Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarkers discovery, organ function assessment, and personalized medication. Therefore, it is of utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples and extracted comprehensive biological information, including the primary components and compositions present in the blood. We conducted short-wavelength (532 nm green light) Raman scattering spectroscopy on blood samples, plasma, and serum, and subsequently analyzed the biological characteristics detected in each sample type. Our results indicated that whole blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. Additionally, in the field of medical blood testing, the novel 3D Silicon micro-channel device technology holds immense potential. It can serve as a substrate and helps to detect various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used to diagnose clinical diseases.

Object detection is important in many applications, such as autonomous driving. While 2D images are lack of depth information and are sensitive to environmental conditions, 3D point cloud can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving a mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.

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.

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

The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods.

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

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

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

Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using a model-based and optimization-driven approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer and (3) providing SmartScan with global foresight to make it less myopic in its optimization. Sample 3D parts are printed using the proposed generalized SmartScan and compared to those printed using standard heuristic scan sequences. Reductions of up to 93% in temperature inhomogeneity, 87% in residual stress, and 26% in maximum deformation were observed, without significantly sacrificing print speed. However, SmartScan was found to cause minor (<5%) to significant (up to 20%) increases in surface roughness compared to the heuristic approaches, depending on the scan pattern used.

To improve the measurement accuracy of three-dimensional rotation angle of the spherical joint, a novel approach is proposed in this study, which combines the magnetic detection by Hall sensor and surface feature identification by eddy current sensor. Firstly, a permanent magnet is embedded in the ball head of spherical joint, and Hall sensors are set and distributed in the ball socket to measure the variation of magnetic flux density when spherical joint rotating, which are related to 3D rotation angle. In order to further improve measurement accuracy and robustness, we also set grooves on the ball head and use eddy current sensors to synchronously identify the rotation angle of the ball head. After the combination of two signals is adopted, a measurement model is established using the RBF neural network by training, and realized real-time measurement of the 3D rotation angle of spherical joint. The feasibility and superiority of this method are validated through experiments. The experimental results indicated that the measurement accuracy is promoted substantially compared to the preliminary measurement scheme based on spherical coding, the average measurement error of single axis is reduced by 9'9". The root mean square errors for the measurement of 3D rotation angles in this proposed method are as follows: the pitch angle α has an error of 1'8", the yaw angle β has an error of 2'15", and the roll angle γ has an error of 29'6".

We investigate a single-particle density of states in the three-dimensional system described by effective two-band Hamiltonian which describes a ground state in two distant electronic phases: the semimetalic nodal-loop phase and the insulating gapped phase. Analysis of valence bands and Fermi surfaces in both phases indicates that the density of states crucially depends on the parameter in the Hamiltonian of the system that controls a topological alternation of the Fermi surface. The signature of that alternation is expected to play an important role in all quantities closely related to the density of electronic states, such as charge transport and optical conductivity of the system for example.

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.

This case study explores the transformative effects of emergent digital technologies, particularly augmented reality (AR), on literacy engagement and reading for pleasure. The United Arab Emirates Ministry of Education implemented the UAE Storytime programme, utilising AR to create an interactive literacy experience for young learners. The programme incorporated bilingual episodes, featuring stories based on local and global contexts, with 3D objects and interactive games to enhance understanding. The results indicate a positive correlation between the use of AR and literacy engagement. A high proportion of learners scanned the QR codes to access the AR objects, which enhanced their engagement and understanding of the stories. The programme fostered a learner agency, engaged family members in the learning process, and established a sense of global community. The study recommends the integration of similar initiatives into the curriculum to promote collaboration, creativity, and holistic learning experiences.

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

The present work fronts the human pose estimation problem, developing a method that enables automated ergonomic risk assessment. A research methodology is developed to calculate joint angles from digital snapshots or videos by using computer vision and machine learning techniques to get a more accurate ergonomic risk assessment. Starting from an ergonomic analysis, the study explores the use of a semi-supervised training method to detect the skeletons of workers. The research methodology developed aims to infer the positions and angles of the joints, to calculate the criticality index based on the RULA scores and fuzzy rules. Then, to prevent work-related musculoskeletal disorders (WMSD), improve production capacity and decrease the ergonomic risk, the system uses joint with a collaborative robot to support the worker in carrying out the most critical operations. The method has been tested on a real industrial case in which manual assembly activities of electrical components are conducted. The approach developed can overcome the limitations of recent developments based on computer vision or wearable measurement, sensors by performing an assessment with an objective and flexible ap-proach to postural analysis development.

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

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

This article addresses the need to know the thermal conductivity in different filaments that this FDM technology uses. Because there are multiple applications that these additive manufacturing products play in the field of thermal insulation. For the study of thermal conductivity, the DTC-25 commercial conductivity measurement bench is used, where the tests will be carried out on a set of seven different materials with 100% fabrication density. From base materials such as acrylonitrile butadiene styrene (ABS) or polylactic acid (PLA), materials of high mechanical and thermal resistance, polyether ether ketone (PEEK) and high-performance polyetherimide thermoplastic (ULTEM), to materials with metal inclusions (aluminum 6061) that would later be subjected to thermal aftertreatments. The study shows how the parts manufactured with aluminum inclusion have a higher thermal conductivity, 0.40 ±0.05 W/m·K, compared to another material, such as TPU, of high mechanical and thermal resistance, with a conductivity of 0.26 ±0.05 W/m·K.

In the bioprinting process, controlling the motion of bioprinters involves a computer-aided design (CAD) model, converting that model into g-code, and transmitting the motion commands to the bioprinters. The g-code file contains information about the motion of the axes and can be generated using various software. However, a challenge arises when using g-code between different bioprinters. Therefore, the objective of this study was to propose a reliable method for analyzing the differences in 3D bioprinting devices by parameterizing a crucial variable for extrusion bioprinting: the E-value. Four bioprinters from three different manufacturers were used to investigate the extrusion behavior when subjected to the same E-value.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, partic-ularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Genera-tive Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer, by incorporating segmentations of spinal vertebrae in CT scans for the region of interest focussing. The approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of Mean Squared Error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n=440 CT data were processed. We evaluated the performance of our model using multiple metrics, in-cluding Mean Absolute Error (MAE) (for each slice of the 3D volume [MAE0] and for the entire 3D volume [MAE]), Cosine Similarity, Peak Signal-to-Noise Ratio (PSNR), 3D Peak Sig-nal-to-Noise Ratio (PSNR-3D), and Structural Similarity Index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of the approach in reconstruct-ing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Gain-scheduled autopilots have emerged as a dominant strategy to achieve adaptive control of coupled, non-linear engineering complexities, owing to an ability to adapt to changing operational conditions and uncertainties. This study focuses on utilizing bilinear interpolation of gain-scheduled autopilots, emphasizing enhanced system performance and robustness. Through a comprehensive investigation and comparative analysis using three disparate cases, advantages over conventional methods are revealed. Strengths and weaknesses of both simple and specialized variants (such as linear, and real-time gain-scheduling) are introduced. Three missile guidance case–studies utilize simulation time and miss distance figures of merit. Potential to achieve precise control across various mission scenarios, while ensuring reduced computational complexity is revealed by nearly two–hundred percent improved missile miss distances with comparable distances traveled and slightly improved computational burden.

