Solid lipid nanoparticles(SLN)could enhance the oral bioavailability of loaded protein and peptide drugs through lymphatic transport.Natural oligopeptides regulate nearly all vital processes and serve as a nitrogen so...Solid lipid nanoparticles(SLN)could enhance the oral bioavailability of loaded protein and peptide drugs through lymphatic transport.Natural oligopeptides regulate nearly all vital processes and serve as a nitrogen source for nourishment.They are mainly transported by oligopeptide transporter-1(PepT-1)which are primarily expressed in the intestine with the characteristics of high-capacity and low energy consumption.Our preliminary research discovered the transmembrane transport of SLN could be improved by stimulating the oligopeptide absorption pathway.This implied the potential of combining the advantages of SLN with oligopeptide transporter mediated transportation.Herein,two kinds of dipeptide modified SLN were designed with insulin and glucagon like peptide-1(GLP-1)analogue exenatide as model drugs.These drugs loaded SLN showed enhanced oral bioavailability and hypoglycemic effect in both type I diabetic C57BL/6mice and type II diabetic KKAymice.Compared with un-modified SLN,dipeptide-modified SLN could be internalized by intestinal epithelial cells via PepT-1-mediated endocytosis with higher uptake.Interestingly,after internalization,more SLN could access the systemic circulation via lymphatic transport pathway,highlighting the potential to combine the oligopeptide-absorption route with SLN for oral drug delivery.展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
Smart transportation is a key direction and trend in the development of China’s public transportation sector.Under this background,new opportunities for the development of transportation engineering education have em...Smart transportation is a key direction and trend in the development of China’s public transportation sector.Under this background,new opportunities for the development of transportation engineering education have emerged,necessitating the active promotion of hybrid teaching in transportation engineering courses.This approach aims to achieve innovation in teaching content and enhance the quality and effectiveness of education.Therefore,to improve the quality of transportation engineering education,this paper conducts research and exploration on the reform of hybrid teaching content.It proposes several measures,including constructing a dynamic teaching content system,strengthening faculty education and training,improving teaching facilities and technical support,and reinforcing students’self-discipline in learning.These initiatives aim to promote the reform of transportation engineering courses under the current smart transportation background and enhance the overall level and quality of education.展开更多
The Chengdu-Chongqing twin city economic circle has become a national strategy.As the westwards gateway of Chongqing central city,the High-tech Zone should further strengthen its traffic integration and interconnectiv...The Chengdu-Chongqing twin city economic circle has become a national strategy.As the westwards gateway of Chongqing central city,the High-tech Zone should further strengthen its traffic integration and interconnectivity with the Chengdu-Chongqing main Corridor.Combining the design concept and functional positioning of Jinfeng Hub,the research should be carried out from the aspects of hub scale control,three-dimensional spatial layout,rail transit connection,road collection and distribution system construction,and integrated development with the city.It is expected to provide a good reference for the planning and design of modern integrated transportation hub.展开更多
The Janus fabrics designed for personal moisture/thermal regulation have garnered significant attention for their potential to enhance human comfort.However,the development of smart and dynamic fabrics capable of mana...The Janus fabrics designed for personal moisture/thermal regulation have garnered significant attention for their potential to enhance human comfort.However,the development of smart and dynamic fabrics capable of managing personal moisture/thermal comfort in response to changing external environments remains a challenge.Herein,a smart cellulose-based Janus fabric was designed to dynamically manage personal moisture/heat.The cotton fabric was grafted with N-isopropylacrylamide to construct a temperature-stimulated transport channel.Subsequently,hydrophobic ethyl cellulose and hydrophilic cellulose nanofiber were sprayed on the bottom and top sides of the fabric to obtain wettability gradient.The fabric exhibits anti-gravity directional liquid transportation from hydrophobic side to hydrophilic side,and can dynamically and continuously control the transportation time in a wide range of 3–66 s as the temperature increases from 10 to 40℃.This smart fabric can quickly dissipate heat at high temperatures,while at low temperatures,it can slow down the heat dissipation rate and prevent the human from becoming too cold.In addition,the fabric has UV shielding and photodynamic antibacterial properties through depositing graphitic carbon nitride nanosheets on the hydrophilic side.This smart fabric offers an innovative approach to maximizing personal comfort in environments with significant temperature variations.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
In this paper,we prove the transportation cost-information inequalities on the space of continuous paths with respect to the L~2-metric and the uniform metric for the law of the mild solution to the stochastic heat eq...In this paper,we prove the transportation cost-information inequalities on the space of continuous paths with respect to the L~2-metric and the uniform metric for the law of the mild solution to the stochastic heat equation defined on[0,T]×[0,1]driven by double-parameter fractional noise.展开更多
Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英...Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。展开更多
Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy ...Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.展开更多
Inactivation of carbon-based transition metal catalysts,which was caused by electron loss,limited their application in advanced oxidation processes.Therefore,Co and TiO_(2) double-loaded carbon nanofiber material(Co@C...Inactivation of carbon-based transition metal catalysts,which was caused by electron loss,limited their application in advanced oxidation processes.Therefore,Co and TiO_(2) double-loaded carbon nanofiber material(Co@CNFs-TiO_(2))was synthesized in this study.Photocatalytic and chemical catalytic systems were synergized efficiently.Tetracycline was eliminated within 15 min.The degradation rate remained above 90%after five cycles,and the 50%promotion proved the high stability of Co@CNFs-TiO_(2).The main reactive oxygen species in this system were sulfate radicals,whereas Co and TiO_(2) represented the active sites of the catalytic reaction.Electrons generated from TiO_(2) during the photocatalytic process were transferred to Co,which promoted the Co(Ⅲ)/Co(Ⅱ)cycle and maintained Co in a low-valence state,thereby stimulating the generation of sulfate radicals.In this study,the effective regulation of reactive oxygen species in the reaction system was realized.The results provided a guidance for in situ electron replenishment and regeneration of carbon-based transition metal catalysts,which will expand the practical application of advanced oxidation processes.展开更多
The Third International Conference on Rail Transportation(ICRT),which was initiated by Southwest Jiaotong University and hosted by Tongji University,took place successfully in Shanghai,China,from August 7 to 9,2024.As...The Third International Conference on Rail Transportation(ICRT),which was initiated by Southwest Jiaotong University and hosted by Tongji University,took place successfully in Shanghai,China,from August 7 to 9,2024.As the chairman of the ICRT conference,I am delighted to witness its remarkable achievement.Based on the success of previous editions held in Chengdu in 2017 and 2021,this conference aims to provide a premier platform for extensive interaction and collaboration among universities,research institutions,and enterprises worldwide.展开更多
This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fissi...This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fission from ARCs(advanced reactor concepts)and SMRs(small modular reactors),and future-focused nuclear fusion methods-MCF(magnetic confinement fusion)and ICF(inertial confinement fusion).Central to this integration is the use of AI(artificial intelligence)to enhance smart grid efficiency,enable real-time optimization,and ensure resilient energy delivery.The synergy between these zero-carbon energy sources and AI-driven infrastructure promises a transformative impact on electric mobility,hydrogen-powered systems,and autonomous transport.By detailing the architecture of an AI-augmented,carbon-neutral transport ecosystem,this paper contributes to the roadmap for future global mobility.展开更多
Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into th...Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into the modern logistics system.It seeks to address the need for a more nuanced understanding of the“road to rail”policy,emphasizing the importance of intermodal collaboration and service of fragmented market demands.Design/methodology/approach–The study employs a transport economics perspective to evaluate the achievements and shortcomings of China’s transportation structure optimization.It bases its assessment of the current state of railway freight logistics,multi-modal transportation and the broader implications for the transportation service market on data analysis.The methodology includes a review of existing policies,an examination of industry practices and a comparative analysis with global trends in railway logistics.Findings–The research underscores the importance of focusing on the development of non-bulk materials,noting the insufficiency in the development of China’s rail multi-modal transportation and highlighting the instructive value of successful cases in open-top container road-rail intermodal transportation.The study posits that the railway sector must enhance cooperation with other market entities,aligning with the lead enterprises in the logistics chain that are characterized by speed,high value and strong coordination capabilities,in order to better serve the transportation market.This approach moves away from a reliance on the railway’s own capabilities alone.Originality/value–This paper offers original insights into the transformation of railway freight in China,contributing to the body of knowledge on transportation economics and logistics.It provides valuable recommendations for policymakers and industry practitioners,emphasizing the strategic importance of railway logistics in the context of China’s economic development and intense competition in the supply chain.The value of the article lies in its comprehensive understanding of the complexities involved in the adjustment of transportation structures,providing direction for the market-oriented reform of China’s railway freight sector.展开更多
