作者机构：
[Yan, Hongyu; Xie, SC; Xie, Suchao; Jing, Kunkun] Cent South Univ, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Peoples R China.;[Yan, Hongyu; Xie, SC; Xie, Suchao; Jing, Kunkun] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.;[Zhang, Yifan] Tiangong Univ, Inst Composite Mat, Key Lab Adv Text Composite Mat, Minist Educ, Tianjin 300387, Peoples R China.;[Zhou, Hui] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha 410004, Peoples R China.
通讯机构：
[Xie, SC ] C;Cent South Univ, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Peoples R China.;Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.
关键词：
3D woven fabrics;3D woven composites;Out-of-plane impact performance;Multi-scale analysis;Engineering applications
摘要：
Three-dimensional(3D) woven composites are widely used as structural materials in fields such as personal protection, aerospace, automotive, and rail vehicles due to their lightweight, high strength, superior out-of-plane impact performance, and high design flexibility. This systematic review focuses on the impact resistance of 3D woven fabrics and their composites, discussing synthetic fibers, natural fibers, and their hybrid configurations. Particular emphasis is placed on the mechanical response of 3D woven fabrics and composites under out-of-plane impact, with a detailed exploration and comparison of the factors influencing their impact performance. The current status of multiscale simulation analysis methods is also highlighted. Finally, the applications of impact-resistant 3D woven fabrics and composites are summarized and some existing problems in this field are elaborated. Overall, this paper summarizes the key progress and future prospects in the field of 3D woven composites and their behavior under impact loading, aiming to provide new insights into the design of new lightweight impact-resistant structural materials.
Three-dimensional(3D) woven composites are widely used as structural materials in fields such as personal protection, aerospace, automotive, and rail vehicles due to their lightweight, high strength, superior out-of-plane impact performance, and high design flexibility. This systematic review focuses on the impact resistance of 3D woven fabrics and their composites, discussing synthetic fibers, natural fibers, and their hybrid configurations. Particular emphasis is placed on the mechanical response of 3D woven fabrics and composites under out-of-plane impact, with a detailed exploration and comparison of the factors influencing their impact performance. The current status of multiscale simulation analysis methods is also highlighted. Finally, the applications of impact-resistant 3D woven fabrics and composites are summarized and some existing problems in this field are elaborated. Overall, this paper summarizes the key progress and future prospects in the field of 3D woven composites and their behavior under impact loading, aiming to provide new insights into the design of new lightweight impact-resistant structural materials.
期刊：
Expert Systems with Applications,2025年261:125532 ISSN：0957-4174
通讯作者：
Ruihuan Liu
作者机构：
[Zhao, Chengwei] School of Business, Central South University of Forestry and Technology, Changsha, China;[Liu, Ruihuan] School of Logistics, Central South University of Forestry and Technology, Changsha, China;[Xu, Xuanhua; He, Jishan] School of Business, Central South University, Changsha, China
通讯机构：
[Ruihuan Liu] S;School of Logistics, Central South University of Forestry and Technology, Changsha, China
摘要：
To achieve novel core with high energy absorption and strong water intrusion resistance, a composite structure was created by employing in-situ foaming of closed-cell polyurethane (PU) in a honeycomb structure. Experimental, theoretical, and finite element analyses revealed that the in-situ foaming technique increased the honeycomb's energy absorption by 312.84 % and improved impact efficiency by 61.45 % while maintaining specific energy absorption (SEA). Remarkably, this composite structure surpassed the energy absorption capacity of individual honeycomb and foam. The coupling gain ratio introduced by the foam increases within a certain range as the honeycomb cell size decreases and wall thickness increases, with little influence from the foam's mechanical properties. Water intrusion, leading to ice formation at low temperatures, drastically reduced honeycomb SEA by 94.61 % and impaired mechanical properties significantly. Closed-cell PU in-situ foaming provided exceptional water intrusion resistance, preserving the honeycomb's mechanical performance, including energy absorption and SEA.
