In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud using temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCF), and a key PCF is selected. After the 3D skeleton is predicted through 3D pose estimation, the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCF by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.

While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles pose significant hindrances for many potential cyclists. Large-scale analysis of overtaking maneuvers could inform traffic researches and city planners on how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and of LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, consisting of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3d object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

Additive Manufacturing (AM) simplifies the fabrication of complex geometries. Its scope has rapidly expanded from the fabrication of pre-production visualization models to the manufacturing of end use parts driving the need for better part quality assurance in the additively manufactured parts. Machine learning (ML) is one of the promising techniques that can be used to achieve this goal. Current research in this field includes the use of supervised and unsupervised ML algorithms for quality control and prediction of mechanical properties of AM parts. This paper explores the applications of supervised learning algorithms - Support Vector Machines and Random Forests. Support vector machines provide high accuracy in classifying the data and is used to decide whether the final parts have the desired properties. Random Forests consist of an ensemble of decision trees capable of both classification and regression. This paper reviews the implementation of both algorithms and analyzes the research carried out on their applications in AM.

Building Information Modelling (BIM) has gained significant relevance in civil engineering, particularly in the infrastructure sector. It offers the potential to increase efficiencies, optimize processes, and enhance sustainability throughout infrastructure projects’ life cycles. BIM consolidates information about asset components into a single model, enabling real-time updates, cost reduction, and improved consistency. Digital twins serve as valuable extensions of BIM in infrastructure, playing a crucial role during operation. By capturing real-time data from sensors and IoT devices, digital twins enabls continuous monitoring, proactive maintenance, efficient energy management, and informed decision-making for optimized operational efficiency. Advancements in 3D point cloud technologies, such as 3D laser scanning, have expanded BIM’s application in operation and maintenance. These technologies rapidly capture accurate and detailed three-dimensional information, prompting the exploration of automated alternatives to manual point cloud modeling. This paper demonstrates the application of supervised machine learning, specifically support vector machines, for analyzing and segmenting 3D point clouds, a crucial step in 3D modeling. Various approaches for semantic segmentation are introduced, investigated, and evaluated using diverse data sets. The results highlight the effectiveness of supervised machine learning techniques in achieving accurate segmentation of 3D point clouds.

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topo-logical structure generated from the supervoxelization of the 3D point clouds, which is used to make the clo-sure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrates the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

Geotechnical investigation is an important site characterization process in engineering construction. Excavation pits and boreholes are popular geotechnical investigation methods for accessing subsurface soil and rock characteristics. Although they are accurate, they are limited to the discrete locations where tests are carried out. The demand for 3D representation has been increasing as an alternative for improved visualization of geotechnical investigations. This study developed a procedure for 3D modelling of geotechnical investigations using open-source software. It improves 3D visualization of geotechnical investigations and characterization of accuracy and uncertainty assessment. Presently, most geotechnical investigations do not regularly report uncertainty assessment despite its importance in risk analysis of the investigations. The procedure developed in this study was tested in a proposed site for construction of an Inland Container Depot where it depicted geotechnical soil properties with more than 80% accuracy on holdout samples. It was able to model both horizontal and vertical variations of the soil properties with quantification of uncertainty. More testing and wide applications are recommended.

In the realm of autonomous vehicle environment perception, the primary objective of point cloud target detection is to swiftly and accurately identify three-dimensional objects from point cloud data. To meet this requirement, the prevalent network architecture employed in the industry is the voxel-based PointPillars model. Nonetheless, this model faces challenges in maintaining detection accuracy when objects are obscured or diminutive in size. In response to this issue, we introduce AGPNet, a novel model that seamlessly integrates four key modules: Data Augmentation, Dynamic Graph CNN, Pillar Feature Net, and Detection Head (SSD). The Data Augmentation module enhances the adaptability of point cloud data to complex and ever-changing real-world environments. The Dynamic Graph CNN module endows the network structure with geometric features, which encapsulate not only the point itself but also its adjacent points. The Pillar Feature Net module translates three-dimensional point cloud data into pseudo-image data through the utilization of voxels. Subsequently, the Detection Head (SSD) module leverages this pseudo-image data to conduct target detection of three-dimensional objects. Our experiments, conducted on the KITTI dataset, demonstrate that our proposed method boosts object detection accuracy by 6-7 percentage points compared to the PointPillars model, while maintaining similar detection times.

3D Zernike moments based on 3D Zernike polynomials have been successfully applied to the field of voxelized 3D shape retrieval and have attracted more attention in biomedical image processing. As the order of 3D Zernike moments increases, both computational efficiency and numerical accuracy decrease. Due to this phenomenon, a more efficient and stable method for computing high-order 3D Zernike moments was proposed in this study. The proposed recursive formula for computing 3D Zernike radial polynomials combines the recursive calculation of spherical harmonics to develop a voxel-based algorithm for the calculation of 3D Zernike moments. The algorithm was applied to the 3D shape Michelangelo's David with a size of 150×150×150 voxels. As compared to the method without additional acceleration, the proposed method uses a group action of order sixteen orthogonal group and saving unnecessary iterations, the factor of speed-up is 56.783±3.999 when the order of Zernike moments is between 10 and 450. The proposed method also obtained an accurate reconstructed shape with the error rate (normalized mean square error) of 0.00 (4.17×10^-3) when the reconstruction was computed for all moments up to order 450.

3D reconstruction from a single 2D input is a classic problem in the field of computer vision. With the advancements in deep learning, the performance of 3D reconstruction has also significantly improved. The reconstruction task is more difficult for objects with no textures or complex deformations. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to lack of standard datasets or 3D shape representation methods, it is hard make direct comparisons between all reviewed methods. However, this paper reviews different approaches for reconstructing 3d shape as depth maps, surface normals, point clouds and meshes; along with various loss functions and evaluation metrics used to train and evaluate these methods.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

High-precision, ultra-thin angular detectable imaging upon a single pixel holds significant promise for light field detection and reconstruction, thereby catalyzing advancements in machine vision and interaction technology. Traditional light-direction angle sensors relying on optical components like gratings and lenses face inherent constraints from diffraction limits in achieving device miniaturization. Recently, angle sensors via coupled double nanowires have demonstrated prowess in attaining high-precision angle perception of incident light at sub-wavelength device scales, which may herald a novel design paradigm for ultra-compact angle sensors. However, the current approach to measure the three-dimensional (3D) incident light direction is unstable. In this paper, we propose a flexible segmented concentric nanorings structure capable of discerning the 3D light-direction based on its sensitivity character to both elevation (θ) and azimuth (ϕ) angles at a micrometer device scale. Through deep learning (DL) analysis and prediction, our simulations reveal that for angle scanning with a step size of 1o, the device can still achieve a detection range of 0∼360o for azimuth and 45o∼90o for elevation, with an average accuracy of 0.19o, and DL can further solve some data aliasing problems to expand the sensing range. Our design broadens the angle sensing dimension based on mutual resonance coupling among nanorings segments, and through waveguide implementation or sensor array arrangements, the detection range can be flexibly adjusted to accommodate diverse application scenarios.

3D human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.

Along with the popularity of the Internet, people are exposed to more and more ways of micro-videos, and a huge amount of micro-video data has emerged. micro-videos have gradually become the Internet content preferred by the public, and a large number of micro-video apps have also emerged, such as Tiktok and Kwai. Intelligent classification and mining of micro-videos can greatly enhance user experience, improve business operation efficiency and enhance user experience. Through deep intelligent analysis and mining of micro-videos, important information in micro-videos can be extracted to provide an important basis for beautifying videos, content appreciation, video recommendation, content search, etc. In the past, content understanding for short videos often used human work annotation, but in recent years, with the great success of deep convolutional neural networks in image recognition, short video content understanding based on this method has gradually developed. Nowadays, most recognition algorithms extract the feature representation of each frame independently and then fuse them. However, while extracting the feature representation, some low-level semantic features are lost, which makes the algorithm unable to accurately distinguish the category of the video. At present, the algorithm of micro-video recognition based on deep learning has surpassed the iDT algorithm, making these traditional methods fade out of people’s view. In this paper according to the micro-video classification task, a new network model is proposed to concatenate features of each modality into the overall features of various modalities through the network, and then fuse the various modal features with the attention mechanism to obtain the whole micro-video features, which will be used for classification. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

Recently, new semantic segmentation and object detection methods have been proposed for direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information and its robustness to dynamic changes in the environment. In addition, the cost of LiDAR sensors has decreased in recent years, which is an important factor for many application scenarios. The challenge with 3D LiDAR sensors like the Velodyne HDL-64E S2 is that they can output large 3D data at up to 1,300,000 points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real-time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed, but although it is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, we propose a new approach to semantic segmentation in forestry that uses a systematic sampling method in which the local neighbors of each point are retained to preserve geometric details. Our approach has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation in large datasets such as Semantic3D. We also present a preliminary study on the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

Accurate weight measurement is pivotal for monitoring the growth and well-being of cattle. However, the conventional weighing process, which involves physically placing cattle on scales, is labor-intensive and distressing for the animals. Hence, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. In the initial phase, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment two dominant parts of the cattle. From these segmented parts, three crucial dimensions of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, Polynomial regression, Random Forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.81% using the random forest regression model.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, bio-mass estimation, etc. Deep Neural Networks (DNN) have shown superior results when comparing with conventional machine learning methods such as Multi-Layer Perceptron (MLP) in cases of huge input data. The objective of this research was to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, RGB channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Accurate geometric modeling of blood vessels lumen given their 3D images is a basis for vessel quantification in support of cardiovascular diseases diagnosis, treatment and monitoring. Unlike other approaches, which assume circular or elliptical vessel cross-section, the proposed method employs parametric B-splines combined with the image formation system equations to accurately localize the highly curved lumen boundaries. No image segmentation is involved which might reduce the localization accuracy in effect of spatial discretization. It is shown that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. Two example applications: modeling the lower extremities artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA), are presented and discussed to demonstrate the method potential. Besides application to medical diagnosis, blood-flow simulation and vessel-phantom design, it can serve as a tool for automated annotation of image datasets required to train machine-learning algorithms.

