Map Network Research Articles

DSpix2pix: A New Dual-Style Controlled Reconstruction Network for Remote Sensing Image Super-Resolution

Super-resolution reconstruction is a critical task in remote sensing image classification, and generative adversarial networks (GANs) have emerged as a dominant approach in this field. Traditional generative networks often produce low-quality images at resolutions like 256 × 256, and current research on single-image super-resolution typically focuses on resolution enhancement factors of two to four (2×–4×), which do not meet practical application demands. Building upon the framework of StyleGAN, this study introduces a dual-style controlled super-resolution reconstruction network referred to as DSpix2pix. It uses a fixed style vector (Style 1) from StyleGAN-v2, generated through its mapping network and applied to each layer in the generator. And an additional style vector (Style 2) is extracted from example images and injected into the decoder using AdIn, enhancing the balance of styles in the generated images. DSpix2pix is capable of generating high-quality, smoother, noise-reduced, and more realistic super-resolution remote sensing images at 512 × 512 and 1024 × 1024 resolutions. In terms of visual metrics such as RMSE, PSNR, SSIM, and LPIPS, it outperforms traditional super-resolution networks like SRGAN and UNIT, with RMSE consistently exceeding 10. The network excels in 2× and 4× super-resolution tasks, demonstrating potential for remote sensing image interpretation, and shows promising results in 8x super-resolution tasks.

An unsupervised automatic texture classification method for ultrasound images of thyroid nodules

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

Deep neural network and transfer learning for annual wetland vegetation mapping using sentinel-2 time-series data in the heterogeneous lake floodplain environment

ABSTRACT The lake-floodplain wetlands are characterized by high biodiversity, difficult access, and significant environmental changes. Traditional remote sensing mapping methods struggle to generate consistent time-series data on wetland vegetation communities. Current research has endeavoured to address this issue through the application of deep learning methodologies. However, a significant limitation of these models is their reliance on a substantial volume of training samples, which contradicts the difficulty and high cost of obtaining samples from the lake-floodplain wetlands. Whether it is possible to construct a transferable deep learning model under small sample conditions and apply it to the mapping of lake-floodplain wetlands is an urgent issue that needs to be addressed. To solve this problem, this study first constructed a deep neural network (DNN) designed specifically for mapping complex lake-floodplain wetland vegetation under conditions of limited sample size. Subsequently, using 2021 as a reference year, a novel histogram threshold method was proposed to identify the unchanged samples for the target transfer years of 2019, 2020, 2022, and 2023. Finally, annual wetland vegetation mapping was performed in Poyang Lake using DNN and sample transfer learning (STL). The results showed that high-quality annual time-series data of wetland vegetation can be generated using the constructed DNN and STL, with all overall accuracies exceeding 80%. The histogram threshold method, which combines SAD and NDVI indicators of key phenological period, can effectively solve the problem of difficulty in determining the unchanged samples in transfer learning for heterogeneous lake wetlands. Furthermore, the performance of STL based on the constructed DNN model was significantly superior to those based on support vector machine and random forest algorithms for mapping annual wetland vegetation communities using limited training samples. This study demonstrates that the effective application of DNN and STL will be highly beneficial for long-term monitoring of vegetation in lake-floodplain wetlands, particularly where sample availability is limited.

Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.

Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question. Any adjustment to this hypothesis necessitates acquiring a new set of landmarks or revising them significantly, which can be impractical for large datasets. There is a pressing need for more efficient and flexible methods for landmark placement that can adapt to different hypotheses without requiring extensive human effort. This study investigates the precision and accuracy of landmarks derived from functional correspondences obtained through the functional map framework of geometry processing. We utilize a deep functional map network to learn shape descriptors, which enable us to achieve functional map-based and point-to-point correspondences between specimens in our dataset. Our methodology involves automating the landmarking process by interrogating these maps to identify corresponding landmarks, using manually placed landmarks from the entire dataset as a reference. We apply our method to a dataset of rodent mandibles and compare its performance to MALPACA's, a standard tool for automatic landmark placement. Our model demonstrates a speed improvement compared to MALPACA while maintaining a competitive level of accuracy. Although MALPACA typically shows the lowest RMSE, our models perform comparably well, particularly with smaller training datasets, indicating strong generalizability. Visual assessments confirm the precision of our automated landmark placements, with deviations consistently falling within an acceptable range for MALPACA estimates. Our results underscore the potential of unsupervised learning models in anatomical landmark placement, presenting a practical and efficient alternative to traditional methods. Our approach saves significant time and effort and provides the flexibility to adapt to different hypotheses about critical geometrical features without the need for manual re-acquisition of landmarks. This advancement can significantly enhance the scalability and applicability of geometric morphometrics, making it more feasible for large datasets and diverse biological studies.