The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials...The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials to concurrently reduce vehicle weight and enhance efficiency,sustainability,and safety.Challenges faced include high production and processing costs,durability issues in harsh railway environments,and environmental impacts associated with alloy production.Research findings indicate that innovative alloy design and advanced processing techniques,such as powder metallurgy,additive manufacturing,and surface treatment,significantly improve the applicability of titanium alloys in railway applications.These methods substantially increase energy efficiency and safety.Additionally,advancements in environmentally sustainable practices in the production of titanium alloys address ecological concerns.As research progresses,the study and development of low-cost,high-performance titanium alloys highlight the need for more efficient and environmentally friendly manufacturing processes.Exploring new alloy compositions and applying emerging technologies in processing and manufacturing are key areas for future research.These advancements are expected to enhance the role of titanium alloys in revolutionizing railway transportation,aligning with global trends towards sustainability and performance improvement.This research underscores the significant potential contribution of titanium alloys to future efficient and eco-friendly rail travel.展开更多
While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the se...While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.展开更多
The evolution mechanism of railway transportation network nodes driven by sea-rail intermodal transport(SRIT),a globally prevalent logistics method,has not been thoroughly investigated.From the perspective of SRIT,thi...The evolution mechanism of railway transportation network nodes driven by sea-rail intermodal transport(SRIT),a globally prevalent logistics method,has not been thoroughly investigated.From the perspective of SRIT,this study constructed a framework for understanding the evolution of railway container transport network nodes using Northeast China from 2013 to 2020 as a case study.It leverages proprietary data from 95306 Railway Freight E-commerce Platform.By employing the hybrid EWM-GA-TOPSIS model,complex network analysis,modified gravity model,and correlation and regression analyses,this study delves into the spatiotemporal patterns and dynamic transformations of railway container freight stations(RCFS).Finally,the long-term relationship between the RCFS and SRIT is explored.The results indicate that the spatial and temporal analysis of the RCFS in Northeast China from 2013 to 2020 revealed a clear polarisation trend,with the top-ranked stations mainly concentrated near ports and important transportation hubs.Additionally,the RCFS exhibited an expansionary trend;however,its development was uneven,and there was a significant increase in the number of new stations compared to abandoned stations,indicating an overall positive growth tendency.Moreover,the intensity of the SRIT at the RCFS in Northeast China notably increased.A significant positive linear relationship exists between SRIT and the freight capacity of all stations.A relatively pronounced correlation was observed for high-intensity stations,whereas a relatively weak correlation was observed for low-intensity stations.This study not only provides an effective framework for future research on RCFS within the context of SRIT but also serves as a scientific reference for promoting the implementation of the national strategy for multimodal transportation.展开更多
The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understandi...The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.展开更多
基金supported by National Key Research and Development Program of China(Grant No.2021YFE0115200)the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(Grant No.U22A20356).
文摘Solid lipid nanoparticles(SLN)could enhance the oral bioavailability of loaded protein and peptide drugs through lymphatic transport.Natural oligopeptides regulate nearly all vital processes and serve as a nitrogen source for nourishment.They are mainly transported by oligopeptide transporter-1(PepT-1)which are primarily expressed in the intestine with the characteristics of high-capacity and low energy consumption.Our preliminary research discovered the transmembrane transport of SLN could be improved by stimulating the oligopeptide absorption pathway.This implied the potential of combining the advantages of SLN with oligopeptide transporter mediated transportation.Herein,two kinds of dipeptide modified SLN were designed with insulin and glucagon like peptide-1(GLP-1)analogue exenatide as model drugs.These drugs loaded SLN showed enhanced oral bioavailability and hypoglycemic effect in both type I diabetic C57BL/6mice and type II diabetic KKAymice.Compared with un-modified SLN,dipeptide-modified SLN could be internalized by intestinal epithelial cells via PepT-1-mediated endocytosis with higher uptake.Interestingly,after internalization,more SLN could access the systemic circulation via lymphatic transport pathway,highlighting the potential to combine the oligopeptide-absorption route with SLN for oral drug delivery.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
文摘Smart transportation is a key direction and trend in the development of China’s public transportation sector.Under this background,new opportunities for the development of transportation engineering education have emerged,necessitating the active promotion of hybrid teaching in transportation engineering courses.This approach aims to achieve innovation in teaching content and enhance the quality and effectiveness of education.Therefore,to improve the quality of transportation engineering education,this paper conducts research and exploration on the reform of hybrid teaching content.It proposes several measures,including constructing a dynamic teaching content system,strengthening faculty education and training,improving teaching facilities and technical support,and reinforcing students’self-discipline in learning.These initiatives aim to promote the reform of transportation engineering courses under the current smart transportation background and enhance the overall level and quality of education.
基金The 2024 Chongqing Municipal Education Commission Science and Technology Research Project,“Research on the Planning and Design of Modern Comprehensive Transportation Hubs under the Background of the Construction of the Chengdu-Chongqing Twin-City Economic Circle:A Case Study of Jinfeng Hub in Chongqing High-tech Zone”(Project No.:KJQN202405604).