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.

Drone imagery has the potential to enrich urban planning and preservation, especially where it converges with the growing creation and use of 3D models. The authors have conducted a systematic literature review of articles published between 2002 and 2022, drawing from reputable academic repositories including Science Direct, Web of Science, and China National Knowledge Infrastructure (CNKI), to identify current gaps in existing research in the application of UAVs to the creation of 3D models in urban planning and preservation. Findings indicate five research shortcomings: limited participation of planning experts, research focus imbalance, lack of usage for special scenarios, lack of integration with smart city planning, and limited interdisciplinary collaboration. The study also acknowledges current limitations with UAV applications and discusses possible countermeasures as well as future prospects.

Introduction Despite efforts to use scaffolds to treat meniscus tears, minimal progress has been made in facilitating meniscus regeneration and return of function. Our research objective was to develop a meniscus repair and regeneration implant by applying a resorbable scaffold in combination with cells and growth factors. We report here the results of using platelet-rich plasma (PRP) as a source of growth factors to induce fibrochondrogenic differentiation of human adipose-derived mesenchymal stem cells (hADSC) in a three-dimensional (3D) Type I collagen-based scaffold in vitro. Methods Scaffold Preparation. Type I collagen scaffolds were prepared following a protocol previously published. Two different densities of scaffolds, high density (HD) and low density (LD), were produced for in vitro study. hADSC and PRP Preparation. hADSCs were cultured to the fifth passage to reach the desired number for experimentation. PRP was collected from human blood and activated. Cell Culture Procedure. Effects of PRP on hADSC proliferation and differentiation into fibrochondrogenic cells were examined in four scaffold groups: LD, HD, LD+PRP and HD+PRP. hADSCs were seeded onto scaffolds (n=5) at a concentration of 2 × 106 cells/scaffold. 1% of PRP was added to the experimental media. Cellular proliferation was assessed at 1, 7, 14, and 21 days. Differentiation was measured using qRT-PCR on Days 14 and 21. qRT-PCR analysis of gene expression was completed with primers for COLLAGEN 1 and AGGRECAN. Data Analysis. ANOVAs were conducted (two-tailed tests) at the .05 significance level. Results Cellular proliferation of hADSCs seeded on each scaffold increased over time. Similar trend was observed for cells seeded on HD scaffolds with and without PRP. hADSC showed significant increase in cellular proliferation on the LD scaffolds at Days 1 and 7. At Day 21, PRP treatment and LD scaffold had a synergistic positive effect on Type I collagen gene expression. PRP did not elevate type I collagen gene in the HD group, the HD scaffold alone had the same level of type I collagen gene expression as LD+PRP. Aggrecan expression was elevated in the presence of PRP in both the HD and LD scaffold groups, indicating enhanced fibrochondrogenic differentiation of hADSCs. Effective cell infiltration was observed across both HD and LD scaffolds with and without PRP treatment. HD scaffolds displayed larger cell clusters and more extensive cell migration over time compared to LD scaffolds. However, LD scaffolds resulted a more uniform cellular distribution than HD scaffolds. Conclusion Our study demonstrates that PRP can play an important role in directing hADSCs towards fibrochondrogenic differentiation in Type I collagen-based scaffolds in vitro. Additionally, our study shows that collagen scaffold density can influence the spatial distribution and cellular behavior of infiltrated cells.

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.

Introduction: There is no standardised, universal method to assess physical conditions such as pressure in the pelvicalyceal system in real time during RIRS (retrograde intrarenal surgery). Therefore, the problem concerning increased pressure in the upper urinary tract during the procedure is underestimated. Moreover, it potentially may cause micro-damage and longer postoperative recovery. The aim of this study was to evaluate intrapelvic pressure (IPP) during RIRS procedures. Materials and methods: The 3D printed models of the pelvicalyceal system were printed based on CT-scan of real patient. They were used to perform 50 RIRS procedures with laser lithotripsy of artificially synthesised kidney stones with two different sizes of ureteral access sheath-UAS (10/12Fr vs. 12/14Fr) together with different energy settings generated by the holmium:YAG laser. IPP monitoring during RIRS was performed with the use of the PressureWire X Guidewire compatible with the CoroFlow system. Results and Conclusions: The results showed that high IPP of up to 400 cmH2O would be achieved using a 10/12Fr UAS, while the use of a 12/14Fr UAS would significantly reduce the peak pressure to approximately 100 cmH2O, hence the size of the UAS is a pivotal factor of the IPP generated during the procedure.

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

(1) Background: Type-2 Diabetes (T2DM) is a long-term medical disorder characterized by Insulin deficiency and high blood glucose levels. Among other medication to cure T2DM, the review of the literature found that various Pyrimidine derivative act as an agonist for G-protein-coupled receptor 119 (GPR119) was proposed to control blood glucose levels by enhancing the function of pancreatic beta-cells and its mechanism of action with fewer adverse effects.; (2) Methods: In the present research work, in-silico investigations were carried out to investigate the potential of Pyrimidine analogue as an agonists to the protein target GPR119 receptor. We performed exhaustive molecular modelling and protein modelling methodologies such as homology mod-elling, molecular docking along with various drug designing tools such as 3D-QSAR and pharmacophore mapping to ascertain the design of better GPR119 agonists.; (3) Results: On the basis of in-depth computa-tional studies, we designed new pyrimidine moiety and analyzed them for GPR-119 receptor agonist and fur-ther explored the ADME properties. Designed compounds found to exhibit better predicted activities as com-pared to reference compound; (4) Conclusions: The current research on pyrimidine derivatives, using molec-ular docking, 3D-QSAR and Pharmacophore mapping demonstrated that the obtained computational model has significant properties and the designed molecules and Dataset from this model, produced antidiabetic compound against the target GPR119 i.e., compound 1S, 1Z and 1D with the docking score of −11.696, -9.314 and -8.721 respectively. The pharmacokinetics and drug-likeness studies revealed that these compounds may be the future candidate for the treatment of diabetes acting via GPR119 agonist mechanism.

Robin sequence is a congenital anomaly characterized by a triad of features that include micrognathia, glossoptosis, and airway obstruction. This comprehensive historical review maps the evolution of approaches and appliances for its treatment from the past until current modern possibilities of interdisciplinary combination of modern engineering, medicine, materials, and computer science combined approach with emphasis to design appliances inspired by nature - individual human anatomy. Current biomimetic designs are clinically applied resulting in appliances that are more efficient, comfortable, sustainable, and safer than legacy traditional designs. Review maps the treatment modalities that have been used for patients with Robin sequence over the years. Early management of Robin sequence focused primarily on airway maintenance and feeding support, while current management strategies involve both nonsurgical and surgical interventions and biomimetic biocompatible personalized appliances. The goal of this paper was to provide a review of the evolution of management strategies for patients with Robin sequence that led to the current interdisciplinary biomimetic approaches impacting the future of Robin Sequence treatment with biomimetics at the forefront.

