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Abstract Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

In the context of the 5G network, the proliferation of access devices results in heightened network traffic and shifts in traffic patterns, and network intrusion detection faces greater challenges. A feature selection algorithm is proposed for network intrusion detection systems that uses an improved binary pigeon-inspired optimizer (SABPIO) algorithm to tackle the challenges posed by the high dimensionality and complexity of network traffic, resulting in complex models, reduced accuracy, and longer detection times. First, the raw dataset is pre-processed by uniquely one-hot encoded and standardized. Next, feature selection is performed using SABPIO, which employs simulated annealing and the population decay factor to identify the most relevant subset of features for subsequent review and evaluation. Finally, the selected subset of features is fed into decision trees and random forest classifiers to evaluate the effectiveness of SABPIO. The proposed algorithm has been validated through experimentation on three publicly available datasets: UNSW-NB15, NLS-KDD, and CIC-IDS-2017. The experimental findings demonstrate that SABPIO identifies the most indicative subset of features through rational computation. This method significantly abbreviates the system’s training duration, enhances detection rates, and compared to the use of all features, minimally reduces the training and testing times by factors of 3.2 and 0.3, respectively. Furthermore, it enhances the F1-score of the feature subset selected by CPIO and Boost algorithms when compared to CPIO and XGBoost, resulting in improvements ranging from 1.21% to 2.19%, and 1.79% to 4.52%.

Digital Twin (DT) is widely regarded as a highly promising technology with the potential to revolutionize various industries, making it a key trend in the Industry 4.0 era. In a cost-effective and risk-free setting, digital twins facilitate the interaction and merging of the physical and informational realms. The application of digital twins spans across different sectors, including aerospace, healthcare, smart manufacturing, and smart cities. As electric vehicles have experienced rapid growth, there is a growing demand for the development of innovative technologies. One potential area for digital twins application is within the automotive sector. The powertrain system of electric vehicles (EVs) consists of three parts, power source, power electronic system, and electric motor, which are considered as the core components of electric vehicles. The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

In the present study, PMMA/PE-ɤ-Fe2O3nanocomposites of various compositions were produced through ultrasound-assisted technique. Thermogravimetric analysis and UV-visible results indicated that the thermal stability is enhanced distinctly, without a sacrifice in optical clarity. The improvement of thermal properties was attributed to the homogeneous and good dispersion of ɤ-Fe2O3nanoparticles in PMMA/PE/ɤ-Fe2O3. And the excellent thermal properties performance of the ɤ-Fe2O3fillers improved the tribological properties of PMMA/PE composites. [ABSTRACT FROM AUTHOR]

Carbon nanotube (CNT) was employed as a reinforcing material to prepare polyoxymethylene (POM)-based composite film through a simple melting extrusion. An effective approach was developed to clean and modify the surface of as-received CNT with nitric acid and then with a silane coupling agent. The mechanical evaluation demonstrated that a significant reinforcement was achieved for POM/CNT composites due to the improved interfacial adhesion between CNT and the matrix. The thermal stabilities of the composites were also improved in the presence of CNT. The studies on crystallization behaviors showed that CNT acted as a nucleation agent for the crystallization of POM domain in composites, and therefore, the crystallization rate and nucleation density increased remarkably due to the heterogeneous nucleating effect of CNT. [ABSTRACT FROM AUTHOR]

In this paper, the effect of carbon fiber and SiO2 on the mechanical and tribological properties of PI composites was investigated. Reinforced polyimide composite with short carbon fiber and SiO2 was prepared by hot molding press technology and a dramatic improved tensile strength of SiO2-filled carbon fiber/PI composites was observed. Approximately, an increase of 136% in tensile strength was obtained in case of composites with SiO2 compared with the CF/PI composites. The addition of SiO2 in composites can significantly improve the anti-wear properties and the load-carrying capacity of the CF/PI composites. The research extend the application of CF/PI composites in engineering area. [ABSTRACT FROM AUTHOR]