Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core cros...Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.展开更多
Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emi...Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emission networks and driver patterns)and the mutual influence of gross domestic product(GDP)and GHG emissions remains limited at a global level in the 21st century,which is not conducive to forming a consensus in global climate change negotiations and formulating relevant policies.To fill these gaps,this study comprehensively analyzes the complex network and driver pattern of GHG emissions,as well as the corresponding mutual influence with GDP for 185 countries during 2000-2021,based on social network analysis,the logarithmic Divisia decomposition approach,and panel vector autoregression model at global and regional levels.The results indicate that significant heterogeneity and inequality exist in terms of GHG emissions among regions and countries in different geographical areas and economic income levels.Additionally,GDP per capita and GHG emission intensity are the largest positive and negative drivers,respectively,affecting the increase in global GHG emissions.Furthermore,key countries,such as Germany and Canada,that could serve as coordinating bridges to strengthen collaboration in the global emission network are identified.This study highlights the need to encourage key participants in the emission network and foster international cooperation in governance,energy technology,and economic investment to address climate change.展开更多
With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extract...With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.展开更多
In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this...In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interactio...Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.展开更多
This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-...This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-time acquisition and quality control of seismic data,the real-time monitoring of equipment location and health status,the synchronous transmission of collected data between the cloud and client,and the real-time issuance of operational instructions.It addresses the critical limitation of existing seismic node equipment,which is often restricted to mining and blind storage due to the absence of a wired or wireless communication link between the acquisition node device and the central control unit.This limitation necessitates local data storage and rendering real-time quality control unfeasible.Typically,quality control is conducted post-task completion,requiring the overall retrieval and downloading of data.If data issues are identifi ed,it becomes necessary to eliminate faulty tracks and determine the need for supplementary acquisition,which can lead to delays in the acquisition process.The implementation of real-time monitoring and early warning systems for equipment health status aims to mitigate the risk of poor data quality resulting from equipment anomalies.Furthermore,the real-time synchronous transmission between the cloud and server addresses the bottleneck of slow download speeds associated with the centralized retrieval of data from multiple node devices during blind acquisition and storage.A real-time microseismic data acquisition test and verifi cation were conducted at a fracturing site in an eastern oil and gas fi eld.Analysis of the test data indicates that the overall performance indicators of the system are comparable to those of existing mainstream system equipment,demonstrating stability and reliability.The performance parameters fully satisfy the technical requirements for oilfield fracturing monitoring scenarios,suggesting promising prospects for further promotion and application.展开更多
Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their excep...Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their exceptional ability to decompose organic matter.However,accurately identifying larval instars is critical for optimizing feeding efficiency and downstreamapplications,as different stages exhibit only subtle visual differences.This study proposes a real-timemobile application for automatic classification of BSFL larval stages.The systemdistinguishes between early instars(Stages 1–4),suitable for food waste processing and animal feed,and late instars(Stages 5–6),optimal for pupation and industrial use.A baseline YOLO11 model was employed,achieving a mAP50-95 of 0.811.To further improve performance and efficiency,we introduce YOLO11-DSConv,a novel adaptation incorporating Depthwise Separable Convolutions specifically optimized for the unique challenges of BSFL classification.Unlike existing YOLO+DSConv implementations,our approach is tailored for the subtle visual differences between larval stages and integrated into a complete end-to-end system.The enhanced model achieved a mAP50-95 of 0.813 while reducing computational complexity by 15.5%.The proposed system demonstrates high accuracy and lightweight performance,making it suitable for deployment on resource-constrained agricultural devices,while directly supporting circular economy initiatives through precise larval stage identification.By integrating BSFL classification with realtime AI,this work contributes to sustainable food wastemanagement and advances intelligent applications in precision agriculture and circular economy initiatives.Additional supplementary materials and the implementation code are available at the following link:YOLO11-DSConv,Server Side,Mobile Application.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16...Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.展开更多
We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwi...We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.展开更多
In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increase...In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.展开更多
E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that m...E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.展开更多
Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates...Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.展开更多
The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization i...The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.展开更多
The efficient development of the urban economy is a major concern of scholars in the fields of geography and urban science.In the context of globalization,informatization,industrialization,and urbanization,the externa...The efficient development of the urban economy is a major concern of scholars in the fields of geography and urban science.In the context of globalization,informatization,industrialization,and urbanization,the external relationships of China's cities are experiencing the joint action of urban scale hierarchies and connection networks(“hierarchy-network”).However,under the interactive effect of the two,the mechanism of urban economic efficiency(UEE)is unclear.Therefore,based on Baidu migration data,the regionalization with dynamically constrained agglomerative clustering and partitioning(REDCAP)method,and a spatial simultaneous equation model,this paper analyzes the UEE spatial pattern and mechanism in China.The results indicate that:(1)the urban economy has a superlinear relationship with the population size.However,the benefit of this superlinear growth is in marginal decline.(2)The UEE shows a pattern of differentiation between China's eastern,then central,and then western region.Also,local differences are found within the three major sub-regions.(3)The increase of urban network centrality can promote UEE,while the impact of urban scale is negative.(4)There is regional heterogeneity of the interactive effect of“hierarchy-network”on UEE.This study reveals the influencing mechanism of UEE and also provides policy implications for the development of UEE.展开更多
文摘Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.
基金supported by the Humanities and Social Sciences Youth Foundation,Ministry of Education of China[Grant No.24YJC630248]Sichuan Office of Philosophy and Social Science,China[Grant No.SCJJ24ND299].
文摘Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emission networks and driver patterns)and the mutual influence of gross domestic product(GDP)and GHG emissions remains limited at a global level in the 21st century,which is not conducive to forming a consensus in global climate change negotiations and formulating relevant policies.To fill these gaps,this study comprehensively analyzes the complex network and driver pattern of GHG emissions,as well as the corresponding mutual influence with GDP for 185 countries during 2000-2021,based on social network analysis,the logarithmic Divisia decomposition approach,and panel vector autoregression model at global and regional levels.The results indicate that significant heterogeneity and inequality exist in terms of GHG emissions among regions and countries in different geographical areas and economic income levels.Additionally,GDP per capita and GHG emission intensity are the largest positive and negative drivers,respectively,affecting the increase in global GHG emissions.Furthermore,key countries,such as Germany and Canada,that could serve as coordinating bridges to strengthen collaboration in the global emission network are identified.This study highlights the need to encourage key participants in the emission network and foster international cooperation in governance,energy technology,and economic investment to address climate change.
基金supported by the Natural Science Foundation of Henan under Grant 242300421220the Henan Provincial Science and Technology Research Project under Grants 252102211047 and 252102211062+3 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126.
文摘With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72031009 and 61473338)。
文摘In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金National Natural Science Foundation of China,No.42361040。
文摘Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
基金funded by the National Natural Science Foundation of China (42074127)the Key Program of National Natural Science Foundation of China (41930425)Research on Key Technologies for the Production, Exploration, and Development of Continental Shale Oil (2023ZZ15YJ02)。
文摘This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-time acquisition and quality control of seismic data,the real-time monitoring of equipment location and health status,the synchronous transmission of collected data between the cloud and client,and the real-time issuance of operational instructions.It addresses the critical limitation of existing seismic node equipment,which is often restricted to mining and blind storage due to the absence of a wired or wireless communication link between the acquisition node device and the central control unit.This limitation necessitates local data storage and rendering real-time quality control unfeasible.Typically,quality control is conducted post-task completion,requiring the overall retrieval and downloading of data.If data issues are identifi ed,it becomes necessary to eliminate faulty tracks and determine the need for supplementary acquisition,which can lead to delays in the acquisition process.The implementation of real-time monitoring and early warning systems for equipment health status aims to mitigate the risk of poor data quality resulting from equipment anomalies.Furthermore,the real-time synchronous transmission between the cloud and server addresses the bottleneck of slow download speeds associated with the centralized retrieval of data from multiple node devices during blind acquisition and storage.A real-time microseismic data acquisition test and verifi cation were conducted at a fracturing site in an eastern oil and gas fi eld.Analysis of the test data indicates that the overall performance indicators of the system are comparable to those of existing mainstream system equipment,demonstrating stability and reliability.The performance parameters fully satisfy the technical requirements for oilfield fracturing monitoring scenarios,suggesting promising prospects for further promotion and application.
