Information Visualization Research Articles

The Evolutionary Path and Emerging Trends of Circulating Fluidized Bed Technology: An Integrated Analysis through Bibliometric Assessment and Data Visualization

Confronted with the significant challenges of global climate change and environmental deterioration, the pursuit of carbon emission peaks and the realization of carbon neutrality have become a collective goal for countries worldwide. As an exemplary combustion technology noted for its efficiency and environmental friendliness, the circulating fluidized bed (CFB) is instrumental in curbing the release of carbon dioxide alongside other deleterious gases. The technology is pivotal in promoting the clean and efficient use of coal, simultaneously expediting the global shift towards a sustainable, green, and low-carbon future. This study employs a bibliometric analysis, a social network analysis, and information visualization techniques to delve into the evolution of CFB technology, leveraging the Web of Science database (SCI-EXPANDED and CPCI-S) and the Derwent Innovations Index (DII). Through a meticulous examination of academic papers and patent literature related to CFB technology, this research unveils the developmental trajectory and trends of CFB technology, providing a scientific foundation and reference for strategic technology planning and focused research in key areas. The findings indicate that, while there is a downward trend in the global publication of academic papers on CFB technology, the number of patent applications continues to grow steadily. CFB technology has achieved significant advancements in enhancing combustion efficiency, environmental protection, energy utilization, and waste management, and is progressing towards a direction of diversification and greater efficiency. Moving forward, the development of CFB technology should concentrate on pivotal areas such as material science, fluid dynamics simulation, environmental impact assessment, system integration, and intelligentization, to foster ongoing innovation and a broad application of the technology.

Intelligent Chinese Typesetting Model Based on Information Importance Can Enhance Text Readability

In today’s era of information overload, efficiently extracting valuable information from a large volume of textual data has become a crucial challenge in reading. This study aimed to explore the enhancement of Chinese readability in the digital domain through intelligent typesetting method. This study involved two experiments. The purpose of the first experiment was to achieve the machine learning-based assessment of the importance of individual words in Chinese articles and to automate typesetting based on the importance of words. For the second experiment, readability tests and eye-tracking reading tests were performed and the reading performance and reading attention between intelligent typesetting and general typesetting style was compared. This work proposed three Chinese typesetting methods that distinguished the importance of Chinese text information, based on font size and color. The results showed that first, when reading Chinese text, visual attention was more likely to be drawn to larger font sizes, darker brightness, or warmer-colored characters. Second, intelligent Chinese typography that distinguishes information importance through font size, color brightness, and color hue can enhance Chinese reading comprehension accuracy and subjective evaluation. It was concluded that using the TextRank model to distinguish importance of Chinese vocabulary and intelligent typesetting methods based on visual features of font could obviously improve text readability. Specifically, readability achieved through intelligent typesetting method distinguishing information importance through font color brightness surpasses that of general typesetting significantly. These intelligent typesetting methods can be widely applied in Chinese reading scenarios, such as web pages, e-books, information visualization, and other Chinese reading contexts.

The impact of clock timing on VDT visual search performance under time constraint.

Conducting Visual Display Terminal (VDT) visual search tasks under time constraint has broad applications in fields such as security checks, medical diagnostics, and rescue operations. While excessive time pressure can impair performance, moderate time pressure can motivate individuals to complete tasks and increase productivity. Investigating the positive impact of time pressure on visual search tasks has become a crucial area of study. Clock timing plays a vital role in the visual interface, influencing the perception of time pressure and impacting visual search performance. However, existing research has paid little attention to the induction of time pressure and the impact of clock timing in VDT visual interfaces on visual search performance. Hence, the objective of this study is to investigate the impact of clock timing on VDT visual search performance under time constraint. The content of the experimental tasks was determined through a pilot experiment. The formal experiment was conducted in two phases over six sessions. Participants were tasked with locating the letter "E" embedded within the distractor letter "F," displayed with a clock area above the interface. The first phase of experiments included conditions of no clock, 4-min clock timing, and 4-min countdown clock timing. In the second phase of the experiment, the clock display method was a countdown clock, with three experiments conducted featuring long time, medium time, and short time. Search speed and accuracy were used as primary performance evaluation metrics to examine the impact of clock timing methods and duration on visual search performance. Twenty-one undergraduate students participated in the formal experiment. In the first phase of experiments, participants demonstrated significantly faster reaction times (RTs) in tasks where a clock display was present compared to tasks without (ANOVA, F(2, 60) = 4.588, P = 0.014). However, there were no significant differences in accuracy rates across different timing conditions (ANOVA, F(2, 60) = 0.146, P = 0.865), and no significant correlation between RTs and accuracy was found (Kendall's R = 0.11, P = 0.914). During the second phase, RTs decreased significantly as time constraints became more stringent (ANOVA, F(2, 60) = 7.564, P < 0.05). Conversely, accuracy rates decreased significantly under shorter time constraints (ANOVA, F(2, 60) = 4.315, P < 0.05), with a negative correlation observed between RTs and accuracy (Kendall's R = 0.220, P < 0.01). Compared to conditions without clock displays, having clock displays significantly improved the speed of the visual search task, although the difference in accuracy was not statistically significant. In the context of shorter clock countdown limits, Shorter timing constraints resulted in faster search speeds but also led to reduced accuracy and increased fatigue. Overall, a correlation exists between search speed and accuracy in visual tasks, where higher speed often correlates with lower accuracy. These findings provide valuable insights into clock timing design for visual search interfaces under time pressure.