We explore 3D printing modules for self-assembling spacecraft and robots – structure, actuators, electrics. Self-assembly has long been viewed as a highly desirable capability for autonomous construction of large space structures. We review self-assembly in space which focusses on the self-assembly of modules that demonstrate the range of applications. However, self-assembly may be synergised with 3D printing to offer an automated capability from raw material to modules for assembling new spacecraft or habitats. One application of 3D printing is using space debris on-orbit as an in-situ resource - defunct spacecraft may be salvaged as raw material for in-situ construction on demand. The common features of all self-assembling modules are that the modules constitute a structure housing a computer-controlled actuator internally and a reversible latching mechanism externally. We have demonstrated a 3D printed DC electric motor in which the only components that were not 3D printed are the wire coils. We have married our 3D printed motor prototype as an actuated joint between two 3D printed trigon-type panels developed as part of a trigon self-assembling system concept. The trigon concept underlies a modular approach to self-assembling and self-deploying structures. The 3D printed motorised panel system demonstrates that the motor aspect and structural aspects of robotic self-assembling machines are amenable to 3D printing. This has implications for self-assembling systems into modular satellites as a solution to space debris.

To enable purposeful design and implementation of automated concrete technologies, precise assessment and prediction of the complex material flow at various stages of the process chain are necessary. This paper investigates the intermediate stage of the extrusion and deposition processes in extrusion-based 3D-concrete-printing, using a numerical model based on the Particle Finite Element Method (PFEM). In PFEM, due to the Lagrangian description of motion, remeshing algorithms and the alpha shape method are used to track the free surface during large deformation scenarios. The Bingham constitutive model was used for describing the rheological behaviour of fresh concrete. This model is validated by comparing the numerically predicted layer geometries with those obtained from laboratory 3D printing experiments. Extensive parametric studies were then conducted using the numerical simulation, delineating the influence of process and material parameters on the layer geometries, the dynamic surface forces generated under the extrusion nozzle and the inter-layer interactions.

Three-dimensional flexible piezoresistive porous sensors have been used in health diagnosis and wearable devices. By utilizing 3D printed sacrificial mold casting with highly conductive polymer composites, porous sensors with high conductivity and complex structures were achieved. Traditional conductive polymer composites are usually heated using external heat sources, which are time and energy consuming. However, in this study, conductive porous sensors with triply periodic minimal surface (TPMS) structures were manufactured using self-resistance electric heating by utilizing the conductive nature of the polymers. The porous structures were designed based on Diamond (D), Gyroid (G), and I-WP (I) unit cells of the TPMS. The use of self-resistance electric heating has improved the compression performance and piezoresistive response of the structures. Among these structures, the D-based structure exhibited the maximum response strain (61 %), with a corresponding resistance response value of 0.97, which increased by 10.26 % compared to that of the structures heated by using the external heat sources. This study offers a new perspective on designing and optimizing porous materials with specific functionalities, particularly in the realm of flexible and portable piezoresistive sensors.

On using a 3D model to teach students a topic in astronomy, feedback was evaluated via a questionnaire. Analysis of students' responses to the questions revealed that user interface had a significant impact on their attitudes, which in turn could increase users' willingness to use immersive technology in e-learning. The results also showed that improving visual features through immersive technology and augmented reality tools were effective. The results confirm all hypotheses about positive relationships between facilitating conditions and usability of metaverse technology for students' attention and study motivation.

A novel and simple method to improve the corrosion resistance of copper by constructing a 3D 1-dodecanethiol self-assembled monolayers (SAMs) in 3.5% NaCl solution is reported in this study. Several drops of 1% H3PO4 solution are thinly and uniformly distributed on copper surface to form a 3D nanostructure constituted by Cu3(PO4)2 nanoflowers. The anticorrosion properties of 1-dodecanethiol SAMs on copper surface and on copper surface treated with H3PO4 solution were evaluated. Results demonstrated that 1-dodecanethiol SAMs on bare copper surface exhibit good protection capacity, whereas a copper surface pretreated with H3PO4 solution can substantially enhance the corrosion resistance of 1-dodecanethiol SAMs.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

Manufacturing of facial epithesis is a complex technological process including several traditional stages of production, accompanied by aggravated psycho-emotional status of the patient. One of the main factors causing discomfort in patients with facial defects is the process of obtaining silicone impressions from the wound surface and the long waiting period for the final epithesis. Active development of digital technologies in medicine and dentistry helps specialists not only to automate most of the traditional stages of manufacturing of dento-mandibular and facial prostheses, but also improves the quality of life of patients by increasing the efficiency of services and reducing the production time of prosthetic structures. The application of digital technologies in the production of facial epithesis was difficult due to the lack of specialized 3D-software for modeling and material for further additive manufacturing of structures. The authors developed a unique technology for 3D modeling and additive manufacturing of facial epithesis, including a 3D CAD module and material for 3D printing. The primary objective was to fabricate immediate facial prostheses in the postoperative period in the shortest possible time until the final epithesis was fabricated.

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.

3D printing is playing an important role in cardiovascular disease by use of personalised models with replication of normal anatomy and pathology with high accuracy and reliability. While 3D printed heart and vascular models are shown to improve medical education, preoperative planning and simulation of cardiac procedures, and enhance communication with patients, 3D bioprinting however, represents a potential advancement of 3D printing technology by printing cellular or biological components, functional tissues and organs that can be used in a variety of applications in cardiovascular disease. Recent advances in bioprinting technology have shown the ability to support vascularisation of large scale constructs with enhanced biocompatibility and structural stability, thus creating opportunities to replace damaged tissues or organs. In this review, we provide an overview of the use of 3D bioprinting in cardiovascular disease with a focus on technologies and applications in cardiac tissues, vascular constructs and grafts, heart valves and myocardium. Limitations and future research directions are highlighted.

Tumor cells evolve in a complex and heterogeneous environment composed of different cell types and an extracellular matrix. Current 2D culture methods are very limited in their ability to mimic the cancer cell environment. In recent years, various 3D models of cancer cells have been developed, notably in the form of spheroids/organoids, using scaffold or cancer-on-chip devices. However, these models have the disadvantage of not being able to precisely control the organization of multiple cell types in complex architecture and are sometimes not very reproducible in their production, and this is especially true for spheroids. Three-dimensional bioprinting can produce complex, multi-cellular, and reproducible constructs in which the matrix composition and rigidity can be adapted locally or globally to the tumor model studied. For these reasons, 3D bioprinting seems to be the technique of choice to mimic the tumor microenvironment in vivo as closely as possible. In this review, we discuss different 3D-bioprinting technologies, including bioinks and crosslinkers that can be used for in vitro cancer models, and the techniques used to study cells grown in hydrogels; finally, we provide some applications of bioprinted cancer models.

3D printing facilitates the straightforward construction of microchannels with complex three-dimensional architectures. Here, we demonstrate 3D-printed modular mixing components that operate on the basis of splitting and recombining fluid streams to decrease interstream diffusion length. These are compared to helical mixers that operate on the principle of chaotic advection.

