of $z$ bits after the radix point, E can ensure that the deviation of the squeezed bits is bounded by $2^{-z}$ . Further simulation results demonstrate that the new method not only guide the 2D CML model to exhibit complex dynamic behavior, but also generate uniformly distributed independent bits. In particular, the squeezed pseudo random bits can pass both NIST 800-22 and TestU01 test suites in various settings. This study thereby provides a theoretical basis for effectively applying the 2D CML to secure communications., Comment: 10 pages, 11figures

Fluoride contamination in groundwater is a major concern in many parts of India and all over the world. Researches paying attention for the removal of fluoride through the application of biosorbents prepared from different parts of plants are finding greater scope and importance. The present research work focuses on Senna auriculata L., flower petal biomass as biosorbent, and evaluated its feasibility for fluoride ion elimination from aqueous solutions. Batch experiments were conducted to remove fluoride under different experimental conditions have been optimized for the maximum removal of fluoride; 80% removal was observed at pH: 6, sorbent dosage: 0.25 g/100 mL, time of agitation: 90 min, and initial concentration of the fluoride ions: 5 mg/L. Characterization studies of the biosorbent revealed its favorability towards the sorption of fluoride. In the isothermal modeling studies, Langmuir isotherm model was obeyed by the biosorption process with R2 value of 0.98 and from a kinetic perspective, the biosorption of fluoride onto the biosorbent observed the pseudo-second-order reaction with R2 value of 0.98. The developed biosorbent has been applied to real field fluoride-contaminated water samples and found to be successful.

We introduce a multivariate stochastic volatility model that imposes no restrictions on the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. Inference is achieved via a carefully designed feasible and scalable MCMC that has quadratic, rather than cubic, computational complexity for evaluating the multivariate normal densities required. We illustrate how our model can be applied on macroeconomic applications through a stochastic volatility VAR model, comparing it to competing approaches in the literature. We also demonstrate how our approach can be applied to a large dataset containing 571 stock daily returns of Euro STOXX index.

In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

This paper investigates suppressing the pitching oscillation of a NACA 0012 airfoil at a Reynolds number of [Formula: see text] by using phase-shifted trailing-edge morphing. The latter considering geometric nonlinearity is simulated numerically based on spatiotemporal polynomial surface fitting. Flowfield results and aerodynamic forces are obtained by solving the unsteady Reynolds-averaged Navier–Stokes equations accompanied by a low-Reynolds-number modified-shear-stress transport model. Dynamic meshing combining a sliding mesh and Laplacian diffusion is developed to deal with the deformation of the computational grid due to the large-amplitude coupled pitching and morphing motion of the airfoil. The airfoil is subjected to stall-flutter analysis from an energy perspective by calculating the energy extracted by the airfoil from the freestream in an oscillation cycle. The flow control mechanism of the phase-offset trailing-edge morphing is analyzed in terms of the pressure distribution and moment contribution. The results suggest that trailing-edge motion with a phase of [Formula: see text] reduces the energy extraction by more than 300% and could be effective for suppressing the stall-flutter limit-cycle oscillation amplitude in specific ranges of amplitude and frequency.

In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multiple subproblems. However, these subproblems may be unnecessary or resolved. Operating on such subproblems can cause server inefficiencies, especially in the case of expensive optimization. To overcome this shortcoming, we propose an adaptive subproblem selection (ASS) strategy to identify the most promising subproblems for further modeling. To better leverage the cross information between the subproblems, we use the collaborative multioutput Gaussian process surrogate to model them jointly. Moreover, the commonly used acquisition functions (also known as infill criteria) are investigated in this article. Our analysis reveals that these acquisition functions may cause severe imbalances between exploitation and exploration in multiobjective optimization scenarios. Consequently, we develop a new acquisition function, namely, adaptive lower confidence bound (ALCB), to cope with it. The experimental results on three different sets of benchmark problems indicate that our proposed algorithm is competitive. Beyond that, we also quantitatively validate the effectiveness of the ASS strategy, the CoMOGP model, and the ALCB acquisition function.