Attention-enhanced feature mapping network for visible-infrared person re-identification

Attention-enhanced feature mapping network for visible-infrared person re-identification

High-resolution hemodynamic estimation from ultrafast ultrasound image velocimetry using a physics-informed neural network

Objective.Estimating the high-resolution (HR) blood flow velocity and pressure fields for the diagnosis and treatment of vascular diseases remains challenging.Approach. In this study, a physics-informed neural network (PINN) with a refined mapping capability was combined with ultrafast ultrasound image velocimetry (u-UIV) to predict HR hemodynamic parameters. Specifically, the Navier-Stokes equations were encoded into the PINN to dynamically optimize the network performance under physical constraints, and a refined mapping network was added at the input to achieve data refinement. During the prediction of HR ultrasound hemodynamic parameters, only the sparse spatial coordinates in the time series were input into the PINN, and the velocity vectors generated from the u-UIV were used together with physical residuals to enhance the physical correctness of HR predictions during the iterative process.Main results.The performance of the refined mapping network was validated via simulations, with a 1.9-fold increase in the radial resolution and a 2.5-fold increase in the axial resolution. HR velocity field estimation fromin vitroandin vivodata showed good agreement with theoretical values and u-UIV measurements, with micrometer-level spatial resolution (88µm×115µm for straight vessel, 75µm×120µm for stenotic vessel and 63µm × 79µm forin vivodata), while the pressure field could be inferred from physical laws.Significance.The proposed method performs well when few data samples are available and has the potential to assist in the clinical diagnosis of vascular diseases.

A MultiScale Coordinate Attention Feature Fusion Network for Province-Scale Aquatic Vegetation Mapping From High-Resolution Remote Sensing Imagery

A MultiScale Coordinate Attention Feature Fusion Network for Province-Scale Aquatic Vegetation Mapping From High-Resolution Remote Sensing Imagery

Ecological networks construction and optimization in the Longdong Loess Plateau: The advantages of self-organizing map and complex networks

Ecological networks construction and optimization in the Longdong Loess Plateau: The advantages of self-organizing map and complex networks

EXPLORING GAMIFICATION ELEMENTS IN EDUCATION: A BIBLIOMETRIC STUDY

This bibliometric study examines the integration of gamification elements within educational contexts, highlighting trends, influential research, and collaborative networks in the field. Gamification, increasingly recognized for its potential to enhance student engagement and motivation, has become a focal point in educational research. However, there remains a need for a comprehensive analysis to map its development, identify key contributors, and evaluate its impact. This study addresses these gaps by analyzing 1,637 documents from the Scopus database, focusing on publications related to gamification in education. Methodologically, Scopus Analyzer was used to identify publication trends, while VOSviewer software facilitated keyword mapping and co-authorship network analysis. The results reveal a steady increase in research activity, particularly since 2020, reflecting a growing acceptance of gamification as a viable educational tool. Key findings reveal that "gamification," "engagement," and "motivation" are frequently occurring keywords, indicating core themes within the field. The United States, Spain, and the United Kingdom emerged as the leading contributors in terms of publications and collaborations, highlighting significant international interest. Additionally, prominent authors have contributed substantially to advancing theoretical and practical knowledge on gamification's application in diverse educational settings. Despite this progress, gaps remain in standardized frameworks and longitudinal studies assessing gamification's long-term impact. In conclusion, the study underscores the extensive research interest in gamification within education, its established benefits, and the collaborative efforts across countries. Thus, future research should focus on addressing identified gaps by developing structured frameworks and conducting studies that evaluate the sustained effectiveness of gamified interventions across various educational contexts. This bibliometric analysis provides a foundation for educators, researchers, and policymakers to understand the evolving landscape of gamification in education.

