Visualization Model Research Articles

DEVELOPING SKILLS TO RECOGNIZE THE CONCEPT OF NUMBERS USING THE PROJECT VISUALIZATION MODEL AT TK TUNAS MUDA

The problem in this study is the low cognitive development of number concept children. The solution to this problem is to use the Project visualization model. The purpose of this study is to describe the activity of teachers, analyze children's activities and cognitive development. This research uses a qualitative approach with the type of Classroom Action Research (CAR), which is carried out with four meetings. The subjects of the study were group A children of Tunas Muda Kindergarten, the number of children was 13 people. The type of data in this study is qualitative data. The results of this study showed that teacher activities at meeting I got a score of 32, meeting II got a score of 42, meeting III got a score of 48, and meeting IV got a score of 52. Children's activities at meeting I obtained a percentage of 53.84%, meeting II obtained a percentage of 68.50%, meeting III obtained a percentage of 83.17%, and meeting IV obtained a percentage of 94.95%. Cognitive Development of children meeting I obtained a percentage of 75.96%, meeting II obtained a percentage of 82.05%, meeting III obtained a percentage of 89.10%, and meeting IV obtained 96.15%. Based on the results of the study, it can be concluded that teacher activity, child activity, and child development results have successfully increased and developed.

Weather Forecasting

My Project is on implementation of a weather forecasting using python and machine learning. real-time weather data from locations across the world. Through Python scripting, the project generates accurate forecasts for diverse regions, providing essential meteorological parameters such as temperature, wind speed, humidity, and weather conditions. Weather forecasting plays a crucial role in various aspects of human life, from planning outdoor activities to making strategic decisions in sectors like agriculture, transportation, and emergency management. In this project, we explore the application of Python programming language in weather forecasting, employing both open-source libraries and APIs to access and analyze weather data. Through this project, we aim to demonstrate the versatility of Python in harnessing weather data for forecasting purposes, showcasing its capabilities in data retrieval, analysis, visualization, and predictive modelling. By empowering users with accessible tools and methodologies, we envision a future where weather forecasting becomes more accurate, reliable, and actionable, contributing to informed decision-making and enhanced societal resilience in the face of changing weather patterns. Weather forecasting is the scientific process of predicting atmospheric conditions at a specific location and time. It involves the collection and analysis of data from various sources, such as satellites, weather stations, and radar systems. This data is then input into numerical weather prediction models, which use mathematical equations to simulate the behavior of the atmosphere. Advances in technology, such as high-performance computing and machine learning, have significantly improved the accuracy and reliability of weather forecasts. These predictions are crucial for a wide range of applications, including agriculture, disaster management, transportation, and daily planning, helping to mitigate the impacts of severe weather events and enhance societal preparedness and safety. Despite significant progress, challenges remain due to the inherently chaotic nature of the atmosphere, which limits the precision of long-term forecasts

A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete

Compressive strength (CS) of concrete is one of the most important factors in the construction industry and various time and effort-consuming tasks are required to measure it. To tackle such problems, the use of machine learning (ML), a branch of artificial intelligence, has recently resulted in a dramatic revolution in the construction sector, resulting in increased efficiency, accuracy, and creativity. Taking these factors into consideration, the current research was conducted on concrete manufactured with recycled coarse aggregate and fly ash generated as a byproduct of construction and demolition activities and thermal power plants. A large dataset consisting of 444 data points, along with ten input parameters, has been collected from the literature to forecast the CS of fly ash and recycled coarse aggregate-based self-compacting concrete. In this regard, ten advanced ML models, including K-Nearest Neighbors (KNN), Extra Tree Regressor (ETR), Bagging Regressor (BR), Adaboost Regressor (AR), Extreme Gradient Boosting (XGB), Linear Regression (LR), Random Forest (RF), Decision Tree Regression (DTR), Support Vector Regression (SVR) and Gradient Boosting Regression (GBR) have been considered. Furthermore, various data visualization plots and model’s performance matrices such as scatter plot, histograms, heatmaps, Shapley Additive Explanation (SHAP) Analysis, Regression Error Characteristics (REC), and errors have been utilized. In order to evaluate the most influential input parameter and depict the overall performance of ML models, sensitivity analysis and Taylor’s diagram are used. As a method of validation, the Kfold cross-validation approach has been implemented to justify the obtained output. Based on the outcome of the study, the BR model has displayed remarkable accuracy with insignificant errors and high R-squared values (R2 = 0.961), while XGB (R2 = 0.959), and DTR (R2 = 0.952) models also achieved commendable, as compared to other ML models. Additionally, water content, curing days, fly ash, and w/c ratio were found to be the most critical components that directly impact the CS of fly ash and RCA-based SCC. To cater to diverse and extensive practices, a graphical user interface has been developed to assist researchers and engineers in getting instant results of their fly ash and RCA-based SCC mixes prior to the execution of time- and resource-consuming laboratory work.