3D meso-scale structures that can reach up to centimeters in overall size but retain micro- or nano-features, proved to be promising in various science fields ranging from micro-mechanical metamaterials to photonics and bio-medical scaffolds. In this work we present synchronization of the linear and galvano scanners for efficient femtosecond 3D optical printing of objects at the meso-scale (from sub-μm to sub-cm spanning five orders of magnitude). In such configuration the linear stages provide stitch-free structuring at nearly limitless (up to tens-of-cm) working area, while galvo-scanners allow to achieve translation velocities in the range of mm/s-cm/s without sacrificing nano-scale positioning accuracy and preserving undistorted shape of the final print. The principle behind this approach is demonstrated, proving its inherent advantages in comparison to separate use of only linear stages or scanners. The printing rate is calculated in terms voxels/s, showcasing the capability to maintain an optimal feature size while increasing throughput. Full capabilities of this approach are demonstrated by fabricating structures that reach millimeters in size but still retain μm-scale features: scaffolds for cell growth, microlenses and photonic crystals. All this is combined into a benchmark structure: a meso-butterfly. Provided results show that synchronization of two scan modes is crucial for the end goal of industrial-scale implementation of this technology and makes the laser printing well aligned with similar approaches in nanofabrication by electron and ion beams.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, micron-to-millimeter scale structures represented by Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photo-chemical reaction curve is developed in this paper, and a sub-domain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results showed the superiority of the proposed 3D OPC method in the fabrication of the Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1 %, and the profile error of the fabricated Fresnel lens reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image increased by 18.92 %, and the average contrast of the image enhanced by 36 %. We believe the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

Introduction: The current generation of 3D printers are lighter, cheaper, and smaller, making them more accessible to the chairside digital dentist than ever before. 3D printers in general in the industrial and chairside setting can work with various types of materials including, metals, ceramics, and polymers. Evidence presented in many studies show that an ideal material used for dental restorations is characterised by several properties related to durability, cost-effectiveness, and high performance. This review is the second part in a 3D Printing series that looks at the literature on material science and applications for these materials in 3D printing as well as a discussion on the potential further development and future evolution in 3D printing materials. Conclusions: Current materials in 3D printing provide a wide range of possibilities for providing more predictable workflows as well as improving efficiency through less wasteful additive manufacturing in CAD/CAM procedures. Incorporating a 3D printer and a digital workflow into a dental practice is challenging but the wide range of manufacturing options and materials available mean that the dentist should be well prepared to treat patients with a more predictable and cost effective treatment pathway. As 3D printing continues to become a commonplace addition to chair side dental clinics, the evolution of these materials, in particular reinforced PMMA, resin incorporating zirconia and glass reinforced polymers offer increased speed and improved aesthetics that will likely replace subtractive manufacturing milling machines for most procedures.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

AbstractWhile the technology is relatively new, low cost 3D printing has impacted many aspects of human life. 3D printers are being used as manufacturing tools for a wide variety of devices in a spectrum of applications ranging from diagnosis to implants to external prostheses. The ease of use and availability of 3D design software and low cost has made 3D printing an accessible manufacturing and fabrication tool in many research laboratories. 3D printers can print materials with varying density, optical character, strength and chemical properties providing platforms for a huge number of strategies that can be chosen for user’s needs. In this review, we focus on applications in biomedical diagnostics and how this revolutionary technique is facilitating development of low cost, sensitive and often geometrically complex tools. 3D printing in fabrication of microfluidics, supporting equipment, optical and electronic components of diagnostic devices is presented. Emerging diagnostic 3D bioprinting as a tool to incorporate living cells or biomaterials into 3D printing is also discussed.

Introduction: The term 3D printing is commonly used to depict an assembling method whereby the final form of an object is the result of the addition of different layers to build the frame of an object. This procedure is more accurately portrayed as additive manufacturing and is likewise alluded to as fast prototyping. The term 3D printing, in any case, is generally new and has been an active part of current developments in Dentistry. Much publicity encompasses the evolution of 3D printing, which is hailed as an innovation that will perpetually change CAM manufacturing, including in the dental sector. This review is the first part in a 3D Printing series that looks at the history of 3D Printing, the technologies available and reviews the literature relating to the accuracy of these technologies. Conclusions: The recent advancement in digital dentistry to incorporate these tools has modernised dental practices by paving the way for computer-aided design (CAD) technology and rapid prototyping. The use of 3D printing has led to 3D digital models produced with intraoral scanners (IOS), which can be manipulated easily for diagnosis, treatment planning, mockups, and a multitude of other uses. Combining 3D Printing with a 3D intraoral scan eliminates the need for physical storage but makes it to retrieve a 3D models for use within all dental modalities.

Path planning in 3D environment is a fundamental research area for robots and autonomous vehicles. Based on the principle ``the shortest path consists of tangents'', RimJump* is proposed as a tangent-based path planning method suitable for finding the shortest path (both off-ground and on-ground) in 3D space (e.g., octomap and point cloud) for mobile platform to follow. RimJump* searches the tangent graph in the form of a path tree and considers the geometrical properties of the locally shortest path. Therefore, the method can provide all of the locally shortest paths that connect the starting point and the target, including the globally shortest path. And the time cost of RimJump* is insensitive to map scale increases in comparison to methods that search the whole passable space rather than the surface of the obstacle, e.g., Dijkstra and A*. In the Results, RimJump* is compared with other methods in terms of path length and time cost.

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

Pure monocular 3D reconstruction is an ill-posed problem that has attracted the research community's interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion, so many methods have been proposed. However, most of these methods were not evaluated in large datasets, under various motion patterns, had not been tested under the same metrics, and most of them had not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine learning-based methods, and two updates of the best methods, to identify the advantages and limitations of each category of the taxonomy and test if the addition of machine learning or the updates made on those methods improved them significantly. Thus, we evaluated each algorithm under the TUM-Mono benchmark and performed an inferential statistical analysis to identify significative differences through its metrics. Results determined that sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly improves the performance of geometric-based methods from different perspectives.

In recent years, Additive Manufacturing (AM), commonly referred to as 3D printing, has garnered the attention of the scientific community due to its capacity to transform ordinary and traditional items into customized materials at an affordable cost, through various AM processes. Antimicrobial/antibiofilm 3D printed materials are one of the most trending research topics, owing to the growing concerns over the emergence of complex microbial structures called "biofilms" on various surfaces. The review provides an overview of the evolution of additive manufacturing (AM) technologies and their various derivatives, along with a brief description of their materials and applications. It also introduces how biofilms can represent an advantageous lifestyle for microbial populations. The primary objective of this research was to conduct a systematic review on the development of planctonic or biofilm forms of microorganisms on 3D printed materials. The article summarizes commonly studied microorganisms on these materials and presents their 3D printing process, materials, as well as the fields covered by each of the analyzed papers. To the best of our knowledge, this is the first all-inclusive systematic review that amalgamated research conducted in diverse fields to assess the development of biofilms on surfaces produced through three-dimensional printing. Most notably, this review presents a comprehensive account of sustainable approaches for producing antimicrobial materials through 3D printing. Additionally, we assess their advancements in various fields such as medicine, environment, agri-food, and other relevant sectors. The findings of our literature review can be used to recommend appropriate microorganisms, 3D printing materials, and technologies for academic and industrial research purposes focusing on the development of microbial biofilms on 3D printed surfaces. Furthermore, it highlights the potential of environmentally-friendly modified AM technologies to combat biofilms in clinical and non-clinical areas. Our goal with this review is to help readers gain a better understanding of fundamental concepts, inspire new researchers, and provide valuable insights for future empirical studies focused on eradicating biofilms from 3D printed materials.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

While Neural Radiance Fields (NeRF) are gaining increasing interest in various domains as innovative methods for novel view synthesis and image-based reconstruction, their potential application in the realm of Cultural Heritage remains unexplored. Purpose of this paper is to assess the effectiveness of applying NeRF to sets of images of digital heritage objects and sites. The study’s findings demonstrate that NeRF could be valuable when used in combination with or as a comparison to other well-established techniques such as photogrammetry, to expand the possibilities of preserving and presenting heritage assets with enhanced visual fidelity and accuracy. Particularly, NeRF show promising results in improving the rendering of translucent and reflective surfaces, objects with homogeneous textures, and elements with intricate details. In addition, we demonstrate that, when considering the same set of input images (with known camera poses), reducing the image quality or the number of images results in significantly less information loss with NeRF compared to photogrammetry. This suggests that NeRF is preferentially suited for scenarios involving sparse information or low-quality photos or videos, which could be especially valuable in risky or challenging situations.

Polystyrene as a thin film on arbitrary substrates or pellets form defective graphene films or powders that can be dispersed in water and organic solvents. The materials were characterized by visible absorption, Raman and X-ray photoelectron spectroscopy, electron and atomic force microscopy and electrochemistry. Raman spectra of these materials show the presence of the expected 2D, G and D peaks at 2750, 1590 and 1350 cm^-1, respectively. The relative intensity of the G vs. the D peak is taken as a quantitative indicator of the density of defects in the G layer.

