Perceptual Information Research Articles

Art Exhibition as a Factor in the Development of Visual Culture of Future Artists-teachers in the Educational Process

This study is devoted to the study of the role of art exhibitions in the professional training of future artist-teachers. Thanks to the exhibition, interactions between artist-teachers and viewers occur. In addition, the process of visualization of modern culture is currently rapidly gaining momentum, influencing the perception of information. In these conditions, the issue of developing the visual culture of the individual is relevant, which is understood as the ability to competently perceive art, visual forms and images, based on knowledge of various traditions and the history of world artistic culture. In this context, the exhibition becomes an important tool for gaining experience in reading new images and meanings of genuine works of art. The question of developing the visual culture of future artist-teachers by means of art exhibitions opens new horizons for studying the interaction of art and education. The purpose of the article. The purpose of the article is to reveal the role of the exhibition not only in the opportunity for the artist to demonstrate his work to a wide audience of visitors, but also in building a non-verbal dialogue, helping to convey the ideas, thoughts, and vision of the author. Methodology and research methods. The methodology of the study examines the main characteristics of visual culture from the standpoint of research in the field of cultural studies, psychological and pedagogical sciences. Methodically, using the analysis and synthesis of theoretical research, the relationship between an art exhibition and the development of visual culture of future artist-teachers is revealed. Results of the study. The article presents the results of a theoretical and practical study of the role of art exhibitions for students majoring in art and pedagogy. They were tested in holding exhibitions within the framework of student scientific and practical conferences at the Department of Decorative and Applied Arts and Design of KubSU in 2022-2024. Conclusion. The conclusion formulates general conclusions about the role of an art exhibition in the professional education of artists-teachers, in the development of visual culture as a personal quality expressed by knowledge of traditions in art and understanding of the multidimensionality of the modern cultural environment.

Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background

Tea disease detection is of great significance to the tea industry. In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. Finally, the map values of 89.7% and 68.5% were obtained on a self-made tea data set and a public tea disease data set, which were improved by 3.9% and 4.3%, respectively, compared with the original benchmark model, and the reasoning speed of the model was 164.3 fps. Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes.

The interaction of task complexity and gender on mental rotation skills in primary school students

The perception and manipulation of spatial information are essential for mathematical learning, and research highlights gender differences in spatial abilities. The present study contributes to the question of whether these differences are evident at earlier ages and how they interact with task complexity in mental rotation. We developed the ‘MEntal Reflection and ROtation’ (MERRO) test for group testing in elementary schools, distinguishing between difficulty- and complexity-generating factors, such as context, dimensionality, rotation angle, and number of rotation axes. A pre-study with 148 secondary school students validated the MERRO as suitable for assessing mental rotation skills with another available instrument appropriate for that age group. The main study, involving 387 primary school students (grade 1-grade 4), identified difficulty-generating and complexity-generating factors and investigated potential early gender differences in spatial abilities. Results revealed that stimulus characteristics significantly impact task complexity, and that these characteristics partly interact with gender. These findings offer new insights into the nuanced development of spatial abilities in early mathematics education.

Multi-dimensional visual information processing under complex light environments using time-evolved polarization-sensitive synaptic electronics

Biological vision system-inspired optoelectronic synapses integrate sensing, memory, and processing for external information perception. However, most efforts focus on spatial expansion while overlooking critical dimensions like polarization and temporal evolution, which are critical for information extraction in complex environments. Inspired by the polarization-sensitive properties of kingfisher vision, we develop a polarization-sensitive optoelectronic synapse array device based on PEA2SnI4 microwires array. Their anisotropic properties ensure polarization recognition, achieving a dichroic ratio of 1.38. And the asymmetric electrode designs create differentiated contact barriers, facilitating efficient charge storage and erasure under low power consumption. By employing four polarization-state-dependent convolutional kernels, the device demonstrates edge extraction capabilities even under 50% salt pepper noise. Furthermore, it enables high-precision in-sensor reservoir computing, with 100% accuracy in extracting fish trajectories under complex light environments. This work demonstrates motion perception in complex environments and provides a foundation for developing multi-dimensional, time-resolved visual systems for intelligent sensing and recognition.

