Current 3D Research Articles

Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System

Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D.

Improving the Effective Spatial Resolution in 1H-MRSI of the Prostate with Three-Dimensional Overdiscretized Reconstructions.

In in vivo 1H-MRSI of the prostate, small matrix sizes can cause voxel bleeding extending to regions far from a voxel, dispersing a signal of interest outside that voxel and mixing extra-prostatic residual lipid signals into the prostate. To resolve this problem, we developed a three-dimensional overdiscretized reconstruction method. Without increasing the acquisition time from current 3D MRSI acquisition methods, this method is aimed to improve the localization of metabolite signals in the prostate without compromising on SNR. The proposed method consists of a 3D spatial overdiscretization of the MRSI grid, followed by noise decorrelation with small random spectral shifts and weighted spatial averaging to reach a final target spatial resolution. We successfully applied the three-dimensional overdiscretized reconstruction method to 3D prostate 1H-MRSI data at 3T. Both in phantom and in vivo, the method proved to be superior to conventional weighted sampling with Hamming filtering of k-space. Compared with the latter, the overdiscretized reconstructed data with smaller voxel size showed up to 10% less voxel bleed while maintaining higher SNR by a factor of 1.87 and 1.45 in phantom measurements. For in vivo measurements, within the same acquisition time and without loss of SNR compared with weighted k-space sampling and Hamming filtering, we achieved increased spatial resolution and improved localization in metabolite maps.

Recent Updates on Metal-Polymer Nanocomposites in 3D Bioprinting for Tissue Engineering Applications

Rapid tooling using additive manufacturing, or 3D printing, is an emerging manufacturing technology that has the potential to revolutionize the production of complex parts using only a computer and a design program. Lightweight structures with excellent dimensional precision and lower cost for customizable geometries are possible with these printed parts. In recent years, inherent constraints of polymers, metals, and ceramics have pushed researchers toward superior alternative composite materials to boost mechanical and other critical features; current 3D printing research follows this route from neat to composite materials. The characteristics, performance, and future uses of composite materials produced using additive manufacturing methods are discussed in this review. In addition, to discuss the state of the art in additive manufacturing, this article also fabricated many technologies, including robotics, machine learning, organ-on-a-chip, and 4D bioprinting.

Design and Validation of a 3D Printed Cranio-Facial Simulator: A Novel Tool for Surgical Education.

To assess the ability of current 3D printing technology to generate a craniofacial bony and soft tissue anatomical model for use in simulating the performance of a fronto-orbital advancement (FOA) osteotomy and then to further assess the value of the model as an educational tool. Anatomic models were designed with a process of serial anatomic segmentation/design, 3D printing, dissection, and device refinement. A validation study was conducted with 5 junior and 5 senior plastic surgery residents. The validation study incorporated a multiple-choice Knowledge Assessment test (KA), an Objective Structured Assessment of Technical skills (OSATs), a Global Rating Scale (GRS) and a Michigan Standard Simulation Experience Scale (MiSSES). We compared the scores of both the junior and senior residents and compared junior resident scores, before and after viewing a lecture/demonstration. MiSSES showed high face validity with a score of 85.1/90, signifying high satisfaction with the simulator learning experience. Simulation and the lecture/demonstration improved the junior resident average KA score from 5.6/10 to 9.6/10 (P = .02), OSATs score from 32.4/66 to 64.4/66 (P < .001) and GRS score from 13.9/35 to 27.5/35 (P < .001). The senior residents OSATs score of 56.3/66 was higher than the pre-lecture juniors (32.4/66) (P < .001), but lower than the post-lecture juniors (64.4/66) (P < .001). We have successfully fabricated a 3D printed craniofacial simulator capable of being used as an educational tool alongside traditional surgical training. Next steps would be improving soft tissue realism, inclusion of patient and disease specific anatomy and creation of models for other surgical specialties.

