Alginate-gelatin hydrogels are extensively used in bioengineering. However, despite different formulations being utilized for growing different cell types in vitro, their pH and its effect, together with the crosslinking ions, of those formulations are still infrequently assessed. In this work we studied how these elements can affect hydrogel stability and printability and influence U2OS and NIH/3T3 cell viability and metabolism on the resulting 3D prints. In this context, 6% alginate + 2% gelatin hydrogels were prepared with 0.1 M MES buffer with pH 5.5, 6.5, 7.0 or 8.0, printed by extrusion-based 3D printing, and crosslinked immediately after printing with either CaCl2 or BaCl2. Our results showed that both the buffer pH and the crosslinking ion (Ca2+ or Ba2+) influence the swelling and degradation rates of the prints. Moreover, the buffer pH influenced the printability of the hydrogel in air, but when printed directly in a fluid-phase CaCl2 or BaCl2 crosslinking bath. In addition, both U2OS and NIH/3T3 cells showed greater cell metabolic activity on one-layer prints crosslinked with Ca2+. Besides, Ba2+ increased cell death of NIH/3T3 cells while had no effect on the U2OS cell viability. The pH of the buffer also caused an important impact on the cell behaviour. U2OS cells showed a 2.25-fold cell metabolism increase on one-layer prints prepared at pH 8.0 in comparison to those prepared at pH 5.5. Whereas, NIH/3T3 cells showed greater metabolism on one-layer prints with pH 7.0. Finally, we observed a difference on cell arrangement of the U2OS cells growing on prints prepared from hydrogels with acidic buffer in comparison to cells growing on those prepared using neutral or basic buffer. These results show that both pH and crosslinking ion influence hydrogel strength and cell behaviour.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

Ledge formation presents a significant challenge in endodontic treatment. Yet, there is still a lack of educational tooth models for hands-on practice. This study aimed to create and evaluate a tooth model for ledge management practice. A natural tooth with curved roots was collected for scientific use under ethics committee approval. Following initial root canal preparation, the tooth was scanned using micro-computed tomography (μCT) and 3D reconstructed. A K-file, created via computer-aided design (CAD), was partly inserted into the root canal wall of the 3D-reconstructed tooth. By subtracting the K-file from the tooth, a tooth model with a root canal ledge was produced. The models were then 3D printed for a hands-on workshop. A seven-item Likert scale questionnaire was administered to 20 postgraduate students and 10 endodontists to assess the model's quality and training effectiveness. The feedback from both students and experts was positive, with no statistically significant differences found between the two groups (p > 0.05). This novel tooth model is expected to address the existing gap in endodontic education and provide benefits for dental practitioners in future applications.

In order to study the effect of TiB2 particles on the mechanical properties of TiB2/6061Al composites, a series of TiB2/6061Al 3D representative volume element (RVE) were established based on SEM photos. This model took into account the ductile damage of the matrix and the traction separation behavior of the interface, and the linear damage evolution law was introduced to characterize the stiffness degradation behavior of the matrix elements. The mixed boundary conditions were used for RVE tensile experiments, and the accuracy of the predicted result was verified by the agreement of the experimental stress-strain curve. Results show that the addition of TiB2 particles can effectively promote the load-bearing capacity of the composite, but elongation is reduced. With the weight fraction of TiB2 increasing from 2.5% to 12.5%, the elastic modulus, yield strength, and tensile strength are increased by 8%, 10.37%, and 11.55% respectively, while the elongation decreased by 10%. The clustering rate of the TiB2 particles is also an important factor affecting the toughness of the composites. With the increase in the clustering rate of TiB2 particles from 20% to 80%, the load-bearing capacity of the composites is not improved, and the elongation of the composites is reduced by 8%. Moreover, the high strain region provides a fast expansion path for crack propagation and the particle spacing is a crucial factor that affects the stress field.

This study aimed to develop chitosan-based apigenin spray dried solid dispersion using 1:1,1:1.5,1:2 w/w ratios. The results of process yield and drug assay were found to be (87.5%, 94.2%, and 95.86%), (95.2 ±1.34%), (99.5 ±0.85%), and (97.6 ±2.42%), respectively, for AC1, AC2, and AC3. Flow properties indicate the free flow of powders. FTIR revealed compatibility among chitosan and apigenin, and DSC and XRD showed prepared solid dispersion was on a molecular level and available in amorphous. In contrast, SEM images reflect irregular block and near-spherical shape elongated particles in the selected AC2. The antimicrobial examination reflects that AC2 was more effective against Gram-positive, negative, and fungal strains. AC2 SDSDs has more anti-oxidant property compared to pure flavonoid (AGN). Anti-proliferative activity against A549 lung cancer cell lines showed better anti-cancer activity by AC2 SDSDs. The selected AC2 SDSD filled in a 3D shell pill and further characterized for 3D shell pill for dissolution profiles and stability studies. The product has sustained release and similar release profiles after three months of storage. The findings suggest that the AC2 SDSDs may be a promising candidate for further development as a 3D printed drug delivery system for treating multiple disease conditions using model flavonoid- apigenin.

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK targeting inhibitors using 3-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using the MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (〖∆∆G〗_RBFE^(A→B)) values of analogous ligands were estimated using the alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. The overall results using ML and physics-based hybrid approaches could be useful for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.

The Galleon was considered a masterpiece of shipbuilding in the sixteenth century, but from a modern point of view, the shape of her hull looks archaic and primitive. However, how accurate is this perception? Is the hull form of Galleon primitive? What were the reasons for its unique design features? This article investigates these questions by directly analysing the hull’s features from the point of view of modern ship’s theory, as well as from a historical perspective. A careful evaluation of specific design criteria of a typical Galleon, together with analysing a 3D model of its hull on modern software, with further insight for the seakeeping behaviour of Galleon with a comparison to more modern full-rigged ship (Barque of 19 CE), showed that these features were not random, but instead had a good rationale behind it, and served specific and carefully decided functions, required at the time.

The extrusion-based 3D concrete printing (3DCP) technology allows the fabrication of permanent formwork with intricate shapes, into which fresh concrete is cast to build structural members with complex geometry. This significantly enhances the geometric freedom of concrete structures without the use of expensive temporary formwork. In addition, with proper material choice for the permanent formwork, the load-bearing capacity and durability of the resulting structure can be improved. This paper investigates 3DCP of permanent formwork for reinforced concrete (RC) beam construction. A 3D-printable engineered geopolymer composite (3DP-EGC, or strain-hardening geopolymer composite, 3DP-SHGC) recently developed by the authors was used for fabrication of the permanent formwork. The 3DP-EGC exhibits strain-hardening behaviour under direct tension. Two different printing patterns were used for the soffit of the permanent formwork to investigate the effect of this parameter on the flexural performance of RC beams. A conventionally mould-cast RC beam was also prepared as the control beam for comparison purposes. The results showed that the RC beams constructed using the 3DP-EGC permanent formwork exhibited superior flexural performance to the control beam. Such beams yielded significantly higher cracking load (up to 43%), deflection at ultimate load (up to 60%), ductility index (50%) and absorbed energy (up to 107%) than those of the control beam. The ultimate load was comparable with or slightly higher than that of the control beam. Furthermore, the printing pattern at the soffit of the permanent formwork was found to have a significant influence on the flexural performance of the RC beams.

One of the major limitations of the current 3D-concrete-printing technology is incorporation of reinforcement. Furthermore, there is a need to decrease the ecological footprint of printable concrete. As a possible solution for these challenges, this paper presents a 3D-printable strain-hardening geopolymer composite (3DP-SHGC) that shows pseudo-ductile behaviour under direct tension. The developed 3DP-SHGC is composed of one-part (just-add-water) geopolymer binder made of slag (GGBFS), fly ash (FA) and solid activator. The one-part geopolymer binder eliminates the need for elevated temperature curing and handling of corrosive alkaline liquids. At first, an optimum matrix was identified by studying the effects of FA to GGBFS ratio on the rheological properties and compressive strength. Subsequently the optimum matrix was reinforced by PVA fibres to make the 3DP-SHGC, which printing performance and rheological properties were evaluated. In addition, the influences of curing temperature on the compressive, flexural and tensile performances of the printed specimens were also investigated. The results were compared with those obtained for the mould-cast specimens. The 3DP-SHGC exhibited superior flexural performance, higher tensile strength, and comparable tensile strain capacity to the mould-cast counterpart. Further, the curing temperature had influence on the mechanical properties of both 3D-printed and mould-cast SHGCs. The underlying reasons for the differences are discussed.

Bone tissue engineering has been developed in the past decades, with the engineering of bone substitutes on the vanguard of this regenerative approach. PCL based scaffolds are fairly applied for bone regeneration, and several composites have been incorporated, as to improve the devices’ mechanical properties and tissue ingrowth. In this study, HA was incorporated on PCL based scaffolds with two different proportions, 80:20 and 60:40. Devices were produced with two different techniques, SC and MB, and further investigated with regards to their mechanical characteristics and in vitro cytocompatibility. Results show the MB devices to present more promising mechanical properties, along with the incorporation of HA. The latter is also related to an increase in osteogenic activity and promotion. Overall, this study suggest PCL:HA scaffolds to be promising candidates for bone tissue engineering, particularly when produced by the MB method.