An initial user model design for adaptive interface development in learning management system based on cognitive load

The cognitive aspect is crucial in developing interactive and adaptive systems, including learning management systems (LMS). By understanding human cognitive processes, developers can create adaptive systems that are sustainably used by targeted users. Cognitive traits need to be explored to become part of the user model, especially especially to develop an LMS with an adaptive interface based on cognitive load. However, there is limited research exploring the correlation between cognitive load and adaptive interfaces, as well as the visualization of information, within online learning settings, including those specifically within LMS platforms. Additionally, a lack of understanding of cognitive processes renders the use of LMS static and unable to adapt to learners’ abilities. Therefore, information regarding the specific characteristics or model of cognitive load that accurately represent LMS users as triggers for adaptive interfaces needs to be further examined. A total of four experts in the cognitive field were interviewed. The interview process was conducted in a semi-structured manner to obtain information about problems in the use of e-learning, the nature of cognitive, and the process of capturing cognitive load during learning using LMS. By using a soft system methodology, the results from the interviews are mapped into a cognitive load-based LMS user model design. The initial model design includes information on working memory capacity, task performance (time, true/false rate), learning behavior (learner log), physiological (in a lab scale), and self-reporting (Likert scale). This model design is a promising first step towards a more technical process in developing adaptive and interactive systems.

NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation.

Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.

Iridium-catalysed synthesis of C,N,N-cyclic azomethine imines enables entry to unexplored nitrogen-rich 3D chemical space

Three-dimensional nitrogen-rich bridged ring systems are of great interest in drug discovery owing to their distinctive physicochemical and structural properties. However, synthetic approaches towards N–N-bond-containing bridged heterocycles are often inefficient and require tedious synthetic strategies. Here we delineate an iridium-catalysed reductive approach to such architectures from C,N,N-cyclic hydrazide substrates using IrCl(CO)[P(OPh)3]2 and 1,1,3,3-tetramethyldisiloxane (TMDS), which provided efficient access to the unstabilized and highly reactive C,N,N-cyclic azomethine imine dipoles. These species were stable and isolable in their dimeric form, but, upon dissociation in solution, reacted with a broad range of dipolarophiles in [3 + 2] cycloaddition reactions with high yields and good diastereoselectivities, enabling the direct synthesis of nitrogen-rich sp3-hybridized pyrazoline polycyclic ring systems. Density functional theory calculations were performed to elucidate the origin of the diastereoselectivity of the cycloaddition reaction, and principal moment of inertia (PMI) analysis was conducted to enable visualization of the topological information of the dipolar cycloadducts.

