Aquifer karstic structures, due to their complex nature, present significant challenges in accurately mapping their intricate features. Traditional methods often rely on invasive techniques or sophisticated equipment, limiting accessibility and feasibility. In this paper, we propose a new approach to non-invasive, low-cost 3D reconstruction using a camera that observes the light projection of a simple diving lamp. Our method capitalizes on the principles of structured light, leveraging the projection of light contours onto the karstic surfaces. By capturing the resultant light patterns with a camera, we reconstruct three-dimensional representations of the structures. The simplicity and portability of the equipment required make this method highly versatile, enabling deployment in diverse underwater environments. We validate our approach through extensive field experiments conducted in various aquifer karstic settings. The results demonstrate the efficacy of our method in accurately delineating intricate karstic features with remarkable detail and resolution. Furthermore, the non-destructive nature of this technique minimizes disturbance to delicate aquatic ecosystems while providing valuable insights into the subterranean landscape. This innovative methodology not only offers a cost-effective and non-invasive means of mapping aquifer karstic structures but also opens avenues for comprehensive environmental monitoring and resource management. Its potential applications span hydrogeological studies, environmental conservation efforts, and sustainable water resource management in karstic terrains worldwide.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

In this paper, we aim to present real implementation cases where non-visible elements of environmental impact assessments (EIA), wind flow, are visualized using a 3D digital twin technology. The conventional EIA process serves as a crucial tool in the evaluation of potential environmental impacts prior to the initiation of development projects. This evaluation helps to establish a legal and institutional framework that seeks to minimize the adverse effects on the environment. However, traditional EIA methods present challenges, particularly for non-experts, in comprehending the results of assessments involving non-visible environmental factors such as noise, air quality, and wind flow. These elements are often represented in static images or numerical tables, limiting accurate understanding and engagement. Our proposed method, termed Digital Twin based EIA, addresses these limitations by leveraging digital twin technology. This advanced methodology allows for the visualization of non-visible environmental elements in a spatio-temporal dimension, providing a more interactive and comprehensive user interface/experience for both citizens and stakeholders. In this context, we present a unique case study that focuses on visualizing wind flow using this technology. This paper serves as an example of how digital twin platforms can enhance traditional EIA processes, offering a more immersive and informative experience. The evolution of EIA into the digital realm signifies an important step forward in fostering public understanding and engagement in environmental protection efforts.

A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.

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.

3D object reconstruction from depth image streams using Kinect-style depth cameras has been extensively studied. In this paper, we propose an approach for accurate camera tracking and volumetric dense surface reconstruction assuming a known cuboid reference object is present in the scene. Our contribu¬tion is three-fold. (a) We maintain drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. (b) We reformulate the problem of depth stream fusion as a binary classification problem, enabling high-fidelity surface reconstruction, especially in the con¬cave zones of objects. (c) We further present a surface denoising strategy to mitigate the topological inconsistency (e.g., holes and dangling triangles), which facilitates the generation of a noise-free triangle mesh. We extend our public dataset CU3D with several new image sequences, test our algorithm on these sequences and quantitatively compare them with other state-of-the-art algorithms. Both our dataset and our algorithm are available as open-source content at https://github.com/zhangxaochen/CuFusion for oth-er researchers to reproduce and verify our results.

Diseases in articular cartilages have affected millions of people globally. Although the biochemical and cellular composition of articular cartilages is relatively simple, there is the limitation in self-repair ability of cartilage. Therefore, developing the strategies for cartilage repair is very important. Here, we reported a new manufacturing process of water-based polyurethane based photosensitive materials with hyaluronic acid and applied the materials for 3D printed customized cartilage scaffolds. The scaffold has high cytocompatibility and is one that closely mimics the mechanical properties of articular cartilages. It is suitable for culturing human Wharton's jelly mesenchymal stem cells (hWJMSCs) and the cells showed an excellent chondrogenic differentiation capacity. We consider that the 3D printing hybrid scaffolds may have potential in customized tissue engineering and facilitate the development of cartilage tissue engineering.