Hydrogenated amorphous silicon (a-Si:H) can be produced by plasma-enhanced chemical vapour deposition (PECVD) of SiH4 (Silane) mixed with Hydrogen. The resulting material shows outstanding radiation resistance properties and can be deposited on a wide variety of different substrates. These devices have been used to detect many different kinds of radiation namely: MIPs, x-rays, neutrons and ions as well as low energy protons and alphas. However, MIP detection using planar diodes has always been difficult due to the unsatisfactory S/N ratio arising from a combination of high leakage current, high capacitance and a limited charge collection efficiency (50% at best for a 30 µm planar diode). To overcome these limitations the 3D-SiAm collaboration proposes to use a 3D detector geometry. The use of vertical electrodes allows for a small collection distance to be maintained while conserving a large detector thickness for charge generation. The depletion voltage in this configuration can be kept below 400 V with consequent reduction in the leakage current. In this paper, following a detailed description of the fabrication process, the results of the tests performed on the planar p-i-n structures made with ion implantation of the dopants and with carrier selective contacts will be illustrated.

This article presents, for the first time, comprehensive model based on the proposed mechanism of Rahal et al [17], Bonardi {20], Tar et al [20], and Mau et al [24], for 3-component systems of G1/Iod/EDB, G1/Iod/amine (in gold chloride), and Iod/Benzoic/Borate (for reduced oxygen inhibition) for the hybri free radical (FRP) and cationic (CP) photopolymerization of interpenetrated polymer network (IPN) systems. Analytic formulas are developed to explore the new features including: (i) conversion efficacy(CE) of FRP is an increasing function of the light intensity (I), the effective absorption coefficient (b), for transient state, whereas, CE at steady-state is independent to the light intensity; (ii) initiator regeneration (RGE)
provides a catalytic cycle for improved CE for FRP and CP; (iii) in the IPN system, the synergic effects due to the co-exist of FRP and CP include: (i) CP can increase the medium viscosity limiting the diffusional oxygen replenishment, such that OIH is reduced; (ii) the cationic monomer also acts as a diluting agent for the radical polymer network, and (iii) the exothermic property of the CP polymerization. We have proposed ascaling law for the transient and steady-state dependence of CE on the key parameter P=bIC₀, given by a an m-order power law of P^m, with m = 0.5 to 1.5, depending on various conditions. The CE also has an optimal value for maximum CE. The presented comprehensive model (with minimum mathematics) focusing on the enhancement mechanisms/pathways, provides analytic formulas which can be used to analyze reported data, and, more importantly, serves as guidance for exploring new functional materials or new kinetic schemes for improved conversion or procedures for both industrial and medical applications such as additive manufacturing (AM), 3D and 4D bioprinting. Finally, we have proposed new directions/experiments based on our theoretical predictions.

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

The accuracy of wind field simulation and prediction is one of the most significant parameters in the field of atmospheric science and wind energy. Limited by the observation data, there are few researches on wind energy development. A 3D Doppler wind lidar (DWL) providing the high-vertical-resolution wind data over the urban complex underlying surface in February 2018 was employed to evaluated the accuracy of vertical wind field simulation systematically for the first time. 11 PBL schemes of the Weather Research and Forecasting Model (WRF) were employed in simulation. The model results were evaluated in groups separated by weather (sunny days, haze days and windy days), observation height layers, and various observation wind speeds. The test results presented that the vertical layer altitude of the observation point position was the most important factor. The simulation is fairly well at a height of 1000-2000m, as most of the relative mean bias of wind speed and wind direction are less than 20% and 6% respectively. Below 1000 m, the wind speed and direction biases are about 30%-150% m.s-1 and 6%-30% respectively. Moreover, when the observed wind speed was lower than 5 m.s-1, the bias were usually large, and the wind speed relative mean bias is up to 50-300%. In addition, the accuracy of simulated wind profile is better in 10-15m.s-1 than other speed ranges, and is better up 1000m than below 1000m in the boundary layer. We see that the WRF boundary layer schemes have different applicability to different weather conditions. The WRF boundary layer schemes have significant differences in wind field simulation with larger error under the complex topography. A PBL scheme is not likely to maintain its advantages in the long term under different conditions including altitude and weather conditions.

The paper formulates requirements for a polymer material for molding products from it by fused deposition modeling. A methodology for evaluating the suitability of a polymer or composite of thereof in 3D printing technology has been developed. A graphic representation of the developed methodology in the form of a temperature-shear rate logarithm diagram is proposed. Application of the proposed methodology makes it possible to simplify the development of new materials for 3D printing by fused deposition modeling both at the stage of selecting the components of the polymer composition and at the stage of its subsequent approbation.

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.

The 2019 Novel Coronavirus (COVID-19) has caused an acute reduction in world supplies of personal protective equipment (PPE) due to increased demand. To combat the impending shortage of equipment including N95 masks, the George Washington University Hospital (GWUH) developed a 3D printed reusable N95 comparable respirator that can be used with multiple filtration units. We evaluated several candidate prototype respirator models, 3D printer filaments, and filtration units detailed here. Our most recent working model was based on a respirator found on an open source maker website and was developed with PLA (printer filament), a removable cap, a removable filtration unit consisting of two layers of MERV 16 sandwiched between MERV 13, and removable elastic bands to secure the mask. Our candidate mask passed our own suction test protocol to evaluate leakage and passed a qualitative Bitrix N95 fit test at employee health at GWUH. Further efforts are directed at improving the current model for seal against face, comfort, and sizing. The 3D model is available upon request and in the supplement of this paper. We welcome collaboration with other institutions and suggest other facilities consider mask fit for their own population when exploring this concept.

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and gas cell length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20-40 eV). We discuss the versatility and adaptability of the presented method.

Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and E-participation on public participation in Kisumu, Kenya. A 3D City model, created with CityEngine2016, was exported into a web-based geo-portal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. For e-participation, 300 questionnaires given out to planning practitioners. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varies within different professional groups while the questionnaires showed strong preference for e-participation methods, especially SMSs/USSDs and emails. The study concludes that the use of 3D visualization and E-participation has the potential for improving the quality and quantity of public participation and recommends further research on the subject.

This systematic review explores how blockchain technology (BcT) can enhance the sustainability, efficiency, and transparency of 3D concrete printing (3DCP) in the construction industry. BcT offers promising solutions to persistent challenges such as resource inefficiencies, lack of transparency, and complex project management. 3DCP, as a cutting-edge construction method, has shown potential in reducing waste, optimizing resources, and enabling intricate designs that traditional methods struggle to achieve. However, its adoption is slowed by supply chain and execution issues. Blockchain’s decentralized and immutable nature allows for secure, traceable, and transparent data management, improving material tracking and automating workflows via smart contracts. Following PRISMA guidelines, an initial search identified 767 papers from Web of Science and Scopus, and through an extended search using additional keywords, a total of 3,227 materials were found. After applying inclusion/exclusion criteria, 10 studies were analyzed through scientometric and content analysis. The review highlights blockchain’s potential to promote material reuse and support circular economy principles, improving 3DCP’s sustainability. While promising, the integration of these technologies lacks real-world case studies, underscoring the need for further research. This review positions blockchain as a key driver for advancing sustainable practices in 3DCP.

Subtilisin-like proteins are serine proteases that use two types of catalytic triads: Ser-His-Asp and Ser-Glu-Asp. Here, we investigate the two known families of subtilisin-like proteins, the subtilases (Ser-His-Asp triad) and the serine-carboxyl proteinases (Ser-Glu-Asp triad), and describe local structural arrangements (cores) that govern the catalytic residues in these proteins. We show separation of the cores into conserved structural zones, which can be repeatedly found in different structures, and compare the structural cores in subtilisin-like proteins with those in trypsin-like serine proteases and alpha/beta-hydrolases.

As a long lifeline system of buried structures, Utility tunnel (UT) is vulnerable to earthquake invading. For utility tunnels with inverted siphon arrangement crossing the river, the seismic response is more complex due to the basin effect of acceleration in the topography and the influence of fluctuating hydrodynamic pressure. Based on a UT project in Haikou, this paper studied seismic response of cast-in-place UT considering the coupled model of water-soil-tunnel structure on ABAQUS software. Herein, the dynamic fluctuation of hydrodynamic pressure is simulated using acoustic-solid interaction model. A viscoelastic artificial boundary was used to simulate the soil boundary effect, and seismic loads were equivalent to nodal forces. Considering seismic invading direction and varying water elevation, the paper investigates the dynamic response characteristics and damage mechanisms of river-crossing utility tunnels. The study shows that basin effects cause the different acceleration amplifying of the soil around the UT in different sections, and the greater damage to the tunnels in bidirectional seismic excitation, which the damage concentrated at the junction of the transition between the horizontal section and the vertical section; Bending moments and axial forces are the dominant mechanical behaviors along the axial direction; and the river water level change influences the internal distribution of the tunnels, showing a certain amount of energy dissipation and seismic damping effects. For the aseismic design of cross-river cast-in-place utility tunnels, bidirectional seismic calculations should be performed, and the influence of river hydrodynamic pressure should not be neglected.