Quantifying and Predicting Tennis Match Momentum: An LGBM-Based Analysis and Visualization Model

In any competitive games, the most exciting thing for the audience is the change of the scores of both players, and quantifying the trend of the scores of both players can help coaches provide timely guidance to the players during the games and also help the players in the preparation for the games. The author firstly obtains the data of the players' psychological state, physical reserve and athletic skills during the game, and then compares the LGBM, SVC, MLP and LR models, analyzes the accuracy and regression rate of each model, and then selects the optimal LGBM model, and then calculates the weights of each parameter through the selected model, and sums them up to obtain the quantitative value of "momentum". Finally, the fluctuating images of the two sides' "momentum" in the whole game are drawn, through which the scoring trend can be visualized and the turning points of the momentum can be marked. The use of such methods is a kind of innovation for sports events, which can scientifically help coaches to make decisions, and can bring more exciting sports events to the audience to fully reflect the athletes' style.

Effect of Janus nanoparticles on foam snap off in porous media

Abstract Nanoparticles can be adsorbed at the gas-liquid interface to improve the stability of foam. However, homogeneous nanoparticles exhibit low surface activity, and their migration to the gas-liquid interface requires significant energy input. This leads to harsh foaming conditions and severely limits the application of homogeneous nanoparticles in foam stability. A microfluidic visualisation model for the study of Janus nanoparticle complex systems was used to investigate the formation behaviour of trapped bubbles in a single connected pore-throat model. The foam generated in the pore showed reduced quantities, sizes, improved quality, and enhanced stability compared to both surfactant systems and hydrophilic nanoparticle complex systems.

MLMSign: Multi-lingual multi-modal illumination-invariant sign language recognition

Sign language (SL) serves as a visual communication tool bearing great significance for deaf people to interact with others and facilitate their daily life. Wide varieties of SLs and the lack of interpretation knowledge necessitate developing automated sign language recognition (SLR) systems to attenuate the communication gap between the deaf and hearing communities. Despite numerous advanced static SLR systems, they are not practical and favorable enough for real-life scenarios once assessed simultaneously from different critical aspects: accuracy in dealing with high intra- and slight inter-class variations, robustness, computational complexity, and generalization ability. To this end, we propose a novel multi-lingual multi-modal SLR system, namely MLMSign, by taking full strengths of hand-crafted features and deep learning models to enhance the performance and the robustness of the system against illumination changes while minimizing computational cost. The RGB sign images and 2D visualizations of their hand-crafted features, i.e., Histogram of Oriented Gradients (HOG) features and a∗ channel of L∗a∗b∗ color space, are employed as three input modalities to train a novel Convolutional Neural Network (CNN). The number of layers, filters, kernel size, learning rate, and optimization technique are carefully selected through an extensive parametric study to minimize the computational cost without compromising accuracy. The system’s performance and robustness are significantly enhanced by jointly deploying the models of these three modalities through ensemble learning. The impact of each modality is optimized based on their impact coefficient determined by grid search. In addition to the comprehensive quantitative assessment, the capabilities of our proposed model and the effectiveness of ensembling over three modalities are evaluated qualitatively using the Grad-CAM visualization model. Experimental results on the test data with additional illumination changes verify the high robustness of our system in dealing with overexposed and underexposed lighting conditions. Achieving a high accuracy (>99.33%) on six benchmark datasets (i.e., Massey, Static ASL, NUS II, TSL Fingerspelling, BdSL36v1, and PSL) demonstrates that our system notably outperforms the recent state-of-the-art approaches with a minimum number of parameters and high generalization ability over complex datasets. Its promising performance for four different sign languages makes it a feasible system for multi-lingual applications.