TDMSANet: A Tri-Dimensional Multi-Head Self-Attention Network for Improved Crop Classification from Multitemporal Fine-Resolution Remotely Sensed Images

Accurate and timely crop distribution data are crucial for governments, in order to make related policies to ensure food security. However, agricultural ecosystems are spatially and temporally dynamic systems, which poses a great challenge for accurate crop mapping using fine spatial resolution (FSR) imagery. This research proposed a novel Tri-Dimensional Multi-head Self-Attention Network (TDMSANet) for accurate crop mapping from multitemporal fine-resolution remotely sensed images. Specifically, three sub-modules were designed to extract spectral, temporal, and spatial feature representations, respectively. All three sub-modules adopted a multi-head self-attention mechanism to assign higher weights to important features. In addition, the positional encoding was adopted by both temporal and spatial submodules to learn the sequence relationships between the features in a feature sequence. The proposed TDMSANet was evaluated on two sites utilizing FSR SAR (UAVSAR) and optical (Rapid Eye) images, respectively. The experimental results showed that TDMSANet consistently achieved significantly higher crop mapping accuracy compared to the benchmark models across both sites, with an average overall accuracy improvement of 1.40%, 3.35%, and 6.42% over CNN, Transformer, and LSTM, respectively. The ablation experiments further showed that the three sub-modules were all useful to the TDMSANet, and the Spatial Feature Extraction Module exerted larger impact than the remaining two sub-modules.

From Private to Public: The Movement of Pahari Paintings from Royal Collections to the Chandigarh Museum in Post-Independence India

The Government Museum and Art Gallery of Chandigarh was established to fill a cultural vacuum in post-partition Punjab. It received the seed of its collection from Lahore and the rest was amassed by M. S. Randhawa (1909–1986), an Indian Civil Service officer with a love for painting that at the time was identified as “Kangra” for its style and association with the eponymous region and kingdom. In the 1950s, he spent considerable time traveling in the western Himalayas, tracking down paintings and acquiring them for the museum. The many volumes of correspondence that he later bequeathed to the museum reveal how collections of early modern Pahari paintings were rapidly dispersing to form new collections elsewhere. Through the lens of the bureaucrat-collector Randhawa, this article sheds light on the complex history of collecting in mid-twentieth-century South Asia. In tracing the movement of paintings from private royal collections to a public government museum, the article approaches provenance as biography with the goal to contribute to collective efforts of mapping networks that connect collections and collectors.

Advanced depth-layering using a synthetic-depth map and deep neural network for real-world object-based holographic displays

We propose an advanced layering method for real-world object-based holographic displays, leveraging what we believe to be a novel synthetic-depth map and deep neural network. The proposed system aims to implement a holographic display that displays natural-like three-dimensional visualizations of real objects by enhancing data quantity and ensuring accurate depth layers. A simplified light-field image acquisition system combined with a deep neural network is employed to efficiently gather organized omnidirectional three-dimensional information from the object, achieving high quality while minimizing processing time. Subsequently, a novel high-accuracy synthetic-depth map containing data from both initial depth and position maps is estimated. Finally, the sub-holograms for each depth layer are generated and integrated as a single main hologram by encompassing comprehensive object information, which is displayed on the spatial light modulator of a holographic display system and illuminated by a coherent light source. Experimental results confirm the superiority of the proposed system, particularly demonstrating its effectiveness for objects with a wide depth range or multiple objects separated by considerable distances.

Red tide identification in coastal regions based on the logical operation of multi-band spectral indices

ABSTRACT Optical remote sensing is an applicable approach for red tide monitoring in wide observation range. This study proposed a red tide identification method using the logical operation of multi-band spectral indices (MBSIs). Five candidate MBSIs from two categories that could potentially indicate red tide outbreaks are selected and integrated into different combinations. The reflectance spectra of red tide measured in the Xinghai Bay, China, are used to determine the optimal combination of MBSIs through the self-organized map network. According to the result, the combination of fluorescence baseline height and the spectral difference indices at red and near-infrared bands achieves the highest accuracy at 0.95 among all the combinations of MBSIs. Therefore, the logical operation of these three MBSIs is applied to Sentinel-2 satellite images to identify red tide in coastal regions. It is found that the logic operation method of MBSIs can work robustly in different sea areas and provides more reasonable results than using any single MBSI. The proposed method is able to detect the red tide that are often undetectable by human eyes with low computational complexity, and can be applied to the rapid detection and daily surveillance on the spatial distribution of red tide outbreaks.

