Mixed Network Research Articles

End-to-End No-wait Scheduling for Time-Triggered Streams in Mixed Wired-Wireless Networks

Proprietary communication technologies for time-critical communication in industrial environments are being gradually replaced by Time-sensitive Networking (TSN)-enabled Ethernet. Furthermore, attempts have been made to bring TSN features into wireless networks so that the flexibility of wireless networks can be utilized, and the end-to-end timings for Time-Triggered (TT) streams can be guaranteed. Given a mixed wired-wireless network, the scheduling problem should be solved for a set of TT stream requests. In this paper, we formulate the no-wait scheduling problem for mixed wired-wireless networks as a Mixed Integer Linear Programming (MILP) model with the objective of minimizing the flowspan. We also propose a relaxation of the original MILP in the form of a 2-stage MILP formulation. Next, a scalable approach based on the greedy heuristic is proposed to solve the problem for realistic-size networks. Evaluation results show that the greedy heuristic is suitable for realistic problem sizes where the MILP-based approach is found to be practically infeasible. Furthermore, the impact of wireless requests on the performance of the greedy heuristic is reported.

Why is the composition of older adults’ care network associated with psychological wellbeing: an application of the self-determination theory

Objectives Older care recipients have different types of care networks, varying from spouse-only to large mixed care networks, that add to different levels of wellbeing. Applying Self-Determination Theory (SDT) to the care context, we argue that the care network composition may foster or hamper the three basic needs for wellbeing: relatedness, autonomy and competence. Method Data are from ten observations between 1992 and 2022 of the Longitudinal Aging Study Amsterdam (N = 18,434 observations from 4,837 older Dutch adults). Five care network types are used: no care, partner, informal, formal or privately paid care. Mixed-hybrid-multilevel regression analysis of depressive symptoms as measure of wellbeing is applied on care network type and loneliness, mastery and care sufficiency as indicators of the three basic needs for wellbeing. Results Receiving care from a partner care network is, compared to the formal care network, the most negatively associated with depressive symptoms, followed by informal care and privately paid care. Differences in care network types existed in loneliness and care sufficiency, but not in mastery, and in part explained the association between care network types and depressive symptoms. Results of between and within effects are comparable. Conclusion Using a rich data set and advanced methodology support the hypotheses that formal care networks hamper wellbeing due to insufficient care and increased loneliness, in particular compared to partner and informal care. The role of mastery was less important, possibly because it does not measure care related level of control.

A Mixed Reality-based Tele-Supervised Ultrasound Education Platform on 5G network compared to Direct Supervision: Prospective Randomized Pilot Trial.

Ultrasound education is transitioning from in-person training to remote methods using mixed reality (MR) and 5G networks. Previous studies are mainly experimental, lacking randomized controlled trials in direct training scenarios. This study aimed to compare an MR-based tele-supervised ultrasound education platform on private 5G networks with traditional in-person training for novice doctors. Conducted at a tertiary academic hospital from November to December 2023, the prospective unblinded randomized controlled pilot study assigned doctors without prior abdominal ultrasound education experience to either the tele-supervision group (TG; n = 20) or direct supervision group (DG; n = 20). Participants received a 15-min video lecture, conducted ultrasound on a phantom, and had 18 images scored by two blinded experts. Additionally, the TG received five minutes of training on basic operation of a head-mounted display (HMD). Communication between doctors in the TG and supervisors was facilitated through an HMD, whereas those in the DG interacted directly with supervisors. Primary outcomes were image quality scores, while secondary outcomes included procedure time, number of supervisor interventions, user experience using NASA-Task load index (NASA-TLX), System Usability Scale (SUS), and self-confidence through pre- and post-surveys. Image quality scores and procedure times showed no significant differences between the groups (TG: 66.8 ± 10.3 vs DG: 66.8 ± 10.4, P = .844; TG: 23.8 ± 8.0 min vs DG: 24.0 ± 8.1 min, P = .946). However, the TG engaged in more educational interventions (TG: 4.0 ± 2.5 vs DG: 0.8 ± 1.1, P <.001), reflecting a more interactive training environment. TG participants reported lower NASA-TLX scores for mental demand (43.8 ± 24.8 vs 60.6 ± 22.4, P = .03), effort (43.1 ± 22.9 vs 67.9 ± 17, P < .001), and frustration (26.9 ± 20.3 vs 45.2 ± 27.8, P = .022), indicating a reduced cognitive load compared the DG. The mean SUS score was also higher in the TG (66.6 ± 9.1 vs 60.2 ± 10.4, P =.046), suggesting better usability. Both groups showed significant improvements in confidence, with the TG showing notably greater improvement in abdominal ultrasound proficiency (Pre-education ━ TG: 1.6 ± 0.9 vs DG: 1.7 ± 0.9, P =.728; Post-education ━ TG: 3.8 ± 0.9 vs DG: 2.8 ± 1.0, P =.006). Although no significant differences in image quality scores were observed between groups, considerable differences in positive educational interactions, workload, and usability were evident. These findings emphasize the platform's potential to enhance the ultrasound training experience, suggesting more interactive and efficient learning. ClinicalTrials.gov, NCT06171828.