文摘Food waste presents a major global environmental challenge,contributing to resource depletion,greenhouse gas emissions,and climate change.Black Soldier Fly Larvae(BSFL)offer an eco-friendly solution due to their exceptional ability to decompose organic matter.However,accurately identifying larval instars is critical for optimizing feeding efficiency and downstreamapplications,as different stages exhibit only subtle visual differences.This study proposes a real-timemobile application for automatic classification of BSFL larval stages.The systemdistinguishes between early instars(Stages 1–4),suitable for food waste processing and animal feed,and late instars(Stages 5–6),optimal for pupation and industrial use.A baseline YOLO11 model was employed,achieving a mAP50-95 of 0.811.To further improve performance and efficiency,we introduce YOLO11-DSConv,a novel adaptation incorporating Depthwise Separable Convolutions specifically optimized for the unique challenges of BSFL classification.Unlike existing YOLO+DSConv implementations,our approach is tailored for the subtle visual differences between larval stages and integrated into a complete end-to-end system.The enhanced model achieved a mAP50-95 of 0.813 while reducing computational complexity by 15.5%.The proposed system demonstrates high accuracy and lightweight performance,making it suitable for deployment on resource-constrained agricultural devices,while directly supporting circular economy initiatives through precise larval stage identification.By integrating BSFL classification with realtime AI,this work contributes to sustainable food wastemanagement and advances intelligent applications in precision agriculture and circular economy initiatives.Additional supplementary materials and the implementation code are available at the following link:YOLO11-DSConv,Server Side,Mobile Application.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
基金This study was supported by the National Water Pollution Control and Treatment Science and Technology Major Project(2017ZX07101-002).
文摘Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.
基金This work was supported by the National Natural Science Foundation of China(Grant No.12072340)the China Postdoctoral Science Foundation(Grant No.2022M720727)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2022ZB130).
文摘We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.
基金supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2018R1A6A1A03024003)the MSIT(Ministry of Science and ICT),Korea,under the Innovative Human Resource Development for Local Intellectualization support program(IITP-2023-2020-0-01612)supervised by the IITP(Institute for Information&communications TechnologyPlanning&Evaluation).
文摘In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.
文摘E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.
基金supported by the China Fundamental Research Funds for the Central Universities(2022JBQY006)。
文摘Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.
基金supported by National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800)National Natural Science Foundation of China (Key Program: 62136003)+2 种基金National Natural Science Foundation of China (62073142)Fundamental Research Funds for the Central Universities (222202417006)Shanghai Al Lab
文摘The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.
基金National Natural Science Foundation of China,No.42371222,No.41971167Fundamental Scientific Research Funds of Central China Normal University,No.CCNU24ZZ120,No.CCNU22JC026。
文摘The efficient development of the urban economy is a major concern of scholars in the fields of geography and urban science.In the context of globalization,informatization,industrialization,and urbanization,the external relationships of China's cities are experiencing the joint action of urban scale hierarchies and connection networks(“hierarchy-network”).However,under the interactive effect of the two,the mechanism of urban economic efficiency(UEE)is unclear.Therefore,based on Baidu migration data,the regionalization with dynamically constrained agglomerative clustering and partitioning(REDCAP)method,and a spatial simultaneous equation model,this paper analyzes the UEE spatial pattern and mechanism in China.The results indicate that:(1)the urban economy has a superlinear relationship with the population size.However,the benefit of this superlinear growth is in marginal decline.(2)The UEE shows a pattern of differentiation between China's eastern,then central,and then western region.Also,local differences are found within the three major sub-regions.(3)The increase of urban network centrality can promote UEE,while the impact of urban scale is negative.(4)There is regional heterogeneity of the interactive effect of“hierarchy-network”on UEE.This study reveals the influencing mechanism of UEE and also provides policy implications for the development of UEE.