Risk prediction of computer investment database information management system based on machine learning algorithms

In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regression algorithms, and Gradient Boosting Decision Tree (GBDT) algorithms to compare and analyze the prediction accuracy of these three algorithms. This article found that the GBDT algorithm has the highest prediction accuracy. Through a large amount of experimental data, it has been proven that the average testing accuracy using regression algorithms was 3.5% higher than that using neural network algorithms. It was found that the average test accuracy using the GBDT algorithm was 7.2% higher than the average test accuracy using the regression algorithm. The study also explores the combination of physiological and behavioral data collected by wearable devices to provide more comprehensive risk assessment and decision support, which provides an important reference for the optimization of enterprise risk management. Through this innovative data source integration, this paper provides a new perspective for the application and development of machine learning algorithms in computer investment database IMS.

TDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.

Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require both densely sampled q-t space data for microstructural fitting, leading to very time-consuming acquisition protocols. To overcome this problem, we present a joint q-t space model-tDKI-Net to estimate diffusion-time dependent kurtosis and the transmembrane exchange, using downsampled q-t space data. The tDKI-Net is composed of several q-Encoders and a t-Encoder, designed based on the extragradient mechanism, each integrated with their respective mapping networks. In the tDKI-Net, two types of encoders along with their mapping networks are employed sequentially to generate kurtosis at individual diffusion times and to fit the transmembrane exchange time ( τm) using the time-dependent kurtosis according the Kärger's model. Meanwhile, we proposed a three-stage training strategy, including physics-informed self-supervised pretraining, DKI warm-up, and joint training, to match the network structure. Our results demonstrated that the proposed tDKI-Net could effectively accelerate tDKI scans, resulting in lower estimation error compared with other methods. Our proposed three-stage training strategy demonstrated superior results than those training from scratch, e.g., the normalized root mean square error (NRMSE) of τm decreased by up to 1.4%. We also investigated the training data size effects and found that although we used one-subject training, the network achieved lower NRMSEs for Kavg, K0 and τm (2.50%, 3.04%, 10.86%) than previous work that used three-subject training (3.8%, 9.5%, 12.1%). tDKI-Net can considerably reduce the scan time by 10.5- fold by joint downsampling the q-t space data without compromising the estimation accuracy.

Effects of the long-term rice expansion on ecosystem carbon budget in the typical agricultural area of Northeast China

Extensive rice expansion in northeast China has significantly altered land use and land cover (LULC) changes. However, the impact of long-term rice expansion on regional carbon budget dynamics remains unclear. A major obstacle in addressing this gap is the absence of agricultural LULC information with high spatiotemporal resolution. Here, we selected the Sanjiang Plain experienced dramatic rice expansion as the study area, presenting a framework that integrates high-resolution crop distribution data with a process-based model to quantify the impact of rice expansion on regional carbon budgets. Specifically, we employed a robust deep-learning network for crop mapping based on Landsat data, to reconstruct the spatiotemporal dynamics of rice expansion from 1985 to 2020. We then incorporated these long-term crop maps into the Denitrification-Decomposition (DNDC) model, to explore the effects of rice expansion on the regional carbon budget over this period. Analysis results showed that the rice planting area expanded by 708.64 km2/yr, resulting in more than a tenfold increase over the past 36 years. Spatially, rice planting expanded from the southwest to the northeast and from the interior to the exterior. This expansion has resulted in approximately 275 Mt. of CO2 and 6.92 Mt. of CH4 greenhouse gas emissions, altering the dynamics of the regional carbon budget and shifting the ecosystem from a carbon sink to a carbon source since 2016. Although the long-term expansion of rice increased soil respiration and CH4 emissions, it also enhanced soil carbon sequestration through agricultural management practices. These findings greatly enhance our understanding of the ecosystem carbon cycle's response to long-term agricultural LULC changes, providing more accurate data support and scientific evidence for developing low-carbon agricultural policies.