Graph reinforcement learning for flexible job shop scheduling under industrial demand response: A production and energy nexus perspective

Amidst the global energy crisis, the industrial sector is facing unparalleled energy conservation challenges as a primary energy consumer. Industrial demand response (IDR) is a promising strategy to enhance the energy efficiency of manufacturing enterprises. This paper introduces a graph reinforcement learning (GRL)-based method for flexible job shop scheduling under IDR from a production and energy nexus perspective. The scheduling problem is initially formulated as a Markov Decision Process (MDP). On this basis, the scheduling state is represented by a unique heterogeneous disjunctive graph incorporating IDR features, and the reward is constructed through a tailored generalized electricity consumption index (GECI). Moreover, a mixed graph neural network scheduler is designed to tackle this MDP, which integrates the heterogeneous attention mechanism and adaptive greedy sampling strategy. Furthermore, a proximal policy optimization algorithm is employed for training to obtain optimal scheduling schemes. Empirical case studies indicate that the proposed method can reduce GECI by up to 14.44% and 2.22%, respectively, outperforming existing well-known dispatching rules and another scheduling method based on GRL.

Governance of the wildlife trade and the prevention of emerging zoonoses: a mixed methods network analysis of transnational organisations, silos, and power dynamics

IntroductionThe wildlife trade is an important arena for intervention in the prevention of emerging zoonoses, and leading organisations have advocated for more collaborative, multi-sectoral approaches to governance in this area. The aim of this study is to characterise the structure and function of the network of transnational organisations that interact around the governance of wildlife trade for the prevention of emerging zoonoses, and to assess these network characteristics in terms of how they might support or undermine progress on these issues.MethodsThis study used a mixed methods social network analysis of transnational organisations. Data were collected between May 2021 and September 2022. Participants were representatives of transnational organisations involved in the governance of wildlife trade and the prevention of emerging zoonoses. An initial seed sample of participants was purposively recruited through professional networks, and snowball sampling was used to identify additional participants. Quantitative data were collected through an online network survey. Measures of centrality (degree, closeness, and betweenness) were calculated and the network’s largest clique was identified and characterised. To understand the extent to which organisations were connected across sectors, homophily by sector was assessed using exponential random graph modelling. Qualitative data were collected through semi-structured interviews. The findings from the quantitative analysis informed the focus of the qualitative analysis. Qualitative data were explored using thematic analysis.ResultsThirty-seven participants completed the network survey and 17 key informants participated in semi-structured interviews. A total of 69 organisations were identified as belonging to this network. Organisations spanned the animal, human, and environmental health sectors, among others including trade, food and agriculture, and crime. Organisation types included inter-governmental organisations, non-governmental organisations, treaty secretariats, research institutions, and network organisations. Participants emphasised the highly inter-sectoral nature of this topic and the importance of inter-sectoral work, and connections were present across existing sectors. However, there were many barriers to effective interaction, particularly conflicting goals and agendas. Power dynamics also shaped relationships between actors, with the human health sector seen as better resourced and more influential, despite having historically lower engagement than the environmental and animal health sectors around the wildlife trade and its role in emerging zoonoses.ConclusionThe network of transnational organisations focused on the governance of wildlife trade and the prevention of emerging zoonoses is highly multi-sectoral, but despite progress catalysed by the COVID-19 pandemic, barriers still exist for inter-sectoral interaction and coordination. A One Health approach to governance at this level, which has gained traction throughout the COVID-19 pandemic, was shared as a promising mechanism to support a balancing of roles and agendas in this space. However, this must involve agreement around equity, priorities, and clear goal setting to support effective action.

