Map Features Research Articles

Radiomics analysis of dual‐energy CT‐derived iodine maps for differentiating malignant from benign thyroid nodules

AbstractBackgroundMany thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual‐energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking.PurposeTo determine the ability of an iodine map‐based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules.MethodsA total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad‐score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed.ResultsAccording to the clinical model, age (0.989 [0.984, 0.995]; p < 0.001), NIC (0.778 [0.640, 0.995]; p = 0.01), and cyst changes (0.617 [0.507, 0.751]; p < 0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p = 0.01), NIC (0.797 [0.674, 0.941]; p = 0.008), cyst changes (0.786 [0.653, 0.947]; p = 0.01), and the rad‐score (1.106 [1.070, 1.143]; p < 0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96 vs. 0.87; p = 0.01) and test (AUC, 0.90 vs. 0.79; p = 0.04) cohorts than did the clinical model.ConclusionsThe radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.

CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Multi-channel Fused Vision Transformer Network for Bearing Fault Diagnosis under Different Working Conditions

Abstract Many of the current fault diagnosis methods rely on time-domain signals. While the richest information are contained in these signals, their complexity poses challenges to network learning and limits the ability to fully characterize them. To address these issues, a novel Multi-channel Fused Vision Transformer Network (MFVTN) is proposed in this paper. Firstly, the Overlapping Patch Embedding (OPE) module is introduced to overlap the time-domain map with edge information, preserving the global continuous features of the time-domain map and adding positional encoding for sorting. This integration helps the Vision Transformer (ViT) merge detailed features and construct the global mapping. Secondly, multiple dimensional time domain signal features are extracted and fused in parallel, enabling multi-domain fault diagnosis of bearings. In order to enhance the network ability to extract domain-invariant features, an adversarial training strategy combined with Wasserstein distance is utilized. The results demonstrate that the diagnostic accuracy of the proposed MFVTN can reach 98.2%.

Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study.

To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC). This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring. K-means clustering (K = 4-6) was applied to obtain multiple sub-regions (n = 3-5), and corresponding histogram features (including mean, standard deviation, minimum, maximum, and 10th, 25th, 50th, 75th, and 90th quantile) of APT and apparent diffusion coefficient (ADC) maps were extracted and filtered using stepwise regression. When K = 5, the K-means clustering is four sub-regions, showing the best prediction for TB grade compared to K = 4 or 6. When K = 5, there were significantly higher histogram features of the APT map in sub-regions 3 and 4 in the high TB grade group compared to the low-intermediate TB grade group. Receiver operating characteristic (ROC) curve and internal validation suggested that the predictive efficiency of the model was highest when K = 5, with AUC, sensitivity, specificity, accuracy, and kappa values of 0.92, 93%, 71%, 87%, and 0.65, respectively. There were no significant differences in the histogram features of each sub-region in the ADC map (p > 0.05). The sub-regional histogram features of APTw images can help to distinguish the heterogeneous regions of RC, which can be used to predict the TB grade of RC. Question Can the sub-regional histogram features of APTw MRI predict the tumor budding (TB) grade of rectal cancer (RC)? Findings Differences exist in histogram features of APT map subregions between high and low-intermediate TB grade groups; subregions of the APT map have different predictive abilities. Clinical relevance APT-weighted imaging might outperform DWI in predicting TB grade in RC.

Dynamical analysis and hardware verification of a new multistable memristive hyperchaotic map

In this paper, a three-dimensional (3D) memristive hyperchaotic map is designed by coupling discrete memristor (DM). Structurally, the map features the existence of infinite fixed points. Based on the differential setting of parameters for various initial values, it can produce an infinite number of heterogeneous coexisting attractors or homogeneous coexisting attractors. Simultaneously, it can realize the attractors symmetric control. The numerical analysis and simulation results highlight its rich dynamical behaviors and excellent performance. A digital hardware platform based on a microcontroller is designed, clearly demonstrating the efficacy of the design through distinct visualization of attractors on an oscilloscope, providing tangible evidence of the successful hardware platform implementation.

High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields.

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://github.com/muyuWang/HHNeRF.

Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference

Citywide fine-grained urban flow inference (FUFI) problem aims to infer the high-resolution flow maps from the coarse-grained ones, which plays an important role in sustainable and economic urban computing and intelligent traffic management. Previous models tackle this problem from spatial constraint, external factors, and memory cost. However, utilizing the new urban flow maps to calibrate the learned model is very challenging due to the “catastrophic forgetting” problem and is still under-explored. In this article, we make the first step in FUFI and present CUFAR—Continual Urban Flow inference with augmented Adaptive knowledge Replay—a novel framework for inferring the fine-grained citywide traffic flows. Specifically, (1) we design a spatial-temporal inference network that can extract better flow map features from both local and global levels; (2) then, we present an augmented adaptive knowledge replay (AKR) training algorithm to selectively replay the learned knowledge to facilitate the learning process of the model on new knowledge without forgetting. We apply several data augmentation techniques to improve the generalization capability of the learning model, gaining additional performance improvements. We also propose a knowledge discriminator to avoid the “negative replaying” issue introduced by noisy urban flow maps. Extensive experiments on two large-scale real-world FUFI datasets demonstrate that our proposed model consistently outperforms strong baselines and effectively mitigates the forgetting problem.