This comprehensive overview of the historical milestones in cell culture underscores key breakthroughs that have shaped the field over time. It begins with Wilhelm Roux's seminal experiments in the 1880s, followed by the pioneering efforts of Ross Granville Harrison, who initiated groundbreaking experiments that fundamentally shaped the landscape of cell culture in the early 20th century. Carrel's influential contributions, notably the immortalization of chicken heart cells, mark a significant advancement in cell culture techniques. Holtfreter, Moscona, and Leighton introduced innovations such as 3D cell culture, laying the groundwork for further exploration. The advent of induced pluripotent stem cells by Takahashi and Yamanaka in 2006 was revolutionary, enabling the reprogramming of differentiated cells into a pluripotent state. Since then, recent innovations include spheroids, organoids, and organ-on-a-chip technologies, aiming to mimic the structure and function of tissues and organs in vitro, pushing the boundaries of biological modeling and disease understanding. In this review, we overview the history of cell culture shedding light on the main discoveries, pitfalls and hurdles that were overcome during the transition from 2D to 3D cell culture techniques. Finally, we discussed the future directions for cell culture research that may accelerate the development of more effective and personalized treatments.

As the use of AI in medical imaging has increased, so has the need to explain a model’s results. Segmentation models are one technique used to produce explainable results. Due to larger size and sophistication, segmentation models which operate on 2D data can often produce better results than models operating on 3D data. In the real world, imaging is combined with clinical factors for diagnosis. To replicate this, multimodal models are used which combine image and text modalities. I propose a multimodal arrangement that converts 3D scans to 2D and uses a 2D segmentation model (DeepLabV3) to analyze the images. This is combined with clinical biomarkers to achieve a complete confidence score. Using the Medical Segmentation Decathlon Lung (MSDL) dataset and the LUng CAncer Screening dataset (LUCAS), I achieved a testing Dice coefficient of 0.91 on segmentation with a receiver operating characteristic (ROC), average precision (AP), and F-Score of 0.89, 0.91, and 0.85 respectively on the final multimodal results. This approach raises the F-score from the previously achieved value of 0.508 to 0.85, creating a new baseline of what is achievable with multimodal lung cancer diagnostics and possible methods to achieve these accuracies.

3D Printing /Additive manufacturing holds a promising future in healthcare as it aids in visualizing things and making 3D objects which is very important in different areas of medicine. The concept of one size fits all has failed the quality of care delivered by us in healthcare which demands customised needs for patient care to create 3D printed tissues, make personalized prosthetics, implants and physical models of the human body. The materials used for printing models and other healthcare devices for medical education, patient needs, and prototyping are increasing. The application of different materials for use in healthcare is not studied. New materials are also available for the various applications in healthcare industry. This experimental study was conducted to review the nine distinct 3D printing filament materials like PETG, PA, ABS, PLA, HIPS, PMMA, Ceramic, Carbon Fiber, and (PLA + Metal Composite) with the help of a 3D printed physical model of T-12 thoracic vertebrae using all the abovementioned materials. The evaluation of materials was based on diverse mechanical properties, applications across medical fields, performance metrics, costs, accuracy, and quality. The assessment This study also focuses on comparison of mechanical properties such as tensile strength, compressive strength of the abovementioned materials with human bone. The study also reviews the 3D printing process and explores its utilization in biomedical applications. The Insights about the application of various materials holds a promising future for development of newer materials for healthcare industry.

Current Micro-Computed Tomography (Micro-CT) protocols for microvasculature analysis often necessitate organ dissection and non-clearable contrast agents. We describe a non-destructive technique for achieving high-resolution images of the human coronary microvasculature. Formalin-fixed human hearts were cannulated using venogram catheters designed for balloon occlusion. Catheters were anchored at the aortic root. Hearts were placed in sealable, radiolucent containers filled with insulating polyurethane foam. Iodine potassium iodide (IKI, Lugol's solution;6.3% Potassium Iodide, 4.1% Iodide) was injected into the coronary arteries. Contrast distribution was monitored using a North Star Imaging X3000 Micro-CT scanner with low-radiation settings, followed by high-radiation scans (3600 rad, 60 kV, 900 mA). We successfully imaged two intact human hearts with chronic total coronary occlusions of the right coronary artery. The scans enabled detailed analysis of the vasa vasorum surrounding stenosed and occluded segments. After imaging, both hearts were cleared of iodine and excess polyurethane foam and then returned to their initial formalin-fixed state for indefinite storage. Conclusions: This method allows for the non-destructive, high-resolution micro-CT imaging of coronary microvasculature in intact human hearts, paving the way for 3D microvascular reconstruction in a macrovascular context.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.

Fused deposition modeling (FDM) is a rather new technology in the production of personalized dosage forms. Melting and printing of polymer–active pharmaceutical ingredient (API) – mixtures can be used to produce oral dosage forms with different dosage as well as release behavior. This process is utilized to increase the bioavailability of pharmaceutically relevant active ingredients that are poorly soluble in physiological medium by transforming them into solid amorphous dispersions (ASD). Release from such ASDs is expected to be faster and higher compared to the raw materials and thus enhance bioavailability. Printing directly from powder while forming ASDs was realized. Different techniques to change release patterns as well as a non-destructive way for determination of API distribution were shown. By measuring the melt viscosities of mixtures printed a rheological model for the printer used is proposed.

The interconnection between the objectives of territorial development and the agro-ecological transition highlights the value of past knowledge in the sustainable management of resources and agro-ecological systems. However, retrospection in rural areas presents difficulties due to a lack of data. This paper contributes to the search for such knowledge from the past by developing an interactive methodology capable of combining heterogeneous sources of information and the activation of local collective memory. Its effectiveness is based on ensuring the interoperability of information and communication in an environment simultaneously shaped by geoinformatics and 3D geo-visualizations. This virtual environment fosters participation and interactivity, supported by representations of the paleo-landscape. Furthermore, synergies were achieved between information sources and their integration into local spatial systems. The application example involved identifying a rice field that existed from the 15th to the 19th century in Thessaly, Greece. It is an interesting case because the location and organization of cultivation in combination with its spatio-temporal coordination ensures the sustainable use of natural resources. It appears that the interplay between information and communication facilitates community participation and the activation of its collective memory as a source of information that enriches the search and local intelligence.

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.

This study aims at relating the microstructural arrangement, in particular the symmetry materialised by filament sequencing in fused filament fabrication process on mechanical behaviour of printed polyamide. Dogbone structures are printed using various printing temperatures, which are combined with part orientation and raster angle that represent the in-plane and out-of-plane symmetrical arrangement of filament. Mechanical testing is conducted on both as-received filaments and printed structures to derive the effect of filament arrangement symmetry and process-generated defects on mechanical loss. In addition, microstructural analysis using scanning electron microscopy is used to share more light on the filament arrangement and their consequence on deformation mechanisms with respect to the printing conditions. The results show that 3D printed polyamide-based material exhibit remarkable tensile performance with strain stiffening behaviour and large elongation at break thanks to particular filament layout. Among the considered printing conditions, the part orientation is found to have the largest influence on tensile behaviour, which modulates the behaviour from complete restoration of the filament performance to mechanical loss.

The issue of equipment layout in multi-floor satellites consists of two primary parts: allocating the equipment to the satellite's layers and placing the equipment in each layer individually. In reviewing the past literature in this field, firstly, the issue of assigning equipment to layers is observed in a few articles and next in the layout part, the non-overlapping constraint has always been a challenge, particularly for components that do not have a circular cross-section. In addition to presenting a heuristic method for allocating equipment to different layers of the satellite, this article presents a robust flexible programming model (RFPM) for the placement of equipment at different layers, taking into account the inherent flexibility of the equipment in placement and the subject of uncertainty. This model is based on the existing uncertainty between the distances of cuboid equipment which has not been addressed in any of the previous research, and by comparing its outputs with cases from past studies, it demonstrates a significantly higher efficiency in placing the equipment and meeting the limit of non-overlapping constraints between the equipment. Finally, it would be possible to reduce the design time in the conceptual and preparatory stages, as well as the satellite's overall size, while still satisfying other constraints like stability and thermal limitations, moments of inertia and centre of gravity.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Abstract: The widespread and popular use of TADs for orthodontic anchoring has become a daily routine in clinical orthodontic treatment. However, as there have been many accidents over the past decade, safety action is needed to help reduce these accidents. We have advocated using the surgical guide and developed a procedure that can benefit patients and orthodontists. The first part of this paper is about the rationale for making the surgical guide for various implant placements that were observed. Due to their anatomical structure, some placements may require particular attention, which is focused on and discussed in length. The second part deals with fabricating the surgical guide in the laboratory procedure. The data from the intraoral SLT acquisition was extracted, and with the DICOM data from CBCT and in a 3-Shapes software, the guidance was designed. The detailed and step-by-step laboratory procedure, CAD/CAM, and 3D printers to make the surgical guide for TADs are explained. The procedure is performed in an easy-to-understand manner to make using the surgical guide possible for the daily practice of orthodontics (Pubmed).