Feedback control system for vibration construction of fresh concrete

To address the issue of uncontrolled vibration in concrete construction. A control system with visual and auditory feedback functionality is developed. Worker posture is represented by Euler angles and size of body to establish palm positioning model. Combined with the construction characteristic of the vibrator, the position relationship between the worker head and vibrator tip is established using the palm as medium. The working state judgment algorithm for vibrator is proposed using the variation characteristic of input current in vibrator to calculate the vibration duration and insertion depth. Based on these algorithms, developing devices are suitable for manual vibration to collect calculated parameters. The developed software system accesses parameters from on-site devices to evaluate vibration quality based on energy transfer model, and sends the construction warning information to site through visualization model and the main control device installed on safety helmet. The application proves that the developed feedback control system is helpful to improve the vibration quality.

Analysis of Brain Regions Activated by Afferent Renal Nerves in DOCA-Salt Hypertensive Mice

We have reported that the neurogenic component of DOCA-salt HTN is dependent on afferent renal nerves (ARNs) that modulate central autonomic networks, however, the central pathways are poorly understood. Targeted recombination in active populations (TRAP2) mice provide a model for global visualization and quantification of neuronal activity via the expression of the fluorescent reporter protein tdTomato which is expressed under the immediate-early gene, c-Fos, a neuronal activation marker, via tamoxifen-inducible Cre. In combination with the deoxycorticosterone-acetate (DOCA)-salt model of hypertension (HTN), neuronal activation in central autonomic centers can be evaluated in TRAP2 mice during DOCA-salt-induced HTN to identify the role of ARNs in the control of blood pressure. We evaluated the expression of tdTomato in TRAP2 mice as a measure of c-Fos activation following 7 days of DOCA-salt HTN and compared it to immunolabeled c-Fos at day 21 of DOCA-salt HTN in brain regions known to be involved in blood pressure regulation. We hypothesized that DOCA will increase the expression of tdTomato and c-Fos in mice with intact renal nerves and that afferent renal denervation (ARDN) will attenuate this DOCA-induced neuronal activity. Three groups of TRAP2-DOCA mice were studied: normotensive (Vehicle-Sham), hypertensive (DOCA-Sham), hypertensive with ARDN (DOCA-ARDN). Mice received a nephrectomy, sham or ARDN, implant of 50mg DOCA and 0.9% saline drinking water for the length of the study (drug-free pellet and diH2O for vehicle). TRAP2-DOCA-salt mice received an injection of 5mg/mL 4-hydroxytamoxifen (4-OHT), a short acting tamoxifen metabolite, on day 7 to induce Cre recombination and expression of tdTomato during the development of HTN. On day 21, mice were anesthetized and perfused with 4% paraformaldehyde. Brains were immunolabeled for c-Fos expression indicating day 21 neuronal activation and compared to endogenous Cre-induced tdTomato expression from day 7 4-OHT. Preliminary data indicate increased neuronal activity in TRAP2 DOCA-Sham mice compared to Vehicle-Sham in the paraventricular nucleus of the hypothalamus, lateral parabrachial nucleus, and nucleus of the solitary tract. This activation pattern was reduced in TRAP2 DOCA-ARDN compared to TRAP2 DOCA-Sham mice. A future study will evaluate c-Fos expression in wild-type DOCA-salt mice at day 7 and 21 of DOCA-salt HTN development to validate the observations in the TRAP2-DOCA model. This experiment will also validate TRAPing as a method for measuring neuronal activation at two time points within the same animal. These preliminary results establish an anatomical foundation for future investigations into the physiological role of ARN input to central autonomic control centers that regulate blood pressure in a salt-induced hypertensive model. NIH R01 HL116476. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Asymmetric instability damage patterns of curved shield tunnels based on transparent soil visualization model tests