The addition of biocarriers can improve the biological processes appearing in a bioreactor since their surface allows the immobilization, attachment, protection, and growth of microorganisms. In addition, the development of a biofilm layer allows the colonization of microorganisms in the biocarriers. The structure, composition, and roughness of the biocarriers' surface are crucial factors that affect the development of the biofilm. In the current work, the aluminosilicate zeolites 13X and ZSM-5 were examined as the main building components of the biocarrier scaffolds, using bentonite, montmorillonite, and halloysite nanotubes as inorganic binders in various combinations. 3D printing was utilized to form pastes into monoliths that underwent heat treatment. The 3d-printed biocarriers were subjected to a mechanical analysis, including density, compression, and nanoindentation tests. Furthermore, the 3d-printed biocarriers were morphologically and structurally characterized using nitrogen adsorption at 77 K (LN2), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The stress-strain response of the materials being studied was obtained through nanoindentation tests combined with finite element analysis (FEA). These tests were also utilized to simulate the lattice geometries under compression loading conditions to investigate their deformation and stress distribution in relation to experimental compression testing. The results indicated that the 3d-printed biocarrier 13X/halloysite nanotubes was endowed with high specific surface area and extended mesoporous structure. Due to these assets, its bulk density was one of the lowest observed amongst the biocarriers derived from the various combinations of materials. The biocarriers based on the 13X zeolite exhibited the highest mechanical stability and appropriate morphological features. The 13X/halloysite nanotubes scaffold exhibits a moderate hardness value compared to the rest, while it presents the highest value of modulus of elasticity. In conclusion, Aluminosilicate zeolites and their combination with clays and inorganic nanotubes afford 3d-printed biocarriers of various textural and structural properties, which can be utilized to improve biological processes.

Fabrication of a true-3D inorganic ceramic with resolution down to nanoscale using sol-gel resist precursor is demonstrated. The method has an unrestricted free-form capability, control of the fill-factor, and high fabrication throughput. A systematic study of the proposed approach based on ultrafast laser 3D lithography of organic-inorganic hybrid sol-gel resin followed by a heat treatment enabled formation of inorganic amorphous and crystalline composites guided by the composition of the initial resin. The achieved resolution of 100 nm was obtained for 3D patterns of complex free-form architectures. Fabrication throughput of 50×10³ voxels/s is achieved; voxel - a single volume element was recorded by a single pulse exposure. After a subsequent thermal treatment, ceramic phase was formed depending on the temperature and duration of the heat treatment as validated by Raman micro-spectroscopy. The X-ray diffraction (XRD) revealed a gradual emergence of the crystalline phases at higher temperatures with a signature of cristobalite SiO₂, a high-temperature polymorph. Also, the tetragonal ZrO₂ phase known for its high fracture strength was observed. This 3D nano-sintering technique is scalable from nano- to millimeter dimensions and opens a conceptually novel route for optical 3D nano-printing of various crystalline inorganic materials defined by an initial composition for diverse applications for microdevices in harsh physical and chemical environments and high temperatures.

Background: The main objective of this study is to develop a customized metatarsophalangeal implant for the treatment of various conditions affecting this specific joint. This approach involves replacing the joint with a custom implant designed specifically to fit each patient's unique anatomical structure. The need for custom implants stems from the obvious lack of size variety available, which presents a major challenge when, the search for the ideal size implant can be a hard task, also when considering the possibility of extensive modification of the bone to adapt it to the available implant. This adaptation, often synonymous with bone reduction, can cause significant post-operative pain for the patient and impact healing time. Methods: A comparative study of modeling software was carried out to select the perfect software, by defining and comparing the relevant criteria. Similarly, the different biomaterial options were examined and compared to select the one best suited to this project, mentioning the imaging modality used to determine the types of images used for our approach. Finally, the 3D printers used in this work were represented, highlighting their technical characteristics. Findings: A resin metatarsophalangeal implant prototype was created using 3D modeling and reconstruction software and tools. Interpretation: Reconstruction and 3D modeling techniques are revolutionizing implant creation, offering customized solutions for patients suffering from metatarsophalangeal joint disorders caused by a wide range of diseases. These results have significant implications for disability management and rehabilitation, encouraging further exploration and progress.

A pilot study on laser 3D printing of inorganic free-form micro-optics is experimentally validated. Ultrafast laser nanolithography is employed for structuring hybrid organic-inorganic material SZ2080TM followed by high-temperature calcination post-processing. The combination allows production of 3D architectures and the heat-treatment results in converting the material to inorganic substance. The produced miniature optical elements are characterized and their optical performance demonstrated. Finally, the concept is validated for manufacturing compound optical components such as stacked lenses. This is opening for new directions and applications of laser made microoptics under harsh conditions such as high intensity radiation, temperature, acidic environment, pressure variations, which include open space, astrophotonics, and remote sensing.

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic and difficulty to keep clean from treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which becomes time-consuming and error prone. From another point-of-view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation have emerged. This article describes the use of various types of scanners capable of digitizing the human body, to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology in clinical environment.

Direct laser writing three-dimensional nano-lithography is an established technique for manufacturing functional 3D micro- and nano-objects via non-linear absorption induced polymerization process. In this Chapter an underlying physical mechanisms taking place during nano-confined polymerization reaction, induced by tightly focused ultra-short laser pulses, are reviewed and discussed. The special attention is paid on the effects that directly impact structuring resolution and minimum achievable feature size. Analysis of possible photo-initiation mechanisms as contributing multi-photon absorption and avalanche ionization in pre-polymers under diverse exposure conditions (wavelength, pulse duration) is presented. Feasible structuring of pure (non-photosensitized) and functional nanoparticles doped polymer precursors is justified and benefits of such materials/structures for microoptics, photonics and cell scaffolds are highlighted. The influence of temperature effects (induced by writing process itself or determined by ambient conditions) on polymerization process, observed in different pre-polymers under diverse exposure regimes is outlined. The further adjustment of the structuring resolution is possible via precise control of light polarization and diffusion assisted radical quenching. The work is concluded with a brief outlook on future challenges and perspectives related to refinement of 3D ultra-fast laser lithography fabrication process in the means of application of diverse post-processing methods and research into novel photo-curable materials including inorganic ones.

This paper introduces an innovative approach to lipreading, leveraging a web application designed to generate subtitles for videos where the speaker's mouth is visible and a comprehensive literature review that precedes the discussion, encompassing a thorough examination of various lipreading methods employed over the past decade. Our method employs a powerful deep learning model, featuring a 3D-convolution network and bidirectional LSTM, enabling accurate sentence-level predictions based solely on visual lip movements. With an impressive accuracy of 97%, our model is trained using pre-segmented lips regions, transformed into animated GIFs for effective pre-training. This work stands as a significant contribution to the evolving landscape of lipreading research, offering a practical and accurate solution for real-world applications.

In the last decade, three-dimensional (3D) printing has allowed the field of complex microfluidic channels to expand rapidly towards channels with variable cross-sections (i.e., beyond simple rounded media with a constant diameter) and channels. Whose trajectory can be outside a single plane and has gained numerous macro- and nanoscale manufacturing advances. Three-dimensional printing is being explored for various biomedical applications and the fabrication of nanomedicines using additive manufacturing techniques. Employing 3D-printed templates for macro-to-micro interfacing, a passively operated microfluidic device was designed. The research seeks to fabricate a biodevice that enables insulin microdosing, creating a subsystem like a microvalve and a microchannel matrix using additive manufacturing techniques. Our systems feature designs that enhance fluid dynamics without affecting the stability of the insulin. We propose using Humalog insulin in tropical climates (up to 30°C) without refrigeration for 28 days. The microvalve has a spiral trajectory with four outlet channels distributed along it. These channels align with the microchannels organized in a 10 mm x 15 mm matrix with 25 microchannels, each with a 770 µm internal diameter. The system delivers a dose of 2,3 mL in 6 minutes. We designed these systems for insulin micro-dosing, but they can also serve similar devices for other drugs.

Bismaleimide (BMI) is a high-performance thermosetting resin widely used in the aerospace, automotive, and mechanical and electronics industries due to its chemical and mechanical stability. It also has the capability of being used as an ink in polyjets used for 3D printing evident from thinner mellamide FTIR bands and faster curing rates as observed from TGA-DSC studies.

The maker movement has reached the optics labs, empowering researchers to actively create and modify microscope designs and imaging accessories. 3D printing has especially had a disruptive impact on the field, as it entails an accessible new approach in fabrication technologies, namely additive manufacturing, making prototyping in the lab available at low cost. Examples of this trend are taking advantage of the easy availability of 3D printing technology. For example, inexpensive microscopes for education have been designed, such as the FlyPi. Also, the highly complex robotic microscope OpenFlexure represents a clear desire for the democratisation of this technology. 3D printing facilitates new and powerful approaches to science and promotes collaboration between researchers, as 3D designs are easily shared. This holds the unique possibility to extend the open-access concept from knowledge to technology, allowing researchers from everywhere to use and extend model structures. Here we present a review of additive manufacturing applications in microscopy, guiding the user through this new and exciting technology and providing a starting point to anyone willing to employ this versatile and powerful new tool.

Free-edge effect is one of the factors affecting the mechanical properties of three-dimensional woven composites under tensile load. However, current research is relatively poorly understood regarding the effect of free-edge on the stiffness and strength of the material. This paper aims at examining the influence of free-edge effect on the mechanical properties of 3D woven composites under tension through experimental and simulation mathods. The three-dimensional DIC technique is used to collect the full-field strains on the specimen surface during the test, and the stress-strain differences in different regions in the width direction are analyzed, and the overlap of the curves in each region is found to be high. Experimental studies are conducted on specimens of different widths (within the range of 15-20 mm), and the results indicate that the differences in mechanical properties of 3D woven composites under tension loading in this width range are not significant. A progressive damage finite element model is developed for calculation and compared with experimental results.