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods, first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, a non-linear mapping is a better choice. Our paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images into Type-I or Type-II. In the second phase, depending on the classification, the Deep Line Model (DLM) for Type-I images or the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, DML creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio(PSNR) and root mean squares error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques.

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.

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.

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

Laser scanning 3D imaging technology, because it can get accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater rotating laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve the problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed-light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging and the dynamic refraction mathematical model is established by the motion of the imaging device. During the imaging process, the incident angle between the laser and the light window varies due to the scanning mode of the system. The experimental results show that the reconstruction radius error is reduced by 60% (from 2.5 mm to about 1 mm) when the measurement data for a standard sphere with a radius of 20 mm are compensated. Moreover, the compensated point cloud data exhibits a higher degree of correspondence with the model of the standard spherical point cloud. This study has a specific reference value for the refractive error analysis of an underwater laser scanning imaging system, and it provides certain research ideas for the subsequent refractive error analysis of various scanning imaging modalities.

It is important for researchers in material domain to reconstruct and visualize the microstructure of alloy, which helps discover morphologies and properties that can only be found in three dimensional and microscopic space. However, because of the limitation of state-of-the-art sensors, the internal microstructure of the material is not easy to obtain. In this paper, a novel automatic 3D reconstruction method is proposed using diffraction contrast tomography technology to capture the 3D microstructure of Al-La alloy. The pipeline of the proposed method starts from the imaging of Al-La alloy with the LabDCT technique from Carl Zeiss AG, which produces a sequence of 2D microstructure sections. Then, a segmentation algorithm based on superpixel and lifted multicut is proposed to extract the 2D microstructure in an image section. Finally, 2D segmentations are joined together to reconstruct and visulize the 3D microstructure. As a result, a novel morphology of Al-La alloy is recovered with both dendrite and lamellar morphologies. The proposed method has the advantage of the ability to losslessly recover the internal microstructure.

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

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

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.

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

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness consistency recovery method is pro-posed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from the images. After image-level processing, a combined underwater acousto-optic feature association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for the visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for performance against the OKVIS framework. The system achieves better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Crop phenotyping data collection is the basis for precision agriculture and smart decisionmaking applications. In this study, a dynamic slicing and reconstruction canopy volume (DR) algorithm is proposed to accurately estimate citrus canopy volume. The algorithm dynamically slices nearby slices based on their proportional area change and density difference, subsequently conducting AS reconstruction and volume calculation for each slice using an iterative mean point spacing as the α-value. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal. In conclusion, we recommend selecting appropriate operating environments for each algorithm based on their principles, geometric properties, volumetric values, running time, and linear relationships with one another to guide orchard mechanization and intelligent operation.

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

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

Additive manufacturing (3D printing) and computer-aided design (CAD) still have limited up-take in biomedical and bioengineering research and education, despite the significant potential of these technologies. The utility of organ-scale 3D-printed models of living structures is widely appreciated, while the workflows for microscopy data translation into tactile-accessible replicas are not well developed yet. Here, we demonstrate an accessible and reproducible CAD-based methodology for generating 3D-printed scalable models of human cells cultured in vitro and imaged using conventional scanning confocal microscopy and fused deposition modelling (FDM) 3D printing. We termed this technology CiTo-3DP (Cells-in-Touch for 3D Printing). As a proof-of-concept, we created CiTo-3DP models of human pancreatic cancer cells and healthy dermal fibroblasts by using selectively stained nuclei and the cytoskeleton components (f-actin and α-smooth muscle actin). The production of dismountable sets of cellular components was al-so shown. The CiTo-3DP approach can be adapted to comprehensively present various cell types, subcellular structures and extracellular matrices. We envisage that the resulting CAD and 3D printed models could be used for further applications, including but not limited to in silico simulations for biology, medicine, pharmacological research, tissue engineering, morphometrical analysis, multiphysics modelling, education, rehabilitation of visually impaired people, and integration into virtual reality.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

