Three-dimensional Data Research Articles

Functional task kinematics in older adults: The role of fear of falling and fall history as measured by themovement deviation profile.

Functional task kinematics in older adults: The role of fear of falling and fall history as measured by themovement deviation profile.

Convolutional neural network for automated quantitative analysis of non-destructively acquired three-dimensional corrosion pit morphology data

Convolutional neural network for automated quantitative analysis of non-destructively acquired three-dimensional corrosion pit morphology data

Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.

Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.

Visualization of oral function during playing a wind instrument by a lateral dental impression: a proof-of-concept investigation

Embouchure describes the interaction of the teeth, lips, tongue, buccal mucosa, and surrounding muscles when playing woodwind and brass instruments. In dental practice, impression material is used to capture the oral structure and function. In this proof-of-concept investigation, embouchure was examined using a silicone-based dental impression material. The participants were 1 oboe player, 1 alto saxophone player, and 4 tenor saxophone players. Four of the participants were amateurs and 2 were professionals. The dental impression material was mixed and inserted onto the buccal aspect of the upper molars. The musicians blew test tones and maintained embouchure for 30 s while a lateral embouchure impression was taken. The hardened material was removed and scanned using cone-beam computed tomography. The three-dimensional surface data of the impression were exported, and the mean thickness was analyzed. The impressions showed the space between the alveolar and buccal mucosae. The mean thickness ± standard deviation of the impressions was 2.35 ± 0.85 mm. The oboist showed the smallest thickness, while the tenor saxophonists showed the greatest thickness. The method enabled visualization of the unique morphology of each participant. The results suggest that embouchure can be objectively evaluated using the presented technique. Making embouchure impressions to assess oral problems should enable dentists to evaluate changes in music playing resulting from oral problems or for their treatment. Further studies in a larger population are needed to generalize the results.

Transforming 3D MRI to 2D Feature Maps Using Pre-Trained Models for Diagnosis of Attention Deficit Hyperactivity Disorder.

Background: According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorder (ADHD). This disorder can have significant negative consequences on people's lives, particularly children. In recent years, methods based on artificial intelligence and neuroimaging techniques, such as MRI, have made significant progress, paving the way for development of more reliable diagnostic tools. In this proof of concept study, our aim was to investigate the potential utility of neuroimaging data and clinical information in combination with a deep learning-based analytical approach, more precisely, a novel feature extraction technique for the diagnosis of ADHD with high accuracy. Methods: Leveraging the ADHD200 dataset, which encompasses demographic information and anatomical MRI scans collected from a diverse ADHD population, our study focused on developing modern deep learning-based diagnostic models. The data preprocessing employed a pre-trained Visual Geometry Group16 (VGG16) network to extract two-dimensional (2D) feature maps from three-dimensional (3D) anatomical MRI data to reduce computational complexity and enhance diagnostic power. The inclusion of personal attributes, such as age, gender, intelligence quotient, and handedness, strengthens the diagnostic models. Four deep-learning architectures-convolutional neural network 2D (CNN2D), CNN1D, long short-term memory (LSTM), and gated recurrent units (GRU)-were employed for analysis of the MRI data, with and without the inclusion of clinical characteristics. Results: A 10-fold cross-validation test revealed that the LSTM model, which incorporated both MRI data and personal attributes, had the best diagnostic performance among all tested models in the diagnosis of ADHD with an accuracy of 0.86 and area under the receiver operating characteristic (ROC) curve (AUC) score of 0.90. Conclusions: Our findings demonstrate that the proposed approach of extracting 2D features from 3D MRI images and integrating these features with clinical characteristics may be useful in the diagnosis of ADHD with high accuracy.

Exploration of crystal chemical space using text-guided generative artificial intelligence

The vastness of chemical space presents a long-standing challenge for the exploration of new compounds with pre-determined properties. In materials science, crystal structure prediction has become a mature tool for mapping from composition to structure based on global optimisation techniques. Generative artificial intelligence now offers the means to efficiently navigate larger regions of crystal chemical space informed by structure-property datasets of materials. Here, we introduce a model, named Chemeleon, designed to generate chemical compositions and crystal structures by learning from both textual descriptions and three-dimensional structural data. The model employs denoising diffusion techniques for compound generation using textual inputs aligned with structural data via cross-modal contrastive learning. The potential of this approach is demonstrated for multi-component compound generation, including the Zn-Ti-O ternary space, and the prediction of stable phases in the Li-P-S-Cl quaternary space of relevance to solid-state batteries.