With accelerated urbanization, underground structures have imposed significant restrictions on underground spaces. Urban shield tunnels must be designed using curved lines to avoid existing tunnels, pile foundations, and other structures. Over-excavation on the inner side of a curved tunnel during tunneling and the difference between the inner and outer jacking forces complicate research on excavation surface stability. In this study, a combination of transparent soil visualization model tests and numerical simulation methods was used to investigate the spatially asymmetric distribution patterns, focusing on the progressive destabilization damage of the soil and the soil arching effect in front of the excavation during construction of curved shield tunnels. Furthermore, the effect of the radius of curvature on the soil arch morphology in sandy soils was analyzed in detail. The results show that the soil displacement in front of the working face of the curved tunnel exhibited a crescent-shaped deflection toward the over-excavation side. The settlement tanks in the curved tunnel exhibited an asymmetric distribution along the center of the tunnel and were deflected to the over-excavation side. The smaller the radius of curvature, the greater was the deflection of the soil in front of the curved tunnel to the over-excavation side.

WITHDRAWN: Visualized neural network-based vibration control for pigeon-like flexible flapping wings

This study investigates pigeon-like flexible flapping wings, which are known for their low energy consumption, high flexibility, and lightweight design. However, such flexible flapping wing systems are prone to deformation and vibration during flight, leading to performance degradation. It is thus necessary to design a control method to effectively manage the vibration of flexible wings. This paper proposes an improved rigid finite element method (IRFE) to develop a dynamic visualization model of flexible flapping wings. Subsequently, an adaptive vibration controller was designed based on non-singular terminal sliding mode (NTSM) control and fuzzy neural network (FNN) in order to effectively solve the problems of system uncertainty and actuator failure. With the proposed control, stability of the closed loop system is achieved in the context of Lyapunov’s stability theory. At last, a joint simulation using MapleSim and MATLAB/Simulink was conducted to verify the effectiveness and robustness of the proposed controller in terms of trajectory tracking and vibration suppression.The obtained results have demonstrated great practical value of the proposed method in both military (low-altitude reconnaissance, urban operations, and accurate delivery, etc.) and civil (field research, monitoring, and relief for disasters, etc.) applications.

Application Research on Optimal Allocation of Drainage Rights Based on Computer Big Data in Anhui Province of Huai-River Basin

In this paper, a three-dimensional visualization model of river water body based on Vega is proposed. This system can not only display the water quality information of each monitoring point on the geographic information platform, but also carry out virtual reality roaming of the river. Then the mechanism of interactive allocation of initial water discharge rights is analyzed. According to the principle of interaction and negotiation between different regions and industries, a two-level diagnosis and optimization model is established. Secondly, the optimal equation and diagnostic criteria of comprehensive benefits based on regional economy and society are constructed based on the strict control of the maximum emission permit. The initial emission rights are interactively configured between the primary and secondary levels. Emission reduction targets for each region and industry were proposed. Considering the fairness and effectiveness of the allocation of water discharge rights between regions and industries, the comprehensive benefits of each region and the whole basin are maximized. The feasibility of the scheme is illustrated by an example analysis. It is urgent to carry out the research on the rational allocation of drainage rights in Huaihe River, and the relevant research results will contribute to the sustainable supply of grain in Anhui province, help avoid water disputes around the Huaihe River, and promote the sustainable economic and social development of Anhui province.

