Visual Description Research Articles

An integrated, non-destructive method for evaluating core quality from R/V Legend’s giant piston coring system (LGD-GPC)

Despite technological advances, some core disturbances due to piston coring process still occur and can be extremely difficult to identify characteristics of sedimentary structures during qualitative visual core description and core lithologies. Few quantitative methods have offered plausible ways to define the exact magnitude of the distorted sedimentary structures in imperfect sedimentary records in the context of overly limited sedimentary records. In this study, an investigation was conducted on the Kaoping slope offshore southwestern Taiwan, a region known for its high sedimentation rate (> 0.9 g/cm2/year), using both the LGD-GPC (giant piston corer) and giant gravity corer techniques. To examine how GPC procedures have shaped the retrieval of sedimentary sequences and affected core quality, we apply well-developed, non-destructive analyses, including physical property measurements, visible color reflectance and digital core images with quantitative curvature index (CI), to recovered sediments. Cores at 22° 23′N, 120° 13′E were as the basis for non-destructive measurement comparison. Evidence from digital core imaging and the CI calculation further supports the mechanism of piston suction. We suggest that shear deformation of sediment against the core barrel would be the prominent factor that govern the observed core disturbances in LGD-GPC cores. Meanwhile, excessive pulling forcing from a modified TORI piston and a slowly lift speed during LGD-GPC coring process may further aggravate core disturbances at the revisited site. The sedimentary length of core LGD-T43-B-PC is overestimated by more than 200%. We further demonstrate that an integration of the CI and color reflectance is an effective way to evaluate the transformation and distorted sedimentary sequences.

Visual Representation of the Stress Distribution with a Color‐Manipulated Mechanoluminescence of Fluoride for Structural Mechanics

AbstractMechanoluminescence (ML) materials are capable of converting applied stress into luminescent signals, and offer fascinating potential applications in biomedicine, safety testing, structural diagnosis, and mechanical sensing. Color‐responsive ML sensing, combined with quantitative evaluation of loaded stress, is preferred for visual description and sensing reliability. In this work, Yb2+ and Mn2+ ions co‐doped MgF2 with a fascinating ratiometric ML performance are successfully explored. ML color can be manipulated based on the distinct stress‐dependent ML intensity of the activators. Furthermore, a self‐activated orange persistent ML is observed even after the removal of the mechanics stimuli, allowing a delayed stress sensing with the memorized optical signals. Accordingly, a visualized stress sensing with the as‐explored ML composites using the ultraviolet curing 3D printing technique is demonstrated for structural mechanics. Herein, a feasible optical description of the loaded stress distribution in space for objects free from concerns with dimensions and geometrical shapes can be realized, benefiting the study of 3D structural mechanics, especially for complex structures.

Rheological behavior of polyvinylidene fluoride/dimethylacetamide solutions: Experimental investigation and machine learning modeling

This study aimed to investigate the rheological properties of polyvinylidene fluoride/dimethylacetamide solutions. The influences of shear rate, temperature, and additives fractions on solution viscosity were experimentally studied. The results indicated that the viscosity is noticeably reduced by increasing the temperature at low shear rates, while the temperature dependence of viscosity was diminished at high levels of shear rate. On the other hand, adding water to the solution tended the viscosity to increase, especially at lower shear rates and temperatures. Furthermore, an exponential enhancement was observed in the viscosity by growing the inorganic/organic additive ratio. The measured data were employed to design predictive methods for solution viscosity by defining six factors including temperature, shear rate, and the fractions of solvent and additives as input variables. The modeling phase was carried out by implementing three computer-aided approaches, among which the radial basis functions (RBF) method overcame the others from the standpoint of accuracy and reliability with total average absolute relative errors and R2 values of 1.29% and 99.86%, respectively, for 1064 measured data. Also, a visual description through the contour diagram affirmed that the RBF model performs excellently in the suit of describing the rheological behavior of the polymeric solution under various operating conditions. Eventually, the factors with the greatest influences on solution viscosity were designated through a sensitivity analysis based on the recently proposed model.

Unified Transformer With Isomorphic Branches for Natural Language Tracking

Natural language tracking aims to localize the target object referred to by a language description using a sequence of bounding boxes in video frames. Compared with traditional visual single object tracking initialized with only a bounding box (BBox), this task introduces high-level semantic information to reduce the ambiguity of BBox and enhance the ability to retrieve the target in a global manner. Thus, it can yield more accurate and robust tracking results. Previous methods usually adopt off-the-shelf grounding and tracking branches to tackle this task, where feature representations are learned in isolation without benefiting each other. The two branches can associate with each other to discover crucial clues since the language description and the template image provide information from different sources. Therefore, we propose a unified transformer method for natural language tracking named TransNLT, which utilizes isomorphic Transformer structures for grounding and tracking branches where collaborative learning is enabled to construct comprehensive features for the target. In addition, we propose a Selective Feature Gathering (SFG) module which can integrate cross-modal global information of the tracking target from the visual template and language description. Through effective interaction of visual and language information, we can achieve better results than tracking with only a single modality. Extensive experiments on three popular natural language tracking benchmarks show our proposed TransNLT outperforms previous state-of-the-art methods.