Dextran-assisted ultrasonic exfoliation of two-dimensional metal-organic frameworks to evaluate acetylcholinesterase activity and inhibitor screening

Acetylcholinesterase (AChE) is regarded as a biomarker of Alzheimer's disease (AD), and its inhibitors show great potential in AD therapy as AChE can increase the neurotoxicity of the amyloid component that induces AD. Because of this, it is crucial and significant to develop a simple and highly sensitive strategy to monitor AChE levels and screen highly efficient AChE inhibitors. Herein, we synthesize an ultrathin two-dimensional (2D) metal-organic framework (MOF) based on copper-catecholate (Cu-CAT) via dextran assisted ultrasound exfoliation, followed by construction of a sensitive sensor for the monitoring AChE and screening of its inhibitors. By adding AChE, the acetylthiocholine (ATCh) substrate is hydrolyzed to be thiocholine (TCh), which decreases the peroxidase-like activity of Cu-CAT nanosheets (Cu-CAT NSs), impairing the signal reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) to oxidized-TMB (ox-TMB). In the presence of an AChE inhibitor, the signal can be gradually restored. The newly developed sensor shows high sensitivity and selectivity for AChE and huperzine A (HA, an effective drug for AD, an acetylcholine receptor antagonist), as well as for AD drug discovery from traditional Chinese herbs. The limit of detection of the sensor for AChE is 0.01 mU mL−1 and the average IC50 value of HA is 30.81 nM under the optimal of catalysis conditions. Compared with the 3D bulk Cu-CAT, the current 2D Cu-CAT NSs exhibit higher peroxidase activity due to more catalytic active site exposure. This study provides a strategy to prepare an ultrathin 2D MOF with high catalytic activity and new insights for the construction of a biosensor to monitor AChE and new AD drugs.

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays. Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works, and it is poised to become a potential alternative to the traditional wall and portable display forms. However, a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information. To address the viewing angle and perspective issues, a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array, two spliced 8K liquid crystal display panels, and a light shaping diffuser screen. The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme, which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters. The proposed display has a radial viewing angle of 68.7° in a large display size of 43.5 inches, which is larger than the conventional tabletop light field 3D displays. The radial perspective and parallax are correct, and high-resolution 3D images can be reproduced in large radial viewing positions. We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

A vectorized spherical convolutional network for recognizing 3D mesh models with unknown rotation