Increasing the availability of alternative protein from insects is important to solving food shortages. Not only are insects a rich source of protein, the use of insect ingredi-ents can reduce food waste. Insects are thus a potentially valuable ingredient for food industries and even food eaten on deep-space missions. The three-dimensional produc-tion of food on space missions has gained attention owing to its potential to reduce au-tonomous food production and produce sustainable food for long-duration space mis-sions. This study investigated the printability and rheological properties of a high-protein food system derived from mealworms and guar gum used as a stabilizer to im-prove printability. Stability and rheological properties were analyzed for various print-ing parameters. The results indicate that as the guar gum concentration was increased from 0 to 1.75%, the yield stress of the mealworm paste increased from 39 to 1096 Pa. Increasing the guar gum concentration thus resulted in a mealworm paste that was more viscous, exhibited shear thinning behavior, could support itself and was thus more stable. In summary, introducing guar gum resulted in a mealworm paste with rheological properties more suitable for printing in terms of printability and stability.

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.

Self-driving vehicles demand efficient and reliable depth sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in-which the exposed time widow of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

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

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

Type 1 diabetes is a chronic condition that results from the destruction of insulin-producing β-cells in the pancreas. Current treatments for type 1 diabetes, such as insulin therapy and pancreatic islet transplantation, have several limitations and, hence not quite effective in the long run. As current therapy methods fail to slow disease development, novel strategies such as the development of a bioartificial pancreas are being seriously considered. Over the last decade, research has focused on tissue engineering, which aids in the design of biological alternatives for the repair and replacement of non-functional or damaged organs. Three dimensional (3D) bioprinting technology which employs 3D printing technology to generate 3D tissue-like structures from biomaterials and cells, offers a promising solution for the treatment of type 1 diabetes by providing the ability to generate functional endocrine pancreatic tissue. Bioprinted structures are therefore an important aspect of tissue engineering because they have been found to replicate the native extracellular matrix, promoting cell survival and proliferation. In this review, we extensively discussed about bioprinting endocrine pancreas for the treatment of type 1 diabetes particularly focussing on the choice of cells, biomaterials, growth factors, and essential considerations. Additionally, the key challenges and perspectives towards recapitulation of the pancreatic function of the pancreatic organ engineering technologies have also been discussed.

Coronary artery disease is the most prevalent cardiovascular disease, claiming millions of lives annually around the world. The current treatment includes surgically inserting a tubular construct called as a stent inside arteries to restore blood flow. However, due to lack of patient-specific design, the commercial products cannot be used with different vessel anatomies. In this review, we have summarized the drawbacks in existing commercial metal stents which face problems of restenosis and inflammatory responses, owing to the development of neointimal hyperplasia. Further, we have highlighted the fabrication of stents using biodegradable polymers, which can circumvent most of the existing limitations. In this regard, we elaborated on utilization of new fabrication methodologies based on additive manufacturing such as three-dimensional printing to design patient-specific stents. Finally, we have discussed on the functionalization of these stent surfaces with suitable bioactive molecules which can prove to enhance their properties in preventing thrombosis and better healing of injured blood vessel lining.

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

The aim of this study is to introduce and evaluate a new approach by using 3D printed anthropomorphic breast phantoms in teaching X-ray mammography techniques Radiologic technologists. For this purpose, a physical anthropomorphic breast phantom is created, based on a computational breast version. The phantom is created by a stereolithography 3D printer. The practical exercises were conducted in three sessions with the participation of radiographers students first, second and third year of training, respectively. A survey was prepared to summarise the participants´ opinion about this new form of training in mammography technique. A total of 83 students were trained with the new approach. From these 52 responded to the survey questions. More than half of the students (71.2%) successfully handled the exercise with the physical breast phantom. The training provoked a substantial number of students to express the belief that incorporating modern methods based on digital technologies into their X-ray device training is essential. The instructor was well prepared and with the needed competence. The experience within this training convinces the future professionals to introduce the new technologies in their routine work, particularly for exercising the compression technique and breast positioning.

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).

Fly ash cement has rarely been used in Japan, mainly because its strength development is slower than ordinary Portland cement. In this research, the effect of the new fly ash cement with both high alite (C3S) cement and fly ash modified by electrostatic belt separation method on cracking resistance of precast concrete prepared by steam curing was studied. The mechanical and shrinkage properties of the proposed fly ash concrete were compared with those of concrete made using OPC cement without fly ash. In order to study the cracking tendency of precast concrete with the proposed fly ash cement, thermal stress analysis was conducted taking into consideration of the experimental data of concrete properties with the different concrete mix proportions. A standard precast concrete box culvert model was used in this 3D FEM analysis and the distribution of temperature and relative humidity in cross section and induced restraint stress during and after steam curing were discussed. Steam-cured concrete with fly ash and high alite cement developed higher compressive strength on the first day of age than concrete with OPC. The proposed fly ash concrete developed high cracking resistance in the early days. On the other hand, the results showed that the drying shrinkage at later ages was the main cause of cracking.

Unmanned aerial vehicles (UAV) have emerged as a solution to day to day survey tasks, allowing users to visualize phenomena in real-time. This paper explores the capabilities of UAV or drones in the collection of accurate, geo-tagged data quickly, including photogrammetry software processes to deliver standardized data output. In order to explore the capabilities of UAV, Gatu Township in Centenary, Muzabani District of Zimbabwe was chosen from the national mapping topographic series. This study demonstrates the efficiency of data collection using drones, generate 2D orthomosaics in real time, so that analysts can easily visualize land cover and identify any changes, map and model large areas to produce data for 2D and 3D models. The recent development of innovative optical image processing has further lowered the costs high resolution topographic surveys.

The three-dimensional Ising model on the m$\times$n$\times$l cubic lattice with the screw boundary condition along the ${\bf X}$ direction and the periodic boundary conditions along both ${\bf Y}$ and ${\bf Z}$ directions is exactly solved by using the $2^{mn}$-dimensional representation of the rotation group in $2mn$-dimensions, similar to the Kaufman's spinor approach in two dimensions. The exact partition function is obtained from two sets of $2^{mn-1}$ eigenvalues of the $2^{mn}$-dimensional transfer matrix {\bf V} corresponding to the even and odd eigenvectors, respectively. Such the eigenvalues are determined by the angles of the 2mn-dimensional rotation associated with {\bf V}.

Background: Wilms tumor (WT) is the main renal tumor in children. SIOP-UMBRELLA Guidelines admit Nephron sparing surgery (NSS) in syndromic patients, and in small (&lt;300 ml) nonsyndromic unilateral WTs, without lymph node involvement, and with a substantial expected remnant renal function, after neoadjuvant chemotherapy. We report our prechemotherapy transperitoneal robot-assisted partial nephrectomy (RAPN) in a unilateral non-syndromic Wilms tumor. Methods: A 6-year-old-child showed an exophytic 3.6 cm diameter solid rounding mass involving the upper right renal pole at an incidental abdominal echotomography. CT-scan and abdominal MRI proved no local infiltration neither lymph node involvement was appreciated, suggesting an exophytic mass easily resectable through a NSS robotic approach, with a low risk of rupture and no subverted renal anatomy. The preoperative imaging was not suggestive for WT. A virtual 3D reconstruction of the tumor was performed. Results: the oncologic board approved a robot-assisted partial nephrectomy with the intraperitoneal approach. The histopathologic analysis displayed a WT. The patient underwent 10 vincristine-doses adjuvant chemotherapy. 28-months follow-up showed no tumor recurrence. Conclusions: Intraperitoneal RAPN can represent an opportunity for WT and needs to be assessed as a challenging possibility. This case suggests a potential role of prechemotherapy RAPN in the near future for highly selected patients.