文摘The Chengdu-Chongqing twin city economic circle has become a national strategy.As the westwards gateway of Chongqing central city,the High-tech Zone should further strengthen its traffic integration and interconnectivity with the Chengdu-Chongqing main Corridor.Combining the design concept and functional positioning of Jinfeng Hub,the research should be carried out from the aspects of hub scale control,three-dimensional spatial layout,rail transit connection,road collection and distribution system construction,and integrated development with the city.It is expected to provide a good reference for the planning and design of modern integrated transportation hub.
基金support of this work by National Key Research and Development Program of China(2019YFC19059003)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(23KJB430024)+1 种基金Jiangsu Funding Program for Excellent Postdoctoral Talent(2023ZB680)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)are gratefully acknowledged.
文摘The Janus fabrics designed for personal moisture/thermal regulation have garnered significant attention for their potential to enhance human comfort.However,the development of smart and dynamic fabrics capable of managing personal moisture/thermal comfort in response to changing external environments remains a challenge.Herein,a smart cellulose-based Janus fabric was designed to dynamically manage personal moisture/heat.The cotton fabric was grafted with N-isopropylacrylamide to construct a temperature-stimulated transport channel.Subsequently,hydrophobic ethyl cellulose and hydrophilic cellulose nanofiber were sprayed on the bottom and top sides of the fabric to obtain wettability gradient.The fabric exhibits anti-gravity directional liquid transportation from hydrophobic side to hydrophilic side,and can dynamically and continuously control the transportation time in a wide range of 3–66 s as the temperature increases from 10 to 40℃.This smart fabric can quickly dissipate heat at high temperatures,while at low temperatures,it can slow down the heat dissipation rate and prevent the human from becoming too cold.In addition,the fabric has UV shielding and photodynamic antibacterial properties through depositing graphitic carbon nitride nanosheets on the hydrophilic side.This smart fabric offers an innovative approach to maximizing personal comfort in environments with significant temperature variations.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金Partially supported by Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX22-2211,KYCX22-2205)。
文摘In this paper,we prove the transportation cost-information inequalities on the space of continuous paths with respect to the L~2-metric and the uniform metric for the law of the mild solution to the stochastic heat equation defined on[0,T]×[0,1]driven by double-parameter fractional noise.
文摘Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。
文摘Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.
基金the financial support from the National Natural Science Foundation of China(Nos.52074176,52300165,52300056,52300099)Natural Science Foundation of Shandong Province Youth Project(No.ZR2022QB155)Open Project Program of Engineering Research Center of Groundwater Pollution Control and Remediation,Ministry of Education of China(No.GW202203)。
文摘Inactivation of carbon-based transition metal catalysts,which was caused by electron loss,limited their application in advanced oxidation processes.Therefore,Co and TiO_(2) double-loaded carbon nanofiber material(Co@CNFs-TiO_(2))was synthesized in this study.Photocatalytic and chemical catalytic systems were synergized efficiently.Tetracycline was eliminated within 15 min.The degradation rate remained above 90%after five cycles,and the 50%promotion proved the high stability of Co@CNFs-TiO_(2).The main reactive oxygen species in this system were sulfate radicals,whereas Co and TiO_(2) represented the active sites of the catalytic reaction.Electrons generated from TiO_(2) during the photocatalytic process were transferred to Co,which promoted the Co(Ⅲ)/Co(Ⅱ)cycle and maintained Co in a low-valence state,thereby stimulating the generation of sulfate radicals.In this study,the effective regulation of reactive oxygen species in the reaction system was realized.The results provided a guidance for in situ electron replenishment and regeneration of carbon-based transition metal catalysts,which will expand the practical application of advanced oxidation processes.
文摘The Third International Conference on Rail Transportation(ICRT),which was initiated by Southwest Jiaotong University and hosted by Tongji University,took place successfully in Shanghai,China,from August 7 to 9,2024.As the chairman of the ICRT conference,I am delighted to witness its remarkable achievement.Based on the success of previous editions held in Chengdu in 2017 and 2021,this conference aims to provide a premier platform for extensive interaction and collaboration among universities,research institutions,and enterprises worldwide.
文摘This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fission from ARCs(advanced reactor concepts)and SMRs(small modular reactors),and future-focused nuclear fusion methods-MCF(magnetic confinement fusion)and ICF(inertial confinement fusion).Central to this integration is the use of AI(artificial intelligence)to enhance smart grid efficiency,enable real-time optimization,and ensure resilient energy delivery.The synergy between these zero-carbon energy sources and AI-driven infrastructure promises a transformative impact on electric mobility,hydrogen-powered systems,and autonomous transport.By detailing the architecture of an AI-augmented,carbon-neutral transport ecosystem,this paper contributes to the roadmap for future global mobility.