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

Background: Children with anorectal malformation (ARM) require long-term follow-up, especially for fecal continence and main predictors of longer-term success are the type of ARM, associated anomalies and sacral integrity. Three-Dimensional High Resolution Anorectal Manometry (3D-HRAM) provides an image of the pressure profile of anal sphincter complex. Aim of study was to evaluate the anal sphincter function in ARM patients with 3D HRAM correlating this analysis to clinical outcome and to associated malformations. Methods: Forty ARM patients, were submitted to 3D HRAM: manometric, anatomical and clinical score were correlated to response to bowel management (BM). Results: A positive correlation between all scores and type of ARM was found: in high ARM and in patients with spinal anomalies (regardless to ARM type) lower scores were reported and even after BM they do not achieve good continence. Conclusions: 3D-HRAM provides information on the functional anatomy of the sphincter complex. Our study shows good correlation between the manometric results and clinical outcome, confirming spinal malformations and ARM type as most important prognostic risk factors for poor outcome. Specific sphincteric defects can be explored with manometry, allowing for tailored bowel management strategies.

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

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

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

Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

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

Underwater visual detection technology plays a pivotal role in fields such as marine exploration. With the increasing demand for underwater monitoring, the quest for efficient and reliable methods for underwater target recognition has become particularly significant. To address this requirement, this study developed an innovative underwater object detection architecture based on YOLOv8, named YOLOv8-MU, aimed at significantly enhancing detection accuracy.By integrating the LarK module proposed in UniRepLKNet to optimize the backbone network, YOLOv8-MU aims to achieve a larger receptive field without increasing the model’s depth. Further, this research introduces C2fSTR, an innovative method that combines Swin Transformer with the C2f module. Additionally, we have incorporated the SPPFCSPC_EMA module, which combines Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, significantly improving the detection accuracy and robustness of various biological targets. Moreover, by introducing a fusion block based on DAMO-YOLO into the neck of the model, we further enhanced the model’s capability in multi-scale feature extraction. Finally, the adoption of the MPDIoU loss function, designed around vertex distance, effectively tackles the challenges of localization accuracy and boundary clarity in underwater organism detection. Experimental results on the URPC2019 dataset demonstrate that the YOLOv8-MU model achieved an [email protected] of 78.4%, marking improvements of 5.6%, 1.1%, and 4.0% over YOLOv5s, YOLOv7, and YOLOv8n respectively, indicating the leading performance (SOTA) of this method. On the other hand, further evaluation on the URPC2020 dataset confirmed the generalization capability of the YOLOv8-MU architecture, with its [email protected] reaching 80.4%, surpassing various models including YOLOv5x and YOLOv8n, showcasing the wide applicability of our proposed improved model architecture.

This paper presents several novel designs of underwater portable radio antennas operating in the 2 MHz, 50 MHz and 2.4 GHz bands and efficient for launching surface electromagnetic waves at the seawater/air interface. The antenna operation is enabled by an impedance matching antenna enclosure, which is filled with de-ionized water. Enhanced coupling to surface electromagnetic waves is based on the field enhancement at the antenna tip. These design features allow us to reduce antenna dimensions and improve the coupling of electromagnetic energy to the surrounding saltwater medium. Since surface wave propagation length far exceeds the skin depth of conventional radio waves at the same frequency, this technique is useful for broadband underwater wireless communication over distances, which far exceed the skin depth in seawater. We conclude that the developed broadband underwater radio communication technique will be useful in networking of unmanned underwater vehicles.

Pure orbital blowout fractures occur within the confines of the internal orbital wall. Restoration of orbital form and volume is paramount to prevent functional and esthetic impairment. The anatomical peculiarity of the orbit has encouraged surgeons to develop implants with customized features to restore its architecture. This has resulted in worldwide clinical demand for patient-specific implants (PSIs) designed to fit precisely in the patient's unique anatomy. Fused filament fabrication (FFF) three-dimensional (3D) printing technology has enabled the fabrication of implant-grade polymers such as Polyetheretherketone (PEEK), paving the way for a more sophisticated generation of biomaterials. This study evaluates the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the relevant design, biomechanical, and morphological parameters. The performance of the implants is studied as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predict the high durability of the implants, and the maximum deformation values were under one-tenth of a millimeter (mm) domain in all the implant profile configurations. The circular patterned implant (0.9 mm) had the best performance score. The study demonstrates that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor.