VIRTual autOPSY-applying CT and MRI for modern forensic death investigations.

Virtual autopsy, an advanced forensic technique, utilizes cutting-edge imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) to investigate the cause and manner of death without the need for physical dissection. By creating detailed, three-dimensional data of the entire body or specific areas of interest, these post-mortem imaging modalities provide a comprehensive, non-invasive approach to examining decedents. This article explores the historical development of virtual autopsy, its current applications in forensic medicine, and its promising future. It highlights the crucial roles of CT and MRI in forensic death investigations, while also addressing the challenges and limitations associated with these imaging techniques in post-mortem examinations.

Study on the Relationship Between 3D Landscape Patterns and Residents’ Comfort in Urban Multi-Unit High-Rise Residential Areas: A Case Study of High-Density Inland City

As urbanization accelerates, the increasing density of urban buildings and the prevalence of multi-unit high-rise residential areas have emerged as significant factors affecting residents’ comfort. Effective green space planning within residential areas can mitigate residents’ thermal discomfort. This study utilizes methods including the construction of two-dimensional and three-dimensional landscape indices and meteorological data simulation to examine the relationship between residents’ comfort levels at various heights in residential buildings and the 3D landscape patterns of residential areas, based on semantic three-dimensional grid data from a residential complex in Wuhan. The results indicate that (1) The characteristics of 3D landscape patterns vary across different regions within multi-unit high-rise residential areas. The landscape patches in the central and southern regions are more balanced compared to other areas, while there is minimal height variation in residential buildings in the northeastern region. (2) There are notable differences in comfort levels at varying heights across different areas of the residential district. In summer, residents expressing satisfaction with environmental comfort are primarily located in high-rise buildings in the central-southern region, whereas in winter, satisfaction is concentrated among residents in lower and mid-rise buildings in both the northern center and southern areas. (3) The degree of landscape fragmentation, the dominance of certain patches, and the distribution of buildings and vegetation at different heights significantly influence residents’ comfort. Achieving a balanced distribution of green spaces, reducing building density, and ensuring a uniform arrangement of trees of varied heights can effectively enhance the living environment for residents on lower floors, providing practical strategies for the planning of green spaces and built environments that improve overall resident quality of life. This research provides a theoretical foundation and reference for evaluating thermal comfort in high-rise residential areas and optimizing green space configurations.

Return-To-Sport Assessments After Anterior Cruciate Ligament Injury: Which Jump-Landing Test Is Sensitive to an ACL-Injury History Under Fatigued or NonFatigued Conditions?

Accurately identifying residual biomechanical deficits following an anterior cruciate ligament injury is critical for effective rehabilitation and safe return to sport. This study aimed to determine which of four jump-landing tasks demonstrated the greatest sensitivity in distinguishing individuals with a history of ACL injury from healthy controls. Forty-three participants formed the ACL (n=21, 11 females) and the control group (n=22, 12 females). Three-dimensional motion data (Vicon, 250Hz) were recorded during a single-leg hop, unilateral countermovement jump, unilateral crossover hop, and medial-rotation hop before and after a fatigue-inducing intervention (single-leg squats and step-ups). Logistic regression models to classify participants were built using 13 lower-body, trunk, and pelvis joint angles at 50ms after initial ground contact, angular changes in these angles between 50 and 80ms, and principal components derived from these variables. Classification rates and individual classification outcomes were assessed. The results revealed that no single jump-landing task consistently outperformed others in detecting ACL injury history. Classification outcomes were influenced by fatigue state and analytical approaches. Fatigue was found to enhance classification rates. Combining joint angles with their temporal changes improved classification rates compared to using joint angles alone. However, applying principal component analysis as a preprocessing step did not consistently enhance model performance. Overall, the study demonstrated that jump-landing tasks, combined with a variety of analytical approaches, can effectively detect ACL injury history. Fatigue enhanced classification outcomes, suggesting that it amplifies differences between post-injury and healthy movement characteristics.

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Flight route EDR estimation using MHE to fuse three-dimensional wind information in the QAR data analysis.

