The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.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
The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particu...The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particularly from end-of-life vehicles(ELVs),which require proper dismantling and disposal to minimize environmental harm.Millions of tonnes of automotive waste are generated annually,necessitating efficient waste management strategies to mitigate environmental and health risks.ELVs contain hazardous substances such as heavy metals,oils,and plastics,which,if not properly managed,can contaminate soil and water resources.To address this challenge,reverse logistics networks play a crucial role in optimizing the recovery of used components,enhancing recycling efficiency,and ensuring the safe disposal of hazardous and non-recyclable waste.This paper introduces a mathematical programming model designed to minimize the total costs associated with ELVs collection,treatment,and transportation while also accounting for revenues from the resale of repaired,directly reusable,or recycled components.The proposed model determines the optimal locations for processing facilities and establishes efficient material flows within the reverse logistics network.By integrating economic and environmental considerations,this model supports the development of a sustainable and cost-effective automotive waste management system,ultimately contributing to a circular economy approach in the industry.展开更多
The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and int...The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.展开更多
To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross ...To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.展开更多
According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are ...According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.展开更多
First a remanufactming logistics network is con- structed, in which the structure of both the forward logistics and the reverse logistics are of two levels and all the logistics facilities are capacitated. Both the re...First a remanufactming logistics network is con- structed, in which the structure of both the forward logistics and the reverse logistics are of two levels and all the logistics facilities are capacitated. Both the remanufactming products and the new products can be used to meet the demands of customers. Moreover, it is assumed that homogeneous facilities can be designed together into integrated ones, based on which a mixed integer nonlinear programming （MINLP） facility location model of the remanufacturing logistics network with six types of facilities to be sited is built. Then an algorithm based on enumeration for the model is given. The feasible combinations of binary variables are searched by enumeration, and the remaining sub-problems are solved by the LP solver. Finally, the validities of the model and the algorithm are illustrated by means of an example. The result of the sensitivity analysis of parameters indicates that the integration of homogeneous facilities may influence the optimal solution of the problem to a certain degree.展开更多
Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(AN...Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.展开更多
In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper c...In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.展开更多
To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method of multi-layer air transportation networks is put forward based on Laplacian energy maximization. The effect...To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method of multi-layer air transportation networks is put forward based on Laplacian energy maximization. The effectiveness of taking Laplacian energy as a measure of network robustness is validated through numerical experiments. The flight routes addition optimization model is proposed with the principle of maximizing Laplacian energy. Three methods including the depth-first search( DFS) algorithm, greedy algorithm and Monte-Carlo tree search( MCTS) algorithm are applied to solve the proposed problem. The trade-off between system performance and computational efficiency is compared through simulation experiments. Finally, a case study on Chinese airport network( CAN) is conducted using the proposed model. Through encapsulating it into multi-layer infrastructure via k-core decomposition algorithm, Laplacian energy maximization for the sub-networks is discussed which can provide a useful tool for the decision-makers to optimize the robustness of the air transportation network on different scales.展开更多
The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on...The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.展开更多
In multi-layer satellite-terrestrial network, Contact Graph Routing(CGR) uses the contact information among satellites to compute routes. However, due to the resource constraints in satellites, it is extravagant to co...In multi-layer satellite-terrestrial network, Contact Graph Routing(CGR) uses the contact information among satellites to compute routes. However, due to the resource constraints in satellites, it is extravagant to configure lots of the potential contacts into contact plans. What's more, a huge contact plan makes the computing more complex, which further increases computing time. As a result, how to design an efficient contact plan becomes crucial for multi-layer satellite network, which usually has a large scaled topology. In this paper, we propose a distributed contact plan design scheme for multi-layer satellite network by dividing a large contact plan into several partial parts. Meanwhile, a duration based inter-layer contact selection algorithm is proposed to handle contacts disruption problem. The performance of the proposed design was evaluated on our Identifier/Locator split based satellite-terrestrial network testbed with 79 simulation nodes. Experiments showed that the proposed design is able to reduce the data delivery delay.展开更多
