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The purpose of this paper is to present a comprehensive self‐adaptive genetic algorithm (GA) based on fuzzy mechanism, aiming to improve both the optimizing capability and the convergence speed.

Many key factors that affect the performance of GAs are identified and analyzed, and their influences on the optimizing capability and the convergence speed are further elaborated, which prove to be very difficult to be described with explicit mathematical formulas. Therefore, a set of fuzzy rules are used to model these complicated relationships, in order to effectively guide the online self‐adaptive adjustments, such as changing the crossover and mutation probabilities, and thus to improve the optimizing capability and convergence speed.

Simulation results illustrates that, compared with a normal GA and another self‐adaptive GA based on explicit mathematical modeling of the key factors, the new GA is more advanced in terms of the optimizing capability and the convergence speed.

This paper develops a fuzzy‐rule‐based approach to describe the relationships between multiple GA parameters and online states, and the approach is useful in the design of a comprehensive self‐adaptive GA.

The purpose of this paper is to present an approach which can evaluate the ability that successfully achieves command and control, both in qualitative and quantitative modes, to improve decision accuracy and speed, as well as construct an executable architecture for analyzing and verifying different decision projects.

By defining command and control (C2) decision architecture and decomposing C2 decision processes into measurable subfunctions, measures and metrics will be associated with each of the lowest level decomposed functions, and will be used to provide support for performance evaluation. Both Markov decision process analysis and conditional probability (CP) logic are used for modeling the decision‐making process of course of action (COA). Meanwhile, an executable architecture constructed by Petri net is applied to logic structural verification and performance evaluation.

The paper presents an idea and methodology for net‐centric command and control decision‐making process analysis.

The paper describes and decomposes C2 decision processes for complex missions in uncertain environments.

The paper could be an important reference of analysis and application in net‐centric command and control of decision making.

The paper combines methodology with qualitative methods (decision process decomposition), quantitative method (Markov decision process analysis and CP logic), as well as structural verification and performance evaluation.

This study aims to benchmark Chinese TEFL academics’ research productivities, as a way to identify and, subsequently, address research productivity issues. This study investigated 182 Chinese TEFL academics’ research outputs and perceptions about research across three Chinese higher education institutions using a literature‐based survey. ANOVA, t‐tests and descriptive statistics were used to analyse data from and between the three institutions. Findings indicated that more than 70 per cent of the TEFL academics had produced no research in 10 of the 12 research output fields during 2004‐2008. The English Language and Literature Department in the national university outperformed all other departments at the three institutes for most of the research output categories. While a majority of the participants seemed to hold positive perceptions about research, t‐tests and ANOVA indicated that their research perceptions were significantly different across institutes and departments. Developing TEFL research capacity requires tertiary institutions to provide research‐learning opportunities.

“V-bending” is the most commonly used bending process in which the sheet metal is pressed into a “V-shaped” die using a “V-shaped” punch to form a required angular bend. When the punch is removed after the operation, because of elastic recovery, the bent angle varies. This shape discrepancy is known as spring back which causes problems in the assembly of the component in the modern aerospace industry. Regarding the optimization of spring-back accuracy, this research will illustrate the laws of the transition area (TA) of the nondeformation area (NDA) during the 90° “V-shape” bending process.

According to the traditional “V-bending” process to optimize the spring-back accuracy, the bent sheets are divided into deformation area (DA) and NDA. For this reason, the traditional “V-bending” process may prolong error to optimize the spring-back accuracy because NDA has a certain amount of deformation, which the researcher always avoids. Firstly, bent sheets are divided into three parts in this research: DA, TA and NDA to avoid the distortion error in TA that are not considered in the NDA in traditional theory. Then, the stress and strain in the DA and TA were discussed during theoretical derivation and some hypotheses were proposed. In this research, the interval, position and distortion degree of the TA of the bending sheet are used by finite element analysis. Finally, V-shape bending tests for aluminum alloy at room temperature are used and labeled all the work pieces' TAs to realize the deformation amount in the TA.

The bending radius does not affect the range of the TA, it only changes the position of TA in the bending sheet. It is evident that the laws of TA were explored in the width direction and gradually changed from the inner layer to the outer layer based on the ratio of width and thickness of the bending plate/sheet.