We introduce optically clear and resilient free-form micro-optical of pure (non-photosensitized) organic-inorganic SZ2080 material made by femtosecond 3D laser lithography (3DLL). This is advantageous for rapid printing of 3D micro-/nanooptics, including their integration directly onto optical fibers. A systematic study on the fabrication peculiarities and quality of resultant structures is performed. Comparison of microlenses’ resiliency to CW and femtosecond pulsed exposure is determined. Experimental results prove that pure SZ2080 is ∼3 fold more resistant to high irradiance as compared with a standard photo-sensitized material and can sustain up to 1.91 GW/cm² intensity. 3DLL is a promising manufacturing approach for high-intensity micro-optics for emerging fields in astro-photonics and atto-second pulse generation. Additionally, pyrolysis is employed to shrink structures up to 40% by removing organic SZ2080 constituents. This opens a promising route towards downscaling photonic lattices and creation of mechanically robust glass-ceramic structures.

Solar3D is an open-source software application designed to interactively calculate solar irradiation at three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D city models. Solar3D extends GRASS GIS r.sun from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, that is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features which, as a whole, distinguish itself from existing 3D solar irradiation tools: (1) the ability to consume massive heterogeneous 3D city models, including massive 3D city models such as oblique airborne photogrammetry-based 3D city models (OAP3Ds or integrated meshes); (2) the ability to perform near real-time pointwise calculation for duration from daily to annual; (3) the ability to integrate and interactively explore large-scale heterogeneous geospatial data. (4) the ability to calculate solar irradiation at arbitrary surface positions including at rooftops, facades and the ground. Solar3D is publicly available at https://github.com/jian9695/Solar3D.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that the method achieves an accuracy of 87.4% on the MuPoTS-3D dataset, significantly improving the accuracy of end-to-end 3D human pose estimation methods.

Two-dimensional ultrasound (2D US) is commonly used in clinical settings for its cost-effectiveness and non-invasiveness, but it is limited by spatial orientation and operator dependency. Three-dimensional ultrasound (3D US) overcomes these limitations by adding a third dimension and enhancing integration with other imaging modalities. Advances in deep learning (DL) have further propelled the viability of freehand image-based 3D reconstruction, broadening clinical applications in intraoperative and point-of-care (POC) settings. This review evaluates state-of-the-art freehand 3D US reconstruction methods that eliminate the need for external tracking devices, focusing on experimental setups, data acquisition strategies, and reconstruction methodologies. PubMed, Scopus, and IEEE Xplore were searched for studies since 2014 following PRISMA guidelines, excluding those using additional imaging or tracking systems other than inertial measurement units (IMUs). Fourteen eligible studies were analyzed, showing a shift from traditional speckle decorrelation towards DL-based methods, particularly Convolutional Neural Networks (CNNs). Variability in datasets and evaluation methods hindered a comprehensive quantitative comparison, but notable accuracy improvements were observed with IMUs and integration of contextual and temporal information within CNNs. These advancements enhance freehand 3D US reconstruction feasibility, though variability limits definitive conclusions about the most effective methods. Future research should focus on improving precision in complex trajectories and adaptability across clinical scenarios.

3D printing technology has brought about a paradigm shift in the delivery of clinical care in medicine and dentistry. There are several different 3D printing technologies, which, along with different printable materials with unique biomechanical properties, allows for a wide range of applications for 3D printing in dentistry. The clinical use of 3D printing has created versatile applications which streamline our digital workflow. Technological advancements have also paved the way for the integration of new dental materials in dentistry. This paper provides an overview of the mechanisms of 3D printing, the dental materials relevant to each mechanism and the possible applications of these materials within different areas of dentistry. Understanding the existing spectrum of 3D printing applications in dentistry will serve to further expand its use in the dental field.

Human Papillomavirus (HPV) related cancers account for a large percentage of cancers. The tumor microenvironment (TME) is a complex structure and contributes significantly to tumor development, progression, and invasion. The TME is particularly pronounced in HPV-related cancers and HPV is known to influence the TME. In cancer research, there is an urgent need for new models to deepen our understanding of the pathophysiology of the disease and to facilitate the development of innovative therapies. In this context, 3D cell culture has emerged as a promising method in cancer research, that offers significant potential compared to conventional approaches. 3D modelling can produce results that are more consistent with clinical outcomes without the ethical concerns and problems of animal models. In this review, our aim is to discuss the tumor microenvironment in HPV-related cancers and demonstrate the importance of advancing 3D cell culture models for this purpose.

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. Leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms and extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results.

UAV has become powerful devices for both practitioners and researchers. In recent years, there has been a substantial growth in the usage of UAV for a wide range of applications in research and engineering. UAV are mostly utilized for image capturing techniques due to their time savings, low cost, minimum field work, and high precision. The purpose of this study is to generate an accurate and reliable 3D modelling process using UAVs photogrammetric technology; hence this study allows the techniques to evaluate the practicability of modelling and mapping inaccessible tunnels using images recorded by UAVs and photogrammetry. As a result, the approach was always employed to depict the true state of the place. This research incorporates three experiments. The first experiment is to build a 3D model using only one-directional light source and evaluate the accuracy of 3D photogrammetric model and conducted at the concrete tunnel. The second one is to build a 3D model of underground sewer tunnel using different light sources one-directional and omni-directional. The last experiment was to build 3D model using an UAV system which is developed based on first and second experiment’s result, evaluate an accuracy of 3D photogrammetric model and conducted at the tunnel named in the research as a “pedestrian tunnel. There is certain limitation in this study such as Adding a payload to a drone changed its weight distribution, which affected its stability. If the payload was not properly balanced, it caused the drone to fly unevenly, making it more difficult to control.

Pollen analysis as a part of palynology deals with the morphological determination of pollen and spores. Different technologies with different resolutions varying from simple light microscopy to highly elaborate electron microscopy are used for the examination, depending on the area of application (e.g. sedimentology, melissopalynology, forensic palynology, etc.). To answer the question of whether laser scanning microscopy (LSM) can replace scanning electron microscopy (SEM) for the determination of pollen species, 168 species were examined using LSM. It was concluded that LSM is both efficient and easy to handle. After preparing the fresh pollen, a 3D laser scan takes 5-10 minutes and unlike using SEM, the pollen does not have to be sputtered or processed. The 3D scans can be measured quickly and easily with the integrated software and there were no observable artifacts. At magnifications up to 8545x, the image quality is comparable to that of a sputtered SEM sample whereas at higher magnifications, the SEM method is superior. Overall, pollen display by LSM is much less time consuming and more cost effective than with the SEM method.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

OBJECTIVE: Although 3D-printed anatomic models are not new to medicine, the high costs and lengthy production times entailed have limited their application. Our goal was developing a new and less costly 3D modeling method to depict organ-tumor relations at faster printing speeds. METHODS: We have devised a method of 3D modeling using DICOM images. Coordinates are extracted at a specified interval, connecting them to create mesh-work replicas. Adjacent constructs are depicted by density variations, showing anatomic targets (ie, tumors) in contrasting color. RESULTS: An array of organ solid-tumor models were printed via Fused Deposition Modeling 3D printer at significantly less cost ($0.05/cm3) and time expenditure (1.73 min/cm3; both, p<.001). Printed models helped promote visual appreciation of organ-tumor anatomy and adjacent tissues. Our mesh-work 3D thyroidal prototype reproduced glangular size/contour and tumor location, readily approximating the surgical specimen. CONCLUSIONS: This newly devised mesh-type 3D printing method may facilitate anatomic modeling for personalized care and improve patient awareness during informed surgical consent.

Additive manufacturing, commonly referred to as 3D printing, is an exciting and versatile manufacturing technology that is gaining traction and interest in both academic and industrial settings. Polymeric materials are essential components in a majority of the “inks” used across the various 3D printing technologies. As the environmental ramifications of sole or primary reliance on petrochemicals as a resource for industrial polymers continue to manifest themselves at a global scale, a transition to more sustainable bioderived alternatives could offer solutions. In particular, cellulose is promising due to its global abundance, biodegradability, excellent thermal and mechanical properties, and ability to be chemically modified to suit various applications. Thus far, cellulose has typically been incorporated in additive manufacturing applications as a substrate, filler, or reinforcement for other materials. However, due to its versatility and desirable properties, the exploration of all-cellulose 3D printing materials and associated methodologies is increasing. This review will discuss the progress and status of all-cellulose 3D printing approaches, associated challenges, and their potential as a key player in a more sustainable industrial future.