The impact of virtual and augmented reality on presence, user experience and performance of Information Visualisation

The fast growth of virtual reality (VR) and augmented reality (AR) head-mounted displays provides a new medium for interactive visualisations and visual analytics. Presence is the experience of consciousness within extended reality, and it has the potential to increase task performance. This project studies the impact that a sense of presence has on data visualisation performance and user experience under AR and VR conditions. A within-subjects design recruited 38 participants to complete interactive visualisation tasks within the novel immersive data analytics system for genomic data in AR and VR, and measured speed, accuracy, preference, presence, and user satisfaction. Open-ended user experience responses were also collected. The results implied that VR was more conducive to efficiency, effectiveness, and user experience as well as offering insight into possible cognitive load benefits for VR users.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Simulation study of jet trajectory based on Runge-Kutta method

Abstract To solve the problem of not being able to accurately spray to the designated target point due to wind and distance factors when firefighting at a long distance, this paper adopts the Runge-Kutta algorithm to simulate the trajectory of the fire cannon, and establishes the mathematical model of the trajectory of the jet under the ideal state and the trajectory of the jet under the influence of the wind. We carry out three-dimensional visualization research and compare the results of the simulation experiment with the real experimental data, and the range, height, and trajectory have a high degree of fit. Comparing the simulation results with the real experimental data, the range and height of the jet and the trajectory have a high degree of affinity, and adding the wind power value panel in the visualization interface can simulate the changes of the jet trajectory under different wind speeds and wind power and effectively simulate the trajectory of the jet under natural conditions.

Scanpath Prediction on Information Visualisations.

We propose Unified Model of Saliency and Scanpaths (UMSS)- a model that learns to predict multi-duration saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5% for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6% for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment.

Enhancing Archaeological Research Through Immersive Virtual Reality.

Virtual reality (VR) is increasingly employed in archaeology to showcase reconstructions of ancient sites to the general public, yet its utilization for professional purposes by archaeologists remains less common. To address this gap, we introduce a VR application specifically designed to streamline the storage and access of critical data for archaeological studies. This application provides experts with an immersive visualization of excavation sites and related information during the postexcavation analysis phase. The application interface facilitates direct interaction with 3-D models generated through photogrammetry and modeling techniques, enabling detailed examination of collected data and enhancing research activities. We applied this system to the case study of excavations at the Temple of Juno in Agrigento, Italy. In addition, we present the findings of a pilot user study involving archaeologists, which evaluates the effectiveness of immersive technologies for professionals in documenting, preserving, and exploring archaeological sites, while also driving potential future developments.

A Visual Interface for Exploring Hypotheses About Neural Circuits.

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned tasks by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

Development of a wearable device for heart rate monitoring and fall detection using machine learning to analyze and detect early anomaly of cardiovascular conditions

Cardiovascular disease is the leading cause of death globally. This research implements a system for monitoring heart rate, electrocardiogram, and providing alerts for potential risks to patients based on data collected using the LSTM machine learning model. The wearable device is compact in size with a long battery life. The information collected from the device can be remotely monitored by doctors through a visual interface on a web server model, and patients can self-monitor their electrocardiogram status through an application on a mobile device. By integrating the LSTM model into the design, this study has addressed two issues: predicting the trend of the electrocardiogram signal and detecting abnormalities in the predicted signal. This allows users to self-monitor their personal status and doctors to better adjust the treatment method for the patient’s health.

AUTOMATED REHABILITATION TOOL FOR THE LOWER LIMB

An urgent problem today is the need to develop rehabilitation tools that can be used by the patient in self-care mode. Modern methods of rehabilitation of the lower limb are often limited by the availability of specialists and require constant supervision by medical personnel. The problem of incorrect foot posture can lead to numerous health complications, such as flat feet, which can be associated with diabetes and coronary heart disease. Therefore, the development of a tool for self-diagnosis of pressure on different areas of the foot and control of correction is an urgent task. The availability of this tool will contribute to effective rehabilitation in case of disorders of the lower limbs, improve posture, and slow down the progression of a number of diseases. Previous studies confirm the importance of active participation of patients in the rehabilitation process. The trend toward self-correction requires integrated technologies to provide real-time feedback. The main goal is to create the ability to adjust the position of the foot in shoes using data processing and visualiza-tion on a mobile application. The work presents the development of an automated rehabilitation tool for the lower limb, namely the electrical schematic diagram for the tool. The device uses advanced data processing methods to accurately determine the position of the foot. The analysis of the dynamics of movements and the interaction of the tool with the leg area are aimed at achieving maximum accuracy and efficiency of rehabilitation. The developed tool allows users to receive information about the position of the foot in the shoe in real time. The mobile application in the smartphone becomes a visual interface that displays important information that provides an opportunity for self-correction of posture, running technique, and various physical exercises. The automated rehabilitation tool defines a new stage in the development of technologies for self-correction of the lower limb in case of flat feet. By providing patients with a tool to actively control the rehabilitation process, this tool improves quality of life and reduces the need for ongoing medical supervision. In the future, there will be pro-gramming of the electrical circuit and development of the application for the device according to algorithms.