To achieve novel core with high energy absorption and strong water intrusion resistance, a composite structure was created by employing in-situ foaming of closed-cell polyurethane (PU) in a honeycomb structure. Experimental, theoretical, and finite element analyses revealed that the in-situ foaming technique increased the honeycomb's energy absorption by 312.84 % and improved impact efficiency by 61.45 % while maintaining specific energy absorption (SEA). Remarkably, this composite structure surpassed the energy absorption capacity of individual honeycomb and foam. The coupling gain ratio introduced by the foam increases within a certain range as the honeycomb cell size decreases and wall thickness increases, with little influence from the foam's mechanical properties. Water intrusion, leading to ice formation at low temperatures, drastically reduced honeycomb SEA by 94.61 % and impaired mechanical properties significantly. Closed-cell PU in-situ foaming provided exceptional water intrusion resistance, preserving the honeycomb's mechanical performance, including energy absorption and SEA.
摘要：
The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.
The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.
摘要：
With the intensification of global resource competition, the issue of timber supply has escalated from an economic concern to a significant strategic challenge. This study focuses on the evolution of disruption resilience in the global trade network for wood forest products, aiming to reveal the patterns of resilience dynamics under disruption risks by simulating underload cascading failure phenomena. The study provides theoretical support for enhancing the security and stability of the global wood forest product supply chain. Utilizing global trade data from the UN Comtrade Database 2023, a directed weighted complex network model was constructed, spanning upstream, midstream, and downstream sectors, with trade intensity distances serving as edge weights. By developing an underload cascading failure model, the evolution of disruption resilience was simulated under various disruption scenarios from 2002 to 2023, and the long-term impacts of critical node failures on network performance were analyzed. The results demonstrate significant spatiotemporal heterogeneity in the disruption resilience of the global wood forest product trade network. The upstream network exhibits improved resilience in total node strength but reduced global efficiency. The midstream network shows marked volatility in resilience due to external shocks, such as the global financial crisis, while the downstream network remains relatively stable. Simulations reveal that failures in core nodes (e.g., China, the United States, and Germany) disproportionately degrade global efficiency and node strength, with node centrality metrics positively correlated with network performance loss. This study elucidates the evolutionary mechanisms of disruption resilience in the wood forest product trade network under risk propagation, offering actionable insights for optimizing network robustness and supply chain stability. It is recommended that policymakers promote green supply chain initiatives, accelerate afforestation projects, and enhance domestic timber self-sufficiency to reduce reliance on imported timber, thereby strengthening node resilience and fostering sustainable forest resource utilization for economic and environmental benefits.
作者机构：
[Huang, Qian; Pan, Shuangli; Liao, Huiyu; Jiang, Zehua] Cent South Univ Forestry & Technol, Sch Logist, Changsha, Hunan, Peoples R China.;[Zheng, Guijun] Cent South Univ Forestry & Technol, Business Sch, Changsha, Hunan, Peoples R China.;[Pan, Shuangli] Hunan Key Lab Intelligent Logist Technol, Changsha, Hunan, Peoples R China.
通讯机构：
[Pan, SL ] C;Cent South Univ Forestry & Technol, Sch Logist, Changsha, Hunan, Peoples R China.;Hunan Key Lab Intelligent Logist Technol, Changsha, Hunan, Peoples R China.
摘要：
The effective circulation of fresh agricultural products is conducive to increasing farmers' income and improving the living standards of urban residents. Cold chain storage facilities in agricultural producing areas play an important role in ensuring the quality of agricultural products, extending the freshness period of goods, and improving logistics efficiency. Different types of fresh produce have different requirements for refrigeration and often require transshipment due to quantity constraints. In addition, there are economies of scale in the construction and operation of cold chain storage facilities. Based on the above considerations, with the aim of minimizing the total daily cost, an optimization model for the layout of multi-level cold chain storage facilities is established to determine the number, location, type and capacity of cold chain storage facilities at the same time. Genetic algorithm is chosen to solve the model according to the characteristics of the model. Taking J County of China as an example, the model is proved to have strong operability and applicability. It is of guiding significance and reference value to optimize the layout of cold chain storage facilities in rural areas.