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.

Three-dimensional (3D) and four-dimensional (4D) printing emerged as the next generation of manufacturing techniques, spanning several research areas such as engineering, chemistry, biology, computing, and materials science. Three-dimensional printing allows the manufacture of complex shapes with high precision, by adding layer by layer of different materials. The use of smart materials that change shape or color, produce an electrical current, become bioactive, or perform an intended function in response to an external stimulus. Shape memory materials (SMMs) in 3D printing technology have attracted a lot of attention due to their ability to respond to external stimuli, leading this technology towards an emerging area of research, "4D printing technology." The core part of this review summarizes the effect of the main external stimuli on 4D textile materials followed by the main applications 4D printed textiles can change their shape over time due to external stimuli such as temperature.

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.

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.

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

Melanoma is a highly aggressive type of skin cancer produced by malignant transformation of melanocytes and it is usually associated with a poor prognosis. Clinically, melanoma has several stages associated with migration and invasion of the cells through the skin layers, rapid cell spreading and formation of tumors in multiple organs. The main problem is the emergence of resistance of melanoma to the applied methods of treatment, thus it is of primary importance to find more crucial signaling pathways that control the progression of this type of cancer and could be targeted to prevent melanoma spreading. The main life-threatening stage of melanoma is metastasis formation, and 3D cell spheroids are better suited for the study of this process. Using a combinative approach (RT-PCR, immunofluorescence, patch-clamp and calcium imaging) we showed the functional expression of mechanosensitive Piezo1 channels in SK-MEL-2 aggressive melanoma cell line. We found that chemical activation of Piezo1 by its selective agonist Yoda1 completely prevents melanoma spheroid formation, and could be considered as a novel approach for the prevention of melanoma development.

Forests play a pivotal role within the ecological milieu, with alterations to forested areas not only impacting the local environment but also serving as a significant contributing factor to the occurrence of landslide disasters. However, the significance of forests in connection with deep seated landslides is diminished. In instances of extreme events, heavy rainfall, highly weak slopes, or seismic events, it is improbable for forest cover to exert any influence. Due to extensive agricultural irrigation, the Heifangtai terrace of China has experienced a significant rise in groundwater levels, leading to the occurrence of numerous landslides. Through field investigations and remote sensing interpretation, this study has revealed dramatical geomorphological changes in the Heifangtai terrace due to the occurrence of widespread landslides. These changes are likely to disrupt the pre-existing hydrological balance within the study area, thereby altering the conditions for landslides. Therefore, understanding how the evolution patterns of loess landslides can be assessed is a crucial scientific issue for predicting the conditions of landslide occurrence. Taking the evolution of landslides along the edge of the Heifangtai terraces as an example, this study first classified different types of loess landslides. Subsequently, the temporal and spatial distribution characteristics of these landslides were analyzed. Finally, utilizing the FLAC3D software, the stability of the loess slopes at the edge of the Heifangtai terrace was examined under the lateral evolution mode. The research findings indicate that the current loess landslides in the Heifangtai terrace exhibits longitudinal multi-periodicity and lateral group-occurring behavior, which is significant for assessing loess landslides along the edge of Heifangtai terraces.

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.

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

Traditional lithium-ion batteries cannot meet the high flexibility and bendability requirements of modern flexible electronic devices due to the limitations of their electrode materials. Therefore, the development of high-performance flexible energy storage devices is of great significance for promoting flexible electronics. In recent years, one-dimensional flexible fiber lithium-ion batteries have been rapidly developed due to the advantages of high flexibility and bendability. However, it is still a big challenge to realize 1D flexible fiber lithium-ion batteries with excellent electro-chemical properties and good mechanical performance. In this work, a reduced graphene ox-ide-based printing ink is proposed to fabricate flexible Li4Ti5O12/graphene fiber electrodes through a 3D printing assembly strategy. It is noteworthy that the green reducing agent vitamin C was used to reduce graphene oxide in one step, which improved the conductivity of the fiber elec-trode. Furthermore, a 3D conductive network is constructed inside the fiber electrodes due to the high specific surface area of reduced graphene oxide, which enhances the electronic conductivity and ion mobility. The fiber electrode not only has good mechanical performance, but also has ex-cellent electrochemical properties. Equally important, the method is simple and efficient, and the working environment is flexible. It can precisely control the shape, size and structure of the one-dimensional fiber flexible electrode, which is of great significance for the development of fu-ture flexible electronic devices.

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

This paper presents the use of Graph Neural Networks (GNNs) to predict the tensile strength of Fused Deposition Modeling (FDM) specimens. In the present work, there are four main input parameters i.e. Infill percentage, Layer height, Print speed and Extrusion temperature while the Tensile Strength is an output parameter were considered. This study includes use of central composite design based response surface methodology to finalize the experimental layout. 3D printed specimen were manufactured as per the ASTM E8 standard on FDM printer using Polylactic Acid (PLA) as filament. Micro-tensile test were performed on the printed specimen as per ASTM E8 standard. The GNN algorithm was trained on a dataset of FDM specimens, achieving a mean squared error (MSE) of 2.47, mean absolute error (MAE) of 1.14, and R-squared value of 0.78. An adjacency matrix, which shows the connections between nodes in a graph. The obtained plot for nodes and weights in a GNN provide valuable information about the model and its performance. The results show the potential of using GNNs in predicting the mechanical properties of additively manufactured specimens and provide a promising direction for further research in this field.

Copper(I) sulfide (Cu2S) is a low-cost, earth-abundant, and non-toxic thermoelectric material for applications in the middle-high temperature range (&gt;650 K). Although 3D printing these materials can simplify their manufacturing, elevated temperatures observed during sintering impair their crystal structure and energy conversion efficiency. In this study, we demonstrated a novel post-processing methodology to revert the thermoelectric properties of the 3D printed Cu2-xS materials back to the unimpaired state via sulfur infusion. After printing and sintering, sulfur was infused into the specimens under vacuum to optimize their crystal structure and achieve high thermoelectric efficiency. Chemical analysis and X-ray Diffraction (XRD) tests showed that after the sulfur infusion process, the Cu/S ratio was reverted close to the stoichiometric level. 3D printed Cu2-xS showed p-type thermoelectric behavior with electrical conductivity peaking at 143 S-cm-1 at 750 K and Seebeck coefficient of 175 µV-K-1 at 627 K. Figure of merit (ZT) value of 1.0 at 780 K was achieved which is the highest value ever reported for a 3D printed Cu2-xS thermoelectrics at this temperature. Fabrication of environmentally friendly thermoelectric materials with extended dimensional freedom and conversion efficiency has the potential to impact the thermoelectric industry with new energy conversion applications and lowered manufacturing costs.