目的3维目标分类是视觉领域的一个基本问题，3维目标的旋转变化给分类带来极大挑战。同时不规则3维网格模型难以运用传统2维卷积网络提取特征。针对这两个问题，提出一种基于矢量型球面卷积网络的分类方法，用于识别未知旋转的3维网格模型。方法 使用矢量型神经元作为网络的基础神经元，并提出一种新型矢量层间的卷积方式。首先，将3维模型规范化并映射到单位球上，获取球面的信号表示；然后，使用矢量型分类网络和重建网络学习等变的3维模型特征；最后，使用分类网络完成3维模型分类。结果 经过消融实验对比，使用本文提出的球面卷积模块和矢量卷积层，并在训练时加入重建模块。对原本未旋转（no rotation，NR）数据集进行任意旋转（arbitrary rotation，AR），并设定NR/AR，AR/AR，NR/NR共3种训练/测试策略的分类任务，其中NR/AR任务衡量模型识别未知旋转的能力。在刚性数据集ModelNet40上，相比基于球面卷积网络（spherical convolutional neural network，SCNN）的分类方法，在3种任务上分别提高了7.7%，1.8%，3.1%。为验证本文方法在识别非刚性3维网格目标的优越性，在非刚性数据集SHREC15（shape retrieval contest 2015）上，相比SCNN，本文方法在3种任务上分别提高了8.8%，4.5%，5.0%。结论 本文提出一种将矢量型网络运用在3维目标分类的思路，使用光线投射法获得分布在球面空间的特征，便于使用统一的球面卷积算子进行处理；设计一种球面残差模块避免梯度消失；使用矢量型神经元并设计矢量层之间的卷积方式以保证网络的等变性，使得识别任意旋转的3维模型时更加准确。;Objective The 3D meshes are concerned of spatial information-demonstrated surface triangles，which can optimize surface information than other related representations like voxel or point cloud. The 3D shape analysis is still to be resolved in relevant to mesh representation on two aspects：1）the irregular data structure of the mesh model is challenged for feature extraction using traditional 2D convolutional networks，and 2）the 3D rotation transformation is challenged for object recognition as well. The emerging convolutional neural networks（CNNs）have been developing dramatically in the context of 2D vision like classification，segmentation，detection，as well as 3D objects-oriented applications. Current CNNbased 3D mesh classification is developed from two aspects：1）the 3D object is transferred to 2D images and the following 2D-based CNN methods are used，and 2）convolution methods are designed on 3D mesh data. However，it is still challenged to recognize rotated objects due to traditional CNN-equivariant-lacked pooling operation. The lack of rotation equivariance can be improved on the basis of two networks which are vectorized and equivariant networks. The vectorized network，known as capsule network，has shown its potentials in learning spatial transformation on 2D images，but convolutionconstrained method is required to be applied on 3D mesh further. To apply the vectorized neural network to 3D mesh data and preserve rotation equivariance，we develop a vectorized spherical neural network-derived method for 3D mesh classification. Method Our method can be segmented into three categories as mentioned below：First，the 3D mesh model is preprocessed to signals on the sphere. We normalize the 3D mesh into a unit sphere and get the spherical signals on the unit sphere using the ray casting scheme. The obtained spherical signals are nearly equivalent 3D shape representations and can be further processed by spherical convolution methods. The aims of processing 3D mesh to spherical signals are 1）to utilize the spherical signals-defined equivariant spherical convolution operators，and 2）to design vectorized neurons in a coordinated manner. Second，the autoencoder-structured model is used to learn the feature of the spherical signal. The model is composed of two sub-networks：i）a vectorized spherical convolutional neural network（VSCNN）to encode the equivariant feature and classify the 3D object，and ii）a multilayer perceptron decoder to decode the extracted feature back to sphere signal. The VSCNN is based on two kinds of spherical residual convolution block and the vector convolution layer. To train deeper networks and resist overfitting，we develop two spherical convolution modules which are S2 convolution block and SO(3) convolution block，and the primary vectorized neurons are obtained after that. The vector convolution layer is used to learn high-level vectorized features derived from the lower layer. The vector convolutional layer can be used to transfer the primary vectorized neurons to get high-level ones. A deep vectorized network can be constructed through the vector convolutional layer-based stacking. To guarantee the rotation-equivariant spherical vector neurons can be learned well during convolution，we use the SO(3) convolution operator to predict the high-level neurons. The VSCNN and the network-reconstructed are trained simultaneously. Third，the VSCNN-based 3D object classification is demonstrated. For validation，we use VSCNN to clarify the category information of the 3D model only. Result The ModelNet40 and SHREC15 of two 3D datasets are verified for the effectiveness of the proposed method. Our model is trained on the non-rotated（NR） and arbitrarily rotated（AR）training set，and it is tested on the non-rotated and rotated test set as well. The robustness of the model is demonstrated to rotation. For the rigid data set ModelNet40，the accuracy of rotation-unidentified targets can be reached to 85. 2%，surpassing the baseline method by 7. 7%. The comparative analysis shows that our method proposed can surpass most of multi-view and point cloud methods compared to other related 3D data representations. The NR/NR result can show its optimization ability in comparison with the benchmarks. At the same time，to identify non-rigid threedimensional grid targets，we carry out a rotation classification experiment on the non-rigid data set SHREC15，and the accuracy rate can be reached to 90. 4%，surpassing the baseline method by 8. 8%. Conclusion We develop a 3D object classification method for rotated mesh. The robustness to 3D rotation can be optimized in terms of vectorized neurons and the equivariant vector convolution layer. The 3D models-rotated recognition is facilitated excluding rotation augmentation， and it shows the learning ability for vectorized networks-based transformation.

