Visual Results Research Articles

Development of CadR-based cadmium whole cell biosensor for visual detection of environmental Cd2+

BackgroundAs a threat to human health and public health, cadmium (Cd) pollution has received widespread social concern. Our previously constructed CadR-based bacterial whole cell biosensor (WCB) epCadR5 showed high sensitivity and specificity in cadmium detection. However, the application of the sensor is still hindered by the need for laboratory equipment to read the fluorescence signal output. In this study, we aimed to optimizing the sensor to make it available for visual detection of environmental cadmium and simplify the detection process to advance practical application of the sensor. ResultsBy replacing the constitutive promoter with J110, the fluorescence signal output of the sensor was significantly increased and the fluorescence leakage was decreased. In addition, the fluorescence signal output of green fluorescence protein (GFP) was enhanced by the addition of a 5′ untranslated region (5′-UTR) mlcR10. The fluorescence signal output of the WCB is sufficiently robust to be visible and distinguishable to the naked eye, which is of paramount importance for visual detection. The sensor readout can be conveniently recorded by mobile phone camera and quantified. For ease of on-site application, the WCB's visual detection procedures and conditions were further optimized and simplified. The WCB demonstrated good linearity and detection limit (1.81 μg/L) for visual detection of Cd2+ without the assistance of bulky laboratory equipment. For the detection of real environmental samples, the WCB visual detection results were close to those of WCB-flow cytometry (FACS) and graphite furnace atomic absorption spectroscopy (GFAAS). SignificanceIn this work, we developed an easy-to-use, on-site and visual detection biosensor for monitoring environmental Cd2+. It will advance the utilization of cadmium WCBs in practical settings. The optimization and simplification strategy in the study also provide new insights into the visualization of other bacterial biosensors, and will advance the practical application of WCBs.

A SEMIOTIC STUDY OF CARICATURE: THE STRUGGLE BETWEEN AMERICA AND CHINA

Visual communication is a compound process, which requires broad knowledge of semiotics. Semiotics is the education of signs and symbols. In what way these signs and symbols are interpreted is studied under semiotics. It has many concealed signs and connotations, for example: brand name, logo, package design, color, punch line and trade mark, etc. The present study presents one of the most significant philosophies and replicas of visual social semiotics, namely, Kress and van Leeuwen’s "Grammar of Visual Design". It involves the description of semiotic resources, what can be said and done with images (and other visual means of communication) and how things people say and do with images can be interpreted. Thus, the communicative values of signs in a caricature are a portrait with the volume turned up. A caricature is a painting, or more commonly a picture, of an object or item in which the characteristics and structure have been blurred and exaggerated to ridicule or satirize the topic. Thus the current study aims at studying some caricatures nonverbally through analyzing the image and colours of it. The study tackles semiotics; specifically social semiotics, caricature, the elements of analyzing caricature, which are: image, word, and colour. The first part carries on the introduction of the current study as well as tackling the problem of the study, the literature review of semiotics in general and social semiotics in particular. The second part carries on the data and its analysis through the model, as well as the visual results, finally, giving the conclusion.

Visual Communication Method of Multi frame Film and Television Special Effects Images Based on Deep Learning