In the modern aerospace industry, aircraft manufacturing technology must maintain high accuracy. This research has practical value in the 90° “V-shape” bending of metal sheets and the development of its spring-back accuracy.

The purpose of this study is to develop a 3D wheel-rail adhesion model under wet condition, which considers the generated surface roughness topography and the traditional braking procedure for high-speed trains.

Wheel-rail adhesion has an important effect on the braking ability of railway vehicle. Based on the deterministic mixed lubrication approach, the model was solved to get the adhesion characteristics of the train during braking. The elastic deformation was calculated with the discrete convolution and fast Fourier transform method. The simulation results of adhesion coefficient were compared with the experimental values. The wheel-rail adhesion characteristics of train braking at several different initial speeds were investigated. The effects of the time-step length and roughness orientation on the contact load ratio were also discussed.

The results show that the adhesion coefficient of the numerical model is in good agreement with the experimental results. At the instant of braking, the adhesion coefficient drops to a lower adhesion level, the value of adhesion coefficient is lower than 0.06, especially at a higher speed (200, 300 and 400 km/h).

It can provide a better understanding of the low adhesion phenomenon of train braking under wet condition.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0040/

The purpose of this study is to develop a transient wheel–rail rolling contact model to primarily investigate the rail damage under wet condition when the train passes through the welded joints.

The impact force induced by welded joints is obtained through vehicle–track coupling dynamics. The normal and tangential wheel–rail contact pressures were solved by elastohydrodynamic lubrication (EHL) theory and simplified third-body layer theory, respectively. Then, the obtained tangential pressure and normal pressure were applied to the finite element model as moving loads, simulating cyclic loading. Finally, the shakedown map and critical plane method were used to predict rolling contact fatigue (RCF) and the initiation of fatigue cracks.

The results indicate that RCF will occur and fatigue cracks are more prone to appear on the subsurface of the rail, specifically around 2.7 mm below the rail surface in the vicinity of the welded joint and its heat-affected zone.

The cosimulation of numerical model and finite element model was implemented. The influence of surface roughness and fluids was considered. In this model, the normal and tangential wheel–rail contact pressure, the stress and strain and the rail fatigue cracks were obtained under a rail-welded joint excitation.

The Internet of Things (IoT) is used in the fresh agricultural product (FAP) supply chain, which can be coordinated through a revenue-sharing contract. The purpose of this paper is to make the three-level supply chain coordinate in IoT by considering the influence of FAP on market demand and costs of controlling freshness on the road.

A three-level FAP supply chain that comprises a manufacturer, distributor, and retailer in IoT is regarded as the research object. This study improves the revenue-sharing contract, determines the optimal solution when the supply chain achieves maximum profit in three types of decision-making situations, and develops the profit distribution model based on the improved revenue-sharing contract to coordinate the supply chain.

The improved revenue-sharing contract can coordinate the FAP supply chain that comprises a manufacturer, distributor, and retailer in IoT, as well as benefit all enterprises in the supply chain.

Resource utilization rate can be improved after coordinating the entire supply chain. Moreover, loss in the circulation process is reduced, and the circulation efficiency of FAPs is improved because of the application of IoT. The validity of the model is verified through a case analysis.

This study is different from other research in terms of the combination of supply chain coordination, FAPs, and radio frequency identification application in IoT.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

This study aims to explore the influence of a region’s network location characteristics and indirect connections on its innovation capability. The aim is to assist regions in different network locations to use innovation resources to improve their innovation capabilities more efficiently.

This paper represents the Chinese regional innovation network using the gravity model. A theoretical framework is developed to explore the relationships between a region’s innovation capability and its network location. Hypotheses are tested using hierarchical regressions.

First, this paper finds that a region’s network centrality can promote its innovation capability. Second, a structural hole can positively adjust the relationship between a region’s centrality and innovation capability. Third, a region’s indirect connections can inhibit its innovation capability while exhibiting a U-shaped relationship in moderating centrality and innovation capability.

This study uses a multi-index system to construct an innovation network covering 29 regions in China. This network represents the innovation cooperation and overall situation of innovation in China. The paper is one of the first attempts at investigating the relationship between regional network locations and innovation capability. It is also the first attempt at testing the influence of indirect connections on a region’s innovation capability. The findings provide a new perspective on the factors influencing regional innovation capacity and a new way for regions to improve their innovation capability.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.