An End‐to‐End Robotic Visual Localization Algorithm Based on Deep Learning

Efficient localization plays a significant role in mobile autonomous robots’ navigation systems. Traditional visual simultaneous localization systems based on point feature matching suffer from two shortcomings. First one is that the method of tracking features is not robust for environments with frequent changes in brightness. Another one is the large of consecutive visual keyframes can consume expensive computational and storage resources in complex environments. To solve these problems, an end‐to‐end real‐time six degrees of freedom object pose estimation algorithm is proposed to solve the robust and efficient challenges through a deep learning model. First, preprocessing operations such as cropping, averaging, and timestamp alignment are performed on datasets to reduce computational cost and time. Second, the processed dataset is fed into our neural network model to extract the most effective features for matching. Finally, the robot’s current 3D translation and 4D angle information are predicted and output to achieve an end‐to‐end localization system. A broad range of experiments are performed on both indoor and outdoor datasets. The experimental results demonstrate that the translation and orientation accuracy in outdoor scenes improved by 32.9% and 31.4%, respectively. The average improvement of localization accuracy in indoor scenes is 38.4%, and the angle improvement is 13.1%. Moreover, the effectiveness of predicting the global motion trajectories of sequential images algorithm has been verified and is superior to other convolutional neural network methods.

Laser doping of 2D material for precise energy band design.

The number of excellent 2D materials is finite for nano optoelectric devices including transistors, diodes, sensors, and so forth, thus the modulation of 2D materials is important to improve the performance of the current eligible 2D materials, and even to transform unqualified 2D materials into eligible 2D materials. Here we develop a fine laser doping strategy based on highly controllable laser direct writing, and investigate its effectivity and practicability by doping multilayer molybdenum ditelluride (MoTe2). Power-gradient laser doping and patterned laser doping, for the first time, are presented for designable and fine doping of 2D materials. The laser-induced polar transition of MoTe2 indicates good controllability of the method for the carrier concentration distribution in MoTe2. Multiple devices with finely tuned energy band structures are demonstrated by means of power-gradient laser doping and patterned laser doping, further illustrating the design capability of a precise energy band in 2D materials.

Quantitative analysis of 3D food printing layer extrusion accuracy: Contextualizing automated image analysis with human evaluations: Quantifying 3D food printing accuracy

3D food printing can customize food appearance, textures, and flavors to tailor to specific consumer needs. Current 3D food printing depends on trial-and-error optimization and experienced printer operators, which limits the adoption of the technology by general consumers. Digital image analysis can be applied to monitor the 3D printing process, quantify printing errors, and guide optimization of the printing process. We here propose an automated printing accuracy assessment tool based on layer-wise image analysis. Printing inaccuracies are quantified based on over- and under-extrusion with reference to the digital design. The measured defects are compared to human evaluations via an online survey to contextualize the errors and identify the most useful measurements to improve printing efficiency. The survey participants marked oozing and over-extrusion as inaccurate printing which matched the results obtained from automated image analysis. Although under-extrusion was also quantified by the more sensitive digital tool, the survey participants did not perceive consistent under-extrusion as inaccurate printing. The contextualized digital assessment tool provides useful estimations of printing accuracy and corrective actions to avoid printing defects. The digital monitoring approach may accelerate the consumer adoption of 3D food printing by improving the perceived accuracy and efficiency of customized food printing.

3D bioprinting of a gradient stiffened gelatin-alginate hydrogel with adipose-derived stem cells for full-thickness skin regeneration.