Discurso de Salud en la Pandemia: Revisión de Estudios sobre Medios Tradicionales y Redes Sociales en Colombia

This review article analyzes recent research addressing health discourses during the first phase of the COVID-19 pandemic, which lasted from March to May 2020 in Colombia. The aim was to delve deeper into the analysis of discourse in Colombian television media and how these discourses were projected on social networks such as Facebook. The main objective was to analyze the predominant discourses, how some actors immersed in this scenario were represented, the interactions between users of the digital world, and the public perception of information. Throughout the research, an informational framework focused on the change produced by the virus and the exaltation of health personnel and entities that contributed to the protection of the population was evident. Also, practices of misinformation or omission in digital communication were evident. One of the most important conclusions is that although there is a significant amount of research regarding the influence of COVID-19 in various settings, especially in education, significant gaps persist in the analysis of audiovisual discourse during the pandemic and its reception on platforms such as Facebook. Therefore, this article focuses on mapping research focused on health discourses during the pandemic and its journalistic coverage.

Thangka Element Semantic Segmentation with an Integrated Multi-Scale Attention Mechanism

Thangka images contain complex and interconnected elements that often share similar hues. Traditional segmentation algorithms struggle to capture fine details, which renders them poorly adaptable to this characteristic of Thangka images. Additionally, the highly saturated and high-contrast color features in Thangka images can induce issues such as “over-segmentation” or “edge misclassification” during the segmentation process. This paper proposes a semantic segmentation approach that integrates HRNet with a multi-scale convolutional attention mechanism to improve the accuracy of detail segmentation in Thangka imagery. A Coordinate Attention mechanism is integrated into the bottleneck section of HRNet to enhance the model’s perception of spatial and channel information. After feature extraction, a SENet module is appended to improve the model’s ability to adaptively adjust channel weights, whereas the HRNet framework is streamlined to optimize overall network efficiency. Moreover, an atrous convolutional attention mechanism module is used to capture and fuse multi-scale features from the processed feature maps, achieving more precise semantic segmentation. Compared with the original model, the modified model achieves a 32% reduction in parameter volume. Concurrently, the mIoU improves from 80.33% to 84.83% on the Thangka dataset and from 77.04% to 78.58% on the Cityscapes dataset.

Using Podcasts as an Innovative Method of Environmental Education

Modern environmental education requires innovative approaches to teaching for several reasons. First, environmental issues are becoming increasingly complex and multifaceted. Traditional methods that focus only on the transfer of knowledge may not be sufficient to understand these problems and develop skills to solve them. Second, today’s youth are more inclined to visual and digital learning formats. Innovative interactive methods based on multimedia technologies may be more effective for this generation. Third, such methods help make environmental education more relevant and meaningful. They allow us to demonstrate the impact of environmental issues on the lives of students and the possibilities of personal contribution to their solution. The use of podcasts as part of modern educational technologies in the field of ecology complements traditional teaching methods, offering flexibility and accessibility to education, and can play a key role in expanding knowledge and building an informed public on environmental issues. However, despite the growing popularity of podcasts in the educational process, there is still limited evidence on their effectiveness in improving environmental learning. In our study, we focused on analysing the comparative perception of information by students in different formats – podcasts, presentations and text documents – using such methods as experimental research and surveys. The analysis included an assessment of the quality of learning, retention, usability, student preferences, flexibility of learning and suitability for practical application. Our experimental study with students revealed that podcast users achieved the highest test score (84.7%) compared with presentation (82.5%) and text document groups (81.2%). Survey results showed that 97% of students valued learning flexibility with podcasts, 87% reported increased engagement, and 80% considered podcasts effective learning tools. Additionally, 99% of students agreed that podcasts improved their understanding of lectures. The findings help to develop recommendations for integrating different learning formats into the educational process and adapting teaching methods to the needs of students. As an innovative teaching tool, eco-podcasts offer considerable flexibility in disseminating science-based information, ease of use, and the ability to reach a wider audience, contributing to the development of communication skills in the environmental community.