摘要：
This paper considers a dual-channel supply chain where the supplier can distribute products through an online platform and a capital-constrained traditional offline retailer. The retailer needs to fund its business by a portfolio financing of trade credit and platform investment to enter the market. With the price-dependent market uncertain demand, the supplier faces choices among direct, traditional, and dual channels for product distribution. We use the CVaR criterion to formulate the retailer’s risk-averse behaviour. Then derive the optimal retail channel strategy in a Stackelberg game model where the platform leads by setting a revenue-sharing rate. Our results unveil a Pareto improvement region in revenue sharing rates within the portfolio financing scheme. As the slotting fee escalates, the supplier tends towards traditional channels. Conversely, with a modest slotting fee, a threshold emerges for the retailer’s risk aversion, delineating the optimal retail channel. High retailer risk aversion favours the dual channel, while lower aversion steers all members towards the direct channel. Theoretical findings are validated through numerical analysis.
摘要：
An Electric Vehicle (EV) is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions. However, decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging time. To resolve the former issue, several transportation companies increases the travel distance of the EV by establishing recharging stations in various locations. The proposed Electric Vehicle-Routing Problem with Time Windows (E-VRPTW) and recharging stations are constructed in this context; it augments the VRPTW by reinforcing battery capacity constraints. Meanwhile, super-recharging stations are gradually emerging in the surroundings. They can decrease the recharging time for an EV but consume more energy than regular stations. In this paper, we first extend the E-VRPRTW by adding the elements of super-recharging stations. We then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging costs. Subsequently, we compare the experimental results of this approach with other algorithms on several sets of benchmark instances. Furthermore, we analyze the impact of super-recharging stations on the total cost of the logistic plan.
关键词：
Green computing;Cloud workflow;Large-scale scheduling;Evolutionary algorithm;Multi-objective optimization
摘要：
Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm , namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.
Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm , namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.
摘要：
The aim of this study was to develop a high-performance energy-absorbing device with both high energy absorption and smooth energy dissipation. Drawing upon the concept of exploiting temporal misalignment of impact force, a novel composite energy-absorbing device with staggered combination of cutting rings (CECR) was investigated. Dynamic impact tests were conducted using a drop hammer system, and a finite element model of CECR was built to study its application in railway vehicles. The study showed that the cutting rings fail into filamentous fine circles under impact loads, exhibiting high metal utilization efficiency. Under the influence of staggered combination of cutting rings, CECR demonstrated impact force misalignment compensation, with smooth impact forces. The average impact force reached 351.59 kN, with a maximum energy absorption of 195.37 kJ. The FE simulation model of CECR provided good simulation of failure modes, impact force, and energy absorption. Application of CECR to railway vehicles, with a collision simulation of the entire vehicle at 36 km/h, showed a 91.14% increase in steady-state impact force and a significant improvement in passive safety protection capability. CECR can provide design concepts and guidance for the development of energy-absorbing devices with smooth energy absorption characteristics.
作者机构：
[Zhang, Yinggui; Mo, Weiwei; Xiao, Yang; Wu, Caiyi] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.;[Xiao, Yuxie] Changsha Planning & Design Inst Co Ltd, Engn Consulting Dept, Changsha 410011, Peoples R China.;[Wang, Juan] Cent South Univ Forestry & Technol, Sch Logist, Changsha 410004, Peoples R China.
通讯机构：
[Wang, J ] C;Cent South Univ Forestry & Technol, Sch Logist, Changsha 410004, Peoples R China.