Current hydrogel-based scaffolds offer a promising approach to accelerate tissue regeneration, but great challenges remain in developing platforms that mimic the physical microenvironment of tissues combined with therapeutic biological cues. Here, a 3D bioprinting gelatin-alginate hydrogel for the construction of gradient composite scaffolds that mimic the dermal stiffness microenvironment was developed for architecture construction by extruding the bioink on calcium-containing substrates to achieve gradient secondary cross-linking, meanwhile, adipose-derived stem cells were encapsulated in the present hydrogels for therapeutic purposes. The gradient-stiffness scaffold exhibited good stability and biocompatibility as well as enhanced proliferation and migration of the adipose-derived stem cells. In addition, the promoted angiogenesis and healing efficiency was demonstrated via the animal wound model and was mainly attributed to the enhanced paracrine secretion of adipose-derived stem cells by the physical microenvironment provided within the gradient stiffness scaffold. The current 3D printed gradient scaffolds provide adipose-derived stem cells with a distinct yet successive architecture rather than the typical uniform microenvironment to accelerate skin regeneration, which may have broader applications in other chronic wounds or tissue defects.

Holographic Mixed Reality Ultra-High-Definition Traffic Signs to Increase Safety and Inclusivity in Transportation

Current 2D windshield head-up displays can lead to driver distractions due to a shift of gaze from the road towards a small area of the windshield. Customizable mixed reality real-time head-up displays can increase safety in transportation due to the holographic road obstacles being aligned with the road scene. Based on accelerated parallel processing algorithms, a 4K spatial light modulator, virtual Gabor lenses and a He-Ne laser, 3D holographic road signs appear within 1.15 seconds in the driver’s gaze on the road.

An all-two-dimensional Fe-FET retinomorphic sensor based on the novel gate dielectric In2Se3-xOx.

Two-dimensional (2D) ferroelectric field-effect transistors (Fe-FETs) have attracted extensive interest as a competitive platform for implementing future-generation functional electronics, including digital memory and brain-inspired computing circuits. In 2D Fe-FETs, the 2D ferroelectric materials are more suitable as gate dielectric materials compared to 3D ferroelectric materials. However, the current 2D ferroelectric materials (represented by α-In2Se3) need to be integrated with other 3D gate dielectric layers because of their high conductivity as a ferroelectric semiconductor. This 2D/3D hybrid structure can lead to compatibility problems in practical devices. In this study, a new 2D gate dielectric material that is compatible with the complementary metal-oxide semiconductor process was found by using oxygen plasma treatment. The 2D gate dielectric material obtained shows excellent performance, with an equivalent oxide thickness of less than 0.15 nm, and excellent insulation, with a leakage current of less than 2 × 10-5 A cm-2 (under a 1 V gate voltage). Based on this dielectric layer and the α-In2Se3 ferroelectric gate material, we fabricated an all-2D Fe-FET high-performance photodetector with a high on/off ratio (∼105) and detectivity (>1013 Jones). Moreover, the photoelectric device integrates perception, memory and computing characteristics, indicating that it can be applied to an artificial neural network for visual recognition.

3D bioprinting complex models of cancer.

Cancer is characterized by the uncontrolled division of cells, resulting in the formation of tumors. The tumor microenvironment (TME) consists of a variety of cell types present within a heterogeneous extracellular matrix (ECM). Current 2D culture methods for mimicking this microenvironment remain limited due to spatial constraints. Many different types of 3D cancer models have been developed in recent years using spheroids/organoids, biomaterial scaffolds, and cancer-on-chip systems. However, these models cannot precisely control the organization of multiple cell types inside of complex architectures. Bioprinted cancer models can incorporate both stromal and cancer cells inside of 3D constructs to generate custom models of this complex disease. 3D bioprinting can generate complex, multicellular, and reproducible constructs where the matrix composition and rigidity are tailored locally to the tumor. These capabilities make 3D bioprinting an attractive method for reproducing the tumor TME found in vivo. Recent advancements in biomaterial-based bioinks enable the generation of 3D bioprinted cancer models that accurately mimic the TM. Here we discuss recent examples of such 3D-bioprinted cancer models, including those of the lungs, prostate, skin, brain, and colon. We then highlight the advantages of using 3D bioprinting compared to other in vitro modeling techniques and detail its limitations.