Maxillofacial defects, arising from trauma, oncological disease or congenital differences, detrimentally affect everyday life. Prosthetic repair offers the aesthetic and functional reconstruction with the help of materials mimicking natural tissues, among which polymers take unprecedented role. The three-dimensional (3D) printing techniques based on the computer-aided design, where polymers are essential, provide a rapid and cost-effective workflow protocol to perfectly restore patient-specific anatomy for prosthetics. This review discusses the main 3D printing approaches to maxillofacial prostheses fabrication: extrusion and lithography, which are radically preferable to the traditional methods. The main assessment criteria, affording the polymer implementation in 3D printing of prostheses, as well as the characteristics of the key advanced polymers, are considered. The success of the prosthesis is shown to be largely dependent on the retention system, predominantly using polymers in the form of adhesives and osseointegrated implants as a support for the prosthesis. The approaches and technological prospects are also discussed in the context of specific aesthetic restoration on the example of the nasal, auricle and ocular prostheses. 3D printing techniques determine the development of personalized approaches to improve aesthetic and functional effect of prosthetics in patients with maxillofacial defects.

Variations in threshold voltage (VTH) and charge distributions in noncircular cells of three-dimensional (3D) NAND flash memory are investigated. The electric field difference in the circular and spike regions causes nonuniform trapped electron density in the charge trap layer (CTL) and influences the VTH variation. Such less-trapped electron (LT) regions in CTL between the circular and spike regions exhibit a lower electric field during the program, resulting in a higher current flow through that region. For the two-spike cells, the charge distribution and VTH variation are analyzed at different heights (HSpike) and angles between spikes. These irregular cells decrease VTH as the HSpike increases.

With a view to sustainable development and circular economy, this work focused on the possibility to valorize a secondary waste stream of recycled carbon fiber (rCF) to produce a 3D printing usable material with PA6,6 polymer matrix. The reinforcing fibers implemented in the present research are the result of a double recovery action: starting with pyrolysis from which long fibers are obtained, used to produce non-woven fabrics and, subsequently, fiber agglomerate wastes obtained from this last process are ground in a ball mill. The effect of a different amount of reinforcement at 5% and 10% by weight on the mechanical properties of 3D printed thermoplastic composites was investigated. Although the recycled fraction was successfully integrated in the production of filaments for 3D printing and therefore in the production of specimens via Fused Deposition Modeling technique, the results showed that fibers did not improve the mechanical properties as expected, due to an unsuitable average size distribution and the presence of a predominant dusty fraction ascribed to the non-optimized ball milling process. PA6,6 + 10 wt.% rCF composites exhibited a tensile strength of 59.53 MPa and a tensile modulus of 2.24 GPa, which correspond to an improvement in mechanical behavior of 21 5 % and 5 21 % compared to the neat PA6,6 specimens, respectively. The printed composite specimens loaded with the lowest content of rCF provided the greatest improvement in strength (+ 9% over the neat sample). Then, a prediction of the “optimum” critical length of carbon fibers was proposed that could be used for future optimizations of the recycled fiber processing.

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.

Natural rocks are highly heterogeneous due to various geological processes that constantly alter their properties. The accumulation, deposition, and cementation of mineral and organic particles continuously modify the spatial characteristics of rock properties. Property variability or anisotropy is commonly observed in most rock types and influences strength, transport, and thermal conductivity behavior. This unpredictability presents a significant challenge for laboratory testing. Binder-jet additive manufacturing (3D printing) has emerged as a valuable technology for characterizing rock properties in geoscience and engineering. This study proposes a methodology to evaluate the variability and repeatability of mechanical properties of 3D-printed sandstones during binder-jet additive manufacturing. The mechanical properties were analyzed statistically for samples located in various parts of the 3D printer build volume. The results showed that the 3D-printed sandstones exhibited significant variations in their strength and stiffness properties when measured from samples produced within the same build volume during binder-jet additive manufacturing. The Uniaxial Compressive Strength (UCS) varied from 23 to 38 MPa, with an average value of 29 MPa. The Young's modulus, on the other hand, ranged from 1.5 to 4.05 GPa, with an average value of 2.33 GPa. The variability of the mechanical properties, quantified by the standard deviation, decreased when the entire population of 3D-printed sandstones was divided into smaller samples situated at different elevations of the build platform.

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.

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.

Supramolecular structures are of great interest due to their applicability in various scientific and industrial fields. These fields of scientific action include promising applications i biomedical areas, in sensing, and in conductive and electrical phases. The sensible definition of supramolecular molecules is being set by investigators, who, because of the different sensitivities of their methods and observational timescales, may have different views as to what constitute these supramolecular structures. Besides, diverse polymers can be found to offer unique avenues for multifunctional systems with properties in medicine industrial applications. Aspects of this review provide different conceptual strategies to address molecular design, properties, and potential applications of self-assembly materials and the use of metal coordination as a feasible and useful strategy for constructing complex supramolecular structures. In this order of appreciations and ideas order, it is that this review report involves classic topics such as hydrogels that range from their relationship with drug delivery systems, to metallo-supramolecular materials, through applications in ophthalmic systems, in applications that require adhesiveness and electrically conductive hydrogels. This review also addresses systems that are largely based on hydrogel chemistry and the enormous opportunities to design very specific structures for applications that demand enormous specificity.

Methacrylated silk (Sil-MA) is a chemically modified silk fibroin specifically designed to be crosslinkable under UV light. This allows the structuring of this material throught additive manufacturing techniques and then to easily prototype patient specific construct. In this study we used Sil-MA to produce single layer crosslinked structures that can be withdrawal and ejected recovering their shape after rehydration. A complete chemical and physical characterization of the material has been conducted. Additionally, we tested the material biocompatibility according to the International Standard Organization protocols (ISO 10993) ensuring the possibility to use it in future trials. The material was also tested to verify its ability to support the osteogenesis. Two different additive manufacturing techniques have been tested (a Digital Light Processing (DLP) UV projector and a pneumatic extrusion technique) to develop Sil-MA grid. Finally, we provide a proof-of-concept that the printed Sil-MA structures are injectable.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61- 0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97(p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

Tri-dimensional (3D) cell aggregates or spheroids are considered to be closer to physiological conditions than traditional 2D cell culture. Mesenchymal stem cells (MSCs) assembling in spheroids have increased the survival of transplanted cells. The regulation of the biological processes that maintain crucial physiological reactions of MSCs is closely related to the functioning of ion channels. The pattern of expression, role and regulatory mechanisms of ion channels could be significantly different in 3D compared to 2D culture, and, thus, needed to be properly analyzed on the level of ionic currents. We developed a specific approach that allowed us to register, for the first time, endogenous ion channels in endometrial MSCs (eMSCs) assembled in spheroids. Particularly, using the single-channel patch-clamp technique, we have recorded the activity of ion channels and observed their functional interplay in mechanosensitive clusters. Our experimental protocol could be applied for identification and studying of ion channels in 3D cell cultures.

Recent improvements within the fields of high-throughput screening and 3D tissue culture have provided the possibility of developing in vitro micro-tissue models that can be used to study diseases and screen potential new therapies. This paper reports a proof of concept study on the use of microvalve-based bioprinting to create laminar MSC-chondrocyte co-cultures as an in vitro model of autologous chondrocyte implantation (ACI), an established cellular therapy for osteoarthritis. Microvalve-based bioprinting uses microvalves to deposit cells suspended in a liquid in a consistent and repeatable manner. In this case MSCs and chondrocytes have been sequentially deposited into an insert based transwell system in order to create a laminar co-culture, with variations in the ratios of the cell types used to investigate the potential for MSCs to stimulate improved repair. Histological and indirect immunofluorescence staining revealed the formation of dense tissue structures within the chondrocyte and MSC-chondrocyte cell co-cultures, alongside the establishment of a proliferative region at the base of the tissue. No stimulatory or inhibitory effect in terms of ECM production was observed through the introduction of MSCs, although the potential for an immunomodulatory benefit remains. This proof-of-concept study therefore provides a novel method to enable the scalable production of therapeutically relevant micro-tissue models that can be used for in vitro research to optimise ACI procedures.

As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. The paper’s objective is to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. From the results, there was less than 1 second difference between the means of gaming task (mean = 2.55s, std = 0.1002s) to no-task (mean = 2.55s, std = 0.1002s). Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments AR-Game focusing on real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

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.

Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here we describe a ClearSee-based clearing protocol, which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining, and enables high quality 3D imaging of embryo morphology and expression of a fluorescent reporter with the cellular resolution.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

The extrusion-based additive manufacturing is a popular fabrication method, which has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the feed of filament that is presented in the production G-code. The feed of filament is calculated according to the layer height, the extrusion width and the length of printing path. All the required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs that are placed next to each other at a fixed distance. In special curved paths, the distance of adjacent ones is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. Therefore, making a special computer code that can be used to create various functions of curved paths is investigated in this research, and also the feed of filament parameter is studied in detail. Next, by introducing a correction technique, the feed of filament is changed during the curved path to distribute the polymer material uniformly. Finally, composite samples (which have variable stiffness) consisting of curved fibers are produced with the proposed method, and the high quality of printed samples confirm the suggested code and technique.

Emerging pollutants as pharmaceuticals have been focusing international attention for few decades. Ciprofloxacin (CIP) is a common drug widely found in effluents from hospitals, industrial and different wastewater treatment plants, as well as rivers. In this work, the lab-scale 3D-BER system was established, and more than 90% of the antibiotic CIP removal from the Low C/N wastewater. Best results were obtained with current intensity, and different C/N ratio significantly improve the removal of CIP and nitrates, when the ideal conditions were; C/N = 1.5-3.5, pH =7.0-7.5, and I = 60 mA. The highest removal efficiency of CIP = 94.20 %, NO3--N= 95.53 % and total nitrogen (TN) = 84.27 %, respectively. In this novel system, the autotrophic-heterotrophic denitrifying bacteria played vital role for the removal of CIP and enhanced denitrification process. Thus, autotrophic denitrifying bacteria uses CO2 and H2 as carbon sources to reduce nitrates to N2. This system has the assortment and prosperous community revealed at the current intensity of 60 mA, and the analysis of bacterial community structure in effluent samples fluctuates under different condition of C/N ratios. According to the results of LC-MS/MS analysis, the intermediate products were proposed after efficient biodegradation of CIP. Microbial community on biodegrading was mostly found at phylum, and class level was dominantly responsible for the NO3--N and biodegradation of CIP. This work can provide some new insights towards the biodegradation of CIP and the efficient removal of nitrates from low C/N wastewater treatment by the novel 3D-BER system.

The integration of 3D city models with Building Information Models (BIM), abbreviated as GeoBIM, facilitates improved data support to several applications, e.g. 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e. harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas.

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.

This paper presents the systematic development and performance characterization of a non-proprietary 3D-printable ultra-high-performance fiber-reinforced concrete (UHPFRC) for digital construction. Several fresh and hardened properties of the developed 3D-printable UHPFRC matrix (without fiber) and composite (with 2% volume fraction of steel fibers) were evaluated and compared to that of conventionally mold-cast UHPFRC. Additionally, the effects of testing direction on the compressive and flexural strengths of the printed UHPFRC were investigated. The fresh properties of the UHPFRC developed in this study satisfied the criteria for extrudability, buildability, and shape-retention-ability, which are relevant for ensuring printability. The printed UHPFRC exhibited superior flexural performance to the mold-cast UHPFRC due to alignment of the short fibers in the printing direction. The high compressive and flexural strengths, along with the deflection-hardening behavior, of the developed UHPFRC can enable the production of thin 3D-printed components with significant reduction or complete elimination of conventional steel bars.

The diffusion coefficients of ions are measured in a microchip filled with a cationic charged 3D hydrogel in order to study the effect of cationic charged 3D hydrogel on the diffusivity of ions. In this study, poly-diallyl-dimethyl-ammoniumchloride (poly-DADMAC) is used to produce a 3D hydrogel. Four different fluorophores are used in the 3D hydrogel rhodamine 6G, rhodamine-BSA, fluorescein isothio-cyanate (FITC) and FITC-BSA. The rhodamine 6G and rhodamine-BSA are positively charged (cations), while fluorescein isothio-cyanate (FITC) and FITC-BSA are negatively charged (anions). Two widely used techniques which are short time diffusivity measurement technique and long time diffusivity measurement techniques are used to measure the diffusion coefficients. For the short time measurement, Fluorescence recovery after photo-bleaching (FRAP) is used by a 3D confocal microscope. For the long time measurement, fluorescence images are taken for 11 days to observe a pure diffusivity without any convective movement. As a result, the diffusivity of the cations was found to be lower than that of the anions in the cationic charged hydrogel.

Abstract: The promise of regenerative medicine and tissue engineering is founded on the ability to regenerate diseased or damaged tissues and organs into functional tissues and organs or the creation of new tissues and organs altogether. In theory, all damaged and diseased tissues and organs can be regenerated or created using different configurations and combinations of extracellular matrix, cells and inductive biomolecules. Currently, regenerative medicine and tissue engineering can allow the improvement of patients’ quality of life through availing novel treatment options. Tissues and organs have a specific ECM, with specific proteins and factors released by cells residing within the local microenvironment. The coupling of regenerative medicine and tissue engineering field with 3D printing is revolutionizing the treatment of patients in a huge way. 3D bioprinting allows the proper placement of cells and ECMs, allowing the recapitulation of native microenvironments of tissues and organs. 3D bioprinting utilizes different bioinks made up of different formulations of ECM/biomaterials, biomolecules and even cells. The choice of the bioink used during 3D bioprinting is very important as properties such as printability, compatibility and physical strength influence the final construct printed. The extracellular matrix (ECM) provides both physical and mechanical microenvironment needed by cells to survive and proliferate. Decellularized ECM bioink contains biochemical cues from the original native ECM and also the right proportions of ECM proteins. Different techniques and characterization methods are used to derive bioinks from several tissues and organs and to evaluate their quality. This review discusses the uses of decellularized ECM bioinks and argues that they represent the most biomimetic bioinks available. In addition, we briefly discuss some polymer-based bioinks utilized in 3D bioprinting.

The parts obtained by FFF technology are anisotropic in any direction; while the whole part constitutes out of layers, each of the layers include one or more perimeters of shell and the infill with density from 0 to 100%. Mechanical performance of FFF parts depends on its shape, orientation, constitution and 3D printing parameters. A classification for shapes of parts obtained by FFF and stressed during its operation is provided. For the most questionable class (parts with interrupted shell critically stressed on interlayer boundaries) a representative sample and testing apparatus is developed. Different approaches to the sample shape modification were applied and tested. The part shape optimization resulted in increment of the force, required to fracture the part from 483 to 1096 N.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

Most of the phase change materials (PCMs) have been limited to use as functional additions or sealed in containers, and extra auxiliary equipment or supporting matrix is needed. The emergence of 3D printing technique has dramatically advanced the developments of materials and simplified production processes. This study focuses on a novel strategy to model thermal energy storage crystalline gels with three-dimensional architecture directly from liquid resin without supporting materials through light-induced polymerization 3D printing technique. A mask-projection stereolithography printer was used to measure the 3D printing test, and the printable characters of crystalline thermal energy storage P(SA-DMAA) gels with different molar ratios were evaluated. For the P(SA-DMMA) gels with small fraction of SA, the 3D fabrication was realized with higher printing precision both on mili- and micro-meter scales. As a comparison of 3D printed samples, P(SA-DMAA) gels made by other two methods, post-UV curing treatment after 3D printing and UV curing using conventional mold, were prepared. The 3D printed P(SA-DMAA) gels shown high crystallinity. Post–UV curing treatment was beneficial to full curing of 3D printed gels, but did not lead to the further improvement of crystal structure to get higher crystallinity. The P(SA-DMAA) crystalline gel having the highest energy storage enthalpy that reached 69.6 J·g⁻¹ was developed. Its good thermoregulation property in the temperature range from 25 to 40 °C was proved. The P(SA-DMAA) gels are feasible for practical applications as one kind of 3D printing material with thermal energy storage and thermoregulation functionality.