The development of a method for visualizing the states of the national security system

The scientific task, which is solved in the research, is the cognitive display of the state of the national security system with a complex hierarchical structure. As a rule, images are created individually taking into account a specific application field and interpreted by an expert (a group of experts) based on accumulated knowledge. Cognitive mapping is designed to support decision making by an expert (group of experts), monitoring and managing in real time. The object of research is the system of ensuring national security. The subject of the research is the functioning of the national security system. The research developed a method of visualization of the states of the national security system. An overview of the methods of visual graphic presentation of information about the state of multidimensional objects and systems was carried out. The novelties of the proposed method are: ‒ creation of a visual, multi-level and interconnected description of the national security system; ‒ increasing the efficiency of decision making while assessing the state of the national security system; ‒ solving the problem of falling into global and local extremes while assessing the state of the national security system; ‒ combination of graphic and numerical display of controlled state parameters of the national security system; ‒ avoiding the problem of loops while visualizing the state of the national security system in real time. The specified method should be implemented in specialized software, which is used to analyze the state of the national security system and make management decisions.

Artifacts resembling Ediacaran or Cambrian fossils: how to identify them and avoid their generation

Abstract. The generation of artifacts during sample preparation must be considered in paleobiological studies, particularly during the Ediacaran and Cambrian, since such artifacts can assume forms similar to those of cloudinids and other problematic taxa commonly described in samples from these systems. Chemical reactions between hydrogen peroxide and sulfides from the samples can lead to the formation of tubular and vase-shaped structures. The visual description alone does not allow a conclusion about whether their origin is organic or inorganic. In these cases, chemical composition and ultrastructure analysis are tools that help to distinguish artifacts from bona fide fossils. Scanning electron microscopy can be successfully employed to characterize and differentiate fossils from artifacts. The presence or absence of these structures in thin sections is also an essential piece of information to discuss their biogenicity. Furthermore, not using hydrogen peroxide avoids the risk of formation of the artifacts described here.

A dual-path residual attention fusion network for infrared and visible images

Infrared image contains typical targets characterized by pixel intensity distribution, while visible image preserves structural details represented by edges and gradient. Infrared and visible image fusion aims to merge their respective key information, and produce a more comprehensive image with superior target perception and better detail representation. Most existing methods cannot reach this goal well, and are easy to generate a limited fused result. Towards this end, a dual-path residual attention fusion network, namely DRAFusion, is proposed in this paper. We construct a multi-scale dense network architecture, which adequately aggregates horizontal and vertical intermediate features at all scales and layers. In the fusion layer, we develop a residual attention fusion module, cascading channel-wise and spatial-wise attention models, which is used to model the global contextual dependencies, and interact with feature maps to more focus on their respective key information. In addition, we design a novel feature adaptation loss function to control the proportion of their key information in the training phase, which can keep a better balance between fused results and source images. Extensive experiments demonstrate that our DRAFusion produces a remarkable performance on the three testing datasets, and is superior to other methods in terms of qualitative visual description and quantitative evaluation validation.

Aging characteristics of colorectal cancer based on gut microbiota.

Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age-differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age-differentiated bacteria. The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age-related differential bacteria and model of CRC patients based on the 58 age-related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease-differentiated bacteria and the age-differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.

Models of waveguides combining gradient and nonlinear optical layers

Objectives. Theoretical studies of the waveguide properties of interfaces between nonlinear optical and graded-index media are important for application in optoelectronics. Waveguides combining layers with different optical properties seem to be the most promising, since they can be matched to optimal characteristics using a wide range of control parameters. The paper aims to develop a theory of composite optically nonlinear gradedindex waveguides with an arbitrary profile, within which it is possible to obtain exact analytical expressions for surface waves and waveguide modes in an explicit form. The main feature of the theory proposed in this paper is its applicability for describing surface waves and waveguide modes, in which the field is concentrated inside the gradient layer and does not exceed its boundary, avoiding contact with the nonlinear layer.Methods. Analytical methods of the theory of optical waveguides and nonlinear optics are used.Results. A theoretical description of the waveguide properties of the interface between two media having significantly different optical characteristics is carried out. The formulated model of a plane waveguide is applicable to media having an arbitrary spatial permittivity profile. An analytical expression describing a surface wave propagating along the interface between a medium having stepwise nonlinearity and a gradient layer with an arbitrary permittivity profile is obtained. Additionally, analytical expressions for surface waves propagating along the interface between a medium with Kerr nonlinearity (both self-focusing and defocusing), as well as graded-index media characterized by exponential and linear permittivity profiles, are obtained.Conclusions. The proposed theory supports a visual description in an explicit analytical form of a narrowly localized light beam within such waveguides. It is shown that by combining different semiconductor crystals in a composite waveguide, it is possible to obtain a nonlinear optical layer on one side of the waveguide interface and a layer with a graded-index dielectric permittivity profile on the other.