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-groups PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies” of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore “pathologies” of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed previous results of other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA reduced strongly as the sample sizes per group were increased. Eigenvalues via mPCA were also found to be strongly effected by imbalances in sample sizes per group, although this problem was removed by using weighted forms of covariance matrices suggested by the maximum likelihood solution of the two-level model. However, this did not solve problems of spurious differences between groups in these simulations, which was driven by very small sample sizes in one group here. As a “rule of thumb” only, all of our experiments indicate that reasonable results are obtained when sample sizes per group in all groups are at least equal to the number of variables. Interestingly, the sum of all eigenvalues over both levels via mPCA scaled approximately linearly with the inverse of the sample size per group in all experiments. Finally, between-group variation was added explicitly to the MC data generation model in two experiments considered here. Results for the sum of all eigenvalues via mPCA predicted the asymptotic amount for the total amount of variance correctly in this case, whereas standard “single-level” PCA underestimated this quantity.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

3D printing is described as the third industrial revolution: its impact is global in industry and progresses every day in society. It presents a huge potential for ecology and evolution, sciences with a long tradition of inventing and creating objects for research, education and outreach. Its general principle as an additive manufacturing technique is relatively easy to understand: objects are created by adding material layers on top of each other. Although this may seem very straightforward on paper, it is much harder in the real world. Specific knowledge is indeed needed to successfully turn an idea into a real object, because of technical choices and limitations at each step of the implementation. This article aims at helping scientists to jump in the 3D printing revolution, by offering a hands-on guide to current 3D printing technology. We first give a brief overview of uses of 3D printing in ecology and evolution, then review the whole process of object creation, split into three steps: (1) obtaining the digital 3D model of the object of interest, (2) choosing the 3D printing technology and material best adapted to the requirements of its intended use, (3) pre- and post-processing the 3D object. We compare the main technologies available and their pros and cons according to the features and the use of the object to be printed. We give specific and key details in appendices, based on examples in ecology and evolution.

Diabetes is a major health concern that the next-generation of on-demand insulin releasing implants may overcome for personalized therapy. Therein, 3D-printed phenylboronic acid-containing subcutaneous implants with on-demand glucose-triggered drug release abilities are produced using high resolution stereolithography technology. To that end, the methacrylation of phenylboronic acid is targeted following a two-step reaction. The resulting photocurable phenylboronic acid derivative is accordingly incorporated within bioinert polyhydroxyethyl methacrylate-based hydrogels at varying loadings. The end result are sub-centimeter scaled 3D-printed bionert implants that can be remotely activated with 1,2-diols and 1,3-diols such as glucose for on-demand drug administration such as insulin. As a proof of concept, varying glucose concentration from hypoglycemia to hyperglycemia levels readily allow the release of pinacol, i.e. a model molecule with similar release properties than insulin, at respectively low and high rates. In addition, the results demonstrated that adjusting the geometry and size of the 3D-printed part is a simple and suitable method for tailoring the release behavior and dosage.

Most common classifications for acetabular bone defects are based on radiographic two-dimension imaging, with low reliability and reproducibility. With the rise of modern processing techniques based on 3D modelling, methodologies for volumetric quantification of acetabular bone loss are available. Our study aims to describe a new methodology for quantitative assess-ment of acetabular defects, based on 3D modelling, focused on the surface analysis of the integrity of the main anatomical structures of the acetabulum represented by four corresponding sectors (posterior, superior, anterior, and medial). The defect entity is measured as the Area Increase Ra-tio (AIR) detected in all the sectors analyzed on three planes of view (frontal, sagittal and axial) compared to healthy hemipelvises. The analysis was performed on 3D-models from the CT-scan of six exemplary specimens with a unilateral pathological hemipelvis. AIR between the native and the pathological hemipelvis was calculated for each sector, for a total of 48 analysis (range, +0.93% - +171.35%). AIR of >50% were found in 22/48(45.8%) sectors and affected mostly the posterior, medial and superior sectors (20/22, 90.9%). Qualitative analysis showed consistency of the data with the morphological features of the defects. Further studies with larger samples are needed to validate the methodology and potentially develop a new classification scheme.

New in vitro approaches for the assessment of their toxicity are crucial for creating better risk assessments given the growing use of nanoparticles (NPs) in the production of consumer goods. Humans will be exposed to nanoparticles through the inhalation route from contaminated aerosol. A promising method for evaluating the genotoxicity of NPs in vitro uses human airway epithelial cells. This study aimed to determine whether there were differences in morphology change of human bronchial/tracheal epithelial (HBTEC) cells responded to NPs in 2D and 3D spheroid models and to evaluate genotoxicity in 2D and 3D cultures after 24 hr exposure to TiO2-, Ag- and ZnO-NPs using the comet assay. Our 2D and 3D HBTEC cultures with TiO2-NPs did not show any statistically significant genotoxicity compared to controls for concentration points tested in vitro. HBTEC cells grown as 2D indicated that exposure to ZnO-NPs at low concentrations (10-50 ug/mL) for 24 hr did not cause any genotoxic effect but, at high concentrations, genotoxicity was observed. In contrast, treatment of 2D HBTEC with AgO-NPs led to an elevated in percentage of tail DNA damage from 50 ug/mL to 100 ug/mL, which was concentra-tion-dependent. Using 3D spheroid assays revealed a lower %tail length in ZnO- and AgO-NPs, thus exhibiting in vitro genotoxicity of NPs in this cell model. Spheroids (3D) rather than mono-layer cultures (2D) are believed to more precisely represent in vivo-like cell behavior when evaluating genotoxicity. Together, our findings demonstrate the genotoxic effects of NPs and point to the critical role that cell culture models play in the evaluation of toxicological risk.

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map's semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of both geometry, topology and semantics by incorporating the C-Map data structure in the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

This case study originates as a design experiment for a sustainable housing system built on-site. The context is Niamey, the capital of Niger. The study takes into account the environmental issues in the construction sector and aims to find a solution capable of meeting housing, environmental, and economic needs. In the field of earthen construction, the most important developments have been achieved in manufacturing methods. In particular, the use of an additive digital manufacturing system, such as large-scale 3D-printing, allows the construction of complex shapes derived from structural and thermal studies, maintaining a high degree of automation in the construction process, reducing construction times and labor costs. This paper investigates the possibility of responding to housing and environmental needs with a settlement system made almost entirely of printed earth, maintaining the highest possible degree of automation. Starting from a study on the state of the art of 3D-printing in architecture and printable earthen compounds, the design choices of similar cases are analyzed to understand the construction techniques, potentials, and limitations of the medium. Finally, a design proposal is developed based on the definition of a fully printable functional module, which, upon aggregation, determines the characteristics of the final settlement. This implies a radical change of approach compared to previous prototyping of 3D-printed earthen buildings, as the design of the single functional module is not an exercise that finds completion in itself, but is oriented to the scale of the settlement right from the definition of its basic geometric characteristics. In other words, the settlement is no longer the result of the serial aggregation of independent basic units, but arises spontaneously from the juxtaposition of functional modules designed to interact with each other and merge into a single residential complex. The settlement is therefore the large-scale replication of the alternation between full and empty spaces that characterizes the single functional module and, even more importantly, the replication can take multiple forms. In fact, the full and empty spaces of the functional module are planned to allow multiple combinations of aggregation. This introduces a certain degree of customization into the growth dynamics of the settlement, a factor that is entirely new compared to previous proposals by repeatable modules. No less important are the environmental implications, as designing for the scale of the settlement allows the low carbon footprint typical of earth-based construction to be extended from the single building to the entire settlement.

Microspheres, synthesized from diverse natural or synthetic polymers, are readily utilized in biomedical tissue engineering to improve the healing of various tissues. Their ability to encapsulate growth factors, therapeutics, and natural biomolecules, which can aid tissue regeneration, makes microspheres invaluable for future clinical therapies. While microsphere-supplemented scaffolds have been investigated, a pure microsphere-based scaffold with an optimized architecture has been challenging to create via 3D printing methods due to issues that prevent consistent deposition of microsphere-based materials and their ability to maintain the shape of the 3D-printed structure. Utilizing the extrusion printing process, we established a methodology which not only allows to create large microsphere-based scaffolds but also multicomposite matrices into which cells, growth factors, and therapeutics encapsulated in microspheres can be directly deposited during the printing process. Our 3D-McMap method addresses issues with scaffold shape fidelity during and after printing. Carefully timed breaks, minuscule drying steps, and adjustments to extrusion parameters, generated an evenly layered large microsphere-based scaffold that retains its internal architecture. Such scaffolds are superior to other microsphere-containing scaffolds, as they can release biomolecules in a highly controlled spatiotemporal manner. This capability permits to study cell responses to the delivered signals, to develop scaffolds that precisely modulate new tissue formation.