Memes During the Times of War in the Publications of the Polish News Website: Functions and Narratives/Memy w czasie wojny w publikacjach polskich serwisów informacyjnych: funkcje i narracje

This paper presents findings from a study examining the role and functioning of crisis-time memes, particularly during the full-scale invasion of Ukraine, in Polish news websites Rzeczpospolita, Fakt, and WP Wiadomości. It also explores related primary narratives in the context of media tabloidization, and the influence of stylistics and practices from both citizen journalism and social networks. Linguistic, culturological, thematic, and content analyses indicate that strategies of meme usage are influenced by their socio-cultural nature as unique expressions of public opinion (Vox Populi), as well as by the editorial policy of the media and journalists’ intentions. Authors employ memes not only to illustrate themes but also as informational triggers and even sources. Some publications directly or indirectly raise the issue concerning the role of media in simplifying public perception of contemporary problems. In this context, memes can function both as a tool for tabloidization and as a means of formulating and researching complicated themes, such as resistance to propaganda and populism. Quantitative analysis data confirm that Rzeczpospolita media publishes the most memes about the war, despite positioning itself in the segment of qualitative mainstream media and not excessively using memes as illustrations. Conversely, Fakt and WP Wiadomości, which prefer themes of sports and politics, exploit this format intensely. Simultaneously, authors of Rzeczpospolita primarily use verbal descriptions of memes or employ them to achieve various artistic and expressive effects while preserving journalistic factual material and often avoiding information visualization.

ELECTRONICS DEVICE PURCHASE RECCOMENDATION SYSTEM USING PYTHON

Abstract: The rapid proliferation of electronics devices has led to an overwhelming number of options available to consumers, making it challenging to identify the most suitable products based on individual preferences. In this paper, we propose a novel recommendation system leveraging machine learning techniques to assist users in making informed decisions when purchasing electronics devices. Our system utilizes a collaborative filtering approach combined with feature engineering and natural language processing to analyze user preferences and product characteristics. We present the design, implementation, and evaluation of the recommendation system, demonstrating its effectiveness in suggesting personalized electronics device recommendations. Through experimentation on a realworld dataset, we showcase the system’s ability to accurately predict user preferences and provide relevant product recommendations, thereby enhancing the overall shopping experience for consumers in the electronics domain. Keywords — machine learning , database classification ,data collection,and algorithm Keywords– use rapid api for data, information visualization, gym fitness exercise application.

REAL ESTATE PORTAL

Abstract — We propose to build a real estate portal that allows users to post property for sale as well as search sort property and contact its owner online. It also consists of an innovative feature that allows users to post reviews of the dealing process with a buyer after their deal. This allows the buyers to verify a sellers profile and view his past deal experiences from other user reviews on his profile. Every user on the portal has a profile that can be viewed by to check his past deals status. A user may post bad reviews about another user he has dealt with if any payment issues are encountered during the deal. So users may now verify that particular user before dealing with him. Users may look for property as per area, Bhk, Location as well as other amenities like lift, furnished, semi-furnished etc. The system sorts property and displays results list as desired by user. Users may also post a property for sale along with desired price, location and contact details. Dealers can advertise their new projects to the users with the permission of admin. New users can register with permission of admin. The product also provides also lists available loan strategies at present and directs user to the desired bank’s website. Keywords : Spatial Database, Information Visualization, Real Estate Information

Archery Analytic Workflow in a Web-Based Application

The integration of sports science and camera sensing technology has recently emerged to be an advanced analytical tool in sportsperson performance enhancement. The use of computing power and a web-based application can provide quick information analysis and data reporting between coaches and athletes. The design of an archery analytic workflow is demonstrated in this paper using the Python Flask framework, video analytic algorithms, a structured video inventory framework, MongoDB database setup and integration of the Keypoint R-CNN machine learning backend. A user-friendly data visualisation interface on the front end is integrated in the software to deliver athletes’ analytical capabilities such as thorough frame-by-frame video analysis, posture consistency estimation and joint kinematic analysis. This web application framework is not limited to archery sports, and can be extended to numerous sports, such as shooting, weightlifting and cycling. The significance of integrating camera sensing technology with the sports science field can offer quantitative and qualitative observations to improve training programs and performance evaluation.