Selective laser melting (SLM) is one of the widely used techniques in metallic additive manufacturing, in which high-density laser powder is utilized to selectively melting layers of powders to create geometrically complex parts. Temperature distribution and molten pool geometry directly determine the balling effect, and concentrated balling phenomenon significantly deteriorates surface integrity and mechanical properties of the part. Finite element models have been developed to predict temperature distribution and molten pool geometry, but they were computationally expensive. In this paper, the three-dimensional temperature distributions are predicted by analytical models using point moving heat source and semi-ellipsoidal moving source respectively. The molten pool dimensions under various process conditions are obtained from the three-dimensional temperature predictions and experimentally validated. Ti-6Al-4V alloy is chosen for the investigation. Good agreements between the predictions and the measurements are observed. The presented models are also suitable for other metallic materials in the SLM process.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling loads of air-conditioning systems in summer, and urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D vector building data. The building footprints and the terrain are preprocessed using GIS. The solar calculation uses a raytracing algorithm in 3D CAD at the city scale. To evaluate the performance, two cases were considered at the city and district scale, respectively. The total face count of meshes for the simulations constituted approximately 2 000 000 faces. 64379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualized in a 3D web platform. Using a 3D CAD environment establishes a seamless process of updating 3D models and simulations while preprocessing in GIS ensures working with large-scale datasets. The proposed method showed moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

Background: 3D virtual reality (VR) Motionless Imagery Exercise through Avatar (MIEA) was developed to improve elderly’s psychological (cognitive and emotional) and physical health. This study observed the impact of this program on the cognitive functions and emotional well-being and the electrophysiological changes in older adults. Methods: This study was conducted using a randomized controlled trial design. The participants included 38 adults, 65 to 84 years of age, who were physically mobile and neurologically healthy. Among them, 19 participants were assigned to the experimental group, who underwent exercises using elderly avatars in a VR virtual exercise program. The other 19 participants were assigned to the control group who underwent the VR program. Both groups engaged in the VR intervention for 20 minutes three times a week, over six weeks. Results: The cognitive functions, including attention restraint, working memory, and phonemic fluency, of the experimental group showed significant improvements compared to the control group. Regarding emotional well-being, the experimental group showed greater self-efficacy. The electrophysiological changes revealed similar patterns to exercise-induced reactions in electromyography of the experimental group after intervention. Conclusions: These findings confirm that participation in 3D VR MIEA enhances cognitive functions and physical self-efficacy among the elderly. Therefore, 3D VR MIEA can be utilized in the rehabilitation therapy of patients or elderly with limited physical activity.

An 8-months old male American Staffordshire terrier was referred for a no weightbearing lameness of the right pelvic limb, hyperthermia, lethargy and inappetence caused by hematogenous osteomyelitis. Orthopedic, radiographic and tomographic examinations revealed a bone sequestrum of 4 x 1.4 cm and active periosteal reaction of the caudo-lateral cortical in the metaphysis and the distal third of the right femoral diaphysis, medullary osteolysis and interruption of the cranio-medial cortical profile, with involvement of the femoral trochlea leading to a secondary medial patella luxation. Once skeletally mature, after 4 months from surgical debridement and aggressive antibiotic therapy against Klebsiella oxytoca revealed by bacteriological exam, the patient underwent prosthetic surgery for the application of a custom-made patellar groove replacement (PGR) to fill the bone defect and restore the femoral trochlea surface. Despite the serious injury that afflicted the right pelvic limb, the surgery had satisfactory outcomes until the last 18-months postoperative follow up.

СrAss-like phages play an important role in maintaining ecological balance in the human intestine microbiome. However, their lifestyle and genetic diversity are still insufficiently studied. In this study, a novel CrAssE-Sib phage genome belonging to the epsilon сrAss-like phage genomes was found. Detailed analysis indicated that CrAssE-Sib encodes unusual proteins – DNA primase, virion RNA polymerase, prophage antirepressor, and transcriptional repressor, as well as reverse transcriptase (RT) and receptor binding protein (RBP) that are part of diversity-generating retroelement (DGR). Comparative analysis showed that all epsilon сrAss-like phages are divided into two putative genera, which were proposed to name Epsilonunovirus and Epsilonduovirus; CrAssE-Sib belongs to the first of them. RT from CrAssE-Sib and several Epsilonunoviruses contains a specific motif in its fourth domain that was not previously found in RTs from phage and bacterial DGRs. In addition, RBP from CrAssE-Sib and all Epsilonunoviruses has a unique structure. The presence of the conservative prophage repressor and anti–repressors that belong to the lysogenic-to-lytic cycle switches indicate that CrAssE-Sib and other Epsilonunoviruses could be temperate. Epsilon сrAss-like phage substantially differ from other crAss-like phages, including zeta сrAss-like phages. This fact indicates the need to isolate these phages into a separate family.

Recently, nanotechnologies have become increasingly prominent in the field of bone tissue engineering (BTE), offering substantial potential to advance the field forward. These advancements manifest in two primary ways: the localized application of nanoengineered materials to enhance bone regeneration and their use as nano-vehicles for delivering bioactive compounds. Despite significant progress in the development of bone substitutes over the past few decades, it is worth noting that the quest to identify the optimal biomaterial for bone regeneration remains a subject of intense debate. Ever since its initial discovery, poly(lactic-co-glycolic acid) (PLGA) has found widespread use in BTE due to its favorable biocompatibility and customizable biodegradability. This review provides an overview of contemporary advancements in the development of bone regeneration materials using PLGA polymer. The review covers some of PLGA properties with a special focus on modifications of these properties towards bone regeneration. Furthermore, we delve into the techniques for synthesizing PLGA nanoparticles (NPs), the diverse forms in which these NPs can be fabricated, and the bioactive molecules that exhibit therapeutic potential for promoting bone regeneration. Additionally, we addressed some of the current concerns regarding the safety of PLGA NPs, and PLGA-based products available in the market. Finally, we briefly discussed some of the current challenges and proposed some strategies to functionally enhance the fabrication of PLGA NPs towards BTE. We envisage that the utilization of PLGA NP holds significant potential as a potent tool in advancing therapies for intractable bone diseases.

As the control brain of the petrochemical plant, blast resistant performance requirements of the petrochemical control room should be guaranteed when the vapor cloud explosion occurs in the petrochemical production process. 3D-Kagome truss core sandwich structure is a kind of blast resistant material with high energy absorption. Considering the influence factors of the radius of the truss core rod and thickness of the upper and lower panels, in this paper, blast resistant performance of a real steel petrochemical control room with 3D-Kagome truss core sandwich wall is analyzed. With the optimization goal of plastic deformation energy and panel displacement, the optimal wall thickness and radius of the truss core rode are obtained. The optimized blast resistant wall is assembled, and the dynamic response of the steel petrochemical control room with the 3D-Kagome truss core sandwich blast resistant wall is analyzed. The simulation results show that the truss core layer does not dissipate the blast energy effectively when the radius ratio of the truss core rod is larger than 2.7% of the total thickness of the wall; with the increase of the thickness of the upper and lower panels, the proportion of plastic deformation energy of the truss core layer increases gradually and then tends to be a stable proportion value, that is about 90%; When the thickness ratio of the panel is 6.7% of the total one of the wall and the radius ratio of the truss core rod is 2.7% of the total thickness of the wall, the optimal blast resistant 3D-Kagome sandwich wall is determined.

There has been controversy around the mechanical properties of Mg-alloys such as AZ91D and the large variation of these have been seen. The current paper addresses this controversy through specially fabricated samples combined with tensile testing and advanced metallography, including 3D reconstruction of the phases. The results show that despite a more brittle nature of the fracture, the equiaxed microstructure displays a better elongation as compared to a dendritic microstructure. The main conclusion is that this is primarily caused by the nature, or tortuosity, of the Mg17Al12-network in the material.

One of the key pathogenetic links of type 2 diabetes mellitus (T2DM) is the formation of insulin resistance (IR). Besides a wide selection of synthetic antidiabetic drugs, various plant-origin extracts are also available to support the treatment of T2DM. This study aimed to investigate and gain knowledge of the chemical composition and potential IR correction effect of American cranberry (Vaccinium macrocarpon Aiton) leaf extracts and formulate novel 3D-printed oral dosage forms for such extracts. The bioactivity and IR of L-arginine-loaded cranberry leaf extracts were studied in vivo in rats. The cranberry leaf extracts consisted of quinic, 3-caffeoylquinic (chlorogenic), p-coumaroylquinic acids, quercetin 3-O-galactoside, quercetin-3-O-glucoside, quercetin-3-xyloside, quercetin-3-O-arabino-pyranoside, quercetin-3-O-arabinofuranoside, quercetin 3-O-rhamnoside and quercetin-O-p-coumaroyl-hexoside-2 identified by HPLC. In-vivo studies with rats showed that the oral administration of the cranberry leaf extracts had a positive effect on insulin sensitivity coefficients under ITT test, and affected HOMA-IR levels and liver lipid content with experimental IR. A novel 3D-printed immediate-release dosage form was developed for the oral administration of cranberry leaf extracts using polyethylene oxide (PEO) as a carrier gel in a semi-solid extrusion (SSE) 3D printing. In conclusion, American cranberry leaf extracts loaded with L-arginine could find uses in preventing health issues associated with IR.

There is a fast-growing interest in the use of selective laser melting (SLM) for metal/alloy additive manufacturing. Our current knowledge of SLM printed 316 stainless steel (SS316) is limited and sometimes appears sporadic, presumably due to the complex interdependent effects of a large number of process variables of the SLM processing. This is reflected from the discrepant findings in the crystallographic textures and microstructures in this investigation with those reported in the literatures, which also vary itself. The as-printed material is macroscopically asymmetric in both the structures and crystallographic textures. The <101> and <111> crystallographic directions align parallel with the SLM scanning direction (SD) and build direction (BD), respectively. Like-wise, some characteristic low angle boundary features are reported crystallographic, while this investigation unequivocally proves them non-crystallographic since they always maintain an identical alignment with the SLM laser scanning direction irrespective of the matrix material’s crystal orientation. There is also 500±200 nm columnar or cellular features, depending on the cross-section, generally found all over the sample. These columnar or cellular features are formed with walls made of dense packing of dislocations entangled with Mn, Si and O enriched amorphous inclusions. They remain stable after the ASM solution treatments at 1050 °C temperatures, and therefore, are capable of hindering boundary migration events of recrystallization and grain growth. Thus, the nanoscale structures can be retained at high temperatures. Large 2-4 µm inclusions form during the solution treatment, within which the chemical and phase distribution are heterogeneous.