In the network approach to psychopathology, psychological constructs are conceptualized as networks of interacting components (e.g., the symptoms of a disorder). In this network view, interest is on the degree to which symptoms influence each other, both directly and indirectly. These direct and indirect influences are often captured with centrality indices, however, the estimation method often used with these networks, the frequentist graphical LASSO (GLASSO), has difficulty estimating (uncertainty in) these measures. Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution of centrality indices. This study therefore compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations. Results showed that the Bayesian GLASSO performed better than the Horseshoe, and that the Bayesian GLASSO outperformed the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity. Sensitivity was better for the frequentist GLASSO, but performance of the Bayesian GLASSO is usually close. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well.

In this research, experimental investigation of the coefficient of the heat transfer has been conducted for a set of rectangular fins with different fin height, 0.27, 0.47 and 0.67 m at air velocity test points of 1.1, 1.3, 1.5, 1.7 and 2 m/s, to meet the Reynolds number (Re). Thirty samples from the two cases with changing fin positions were studied to verify the thermal performance of the system. The study was conducted based on the cross-sectional diameter of 21167, 25016, 28864, 32713, and 38486. Total heat flux in all tubes has been settled as 26.3, 58, and 88.7 W/m2. The experimental results showed that the temperature difference between the surface of the block and flowing air increases with increasing Reynolds number for all heat flow states.

In 2002 Mauduit and Sarkozy started to study finite sequences of \begin{document}$ k $\end{document} symbols \begin{document}$ E_{N} = \left(e_{1},e_{2},\cdots,e_{N}\right)\in \mathcal{A}^{N}, $\end{document} where \begin{document}$ \mathcal{A} = \left\{a_{1},a_{2},\cdots,a_{k}\right\}(k\in \mathbb{N},k\geq 2) $\end{document} is a finite set of \begin{document}$ k $\end{document} symbols. Later many pseudorandom sequences of \begin{document}$ k $\end{document} symbols have been given and studied by using number theoretic methods. In this paper we study the pseudorandom properties of the \begin{document}$ k $\end{document} -ary Sidel'nikov sequences with length \begin{document}$ q-1 $\end{document} by using the estimates for certain character sums with exponential function, where \begin{document}$ q $\end{document} is a prime power. Our results show that Sidel'nikov sequences enjoy good well-distribution measure and correlation measure. Furthermore, we prove that the set of size \begin{document}$ \phi(q-1) $\end{document} of \begin{document}$ k $\end{document} -ary Sidel'nikov sequences is collision free and possesses the strict avalanche effect property provided that \begin{document}$ k = o(q^{\frac{1}{4}}) $\end{document} , where \begin{document}$ \phi $\end{document} denotes Euler's totient function.

We use known characterizations of generalized Paley graphs which are cartesian decomposable to explicitly compute the spectra of the corresponding associated irreducible cyclic codes. As applications, we give reduction formulas for the number of rational points in Artin-Schreier curves defined over extension fields and to the computation of Gaussian periods., 20 pages, 2 tables. Added information on Cartesian products of graphs. A general reviwe was made, with small additions improving readability. Some typos corrected. arXiv admin note: text overlap with arXiv:1908.08097

In this paper, we focus on learning optimal parameters for PDE-based image denoising and decomposition models. First, we learn the regularization parameter and the differential operator for gray-scale image denoising using the fractional Laplacian in combination with a bilevel optimization problem. In our setting the fractional Laplacian allows the use of Fourier transform, which enables the optimization of the denoising operator. We prove stable and explainable results as an advantage in comparison to machine learning approaches. The numerical experiments correlate with our theoretical model settings and show a reduction of computing time in contrast to the Rudin-Osher-Fatemi model. Second, we introduce a new regularized image decomposition model with the fractional Laplacian and the Riesz potential. We provide an explicit formula for the unique solution and the numerical experiments illustrate the efficiency.