A Microwave Vision-Enhanced Environmental Perception Method for the Visual Navigation of UAVs

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions of fluctuating illumination and persistent cloud cover. To address this challenge, this paper introduces microwave vision to assist optical vision for environmental measurement and proposes a novel microwave vision-enhanced environmental perception method. In particular, the richness of perceived environmental information can be enhanced by SAR and optical image fusion processing in the case of sufficient light and clear weather. In order to simultaneously mitigate inherent SAR speckle noise and address existing fusion algorithms’ inadequate consideration of UAV navigation-specific environmental perception requirements, this paper designs a SAR Target-Augmented Fusion (STAF) algorithm based on the target detection of SAR images. On the basis of image preprocessing, this algorithm utilizes constant false alarm rate (CFAR) detection along with morphological operations to extract critical target information from SAR images. Subsequently, the intensity–hue–saturation (IHS) transform is employed to integrate this extracted information into the optical image. The experimental results show that the proposed microwave vision-enhanced environmental perception method effectively utilizes microwave vision to shape target information perception in the electromagnetic spectrum and enhance the information content of environmental measurement results. The unique information extracted by the STAF algorithm from SAR images can effectively enhance the optical images while retaining their main attributes. This method can effectively enhance the environmental measurement robustness and information acquisition ability of the visual navigation system.

MCINet: a deep fusion network based on multi-scale context integration for industrial inspection

Abstract Automatic industrial inspection of products has become a key task for improving quality and efficiency in industrial production. Researchers have conducted in-depth research on this task using semantic segmentation technology. However, due to the characteristics of industrial defects, such as diversity, low contrast, blurred boundaries and anisotropic background texture, there are still challenges in small target and fuzzy boundary defect detection. This paper proposes a deep fusion network based on multi-scale context integration (MCINet) for pixel-level industrial inspection. A contextual aggregation pooling module is proposed as an encoder to extract multi-scale defect features effectively. To enhance context information in the decoder, a feature deep fusion module is introduced to carry out information exchange between features at different levels. Moreover, a spatial information perception module is proposed to enable the decoder to focus dynamically for accurate defect location and segmentation. Based on the above modules and the backbone, an efficient coding-decoding industrial defect detection network model is proposed. Extensive experiments on three challenging industrial defect datasets demonstrate that MCINet achieves competitive performance compared to state-of-the-art methods, while balancing accuracy and efficiency.

Flexi-YOLO: A lightweight method for road crack detection in complex environments

Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. The DCNv-C2f module is constructed for the transformation and fusion of feature information, allowing the convolutional kernels to adapt to the complex shape characteristics of cracks dynamically. A Global Attention Module (GAM) is integrated to improve the model’s perception of global information. The AKConv convolution operation is employed to adaptively adjust the size of convolutions, further enhancing local feature capturing. Additionally, a lightweight network design is implemented, establishing G-Head (Ghost-Head) as the detection head to optimize the issue of feature redundancy. Experimental results show that Flexi-YOLO achieves an accuracy increase of 2.7% over YOLOv8n, a recall rate rise of 4.7%, a mAP improvement of 5.3%, a mAP@0.5–0.95 increase of 3.9%, a decrease of 0.5 in GFLOPS, and an F1 score improvement from 0.80 to 0.84. Flexi-YOLO offers higher detection accuracy and robustness and meets the industrial demands for lightweight real-time detection and lower application costs, providing an efficient and precise solution for the automated detection of road cracks.

Flexi-YOLO: A lightweight method for road crack detection in complex environments.

Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model's loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. The DCNv-C2f module is constructed for the transformation and fusion of feature information, allowing the convolutional kernels to adapt to the complex shape characteristics of cracks dynamically. A Global Attention Module (GAM) is integrated to improve the model's perception of global information. The AKConv convolution operation is employed to adaptively adjust the size of convolutions, further enhancing local feature capturing. Additionally, a lightweight network design is implemented, establishing G-Head (Ghost-Head) as the detection head to optimize the issue of feature redundancy. Experimental results show that Flexi-YOLO achieves an accuracy increase of 2.7% over YOLOv8n, a recall rate rise of 4.7%, a mAP improvement of 5.3%, a mAP@0.5-0.95 increase of 3.9%, a decrease of 0.5 in GFLOPS, and an F1 score improvement from 0.80 to 0.84. Flexi-YOLO offers higher detection accuracy and robustness and meets the industrial demands for lightweight real-time detection and lower application costs, providing an efficient and precise solution for the automated detection of road cracks.

A Graph Convolutional Network Framework for Area Attention and Tracking Compensation of In-Orbit Satellite

In order to solve the problems of low tracking accuracy of in-orbit satellites by ground stations and slow processing speed of satellite target tracking images, this paper proposes an orbital satellite regional tracking and prediction model based on graph convolutional networks (GCNs). By performing superpixel segmentation on the satellite tracking image information, we constructed an intra-frame superpixel seed graph node network, enabling the conversion of spatial optical image information into artificial-intelligence-based graph feature data. On this basis, we propose and build an in-orbit satellite region of interest prediction model, which effectively enhances the perception of in-orbit satellite feature information and can be used for in-orbit target prediction. This model, for the first time, combines intra-frame and inter-frame graph structures to improve the sensitivity of GCNs to the spatial feature information of in-orbit satellites. Finally, the model is trained and validated using real satellite target tracking image datasets, demonstrating the effectiveness of the proposed model.

Foveal crowding modifies a target's properties under a brief presentation time

The perception of chromatic and achromatic visual information is combined and processed in the parvocellular stream; however, they are separate processes at the early stage of the visual cortex. In our previous study, we noted that there is difficulty discriminating the color of a letter target presented at the fovea under a crowded presentation for a short time. Visual crowding occurs when an easily identified isolated stimulus becomes very difficult to identify when it is surrounded by stimuli with similar properties. One opinion is that crowding reduces the ability to identify the target but not its features (e.g., color and texture); however, some studies indicated that the ability to recognize features is also impaired under peripheral crowding conditions. Here, we investigated whether the processing of chromatic information can be impaired at the fovea using a classic crowding experiment when tested at brief presentation times (20, 40, and 120 ms). The participants reported both the target's identity and chromaticity (dual task). We found that the target's identification and color discrimination are impaired when presented for 20–40 ms but that they recover for longer presentation times. This effect is increased when temporal backward masking is added. This finding suggests that crowding resembles masking under brief presentation times and occurs at a later processing stage, after an initial masking stage.

The interplay of sensory and motoric information on processing emotions in narrative texts

Abstract This study examines the comprehension of characters’ emotional states in written narrative texts. Recent theories of embodied cognition suggest that emotions are grounded in somatosensory information involving ‘reexperiencing’ of actions and perceptions. In addition, views of graded embodied cognition suggest that there are levels of the embodiment of language, focusing on how perceptual and motor information interact during text comprehension. In the present experiment, one-paragraph stories were written to express six basic emotions: fear, sadness, anger, disgust, happiness, and surprise. Four stories were written for each emotion and for each story four critical sentences were composed. The critical sentences comprised a combination of emotion- and action-based components that matched or mismatched the story. Participants read the stories and the critical sentences. Their task was to respond to a question about the emotional state of the story’s main character, while we measured response latencies and errors. The results of the error rate analyses suggest that, while reading texts, sensory knowledge about characters’ emotional states is activated, but this knowledge is significantly moderated by action-based knowledge. A computational model was used to further confirm these results. The model was trained to predict emotion and action words from critical sentences using linguistic context from stories. Both, the action and emotional words activations showed a distinct effect on participants’ comprehension accuracy.