The cross-sectional and longitudinal interconnectedness of physical, psychological and role functioning following physical trauma: A network analysis

ObjectiveSurviving physical trauma can have a large impact on one's daily life. Patients are at increased risk for poor physical health, psychological complaints, and problems in role functioning – which is often experienced simultaneously. The present study explores the interconnectedness of physical, psychological, and role functioning during the first two years post-injury, both cross-sectionally and longitudinally from a network perspective. Methods3785 trauma patients (Mage = 64.2 years, SDage = 18.9 years, 50.5% female) completed questionnaires on physical, psychological, and role functioning across six measurement occasions during the first two years post-injury. The Injury Severity Score (ISS) was retrieved from the local trauma registry. Mixed graphical network models and cross-lagged network models were estimated to examine which items of recovery played a central role and were mostly related to other items in cross-sectional and longitudinal networks respectively. ResultsThe cross-sectional networks showed especially strong interconnections between impairments of physical and role functioning and also within post-traumatic stress symptoms. The longitudinal networks extended these results by showing that pain, impaired mobility, limitations in self-care, anxiety/depressive symptoms, and several post-traumatic stress symptoms were strong predictors for impairments in functioning at later stages of recovery. ConclusionOur findings showed that impairments in physical, psychological, and role functioning experienced by trauma patients are largely intertwined across the two years following injury. Monitoring physical impairments and psychological complaints early in recovery might help to more promptly provide the best fitting aftercare for trauma patients, which can improve recovery on the long-term.

Parametrical synthesis of various radio devices with the set quantity of identical cascades of type «the nonlinear part – the resistive two-port network»

Background. Presence of possibility of analytical definition of a part of parametres of various radio devices, optimum by criterion of maintenance of preset values of modules and phases of transfer functions on necessary quantity of frequencies, considerably reduces time of numerical optimisation of other part of parametres by criterion of formation demanded PFC and FFC in a strip of frequencies. Till now such problems dared concerning radio devices only with one cascade of type «a nonlinear part – the coordination the device» or «the coordination the device – a nonlinear part». In quality согласующего devices were used the jet, resistive, complex or mixed two-port networks. The problem of multicascade radio devices with jet two-port networks is solved also. Change of basis for the coordination two-port networks and a place of inclusion of a nonlinear part leads to change of area of a physical realizability. Aim. Working out of algorithms of parametrical synthesis of radio devices with any quantity of identical and unequal cascades of type «a nonlinear part -the coordination the resistive two-port network» by criterion of maintenance of the set frequency characteristics. Nonlinear parts are presented in the form of a nonlinear element and parallel either consecutive on a current or pressure of a feedback. Methods. The theory of two-port networks, matrix algebra, a decomposition method, a method of synthesis of actuation devices microwave, numerical methods of optimisation. Results. In interests of achievement of the specified purpose systems of the algebraic equations are generated and solved. Models of optimum two-port networks in the form of mathematical expressions for definition of interrelations between elements of their classical matrix of transfer and for search of dependences of resistance of two-poles from frequency are received. It is shown, that at certain parities between quantity of cascades and values of resistance of a source of a signal and loading of the one-cascade radio device frequency characteristics of one-cascade and multicascade radio devices appear identical or similar. Such schemes are named by equivalent. Conclusion. The comparative analysis of theoretical results (PFC and FFC radio devices, value of parametres), received by mathematical modelling in system MathCad, and the experimental results received by circuit engineering modelling in systems OrCad and MicroCap, shows their satisfactory coincidence.

Analyzing Russia’s propaganda tactics on Twitter using mixed methods network analysis and natural language processing: a case study of the 2022 invasion of Ukraine

This paper examines Russia’s propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of “fascism/Nazism” in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion.