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

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

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

Urinary tract diseases are common in cats, and often require surgical reconstruction. Here, to explore the possibility of urinary tract reconstruction in cats using in-vivo tissue architecture (iBTA), biosheets fabricated using iBTA technology were implanted into the feline bladder and the regeneration process was histologically evaluated. The biosheets were prepared by embedding molds into the dorsal subcutaneous pouches of six cats for 2 months. A section of the bladder wall was removed, and the biosheets were sutured to the excision site. After 1 and 3 months of implantation, the biosheets were harvested and evaluated histologically. Implantable biosheets were formed with a success rate of 67%. There were no major complications following implantation, including tissue rejection, severe inflammation, or infection. Urinary incontinence was also not observed. Histological evaluation revealed the bladder lumen was almost entirely covered by urothelium after 1 month, with myofibroblast infiltration into the biosheets. After 3 months, the urothelium became multilayered, and mature myocytes and nerve fibers were observed at the implantation site. In conclusion, this study showed that tissue reconstruction using iBTA can be applied to cats, and that biosheets have the potential to be useful in both the structural and functional regeneration of the feline urinary tract.

Underwater communication for Underwater Wireless Sensor Networks (UWSNs) faces challenges due to the unique underwater environment. This review explores these challenges (attenuation, multipath propagation, limited bandwidth) across acoustic, optical, and radio frequency (RF) communication channels. It highlights how innovative antenna designs are crucial to mitigate these limitations and enable efficient data transmission. The review analyzes monopole, dipole, and helical antennas, discussing their trade-offs in radiation pattern, efficiency, and mitigating multipath effects. Advancements in materials, metamaterials, and reconfigurable antennas offer promising solutions for improved signal propagation, reduced attenuation, and better environmental adaptability. Research gaps include material exploration for low-conductivity antennas and optimizing antenna designs. Rigorous performance evaluation remains essential. Overall, advancements in underwater antenna technology are key to unlocking the full potential of UWSNs and enabling breakthroughs in marine applications.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

The study of underwater vehicle wake detection is of significant importance within the field of target detection, localisation and tracking of underwater vehicles. Given that propellers are the propellers of modern ships and underwater vehicles, the propeller wake field represents the principal target source for wake detection in underwater vehicles. The objective of this paper is to propose a method for measuring the wake of an underwater propeller based on a position-sensitive detector. A theoretical model of the relationship between the laser spot displacement and the change of refractive index of the wake field is established on the basis of the principle of laser beam deflection. A prototype experimental setup for underwater propeller wake measurement was constructed based on the aforementioned optical measurement method. Furthermore, the simulation of the propeller wake flow field with strong density stratification and linear density stratification was conducted based on the experimental setup. In the experiment, a three-bladed propeller with a diameter of 39mm was selected, the water tank size was 980mm×380mm×380mm, and the laser acted at a distance of 550 mm from the propeller. The experimental results indicate that the wake dissipation time of the propeller in a strong density-stratified water environment is approximately 800s. Following the stabilization of the wake field density, the laser spot position is observed to be stable at 0.341 mm, with a corresponding refractive index change of 2.99×10-6 RIU. The wake dissipation time of the propeller in a linear density-stratified water environment is approximately 750 s. Once the wake field density has reached a stable state, the laser spot position is found to be 0.441mm, corresponding to a refractive index change of 3.87×10-6 RIU. These experimental results are found to be in general agreement with the simulation results of the propeller wake field. A comparison of the experimental device-based wake measurements with the CTD-based wake measurements reveals a consistent trend.