A Comprehensive Analysis of Network Scanning and Security Assessment Tool

An essential component of any network pen testing effort is network reconnaissance. Gaining additional knowledge about the target's network will help us understand its infrastructure as well as any potential attack routes and exploits that could expose vulnerabilities. By employing both passive and active reconnaissance methods and tools, an attacker can possess substantial volumes of data with a reduced likelihood of being discovered. NMAP, short for Network Mapper, is a widely recognized and powerful open-source network scanning and security assessment tool. This research study analyzes NMAP, including its background, features, usage, and impact on network security. The research also explores the ethical issues of employing NMAP and suggests strategies to avoid such hazards.

Evolution of climate-smart agriculture research: A science mapping exploration and network analysis

Abstract The integrated approach of bibliometric mapping and literature review was adopted to critically examine the evolution of climate-smart agriculture (CSA) research climate from 2012 to 2023 using publications data recovered from the Elsevier Scopus database. The publication trends, stakeholder profiles, social networks, and thematic area analysis on CSA research were examined. Findings revealed that the publication trends on CSA research revealed an incremental pattern from 2012 to 2023. Stakeholder profile analysis identified numerous authors, affiliations, and countries actively engaged in CSA research. The most prolific researcher is Mangi L. Jat, whereas Wageningen University & Research is the most prolific affiliation. Finally, the United States is the most prolific nation. Stakeholder’s productivity was ascribed to a combination of factors ranging from access to research funding to collaborations. The most active funder of CSA research is CGIAR (Consortium of International Agricultural Research Centres), which has funded 52 publications, gained 1,529 citations, and an h-index of 20. The CGIAR has greatly supported the works of highly prolific authors such as Mangi L. Jat, Hanuman S. Jat, and Suresh K. Kakraliya. Social network analysis revealed strong, active, and extensive links, particularly between nations such as the US, India, Kenya, and Ethiopia. The cluster and thematic area analyses of the CSA research landscape revealed three areas, namely, Climate-Resilient Agricultural Adoption, Climate-Responsive Agricultural Practices, and Integrated Food Systems, which are the current focus of stakeholders in the field. Overall, the study showed that CSA is a broad and interdisciplinary topic with a significant impact on the social, economic, and environmental aspects of humanity and societies across the globe.

Introducing an integrated self-organizing map and radial basis function network for accurate prediction of water- Fe3O4 nanofluid viscosity versus solid volume fraction and temperature

Nanofluids, colloidal suspensions of nanoparticles in base fluids, promise improved thermal conductivity and energy efficiency. Predicting nanofluid viscosity, influenced by factors like temperature and nanoparticle concentration, is crucial for optimizing performance. This research introduces an innovative approach combining Self-Organizing Map (SOM) and Radial Basis Function (RBF) networks to accurately predict viscosity. The SOM guides RBF center placement for enhanced accuracy, validated with water-Fe3O4 nanofluid data. Temperature significantly affects viscosity, with adjustments from 10 to 50 °C showing viscosity decreases from around 5 to lower than 2 mPa.s. The integrated SOM-RBF model achieves high precision (max absolute error 0.1078 mPa.s, 4 % relative error), suggesting potential for further enhancement. This novel neural network combination enhances efficiency in nanofluid applications, advancing predictive modeling in nanofluid engineering for broader industrial and scientific innovation.

Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.

Accurate Alzheimer's Disease (AD) progression prediction is essential for early intervention. The TADPOLE challenge, involving 92 algorithms, used multimodal biomarkers to predict future clinical diagnosis, cognition, and ventricular volume. The winning algorithm, FROG, utilized a Longitudinal-to-Cross-sectional (L2C) transformation to convert variable longitudinal histories into fixed-length feature vectors, which contrasted with most existing approaches that fitted models to entire longitudinal histories, e.g., AD Course Map (AD-Map) and minimal recurrent neural networks (MinimalRNN). The TADPOLE challenge only utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To evaluate FROG's generalizability, we trained it on the ADNI dataset and tested it on three external datasets covering 2,312 participants and 13,200 timepoints. We also introduced two FROG variants. One variant, L2C feedforward neural network (L2C-FNN), unified all XGBoost models used by the original FROG with an FNN. Across external datasets, L2C-FNN and AD-Map were the best for predicting cognition and ventricular volume. For clinical diagnosis prediction, L2C-FNN was the best, while AD-Map was the worst. L2C-FNN compared favorably with other approaches regardless of the number of observed timepoints, and when predicting from 0 to 6 years into the future, underscoring its potential for long-term dementia progression prediction. Pretrained ADNI models are publicly available: GITHUB_LINK.