Joint Routing and Pricing Control in Bimodal Mixed Autonomy Networks with Elastic Demand and Three-Dimensional Passenger Macroscopic Fundamental Diagram

The interaction between mixed autonomy traffic and public transport vehicles competing for limited road space is a less explored area of research. To evaluate the traffic dynamics in bimodal networks, a three-dimensional (passenger) network fundamental diagram, known as 3D-NFD (3D-pNFD), can be estimated that relates the accumulation of cars (autonomous and human-driven) and buses to the network vehicular (passenger) travel production. In this study, we propose a 3D-pNFD-based congestion pricing scheme considering three vehicle classes of buses, system-optimal (SO) seeking connected and automated vehicles (CAVs), and user-equilibrium (UE) seeking human-driven vehicles (HVs). We develop an iterative tri-level modeling framework for mode choice, pricing, and route choice in a mixed autonomy network. The lower level generates mixed equilibrium traffic flow through an integrated mixed equilibrium simulation-based dynamic traffic assignment model and a transit assignment model. The mid-level finds the optimal toll rates through a 3D-pNFD feedback-based controller. A nested logit-based mode choice model is also applied to capture travelers’ preferences toward three available modes and incorporates elastic demand. To encourage CAVs to follow the SO routing mode, they are provided with a discount on the congestion toll whereas UE-seeking HVs are subject to full price toll. Buses are also entirely exempt from the toll. We explore three scenarios with different discount rates on SO-seeking CAVs to investigate the effect of the incentive plans on the mode choice behaviors of road users in the pricing zone. The performance of the proposed model is evaluated in a large-scale network in Melbourne, Australia.

Attention-Based Intrinsic Reward Mixing Network for Credit Assignment in Multiagent Reinforcement Learning

Credit assignment is a critical problem in cooperative Multi-Agent Reinforcement Learning (MARL). To address this problem, current studies mainly rely on the intrinsic reward, which is directly summed with the global reward to generate a total reward. However, such kinds of intrinsic reward functions ignore the dependence among agents and inevitably limit the adaptivity and effectiveness of MARL methods. In this paper, we propose a novel method, Attention-based Intrinsic Reward Mixing Network (AIRMN), for credit assignment in MARL. Specifically, we design a new intrinsic reward network on the basis of the attention mechanism, in order to enhance the effectiveness of teamwork. Besides, we devise a new mixing network that combines the intrinsic and extrinsic rewards in a nonlinear and dynamic manner, so as to adapt the total reward to the variation of the environment. Experimental results on the battle games of StarCraft II demonstrate that AIRMN outperforms the state-of-the-art methods in terms of the average test win rate, and also validate that AIRMN can dynamically return the precise intrinsic reward to each agent based on their contributions to the team cooperation, thereby better dealing with the credit assignment problem.

Advancing ICU patient care with a Real-Time predictive model for mechanical Power to mitigate VILI

BackgroundInvasive Mechanical Ventilation (IMV) in Intensive Care Units (ICU) significantly increases the risk of Ventilator-Induced Lung Injury (VILI), necessitating careful management of mechanical power (MP). This study aims to develop a real-time predictive model of MP utilizing Artificial Intelligence to mitigate VILI. MethodologyA retrospective observational study was conducted, extracting patient data from Clinical Information Systems from 2018 to 2022. Patients over 18 years old with more than 6 h of IMV were selected. Continuous data on IMV variables, laboratory data, monitoring, procedures, demographic data, type of admission, reason for admission, and APACHE II at admission were extracted. The variables with the highest correlation to MP were used for prediction and IMV data was grouped in 15-minute intervals using the mean. A mixed neural network model was developed to forecast MP 15 min in advance, using IMV data from 6 h before the prediction and current patient status. The model’s ability to predict future MP was analyzed and compared to a baseline model predicting the future value of MP as equal to the current value. ResultsThe cohort consisted of 1967 patients after applying inclusion criteria, with a median age of 63 years and 66.9 % male. The deep learning model achieved a mean squared error of 2.79 in the test set, indicating a 20 % improvement over the baseline model. It demonstrated high accuracy (94 %) in predicting whether MP would exceed a critical threshold of 18 J/min, which correlates with increased mortality. The integration of this model into a web platform allows clinicians real-time access to MP predictions, facilitating timely adjustments to ventilation settings. ConclusionsThe study successfully developed and integrated in clinical practice a predictive model for MP. This model will assist clinicians allowing for the adjustment of ventilatory parameters before lung damage occurs.