For the dynamic film and television special effects industry, creating visually stunning and valuable graphics requires the integration of excellent deep-learning algorithms. This paper presents an enhanced version of the 3-D Convolutional Neural Network (3-D CNN), specifically tailored to meet the demanding needs of multi-frame television and film special effects. The version’s key feature is its efficient data handling through coupled precision training, significantly increasing computational efficiency while reducing memory needs. This method, which combines floating-issue operations of 16 and 32 bits, is ideal for efficiently processing large amounts of high-resolution video data. The proposed 3-D CNN structure excels in extracting and analysing complex spatiotemporal capabilities from video sequences, capturing the spatial and temporal nuances crucial in film imagery. This capability is important for accomplishing excessive fidelity in visual results, ensuring seamless integration with live motion pics. Incorporating a cutting-edge attention mechanism inspired by the aid of transformer-based architectures, the model specialises inside the maximum pertinent components of video frames to enhance the quality and realism of outcomes. Furthermore, the model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalise efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques make the model robust and prepare it to deal with an intensive variety of challenging situations involving computer special effects. Real-time processing competencies make the model a game-changer for on-set visible outcomes and adjustments. Designed for seamless integration with the famous CGI software program software utility, it allows a harmonious aggregate of AI and ingenious creativity. Acknowledging the environmental impact of high-powered computing, the model consists of power-efficient computational techniques that align with sustainable computing practices, lower operational costs, and are related to processing complex video statistics. model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalize efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques, in addition, make the model robust, and prepare it to deal with an intensive variety of computer special effects challenging situations.

The effect of ceramides on skin absorption by Raman spectroscopy.

Ceramides are essential epidermal constituents that play a critical role in skin moisturization treatment as a raw material in cosmetics formulation. Recently, ceramides have been known to be frequently applied in various cosmetic formulations. Despite ceramide's beneficial characteristics, academic research regarding ceramides and their skin absorption remains insufficient. Therefore, our study conducted clinical research employing Raman spectroscopy to investigate the effects of ceramides on skin absorption to enhance the understanding of ceramides' dermatological functionality and their topical application in cosmetics science. Twenty healthy individuals with dry skin have participated in this clinical trial. In this double-arm designed trial, the test group received an investigational product with ceramides (5000ppm) and a control group received an investigational product without the ceramides while all other components remained identical. The subjects visited the clinical research center and acclimatized for 30 min in constant humidity and temperature for equilibrium, subsequently conducting a measurement. Before the trial, the research subject's target site (lower arm area) was kept clean, devoid of any cosmetic administering 24h before the trial when investigational product was topically applied. Our findings with Raman spectroscopy statistically demonstrate that skin absorption amount, speed and depth for both groups improved overall (p<0.05) after administration of the investigational product. Notably, the test group received an investigational product with ceramides (5000ppm) indicating superior effectiveness across all parameters compared to a control group from comparison analysis of each parameter (p<0.05). This study concludes that ceramide-containing cosmetics provide a beneficial effect on skin absorption via visual and statistical results of Raman spectroscopy analysis.

Real-Time Decoding of Snapshot Compressive Imaging Using Tensor FISTA-Net.

Snapshot compressive imaging (SCI) cameras compress high-speed videos or hyperspectral images into measurement frames. However, decoding the data frames from measurement frames is compute-intensive. Existing state-of-the-art decoding algorithms suffer from low decoding quality or heavy running time or both, which are not practical for real-time applications. In this article, we exploit the powerful learning ability of deep neural networks (DNN) and propose a novel tensor fast iterative shrinkage-thresholding algorithm net (Tensor FISTA-Net) as a real-time decoder for SCI cameras. Since SCI cameras have an accurate physical model, we can trade training time for the decoding time by generating abundant synthetic data and training a decoder on the cloud. Tensor FISTA-Net not only learns a sparse representation of the frames through convolution layers but also reduces the decoding time and memory consumption significantly through tensor operations, which makes Tensor FISTA-Net an appropriate approach for a real-time decoder. Our proposed Tensor FISTA-Net obtains an average PSNR improvement of 0.79-2.84 dB (video images) and 2.61-4.43 dB (hyperspectral images) over the state-of-the-art algorithms, along with more clear and detailed visual results on real SCI datasets, Hammer and Wheel, respectively. Our Tensor FISTA-Net reaches 45 frames per second in video datasets and 70 frames per second in hyperspectral datasets, meeting the real-time requirement. Besides, the trained model occupies only a 12 -MB memory footprint, making it applicable to real-time Internet of Things (IoT) applications.

