Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in mariti...Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.展开更多
At Yangpu Port on the northwestern tip of Hainan Island,towering quay cranes are lined in neat rows, hoisting containers on and off cargo vessels in constant motion.“Today, half of the international ships we pilot ar...At Yangpu Port on the northwestern tip of Hainan Island,towering quay cranes are lined in neat rows, hoisting containers on and off cargo vessels in constant motion.“Today, half of the international ships we pilot are ultra-large vessels over 200meters in length,” said Lin Hongpin,head of the Yangpu branch of Hainan Provincial Ship Pilot Station. Since the launch of the island-wide special customs operations, port throughput has continued to climb, with pilotage services for such large ships rising by nearly 20 percent year on year.展开更多
The accelerated decline of Arctic sea ice since the 1980s has paradoxically amplified greenhouse gas(GHG)emissions through increased shipping activities in this ecologically vulnerable region.This study investigates h...The accelerated decline of Arctic sea ice since the 1980s has paradoxically amplified greenhouse gas(GHG)emissions through increased shipping activities in this ecologically vulnerable region.This study investigates how to reconcile the decarbonization of Arctic shipping with conflicting environmental,economic,and geopolitical interests.Through systematic literature review and interest-balancing analysis,our findings identify three systemic barriers:(1)inadequate adaptation of International Maritime Organization(IMO)regulations to Arctic-specific environmental risks,(2)fragmented enforcement mechanisms among Arctic and non-Arctic States,and(3)technological limitations in clean fuel adoption for ice-class vessels.To address these challenges,a tripartite governance framework is proposed.First,legally binding amendments to International Convention for the Prevention of Pollution from Ships(MARPOL)Annex VI introducing Arctic-specific Energy Efficiency eXisting ship Index(EEXI)standards and extending energy efficiency regulations to fishing vessels.Second,a phased fuel transition prioritizing liquefied natural gas(LNG)and methanol,followed by hydrogen-ammonia synthetics.Third,enhanced multilateral cooperation through an Arctic Climate Shipping Alliance to coordinate joint research and development in cold-adapted technologies and ice-route optimization.By integrating United Nations Convention on the Law of the Sea(UNCLOS)obligations with IMO Polar Code implementation,this study advances a dynamic interest-balancing framework for policymakers,offering actionable pathways to achieve Paris Agreement targets while safeguarding Arctic ecosystems.展开更多
The global shipping industry,like many others,is under growing pressure to be more sustainable.Regulation,renewable energy advances and customer demand have created a golden opportunity to make shipping more environme...The global shipping industry,like many others,is under growing pressure to be more sustainable.Regulation,renewable energy advances and customer demand have created a golden opportunity to make shipping more environmentally sustainable,which,however,entails significant funding.Traditional ship financing has been done largely on a secured basis,with relatively few considerations around sustainability and environmental protection.This approach is ripe for innovation,given the industry’s significant environmental footprint.Evidence from other industries suggests that borrowers could benefit in pricing and structure from sustainable borrowing mechanisms,such as green bonds(where proceeds are dedicated to environmental and social investment).The trend is also increasing for sustainable loans,which can help to meet the growing demand for retrofit financing within existing vessels to meet CO_(2)emission targets.This paper aims to explore the attitudes of shipping industry participants,through the use of a survey,to green financing,that is,issuing unsecured and covered green,social,and sustainable bonds and other related financing instruments.These could effectively advance the environmental and social agenda in the industry,strengthening environmental,social,and governance(ESG)structures at the same time.Preliminary results suggest that there is considerable scope for improving knowledge and awareness among marine professionals to bridge the sustainability gap.展开更多
Structural properties of the ship container logistics network of China(SCLNC)are studied in the light of recent investigations of complex networks.SCLNC is composed of a set of routes and ports located along the sea o...Structural properties of the ship container logistics network of China(SCLNC)are studied in the light of recent investigations of complex networks.SCLNC is composed of a set of routes and ports located along the sea or river.Network properties including the degree distribution,degree correlations,clustering,shortest path length,centrality and betweenness are studied in different definition of network topology.It is found that geographical constraint plays an important role in the network topology of SCLNC.We also study the traffic flow of SCLNC based on the weighted network representation,and demonstrate the weight distribution can be described by power law or exponential function depending on the assumed definition of network topology.Other features related to SCLNC are also investigated.展开更多
To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and en...To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and environmental issues of ships,this study aims to improve the transportation efficiency of ships by providing a ship route induction method.Ship data from a certain bay during a defined period are collected,and an improved backpropagation neural network algorithm is used to forecast ship traffic.On the basis of the forecasted data and ship route induction objectives,dynamic programming of ship routes is performed.Experimental results show that the routes planned using this induction method reduce the combined cost by 17.55%compared with statically induced routes.This method has promising engineering applications in improving ship navigation efficiency,promoting energy conservation,and reducing emissions.展开更多