基金supported by the Yuxiu Innovation Project of NCUT(Grant No.2024NCUTYXCX211).
文摘Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into the modern logistics system.It seeks to address the need for a more nuanced understanding of the“road to rail”policy,emphasizing the importance of intermodal collaboration and service of fragmented market demands.Design/methodology/approach–The study employs a transport economics perspective to evaluate the achievements and shortcomings of China’s transportation structure optimization.It bases its assessment of the current state of railway freight logistics,multi-modal transportation and the broader implications for the transportation service market on data analysis.The methodology includes a review of existing policies,an examination of industry practices and a comparative analysis with global trends in railway logistics.Findings–The research underscores the importance of focusing on the development of non-bulk materials,noting the insufficiency in the development of China’s rail multi-modal transportation and highlighting the instructive value of successful cases in open-top container road-rail intermodal transportation.The study posits that the railway sector must enhance cooperation with other market entities,aligning with the lead enterprises in the logistics chain that are characterized by speed,high value and strong coordination capabilities,in order to better serve the transportation market.This approach moves away from a reliance on the railway’s own capabilities alone.Originality/value–This paper offers original insights into the transformation of railway freight in China,contributing to the body of knowledge on transportation economics and logistics.It provides valuable recommendations for policymakers and industry practitioners,emphasizing the strategic importance of railway logistics in the context of China’s economic development and intense competition in the supply chain.The value of the article lies in its comprehensive understanding of the complexities involved in the adjustment of transportation structures,providing direction for the market-oriented reform of China’s railway freight sector.
基金Supported by National Natural Science Foundation of China(Grant No.52375159)Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2025RVL-T14).
文摘The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials to concurrently reduce vehicle weight and enhance efficiency,sustainability,and safety.Challenges faced include high production and processing costs,durability issues in harsh railway environments,and environmental impacts associated with alloy production.Research findings indicate that innovative alloy design and advanced processing techniques,such as powder metallurgy,additive manufacturing,and surface treatment,significantly improve the applicability of titanium alloys in railway applications.These methods substantially increase energy efficiency and safety.Additionally,advancements in environmentally sustainable practices in the production of titanium alloys address ecological concerns.As research progresses,the study and development of low-cost,high-performance titanium alloys highlight the need for more efficient and environmentally friendly manufacturing processes.Exploring new alloy compositions and applying emerging technologies in processing and manufacturing are key areas for future research.These advancements are expected to enhance the role of titanium alloys in revolutionizing railway transportation,aligning with global trends towards sustainability and performance improvement.This research underscores the significant potential contribution of titanium alloys to future efficient and eco-friendly rail travel.
文摘While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.
基金National Natural Science Foundation of ChinaNo.72174035+5 种基金The National Key Research and Development ProjectNo.2023YFB4302200111 Project of ChinaNo.B20082The Talent Planning in DalianNo.2022RG05。
文摘The evolution mechanism of railway transportation network nodes driven by sea-rail intermodal transport(SRIT),a globally prevalent logistics method,has not been thoroughly investigated.From the perspective of SRIT,this study constructed a framework for understanding the evolution of railway container transport network nodes using Northeast China from 2013 to 2020 as a case study.It leverages proprietary data from 95306 Railway Freight E-commerce Platform.By employing the hybrid EWM-GA-TOPSIS model,complex network analysis,modified gravity model,and correlation and regression analyses,this study delves into the spatiotemporal patterns and dynamic transformations of railway container freight stations(RCFS).Finally,the long-term relationship between the RCFS and SRIT is explored.The results indicate that the spatial and temporal analysis of the RCFS in Northeast China from 2013 to 2020 revealed a clear polarisation trend,with the top-ranked stations mainly concentrated near ports and important transportation hubs.Additionally,the RCFS exhibited an expansionary trend;however,its development was uneven,and there was a significant increase in the number of new stations compared to abandoned stations,indicating an overall positive growth tendency.Moreover,the intensity of the SRIT at the RCFS in Northeast China notably increased.A significant positive linear relationship exists between SRIT and the freight capacity of all stations.A relatively pronounced correlation was observed for high-intensity stations,whereas a relatively weak correlation was observed for low-intensity stations.This study not only provides an effective framework for future research on RCFS within the context of SRIT but also serves as a scientific reference for promoting the implementation of the national strategy for multimodal transportation.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,project number MoE-IF-UJ-R2-22-20772-1.
文摘The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
