Map Network Research Articles

Synchronization effects in multiplex networks of chaotic maps with memristive interlayer coupling

Models of two-layer multiplex networks of one-dimensional maps with memristive interlayer coupling are studied. In the absence of coupling, the layers exhibit different types of chimera states. Two types of maps are considered as the elements of networks: modified Ricker maps and cubic maps. The effects of complete and partial synchronization of the spatiotemporal dynamics of layers are observed in the case of a homogeneous and inhomogeneous network, respectively. The dependence of the synchronization threshold for the coupling parameter on the initial states of the memristors has been established. This dependence is partially preserved in the case of non-ideal memristors. Diagrams of the synchronization error are constructed on the plane of the initial state variable of memristors and the interlayer coupling coefficient. The effect of noise in memristive coupling elements on synchronization is considered.

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.

RBFNN-based angles-only orbit determination method for non-cooperative space targets

Angles-only orbit determination during space situational awareness missions for non-cooperative targets such as space debris is subject to the well-known observability problem, that is, the orbital state is weakly observable or even unobservable because of missing range measurements. The key point to solve this problem is how to quickly capture the nonlinearity of orbit dynamics if only one space-based passive sensor is available to observe the target and translational maneuvers are not executed. To solve the short-arc angles-only problem, this study develops a simple and fast orbit determination method by exploiting the advantages of radial basis function neural network (RBFNN). Emphasis was placed on the design of the RBFNN, which was trained to construct a mapping network model from line-of-sight (LOS) measurements to the relative orbit. First, the perturbed relative orbit dynamics considering the Earth's non-spherical J2 term and air drag was established with the LOS measurement model. Second, a RBFNN-based angles-only orbit determination frame was developed, in which the generator of the training dataset, data preprocessing algorithm, and RBF network structure were designed. Finally, the proposed method was verified and tested using a set of numerical simulations of low-Earth orbit cases. The anti-noise and generalization performance of the designed network and the sensitivity of the solution accuracy to the measurement frequency and span were presented and discussed. Additionally, the advantages of the RBFNN-based method were confirmed by comparison with the results obtained using the BPNN (Back Propagation Neural Network)-based method. The simulation results show that the average orbit prediction error for LEO scenarios is approximately 0.08% using the index of initial relative distance, where only three sets of LOS angles are measured within a span of 24 s.

Minimum-Data-Driven Guidance for Impact Angle Control

This paper investigates the impact-angle-control guidance problem for varying-speed flight vehicles with constrained acceleration. A learning-based bias proportional navigation guidance (L-BPN) law is proposed to achieve impact-angle-constrained impact by constructing a deep neural network (DNN) for nonlinear mapping between the impact angle and the bias term. During the process of dataset establishment, the impact of state variables is evaluated by sensitivity analysis to minimize the quantity of training data. This approach also effectively accelerates sample generation and improves the training efficiency. The simulation results verify the effectiveness of the proposed L-BPN law and demonstrate its advantages over the existing algorithms.

Adversarial learning-based camera pose-to-image mapping network for synthesizing new view in real indoor environments

Given a new 6DoF camera pose in an indoor environment, we investigate the challenging problem of predicting the view from that pose based on a set of reference RGBD views. Existing explicit or implicit 3D geometry construction methods are computationally expensive, while those based on learning have primarily focused on isolated views of object categories with regular geometric structures. In contrast to the traditional render-inpaint approach for new view synthesis in real indoor environments, we propose a conditional generative adversarial neural network (P2I-NET) that directly predicts the new view from the given pose. P2I-NET learns the conditional distribution of environment images to establish the correspondence between the camera pose and its view, achieved through innovative designs in its architecture and training loss function. Two auxiliary discriminator constraints are incorporated to ensure consistency between the pose of the generated image and that of the corresponding real-world image in both the latent feature space and the real-world pose space. Moreover, a deep convolutional neural network (CNN) is introduced to further reinforce this consistency in the pixel space. Extensive new view synthesis experiments and pose estimation experiments have been conducted on real indoor datasets. The results demonstrate that P2I-NET outperforms a number of strong baseline models based on NeRF. In particular, it is shown that P2I-NET is 40 to 100 times faster than these competitor techniques while producing images of similar quality. The results also reveal that the discriminator in P2I-NET achieves comparable positioning performance to other positioning methods, despite having a smaller model size. Furthermore, we contribute a new publicly available indoor environment dataset containing 22 high resolution RGBD videos where each frame also has accurate camera pose parameters.