A novel portable in situ analyzer for highly sensitive monitoring of organophosphorus and carbamate pesticides in food and environment

The presence of excessive residues of organophosphorus (OPs) and carbamate (CPs) pesticides in food and the environment poses a significant threat to human health and ecological sustainability. Herein, we reported a dual-readout, portable, and highly sensitive photoelectric analyzer (DPHP-analyzer) to achieve rapid and accurate monitoring of OPs and CPs. The dual signal strategy is mainly achieved by controlling the activity of acetylcholinesterase (AChE) in the presence of OPs to catalyze acetylthiocholine (ATCh) and generate thiocholine (TCh), which specifically induced the conversion of colorless Ellman's reagent (DTNB) into yellow 5-thio-2-nitrobenzoic acid (TNB). Simultaneously, the utilization of positively charged TNB as an absorber can induce a dynamic fluorescence quenching effect based on energy transfer mechanism. Fully cooperating with the above mechanism, a CD like, disposable array-based chip manufactured by 3D printing (CD-AN-chip) was designed and integrated to deliver reagents. By use of this dual-output strategy, the analyzer and chip can provide good sensitivity and selectivity for the monitoring of diazinon and carbendazim (case analysis) with a LOD 0.38 ng/mL for carbendazim. And the reproducibility, stability and practicability analysis of real sample have been thoroughly validated simultaneously. Importantly, with the features of fluorescence fingerprint map, the detector could be employed for the distinction of various analytes and further be used for monitoring the nutritional environment within residential and public settings.

AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8

This study introduces an improved YOLOv8 model tailored for detecting objects in road scenes. To overcome the limitations of standard convolution operations in adapting to varying targets, we introduce Adaptive Kernel Convolution (AKconv). AKconv dynamically adjusts the convolution kernel’s shape and size, enhancing the backbone network’s feature extraction capabilities and improving feature representation across different scales. Additionally, we employ a Multi-Scale Dilated Attention (MSDA) mechanism to focus on key target features, further enhancing feature representation. To address the challenge posed by YOLOv8’s large down sampling factor, which limits the learning of small target features in deeper feature maps, we add a small target detection layer. Finally, to improve model training efficiency, we introduce a regression loss function with a Wise-IoU dynamic non-monotonic focusing mechanism. With these enhancements, our improved YOLOv8 model excels in road scene object detection tasks, achieving a 5.6 percentage point improvement in average precision over the original YOLOv8n on real road datasets.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Comparison of diagnostic accuracy of radiomics parameter maps and standard reconstruction for the detection of liver lesions in computed tomography.

The liver is a frequent location of metastatic disease in various malignant tumor entities. Computed tomography (CT) is the most frequently employed modality for initial diagnosis. However, liver metastases may only be delineated vaguely on CT. Calculating radiomics features in feature maps can unravel textures not visible to the human eye on a standard CT reconstruction (SCTR). This study aimed to investigate the comparative diagnostic accuracy of radiomics feature maps and SCTR for liver metastases. Forty-seven patients with hepatic metastatic colorectal cancer were retrospectively enrolled. Whole-liver maps of original radiomics features were generated. A representative feature was selected for each feature class based on the visualization of example lesions from five patients. These maps and the conventional CT image data were viewed and evaluated by four readers in terms of liver parenchyma, number of lesions, visual contrast of lesions and diagnostic confidence. T-tests and chi²-tests were performed with a significance cut off of p<0.05 to compare the feature maps with SCRT, and the data were visualized as boxplots. Regarding the number of lesions detected, SCTR showed superior performance compared to radiomics maps. However, the feature map for firstorder RootMeanSquared was ranked superior in terms of very high visual contrast in 57.4% of cases, compared to 41.0% in standard reconstructions (p < 0.001). All other radiomics maps ranked significantly lower in visual contrast when compared to SCTR. For diagnostic confidence, firstorder RootMeanSquared reached very high ratings in 47.9% of cases, compared to 62.8% for SCTR (p < 0.001). The conventional CT images showed superior results in all categories for the other features investigated. The application of firstorder RootMeanSquared feature maps may help visualize faintly demarcated liver lesions by increasing visual contrast. However, reading of SCTR remains necessary for diagnostic confidence.

Rolling bearing fault diagnosis method based on wavelet packet logarithmic energy map

Aiming at the fault diagnosis problem of rolling bearings, a diagnosis method based on wavelet packet logarithmic energy map is proposed. Firstly, a new wavelet packet node logarithmic energy formula is improved and proposed to overcome the shortcomings of cumbersome parameter determination and strong subjectivity in the traditional wavelet packet energy formula, improve the recognition of high-frequency faults and the distinction of low-frequency fault categories, so as to fully extract the initial time-frequency domain features; secondly, the Gram angle and field idea is used to realize the conversion from one-dimensional features to two-dimensional image features, so as to construct the logarithmic energy map feature based on wavelet packet, which further considers the spatial information between adjacent features, thereby optimizing the initial time-frequency domain features and improving the significance of the obtained features. On this basis, the residual network is used to improve the accuracy of fault diagnosis classification results. Finally, through the simulation verification of the standard rolling bearing data set of Case Western Reserve University, it can be seen that the fault diagnosis model constructed by the proposed method has high diagnostic accuracy and strong generalization ability.