Marine services-ranging from ocean tourism and maritime transport to public marine services-have become a powerful driver of China’s ocean economy.In 2024,the country’s gross ocean product(GOP)exceeded 10 trillion y...Marine services-ranging from ocean tourism and maritime transport to public marine services-have become a powerful driver of China’s ocean economy.In 2024,the country’s gross ocean product(GOP)exceeded 10 trillion yuan(US$1.4 trillion)for the first time,with marine services contributing 6.28 trillion yuan(US$880 billion),or 59.6 percent of the total.Among them,marine tourism and maritime transport accounted for the lion’s share.展开更多
Singapore leverages its strategic location as a logistics and financial hub serving Southeast Asia to help companies enhance regional operations and build resilient supply chains,according to Teo Siong Seng,Chairman o...Singapore leverages its strategic location as a logistics and financial hub serving Southeast Asia to help companies enhance regional operations and build resilient supply chains,according to Teo Siong Seng,Chairman of the Singapore Business Federation and Executive Chairman of Pacific International Lines(PIL),at the“Singapore Night”event held on the sidelines of the 21st China-ASEAN Expo.He also expressed hopes to work hand in hand with Chinese enterprises to explore emerging areas of cooperation and realize a vision of mutual benefits.展开更多
Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe ...Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe structural damage or even collapse.Accurate measurement of ship dimensions is an effective way to monitor approaching over-height ships and avoid collisions.However,the performance of current techniques for estimating the size of moving objects can be undermined by large sensor-to-object distance,limiting their applicability.In this study,we propose a digital twin-assisted ship size measurement framework that can overcome such limitations through a predictive model and virtual-to-real-world transfer learning.Specifically,a 3D synthetic environment is first established to generate a synthetic dataset,which includes ship images,positions,and dimensions.Then the pixel information and spatial coordinates of ships are adopted as regressors,and ship dimensions are selected as the output variables to pre-train deep learning models using the generated dataset.Coordinate system transformations are applied to address dataset bias between the simulated world and real-world,as well as improve the model’s generalization.The pre-trained models are compared using supervised virtual-to-real-world transfer learning to select the version with optimal real-world performance.The mean absolute percentage error is only 3.74%across varying camera-to-ship distances,which demonstrates that the proposed method is effective for over-limit ship monitoring.展开更多
In cyber-age,with the creation and growth of e-commerce platforms online causing profound changes to the way of business-doing in traditional sectors,"Internet Plus shipping”is now well predicted as an inevitabl...In cyber-age,with the creation and growth of e-commerce platforms online causing profound changes to the way of business-doing in traditional sectors,"Internet Plus shipping”is now well predicted as an inevitable trend shipping business.As e-commerce shipping platforms make advertising,publication of information,business communication and transaction easier and more convenient,they are becoming a driving force accelerating the development of shipping industry.In domestic market today,there are already a variety of comparably matured models with which online shipping platforms operate,however,through evaluating their performances;these models have both unique advantages and disadvantages or deficiencies.The paper in the first place gives a summarization to what exactly e-commerce shipping can do,then introduces some most recognizable operational models on current stage,and lastly,from the perspective of"Internet Plus",offers some proposals with the hope of helping advance its future development.展开更多
For students in higher vocational colleges,the"Internet+"Innovation and Entrepreneurship Competition not only provides them with a rare platform for communication and learning but also significantly promotes...For students in higher vocational colleges,the"Internet+"Innovation and Entrepreneurship Competition not only provides them with a rare platform for communication and learning but also significantly promotes their comprehensive development and substantially enhances their innovative and entrepreneurial capabilities.This paper explores the collaboration models between higher vocational colleges and industries in advancing the"Internet+"Innovation and Entrepreneurship Competition,analyzes the impact of the competition on the educational systems and curriculum design of higher vocational colleges,and proposes pathways for research and practice on innovative and entrepreneurial talent cultivation models,aiming to cultivate high-quality and high-skilled talents who can adapt to industrial transformation and upgrading and achieve high-quality development.展开更多
The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments ...The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments of ships are often subject to changes,which in turn affect their energy efficiency in a complex manner.It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal.In this study,we take a diesel-electric hybrid ship navigating in inland waterways as the research object,and propose a hierarchical optimization method for ship energy efficiency.The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions,and employs the model predictive control(MPC)method to optimize the propulsion motor speed.The lower-layer control utilizes an equivalent fuel consumption minimization method,which is based on improving the equivalence factor.This involves combining the variation of the supercapacitor’s state of charge(SOC)with the propulsion motor speed obtained from the MPC optimization in the upper-layer control.Furthermore,a proportional integral(PI)controller is used to adjust the equivalence factor,in order to adapt the equivalent fuel consumption minimization method to the working conditions.Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator(EEOI)by approximately 11.54%and the fuel consumption by approximately 9.47%in comparison to the pre-optimization scenario.展开更多
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,th...TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo...The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC3701500)the Key R&D Plan Projects of Zhejiang Province(No.2024SSYS0072)Zhejiang Provincial Natural Science Foundation(No.LDT23E0601).