Underwater wireless optical communication (UWOC) is acknowledged as a useful way to transmit data in ocean for short distance application. Carrying UWOC device on mobile platform is quite practical in ocean engineering, which is helpful to exploit its advantages. In application, such platform needs camera to observe surroundings and guide its action. Since the majority of ocean is always dark, active illumination is necessary to imaging. Once UWOC worked in such environment, its performance is affected by the illumination light noise. In this paper, we study the influence of underwater LED illumination on bidirectional UWOC with Monte Carlo method. We simulate forward noise from the LED illumination to the opposite receiver in the cooperative terminal, and the backscattering noise on the adjacent receiver in the same terminal. The results show that the forward noise is reduced with the increase of absorption coefficient, scattering coefficient, transmitting distance, and separated distance between receiver and the optical axis of LED. But it becomes greater with FOV of receiver. The backscattering noise is reduced with the increase of absorption coefficient and separated distance between receiver and LED. But it becomes greater with FOV and scattering coefficient, while it has little relation with transmitting distance. In order to reduce such two kind noises, besides inserting optical filter in the receivers and narrow their field, the optical axis of LED light should keep away from the receivers. The results in this paper is helpful for UWOC application.

Background. Cervical esophageal reconstruction is vital for improving the quality of life in cancer surgery patients. Microsurgery is crucial, providing vascularized tissue for defect repair, particularly in secondary cases with a higher risk of failure due to larger defects and damage from previous surgery and radiotherapy. The purpose of this study was to describe the clinical characteristics of a series of patients who underwent secondary repair of esophageal defects and provide practical information for the management and treatment of such cases based on the authors' experience and the literature review. Methods. We retrospectively reviewed the electronic medical records of the Plastic Surgery Clinic at the University of Trieste to identify cases of patients who underwent secondary esophageal microsurgical reconstructions following oncological surgery. Patient demographics, etiology of esophageal defects, previous surgical history, and preoperative assessments were collected from medical records. Surgical techniques employed for reconstruction, such as pedicled flaps or free tissue transfers, were documented along with intraoperative details. Postoperative outcomes, including complications, graft viability, and functional outcomes, were evaluated during follow-up. Results. We treated 13 cases of secondary esophageal reconstructions from 2011 to 2022. Most commonly, Antero-Lateral Thigh (ALT) flaps were used in 10 cases, while 2 cases employed Radial Forearm Flap (RFF), and 1 a chimeric parascapular flap. No flap failures occurred during a median 50-month follow-up. One ALT flap patient experienced post-op stricture but maintained swallowing ability. A single tracheoesophageal fistula occurred in a RFF patient with a history of radiotherapy and completion lymph node dissection. Conclusions. Cervical esophageal reconstruction significantly impacts patients' quality of life by restoring oral feeding and phonation. When local flaps fall short, microsurgical reconstruction with intestinal flaps is valuable, but burdened by limitations. For challenging secondary cases, ALT or RFF flaps emerge as safer options due to their robust pedicles, yielding low complication rates and positive functional outcomes.

The quest for youthful, healthy skin and full, vibrant hair has long been a driving force in the dermocosmetics field. However, traditional approaches often struggle to address the underlying causes of aging, damage, and hair loss. Regenerative cosmetics, powered by skin tissue engineering, offer a transformative alternative. This review explores the emerging field of using engineered skin tissues for cosmetic purposes, focusing specifically on their potential for anti-aging, repair, and hair restoration applications. We discuss how these technologies aim to rejuvenate aging skin by promoting collagen production, reducing wrinkles, and improving overall skin function. Additionally, the use of engineered skin for wound healing and scar reduction is examined, highlighting their potential to improve the appearance and functionality of damaged skin. Finally, we advance the exciting prospects of utilizing skin tissue engineering techniques to regenerate hair follicles, potentially offering solutions for hair loss and promoting denser hair growth.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