A method and tool for Sustainability-driven Computer-Aided Process Planning with user-tuneable optimization

This paper introduces the methodology framework, and a cloud-based Sustainability-driven Computer-Aided Process Planning (s-CAPP) tool aimed at optimizing efficiency and sustainability aspects of manufacturing operations at shopfloor level, considering a user-tailored optimization goal function. Geometric data extracted from 3D models of the parts to be produced are coupled with process parameters and resource consumption data from the manufacturing equipment in order to analyze the metrics for productivity and sustainability accordingly. By leveraging comprehensive data resources in manufacturing, s-CAPP allows companies to balance their production plans between productivity and sustainability through a user-friendly interface. Its application in a furniture manufacturing case study demonstrates its potential for process planning by prioritizing between productivity or sustainability, showcasing promising results. The presented s-CAPP implementation assists the production managers in taking decisions regarding sustainability and productivity in the production of batches through the prompting of intuitive visual results, impacting on both economic and environmental sides. Keywords: sustainability-driven manufacturing, process planning, cloud-based computing, furniture manufacturing, process simulation, data-driven manufacturing, feature extraction

Navigating subjectivity in AI-generated photography: The quest for ethics and creative agency

This study identifies alternative models for the production of AI-generated images to those currently used by mainstream AI platforms. Based on primitive computational art processes, these systems allow designers to gain greater control over the final visual result while avoiding potential issues with intellectual property theft and breach of privacy. The article starts by analysing the level of artificiality that might be effectively attributed to each part of the creative process involved in the development of AI-generated images. It then moves on to discuss the extent to which individual actors intervene in each of these stages, with the aim of untangling the different subjectivities that would ultimately shape the meaning of AI-generated photographs, and identifying existing AI models that are able to offer practical solutions for every designer to retain creative autonomy over their AI creations.

The Study of Echocardiography of Left Ventricle Segmentation Combining Transformer and Convolutional Neural Networks.

Accurate prediction of echocardiographic parameters is essential for diagnosis and treatment of cardiac disease, especially for segmentation of the left ventricle to obtain measurements such as left ventricular ejection fraction and volume. However, manually outlining left ventricle on echocardiographic images is a time-consuming and physician experience-dependent task. Therefore, it is crucial to develop an accurate and efficient automatic segmentation tool. Therefore, we aimed to explore a model to perform echocardiography of left ventricle segmentation by combining transformer and convolutional neural networks (CNN).ResNet-50 was used in CNN branch. The encoder-decoder architecture was used for transformer branch, which was fused to the corresponding feature maps of the CNN branches. Fusion module was used to effectively combine feature information from the CNN and transformer. Bridge attention used to increase sensitivity and prediction accuracy of model. The entire network was trained end-to-end using the binary cross-entropy with logits loss L.In this work, we propose an automatic left ventricular (LV) segmentation model based on Transformer and CNN that efficiently captures global dependencies and spatial details and create a fusion module using CBAM that fuses Transformer and CNN features. In addition, attention is also computed using multi-level fusion features to obtain the final attention segmentation map. The model was trained and evaluated on a large cardiac image dataset, EchoNet-Dynamic, with test dice coefficient of 92.4%.The results show that our model can better segment left ventricle. We also tested our model on clinical patient ultrasound images, and visualization results proved effectiveness of the model.

Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals. Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.

Reference-Based OCT Angiogram Super-Resolution With Learnable Texture Generation.

Optical coherence tomography angiography (OCTA) can visualize retinal microvasculature and is important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases. However, the resolution of optical coherence tomography (OCT) angiograms inevitably decreases when increasing the field-of-view (FOV) given a fixed acquisition time. To address this issue, we propose a novel reference-based super-resolution (RefSR) framework to preserve the resolution of the OCT angiograms while increasing the scanning area. Specifically, textures from the normal RefSR pipeline are used to train a learnable texture generator (LTG), which is designed to generate textures according to the input. The key difference between the proposed method and traditional RefSR models is that the textures used during inference are generated by the LTG instead of being searched from a single reference (Ref) image. Since the LTG is optimized throughout the whole training process, the available texture space is significantly enlarged and no longer limited to a single Ref image, but extends to all textures contained in the training samples. Moreover, our proposed LTGNet does not require an Ref image at the inference phase, thereby becoming invulnerable to the selection of the Ref image. Both experimental and visual results show that LTGNet has competitive performance and robustness over state-of-the-art methods, indicating good reliability and promise in real-life deployment. The source code is available at https://github.com/RYY0722/LTGNet.