关键词：
oversize and heavyweight cargo;carbon emission;multimodal transportation;genetic algorithm
摘要：
With the increasing global concern over climate change, reducing greenhouse gas emissions has become a universal goal for governments and enterprises. For oversize and heavyweight cargo (OHC) transportation, multimodal transportation has become widely adopted. However, this mode inevitably generates carbon emissions, making research into effective emission reduction strategies essential for achieving low-carbon economic development. This study investigates the optimization of multimodal transportation paths for OHC (OMTP-OHC), considering various direct carbon pricing policies and develops models for these paths under the ordinary scenario-defined as scenarios without any carbon pricing policies-and two carbon pricing policy scenarios, namely the emission trading scheme (ETS) policy and the carbon tax policy, to identify the most cost-effective solutions. An enhanced genetic algorithm incorporating elite strategy and catastrophe theory is employed to solve the models under the three scenarios. Subsequently, we examine the impact of ETS policy price fluctuations, carbon quota factors, and different carbon tax levels on decision-making through a case study, confirming the feasibility of the proposed model and algorithm. The findings indicate that the proposed algorithm effectively addresses this problem. Moreover, the algorithm demonstrates a small impact of ETS policy price fluctuations on outcomes and a slightly low sensitivity to carbon quota factors. This may be attributed to the relatively low ETS policy prices and the characteristics of OHC, where transportation and modification costs are significantly higher than carbon emission costs. Additionally, a comparative analysis of the two carbon pricing policies demonstrates the varying intensities of emission reductions in multimodal transportation, with the ranking of carbon emission reduction intensity as follows: upper-intermediate level of carbon tax > intermediate level of carbon tax > lower-intermediate level of carbon tax = ETS policy > the ordinary scenario. The emission reduction at the lower-intermediate carbon tax level (USD 8.40/t) matches that of the ETS policy at 30%, with a 49.59% greater reduction at the intermediate level (USD 50.48/t) compared to the ordinary scenario, and a 70.07% reduction at the upper-intermediate level (USD 91.14/t). The model and algorithm proposed in this study can provide scientific and technical support to realize the low-carbonization of the multimodal transportation for OHC. The findings of this study also provide scientific evidence for understanding the situation of multimodal transportation for OHC under China's ETS policy and its performance under different carbon tax levels in China and other regions. This also contributes to achieving the goal of low-carbon economic development.
期刊：
Swarm and Evolutionary Computation,2024年91:101751 ISSN：2210-6502
通讯作者：
Jun Li
作者机构：
[Xia, Yangkun; Luo, Xinran; Jin, Ting] School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China;[Li, Jun] School of Management, Hunan Institute of Engineering, Xiangtan 411104, China;[Xing, Lining] Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China
通讯机构：
[Jun Li] S;School of Management, Hunan Institute of Engineering, Xiangtan 411104, China
摘要：
Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.
Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.
摘要：
Autonomous vehicles (AV) can not only improve traffic safety and congestion, but also have strategic significance for the development of the transportation industry. With the continuous updating of core technologies such as artificial intelligence, sensor detection, synchronous positioning, and high-precision mapping, the development of AV has been promoted. When 5G network is combined with Internet of Vehicles, the problems of AV can be solved by taking advantage of 5G ultra-large bandwidth, low latency and high reliability. However, when the user controls the vehicle remotely, a real-time and reliable authentication process is needed, while minimizing the overhead of security protocols. Therefore, this article proposes a practical and secure multifactor user authentication protocol for AV in 5G network. By introducing non-interactive zero-knowledge proof technology and physical uncloning function, the protocol completes mutual authentication and key agreement without revealing any sensitive information. The article proves the security of the protocol through BAN logic and the simulation of Scyther. And it can resist malicious attacks and provide more security features. The informal security analysis shows that the protocol can meet the proposed security requirements. Finally, we evaluate the efficiency of the protocol, and the results show that the protocol can provide better performance.
关键词：
Nonclassical diffusion equation;time-dependent global attractor;nonlinear delay;arbitrary polynomial growth
摘要：
In this paper, we mainly investigate the asymptotic behavior of solutions for the nonclassical diffusion problem with hereditary effects and time-dependent perturbed parameter. The main novelty is that the delay term may be driven by a function under very minimal assumptions, namely, measurability and the fact that the phase space is a time-dependent space of functions that are continuous in time. The existence and regularity of time-dependent global attractors are proved by using a new analytical technique. It is remarkable that the nonlinearity $ f $ has no restriction on the upper growth.
摘要：
This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China’s influence waning, and India’s rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions.