Цифровой двойник судна в период эксплуатации и оценка возможности его использования в целях построения и реализации траектории морских автономных надводных судов

The concept of digital twins, which is the basis and one of the conditions of the fourth industrial revolution in both the general industry and the marine industry, is considered and summarized. The terminology of the concept is analyzed, including many definitions of the term “digital twin”, as well as various approaches to the implementation of this concept in practice. Various types of digital twins, which were introduced by the founder of the concept M. Reeves, are considered: the digital twin of the prototype, the digital twin of the sample, the aggregated digital twin and the operational digital twin. The principles of building a ship's digital twin are substantiated, including individuality, identity, and the need to use mathematical models, machine learning, and artificial intelligence in its structure. A scheme of functioning of a ship's digital twin during operation is constructed, which contains a physical space – a ship, sensors, vessels of the same type, target vessels; navigation space; virtual space – digital twins of ship components, processes, navigation space, other vessels, as well as an operational digital twin designed for targeted information processing. The problems of integrating the concept of digital twins into the development of marine autonomous surface vessels (MASV) are identified, related to the need to store and process a large amount of data, the lack of generally accepted standards for such data, as well as the problem of adequate perception of information by an external captain during remote control of the vessel. In the context of the evolution of the MASV, a predictive assessment of the role of digital twins in the functioning of autonomous vessels in various periods of their evolution has been made, with an emphasis on the task of constructing and implementing the trajectory of the MASV.

Evaluating virtual reality as a tool for improving surgical planning in spinal tumors.

Surgical planning is essential before the surgery, especially for spinal tumors resection. During the surgical planning, medical images are analyzed by surgeon on a standardized display mode using a computer. But this display mode has its limit in term of spatial perception of the anatomical structures. Our purpose in this study is to assess the impact of using another display mode like virtual reality (VR) on the surgical planning of spinal tumors resection by comparing VR with conventional computer-based visualization. A user study was conducted with eight neurosurgeons, who planned six spinal tumor surgeries using both VR and computer visualization modalities. The evaluation focused on the perception of anatomical-functional information from medical images, the identification of anatomical structures, and the selection of surgical approaches represented by the number of anatomical structures traversed to attend the tumor. These parameters were assessed using objective questionnaires developed from a work domain analysis (WDA) already proved in brain surgery. We then adapted the WDA to spinal surgery. VR made it easier to perceive a greater number of anatomical-functional information compared to computer visualization. Surgeons identified a greater anatomical structure with VR compared to computer visualization. Furthermore, surgeons selected additional anatomical structures to be traversed to reach the tumor when using VR, leading to a more precise selection of surgical approaches. These findings can predict the added value of VR in helping surgical decision-making when planning surgery. VR can be a promising tool for surgical planning by providing an immersive and interactive perspective that enhances understanding of anatomy. However, our finding is from an exploratory study, more clinical cases should be conducted to demonstrate its feasibility and reliability.

Dynamics and adaptation of translation in excursion activities: cultural gaps, localization and intercultural communication

The article is devoted to the study of the dynamics and adaptation of oral translation in excursion activities, taking into account the localization of cultural realities, the elimination of information gaps and ensuring effective intercultural communication. Particular attention is paid to the analysis of various types of excursions (historical, ethnographic, gastronomic, industrial) and the specifics of translation associated with each of them. The purpose of the study is to identify key strategies for the adaptation of oral translation that improve the quality of excursion services for foreign tourists, especially in the context of excursions in the North-East of Russia. The methodological basis of the study includes a comparative analytical method, a discourse analysis method. As empirical material, excursion texts related to the natural features of the region, the traditional way of life of the Evens, Koryaks, Itelmens, the history of the development of Kolyma are considered. The influence of translation on the perception of information by tourists is analyzed and typical errors leading to communication failures are identified. The results of the study show that successful translation of excursion material requires the use of various adaptive strategies depending on the topic of the excursion. For ethnographic excursions, explanatory translation and cultural interpretation play a key role, for historical excursions – an accurate transfer of realities taking into account the differences in historical experience, for nature routes – the use of analogies and adaptation of terminology. A conclusion is made about the need for a flexible approach to excursion translation and the integration of adaptive strategies to improve the quality of intercultural communication.