We are witnessing the revival of the CAM model, which was already used in the past by some authors studying angiogenesis and anti-cancer drugs, that now offers a refined model to fill, in the translational meaning, the gap between in vitro-in vivo studies. It can be used for a wide range of purposes, from testing cytotoxicity, pharmacokinetics, tumorigenesis, invasion, to action mechanisms of molecules, and validation of new materials from tissue engineering research. CAM model is easy to use with a fast outcome and makes experimental research more sustainable since allowing to replace, reduce and refine pre-clinical experimentation ("3Rs" rules [1]). This re-view aims to highlight some unique potentials that CAM-assay presents; in particular, the authors intend to use in the next future the CAM model to verify, in a microenvironment comparable to in vivo conditions, albeit simplified, the angiogenic ability of functionalized 3D constructs to be used in regenerative medicine strategies in the recovery of skeletal injuries of critical size (CSD) that do not repair spontaneously. For this purpose, organotypic cultures will be planned on sever-al CAMs set up in temporal sequence: a sort of organ model for assessing CSD conducted in the CAM bioreactor, rather than in vivo.

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 &gt; 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.

The two-phase flow of water and reactive gas in the channel of proton exchange membrane fuel cell (PEMFC) plays an important role in high-quality water management. In this study, the removal effect of 3D wave channel and 2D straight channel for liquid water was studied by volume of fluid method. The dynamic behavior of droplet emerge from the gas diffusion layer (GDL) into the channel under the influence of gas flow was investigated. The effects of droplet growth, deformation, detachment, force, pore size on water critical behavior and water content in the channel are discussed. The results show that the 3D wave channel is superior to the removal of liquid water than the 2D straight channel. The increase of velocity is conducive to the discharge liquid water. While infinite increase of velocity will cause more parasitic power loss. The larger pore size of GDL is not advantageous to the performance of PEMFC, but the appropriate increase of pore size is conducive to the discharge of liquid water. The basic knowledge obtained in this study will help to deepen the understanding of droplet dynamics in PEMFC gas channels, and help to optimize the design and working conditions of these channels.

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.

In this article, metallic additive manufacturing (AM) processes were classified and demonstrated. AM technology can be applied to a wide range of industrial areas because of its great feasibility in the design and manufacturing of various complex parts. The main factor that distinguishes various metallic AM processes from each other is the deposition method. Deposited metal can be imported in the form of melt or semi-solid. AM technology also covers a wide range of materials like aluminum, titanium, and many other alloys. Some of the common applications of metallic AM processes are the quick manufacturing of the parts which utilized in various industries like medical, automotive, aerospace, and jewelry industries. AM can help to fabricate parts that are impossible to be manufactured by other processes.

The emergence of new methodologies and tools for digitizing objects belonging to the Cultural Heritage (CH) changed the paradigms adopted so far. Traditionally, the process to acquire artifacts required specialized and often costly equipment tailored to specific purposes. However, the development of more generalized, adaptable, and affordable tools led to novel approaches. This manuscript provides a glimpse into the evolving landscape of custom-made tools for digital documentation, both hardware and software, and their transformative impact on the digitization techniques, built to meet requirements of specific case studies, including ancient drawings, manuscripts, paintings, and museum objects. The advent of self-built instruments has revolutionized the way professionals work today, by leveraging a new generation of low-cost, adaptable gears, leading to unprecedented flexibility and efficiency, while facilitating the capture of high-quality digital representations of objects with minimal damage and preserving their integrity. The outcomes of instruments and tools specifically produced for the contexts described in this paper highlight their potential for promoting interdisciplinary collaboration, facilitating scholarly research, enhancing conservation efforts, and fostering cultural exchange. Ultimately, this research contributes to illustrate how custom software, in combination with recent devices such as smartphones and 3D printers, underscores the importance of adopting these innovative approaches to generate an ecosystem of tools and methods able to preserve, document, and promote the richness of our collective past for future generations.

Prior to clinical trials, preclinical testing of oncology drug candidates is performed by evaluating drug candidates with in vitro and in vivo platforms. For in vivo testing, animal models are used to evaluate the toxicity and efficacy of drug candidates. However, animal models often display poor translational results as many drugs that pass preclinical testing fail when tested in humans, with oncology drugs exhibiting especially poor acceptance rates. The FDA Modernization Act 2.0 promotes alternative preclinical testing techniques, presenting the opportunity to use higher complexity in vitro models as an alternative to in vivo testing, including 3D cell culture models. 3D tissue cultures address many of the shortcomings of 2D cultures by more closely replicating the tumour microenvironment through a combination of realistic drug diffusion, paracrine signaling, cellular phenotype, and vascularization that can better mimic native human tissue. This review will discuss the common forms of 3D cell culture, including cell spheroids, organoids, organs-on-a-chip, and 3D bioprinted tissues. Their advantages and limitations will be presented, aiming to discuss the use of these 3D models to accurately represent human tissue and as an alternative to animal testing. The use of 3D culture platforms for preclinical drug development is expected to accelerate as these platforms continue to improve in complexity, reliability, and translational predictivity.

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.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from camera. For image feature extraction, the ResNet50+FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and regional voxel fusion methods. After information fusion, the Coordinate attention mechanism and SimAM attention mechanism extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm, the proposed algorithm improves the mAP value by 7.9% in BEV view and 7.8% in 3D view at IOU=0.5 (cars) and IOU=0.25 (pedestrians and cyclist). At IOU=0.7 (cars) and IOU=0.5 (pedestrians and cyclist), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

3D/4D printing technologies are currently among the fastest growing, cutting edge fabrication technologies. The scale of their applications is vast and applicable to nearly all industries. 3D printing technologies are particularly popular in robotics, and especially in advanced design innovative solutions for areas such as manufacturing, space technology and medicine. The development of robotics, and in particular of the precision of manufactured components, such as actuators, pneumatic muscles, power transmission units, etc., means that new prototypes are still being made, where the use of 3D printers reduces the production time severalfold and allows for completing the necessary simulations and tests. In addition, the use of 3D printers allows for the production of thin-walled and cellular structures, which is a great advantage compared to conventional fabrication technologies. In the range of 3D printers available on the market, only a few selected technologies allow for actual use in the construction of advanced robot elements (muscles, vibration dampers, etc.). In the era of rapid growth of the precision of the available 3D printers and modern materials, 3D printing may soon become a major tool in robotics. This article presents an overview of 3D printing technologies and materials in terms of their application in robotics and provides examples of the use of 3D and 4D printing in prototyping and fabrication of robotic elements with particular emphasis on the current state of the art. The study considered the possibilities of using 3D/4D printing in robotics with the use of polymeric materials. The review of the literature and the research work currently being carried out in this area is very promising and it seems that 3D/4D printing in robotics is widely used and is still developing, which allows to conclude that in the near future the number of research works in this field will increase rapidly.