The present study aimed to characterize the microstructure of a temporary 3D printing resin (Resilab Temp), evaluating the optical properties and mechanical behavior according to different post-processing curing times. For the analysis of the surface microstructure and establishment of the best printing pattern, samples in bar format following ISO 4049 (25 x 10 x 3 mm) were designed in CAD software (Rhinoceros 6.0), printed on the W3D printer (Wilcos), and light-cured in Anycubic Photon at different times (without curing, 16 minutes, 32 minutes, and 1 hour). For the structural characterization, analyses were carried out in FTIR (Fourier Transform Infrared Spectroscopy) and Scanning Electron Microscopy (SEM). The mechanical behavior of the resin was based on flexural strength tests and Knoop microhardness. Color and translucency analysis were performed with a spectrophotometer (VITA Easy Shade Advanced 4.0), which was evaluated following CIELab, using gray, black, and white backgrounds. All analyses were performed immediately after making the samples and repeated after thermal aging with 2 thousand cycles (5-55 °C). The results obtained were statistically analyzed with a significance level of 5%. FT-IR analysis showed about a 46% degree of conversion on the surface and 37% in the center of the resin sample. The flexural strength was higher for the groups polymerized for 32 min and 1h, while the Knoop microhardness did not show a statistical difference between the groups. Color and translucency analysis also did not show statistical differences between groups. According to all the analyses carried out in this study, for the evaluated material, the post-polymerization time of 1 hour can be suggested to improve the mechanical performance of 3D-printed devices.

Printed oral splints are a therapeutic alternative in the treatment of functional disorders. Their mechanical properties are crucial to their clinical success and their performance can vary depending on cleaning, post-polymerization, or build orientation. This in vitro study aimed to compare the biaxial flexural strength of the chosen materials depending on the aforementioned parameters. 720 discs (n = 15 per group, 16 mm x 2 mm) were printed from splint materials. Printing was performed under 90°, 45°, or 0° orientation to the building platform with supporting structures. Specimens were either automatically or manually cleaned with isopropanol. Post-polymerization was performed with LED- or Xenon-light. Biaxial flexural strength (BFS) was determined with a piston-on-3-ball test after 24 hours or 60 days water storage (37°C). Test specimens were preloaded with 0.5 N and the load was applied by a piston of 1.6 mm in diameter at 1 mm/min crosshead speed. Statistics were performed by using descriptive statistics, ANOVA, and Levene-tests. Mean BFS after 24 hours of storage varied between 79 MPa and 157 MPa. After 60 hours the BFS significantly decreased and revealed values ranging from 72-127 MPa (mean24 113 MPa, mean60 97 MPa; p&lt;0.001). No significant differences could be determined between the materials (p=0.103) or between the different cleaning procedures (p=0.321). Post-polymerization yielded higher means with LED- (P1: 115 MPa) than with Xenon-light (P2: 95 MPa; p&lt;0.001). Regarding position, the mean values ranged from 101 MPa for 0°, 102 MPa for 45°, and 115 MPa for 90°, which was significantly different (p&lt;0.001). Build orientation of 90° and post-polymerization with LED-light provided significantly higher biaxial flexural strength and should be used to guarantee optimal strength of splint materials. Aging decreased the biaxial flexural strength of the tested specimens.

Drone technology has undoubtedly become an integral part of our everyday life these days. The business and industrial use of unmanned aerial vehicles (UAV) can provide advantageous solutions in many areas of life, and they are also optimal for emergency situations and for accessing hard-to-reach places. However, their application poses numerous technological and regulatory challenges to be overcome. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue authors developed a novel trajectory planning method for UAVs to optimize the energy necessity during flight. First, an “energy map” was created, which was the basis for trajectory planning, i.e., determining the energy consumption of the individual components. This was followed by configuring the 3D environment including partitioning of the work space (WS), in fact, defining the free spaces, occupied spaces (obstacles) and semi-occupied/free spaces. Then the corresponding graph-like path(s) were generated on the basis of the partitioned space, where a graph search-based heuristic trajectory planning was initiated, taking into account the most important wind conditions including velocity and direction. Finally, in order to test the theoretical results, some sample environments were created to test and analyse the proposed path generations. The method eventually proposed was able to determine the optimal path in terms of energy consumption.

This research discusses the development of a software-centered EdTech (Education Technology) class model through the implementation of a course titled "3D Time Machine" at a 4-year university in Republic of Korea over two semesters. The course focused on teaching 3D modeling software, Blender, within the history department. The primary objective of offering this course was to equip students from the digital generation with the capability to manipulate digital technology effectively. Through studying historical materials and accumulating domain knowledge, students could construct their narratives from their unique perspectives. This aimed to foster their proficiency in digital technology operation, preparing them for an education environment increasingly centered around virtual worlds. As the use of virtual worlds gains prominence in educational settings, there is a growing need to incorporate curricula that prepare students to thrive as well-prepared citizens in future societies. Assessing the digital competencies of contemporary university students and designing instructional models with particular attention to their needs is becoming increasingly important. This research draws insights from interviews, conducted in both face-to-face and written formats, with students who participated in the "3D Time Machine" course. The interviews revealed valuable insights that can be actively incorporated into the development of software-centered EdTech instructional models. They reported that they expanded their perceptions as they learned how to make their ideas tangible. The course helped students overcome their fear of digital technology. In conclusion, the course effectively employs 3D modeling technology, a software-centered EdTech, as a core element in helping students develop their narratives rapidly and diversely, thereby playing a crucial role in their ability to articulate their unique perspectives.

In this study, we designed a functional neuro-cardiac model to help us examine the role of neuronal regulation and confirm the importance of neural innervation techniques for cardiac tissue regeneration. A three-dimensional (3D) bioprinted neuro-cardiac scaffold composed of a mixture of gelatin-alginate and alginate-genipin-fibrin hydrogels was developed with a 2:1 ratio of AC16 cardiomyocytes (CMs) and retinoic acid differentiated SH-SY5Y neuronal cells (NCs) respectively. A unique semi-3D bioprinting approach was adopted where the CMs were mixed in the cardiac bioink and printed using an anisotropic accordion design to mimic the physiological tissue architecture in vivo. The voids in this 3D structure were methodically filled in using a NCs-gel mixture and cross-linked. Confocal fluorescent imaging using Microtubule-associated protein 2 (MAP-2) antibodies for labeling NCs, and the MyoD1 antibody for CMs revealed functional coupling between the two cell types in the final cross-linked structure. This data confirmed the development of a physiologically functional neuro-cardiac model that can be used to study neuro-cardiac modulation under physiological and pathological conditions.

Characters are one of the most important elements in composing digital animation. The appear-ance and voice of a character should be designed to express the personality and values of the character. However, it is not easy for animation producers to harmoniously match the appear-ance and voice of a character. Advances in deep learning technology have made it possible to overcome this limitation. To achieve this, firstly, an audio-visual dataset of characters is required. In this study, we construct and verify a Korean audio-visual dataset consisting of frontal face im-ages of various characters and short voice clips. We developed an application that can automati-cally extract the frontal face image and a short voice clip of a character by collecting videos up-loaded to YouTube. Through this, a dataset consisting of a total of 1,522 face images and a total of 7,999 seconds of voice clips was built based on 490 characters. Furthermore, we automatically la-bel characters by gender and age to validate the dataset. The dataset built in this study is expected to be used in various deep learning fields, such as classification, generative adversarial networks, and speech synthesis.

The 3D reconstruction of vermiculite-based sealing material was carried out based on CT scanning experiments and Avizo software. The precision and representation of the reconstructed digital core were tested by calculating the pore structure parameters and comparing them with the experimental results of mercury compression. Avizo is a perfect interface with Fluent software to perform micro-scale seepage simulation to explore the distribution of pressure and velocity fields during fluid transport. In this paper, a new approach is proposed to study the penetration leakage of non-metallic gaskets by using equivalent pore networks.

Background. Currently, most of the research in breast cancer has been carried out in conventional two-dimensional (2D) cell culture due to its practical benefits, however, the three-dimensional (3D) cell culture is becoming the model of choice in cancer research because it allows cell-cell and cell-extracellular matrix (ECM) interactions, mimicking the native microenvironment of tumors in vivo. Methods. in this work, we evaluated the effect of 3D cell organization on the expression pattern of miRNAs (by Small-RNAseq) and mRNAs (by microarrays) in the breast cancer SKBR3 cell line and analyzed the biological processes and signaling pathways regulated by the differ-entially expressed protein-coding genes (DE-mRNAs) and miRNAs (DE-microRNAs) found in the organoids. Results. We obtained well-defined cell-aggregated organoids with a grape cluster-like morphology with a size up to 9.2×105 μm3. The transcriptomic assays showed that cell growth in organoids significantly affected (all p < 0.01) the gene expression patterns of both, miRNAs and mRNAs, finding 20 upregulated and 19 downregulated DE-microRNAs, as well as 49 upregulated and 123 downregulated DE-mRNAs. In silico analysis showed that a subset of 11 upregulated DE-microRNAs target 70 downregulated DE-mRNAs. These genes are involved in 150 gene on-tology (GO) biological processes such as regulation of cell morphogenesis, regulation of cell shape, regulation of canonical Wnt signaling pathway, morphogenesis of epithelium, regulation of cy-toskeleton organization, as well as in the MAPK and AGE-RAGE signaling KEGG-pathways. In-terestingly, hsa-mir-122-5p (Fold Change (FC)=15.4), hsa-mir-369-3p (FC=11.4), and hsa-mir-10b-5p (FC=20.1) regulated up to the 81 % of the 70 downregulated DE-mRNAs. Conclu-sion, the organotypic 3D cell-organization architecture of breast cancer SKBR3 cells impacts the expression pattern of miRNAs-mRNAs network mainly through overexpression of hsa-mir-122-5p, hsa-mir-369-3p, and hsa-mir-10b-5p. All these findings suggest that the interaction between cell-cell and cell-ECM as well as the change in the culture architecture impacts gene ex-pression, and therefore, support the pertinence of migrating breast cancer research from conven-tional cultures to 3D models.