Research on Intelligent Digital Manufacturing Platform Based on Artificial Intelligence Deep Neural Network

In today’s information age, intelligent manufacturing has become a significant trend of future industrial development. The neural network plays a vital role as the basis of smart manufacturing. This is a kind of intelligent algorithm that can simulate human brain organization, with strong learning ability and computing power, and has achieved remarkable results in the application field; the most typical representative is deep neural network, especially in recent years, an artificial intelligence developed based on deep neural network has entered the application stage, providing a new idea and model for industrial production. Based on this, taking the clothing production process as an example, this paper combined transfer learning and network fine-tuning technology to build a Faster RCNN model based on the ResNet-101 network to realize the positioning and recognition of clothing style map features and create an intelligent generation platform for clothing process flow on this basis.

650P Development and implementation of enhanced AI-derived DTI features for precision mapping of neural tract damage in myotonic dystrophy

650P Development and implementation of enhanced AI-derived DTI features for precision mapping of neural tract damage in myotonic dystrophy

Overview of High-Dynamic-Range Image Quality Assessment.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

Эволюция национального состава Саратовской губернии в первой половине 1920-х годов

The article examines the changes in the national composition of Saratov province in the first half of the 1920s based on the materials of the All-Russian Census of 1920, the All-Union Urban Census of 1923 and the first All-Union Population Census of 1926. The factors influencing the evolution of the national structure of the region are being studied. The conclusion is made about the formation in the main features of the ethnic map of the Saratov region. The position is put forward on the development and conduct of the first censuses as the “golden” period of Soviet statistics, which had a huge impact on the country’s modernization policy.

Knowledge‐Guided Automated Cartographic Generalization Process Construction: A Case Study Based on Map Analysis of Public Maps of China

ABSTRACTThe efficacy of conveying information through maps heavily depends on the quality of map generalization. However, automating map generalization poses a complex decision‐making challenge, requiring a profound understanding of the process—specifically, knowledge about the generalization procedure. Currently, there is a scarcity of research on the sequence of generalization operations, particularly for cartographic generalization involving symbolization and labeling. On the contrary, customary maps generated in practical applications consistently adhere to the specified generalization and symbolization protocol, which makes it feasible and credible to construct this overall process based on expert knowledge. To reconcile this incongruity, this paper presents a knowledge‐guided automated cartographic generalization process construction. Firstly, an exhaustive examination of the sequential procedures involved in manual generalization and a well‐applied automated generalization system are delineated, drawing upon map analysis methodologies, observations, and expert interviews. Then, elaborate guidelines governing each phase within this process, particularly concerning the symbolization and labeling of map features, are explored. Ultimately, details of the expert interview are described and a map generalized by the well‐applied system is analyzed. The results show that the automated generalization system follows the knowledge‐guided process in this paper can significantly improve production efficiency in practice, this study serves as a connection between cartographers and developers and may help achieve a higher level of automated cartographic generalization.

Genuine entanglement detection via projection map in multipartite systems

We present a formalism to detect genuine multipartite entanglement by considering projection map which is a positive but not completely positive map. Projection map has been motivated by the no-pancake theorem which repudiates the existence of a quantum operation that maps the Bloch sphere onto a disk along its equator. The not-complete positivity feature of projection map is explored to investigate genuine multipartite entanglement in arbitrary N-qubit quantum systems. Our proposed framework can detect some important classes of genuinely entangled states in tripartite and quadripartite scenarios. We provide illustrative example to show the efficacy of our formalism to detect a class of tripartite PPT bound entangled states. Finally, we construct a suitable witness operator based on projection map to certify genuine tripartite entanglement, which is likely to be feasible experimentally.

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

MGFmiRNAloc: Predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module.

MiRNA has distinct physiological functions at various cellular locations. However, few effective computational methods for predicting the subcellular location of miRNA exist, thereby leaving considerable room for improvement. Accordingly, our study proposes the MGFmiRNAloc simplified molecular input line entry system (SMILES) format as a new approach for predicting the subcellular localization of miRNA. Additionally, the graphical convolutional network (GCN) technique was employed to extract the atomic nodes and topological structure of a single base, thereby constructing RNA sequence molecular map features. Subsequently, the channel attention and spatial attention mechanisms (CBAM) were designed to mine deeper for more efficient information. Finally, the prediction module was used to detect the subcellular localization of miRNA. The 10-fold cross-validation and independent test set experiments demonstrate that MGFmiRNAloc outperforms the most sophisticated methods. The results indicate that the new atomic level feature representation proposed in this study could overcome the limitations of small samples and short miRNA sequences, accurately predict the subcellular localization of miRNAs, and be extended to the subcellular localization of other sequences.