DEXTER (Detection of EXplosives and firearms to counter TERrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. The active microwave component of the DEXTER project is referred to as MIC (Microwave Imaging Curtain), supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real time to address non-checkpoint detection of explosives and fire-arms. The project, led by the ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra Wide-Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC's innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar im-ages using a deep-learning network. This paper will outline the project's objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results ob-tained during a live demonstration in a relevant environment.

In the last 20 years, TADs have become one of the topics in orthodontic science, as it becomes one of the effective anchorages in moving teeth in a 3D movement to treat malocclusion and dental-alveolar orthopedic. Problems with the collusion of the roots of the teeth and trauma have hindered the use of these TADs; one of the solutions to keep TADs safe is a step-by-step innovation of procedure to make this anchorage concept using TAD safe. Making surgical guides for single-standing TADs is essential, and in practice, it is virtually possible with the advance of today's CAD/CAM and 3D printers. The content subject in the paper contains two parts: the consideration of why a surgical guide is necessary for the implantation of TADs and the making of the Surgical guide itself.

In recent years, researchers in the field of cultural heritage have intensified their efforts to develop new ways to enhance the promotion and accessibility of cultural content in order to attract more audiences using virtual representations of physical objects (digital twins). Therefore, they increasingly include new technologies and digital tools in their operation, since their application both to the general public and among the cultural organisations themselves, is considered particularly effective. Simultaneously, the increasing quality of the produced digitizations has opened up new opportunities for further exploitation of digitization outcomes in a broader context than was initially anticipated. Responding to the growing demand of museum visitors for a personalized digital tour experience, especially in the midst of the recent Covid-2019 pandemic, the v-PalM project aims to develop a digital platform for offering virtual guidance and education services at the Museum of Paleontology and Geology that is hosted at the National Kapodistrian University of Athens. The development of the platform will be based on collecting data through several methods including crowdsourcing, innovative information and communication technologies, taking advantage of content digitization using 3D scanning devices. In this paper, we demonstrate a methodology for the digitization of paleontological findings that can be used for creating digital twins suitable for various scenarios including research, education, and entertainment.

The global consumption of vegan foods is experiencing an expressive upward trend, underscoring the critical need for quality control measures based on nutritional and functional considerations. This study aimed to evaluate the functional quality of caviar and salmon analogs food inks, based on pulses combined with nano ingredients. The primary objective of this work was to determine the total antioxidant compounds contained in these samples using a voltammetric technique with a glassy carbon electrode. Approximately 100 mg of samples underwent ethanolic extraction (70%) with 1 hour of stirring. The voltammograms were acquired under specific conditions: 10 mL of phosphate buffer electrolyte with a pH of 3.0; an initial potential (Ei) of 0.0 V; a final potential (Ef) of 1.0 V; a pulse amplitude of 50 mV; and a scan rate of 50 mV s-1. The reference electrode used was Ag/AgCl (KCl 3 mol L-1), and the auxiliary electrode was a platinum wire. The voltammograms revealed prominent anodic current peaks at 0.76-0.78 V, which are attributed to isoflavones. Isoflavones, known secondary metabolites with substantial antioxidant potential commonly found in pulses, were identified. The total isoflavone concentrations obtained ranged from 31.5 to 64.3 mg Eq genistein 100 g-1. The results not only validated the efficacy of the electrochemical sensor for quantifying total antioxidant compounds in the samples but also demonstrated that the concentration of total isoflavones in caviar and salmon analogs fell within the expected limits.

The research on calcium leaching of 3D printed cement pastes (3DPC) exposed in aggressive water is limited, and the mechanism underlying its effect needs to be better understood. The performance anisotropy of degraded 3DPC due to calcium leaching is investigated in this paper. Samples with aggregate micro fines (AMF) and fly ash (FA) were prepared. A 6 mol/L NH₄Cl solution was used in the accelerated experiment. At specific leaching durations, performances were tested on the samples in different leaching directions. Results show that the performances of 3DPC in aggressive water have a significant anisotropic behavior. The evolution of pore defects and hydration products mainly governs the performance anisotropy of degraded 3DPC. The remaining hydration products of the surface of 3DPC leached in the Z direction are less than the other two directions because calcium ions leached in the Z direction are difficult to be compensated through weak interfaces between layers.

Background: Our objective was to conduct a comprehensive analysis of the reproducibility of foot and ankle anthropometric measurements with a three-dimensional (3D) optical scanner. Methods: We evaluated thirty-nine different anthropometric parameters obtained with 3D Laser UPOD-S Full-Foot Scanner in a healthy population of twenty subjects. We determined the variance of the measurements for each foot/ankle, and the average variance among different subjects. Results: For 40 feet and ankles (15 women and 5 men; mean age 35.62 +/- 9.54 years, range 9-75 years), the average variance was 1.4 ± 2 (range 0,1 to 8). Overall, the mean absolute measurement error was < 1 mm, with a maximum variance percentage of 8.3%. Forefoot and midfoot circumferences had a low variance < 2.5, with variance percentages < 1%. Hindfoot circumferences, malleolar heights, and the length of the first and fifth metatarsal to the ground contact points showed the highest variance (range 1 to 7). Conclusion: This optical scanner achieved a good reproducibility of a large set of foot and ankle anthropometric measurements. It is a valuable tool for clinical and research purposes.

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.

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

High-performance electromagnetic interference (EMI) shielding materials with ultralow density and environment friendly properties are greatly demanded to address electromagnetic radiation pollution. Herein, carbon nanotubes/polylactic acid (CNTs/PLA) materials with different CNTs contents, which exhibit characteristics of light weight, environmental protection and good chemical stability, are fabricated by 3D printing technology, where CNTs are evenly distributed and bind well with PLA. The performances of 3D printed CNTs/PLA composite are improved compared to pure 3D printed PLA composite, which include mechanical properties, conductive behaviors and electromagnetic interference (EMI) shielding. The EMI shielding effectiveness (SE) of CNTs/PLA composite could be improved when the contents of CNTs increase. When it reaches 15 wt%, the EMI SE of 3D printed CNTs/PLA composite could get up to 47.1 dB, which shields 99.998% of electromagnetic energy. Meanwhile, the EMI shielding mechanism of 3D printed CNTs/PLA composite is mainly of absorption loss, and it generally accounted for more than 80% of the total shielding loss. These excellent comprehensive performances endow the 3D printed CNTs/PLA composite with great potential for use in industrial and aerospace area.

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

Synchrotron radiation computed tomography (SRCT) allows more accurate calcified plaque and coronary stenosis assessment as a result of its superior spatial resolution, however, typical micro-computed tomography (micro-CT) systems have even higher resolution. The purpose of this study was to compare performance of high-resolution micro-CT with SRCT in the assessment of calcified plaques and a previously published dataset of coronary stenosis assessment. This experimental study involved micro-CT scanning of three-dimensional printed coronary artery models with calcification in situ used in our previously published SRCT study on coronary stenosis assessment. Measurements of coronary stenosis utilizing both modalities were compared using a paired sample t-test. The degrees of stenosis measured on all but one micro-CT dataset were statistically significantly lower than the corresponding SRCT measurements reported in our previous paper (p&lt;0.0005-0.05). This indicates that the superior spatial resolution of micro-CT was able to further reduce over-estimation of stenosis caused by extensive calcification of coronary arteries and hence false positive results. This study shows that high-resolution micro-CT outperforms SRCT in both calcified plaque and coronary stenosis assessment. This finding will become clinically important for cardiovascular event prediction and enable reclassification of individuals with low and intermediate risk into appropriate risk categories when the technical challenges of micro-CT in clinical practice such as the small field of view and demanding on image processing power are addressed.