文摘Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.
文摘At Yangpu Port on the northwestern tip of Hainan Island,towering quay cranes are lined in neat rows, hoisting containers on and off cargo vessels in constant motion.“Today, half of the international ships we pilot are ultra-large vessels over 200meters in length,” said Lin Hongpin,head of the Yangpu branch of Hainan Provincial Ship Pilot Station. Since the launch of the island-wide special customs operations, port throughput has continued to climb, with pilotage services for such large ships rising by nearly 20 percent year on year.
基金supported by the Major Research Projects of the National Social Science Fund of China(NSFC,Grant no.23VHQ015).
文摘The accelerated decline of Arctic sea ice since the 1980s has paradoxically amplified greenhouse gas(GHG)emissions through increased shipping activities in this ecologically vulnerable region.This study investigates how to reconcile the decarbonization of Arctic shipping with conflicting environmental,economic,and geopolitical interests.Through systematic literature review and interest-balancing analysis,our findings identify three systemic barriers:(1)inadequate adaptation of International Maritime Organization(IMO)regulations to Arctic-specific environmental risks,(2)fragmented enforcement mechanisms among Arctic and non-Arctic States,and(3)technological limitations in clean fuel adoption for ice-class vessels.To address these challenges,a tripartite governance framework is proposed.First,legally binding amendments to International Convention for the Prevention of Pollution from Ships(MARPOL)Annex VI introducing Arctic-specific Energy Efficiency eXisting ship Index(EEXI)standards and extending energy efficiency regulations to fishing vessels.Second,a phased fuel transition prioritizing liquefied natural gas(LNG)and methanol,followed by hydrogen-ammonia synthetics.Third,enhanced multilateral cooperation through an Arctic Climate Shipping Alliance to coordinate joint research and development in cold-adapted technologies and ice-route optimization.By integrating United Nations Convention on the Law of the Sea(UNCLOS)obligations with IMO Polar Code implementation,this study advances a dynamic interest-balancing framework for policymakers,offering actionable pathways to achieve Paris Agreement targets while safeguarding Arctic ecosystems.
文摘The global shipping industry,like many others,is under growing pressure to be more sustainable.Regulation,renewable energy advances and customer demand have created a golden opportunity to make shipping more environmentally sustainable,which,however,entails significant funding.Traditional ship financing has been done largely on a secured basis,with relatively few considerations around sustainability and environmental protection.This approach is ripe for innovation,given the industry’s significant environmental footprint.Evidence from other industries suggests that borrowers could benefit in pricing and structure from sustainable borrowing mechanisms,such as green bonds(where proceeds are dedicated to environmental and social investment).The trend is also increasing for sustainable loans,which can help to meet the growing demand for retrofit financing within existing vessels to meet CO_(2)emission targets.This paper aims to explore the attitudes of shipping industry participants,through the use of a survey,to green financing,that is,issuing unsecured and covered green,social,and sustainable bonds and other related financing instruments.These could effectively advance the environmental and social agenda in the industry,strengthening environmental,social,and governance(ESG)structures at the same time.Preliminary results suggest that there is considerable scope for improving knowledge and awareness among marine professionals to bridge the sustainability gap.
基金supported by Youth Foundation for Research of the Waterborne Transportation Institute.
文摘Structural properties of the ship container logistics network of China(SCLNC)are studied in the light of recent investigations of complex networks.SCLNC is composed of a set of routes and ports located along the sea or river.Network properties including the degree distribution,degree correlations,clustering,shortest path length,centrality and betweenness are studied in different definition of network topology.It is found that geographical constraint plays an important role in the network topology of SCLNC.We also study the traffic flow of SCLNC based on the weighted network representation,and demonstrate the weight distribution can be described by power law or exponential function depending on the assumed definition of network topology.Other features related to SCLNC are also investigated.
基金Supported by the National Key R&D Program of China project (2017YFC0805309)the National Natural Science Foundation of China (60602020)。
文摘To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and environmental issues of ships,this study aims to improve the transportation efficiency of ships by providing a ship route induction method.Ship data from a certain bay during a defined period are collected,and an improved backpropagation neural network algorithm is used to forecast ship traffic.On the basis of the forecasted data and ship route induction objectives,dynamic programming of ship routes is performed.Experimental results show that the routes planned using this induction method reduce the combined cost by 17.55%compared with statically induced routes.This method has promising engineering applications in improving ship navigation efficiency,promoting energy conservation,and reducing emissions.