Semantic Context and Attention-driven Framework for Predicting Visual Description Utilizing a Deep Neural Network and Natural Language Processing

Background/Purpose: This literature review's goal is to inspect various machine learning algorithms for visual description and their applications to prediction. Examining the numerous approaches mentioned in this area brings up a fresh avenue for expanding the current research methods. Design/Methodology/Approach: The study results that are provided in different scholarly works are acquired from secondary sources, such as scholarly journal publications. This review study analyses these articles and highlights their interpretations. Findings/Result: This research focuses on several cataloguing methods for isolated identifying images and visions. When developing research topics in the idea of inaccessible detecting geographic information systems, the gaps discovered during analysis using various methodologies have made things simpler. Research limitations/implications: This study examined a range of AI tool uses. The scope of this work is rivetted to a assessment of the many machine-learning implementation strategies for analysis and prediction. More research might be done on the many deep learning constructions for image and video classification. Originality/Value: The articles chosen for this study's review are from academic journals and are cited by other authors in their works. The articles that were selected for the examination have a connection to the investigation and research plan described in the paper. Paper Type: Literature review paper.

The Entangled Intersection of Children’s and Educators’ Engagement in the Educational Environment of a Preschool

AbstractThe study presented in this article shows some aspects of how children between the ages of three and five and their educators perceive and describe the educational environment in early childhood education in Sweden. The data is generated using pedagogical walk-throughs with the educators and camera tours together with the children. The study is conducted as a case study with three municipalities as cases and shows how materiality, places and relationships appear in the intersection of the educators’ and children’s verbal and visual descriptions of the indoor educational environment. We put diffractive readings to work to follow the entangled inter-connections and the intra-acting agency of the sociomaterial assemblages. These assemblages consist of different types of materiality, spatial conditions, the educators’ verbal descriptions, the children’s photographs and their verbal and bodily actions when using the cameras. The findings suggest that children and educators use, engage in and relate to the educational environment and materiality in both different and similar ways. Moreover, the children transform the educational setting organised by the educators as place changers, while materiality constructs places as placemaking materiality.

Proteoglycans in Articular Cartilage and Their Contribution to Chondral Injury and Repair Mechanisms.

Proteoglycans are vital components of the extracellular matrix in articular cartilage, providing biomechanical properties crucial for its proper functioning. They are key players in chondral diseases, specifically in the degradation of the extracellular matrix. Evaluating proteoglycan molecules can serve as a biomarker for joint degradation in osteoarthritis patients, as well as assessing the quality of repaired tissue following different treatment strategies for chondral injuries. Despite ongoing research, understanding osteoarthritis and cartilage repair remains unclear, making the identification of key molecules essential for early diagnosis and effective treatment. This review offers an overview of proteoglycans as primary molecules in articular cartilage. It describes the various types of proteoglycans present in both healthy and damaged cartilage, highlighting their roles. Additionally, the review emphasizes the importance of assessing proteoglycans to evaluate the quality of repaired articular tissue. It concludes by providing a visual and narrative description of aggrecan distribution and presence in healthy cartilage. Proteoglycans, such as aggrecan, biglycan, decorin, perlecan, and versican, significantly contribute to maintaining the health of articular cartilage and the cartilage repair process. Therefore, studying these proteoglycans is vital for early diagnosis, evaluating the quality of repaired cartilage, and assessing treatment effectiveness.

Fleet Profile: Using visual analytics to prospect logistic solutions in industrial vehicles fleet

Fleet planning and management activities are essential to establish the quantity and type of vehicles needed to pursue production plans and reduce costs. In steel companies, logistic analysts are responsible for these fleet activities. However, analysts still face challenges assessing fleet utilization due to the volume and form in which the data is found. Normally, this type of problem is out of the scope of fleet planning models in the literature. For information extraction from massive and complex databases, visual analytics is an alternative that employs data analysis and visualization techniques. This research investigates visual analytics to support industrial vehicle fleet management. As a result, two artifacts were developed: a fleet measurement model and Fleet Profile, a visual analytics solution. Two research cycles, each one producing a Fleet Profile version that was evaluated with real cases after the development. Findings from the analysis of the evaluation contents indicated that the Fleet Profile’s visual description could help to evaluate fleet utilization and identify gaps for fleet optimization.

Three Principles of Intellectualization of Automated Systems

Three Principles of Intellectualization of Automated Systems

Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems.