DIEP-based breast reconstruction necessitates wide undermining at the abdominal donor-site, creating large wound areas. Flap harvesting is usually conducted using electrosurgical dissection devices. This study sought to compare the clinical outcomes in patients after using the PEAK PlasmaBlade (PPB) versus monopolar electrocautery (MPE). This retrospective cohort study included 128 patients with DIEP-based breast reconstruction. Patient characteristics as well as information on the postoperative course were collected and a comparative evaluation was conducted. The MPE group exhibited significantly (p* = 0.0324) higher abdominal drainage volume (351.11 ± 185.96 ml) compared to the PPB group (279.38 ± 183.38 ml). Subgroup analysis demonstrated that PPB significantly reduced postoperative wound fluid in patients with BMI > 30 kg/m² (p* = 0.0284), without prior neoadjuvant chemotherapy (p** = 0.0041), and among non-smokers (p = 0.0046). Furthermore, postoperative pain was significantly (p**** < 0.0001) lower in the PPB cohort. This study confirms the non-inferiority of the PEAK PlasmaBlade to conventional electrocautery for abdominal flap harvesting. The PPB demonstrated advantages, notably reduced drainage volume and lower postoperative pain levels. Recognizing patient subsets that benefit more from the PPB highlights the importance of personalized device selection based on patient characteristics.

The forehead and temporal region are a frequent area of skin cancer development. After tumor removal, reconstruction must be performed maintaining the frontal-temporal line of the scalp and the symmetry of the eyebrows, trying to hide the scars in these marks or in natural folds and wrinkles. Second wound healing and skin grafts generally do not produce an acceptable cosmetic result. When direct closure is not possible, the technique of choice is skin flaps. In the midfrontal line, continuation of the glabella, there is a remnant of skin to be used as a donor area for local flaps; similarly, it occurs in the preauricular cheek, which can move towards the temple. In addition to the classic advancement and rotation flaps, the frontalis myocutaneous transposition flap is an excellent technique for closing defects which are wider than higher on the forehead. Its design is very versatile and can be done between the two pupil lines at different heights depending on the location of the defect. On the other hand, the preauricular skin advancement flap with an infralobular Burow's triangle is also an excellent option for reconstructing tumors in the temporal area.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

The work aims to develop an electromagnetic shielding measurement methodology and design an electromagnetic shielding effectiveness test setup for thin sheet material testing. In brief, multiple shielding effectiveness standards are described and analyzed in the thin sheet material testing framework. Two types of the developed shielding effectiveness test probe in high and low-frequency ranges are validated comprehensively. Extensive descriptions and data of developed testing methodology are also reported.

Background: This study aimed to investigate the impact of the COVID-19 pandemic on breast reconstruction trends in the United States, focusing on implant-based and autologous techniques, as well as the timing of reconstruction (immediate vs. delayed). Methods: A retrospective analysis of data from the American Society of Plastic Surgeons' National Plastic Surgery Statistics from 2015 to 2022 was conducted. Annual trends in breast reconstruction procedures were analyzed, comparing the pre-pandemic (2015-2019) and pandemic (2020-2022) periods. Results: The total number of breast reconstructions increased from 106,338 in 2015 to 151,641 in 2022. The proportion of implant-based reconstructions decreased from 81.41% pre-pandemic to 76.51% during the pandemic (p < 0.001), with a notable rise in direct-to-implant procedures from 10.37% to 19.12% (p < 0.001). Autologous reconstructions increased from 18.59% to 23.49% (p < 0.001). Among autologous techniques, DIEP flaps remained the most popular. Immediate reconstruction rates increased from 72.61% pre-pandemic to 75.57% during the pandemic, while delayed recon-struction rates decreased from 27.39% to 24.43% (p < 0.001). Conclusions: The COVID-19 pandemic has significantly influenced breast reconstruction trends in the United States, with a shift towards autologous techniques, particularly DIEP flaps, and an in-crease in immediate reconstructions. These changes may reflect adaptations in surgical practice and decision-making processes in response to the unique challenges posed by the pandemic. Further research is needed to assess the long-term outcomes and patient satisfaction associated with these trends.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