Large language models as surrogate models in evolutionary algorithms: A preliminary study

Large Language Models (LLMs) have demonstrated remarkable advancements across diverse domains, manifesting considerable capabilities in evolutionary computation, notably in generating new solutions and automating algorithm design. Surrogate-assisted selection plays a pivotal role in evolutionary algorithms (EAs), especially in addressing expensive optimization problems by reducing the number of real function evaluations. However, whether LLMs can serve as surrogate models remains an unknown. In this study, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training. Specifically, we formulate model-assisted selection as a classification problem or a regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data. This involves predicting whether a solution is good or bad, or approximating its value. This approach is then integrated into EAs, termed LLM-assisted EA (LAEA). Detailed experiments compared the visualization results of 2D data from 9 mainstream LLMs, as well as their performance on 5-10 dimensional problems. The experimental results demonstrate that LLMs have significant potential as surrogate models in evolutionary computation, achieving performance comparable to traditional surrogate models only using inference. This work offers new insights into the application of LLMs in evolutionary computation. Code is available at: https://github.com/hhyqhh/LAEA.git.

PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. To broaden upon it, the choice of indicators and their subsequent ranking for the aggregation into an index is subjective in nature. This aggregation is not empirically validated and is prone to omit the nuances of localized resilience changes and causal factors affecting it, while leading to oversimplified conclusions. Meanwhile, there is a lack of scientifically and computationally rigorous, user-friendly tools that can support customized resilience assessment with consideration of local conditions. This study addresses these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customizable Resilience Inference Measurement (RIM), designed for multi-scale community resilience assessment and influential socioeconomic factors identification; 2) To implement a Platform for Resilience Inference Measurement and Enhancement (PRIME) module in the CyberGISX platform backed by high-performance computing, enabling users to apply and customize RIM to compute and visualize disaster resilience; 3) To demonstrate the utility of PRIME through a representative study to understand the geographical disparities of county-level community resilience to natural hazards in the United States and identifying the driving factors of resilience in the social domain. Customizable RIM generates vulnerability, adaptability, and overall resilience scores derived from empirical parameters—hazard threat, damage, and recovery. Computationally intensive Machine Learning (ML) methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. This setup provides a foundation for assessing resilience and strategizing enhancement interventions.

A multi-task spatio-temporal fully convolutional model incorporating interaction patterns for traffic flow prediction

Previous traffic flow prediction studies have utilized spatio-temporal neural networks combined with the multi-task learning framework to seek complementary information for enhancing prediction performance. However, the existing methods still face two challenges: they fail to capture global interaction patterns between regions and lack consideration for inter-correlations within interaction patterns. To solve these issues, we propose a novel multi-task spatio-temporal fully convolutional model named MSTFCM. First, the model includes the interaction tensor and raster tensor as task inputs, where the interaction tensor extends the raster tensor by incorporating global interaction patterns between regions. Second, a multi-task framework combined spatio-temporal convolutional block was used to learn generalized features and interaction features. A channel spatio-temporal attention is added to adaptively adjust feature weights and capture inter-correlations. To train the MSTFCM, the uncertainty loss was designed as the learnable loss functions, which capture various flow fluctuations, to facilitate multi-task optimization. The proposed model was validated on two real-world traffic datasets collected in Xiamen, China. Experimental results showed that MSTFCM outperformed nine baselines in one-step and multi-step prediction, with slower performance degradation as predicted time intervals and steps increased. We further validated the model’s effectiveness through designed variants and visualization results.