文摘Marine services-ranging from ocean tourism and maritime transport to public marine services-have become a powerful driver of China’s ocean economy.In 2024,the country’s gross ocean product(GOP)exceeded 10 trillion yuan(US$1.4 trillion)for the first time,with marine services contributing 6.28 trillion yuan(US$880 billion),or 59.6 percent of the total.Among them,marine tourism and maritime transport accounted for the lion’s share.
文摘Singapore leverages its strategic location as a logistics and financial hub serving Southeast Asia to help companies enhance regional operations and build resilient supply chains,according to Teo Siong Seng,Chairman of the Singapore Business Federation and Executive Chairman of Pacific International Lines(PIL),at the“Singapore Night”event held on the sidelines of the 21st China-ASEAN Expo.He also expressed hopes to work hand in hand with Chinese enterprises to explore emerging areas of cooperation and realize a vision of mutual benefits.
基金supported by the National Natural Science Foundation of China(Nos.52338011 and 52108274)the Start-up Research Fund of Southeast University(No.RF1028624058),Chinasupport from the SEU Innovation Capability Enhancement Plan for Doctoral Students(No.CXJH_SEU 26112),China.
文摘Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe structural damage or even collapse.Accurate measurement of ship dimensions is an effective way to monitor approaching over-height ships and avoid collisions.However,the performance of current techniques for estimating the size of moving objects can be undermined by large sensor-to-object distance,limiting their applicability.In this study,we propose a digital twin-assisted ship size measurement framework that can overcome such limitations through a predictive model and virtual-to-real-world transfer learning.Specifically,a 3D synthetic environment is first established to generate a synthetic dataset,which includes ship images,positions,and dimensions.Then the pixel information and spatial coordinates of ships are adopted as regressors,and ship dimensions are selected as the output variables to pre-train deep learning models using the generated dataset.Coordinate system transformations are applied to address dataset bias between the simulated world and real-world,as well as improve the model’s generalization.The pre-trained models are compared using supervised virtual-to-real-world transfer learning to select the version with optimal real-world performance.The mean absolute percentage error is only 3.74%across varying camera-to-ship distances,which demonstrates that the proposed method is effective for over-limit ship monitoring.
文摘In cyber-age,with the creation and growth of e-commerce platforms online causing profound changes to the way of business-doing in traditional sectors,"Internet Plus shipping”is now well predicted as an inevitable trend shipping business.As e-commerce shipping platforms make advertising,publication of information,business communication and transaction easier and more convenient,they are becoming a driving force accelerating the development of shipping industry.In domestic market today,there are already a variety of comparably matured models with which online shipping platforms operate,however,through evaluating their performances;these models have both unique advantages and disadvantages or deficiencies.The paper in the first place gives a summarization to what exactly e-commerce shipping can do,then introduces some most recognizable operational models on current stage,and lastly,from the perspective of"Internet Plus",offers some proposals with the hope of helping advance its future development.
基金2024 Annual Planning Program of China Vocational Education Association"Research and Practice on the Pathways of Innovative and Entrepreneurial Talent Cultivation Model under the Background of Industry-Education Integration"(ZJS2024YB79).
文摘For students in higher vocational colleges,the"Internet+"Innovation and Entrepreneurship Competition not only provides them with a rare platform for communication and learning but also significantly promotes their comprehensive development and substantially enhances their innovative and entrepreneurial capabilities.This paper explores the collaboration models between higher vocational colleges and industries in advancing the"Internet+"Innovation and Entrepreneurship Competition,analyzes the impact of the competition on the educational systems and curriculum design of higher vocational colleges,and proposes pathways for research and practice on innovative and entrepreneurial talent cultivation models,aiming to cultivate high-quality and high-skilled talents who can adapt to industrial transformation and upgrading and achieve high-quality development.
基金supported by the National Natural Science Foundation of China(No.52571367)and the Commissions Project of China(No.CBG4N21).
文摘The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments of ships are often subject to changes,which in turn affect their energy efficiency in a complex manner.It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal.In this study,we take a diesel-electric hybrid ship navigating in inland waterways as the research object,and propose a hierarchical optimization method for ship energy efficiency.The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions,and employs the model predictive control(MPC)method to optimize the propulsion motor speed.The lower-layer control utilizes an equivalent fuel consumption minimization method,which is based on improving the equivalence factor.This involves combining the variation of the supercapacitor’s state of charge(SOC)with the propulsion motor speed obtained from the MPC optimization in the upper-layer control.Furthermore,a proportional integral(PI)controller is used to adjust the equivalence factor,in order to adapt the equivalent fuel consumption minimization method to the working conditions.Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator(EEOI)by approximately 11.54%and the fuel consumption by approximately 9.47%in comparison to the pre-optimization scenario.
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
文摘Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
文摘TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber(PNURSP2025R97),PrincessNourah bint AbdulrahmanUniversity,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.