Input reconstruction problem is usually encountered in the areas such as virtual sensing, image restoration, sensor linearization, and communications. The input reconstruction problem can be seen as an inverse problem. Given a nominal system, two types of approaches are commonly used for solving the inverse problem. The first one is to directly invert the nominal system, and the second one is to derive an inversion of the nominal system in an indirect way. However, for the first type of approaches, system inversion cannot be directly conducted under some conditions such as there exist nonminimum-phase zeros in the nominal system, while for the second type of approaches, simultaneously guaranteeing stability and causality of the obtained inversion is still not solved well. In order to avoid the drawbacks existing in the two types of approaches, an alternative approach is proposed for input reconstruction. In this approach, the input signal is first modeled as the output of a state-space model, afterwards two Kalman filters for the model resulted by combining the input signal model and the nominal system model are implemented alternatively such that the input signal can be sequentially reconstructed in an infinite horizon. The proposed input reconstruction approach is applied in two examples, and simulation results can illustrate the effectiveness of the proposed approach.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

Elbow dislocation and instability present significant clinical challenges, necessitating a thorough understanding of the underlying bio-mechanical mechanisms. In this study, a quasi-static three-dimensional finite element model of the human elbow joint is developed to investigate stress distribution and stages of dislocation in the human elbow under various loading conditions. The model simulates the elbow joint in different degrees of flexion (30°, 45°, 60°, and 90°) and forearm positions (pronation and supination), providing detailed insights into the progression of dislocation. Significant findings include the identification of high stress concentrations on the humerus at 90° flexion and on the radial and coronoid processes at 30°, 45°, and 60° flexion. Three reproducible stages of dislocation were observed, particularly in flexed positions with forearm pronation or supination. These stages align with experimental observations and highlight the initial occurrence of bony failures, such as radial head and ulnar coronoid fractures, preceding soft tissue tears. Clinically, the study underscores that early-stage low-impact posterior elbow dislocations retain enough stability to be managed with closed reduction and early mobilization. However, as dislocations progress, significant damage to the medial and lateral collateral ligaments is expected, necessitating more invasive treatments. This research provides valuable bio-mechanical insights into elbow dislocation, aiding in the development of improved treatment strategies and enhancing patient outcomes through precise and timely clinical interventions. The validated FEM serves as a powerful tool for pre-surgical planning offering a comprehensive understanding of elbow joint mechanics.

ABSTRACT Introduction: Traditional penile venous surgery and enhancement have been controversial. Based on De Novo penile fibrovascular assembly, we report on penile venous stripping (PVS) and factual penile girth enhancement (FPGE). Methods: From 2013 to 2023, 18 men underwent PVS and FPGE, all confirmed with veno-occlusive dysfunction (VOD). PVS involves the stripping of erection-related veins. FPGE was performed along the tunica albuginea bilaterally; then, the two tunic defects were repaired with a 70.0x30.0 mm venous stripe and covered with either autologous venous walls (AVW, n=10) or Surgiform (SF, n=8). The penile girth was measured, and radio-opaque contrast was used. The abridged 5-item version of the International Index of Erectile Function (IIEF-5) score and the Erection Hardness Scale (EHS) were used yearly during follow-up via the Internet. Results: The follow-up period was 5.3±1.5 years. Radiopacity was consistently enhanced. Although there was indifference between AVW and SF, there was a significant improvement (both P＜0.01) in IIEF-5 and EHS scores (9.7±2.8 vs. 20.8±2.3; 1.7±0.6 vs. 3.2±0.2, respectively). Both the diameters of the glans and penile shaft increased (28.0±2.3mm and 28.3±2.1mm vs 35.3±2.2mm and 36.3±2.1mm respectively). The satisfaction rate was 81.3% (13/16). Conclusions: This novel combination of PVS and FPGE is promising, although a larger sample size is needed for further validation.

Nowadays, semantic segmentation is increasingly used in exploration by underwater robots, for example in autonomous navigation, so that the robot can recognise the nature and elements of its environment during the mission and act according to this classification to avoid collisions. Other applications can be found in the search for archaeological artefacts, in the inspection of underwater structures or in species monitoring. Therefore, it is necessary to try to improve the performance in these tasks as much as possible. To this end, we compare some methods for improving image quality and for data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementations and the SUIM dataset with 8 common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The single result shows that there are 5 classes with an IoU value above 60% and only one class with an IoU value below 30%, which is a more reliable result and easier to use in real contexts.