Semantic-Aligned Cross-Modal Visual Grounding Network with Transformers

Multi-modal deep learning methods have achieved great improvements in visual grounding; their objective is to localize text-specified objects in images. Most of the existing methods can localize and classify objects with significant appearance differences but suffer from the misclassification problem for extremely similar objects, due to inadequate exploration of multi-modal features. To address this problem, we propose a novel semantic-aligned cross-modal visual grounding network with transformers (SAC-VGNet). SAC-VGNet integrates visual and textual features with semantic alignment to highlight important feature cues for capturing tiny differences between similar objects. Technically, SAC-VGNet incorporates a multi-modal fusion module to effectively fuse visual and textual descriptions. It also introduces contrastive learning to align linguistic and visual features on the text-to-pixel level, enabling the capture of subtle differences between objects. The overall architecture is end-to-end without the need for extra parameter settings. To evaluate our approach, we manually annotate text descriptions for images in two fine-grained visual grounding datasets. The experimental results demonstrate that SAC-VGNet significantly improves performance in fine-grained visual grounding.

PERANCANGAN ILUSTRASI SAMPUL BUKU NOVEL ANNE OF AVONLEA

Book cover illustrations were used to provide a visual description of the contents of the book. Anne of Avonlea was one of the translated novels that needs a new illustration for the book cover. By creating a new illustration, it was expected that the appearance of the book would be more attractive and would be able to attract consumers to buy the book. In the process, the making of this novel illustration used the methods of orientation, analysis, concepts, design, and implementation in stages. The results obtained were the illustration implemented in the printed book products of Anne of Avonlea.

Long COVID: a review and proposed visualization of the complexity of long COVID.

Post-Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV-2) infection, or Long COVID, is a prevailing second pandemic with nearly 100 million affected individuals globally and counting. We propose a visual description of the complexity of Long COVID and its pathogenesis that can be used by researchers, clinicians, and public health officials to guide the global effort toward an improved understanding of Long COVID and the eventual mechanism-based provision of care to afflicted patients. The proposed visualization or framework for Long COVID should be an evidence-based, dynamic, modular, and systems-level approach to the condition. Furthermore, with further research such a framework could establish the strength of the relationships between pre-existing conditions (or risk factors), biological mechanisms, and resulting clinical phenotypes and outcomes of Long COVID. Notwithstanding the significant contribution that disparities in access to care and social determinants of health have on outcomes and disease course of long COVID, our model focuses primarily on biological mechanisms. Accordingly, the proposed visualization sets out to guide scientific, clinical, and public health efforts to better understand and abrogate the health burden imposed by long COVID.

Dirty Windows and Troublesome Things: The Problem of Object-Orientation in Alain Robbe-Grillet’s La Jalousie

This article investigates the representation of objects in La Jalousie (1957), a novel in the nouveau roman tradition written by French novelist Alain Robbe-Grillet. If the ‘new novel’ sought to render the material world with objective clarity, and positioned itself against traditional fiction, with its reliance on metaphor, allegory, and other ‘projections,’ this article argues that such an aesthetic program is undercut by its own assumptions about the power of description and the primacy of the visual. In an analysis which hybridizes three separate strands of criticism—object-oriented ontology, Heideggerian phenomenology, and the models of ‘resonation’ proposed by Brian Massumi—I will argue that such a treatment of objects, with its exclusive reliance on visual description, measurement, and enumeration, ends up depriving objects of the vitality and dynamism that would justify such a fictional project in the first place. However, traces of this dynamism do survive the flattening sweep of Robbe-Grillet’s narration, and indeed offer from the cracks and fissures of the novel’s otherwise smoothly controlled style the possibility of an alternate ‘object-orientation’—one, I will argue, which suspends its cool optical detachment to allow, however briefly, the eruption of a messy, entangling register of touch.

RTFusion: A Multimodal Fusion Network with Significant Information Enhancement.

Multimodal medical fusion images are important for clinical diagnosis because they can better reflect the location of disease and provide anatomically detailed information. Existing medical image fusion methods can cause significant information loss in fusion images to varying degrees. Therefore, we designed a residual transformer fusion network (RTFusion): a multimodal fusion network with significant information enhancement. We use the residual transformer to make the image information interact remotely to ensure the global information of the image and use the residual structure to enhance the feature information to prevent information loss. Then the channel attention and spatial attention module (CASAM) is added to the fusion process to enhance the significant information of the fusion image, and the feature interaction module is used to promote the interaction of specific information of the source image. Finally, the loss function of the block calculation is designed to drive the fusion network to retain rich texture details, structural information, and color information, to optimize the subjective visual effect of the image. Extensive experiments show that our method can better recover the significant information of the source image and outperform other advanced methods in subjective visual description and objective metric evaluation. In particular, the color information and texture information are balanced to enhance the visual effect of the fused image.