PFMNet: Prototype-based feature mapping network for few-shot domain adaptation in medical image segmentation

Lack of data is one of the biggest hurdles for rare disease research using deep learning. Due to the lack of rare-disease images and annotations, training a robust network for automatic rare-disease image segmentation is very challenging. To address this challenge, few-shot domain adaptation (FSDA) has emerged as a practical research direction, aiming to leverage a limited number of annotated images from a target domain to facilitate adaptation of models trained on other large datasets in a source domain. In this paper, we present a novel prototype-based feature mapping network (PFMNet) designed for FSDA in medical image segmentation. PFMNet adopts an encoder–decoder structure for segmentation, with the prototype-based feature mapping (PFM) module positioned at the bottom of the encoder–decoder structure. The PFM module transforms high-level features from the target domain into the source domain-like features that are more easily comprehensible by the decoder. By leveraging these source domain-like features, the decoder can effectively perform few-shot segmentation in the target domain and generate accurate segmentation masks. We evaluate the performance of PFMNet through experiments on three typical yet challenging few-shot medical image segmentation tasks: cross-center optic disc/cup segmentation, cross-center polyp segmentation, and cross-modality cardiac structure segmentation. We consider four different settings: 5-shot, 10-shot, 15-shot, and 20-shot. The experimental results substantiate the efficacy of our proposed approach for few-shot domain adaptation in medical image segmentation.

Ecological risk assessment and influencing factors in the Wuhan Metropolitan Area based on supply and demand bundles of ecosystem services

In the context of rapid urbanization, metropolitan areas are facing the risk of supply-demand mismatches among ecosystem services. Investigating the patterns, relationships, and driving factors of multiple supply-demand risks is of great significance to support the efficient management of regional ecological risks. We quantified the single/comprehensive supply-demand risk rates of six ecosystem services in Wuhan Metropolitan Area at the township scale in 2000, 2010, and 2020. By applying the self-organizing feature map network and optimal parameter geo-detector, we identified supply-demand risks bundles of ecosystem services and influencing factors of comprehensive risks. The results showed significant spatial variations in the supply-demand risks of typical ecosystem services from 2000 to 2020. The supply-demand risk associated with grain production, water yield, carbon sequestration, and green space recreation increased, while soil conservation and water purification risks decreased. The comprehensive ecosystem services supply-demand risk increased from 0.41 to 0.45, indicating a 'core area increase and periphery decrease' trend. Throughout the study period, the area exhibited bundles of comprehensive extremely high-risk bundles (B1), comprehensive high-risk bundles (B2), water purification high-risk bundles (B3), and grain production-soil conservation risk bundles (B4). The transition of risk types from B3 to B2 and from B2 to B1 suggested an increase in the combination and intensity of supply-demand risk. Vegetation cover, nighttime light index, and population density were the main driving factors for spatial variations in comprehensive supply-demand risk. Ecologi-cal risk assessment based on ecosystem services supply-demand bundles could provide an effective and reliable way to regulate multiple regional risk issues.

Landscape Mapping: Investigating the Scope of Information Systems and Technology in Contemporary Organizations

In today's fast-paced business environment, the integration of Information Systems (IS) and technology is crucial for organizational success. This research aims to explore the landscape of Information Systems and Technology (IST) within contemporary organizations, investigating its multifaceted dimensions across diverse organizational contexts. Through a systematic examination, this study seeks to shed light on the intricate relationships between technology and organizational functionality, offering insights into IST deployment, utilization, and management strategies. By employing bibliometric analysis, thematic mapping, and author collaboration network analysis, this research provides a comprehensive understanding of IST research dynamics, trends, and future directions. The findings not only contribute to scholarly discourse but also offer practical implications for organizational stakeholders, policymakers, and technology practitioners.

Atmospheric scattering model and dark channel prior constraint network for environmental monitoring under hazy conditions

Environmental monitoring systems based on remote sensing technology have a wider monitoring range and longer timeliness, which makes them widely used in the detection and management of pollution sources. However, haze weather conditions degrade image quality and reduce the precision of environmental monitoring systems. To address this problem, this research proposes a remote sensing image dehazing method based on the atmospheric scattering model and a dark channel prior constrained network. The method consists of a dehazing network, a dark channel information injection network (DCIIN), and a transmission map network. Within the dehazing network, the branch fusion module optimizes feature weights to enhance the dehazing effect. By leveraging dark channel information, the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the output of the deep learning model aligns with physical laws, we reconstruct the haze image using the prediction results from the three networks. Subsequently, we apply the traditional loss function and dark channel loss function between the reconstructed haze image and the original haze image. This approach enhances interpretability and reliability while maintaining adherence to physical principles. Furthermore, the network is trained on a synthesized non-homogeneous haze remote sensing dataset using dark channel information from cloud maps. The experimental results show that the proposed network can achieve better image dehazing on both synthetic and real remote sensing images with non-homogeneous haze distribution. This research provides a new idea for solving the problem of decreased accuracy of environmental monitoring systems under haze weather conditions and has strong practicability.