Enhancing safety in conditionally automated driving: Can more takeover request visual information make a difference in hazard scenarios with varied hazard visibility?

Autonomous driving technology has the potential to significantly reduce the number of traffic accidents. However, before achieving full automation, drivers still need to take control of the vehicle in complex and diverse scenarios that the autonomous driving system cannot handle. Therefore, appropriate takeover request (TOR) designs are necessary to enhance takeover performance and driving safety. This study focuses on takeover tasks in hazard scenarios with varied hazard visibility, which can be categorized as overt hazards and covert hazards. Through ergonomic experiments, the impact of TOR interface visual information, including takeover warning, hazard direction, and time to collision, on takeover performance is investigated, and specific analyses are conducted using eye-tracking data. The following conclusions are drawn from the experiments: (1) The visibility of hazards significantly affects takeover performance. (2) Providing more TOR visual information in hazards with different visibility has varying effects on drivers’ visual attention allocation but can improve takeover performance. (3) More TOR visual information helps reduce takeover workload and increase human–machine trust. Based on these findings, this paper proposes the following TOR visual interface design strategies: (1) In overt hazard scenarios, only takeover warning is necessary, as additional visual information may distract drivers’ attention. (2) In covert hazard scenarios, the TOR visual interface should better assist drivers in understanding the current hazard situation by providing information on hazard direction and time to collision to enhance takeover performance.

COMPUTER VISUALIZATION IN TEACHING GENERAL TECHNICAL DISCIPLINES: THEORETICAL AND PRACTICAL ASPECTS

The article investigates the development of visual aids and visualization ideas in the organization of the educational process, particularly in teaching general technical disciplines. The article aims to review the development of visual aids and visualization ideas in education, clarify the essence of computer visualiza- tion, classify its types and kinds, and determine the directions of computer visualization usage in teaching general technical disciplines and the software applications that ensure their development for educational purposes. The study operates methods of retrospective, content, and semantic analysis, classification, gen- eralization, and modelling. The historical context of introducing electronic visualization tools in education has been considered in the study, starting from the first projectors in the 1930s to modern computer visualization technologies such as 2D and 3D visualization, stereoscopic 3D, virtual reality (VR), and augmented reality (AR). The es- sence of computer visualization as a general term for graphic methods of creating, analysing, and transmit- ting information has been defined. Differences between the didactic principles of visual aids and visualiza- tion have been clarified. It has been highlighted that visualization has a more complex structure since it in- cludes actions for constructing images of investigated objects or phenomena. The article identifies the directions for implementing computer visualization, including scientific, soft- ware, and information visualization. It has been established that all visual content can be divided into three subgroups: structured, artistic, and demonstrative visual objects, which can be static or dynamic, in one plane or three-dimensional space. The impact of computer visualization on the methods of teaching general technical disciplines has been analysed. It has been shown that modern approaches to using computer visualization provide new opportunities for more effective and interactive learning, promoting the development of students’ technical and spatial imagination, which is crucial for future engineers and technical specialists. An approximate list of software for creating computer visualization tools, which can be useful in teaching general technical disciplines, has been provided. Among them are applications for creating presentations and infographics, 3D modelling, VR and AR-based applications and services, simulators, and emulators. The main directions for using computer visualization aids in teaching general technical disciplines are identified, including auxiliary synopsis, diagrams, tables, block diagrams, graphs, frames, word clouds, domain structures, memory cards, comics, heuristic sketches, knowledge animation, infographics, multimedia presentations, educational videos, static and dynamic 2D and 3D models, VR and AR objects. It has been concluded, that the didactic effectiveness of computer visualization in teaching general technical disciplines is ensured by certain provisions, namely: the focus on the quick and complete memorization of studied concepts, theories, and ideas by students; adherence to the principles of reliability, quality, completeness, sufficiency, logic, simplicity of perception, optimality, unity of graphics and text, and emphasis on key points; encouraging students to independently analyse and construct new, non-trivial didactic tasks allowing them to explore concepts, phenomena, or objects in terms of independent search and educational experimentation.