RGD-Coated Polymer Nanoworms for Enriching Cancer Stem Cells

Cancer stem cells (CSCs) are primarily responsible for tumour drug resistance and metastasis; thus, targeting CSCs can be a promising approach to stop cancer recurrence. However, CSCs are small in numbers and readily differentiate into matured cancer cells, making the study of their biological features, including therapeutic targets, difficult. The use of three-dimensional (3D) culture systems to enrich CSCs has some limitations, including low sphere forming efficiency, enzymatic digestion that may damage surface proteins, and more importantly no means to sustain the stem properties. A responsive 3D polymer extracellular matrix (ECM) system coated with RGD was used to enrich CSCs, sustain stemness and avoid enzymatic dissociation. RGD was used as a targeting motif and a ligand to bind integrin receptors. We found that the system was able to increase sphere forming efficiency, promote the growth of spheric cells, and maintain stemness-associated properties compared to the current 3D culture. We showed that continuous culture for three generations of colon tumour spheroid led to the stem marker CD24 gradually increasing. Furthermore, the new system could enhance the cancer cell sphere forming ability for the difficult triple negative breast cancer cells, MBA-MD-231. The key stem gene expression for colon cancer also increased with the new system. Further studies indicated that the concentration of RGD, especially at high doses, could inhibit stemness. Taken together, our data demonstrate that our RGD-based ECM system can facilitate the enrichment of CSCs and now allow for the investigation of new therapeutic approaches for colorectal cancer or other cancers.

Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels. The current unsupervised method mainly uses 3DCNN to regularize the cost volume to regression image depth. This approach results in high memory requirements and long computing time. In this paper, we propose an end-to-end unsupervised multi-view 3D reconstruction network framework based on PatchMatch, Unsup_patchmatchnet. It dramatically reduces memory requirements and computing time. We propose a feature point consistency loss function. We incorporate various self-supervised signals such as photometric consistency loss and semantic consistency loss into the loss function. At the same time, we propose a high-resolution loss method. This improves the reconstruction of high-resolution images. The experiment proves that the memory usage of the network is reduced by 80% and the running time is reduced by more than 50% compared with the network using 3DCNN method. The overall error of reconstructed 3D point cloud is only 0.501 mm. It is superior to most current unsupervised multi-view 3D reconstruction networks. Then, we test on different data sets and verify that the network has good generalization.

In vitro–in vivo correlation of drug release profiles from medicated contact lenses using an in vitro eye blink model

There is still a paucity of information on how in vitro release profiles from drug-loaded contact lenses (CLs) recorded in 3D printed eye models correlate with in vivo profiles. This work aims to evaluate the release profiles of two drug-loaded CLs in a 3D in vitro eye blink model and compare the obtained results with the release in a vial and the drug levels in tear fluid previously obtained from an animal in vivo study. In vitro release in the eye model was tested at two different flow rates (5 and 10 µL/min) and a blink speed of 1 blink/10 s. Model CLs were loaded with two different drugs, hydrophilic pravastatin and hydrophobic resveratrol. The release of both drugs was more sustained and lower in the 3D eye model compared to the in vitro release in vials. Interestingly, both drugs presented similar release patterns in the eye model and in vivo, although the total amount of drugs released in the eye model was significantly lower, especially for resveratrol. Strong correlations between percentages of pravastatin released in the eye model and in vivo were found. These findings suggest that the current 3D printed eye blink model could be a useful tool to measure the release of ophthalmic drugs from medicated CLs. Nevertheless, physiological parameters such as the composition of the tear fluid and eyeball surface, tear flow rates, and temperature should be optimized in further studies.Graphical abstract

Bioprinting on Organ-on-Chip: Development and Applications.