An intelligent recommendation strategy for integrated online courses in vocational education based on short-term preferences

With the swift advancement of online teaching in vocational education, an increasing number of web-based course materials are being made available to students, granting them the freedom to select resources that suit their personal needs. To optimize the effectiveness of artificial intelligence-enabled smart vocational education, this study presents a course recommendation model centered on learning behaviors and interests. The model utilizes short-term preferences reconstruction behavior contribution to identify fluctuations in learners' interests in real-time. A model for recommending courses is proposed based on short-term preferences and enhancements to learning behavior. Its purpose is to tackle the issue of generalization arising from sparsity and weak correlation in learning behavior. The outcomes demonstrated the model put forth in the study achieved higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) values in comparison experiments with multiple models. Hence, this suggested that creating a novel component of historical learning behavior, powered by dynamic interest factors, could resolve the issue of changing learning interests and enhance the efficacy of course recommendation models. Furthermore, the introduction of a correlation mapping network enables the forward mapping transformation from weak to strong learning behavior, thus improving and optimizing input for the agent strategy, reducing data sparsity, and enhancing the performance and generalization of the course recommendation model.

A new driving style recognition method for personalized adaptive cruise control to enhance vehicle personalization

There are limitations of personalization in Advanced Driver Assistance Systems (ADAS) that have a serious impact on driver acceptance and satisfaction. This study investigates driving style recognition method to achieve personalization of longitudinal driving behavior. Currently, driving style recognition algorithms for Personalized Adaptive Cruise Control (PACC) rely on integrated recognition. However, disturbances in the driving cycle may lead to changes in a driver’s integrated driving style. Therefore, the integrated driving style cannot accurately and comprehensively reflect the driver’s driving style. To solve this problem, a new driving style recognition method for PACC is proposed, which considers integrated driving style and driving cycle. Firstly, the method calculates the constructed feature parameters of driving cycle and style, and then reduces the dimensionality of the feature parameter matrix by principal component analysis (PCA). Secondly, a two-stage clustering algorithm with self-organizing mapping networks and K-means clustering (SOM-K-means) is used to obtain the type labels. Then, a transient recognition model based on random forest (RF) is established and the hyperparameters of this model are optimized by sparrow search algorithm (SSA). Based on this, a comprehensive driving style recognition model is established using analytic hierarchy process (AHP). Finally, the validity of the proposed method is verified by a natural dataset. The method incorporates the driving cycle into driving style recognition and provides guidance for improving the personalization of adaptive cruise control system.

Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps

Network Analysis has traditionally been applied to analyzing interactions among learners in online learning platforms such as discussion boards. However, there are opportunities to bring Network Analysis to bear on networks representing learners' mental models of course material, rather than learner interactions. This paper describes the construction and analysis of collaborative educational networks based on concept maps created by undergraduates. Concept mapping activities were deployed throughout two separate quarters of a large General Education (GE) course about sustainability and technology at a large university on the West Coast of the United States. A variety of Network Analysis metrics are evaluated on their ability to predict an individual learner's understanding based on that learner's contributions to a network representing the collective understanding of all learners in the course. Several of the metrics significantly correlated with learner performance, especially those that compare an individual learner's conformity to the larger group's consensus. The novel network metrics based on collective networks of learner concept maps are shown to produce stronger and more reproducible correlations with learner performance than metrics traditionally used in the literature to evaluate concept maps. This paper thus demonstrates that Network Analysis in conjunction with collective networks of concept maps can provide insights into learners' conceptual understanding of course material.

Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis.

Regarding neurophysiological and developmental findings, anxiety and depression are usual comorbidities of gastritis patients. However, research related to anxiety and depression among chronic gastritis patients was conducted on the disease level while ignoring symptoms. Hence, we rendered the network approach to reveal the symptoms of anxiety and depression among chronic gastritis patients. Three hundred and sixty-nine chronic gastritis patients (female = 139, Mage = 55.87 years) were asked to complete the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Three symptom networks and one directed acyclic graph (DAG) network were formed. First, in the anxiety network of chronic gastritis patients, dizziness was the most influential symptom. In the depression network of chronic gastritis patients, depressed affect and psychomotor retardation were the influential symptoms. Second, panic, easy fatiguability, weakness, palpitation, depressed affect, tachycardia, fatigue, and psychomotor agitation bridged the anxiety-depression network of chronic gastritis patients. Third, DAG networks showed that anxiousness and hopelessness could trigger other symptoms in the anxiety-depression networks of chronic gastritis patients. The current study provided insightful information on patients with chronic gastritis by examining the structures of symptoms.