This research focused on the perception of visually impaired individuals regarding tactile image creation using 3D printing. We developed and applied an experiment with 28 participants with visual impairments. They performed tasks based on skills whose foundations are needed and used in their daily lives, such as scanning, tactile discrimination, picture and spatial comprehension. The tactile images used in this study were developed by additive manufacturing and printed over 180 g/m² sulphite paper. The best performances were obtained when (1) the tactile image contour line heights were between 0.2 mm and 0.4 mm; and (2) the texture presented densities between 10% and 50%, when alone, or between 10% and 90%, when combined with another texture. The tested line widths (0.8 mm and 1.2 mm) did not present statistically significant differences in the results. Vision status (low vision or blindness) and moment of vision loss (congenital or late blind) did not influence the results. Schooling affected the activities of identifying objects and locating their copies. In the Conclusion section, there is a detailed recommendation list for the production and application of tactile materials, which range from simplifying forms to encouraging the use of both hands during content assimilation.

The aim of this study was to measure the effects on 3D active cervical Range of Motion (ACROM) and perceived pain of a n elastic taping (ET) application in the cervical area. Thirty participants (n: 22M and 8F, age 35.4±4.4 years; body height 173.1±8.4 cm; body mass 73.5±12.8 kg) for the study group (SG) and twenty subjects (n: 11M and 9F, age 32.6±3.9 years; body height 174.9±10.9 cm; body mass 71.2±12.9 kg) for the control group (CG) were recruited. All subjects were symptomatic for neck and cervical pain in baseline condition. Each group performed on ACROM test and the perceived pain on neck basing as Numerical Rating Scale (NRS 0-10, a.u.): at the baseline (T0); after 20' from ET application (T1); after three days of wearing the ET application (T2). Between T0 and T1 an ET was applied on the cervical area of the SG participants. Statistical analysis did not show any significant change in CG in any measurement session for ACROM and neck pain parameters. Conversely, the SG showed significant improvements for ACROM rotation left (T0 64.8 ± 7.7° - T2 76.0 ± 11.1° p &lt; 0.000) and right (T0 66.0 ± 11.9° - T2 74.2 ± 9.6° p &lt; 0.000); lateral inclination left (T0 37.5 ± 6.9° - T2 40.6 ± 10.8° p &lt; 0.000) and right (T0 36.5 ± 7.9° - T2 40.9 ± 5.2° p &lt; 0.000); flexion posterior (T0 47.0 ± 12.9° - T2 55.1 ± 12.3° p &lt; 0.001) and anterior (T0 55.0 ± 3.6° - T2 62.9 ± 12.0° p &lt; 0.006). A significant decrease was measure also in SG for pain NRS between T0 and T2 (T0 7.5 ± 1.0° - T1 5.5 ± 1.4 - T2 1.4 ± 1.5° p &lt; 0.000). In conclusion, the results of this study underline the usefulness of a bilateral and symmetrical ET cervical application in terms to enhance multiplanar ACROM and to reduce subjective self-perceived cervical pain when it is needed. These findings can be applied for managing musculoskeletal health of different kind of individuals.

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.

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

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.

Single particle analysis with cryogenic electron microscopy (cryo-EM) can determine high resolution structures of macromolecular assemblies. To reconstruct final three-dimension(3D) density map, a reliable initial model is essential and ab-initio reconstruction usually can avoid model bias. The grand challenge for ab-initio reconstruction is to alignment two-dimension(2D) low signal-to-noise ratio(SNR) of particle images with random and unknown parameters. Here we formulate a fast ab initio reconstruction method(named AlignFM) based on Fourier Mellin transform and particle-swarm intelligence optimization(PSO) algorithm. It enables simultaneous 2D alignment and 3D refinement, which can fast generate a reliable 3D map. After experiments on several real datasets. AlignFM shows high performance on fast ab initio reconstruction with a small amount of data.

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

High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision-making.

3D measurement is a high-throughput method that can record a large amount of information. In this study, we have developed new methods that could be used for morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The correlation coefficients between manual measurements and the scale-related methods were all above 0.9. In particular, the differences in LC calculated from the normal vector data allowed us to visualize and quantify the concavity and convexity of leaves. Furthermore, we also found a difference in the time point at which leaf blistering began to develop among the cultivars. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment.

The paper presents the conceptualization and realization of a novel platform for additive manu-facturing, an industrial robot arm-based system for additive manufacturing applications. Tradi-tional 3D printers, especially those employing fused deposition modelling (FDM) processes, are restricted to depositing material in a single toolpath plane (e.g. x-y plane). The focus of this study was to explore the feasibility of integrating commercial off the shelf (COTS) additive manufac-turing technologies with a six degree of freedom industrial robot arm to yield a 3D additive manufacturing system with the capability to perform free-form six degree of freedom fused deposition modelling. The present paper presents the development of a platform from stage 0, i.e. materials to its use, and finally a printed product with the developed extruder.

ORF6 is responsible for suppressing the immune response of cells infected by the SARS-CoV-2 virus. It is also the most toxic protein of SARS-CoV-2, and its actions are associated with the viral pathogenicity. Here we study in silico and in vitro the structure of the protein, its interaction with RAE1, and the mechanism of action behind its high toxicity. We show both computationally and experimentally that SARS-CoV-2 ORF6, embedded in the membranes of the endoplasmic reticulum, binds to RAE1 and sequesters it in the cytoplasm, thus depleting its availability in the nucleus and impairing nucleocytoplasmic mRNA transport. This negatively affects the cellular genome stability by compromising the cell cycle progression into the S-phase and by promoting the accumulation of RNA-DNA hybrids. Understanding the multiple ways in which ORF6 affects DNA replication may also have important implications for elucidating the pathogenesis of SARS-CoV-2 and developing therapeutic strategies to mitigate its deleterious effects on host cells.

For this research, a comparative evaluation was performed in which the addition of 3D-printed biocarriers fabricated with 13X and Halloysite (13Χ-Η) and commercial Kaldnes K1 biocarriers were evaluated as opposed to the non addition of biocarriers in a semi-pilot scale MBBR-MBR unit. For the evaluation of the MBBR-MBR efficiency, various physicochemical parameters were measured while static light scattering and optical microscopy observations were additionally used. Biofilm extracted from the biocarriers was also evaluated. It was observed that in the MBBR-MBR K1 unit, the membrane filtration improved while in the MBBR-MBR 13X-H unit the membrane fouling rate was the same as in the control MBBR-MBR unit. This is due to the production of large amount of SMP which resulted from the large amount of biofilm created in the 13X-H biocarriers. An optimal biodegradation of the organic load was concluded for all three MBBR-MBR units. The nitrification and denitrification processes were improved at the MBBR-MBR units using 13X-H and K1. The dry mass produced on the 13X-H biocarriers was three orders of magnitude larger than that produced on the K1. Finally, it was observed that mostly EPS was produced in the biofilm of K1 biocarriers while in the biofilm of 13X-H mostly SMP.

Medical management of chronic canine pruritic dermatologic conditions is challenging and often frustrating. This is a report that shows one way of aiding the management of pruritic dogs using a remote monitoring device. It is often difficult for veterinarians to get dog owners to return to the clinic once a dog is treated. It is possible that a 3-D accelerometer device could provide information to the clinic staff on the success or failure of a pruritus treatment plan while the dog was cared for at home. Eighty-seven dogs and their owners came to a Florida dermatology specialty clinic or its general practice hospital to be evaluated and treated for pruritus. An ANIMO® 3-D accelerometer was placed on the collar of dogs diagnosed and treated for pruritus. Dogs that completed the study were monitored for 120 days (4 months). The ANIMO app monitored a dog’s daily scratching, shaking, sleeping, activity, resting, barking and calories consumed and summarized this information in a daily report visible on the pet owner’s smart phone. An additional variable (grooming minutes per day) could be seen by the Sure Petcare R&amp;D Team that was not yet available in the app. The use of a 3-D accelerometer enabled veterinarians to continuously monitor dogs at home when they were being treated for itching. Clinic staff kept in touch with the owners by phone and could change therapy or bring the dog back for a recheck if problems were seen. Daily reports were combined into line charts that showed plots of scratching, shaking, grooming and sleeping over four months. Veterinarians were able to remotely monitor dogs that had been treated for pruritus for up to four months through the use of a collar-borne monitoring device. Dog owners and clinic staff used the daily summaries which were accessible through a smart phone app. Dogs seemed to tolerate the device well because of its small size and unobtrusive nature.

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

Antimicrobial application of carbon nanomaterials such as carbon nanotubes (CNTs), capped CNT, C60 fullerene, and C70 fullerene are increasing owing to their low cytotoxicity properties compared to other nanomaterials such as metallic nanoparticles. Enhanced mechanical property and antibacterial activity can be caused by incorporation of CNTs in 3-dimentional (3D) printed nanocomposites (NCs). Interruption of bacterial membrane resulting from cylindrical shape and high aspect ratio properties has been found as the prominent antibacterial mechanism of CNTs. However, unraveling interaction of CNT, capped CNT, C60 fullerene, and C70 fullerene with virulence factors of the main bacterial pathogenesis has not yet been known. Therefore, in the present study, interactions of CNT, capped CNT, C60, and C70 with the eight virulence factors including protein kinase A and ESX-secreted protein B of Mycobacterium tuberculosis, pseudomonas elastase and exotoxin A of Pseudomonas aeruginosa, alpha-hemolysin and penicillin binding protein 2a of Staphylococcus aureus and shiga toxin 2a and heat-labile enterotoxin of Escherichia coli have been evaluated by molecular docking method of AutoDock Vina. This study disclosed the binding strength followed the sequence CNT &gt; capped CNT &gt; C70 &gt; C60 towards alpha-hemolysin of S. aureus compared to other virulence factors with values of -19.6, -18.8, -13.6, and -12.8 kcal/mol, respectively. The lowest and highest binding affinity were found for CNT against 1TII and 1MWT by binding energy values of 97.4 and -20.1 kcal/mol. The stability of CNT-1MWT complex at the different times has resulted according to the normal mode analysis of elNémo and iMOD servers. Future studies should be focused on improving cellular uptake of CNTs, capped CNTs, C70, and C60 by surface functionalization for active targeting of bacteria.

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.

Recently, 3D printing techniques gained much attention in solid dosage form production. Even though the methods are increasingly studied, there are still many challenges in establishing an accurate content of active ingredients in the final products. In the present study, new experimental design techniques and statistical software were developed to identify and optimize a formula to predict the mass of the tablet to be 3D printed before it actually is. The first step was to develop a virtual 3D object in the FreeCad Software, then, using a CURA slicer software, the G-code Format was obtained. The tablets were printed using a Cartesian 3D printer based on the generated data. Finally, adapting the object sequencing mode, a predicted drug mass value was obtained. As expected, the oblong tablet's scaling directly influences its mass. The software generates a correlation coefficient (r) of 0.84, confirming the directly proportional relationship between scaling and tablet weight. However, the r values progressively decreased, indicating a poor correlation between tablet weight and other factors: infill density percent (r = 0.403) and wall thickness (r = 0.185). After repeating the 3D printing with the values ​​suggested by the software, tablets with a practical value of 1066 mg were obtained. Considering that the confidence interval calculated is [834.95—1165.05] and the experimental value is 1066 mg, it can be certified that the proposed model is valid.

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.

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

This study demonstrates the successful synthesis of Ni/Y2O3 nanocomposite particles through the application of ultrasound-assisted precipitation using the Ultrasonic Spray Pyrolysis technique. The particle size was predicted accurately using a modified equation, and they were collected in a water suspension with Polyvinylpyrrolidone (PVP) as the stabiliser. The presence of the Y2O3 core and Ni shell was confirmed with Transmission Electron Microscopy (TEM) and with electron diffraction. The TEM observations revealed the formation of round particles with an average diameter of 466 nm, while the lattice parameter on the Ni particle’s surface was measured to be 0.343 nm. The Ni/Y2O3 nanocomposite particle suspensions were lyophilized, to obtain a dried material that was suitable for embedding into a Polylactic acid (PLA) matrix. The resulting PLA/Ni/Y2O3 composite material was successfully extruded successfully, so that a 3D printed technology Fused Filament Fabrication could be used further for the production of tensile test tubes. The tensile tests showed that the addition of lyophilised Ni/Y2O3 into the PLA matrix decreased the tensile strength by 21.8 %, while the tensile modulus was not influenced. Dynamic Mechanical Analysis (DMA) showed that the addition of the Ni/Y2O3 particles increased the glass transition temperature by 5 °C, and increased the storage modulus in the glass transition range significantly. These findings demonstrate the potential for utilising Ni/Y2O3 nanocomposite particles in 3D printing applications, and warrant further exploration of their mechanical properties and potential applications in various fields.

Small molecules that can modulate or stabilize cell-cell interactions are valuable tools for investigating the impact of collective cell behavior on various biological processes such as development/morphogenesis, tissue regeneration and cancer progression. Recently, we showed that budesonide, a glucocorticoid widely used as anti-asthmatic drug, is a potent regulator of stem cell pluripotency. Here we tested the effect of different budesonide derivatives and identified CHD-030498 as a more effective analogue of budesonide. CHD-030498 was able to prevent stem cell pluripotency exit in different cell-based models, including embryonic stem-to-mesenchymal transition, spontaneous differentiation and 3D gastruloid development, and at lower doses compared to budesonide.