Real-time assessment of ship collision risk using image processing techniques

The poor quality or the miss of Automatic Identification System (AIS) data may cause erroneous judgement of the potential navigational risk. Therefore, this study proposes a real-time framework for assessing ship collision risk using onboard video data in order to improve the risk perception ability of navigators. Firstly, the Squeeze-and-Excitation (SE) attention mechanism and the K-means algorithm are simultaneously utilized for the framework to enhance the multi-scale ship detection capability. The Deep-SORT is employed to complete multi-ship feature matching. Secondly, the distances between two ships and their speeds are measured using the pinhole imaging principle based on the ship visual feature extraction results. Moreover, the ship distance-speed correction method is designed to improve the reliability of estimated results. Finally, the effectiveness of the framework is validated using naturalistic driving data from the “He Hua Hai” ship. The results show that the proposed framework could demonstrate an excellent performance in assessing ship collision risk using the onboard video data. The proposed framework could help precisely detect and promptly provide warnings about potential ship collision risks. This could help prevent catastrophic accidents that pose a threat to oceans and coasts, particularly in situations when AIS data proves to be unreliable or ineffective.

Evaluation of the effect of vitreomacular interface disorders on anti-VEGF treatment in patients with diabetic macular edema in real life: MARMASIA study group report No. 10.

The aim of this study is to investigate the effect of vitreomacular interface disorders (VMID) on treatment response in patients treated with anti-vascular endothelial growth factor (anti-VEGF) due to diabetic macular edema (DME). Three hundred seventy-seven eyes of 239 patients in the MARMASIA Study Group who received intravitreal anti-VEGF treatment (IVT) due to DME were included in the study. The group 1 consisted of 44 eyes of the patients who had not received any treatment before, were followed up regularly for 24months after at least a 3-month loading dose, and suffered from VMID such as epiretinal membrane, vitreomacular adhesion or traction, and lamellar hole. The group 2 consisted of 333 eyes of the patients without VMID. Best-corrected visual acuity (BCVA) and central macular thickness (CMT) of the patients at baseline, 3rd month, 6th month, 1st year and 2nd year follow-ups were measured. The mean age of the Groups 1 and 2 was 67.1 ± 11.3 and 61.9 ± 10.2 years, respectively. 61.3% of the group 1 and 58.8% of the group 2 were female (p > 0.05). The duration of diabetes was 19.2 ± 3.7 and 15.8 ± 3.2 years, respectively, and the number of follow-ups was 16.09 ± 4.68 and 12.06 ± 4.58, respectively in the groups (p < 0.001, 0.001, respectively). The number of IVT was 7.13 ± 2.71 and 7.20 ± 2.22, respectively in the groups 1 and 2 and no statistically significant difference was observed between them (p = 0.860). According to logMAR, BCVA values at baseline were 0.63 ± 0.24 and 0.59 ± 0.26 (p = 0.29), respectively, in the groups and the amount of change in BCVA at the end of the 2nd year was -0.02 ± 0.48 in the group 1 and -0.12 ± 0.48 in the group 2. It was observed as 0.48 (p = 0.13). Although the increase in BCVA was greater at all follow-ups in the group 2 compared to their initial examination, no significant difference was observed between the groups in terms of BCVA change. The CMT values of the groups at baseline were 442.5 ± 131.3µm and 590.9 ± 170.6µm, respectively (p = 0.03) The decrease in CMT after IVT was significantly greater in the group 2 at all follow-ups when compared to the first group (p < 0.05). While the presence of VMID in DME patients receiving IVT did not affect visual results, it negatively affected the anatomical response and macular edema morphology. The presence of VMID at baseline affected the success of IVT. It should be taken into consideration that VMID may resolve spontaneously or with IVT, and new cases of VMID may occur in patients during the treatment process.