Compared with the extensive research on logistics network infrastructures(LNIs)in the developed world,empirical research is still scarce in China.In this paper the theory of LNIs is firstly overviewed.Then a new evalu...Compared with the extensive research on logistics network infrastructures(LNIs)in the developed world,empirical research is still scarce in China.In this paper the theory of LNIs is firstly overviewed.Then a new evaluation index system for LNIs is set up which contains factors that reflect the economic development level,transportation accessibility and turnover volume of freight traffc.An empirical study is carried out by using data envelopment analysis(DEA)and principal component analysis(PCA)approach to classify LNIs into 4 clusters for 25 cities in the Yangtze River Delta Region of China.According to the characteristics of the 4 clusters,suggestions are proposed for improving their LNIs.Finally,after comparing different LNIs of 25 cities in the Yangtze River Delta Region of China,this paper proposes that different LNIs including hub,central distribution center or cross docking center,regional distribution center or distribution center should be built reasonably in order to meet the customer's requirement in the four different cluster cities.展开更多
The uncertainty of time, quantity and quality of recycling products leads tothe bad stability and flexibility of remanufacturing logistics networks, and general design onlycovered the minimizing logistics cost, thus, ...The uncertainty of time, quantity and quality of recycling products leads tothe bad stability and flexibility of remanufacturing logistics networks, and general design onlycovered the minimizing logistics cost, thus, robust design is presented here to solve theuncertainty. The mathematical model of remanufacturing logistics networks is built based onstochastic distribution of uncontrollable factors, and robust objectives are presented. Theintegration of mathematical simulation and design of experiment method is performed to do sensitiveanalysis. The influence of each factor and level on the system is investigated, and the main factorsand optimum combination are studied. The numbers of factors, level of each factor and designprocess of experiment are investigated as well. Finally, the process of robust design based ondesign of experiment is demonstrated by a detailed example.展开更多
Cold-chain demand of fresh agricultural products is increasing in China, while network layout of cold-chain logistics is in disorder and its cost is huge. To address this problem, this paper casts an optimal model of ...Cold-chain demand of fresh agricultural products is increasing in China, while network layout of cold-chain logistics is in disorder and its cost is huge. To address this problem, this paper casts an optimal model of cold-chain logistics network and tackles it with genetic algorithms. This optimal model takes running total cost of logistics network as the objective, and embeds a nonlinear mixed integer programming including two assignment issues. The model determines optimal layout and logistics management for pre-cooling stations and logistics center for fresh agricultural products. Our main contribution is to consider construction cost and operation cost of cold chain logistics simultaneously. Case study illustrates the effectiveness of the model.展开更多
BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation.Recently,artificial neural network(ANN)was reported to have better predictive ability than the ...BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation.Recently,artificial neural network(ANN)was reported to have better predictive ability than the classical logistic regression(LR)for this postoperative outcome.AIM To identify the risk factors of AKI after deceased-donor liver transplantation(DDLT)and compare the prediction performance of ANN with that of LR for this complication.METHODS Adult patients with no evidence of end-stage kidney dysfunction(KD)who underwent the first DDLT according to model for end-stage liver disease(MELD)score allocation system was evaluated.AKI was defined according to the International Club of Ascites criteria,and potential predictors of postoperative AKI were identified by LR.The prediction performance of both ANN and LR was tested.RESULTS The incidence of AKI was 60.6%(n=88/145)and the following predictors were identified by LR:MELD score>25(odds ratio[OR]=1.999),preoperative kidney dysfunction(OR=1.279),extended criteria donors(OR=1.191),intraoperative arterial hypotension(OR=1.935),intraoperative massive blood transfusion(MBT)(OR=1.830),and postoperative serum lactate(SL)(OR=2.001).The area under the receiver-operating characteristic curve was best for ANN(0.81,95%confidence interval[CI]:0.75-0.83)than for LR(0.71,95%CI:0.67-0.76).The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38,respectively.CONCLUSION The severity of liver disease,pre-existing kidney dysfunction,marginal grafts,hemodynamic instability,MBT,and SL are predictors of postoperative AKI,and ANN has better prediction performance than LR in this scenario.展开更多
Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of t...Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.展开更多
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
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.