This article presents, for the first time, the efficacy and curing depth analysis of photo-thermal dual polymerization in metal (Fe) polymer composites for 3D printing of a 3-component (A/B/M) system based on the proposed mechanism of our group, in which the co initiators A and B are Irgacure-369, and charge-transfer complexes (CTC), respectively; and the monomer M is filled by Fe. Our formulas show the depth of curing (Zc) is an increasing function of the light intensity, but a decreasing function of the Fe and photoinitiator concentrations. Zc is enhanced by the additive [B] which produces extra thermal radical for polymerization under high temperature. The heat (or temperature) increase in the system has two components : (i) due to the light absorption of Fe filler, and (ii) heat released from the exothermic photopolymerization of the monomer. The heat is transported to the additive (or co-initiator) [B] to produce extra radical R' and enhance the monomer conversion function (CF). The Fe filler leads to temperature increase, but also limits the light penetration leading to lower CF and Zc, which could be overcome by the additive initiator [B] in thick polymers. Optimal Fe for maximal CF and Zc are explored theoretically.

This paper presents a comparison between indirect methods for assessing the buildability of 3D printed cementitious materials, including CRV or CSR test using rotational rheometry, unconfined uniaxial compression test (UUCT), a newly proposed confined uniaxial compression test (CUCT), and fast penetration test. The experimental program was conducted on the extruded samples of six printable mortars and two printable concretes with various rheological behavior achieved by the use of different additives and admixtures. Predictions of the material failure and the stability failure in hollow cylindrical structures are provided and compared with the results of the direct printing test. Benefits and disadvantages of the employed test methods are discussed, also taking into consideration a perspective of their use for automated in-situ control of the materials’ buildability.

Associations between blood cancer and genetic predisposition, including both inherited variants and acquired mutations and epimutations, have been well characterized. However, the majority of these variants affect noncoding regions, making their mechanisms difficult to hypothesize and hindering the translation of these insights into patient benefits. Fueled by unprecedented progress in next-generation sequencing and computational integrative analysis, studies have started applying combinations of epigenetic, genome architecture and functional assays to bridge the gap between noncoding variants and blood cancer. These complementary tools have not only allowed us to understand the potential malignant role of these variants but also to differentiate key variants, cell types and conditions from misleading ones. Here, we briefly review recent studies that have provided fundamental insights into our understanding of how noncoding mutations at enhancers predispose and promote blood malignancies in the context of spatial genome architecture.

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Non-destructive testing of metallic objects that may contain embedded defects of different sizes is an important application in many industrial branches for quality control. Most of these techniques allow defect detection and its approximate localization, but very few give enough information for its 3D reconstruction. Here we present a hybrid laser – transducer system that combines remote laser-generated ultrasound excitation and non-contact ultrasonic transducer detection. This fully non-contact method gives access to separating scan areas on different object’s faces and defect details from different angles/perspectives can be analysed. This hybrid system can analyse the whole object’s volume data and allow a 3D reconstruction image of the embedded defects. As a novelty for the signal processing improvement, we use a 2D apodization window filtering technique, applied along with the synthetic aperture focusing algorithm in order to remove the undesired effects of side lobes and wide-angle reflections of propagating ultrasound waves, thus, enhancing the resulting 3D image of the defect. We provide both qualitative and quantitative volumetric results with high accuracy and resolution compared with conventional techniques.

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition: it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such a research due to their high variability and the necessity of their monitoring to provide safety of agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of >3000 descriptors was used in QSAR analysis of binding activities (pIC50). Comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR COMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q² (‘leave-many-out’) is equal 0.498, and for monoclonal antibodies q² is equal 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

Manipulation of micro- and nano-sized objects with optical tweezers is a well established, albeit still evolving technique. While many objects can be trapped directly with focused laser beam(s), for some applications indirect manipulation with tweezers-operated tools is preferred. We introduce a simple, versatile micro-tool operated with holographic optical tweezers. The 40 µm long dumbbell-shaped tool, fabricated with two-photon laser 3D photolithography has two beads for efficient optical trapping and a probing spike on one end. We demonstrate fluids viscosity measurements and vibration detection as examples of possible applications.

The paper presents a new method of path planning for an industrial robot manipulator performing thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser trian-gulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed based on the application of discrete exterior calculus methods characterized by high computational efficiency. The developed algorithms and their software implementation were experimentally tested through robotic mi-croplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral raw materials.

Computer Vision using Deep Learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, despite the fact that autistic children are frequently exposed to danger as a result of their activities, many Computer Vision experts have shown little interest in their safety. High-grade autistic children frequently experience the Meltdown Crisis condition, characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children’s parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences in order to extract and learn the spatial and temporal features of the Stereotyped Motor Movements made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. In order to attain a 92% recall and F1 Score, the final decisions made for data preparation, model construction, and training parameters were tweaked and established experimentally. The best loss value obtained was 0.08. The MeltdownCrisis dataset, which includes realistic scenarios of autistic children’s behaviors in the Pre-Meltdown Crisis state and Normal state the Normal state data being used as a negative class was utilized for evaluation.

In the Anthropocene era, lake ecosystems are facing significant human-related pressures, resulting in a decline in both biodiversity and habitat quality. Restoration initiatives can effectively enhance the resilience of freshwater environments. This research delves into various established and novel method aimed at encouraging the growth of a macrophyte, Vallisneria spiralis, in the littoral zone of Lake Como, a southern alpine Italian lake. For this aim, Vallisneria spiralis was collected and placed in tanks filled with four different types of 3D-printed biodegradable substrates. Remarkably, the plant demonstrated a growth rate of 4 mm per day, increasing from an initial number of 12 plants to 400 after four months, with a total surpassing 700 specimens by the conclusion of the study. Following their introduction into the natural environment, the survival rate of plants positioned on substrates that remained stable and in contact with the lakebed reached 85.7%. This research is the first to indicate that Vallisneria spiralis could be a viable option for restoration projects in coastal lake habitats, particularly when utilizing biodegradable substrates. The hope is that this study can serve as a valuable model for future restoration initiatives and improve sustainability of freshwater ecosystems.

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D, and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in recreating 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used including a building and a university campus. As the result, 3D Gaussian data has proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twin.

Test plants or laboratory prototypes are essential for the development of training activities in engineering. In the area of automation and control, in particular, equipment that model to a high degree those generally found in industrial processes is required, allowing engineers in training to be in permanent contact with the challenges of the context in which they work. This paper presents the design and simulation of a fly cutting plant for use in academic environments. In the design, a 3D model was built in SketchUp, considering features present in industrial plants. The simulation of the system was developed in MATLAB through a mathematical modeling of the plant. The main contribution of the work carried out was the design of a low-cost and small-dimension industrial prototype, which includes a conveyor belt and a continuous cutting mechanism through which large-scale industrial processes can be known and operated. As results, performance tests were performed in tools such as MATLAB, Simulink, and Code Composer Studio. After the operation, the operation and cutting tests were implemented using a classical control technique. Additionally, all the design features of a flying cutting plant, which can be easily implemented for the development of process control-oriented activities, are presented.

Over 2.2 billion people across the globe face significant barriers to accessing essential ophthalmic care, with elderly, rural, and refugee populations being disproportionately affected. These obsta-cles deepen existing disparities in eye care. 3D printing is a novel technology that has the potential to transform the field and improve access through alleviating many patient-specific barriers. This article delves into the evolution of 3D printing within ophthalmology, highlighting its current ap-plications and future potential. It explores various 3D printing techniques and biomaterials, dis-cussing their effectiveness in creating advanced solutions such as bioengineered corneas, ocular prosthetics, and innovative treatments for dry eye syndrome. Additionally, 3D printing has rev-olutionized drug delivery systems for conditions like glaucoma, retinal diseases, and for ocular brachytherapy. Despite its promise, the integration of 3D printing into clinical practice presents challenges, which the article addresses alongside strategies for overcoming them. By mapping out the technological advancements and challenges, this review offers a roadmap for enhancing global eye care accessibility and improving patient outcomes on a global scale.