Social Network Patterns of Creative Economy Actors in the Culinary Sector of Tourism Destination Areas in Padang City West Sumatra

This research was conducted to analyze the network patterns of creative economy actors in the culinary sector in Padang City. Data collection methods in this study began with documentation studies, in-depth interviews and non-participant observation. The approach used in this research is a qualitative approach with a descriptive analytical type and the withdrawal of informants through purposive sampling and snowball to see the relationship between creative economic actors in the culinary sector. The unit of analysis in this study is the individual. The results of this study indicate that creative economic actors in the culinary sector in Padang City have been able to build social networks. This can be seen in the distribution of culinary products that have been carried out by creative economic actors in the culinary sector outside the Padang City area. The social network pattern formed in the creative economic actors in the culinary sector is in a mixed social network pattern. In addition, creative economic actors build social network patterns in 4 forms, namely family-based social network patterns, friendships, joint activities and social media and customers. The social network pattern is spread in the distribution of culinary products that have been created and provides information on the acquisition of raw materials, strategies in product marketing and the results of product profits obtained by creative economic actors in the culinary sector. The novelty of this research is a mixed-based social network pattern.

Graph-enhanced deep material network: multiscale materials modeling with microstructural informatics

AbstractThis study addresses the fundamental challenge of extending the deep material network (DMN) to accommodate multiple microstructures. DMN has gained significant attention due to its ability to be used for fast and accurate nonlinear multiscale modeling while being only trained on linear elastic data. Due to its limitation to a single microstructure, various works sought to generalize it based on the macroscopic description of microstructures. In this work, we utilize a mechanistic machine learning approach grounded instead in microstructural informatics, which can potentially be used for any family of microstructures. This is achieved by learning from the graph representation of microstructures through graph neural networks. Such an approach is a first in works related to DMN. We propose a mixed graph neural network (GNN)-DMN model that can single-handedly treat multiple microstructures and derive their DMN representations. Two examples are designed to demonstrate the validity and reliability of the approach, even when it comes to the prediction of nonlinear responses for microstructures unseen during training. Furthermore, the model trained on microstructures with complex topology accurately makes inferences on microstructures created under different and simpler assumptions. Our work opens the door for the possibility of unifying the multiscale modeling of many families of microstructures under a single model, as well as new possibilities in material design.

Study on urban green development efficiency of Jiangsu, Zhejiang and Fujian in China: a mixed network SBM approach

PurposeBy studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.Design/methodology/approachFirst, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.FindingsSubsystem efficiency presents a distribution trend of PCS &gt; EBS &gt; SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.Originality/valueThis study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

An Efficient, Fast and Accurate Online Signature Verification Using Blended Feature Vector and Deep Learning

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research.

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction

Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/ .

Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks

In order to address the issues of predefined adjacency matrices inadequately representing information in road networks, insufficiently capturing spatial dependencies of traffic networks, and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase, thus affecting traffic prediction performance, this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks (AMGCN). The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data, introduces a mixed skip propagation graph convolutional neural network model, which retains the original node states and selectively acquires outputs of convolutional layers, thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow. Finally, the output is fed into Long Short-Term Memory networks to capture temporal correlations. Comparative experiments on two real datasets validate the effectiveness of the proposed model.

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach

The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.

MACN: A cascade defect detection for complex background based on mixture attention mechanism

AbstractDefect detection in complex background is a critical issue. To address this issue, this paper proposes the mixture attention mechanism cascade network, in which the new channel attention network is linked with the spatial attention network to create an effective mixed attention network that takes advantage of their respective advantages, adaptively suppresses background noise, and highlights defect features. To ensure the efficiency and effectiveness of effective mixed attention network, the new channel attention network splices the output features of the global average pooling layer and the global maximum pooling layer and then sends the spliced features into a shared network, which is a one‐dimensional convolutional network, and uses cross‐channel interaction for fusion. Furthermore, in order to provide more discriminative feature representation, the authors extract the intermediate features of the region proposal network and input them into effective mixed attention network. Finally, the cascade head is used to refine the predicted bounding box to achieve high‐quality defect location. To demonstrate the superiority and usefulness of this method, it is compared to the latest method using widely used PCB and NEU data sets. A large number of trials demonstrate that this strategy outperforms other methods for detecting defects in complicated backgrounds.