文摘The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particularly from end-of-life vehicles(ELVs),which require proper dismantling and disposal to minimize environmental harm.Millions of tonnes of automotive waste are generated annually,necessitating efficient waste management strategies to mitigate environmental and health risks.ELVs contain hazardous substances such as heavy metals,oils,and plastics,which,if not properly managed,can contaminate soil and water resources.To address this challenge,reverse logistics networks play a crucial role in optimizing the recovery of used components,enhancing recycling efficiency,and ensuring the safe disposal of hazardous and non-recyclable waste.This paper introduces a mathematical programming model designed to minimize the total costs associated with ELVs collection,treatment,and transportation while also accounting for revenues from the resale of repaired,directly reusable,or recycled components.The proposed model determines the optimal locations for processing facilities and establishes efficient material flows within the reverse logistics network.By integrating economic and environmental considerations,this model supports the development of a sustainable and cost-effective automotive waste management system,ultimately contributing to a circular economy approach in the industry.
基金supported by the National Natural Science Foundation of China under Grant No.62172132.
文摘The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.
基金Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province,China(No.JAS19371)Social Science Research Project of Education Department of Fujian Province,China(No.JAS160571)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)。
文摘To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.
文摘According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.
基金The National Natural Science Foundation of China(No.70472033).
文摘First a remanufactming logistics network is con- structed, in which the structure of both the forward logistics and the reverse logistics are of two levels and all the logistics facilities are capacitated. Both the remanufactming products and the new products can be used to meet the demands of customers. Moreover, it is assumed that homogeneous facilities can be designed together into integrated ones, based on which a mixed integer nonlinear programming （MINLP） facility location model of the remanufacturing logistics network with six types of facilities to be sited is built. Then an algorithm based on enumeration for the model is given. The feasible combinations of binary variables are searched by enumeration, and the remaining sub-problems are solved by the LP solver. Finally, the validities of the model and the algorithm are illustrated by means of an example. The result of the sensitivity analysis of parameters indicates that the integration of homogeneous facilities may influence the optimal solution of the problem to a certain degree.
文摘Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.
基金This project (No. 49070196) is funded by the National Science Foundation of China.
文摘In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.
基金The National Natural Science Foundation of China(No.61573098,71401072)the Natural Science Foundation of Jiangsu Province(No.BK20130814)
文摘To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method of multi-layer air transportation networks is put forward based on Laplacian energy maximization. The effectiveness of taking Laplacian energy as a measure of network robustness is validated through numerical experiments. The flight routes addition optimization model is proposed with the principle of maximizing Laplacian energy. Three methods including the depth-first search( DFS) algorithm, greedy algorithm and Monte-Carlo tree search( MCTS) algorithm are applied to solve the proposed problem. The trade-off between system performance and computational efficiency is compared through simulation experiments. Finally, a case study on Chinese airport network( CAN) is conducted using the proposed model. Through encapsulating it into multi-layer infrastructure via k-core decomposition algorithm, Laplacian energy maximization for the sub-networks is discussed which can provide a useful tool for the decision-makers to optimize the robustness of the air transportation network on different scales.
基金Under the auspices of National Natural Science Foundation of China(No.42071165,41801144)GDAS’Project of Science and Technology Development(No.2023GDASZH-2023010101,2021GDASYL-20210103004)。
文摘The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.
基金supported by National High Technology of China ("863 program") under Grant No. 2015AA015702NSAF under Grant No. U1530118+1 种基金NSFC under Grant No. 61602030National Basic Research Program of China ("973 program") under Grant No. 2013CB329101
文摘In multi-layer satellite-terrestrial network, Contact Graph Routing(CGR) uses the contact information among satellites to compute routes. However, due to the resource constraints in satellites, it is extravagant to configure lots of the potential contacts into contact plans. What's more, a huge contact plan makes the computing more complex, which further increases computing time. As a result, how to design an efficient contact plan becomes crucial for multi-layer satellite network, which usually has a large scaled topology. In this paper, we propose a distributed contact plan design scheme for multi-layer satellite network by dividing a large contact plan into several partial parts. Meanwhile, a duration based inter-layer contact selection algorithm is proposed to handle contacts disruption problem. The performance of the proposed design was evaluated on our Identifier/Locator split based satellite-terrestrial network testbed with 79 simulation nodes. Experiments showed that the proposed design is able to reduce the data delivery delay.