Pseudo‐CT synthesis in adaptive radiotherapy based on a stacked coarse‐to‐fine model: Combing diffusion process and spatial‐frequency convolutions

AbstractBackgroundCone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction.PurposeA novel stacked coarse‐to‐fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial‐frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images.MethodsThe enhancement of low‐quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U‐ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo‐CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT‐CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing.ResultsTo evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross‐correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t‐test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p &lt; 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann–Whitney test (p &lt; 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation.ConclusionThe pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

Symantic Literatur Review Riset Etnomatematika Menggunakan Bibliometrix R Studio Bibblioshiny

The aim of this literature review is to analyze the trend of development of articles published by Scopus with a time span of 40 years from 1984-2024, as a material for study to forecast research that will be carried out by the next researcher. The method of writing this article uses a semantic literature review with the tools used is bibliometric analysis with R Studio biblioshiny. The stages of analysis activities by collecting articles through Scopus API Key with the keyword Ethnomathmematics found as many as 618 documents from journals, books, book chapters and others. Furthermore, an analysis of the results of data visualization from biblioshiny is carried out. The results of the study found that Indonesia,both authors and institutions, wrote many articles related to ethnomathematics. New themes were found that are relevant for ethnomathematics research that are linked to cryptography, curriculum, augmented reality and others, so that prospective researchers can link ethnomathematics with interdisciplinary studies

Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network

Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks.

MSI-DTrans: A multi-focus image fusion using multilayer semantic interaction and dynamic transformer

Multi-focus image fusion (MFIF) aims to utilize multiple images with different focal lengths to fuse into a single full-focus image. This process enhances the realism and clarity of the resulting image. In this paper, a MFIF method called MSI-DTrans was proposed. On the one hand, in order to fully utilize all the effective information that the source image carries, the proposed method adopts a multilayer semantic interaction strategy to enhance the interaction of high-frequency and low-frequency information. This approach gradually mines more abstract semantic information, guiding the generation of feature maps from coarse to fine. On the other hand, a parallel multi-scale joint self-attention computation model is designed. The model adopts dynamic sense field and dynamic token embedding to overcome the performance degradation problem when dealing with multi-scale objects. This enables self-attention to integrate long-range dependencies between objects of different scales and reduces computational overhead. Numerous experimental results show that the proposed method effectively avoids image distortion, achieves better visualization results, and demonstrates good competitiveness with many state-of-the-art methods in terms of qualitative and quantitative analysis, as well as efficiency comparison. The source code is available at https://github.com/ouyangbaicai/MSI-DTrans.

Immunothrombosis: A bibliometric analysis from 2003 to 2023.

Immunothrombosis is a physiological process that constitutes an intravascular innate immune response. Abnormal immunothrombosis can lead to thrombotic disorders. With the outbreak of COVID-19, there is increasing attention to the mechanisms of immunothrombosis and its critical role in thrombotic events, and a growing number of relevant research papers are emerging. This article employs bibliometrics to discuss the current status, hotspots, and trends in research of this field. Research papers relevant to immunothrombosis published from January 1, 2003, to May 29, 2023, were collected from the Web of Science Core Collection database. VOSviewer and the R package "Bibliometrix" were employed to analyze publication metrics, including the number of publications, authors, countries, institutions, journals, and keywords. The analysis generated visual results, and trends in research topics and hotspots were examined. A total of 495 target papers were identified, originating from 58 countries and involving 3287 authors from 1011 research institutions. Eighty high-frequency keywords were classified into 5 clusters. The current key research topics in the field of immunothrombosis include platelets, inflammation, neutrophil extracellular traps, Von Willebrand factor, and the complement system. Research hotspots focus on the mechanisms and manifestations of immunothrombosis in COVID-19, as well as the discovery of novel treatment strategies targeting immunothrombosis in cardiovascular and cerebrovascular diseases. Bibliometric analysis summarizes the main achievements and development trends in research on immunothrombosis, offering readers a comprehensive understanding of the field and guiding future research directions.