To address the problem that vertical wind data can provide information only on turbulence fluctuations in the vertical direction, meaning that the consideration of insufficiently compre-hensive turbulence information leads to low accuracy in eddy dissipation rate(EDR) estimation based on vertical wind (VWE), the paper proposes a flight route EDR estimation using MHE to fuse three-dimensional wind information in the Quick Access Recorder(QAR) data analysis. Quality control is performed on QAR data to eliminate irrelevant features for computing vertical wind and the two horizontal wind components. The corresponding formulas are then applied to obtain three-dimensional wind data information. Subsequently, a multi-head attention mechanism is employed to quantify the relationship between three-dimensional wind characteristics and the feature matrix (vertical acceleration). This process generates feature fusion weights, which are mapped onto the three-dimensional wind matrix and used to convert the three-dimensional wind data into one-dimensional wind data, thereby achieving the fusion of three-dimensional wind features. Finally, through maximum likelihood estimation(MLE) combine the new wind data in the frequency domain, the estimation of the flight route EDR is realized. Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. The proposed algorithm provides highly accurate EDR estimations for flight paths and demonstrates good practical applicability for assessing turbulence intensity along flight routes, thereby enhancing the safety of aircraft during flight.

A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.

Quantitative susceptibility mapping study of deep gray matter iron content in glioma patients.

Iron is associated with the pathophysiology of gliomas. The measurement of brain iron levels plays a vital role in defining the pathophysiologic changes caused by gliomas. This study aimed to analyze the effect of gliomas with different histopathology and molecular characteristics on deep gray matter (DGM) iron content and the relationship between DGM iron content and cognition in glioma patients (PT). In this retrospective study, we included 81 PT, categorized according to different histopathology and molecular characteristics, and 30 age- and gender-matched healthy controls (HC). Brain quantitative susceptibility mapping (QSM) maps were computed from three-dimensional (3D) multi-echo gradient-echo data using Laplacian-based phase unwrapping, a variational sharpening (V-SHARP) background field correction and the streaking artifacts reduction (STAR)-QSM method. ITK-SNAP was used to measure the susceptibility values reflecting the iron content in the regions of interest. Differences in DGM magnetic susceptibility between groups were compared. Pearson's correlation analysis assessed the relationship between DGM magnetic susceptibility and Montreal Cognitive Assessment (MoCA). Compared to HC, PT showed higher DGM magnetic susceptibility. QSM analysis exhibited higher DGM magnetic susceptibility in high-grade gliomas (HGG) than low-grade gliomas (LGG). Isocitrate dehydrogenase (IDH) wild-type gliomas showed higher magnetic susceptibility than IDH mutant-type in the putamen [IDH mutant-type: interquartile range (IQR), 0.052, 0.019 ppm vs. IDH wild-type: IQR, 0.062, 0.015 ppm, P=0.015]. A higher Ki-67 index also correlated with higher magnetic susceptibility in the putamen (high Ki-67 index group: IQR, 0.063, 0.019 ppm vs. low Ki-67 index group: IQR, 0.052, 0.016 ppm, P=0.005). The PT group had significantly lower MoCA scores than the HC group (patients group vs. HC group: 22.30±4.92 vs. 24.70±2.20, P=0.021). There was a negative correlation between MoCA scores and DGM magnetic susceptibility. The DGM iron content, as measured by QSM, was higher in PT than in HC and correlated with tumor grade. Cognitive decline is associated with increased DGM iron content in PT. The DGM iron content could be a potential biomarker in glioma differentiation and prognosis.

An alternative approach to code, store, and regenerate 3D data in dental medicine using open-source software: A scripting-based technique.

An alternative approach to code, store, and regenerate 3D data in dental medicine using open-source software: A scripting-based technique.

Impacts of Vertical Resolution and Diurnally Varying Background Error Covariance on FY-4A/AGRI Data Assimilation over Land