基金the Science Foundation of Ministry of Education of China(No.11YJC630081)the Foundation of Philosophy and Social Science of Zhejiang Province(No.11YD22YB)+3 种基金the Zhejiang Natural Science Foundation(Nos.Y6090015 and Z1091224)the National Natural Science Foundation of China(No.71071141)the Doctoral Fund of Ministry of Education of China(No.20103326110001)the Foundation of Key Research Insitute of Social Sciences and Humanities of Ministry of Education in Zhejiang Gongshang University(Nos.11JDSM02Z and 2011ZS-DSM208)
文摘Compared with the extensive research on logistics network infrastructures(LNIs)in the developed world,empirical research is still scarce in China.In this paper the theory of LNIs is firstly overviewed.Then a new evaluation index system for LNIs is set up which contains factors that reflect the economic development level,transportation accessibility and turnover volume of freight traffc.An empirical study is carried out by using data envelopment analysis(DEA)and principal component analysis(PCA)approach to classify LNIs into 4 clusters for 25 cities in the Yangtze River Delta Region of China.According to the characteristics of the 4 clusters,suggestions are proposed for improving their LNIs.Finally,after comparing different LNIs of 25 cities in the Yangtze River Delta Region of China,this paper proposes that different LNIs including hub,central distribution center or cross docking center,regional distribution center or distribution center should be built reasonably in order to meet the customer's requirement in the four different cluster cities.
基金This project is supported by Provincial Natural Science Foundation of Shanghai, China (No. 02ZH14060).
文摘The uncertainty of time, quantity and quality of recycling products leads tothe bad stability and flexibility of remanufacturing logistics networks, and general design onlycovered the minimizing logistics cost, thus, robust design is presented here to solve theuncertainty. The mathematical model of remanufacturing logistics networks is built based onstochastic distribution of uncontrollable factors, and robust objectives are presented. Theintegration of mathematical simulation and design of experiment method is performed to do sensitiveanalysis. The influence of each factor and level on the system is investigated, and the main factorsand optimum combination are studied. The numbers of factors, level of each factor and designprocess of experiment are investigated as well. Finally, the process of robust design based ondesign of experiment is demonstrated by a detailed example.
文摘Cold-chain demand of fresh agricultural products is increasing in China, while network layout of cold-chain logistics is in disorder and its cost is huge. To address this problem, this paper casts an optimal model of cold-chain logistics network and tackles it with genetic algorithms. This optimal model takes running total cost of logistics network as the objective, and embeds a nonlinear mixed integer programming including two assignment issues. The model determines optimal layout and logistics management for pre-cooling stations and logistics center for fresh agricultural products. Our main contribution is to consider construction cost and operation cost of cold chain logistics simultaneously. Case study illustrates the effectiveness of the model.
文摘BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation.Recently,artificial neural network(ANN)was reported to have better predictive ability than the classical logistic regression(LR)for this postoperative outcome.AIM To identify the risk factors of AKI after deceased-donor liver transplantation(DDLT)and compare the prediction performance of ANN with that of LR for this complication.METHODS Adult patients with no evidence of end-stage kidney dysfunction(KD)who underwent the first DDLT according to model for end-stage liver disease(MELD)score allocation system was evaluated.AKI was defined according to the International Club of Ascites criteria,and potential predictors of postoperative AKI were identified by LR.The prediction performance of both ANN and LR was tested.RESULTS The incidence of AKI was 60.6%(n=88/145)and the following predictors were identified by LR:MELD score>25(odds ratio[OR]=1.999),preoperative kidney dysfunction(OR=1.279),extended criteria donors(OR=1.191),intraoperative arterial hypotension(OR=1.935),intraoperative massive blood transfusion(MBT)(OR=1.830),and postoperative serum lactate(SL)(OR=2.001).The area under the receiver-operating characteristic curve was best for ANN(0.81,95%confidence interval[CI]:0.75-0.83)than for LR(0.71,95%CI:0.67-0.76).The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38,respectively.CONCLUSION The severity of liver disease,pre-existing kidney dysfunction,marginal grafts,hemodynamic instability,MBT,and SL are predictors of postoperative AKI,and ANN has better prediction performance than LR in this scenario.
基金Project(51178061)supported by the National Natural Science Foundation of ChinaProject(2010FJ6016)supported by Hunan Provincial Science and Technology,China+1 种基金Project(12C0015)supported by Scientific Research Fund of Hunan Provincial Education Department,ChinaProject(13JJ3072)supported by Hunan Provincial Natural Science Foundation of China
文摘Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.