Objective: A numerical and theoretical study is developed to analyze the combined effect of activation energy and chemical reaction in the flow of nanofluids due to the thin moving needle using the mathematical nanofluid model offered by Buongiorno. A passively controlled nanoparticle volume fraction boundary is assumed rather than actively controlled. Methods: A similarity transformation is utilized to convert the governing partial differential equations to a set of ordinary differential equations which are then solved numerically by Runge-Kutta Shooting Method (RKSM). The physical characteristics of flow, heat and mass transfer are illustrated via graphs and tables for some set of values of governing parameters. Results: In addition, the basic non-linear governing equations are solved analytically using semianalytical technique called Differential transform method (DTM) and the comparison has been made with the numerical and the published results. Conclusion: The present study reveals that the ratio between the needle velocity and the composite velocity brings out to increases the velocity distribution with λ

The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

Agriculture is the back bone of India, one of the major sources for some notable population from past decade, slowly the agriculture is facing downward growth. Due to less rain fall, they are not getting proper yield, and also expected price for their crops. The farmers are getting loss or less profit. If they know the price of particular commodity, accordingly they can grow selected crop depending upon their geographical area. We can use some of the machine leaning regression algorithms to predict the price of the agricultural commodities that helps the formers to do the decision making. This research work focus on detailed study of predicting the price of the cotton using five different ML regression algorithms namely Linear Regression, Bayesian Linear Regression, Boosted Decision Tree Regression, Decision forest Regression, Poisson Regression. Metrics for evaluating the performance of ML model. The Data set contains the cotton prices of different states of India. The cotton price 2019 data set is used for this work and the prediction values are compared with 2020 data set and the predicted prices are nearing to the actual values. Compare to all other algorithms Boosted decision tree giving the good accuracy, after that Bayesian and Linear regression also performing well.

In this paper, we propose a Hierarchical Multimodal Variational Encoder-Decoder (HMMVED) to predict the popularity of micro-videos by comprehensively leveraging the user information and micro-video content in a hierarchical fashion. In particular, the multimodal variational encoder-decoder framework encodes the input modalities to a lower dimensional stochastic variable, from which the popularity of micro-videos can be decoded. Considering the important role of users social influence in social media for information dissemination, a user encoderdecoder is designed to learn the user Gaussian embedding from the user information, which is informative about the coarsegrained popularity for the micro-videos uploaded by the user. The learned user embedding then acts as the prior of the micro-video latent embeddings in the micro-video encoder-decoder, where the refined posterior embedding of the micro-video is obtained by taking the multimodal content into consideration. The finegrained popularity of each micro-video is generated from the posterior embedding of the micro-video. Based on the multimodal extension of variational information bottleneck theory, we show that the learned latent embeddings of micro-videos are maximally expressive about the popularity whilst maximally compressing the information from input modalities. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method. Codes and datasets are available at: https://github.com/JennyXieJiayi/HMMVED.

The degree distributions of complex networks are usually considered to be power law. However, it is not the case for a large number of them. We thus propose a new model able to build random growing networks with (almost) any wanted degree distribution. The degree distribution can either be theoretical or extracted from a real-world network. The main idea is to invert the recurrence equation commonly used to compute the degree distribution in order to find a convenient attachment function for node connections - commonly chosen as linear. We compute this attachment function for some classical distributions, as the power-law, broken power-law, geometric and Poisson distributions. We also use the model on an undirected version of the Twitter network, for which the degree distribution has an unusual shape. We finally show that the divergence of chosen attachment functions is heavily links to the heavy-tailed property of the obtained degree distributions., Comment: 23 pages, 3 figures

This article mainly explores the local input-to-state stability (LISS) property of a class of nonlinear systems via a saturated control strategy, where both the external disturbance and impulsive disturbance being fully considered. In terms of the Lyapunov method and inequality techniques, some sufficient conditions under which the system can be made LISS are proposed, and the elastic constraint relationship among saturated control gain, rate coefficients, external disturbance, and domain of initial value is revealed. Moreover, the optimization design procedures are provided with the hope of obtaining the estimates of admissible external disturbance and domain of initial value as large as possible, where the corresponding saturated control law can be designed by solving LMI-based conditions. In the absence of an external disturbance, the locally exponential stability (LES) property can also be presented with a set of more relaxed conditions. Finally, two examples are presented to reveal the validity of the obtained results.