Abstract This study aims at improving the assimilation of Fengyun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) clear-sky surface-sensitive brightness temperature over land using the Weather Research and Forecasting and the Gridpoint Statistical Interpolation three-dimensional variational data assimilation for summer convective cases over eastern China. We investigated the influences of the planetary boundary layer on the assimilation of FY-4A/AGRI surface-sensitive channels, focusing on model low-level vertical resolution and diurnal variation of background error covariance. The vertical levels are increased from 48 to 91 by adding more near-surface levels. First, stronger low-level vertical background error correlations of humidity and temperature are found during daytime than during nighttime. A total of five numerical experiments are then conducted to examine the impacts of FY-4A/AGRI assimilation with two vertical resolutions. The experiments WV48 and WV91 assimilate only the water vapor channels, and ALL48, ALL91, and ALL91M assimilate the water vapor and surface-sensitive channels. The diurnally varying background error covariance, which is estimated at 3-h intervals, is incorporated into ALL91M. The 6-h cycling data assimilation at 1-h interval prior to convection initiation is conducted for each of the seven cases. Overall, a comparison between WV48/WV91 and ALL48/ALL91M revealed a consistent positive impact of AGRI brightness temperature assimilation over land on convective precipitation forecasts. The added values of assimilating AGRI surface-sensitive channels are more pronounced by using the finer vertical resolution due to low-level humidity enhancements that are related to the rise of the planetary boundary layer. With the diurnally varying background error covariance, ALL91M outperformed ALL91 because of improved low-level analyzed structures that are associated with proper low-level vertical error correlations of humidity and thermal variables.

Improving PM2.5 forecasts with three-dimensional variation data assimilation of visibility observations in China

Improving PM2.5 forecasts with three-dimensional variation data assimilation of visibility observations in China

Three-dimensional heterogeneous panel data models with multi-level interactive fixed effects

Three-dimensional heterogeneous panel data models with multi-level interactive fixed effects

Prediction of vegetation pattern evolution in arid ecosystems using 3D-Var data assimilation.

In arid regions, ecosystems are fragile, and vegetation exhibits high sensitivity to changes in climatic conditions. Vegetation patterns-non-uniform macroscopic structures formed by vegetation through temporal and spatial self-organization-serve as critical indicators of an ecosystem's adaptive capacity, post-disturbance resilience, and early warning signals of ecosystem degradation. Investigating the formation mechanisms of vegetation patterns using reaction-diffusion (RD) models represents a vital approach to deciphering vegetation evolution dynamics, with significant implications for protecting arid ecosystems. However, heterogeneous steady-state solutions of RD systems, such as Turing patterns, often reside in multistable regions. This implies that minute variations in initial conditions may lead to markedly divergent outcomes. When initial vegetation distribution data are imprecise, predictions of vegetation evolution trends and steady-state distributions in a given spatial position using RD models become highly sensitive to initial errors-a case where "minor discrepancies in input yield vastly divergent results." This study applies the three-dimensional variational data assimilation method to a RD model coupling vegetation, soil moisture, and surface water dynamics in arid regions. The results demonstrate that incorporating a modest amount of observational data can substantially enhance the model's predictive accuracy for vegetation evolution trajectories.

Study on Wind-eroded Concave Surface Morphology Based on Laser Radar and Color Space Encoding

Abstract This article takes a test area in the eastern edge of Greenbulongtan, Barunbireli Town, Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, where a polylactic acid fiber (PLA) sand barrier is installed, as the research object. By using the three-dimensional scanning technology and software such as Sketch Up with the portable device iPhone12 Pro Max equipped with a LiDAR, the boundary length, erosion depth, erosion coefficient, and erosion intensity of the concave surface of the sand barrier in the test area were measured through the method of model size measurement. In addition, the model size measurement method and the novel RGB numerical simulation algorithm proposed in this paper were mutually corroborated in terms of the accumulation rate of the concave surface. The experiment shows that the erosion coefficient of the PLA wind erosion concave surface with a size of 1.5 m×1.5 m in the test area is about 1.34, the accumulation intensity is about 0.8 ml/cm2, and the erosion intensity is about 1.2 ml/cm2. The overall erosion amount in the test area is greater than the accumulation amount, and the stability of the 1.5 m×1.5 m PLA sand barrier in the experimental stage is poor. Exploring the relationship between the specific RGB values and eleva-tion is the highlight of this article. The novel RGB numerical simulation algorithm proposed in this paper calculates the erosion rate, which fits with the erosion rate obtained by measuring the three-dimensional model size data, with a fitting degree of more than 98%. This proves that using LiDAR three-dimensional modeling to study wind erosion concave surfaces is feasible, and the RGB numerical simulation algorithm is simple and effective in quickly and efficiently processing complex concave terrain.

Zygomatic Osteotomy surgery design software based on skull CT scans - Self-supervised algo reduces workload.

Zygomatic Osteotomy surgery design software based on skull CT scans - Self-supervised algo reduces workload.