A critical cartography: mapping chapters, networks, and relationships through regional organization

ABSTRACT The Association for Critical Heritage Studies (ACHS) was created, in part, to address underrepresentation in the study and management of heritage. However, achieving genuinely inclusive representation within global heritage conversations remains elusive, exacerbated by entrenched power dynamics and the dominance of Western epistemologies. Here, we discuss ACHS’s efforts to foster a more democratic, inclusive discourse through its organizational structure and international collaborations. As we argue here, although significant strides towards diversifying heritage discourse are evidenced by the creation of chapters and networks aimed at amplifying underrepresented voices, challenges persist in ensuring equitable participation. We discuss the importance of strategic regionalization and language inclusivity in advancing a platform that reflects the diversity of global heritage perspectives.

Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

The accumulation of operational time in aero-engines leads to irreversible mechanical wear and tear, necessitating accurate measurement of the health evolution trend for effective predictive maintenance, thus reducing the risk of accidents and ensuring personnel safety. In this paper, a parameter-adaptive variational mode decomposition (VMD) method and improved transformer model are proposed to forecast the degradation trend of aero-engine feature hierarchy fusion. Firstly, in order to quantitatively evaluate the engine health evolution process, the health state aggregate indicator (HSAI) is innovatively constructed by employing the deep blend auto-encoder and self-organizing map network, which facilitate the feature-hierarchy fusion of multi-source sensory data. Secondly, for the significant characteristics with nonlinearity and stochastic fluctuation of the HSAI sequence, the multiscale frequency features are extracted by the parameter-adaptive VMD method with the improved gray wolf optimizer, which analyzes the inherent degradation law. Finally, considering the problem of parameter sharing in the transformer model, a simplified mixture of experts routing algorithm is introduced to implement the switch transformer model to further measure the future aero-engine health trends. Extensive experiments on the multi-source dataset of aero-engine confirm that the proposed method accomplishes the more superior performance for health evolution measurement compared with other available methods.

Taylor Shepherd Golden Optimization-Enabled ResUNet for Forest Change Detection Using Satellite Images

The pivotal task of remote sensing image (RSI) processing change detection (CD) highly aims to accurately detect changes in land cover based on multi-temporal images. With the advent of deep learning, technology has delivered remarkable results in the last years in the detection of variations in forest land cover data. Some of the conventional CD techniques are weak and are highly susceptible to errors and can result even in inaccurate outcomes. Thus, certain techniques are not desirable for real-time CD applications. To abridge this gap, this research introduces an innovative work for forest CD utilizing the proposed Taylor Shepherd Golden Optimization_ResUNet (TSGO_ResUNet) and Fuzzy Neural network (Fuzzy NN) for segment mapping. Here, the segmentation process is accomplished using ResUNet to determine the exact boundary or shape of each object for every pixel in the image. Furthermore, TSGO is achieved by consolidating Taylor Shuffled Shepherd Optimization (TSSO) with Golden Search Optimization (GSO). In addition, the devised TSGO_ResUNet + Fuzzy NN has gained maximum accuracy and kappa coefficient of 0.952 and 0.785, and minimum error rate of 0.051.

Tetris-inspired detector with neural network for radiation mapping

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models.

Landslide susceptibility mapping is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility mapping (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility mapping and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study's findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

Dynamic Light Scattering in Biomedical Applications: feature issue introduction

The feature Issue on “Dynamic Light Scattering in Biomedical Applications” presents a compilation of research breakthroughs and technological advancements that have shaped the field of biophotonics, particularly in the non-invasive exploration of biological tissues. Highlighting the significance of dynamic light scattering (DLS) alongside techniques like laser Doppler flowmetry (LDF), diffusing wave spectroscopy (DWS), and laser speckle contrast imaging (LSCI), this issue underscores the versatile applications of these methods in capturing the intricate dynamics of microcirculatory blood flow across various tissues. Contributions explore developments in fluorescence tomography, the integration of machine learning for data processing, enhancements in microscopy for cancer detection, and novel approaches in optical biophysics, among others. Innovations featured include a high-resolution speckle contrast tomography system for deep blood flow imaging, a rapid estimation technique for real-time tissue perfusion imaging, and the use of convolutional neural networks for efficient blood flow mapping. Additionally, studies delve into the impact of skin strain on spectral reflectance, the sensitivity of cerebral blood flow measurement techniques, and the potential of photobiomodulation for enhancing brain function. This issue not only showcases the latest theoretical and experimental strides in DLS-based imaging but also anticipates the continued evolution of these modalities for groundbreaking applications in disease detection, diagnosis, and monitoring, marking a pivotal contribution to the field of biomedical optics.

Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies.