3D Transformation Research Articles

Towards harmonization of SO(3)-equivariance and expressiveness: a hybrid deep learning framework for electronic-structure Hamiltonian prediction

Abstract Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the non-linear expressive capability of networks remains unsolved. To navigate the harmonization between SO(3)-equivariance and expressiveness, we propose HarmoSE, a deep learning method synergizing two distinct categories of neural mechanisms as a two-stage encoding and regression framework. The first stage corresponds to group theory-based neural mechanisms with inherent SO(3)-equivariant properties prior to the parameter learning process, while the second stage is characterized by a non-linear 3D graph Transformer network we propose, featuring high capability on non-linear expressiveness. Their combination lies in the point that, the first stage predicts baseline Hamiltonians with abundant SO(3)-equivariant features extracted, assisting the second stage in empirical learning of equivariance; and in turn, the second stage refines the first stage’s output as a fine-grained prediction of Hamiltonians using powerful non-linear neural mappings, compensating for the intrinsic weakness on non-linear expressiveness capability of mechanisms in the first stage. Our method enables precise, generalizable predictions while capturing SO(3)-equivariance under rotational transformations, and achieves state-of-the-art performance in Hamiltonian prediction tasks under multiple mean absolute error (MAE) metrics, such as the average MAE across all samples and matrix elements, the MAE for challenging samples, the MAE for different Hamiltonian blocks, and the MAE for the challenging blocks. It also demonstrates significant improvements in accuracy for downstream quantities, such as occupied orbital energy and the electronic wavefunction, as measured by MAE and cosine similarity, respectively.

Development of fully automated models for staging liver fibrosis using non-contrast MRI and artificial intelligence: a retrospective multicenter study

Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF. A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n=1208) and internal test (n=518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs-Clinic, Image, and Fusion-were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists). Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging≥F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment. AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF. National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes

Background and Objective:Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details. Methods:To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time. Results:VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at https://github.com/marziehoghbaie/VLFATRollout/.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Learning Internal Representations of 3D Transformations From 2D Projected Inputs.

We describe a computational model for inferring 3D structure from the motion of projected 2D points in an image, with the aim of understanding how biological vision systems learn and internally represent 3D transformations from the statistics of their input. The model uses manifold transport operators to describe the action of 3D points in a scene as they undergo transformation. We show that the model can learn the generator of the Lie group for these transformations from purely 2D input, providing a proof-of-concept demonstration for how biological systems could adapt their internal representations based on sensory input. Focusing on a rotational model, we evaluate the ability of the model to infer depth from moving 2D projected points and to learn rotational transformations from 2D training stimuli. Finally, we compare the model performance to psychophysical performance on structure-from-motion tasks.

Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-patient models exploiting data from multiple participants. The subject-specific models using only low-density 8×8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. The proposed SwinTW decoder enables future speech neuropros-theses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests that such a model can be applied to new patients that do not have paired acoustic and neural data, providing an advance in neuroprostheses for people with speech disability, where acoustic-neural training data is not feasible.

Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer

The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.

A morphological indicator for aortic dissection: fitting circle of the thoracic aorta

BackgroundThis study aims to identify a morphological indicator of aortic dissection (AD) based on the geometrical characteristics of the thoracic aorta.MethodsWe evaluated computed tomographic angiograms of 63 samples with AD (22 with type A AD, 41 with type B AD) and 71 healthy samples. Via centerline extraction and spatial transformation, the spatial entanglement of the aorta was minimized, and the expanded 2D aortic morphology was obtained. The 2D morphology of the thoracic aorta was fit to a circle. The applicability of the fitting circle method for identifying aortic dissection was verified by multivariable logistic regression analysis.ResultsVia the 3D coordinate transformation algorithm, the optimal aortic view was obtained. On this view, the geometrical characteristics of the thoracic aortas of the healthy controls were similar to a portion of a circle (sum of residuals: 3502.45 ± 2566.71, variance: 86.23 ± 56.60), while that of AD samples had poorer similarity to the circle (sum of residuals: 5404.78 ± 3891.69, variance: 129.90 ± 90.09). This difference was significant (p < 0.001). A logistic regression model showed that increased deformation of the thoracic aorta was a significant indicator of aortic dissection (odds ratio: 1.35, p = 0.034).ConclusionsThe morphology of the healthy thoracic aorta could be fit to a circle, while that of the dissected aorta had poorer similarity to the circle. The statistics of the circle are an effective indicator of aortic deformation in AD.Trial registrationThis study is registered in the Chinese Clinical Trial Registry (ChiCTR2000029219).

Fusing binocular vision and deep learning to detect dynamic wheel-rail displacement of high-speed trains

Large displacements between the wheel and rail lead to track damage or even train derailment. Detecting dynamic wheel-rail displacement (DWRD) can provide a technical means for discriminating the operational health status of rail vehicles and formulating active control strategies. In this paper, a new method, which fuses binocular vision (BV) and deep learning (DL), is proposed to detect the DWRD online, the method is abbreviated as BV-DL. First, a binocular camera is used to capture the dynamic wheel-rail contact video online, while two lasers are equipped to focus on the wheel-rail contact region to locate some keypoints in the wheel-rail contact images. Then, an ROI (region of interest) detection network is proposed to recognize the wheel-rail contact region automatically, and a keypoint detection network is proposed to detect the wheel-rail pixel displacement (WRPD). Finally, a 3D coordinate transformation (3DCT) model is built to calculate the wheel-rail world displacement in real-time, i.e., the DWRD between the keypoints. To test the performance of the method, a laser displacement sensor-based method is used to verify the reliability of the 3DCT model. Meanwhile, a traditional image processing method, a MobileNet V3-based method, and a YOLOX-s-based method are introduced to predict the WRPD. Experimental results show that compared with the four methods, the proposed BV-DL method can achieve optimal results in pixel coordinate calculation and 3D coordinate computation.

Camera LiDAR calibration: an automatic and accurate method with novel PLE metrics

Abstract Camera-LiDAR calibration is a crucial aspect of perception systems and sensor fusion in various fields, facilitating the fusion of data from cameras and LiDAR sensors for applications such as autonomous vehicles, robotics, and augmented reality. In this paper, we presented a novel multi-modal fusion method that introduces an efficient camera-LiDAR joint calibration technique using a simple checkerboard. Our method estimates the 3D rigid transformation of the camera system with respect to the LiDAR frame by establishing 2D-to-3D point correspondences for geometric calibration. We improved the idea of the traditional PNP (Perspective-N-Point) algorithm by employing point cloud segmentation and clustering to re-match the checkerboard. When compared with the calibration methods in the Autoware and MATLAB calibration toolbox, the reprojection error was improved by 38.13\% and 58.30\% respectively. The average translation error was improved by 78.26\% and 51.61\% respectively, while the average rotation error is improved by 75\% and 60.78\% respectively, which has a great improvement in all three areas. At the same time, we propose a new evaluation metric for joint calibration results based on reprojected images: PLE (Pixel-Level Evaluation) index to reflect the accuracy of the joint calibration of the camera and LiDAR. An automatic calibration software has been developed for the calibration of the camera's intrinsic parameters as well as for the joint calibration of the camera-LIDAR extrinsic parameters. We have extensively validated our algorithm using the Intel RealSense Depth D435 Camera and LeiShen C16-151B LiDAR.

MeshWGAN: Mesh-to-Mesh Wasserstein GAN With Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation.

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications.

Emission analysis based on mixed traffic flow and license plate recognition model

With the rising ownership of new energy vehicles (NEVs), accurate road traffic emission estimations are crucial. This study combined mixed traffic flow and license plate recognition (LPR) using improved 3D convolutional neural network (3D-CNN) and Transformer models. The 3D-CNN model is designed for vehicle localization and traffic flow data acquisition, while the Transformer-based LPR model accurately recognizes license plates, distinguishing NEVs from conventional vehicles. The overall model is validated by the training and test datasets, and then with field application along a primary arterial segment in the South 2nd Ring Road, Xi’an, China. The results demonstrate capability for the traffic emission approximation of the mixed traffic flow including new energy vehicles, revealing that mixed traffic flow identification plays an important role in road emission approximation. Results and procedures of the study may provide benchmark for the subsequent research and the verification of related transportation policies.

P-289 Optimising the detection of obliterated Pouch of Douglas for endometriosis diagnosis, by combining unpaired endometriosis ultrasounds and magnetic resonance imaging using Artificial Intelligence

Abstract Study question Can we improve the diagnostic accuracy of the detection of POD obliteration in endometriosis magnetic resonance imaging, by leveraging results from unpaired eTVUS data sets? Summary answer We illustrate effective multimodal analysis methods improve POD obliteration detection accuracy from eMRI datasets, with an Area Under the Curve (AUC) from 65.0% to 90.6%. What is known already Traditionally, women investigated for pelvic pain and endometriosis, wait 6.4 years for laparoscopic diagnosis. There is a need for a more timely, non-invasive, accessible diagnostic tool. IMAGENDO is designed to combine eTVUS and eMRI using Artificial Intelligence (AI) to address this delay. We have previously demonstrated detection of pelvic endometriosis, including Pouch of Douglas (POD) obliteration, has a 95% specificity from endometriosis ultrasounds (eTVUS) and 72% from endometriosis magnetic resonance imaging (eMRI). This preliminary data has shown our novel multimodal AI approach, using imaging data from eTVUS and eMRIs, can improve diagnostic accuracy when detecting POD obliteration in endometriosis. Study design, size, duration The IMAGENDO study is a program of research designed to create a new diagnostic algorithm for endometriosis. The first part of the study describes the development of our initial algorithm using retrospective cross sectional transvaginal ultrasounds (n = 749), and magnetic resonance images (n = 89 private, n = 8984 public) from 9822 participants overall aged 18 to 45 years, collected between September 2011 and September 2022. Participants/materials, setting, methods Using public MRIs, we pre-trained a machine learning model, and fine-tuned the algorithm using private eMRIs to detect POD obliteration. Then unpaired eTVUSs were introduced to further improve our diagnostic model. We used a machine learning method known as Masked Autoencoder pretraining, which is unsupervised learning reconstructing masked data to generate a larger dataset. Then we embedded the data, compressing a large dataset into a small representation with the most salient features. Main results and the role of chance Scant training samples limited the generalisability of a 3D Vision Transformer to classify POD obliteration from MRI volumes, with an Area Under the Curve (AUC) of 65.0%. However, the masked auto-encoder pre-training partially mitigates this issue, improving the AUC to 87.2%. Adding knowledge distillation, and training a 3D Vision Transformer from scratch on such a small dataset is still challenging, with an AUC of 66.7%. However, adding both together: The knowledge distillation performance of 3D Vision Transformer with masked auto-encoder pre-training reaches an AUC of 77.2%, worse than without knowledge distillation, with an AUC of 87.2%. This could be due to the excessive domain shift between the pre-training dataset and TVUS dataset. However, fine-tuning the model from masked auto-encoder pre-training, the model improves accuracy from AUC=87.2% to AUC=90.6%. With all the steps using unmatched imaging from an alternative modality, this model ultimately demonstrated improvement in the AUC from 65% to 90% on our private MRI dataset. This is the first POD obliteration detection method that distils knowledge from TVUS to MRI using unpaired data, aiming to improve diagnostic accuracy of endometriosis from MRI; and the first machine learning method automatically detecting POD obliteration from MRI data to diagnose endometriosis. Limitations, reasons for caution Our eMRI datasets had some confounding problems, present as a result of artefacts, mislabelling, and misreporting. These were resolved using model checking, student auditing and expert radiology review. We will further test our algorithm with a diagnostic test accuracy study on at least two test cohorts. Wider implications of the findings Pre-training using digital data from different imaging modalities can improve the diagnosis of endometriosis especially when either imaging modality is missing. Provided specialist scanning is available, women with endometriosis will be able to obtain faster diagnosis prior to surgery. Trial registration number ACTRN12623000646640

Applications of 3D printing and digital transformation in aerospace engineering and telecommunication technologies

The aim of the development is to present the overall process of building functional prototypes for the purposes of aerospace engineering and telecommunication technologies, based on specific examples from the author's scientific and practical work. The scientific-applied contribution of the research is expressed in the correct selection and comparative analysis of compatible software products and devices for 3D modeling, digital transformation and 3D printing of objects from the space infrastructure and. in particular, of radio technical devices as key components of telecommunication satellites. The presented practical results are in the form of a final product that, if the criteria for precise modeling and the selection of a suitable material for 3D printing are met, can satisfy the quality criteria of the relevant industry for which it is intended.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 μm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Comparison of deviceless methods for distant object manipulation in mixed reality

Mixed Reality (MR) applications based on the current generation of Head-mounted displays (HMDs) support a deviceless manipulation of virtual objects based on finger tracking and control of the 3D transformation with gestures. However, when the object manipulation is performed at some distance, and when the transform includes scaling, it is not apparent how to remap the hand motions over the degrees of freedom of the object. The most popular software toolkits used for XR development provide specific solutions, but there are still usability issues and a need for clear guidelines for the interaction design.In this work, we compare three different solutions for the devices and remote translation, rotation, and scaling of virtual objects in a real environment in two user studies aimed at assessing the usability of the methods for different tasks. The tasks are a fast and rough docking of virtual cubes on a tangible shelf from varying distances and the accurate placement of cubes on the shelf in a fixed amount of time.The outcomes of these studies show that the usability of the manipulation methods is strongly affected by the use of separate or integrated control of the degrees of freedom, by the use of the hands in a symmetric or specialized way, by the visual feedback, and by the previous experience of the users and provides valuable guidelines for the design of interactive mixed environments.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

A hierarchical occupancy network with multi‐height attention for vision‐centric 3D occupancy prediction

AbstractThe precise geometric representation and ability to handle long‐tail targets have led to the increasing attention towards vision‐centric 3D occupancy prediction, which models the real world as a voxel‐wise model solely through visual inputs. Despite some notable achievements in this field, many prior or concurrent approaches simply adapt existing spatial cross‐attention (SCA) as their 2D–3D transformation module, which may lead to informative coupling or compromise the global receptive field along the height dimension. To overcome these limitations, we propose a hierarchical occupancy (HierOcc) network featuring our innovative height‐aware cross‐attention (HACA) and hierarchical self‐attention (HSA) as its core modules to achieve enhanced precision and completeness in 3D occupancy prediction. The former module enables 2D–3D transformation, while the latter promotes voxels’ intercommunication. The key insight behind both modules is our multi‐height attention mechanism which ensures each attention head corresponds explicitly to a specific height, thereby decoupling height information while maintaining global attention across the height dimension. Extensive experiments show that our method brings significant improvements compared to baseline and surpasses all concurrent methods, demonstrating its superiority.