Development of dynamic centrifuge models for measurement and visualization of deformation mechanisms in liquefiable soils

Recent advancements in geotechnical physical modeling have enabled visualization and analysis of deformation mechanisms in soil sections through the integration of Particle Image Velocimetry (PIV) in dynamic centrifuge modeling. PIV requires a rigid wall container. In this paper, we summarize the design considerations that enable reliable measurement and visualization of soil deformation mechanisms at the University of Colorado (CU) Boulder’s 400 g-ton, 5.5 m radius centrifuge facility. The primary objective of this system is to investigate the response of complex and stratigraphically variable, saturated granular soil deposits beneath shallow-founded structures, though it can also be used for other configurations that benefit from advanced visualization in the centrifuge. The setup aims to enable quantification and visualization of different deformation mechanisms, including shear, volumetric, and soil ejecta formation, near and away from structures. The paper provides detailed design considerations for the system components, including a rigid container with a transparent Perspex wall and duct seal inclusions, a linear-elastic single-degree of freedom (SDOF) structure, soil texture, a potential ground improvement technique, and a high-speed camera system. Through fully-coupled, three-dimensional (3D) nonlinear finite element analyses, we investigate the influence of container type, domain size, and duct seal geometry on boundary effects, with consideration of nonlinearities within various soil profile configurations with and without the presence of a building model. The findings highlight the critical impact of proximity to lateral boundaries of the container on accelerations, excess pore pressures, foundation settlement, tilt, and shear strains, as well as the benefits of duct seal in reducing those boundary effects. The results also show that the extent of boundary effects on key performance measures depends on the properties of the container, soil layers, and ground motion. The simulations also show that surface ejecta potential within stratigraphic soil profiles is highly sensitive to the depth of the groundwater table and variations in soil permeability, which must be considered in designing experimental programs that investigate the development of soil ejecta.

Understanding Research Dynamics in Job Stress

The objective of this analysis is to conduct a bibliometric analysis to map the research on work stress. This will be done by employing quantitative methods such as network, overlay, and density cluster models to analyze the research on work stress over a span of 11 years. Data are extracted from the scientific literature in the Science Direct database using the keyword "job stress," in which a total of 180 relevant articles were identified. These articles were analyzed using algorithms in Vosviewer to identify common topics in stress research. The analysis revealed that work-related stress studies frequently discuss 39 key topics, as determined by researchers. Through bibliometric analysis utilizing a network visualization model, it has been determined that there are six recurring clusters of keywords. The initial cluster, highlighted in red, comprises a total of 11 keywords. The terms "job stress" and "job satisfaction" have been the primary focus of research in the field of job stress analysis from 2013 to 2023. In response to the analysis of overlay visualization, the keyword "job stress" has been frequently examined from the past to the present. According to the density visualization analysis, the keywords "job stress" and "job satisfaction" exhibit the most intense and prominent colors, suggesting that numerous researchers have conducted studies on job stress.

Patient-specific Advanced Visualization Modeling in Intracranial Aneurysms Featuring Anatomical, Clinical, and Demographic Features (P8-5.024)

Patient-specific Advanced Visualization Modeling in Intracranial Aneurysms Featuring Anatomical, Clinical, and Demographic Features (P8-5.024)

Transport-of-intensity model for single-mask x-ray differential phase contrast imaging

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. The single-mask differential phase contrast imaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask x-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as retrieved images. The model validity as well as the proposed retrieval method are demonstrated via both experimental results on a system developed in house as well as Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.

Visualization and Interaction Model of Outdoor Landscape Design Based on Virtual Reality Technology using Optimized Sparse Spectra Graph Convolutional Neural Network

The artistic thought of traditional garden landscape design has significant effect on garden art, architectural theory, gardening theory are also inextricably linked. In modern gardens, traditional architecture lacks applicability to construction of garden painting environment. In this Manuscript, Visualization and Interaction Model of Outdoor Landscape Design Based on Virtual Reality Technology using Optimized Sparse Spectra is Graph Convolutional Neural Network is proposed (VIM-OLD-VRT-SSGCNN).Initially the data is collected fromArcbazer.com data. Afterwards sparse spectra graph convolutional neural network (SSGCNN) is used to design the Outdoor Landscape. In Generally, SSGCNN doesn’t expose some adoption of optimization systems for calculating optimal parameters for exactly design the Outdoor Landscape. Hence, Piranha Foraging optimization Algorithm (PFOA) is proposed to optimize SSGCNN which precisely design Outdoor Landscape. Finally Visualization and Interaction Model of Outdoor Landscape Designed. The proposed VIM-OLD-VRT-SSGCNN method is implemented python and performance metrics likes accuracy, precision, F1-score, sensitivity, specificity, computational time, RoC are evaluated. The performance of VIM-OLD-VRT-SSGCNN method provides 18.31%, 20.72%, 21.67% greater accuracy, 17.83%, 18.42%, 20.38% greater precision and 21.75%, 22.36%, 23.59% greater computational time while compared with existing techniques like Utilizing Deep Learning to Generate Font with Backyards in Landscape Architecture(UDL-GFB-LA), Urban Landscape Design Depend on Data Fusion with Computer Virtual Reality Technology (ULD-DF-CVRT),perception of urban sounds cape and landscape utilizing different visual environment reproduction techniques in virtual reality (PUSL-DVER-VR) respectively.

The influence of the body position during PMCT: Implications for 3D reconstructions

Objectivesvariations in the positions of organs and bones resulting from two different body positions during PMCT were investigated. MethodologyForty-four bodies were CT-scanned in two positions and compared using 18 landmarks and 27 distances. Results and conclusionDifferences below 1 cm were observed for all measurements. The most significant variation was observed along the cranio-caudal plan (z). Awareness of these movements is important when using 3D models for visualization or 3D reconstructions.

Photonically Projected Atoms: A Paradigm Shift in Atomic Physics

This paper presents the discovery of photonically projected atoms, first observed serendipitously during the development of a smartphone-based technique for material identification through reflected light measurements. Upon illuminating samples with ≥75,000 lumen from a light-emitting diode, we observed exact atomic projections emanating from charged particles within the sample. Analysis determined that protons within each atom bond with and re-emit contacted photons. After proton emission, a spatial lensing effect enlarges the projections of the photons to a diameter of 0.5 mm when measured 1 mm from the surface. The smaller 1⁄3” sensors and pixel size of smartphone cameras provide a better resolution of the fine structural details compared with larger digital camera alternatives. Through seven years of imaging analysis under various conditions, fundamental insights emerged on atomic architectures, particle interactions, and the role of space, empirically revising mainstream quantum theory. This research aims to disseminate recent advancements in the direct visualization and updated modeling of atomic projections to enable ongoing physics discoveries through this accessible technique.

A Spatial Correlation Identification Model for Coherent Structure Extraction and Three-Dimensional Visualization

Multi-scale coherent structures have been observed in ocean currents, which are induced by the interaction of shear flows with different velocities. Understanding the spatial configuration and scale characteristics of coherent structures will promote the explanation of physical ocean phenomena. Considering the self-similarity, we propose a spatial correlation identification model for coherent structure extraction and three-dimensional visualization based on the wavelet transform and time-dependent intrinsic correlation method. The spatial and scale distributions of coherent structures are related to the dissipation rate variation. Most large-scale coherent structures, with the largest length scale of 13 m, are found to exist in stable fluid, such as the water column below 50 m. However, small-scale structures are found in chaotic fluids, such as the upper layer. Furthermore, we found that coherent structures of different scales coexist simultaneously in the same depth range, indicating a simultaneous multi-scale structure pattern for turbulent flow investigations.

Unveiling the Intricacies of the Inner Ear Anatomy: Novel 3D-Printed Model for Detailed Visualization and Functional Demonstrations.

This research aimed to print realistically detailed and magnified three-dimensional models of the inner ear, specifically focusing on visualising its complex labyrinth structure and functioning simulation. Temporal bone computed-tomography data were imported into Mimics software to construct an initial three-dimensional inner-ear model. Subsequently, the model was amplified and printed with precision using a three-dimensional printer. Five senior attending physicians evaluated the printed model using a Likert scale to gauge its morphological accuracy, clinical applicability and anatomical teaching value. The printed inner-ear model effectively demonstrated the intricate internal structure. All five physicians agreed that the model closely resembled the real inner ear in shape and structure, and simulated certain inner-ear functions. The model was considered highly valuable for understanding anatomical structure and disorders. The three-dimensionally printed inner-ear model is highly simulated and provides a valuable visual tool for studying inner-ear anatomy and clinical teaching, benefiting otologists.