Self‐Organized Multitarget Pursuit in Multi‐Unmanned Aerial Vehicle Systems via Hierarchical Probabilistic Graphical Models

The cooperation of multiple unmanned aerial vehicle (multi‐UAV) systems in adversarial and uncertain environments, particularly for multitarget pursuit, has gained significant attention. However, existing pursuit methods face challenges, such as limited adaptability, high communication demands, and reduced interpretability, which weaken their effectiveness in decentralized settings with low system redundancy. To address these challenges, this article proposes a self‐organized multitarget pursuit approach based on hierarchical probabilistic graphical models (PGM) for multi‐UAV systems. Specifically, a scalable hierarchical PGM structure is designed to facilitate a direct mapping from environmental perception to action decision‐making for individual UAVs. To estimate the probability distribution parameters of the designed PGM's nodes, a density‐driven probability parameter estimation method that relies on local perceptual information is proposed, which employs smoothed particle hydrodynamics for local density estimation. A series of simulations and real‐world experiments validates the scalability, adaptability, and robustness of the proposed approach in dynamic, obstacle‐cluttered environments.

Звучащий и письменный коды в пространстве адресата (на материале автоматических субтитров)

The issue of the relationship between spoken and written speech is examined within the fields of linguistic variationology and linguosemiotics. The aim of the study is to identify the types of correspondence between spoken speech and automatic subtitles. The empirical basis of the research consists of 85 YouTube Shorts videos as a synthesis of auditory and graphic codes, as well as the results of a survey of recipients regarding the quality of automatic Russian-language subtitles, their conformity with Russian spoken speech, and the impact of identified modifications on the perception of information and understanding of the meaning conveyed in the video. The scientific novelty lies in the fact that, for the first time, units of code transformation were identified in the correlation of automatic subtitles and spoken speech in the same language; from a theoretical perspective, spoken speech and subtitles are considered in terms of the primacy and secondary status of auditory and graphic codes, with subtitles viewed as secondary texts relative to the original spoken variant. The results showed that most transformations in subtitles do not distort meaning and correspond to the content of the video as a polycode text. The most frequent type of code transformation is differences in the graphic form of a word compared to the grammatical standard in the Russian language.

Візуалізація статистичних даних навчально-тренувального процесу на прикладі спортивних ігор

One of the most effective ways to improve the training process is to use data visualisation. Visualisation is understood as a set of methods for presenting numerical information or physical processes in a form convenient for visual perception and analytical comprehension. This approach allows you to create visual graphic images that reflect the substantive characteristics of the phenomena under study, and at the same time allows you to compactly present large amounts of data, facilitating their analysis, interpretation and use in decision-making. Purpose: to substantiate the feasibility of using statistical diagrams for visualization and graphical representation of statistical data using the example of sports games. Material and methods: The article used theoretical research methods, namely analysis, synthesis and generalization of data from scientific and scientific-methodical literary sources; content analysis of publicly available Internet resources; synthesis of the obtained scientific information; comparative analysis. Results: the means of visualization and graphical display of statistical indicators in sports games were developed and generalized, which significantly contributes to better perception and understanding of information. Conclusions: the feasibility of using visualization and graphical display of statistical data in sports games was proven in view of methodological and didactic considerations. It has been established that the most common types of statistical diagrams used in such studies are random variable distribution diagrams, time interval diagrams, correlation diagrams, and comparative column and line diagrams. Thus, a key and integral stage of graphical support for data analysis is the justified choice of the appropriate type of statistical diagram.