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The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.

Autonomous vehicles represent a transformative technology in modern transportation, promising enhanced safety, efficiency, and accessibility in mobility systems. This paper presents a comprehensive autonomous vehicle system designed specifically for Vietnam’s traffic conditions, featuring a multi-layered approach to perception, decision-making, and control. The system utilizes dual 2D LiDARs, camera vision, and GPS sensing to navigate complex urban environments. A key contribution is the development of a specialized segmentation model that accurately identifies Vietnam-specific traffic signs, lane markings, road features, and pedestrians. The system implements a hierarchical decision-making architecture, combining long-term planning based on GPS and map data with short-term reactive planning derived from a bird’s-eye view transformation of segmentation and LiDAR data. The control system modulates the speed and steering angle through a validated model that ensures stable vehicle operation across various traffic scenarios. Experimental results demonstrate the system’s effectiveness in real-world conditions, achieving a high accuracy rate in terms of segmentation and detection and an exact response in navigation tasks. The proposed system shows robust performance in Vietnam’s unique traffic environment, addressing challenges such as mixed traffic flow and country-specific road infrastructure.

The collapse of ecosystems, the extinction of species, and the breakdown of economic and financial networks usually hinges on topological properties of the underlying networks, such as the existence of self-sustaining (or autocatalytic) feedback cycles. Such collapses can be understood as a massive change of network topology, usually accompanied by the extinction of a macroscopic fraction of nodes and links. It is often related to the breakdown of the last relevant directed catalytic cycle within a dynamical system. Without detailed structural information it seems impossible to state, whether a network is robust or if it is likely to collapse in the near future. Here we show that it is nevertheless possible to predict collapse for a large class of systems that are governed by a linear (or linearized) dynamics. To compute the corresponding early warning signal, we require only non-structural information about the nodes’ states such as species abundances in ecosystems, or company revenues in economic networks. It is shown that the existence of a single directed cycle in the network can be detected by a “quantization effect” of node states, that exists as a direct consequence of a corollary of the Perron–Frobenius theorem. The proposed early warning signal for the collapse of networked systems captures their structural instability without relying on structural information. We illustrate the validity of the approach in a transparent model of co-evolutionary ecosystems and show this quantization in systems of species evolution, epidemiology, and population dynamics.

Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification.

The Internet of Things (IoT) is increasingly creating new market possibilities in several industries’ sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way. We address the problem of traffic engineering with multiple paths for an NFV enabled IoT system (vIoT), taking into account the fluctuation of traffic volume in various time periods. We first formulate the problem as a mixed linear integer programming model for finding the optimal solution of link-weight configuration and traffic engineering. We then develop heuristic algorithms for a vIoT system with a large number of devices. Our solution enables a controller to adjust a link weight system and update a flow table at an NFV switch for directing IoT traffic through a service function chain in a vIoT system. The evaluation results under both synthetic and real-world datasets of network traffic and topologies show that our approach to traffic engineering with multiple paths remarkably improves several performance metrics for a vIoT system.

Geosynthetics have been widely used as a specific reinforcement to support embankments constructed in areas subject to localized sinkholes. The solution works based on several mechanisms such as soil expansion, effect of membrane, friction, and load transfer. However, the load transfer mechanisms have been underestimated due to simple assumptions used in the common existing design methods. The study develops a numerical model to approve the presence of arching within the embankment reinforced by geosynthetics over the cavity. Based on a referenced study that combined experimental and numerical works, the finite element method (FEM) has been used to develop a model of cavity problems. Thereby, the performance of different numerical methods and the ability to reproduce a full-scale experiment have been illustrated according to the analysis of displacement and load distribution acting within the embankment platform over the cavity. The influences on the expansion and the load transfer mechanisms acting inside the reinforcement system have been clarified by a parametric study considering surcharge, embankment height, and friction angle of the embankment.

Piper betle (P. betle) and Piper sarmentosum (P. sarmentosum) are the two members of the Piper genus, have been reported to be rich in phytochemicals and essential oils, which showed strong reducing power, antibacterial, and antifungal activities. P. betle recently has been studied and applied in several commercial products in the antimicrobial respect, meanwhile its relatives, P. sarmentosum has been lesser-known in this field. In this study, the two Piper species—P. betle and P. sarmentosum were studied to compare their ability in silver nanoparticle synthesis and efficacy in antibacterial activity. P. betle and P. sarmentosum were extracted by distilled water at different temperatures and times. Subsequently, their total reducing capacity was determined by DPPH scavenging and Folin-Ciocalteu assays to choose the appropriate extraction conditions. The silver nanoparticle solutions prepared by the extracts of P. betle (Pb.ext) and P. sarmentosum (Ps.ext) were characterized by Dynamic light scattering (DLS), Zeta potential, UV-vis, and Fourier-transform infrared (FTIR) measurements. Finally, the antibacterial activity of the synthesized silver nanoparticle solutions was tested against Escherichia coli using the agar diffusion well–variant method. The Pb.ext showed stronger reducing power with higher total polyphenol content (~125 mg GAE/mL extract) and better DPPH activity (IC50~1.45%). Both the green synthesized silver nanoparticle solutions (Pb.AgNP and Ps.AgNP) performed significantly stronger antibacterial activity on Escherichia coli compared to their initial extracts. Antibacterial tests revealed that Ps.AgNP showed remarkably better growth inhibition activity as compared to Pb.AgNP. This study would contribute useful and important information to the development of antibacterial products based on green synthesized silver nanoparticles fabricated by the extracts of P. betle and P. sarmentosum.

How is the irreversibility of a high-dimensional chaotic system related to its dynamical behavior? In this paper, we address this question by developing a stochastic-thermodynamics treatment of complex networks that exhibit chaos. Specifically, we establish an exact relation between the averaged entropy production rate—a measure of irreversibility—and the autocorrelation function for an infinite system of neurons coupled via random non-reciprocal interactions. We show how, under given noise strength, the entropy production rate can signal the onset of a transition occurring as the coupling heterogeneity increases beyond a critical value via a change in its functional form upon crossing this point. Furthermore, this transition happens at a fixed, noise-independent entropy production rate, elucidating how robust energetic cost is possibly responsible for optimal information processing at criticality.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

Our aim in this paper is to study variants and computational errors of the extragradient method for solving equilibrium problems. First, we consider convergence of the method when domains in the auxiliary subproblems of the extragradient algorithm are replaced by outer and inner approximation polyhedra. Then, computational errors are showed under the asymptotic optimality condition, but the bifunction must satisfy certain Lipschitz-type continuous conditions. Next, by using Armijo-type linesearch techniques commonly used in variational inequalities, we obtain an approximation linesearch algorithm without Lipschitz continuity. Convergence analysis of the algorithms is considered under mild conditions on the iterative parameters.