The ACEtabular rim eXtension (ACE-X) implant is a custom-made 3-dimensional-printed titanium device designed for the treatment of canine hip dysplasia. Thirty-four dogs (61 hips) underwent ACE-X implantation, and assessments were conducted using computed tomography, force plate analysis, Ortolani’s test, and Helsinki Chronic Pain Index (HCPI) questionnaires at five intervals: pre-operative day, surgery day, 1.5-month, 3-month, and 12-month follow-up. Statistically significant increases in femoral head coverage with negative Ortolani subluxation test were observed immediately after surgery and persisted throughout the study. Osteoarthritis (OA) scores remained stable, but osteophyte size significantly increased between the surgery day and the 12-month follow-up, especially in hips with a baseline OA score of 2 compared to those with a score of 1. Force plate data showed no significant changes during the study. The HCPI demonstrated a significant decrease in pain score from pre-operative value to 6-week follow-up and gradually decreased over time. Major complications were identified in 6 hips (9.8%) of 4 dogs. In conclusion, the ACE-X implant effectively increased femoral head coverage, eliminated subluxation, and provided long-term pain relief with minimal complications, benefiting over 90% of the study population. The study supports the ACE-X implant as a secure and valuable treatment for canine hip dysplasia.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

This study aims to demonstrate the possibility of incorporating a natural antioxidant biomolecule, into polymeric porous scaffolds. To this end, Poly-L-Lactic Acid (PLLA) scaffolds were produced using the Thermally Induced Phase Separation (TIPS) technique and additivated with different amounts of Rosmarinic Acid (RA). The scaffolds, with a diameter of 4 mm and a thickness of 2 mm, were characterized with a multi-analytical approach. Specifically, Scanning Electron Mi-croscopy analyses demonstrated the presence of an interconnected porous network, character-ized by a layer of RA at the level of the pore’s surfaces. Moreover, the presence of RA biomole-cules increased the hydrophilic nature of the sample, as evidenced by the decrease in contact angle with water from 128° to 76°. This approach is cost-effective, and it could be customized with different biomolecules, offering the possibility of producing porous polymeric structures con-taining antioxidant molecules. These scaffolds meet the requirements of tissue engineering and could be offered a potential solution to reduce inflammation associated with scaffold implantation thus improving tissue regeneration.

The pharmaceutical industry usually utilizes either hydrophobic or hydrophilic substances extracted from raw plant materials for preparing a final product, However, the plant material waste still contain substances with an opposite solubility. The aim of this study was to enhance the comprehensive usability of plant materials by developing a new non-wasting extraction method for eucalypt leaves and by investigating the phytochemical and pharmacological properties of eucalypt extracts and their 3D-printed dosage forms. The present extraction method enabled to prepare both hydrophobic soft extracts and hydrophilic (aqueous) dry extracts. We identified total 28 terpenes in the hydrophobic soft extract. In the hydrophilic dry extract, total 57 substances were identified, and 26 of them were successfully isolated. The eucalypt extracts studied showed a significant antimicrobial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, Candida albicans, Corynebacterium diphtheriae gravis, and Corynebacterium diphtheriae mitis. The anti-inflammatory activity of the dry extract was studied with a formalin oedema model in mice. The maximum anti-exudative effect of the dry extract was 61.5% at a dose of 20 mg/kg. The composite gels of polyethylene oxide and eucalypt extract were developed, and the key process parameters for semi-solid extrusion (SSE) 3D printing of such gels were verified. The SSE 3D-printed preparations of novel synergistically acting eucalypt extracts could have uses in antimicrobial and anti-inflammatory medicinal applications.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

During the last decades the quantitative description of electrical and magnetotransport properties of solid-state materials has been a remarkable challenge in materials science. Recently, the discovery of a novel class of materials, the topological semimetals, has led to a growing interest for the full understanding of their magnetotransport properties. In this review, the strong interplay among topology, band structure and carrier mobility in recently discovered high carriers mobility topological semimetals is discussed and their effect on their magnetotransport properties is outlined. The huge magnetoresistance effect, especially in the Hall transverse configuration, and a new version of a three-dimensional quantum Hall effect observed in high-mobility Weyl and Dirac semimetals are reviewed. The design of novel quantum sensors based on solid-state semimetals is also encouraged.

The most modern technique utilized to create intricate and complexly manufactured parts for a variety of applications is called additive manufacturing (AM). Fused deposition modeling (FDM) has received the greatest attention in the development and manufacturing sectors. The main objective of this study was to investigate how printing factors affected the mechanical characteristics of printed samples. Samples were produced using an FDM 3D printer in compliance with ASTM D638 using a variety of input settings, including orientation, layer thickness, speed, and infill pattern. On the printed samples, tensile tests and morphological analysis using a scanning electron microscope (SEM) were done. The findings of this study demonstrate that factors including layer thickness, printing speed, and orientation significantly affect the tensile strength of ABS printed samples. Discovering the process settings that could lead to superior mechanical and physical characteristics would undoubtedly assist designers and manufacturers worldwide as the FDM 3D printer becomes more and more crucial in manufacturing engineering parts.

The expression of the Fasciola hepatica Carboxylesterase type B (CestB) gene is induced in response to the anthelmintic triclabendazole (TCBZ), resulting in a significant increase in enzymatic specific activity. Furthermore, the amino acid sequence of CestB exhibits variations that could lead to substantial amino acid substitutions at the ligand-binding site. These substitutions have the potential to affect the interaction between the ligand and the protein, as well as the catalytic properties of the enzyme. The objective of this study was to identify new CestB polymorphisms in TCBZ-resistant parasites using 3D modeling against the metabolically oxidized form of the anthelmintic TCBZSOX. Our aim was to observe the formation of TCBZSOX-specific binding pockets that could potentially explain the resistance to anthelmintic agents. We identified a CestB polymorphism in a TCBZ-resistant strain of parasites exhibiting three radical amino acid substitutions at positions 147, 215, and 243, resulting in the formation of a TCBZSOX-affinity pocket capable of binding the anthelmintic drug. Additionally, our 3D modeling analysis revealed that these amino acid substitutions also had an impact on the configuration of the CestB catalytic site, affecting the enzyme's interaction with chromogenic carboxylic ester substrates and altering its catalytic properties; however, the identified TCBZSOX-binding pocket was located far from the enzyme's catalytic site, making the enzymatic hydrolysis of TCBZSOX theoretically impossible. Nonetheless, the increased affinity for the anthelmintic may explain a drug-sequestration type of anthelmintic resistance and lays the foundation for the development of a molecular diagnostic tool for identifying anthelmintic resistance in F. hepatica.

This comprehensive review explores the advancements in Orthognathic and Oral Maxillofacial Surgery, focusing on the integration of 3D Printing and Virtual Surgical Planning (VSP). Traditional surgical methods, while effective, come with inherent risks and complications, and can lead to variability in outcomes due to the reliance on the surgeon's skill and experience. The shift towards patient-centric care necessitates personalized surgical methods, which can be achieved through advanced technology. The amalgamation of 3D printing and VSP revolutionizes surgical planning and implementation by providing tactile 3D models for visualization and planning, and accurately designed surgical guides for execution. This convergence of digital planning and physical modeling facilitates a more predictable, personalized, and precise surgical process. However, the adoption of these technologies presents challenges, including the need for extensive software training and the steep learning curve associated with computer-aided design programs. Despite these challenges, the integration of 3D printing and VSP paves the way for advanced patient care in orthognathic and oral maxillofacial surgery.

In the present work, a comparative evaluation of an MBBR unit performance was carried out for the following cases: when adding 3D-printed biocarriers fabricated with 13X and bentonite, when using K1 commercial biocarriers and when not adding biocarriers at all. For the evaluation of the MBBR efficiency, various physicochemical parameters were measured, while static light scattering and optical microscopy observations were additionally used. Finally, biofilm extracted from the biocarriers was evaluated. The findings suggest that there is an optimal biodegradation of the organic load in all MBBR units. The nitrification and denitrification process were improved at the 3D MBBR as compared to the control MBBR and MBBR K1. The dry mass of the biofilm in the 3D-printed biocarriers was two orders of magnitude larger than the one in the K1 biocarriers. What is more, in the K1 biocarriers the mass of the biofilm varied in relation to time, due to the fact that it could not be kept inside the holes, something that was not observed to happen with the 3D-printed biocarriers. Finally, it was observed, mostly in the 3D MBBR and less in the K1 MBBR, that the growth of nitrifying bacteria and heterotrophs inside the units increased the biomass production in the form of SMP, which in turn favored the adhesion of biomass on the surface of biocarriers.

The first years of an infant’s life represent a sensitive period for neurodevelopment and see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed instrumented toys as a new task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a Barometer and Inertial Measurement Unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provides a rich dataset describing the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition may be inferred. Such a tool potentially provides an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded Near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information corresponding to different planes. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.

Based on the historical research on the shape of armor in Ming Dynasty, this paper explores the shape characteristics of general armor in Ming Dynasty in China through field research on the general stone statue of Ming Xiaoling Mausoleum in Nanjing. Through Ming Xiaoling Mausoleum general stone carving 3D animation technology restoration project, virtual material heritage practice is carried out. Through the modeling of the general stone statue in Ming Xiaoling Mausoleum by MAYA and Zbrush, UE4 realizes the construction of scenes and the output of interactive effects, so as to explore a mode of application engine animation that can preserve and propagate general stone carving in Ming Xiaoling Mausoleum.