3D cell cultures have improved the evaluation of drugs for cancer therapy, due to their high similarity to solid tumors. In melanoma, autophagy appears to show a dual role depending on the progression of the disease. p62 protein has been proposed for the evaluation of autophagic flux since its expression is an indicator of the state of autophagy. Pentoxifylline (PTX) and Norcantharidin (NCTD) are drugs that have been shown to have anticancer effects in various types of cancer. In this work, B16F1 mouse melanoma cell line was used, from which two-dimensional (2D) monolayer cultures and three-dimensional (3D) organoids were carried out using the hanging drop technique. The expression of p62 was determined by western blot and immunofluorescences. Our results indicate that pentoxifylline reduces p62 expression in both types of culture. While Norcantharidin increases its expression in 3D cultures at 24 h. Therefore, these drugs could have a potential therapeutic use for the regulation of autophagy in melanoma, depending on the state of evolution of the disease.

Cancer cachexia is a multifactorial syndrome that is identified by ongoing muscle atrophy, along with functional impairment, anorexia, weakness, fatigue, anemia, reduced tolerance to antitumor treatments. Thus, reducing the patients’ quality of life. Cachexia alone causes about 22-25% of cancer deaths. This review covers the symptoms, mediators, available treatment, and prospects of 3D bioprinting for cancer cachexia. Studies about cachexia have shown several factors that drive this disease – protein breakdown, inflammatory cytokines activation, and mitochondrial alteration. Even with proper nutrition, physical exercises, anti-inflammatory agents, chemotherapy, and grafting attempts, standard treatment has been unsuccessful for cachexia. But the use of 3D bioprinting shows much promise compared to conventional methods by attempting to fabricate 3D constructs mimicking the native muscle tissues. In this review, some 3D bioprinting techniques with their advantages and drawbacks, along with their achievements and challenges in in-vivo applications have been discussed. Constructs with neural integration or muscle-tendon units aim to repair muscle atrophy. But it is still difficult to properly bio-print these complex muscles. Although progress can be made by developing new bio-inks or 3D printers to fabricate high-resolution constructs. Using secondary data, this review study shows prospects of why 3D bioprinting can be a good alternate approach to fight cachexia.

Fluorescence-linked immunosorbent assay (FLISA) is a commonly used, quantitative technique for detecting biochemical based on antigen–antibody binding reactions using a well-plate platform. With the developments in the manufacturing technology of microfluidic systems, FLISA can be implemented onto microfluidic disk platforms, which allows the detection of trace biochemical with high resolutions. Apart from requiring a lower proportion of reagent (1/10), this method also reduces the time required for the entire process to less than an hour. The incubation process involves antigen–antibody binding reactions as well as the binding of fluorogenic substrates to target proteins. The protocol for FLISA on a microfluidic platform necessitates the appropriate execution of liquid reagent movements during each step in order to ensure sufficient binding reactions. Herein, we propose a novel microfluidic disk comprising a 3D incubation chamber. Vascular endothelial growth factor as concentration with ng mL-1 is detected sequentially using a benchtop process employing this 3D microfluidic disk. The 3D microfluidic disk is implemented without requiring manual intervention or additional procedures for liquid control. During the incubation process, microbead movement is controlled through centrifugal force, generated due to disk rotation, and gravitational force via bead sedimentation on the sloped floor of the chamber.

Objective: The aim of this in vitro study was to evaluate the accuracy of resin-based fixed dental restorations, namely veneers, single crowns, and four-unit fixed partial dental prosthesis (FPDs) using two different 3D printers and polymer-based materials. Materials and Methods: A standard maxillary polyurethane jaw model containing prepared teeth was scanned using an intraoral scanner. The generated STL data were used to design the restorations virtually using CAD software. Two 3D printers were utilized for the provisional digital light processing and stereolithography for the castable resin patterns. Each printer produced 10 specimens of each type of restoration, for a total of 80 restorations. The 3d-printed restorations were then 3D scanned using the same intraoral scanner and evaluated for external and internal dimensional accuracy in terms of trueness and precision. A one-way ANOVA and Two-sample T-Test were implemented to compute the precision (variability between groups) and trueness (with the designed CAD model). A level of statistical significance of p-Value

Abstract: The broad clinical use of synthetic vascular grafts for vascular diseases is limited by their thrombogenicity and low patency rate, especially for vessels with a diameter inferior to 6 mm. Alternatives such as tissue-engineered blood grafts (TEBGs) have gained increasing interest. Among the different manufacturing approaches, 3D bioprinting presents numerous advantages and enables the fabrication of multi-scale, multi-material, and multicellular tissues with heterogeneous and functional intrinsic structures. Extrusion-, inkjet- and light-based 3D printing techniques have been used for the fabrication of TEBG out of hydrogels, cells, and/or solid polymers. This review discusses the state-of-the-art research on the use of 3D printing for TEBG with a focus on the biomaterials and deposition methods.

This paper aims to present the method of producing visual media for communicating the Asinara Island National Park plan. These products are landscape visualizations able to foster landscape preservation both from the point of view of the management of the landscape and of the citizen's involvement in the communication of the historical and environmental values of the landscape. Starting from landscape information gathering, the research has experimented with an operative method for processing different graphic representations from the same geographic database, calibrating the outputs to different audiences, their needs, objectives, and literacy skills. Three different types of products are presented: the first is a digital, dynamic and multisectoral decision-making GIS tool for park management. The second is a 3D model, aimed to the virtual fruition. The third is a map of the zoning park plan drawn to be easily readable to the non-expert public. This case study emerges as a replicable method to process the landscape information to make different visualization tools from a single geographic model, to meet the different requirements that arise from a complex landscape planning process.

Selective laser sintering (SLS) has drawn attention for the fabrication of three-dimensional oral dosage forms due to the plurality of drug formulations that can be processed. The aim of this work was to employ SLS with a CO2 laser for the manufacturing of carvedilol personalised dos-age forms of various strengths. Carvedilol (CVD) and vinylpyrrolidone-vinyl acetate copolymer (Kollidon VA64) blends of various ratios were sintered to produce CVD tablets of 3.125, 6.25 and 12.5 mg. The tuning of the SLS processing parameter laser intensity, improved printability and impacted the tablet hardness, friability, CVD dissolution rate, and the total amount of drug re-leased. Physicochemical characterization showed the presence of CVD in the amorphous state. X-ray micro-CT analysis demonstrated that the applied CO2 intensity affected the total tablet po-rosity, which was reduced with increased laser intensity. The study demonstrated that SLS is a suitable technology for the development of personalised medicines that meet the required specifications and patient needs.

Light Detection and Ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation. To surmount these obstacles, this paper proposes a low-power, low-cost, SPAD-based system-on-chip (SoC) which packages the microlens arrays (MLA) and incorporates with a light-weight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8x8 Single-Photon Avalanche Detectors (SPADs) macro pixel array with time-to-digital converters (TDC) and charge pump, fabricated using a 180nm bipolar-CMOS-DMOS (BCD) process. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8x8 to quarter video graphics array level (QVGA; 256x256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. This integrated state-of-the-art hardware-software solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications.

With increasing demand from unmanned driving and robotics, more attention has been paid to point cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features about reflectivity of the point cloud to maximizing the use of information in point cloud. Secondly, we enhanced the distance features and raw features that we collectively refer to them as self-features of the key points in Set Abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show the enhancement method proposed in this paper has excellent performance.

The process of identifying and approving a new drug is a time-consuming and expensive procedure. One of the biggest issues to overcome is the risk of hepatotoxicity, which is one of the main reasons for drug withdrawal from the market. While animal models are the gold standard in preclinical drug testing, the translation of results into therapeutic intervention is often ambiguous due to interspecies differences in hepatic metabolism. The discovery of human induced Pluripotent Stem Cells (hiPSCs) and their derivatives has opened new possibilities for drug testing. We used mesenchymal stem cells and hepatocytes both derived from hiPSC, together with endothelial cells, to miniaturize the process of generating hepatic organoids. These organoids were then cultivated in vitro using both static and dynamic cultures. Additionally, we tested spheroids composed solely by induced hepatocytes. By miniaturizing the system, we demonstrated the possibility of maintaining the organoids, but not the spheroids, in culture for up to 15 days. This timeframe may be sufficient to carry out a hypothetical pharmacological test or screening. In conclusion, we propose that the hiPSC-derived liver organoids model could complement or, in the near future, replace the pharmacological and toxicological tests conducted on animals.