Organs-on-chips (OoCs) are microfluidic devices that contain bioengineered tissues or parts of natural tissues or organs and can mimic the crucial structures and functions of living organisms. They are designed to control and maintain the cell- and tissue-specific microenvironment while also providing detailed feedback about the activities that are taking place. Bioprinting is an emerging technology for constructing artificial tissues or organ constructs by combining state-of-the-art 3D printing methods with biomaterials. The utilization of 3D bioprinting and cells patterning in OoC technologies reinforces the creation of more complex structures that can imitate the functions of a living organism in a more precise way. Here, we summarize the current 3D bioprinting techniques and we focus on the advantages of 3D bioprinting compared to traditional cell seeding in addition to the methods, materials, and applications of 3D bioprinting in the development of OoC microsystems.

The Drone, the Snake, and the Crystal: Manifesting Potency in 3D Digital Replicas of Living Heritage and Archaeological Places

Creating and sharing 3D digital replicas of archaeological sites online has become increasingly common. They are being integrated in excavation workflows, used to foster public engagement with the site, and provide communication and outreach of research, which now happen on digital media platforms. However, there has been little introspection by the community involved in the 3D documentation field, which has resulted in problematic practices. We critique the western paradigm of archaeological visualisation and propose recommendations for inclusive, decolonised visualisations of living heritage and archaeological places. To begin, we define in broad terms what an archaeological site is, and then we describe how these sites have been recorded and represented using the latest technology for digital re-production, namely laser scanning and photogrammetry. Following that we provide a critical analysis of current 3D visualisations of archaeological sites and develop an approach to ensure that the significance, meaning, and potency of archaeological and living heritage places are transferred to their digital replicas. Our case study at Ga-Mohana Hill in South Africa then offers practical approaches and methodologies that the fields of cultural heritage documentation and archaeological visualisation can employ to address their recurring issues as identified in the critical analysis. We present an online, interactive 3D digital replica of a living heritage and archaeological place that we believe responds appropriately to its political, cultural, and social context along with communicating its archaeological significance.

Effect of fabrication method and surface roughness on spray characteristics for small pressure-swirl atomizers

The paper investigates the effect of the fabrication method, precision and surface roughness on atomization nozzle performance. A comprehensive review of the available knowledge on the topic is an integral part and an indispensable prerequisite of the study. It focuses on the requirements for atomizing nozzles and their production from the point of view of surface quality, dimensional and shape accuracy. The published results on the influence of surface quality and roughness on internal flow and atomizer performance are discussed. Available atomizer manufacturing techniques are reviewed with a focus on Rapid prototyping methods, accuracy and geometrical imperfections.Classical Machining and two 3D printing techniques were applied to produce small pressure-swirl atomizers. They were fabricated in a number of versions. The machined nozzles featured several specific defects. Spray quality measurements of these atomizers, at selected operation regimes, were made on a cold test bench using PDA and mechanical patternation to evaluate discharge coefficient (CD), Sauter mean diameter, spray cone angle, nozzle efficiency (ηn), breakup length and liquid distribution in the spray.The results show the important and systematic effects of surface quality and manufacturing imperfections on the atomizer characteristics. The exit orifice imperfections harm the circumferential uniformity and other spray characteristics. The effect becomes significant when the exit orifice shifted out of the nozzle axis by >2 % of the chamber diameter, and the orifice edge is chamfered by >20 % of the orifice length. The swirl chamber surface modified by scratches has a minor effect.Replacement of machining with current commercial 3D printing techniques, namely Selective laser melting and PolyJet technique, is associated with increased surface roughness. It worsens spray quality, liquid distribution uniformity and ηn and increases the CD when relative surface roughness exceeds 0.007.The result presentation using dimensionless criteria allows for generalisation of the outcomes to other atomizer types and scales.