A morphological feature estimation method is presented, that can be used to monitor fish from low contrast underwater images and videos. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height, mass) and the location of fish body parts of particular interest such as the eyes and gills. The developed system can be exploited both for fish monitoring in open sea as well as aquacultures where non-invasive, low cost morphological feature extraction is essential. Fish health can be assessed from malformations in body shape, the color of the eyes or the gills as well as the fish behavior. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low resolution and low contrast underwater images where the fish can be hardly distinguished from its background. Hardware acceleration can be applied at various stages of the proposed method for real time video processing. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream or similar: diplodous sargus annularis (white seabream), diplodus annularis seabream (spawn), oblada melanura (saddled seabream), pagrus pagrus (common seabream), etc. A large dataset with low resolution underwater videos and images has been created in the context of this work.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

The formal transfer of GNSS under water is not possible. It probably makes sense to talk only about the transformation of GPS into LPS, that is, in the Local Position System. However, the basic methods that are used to solve the problem of Spoofing Detection above water can be used under water. It should be understood that engineering problems are significantly different, since the nature of the propagation of acoustic waves in water and electromagnetic waves in the atmosphere are fundamentally different. In this article, we will limit the navigation with acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position based on the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service areas, GNSS-like UPS are divided into global, regional, zonal and local system. In this article, we will limit ourselves to considering only local GNSS-like UPS. The acoustic signal generator transmits a signal simulation of several satellites. If the level of the simulated signal exceeds the signal strength of real satellites, the receiver of UPS will “capture” the fake signal and calculate the false position based on it.

This paper presents a study of different types of parametric signals with application to underwater acoustic communications. In all the signals, the carrier frequency is 200 kHz, which corresponds to the resonance frequency of the transducer under study and different modulations are presented and compared. In this sense, we study modulations with parametric sine sweeps (4 to 40 kHz) that represent binary codes (zeros and ones), getting closer to the application in acoustic communications. The different properties of the transmitting signals in terms of bit rate, directivity, efficiency and power needed are discussed as well.

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

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

Over the last few decades, ocean research and exploration have made underwater mechanical systems a necessity. Underwater vehicles provide a new kind of marine platforms that could represent a great necessity in many areas of oceanographic research. Until now, the underwater vehicles come in a verity of shapes, sizes and means of propulsion. Depending on these characteristics, the type and mission of the vehicle are also determined. The underwater robots are used for different inspection and intervention missions in e.g. the oil and gas industry, ocean science research. Due to multiple applications to which the vehicle can participate, it can be successfully used and to determine methods of re-use of marine energy. Environmental mapping provides accurate information about the main areas of interest of the energy, as well as the exploitation possibilities of that. Most of the time, biomimetic robots were inspired their senso structure, from different kind of animals, such as insects, fish and birds. Nowadays, the concept of a underwater robotic vehicles capable to move independently, autonomously or remotely, has a great potential and a large application. This is the reason that the last studies have been directed on biomimetic robots. The fish and other underwater animals have evolved superior swimming capabilities in many ways and represent a starting point to explain the fluid-mechanical principles. Furthermore, the underwater animals develop and achieve extraordinary propulsion efficiencies, acceleration and maneuverability. They can also achieve high speed under water. Implanting and creating a vehicle through a biomimetic approach reduces the energy used to maneuver the vehicle as it can automatically correct its position and displacement. The paper presents an examination of the state of biomimetic robotic fishes, underlining the reason why bio-inspiration can help us in the underwater locomotion technology.

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

We propose an asynchronous acoustic chirp slope keying to map bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. Details of the implementation are disclosed and discussed, the performance verified on laboratory scale in a pool measurement, as well as simulated for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products of 50 in single band mode, a raw data rate of 100~bit/s is simulated to achieve bit error rates below 0.001 for signal-to-noise ratios above -6~dB. In dual-band mode and a data rate of 200~bit/s, this bit error level was achieved for signal-to-noise ratios above 0~dB for time-bandwidth product of 25. The packet error rates follow this behavior with an offset of 1~dB.

A nuclear reactor leak is undesirable and unpredictable occurrence in the plant of nuclear energy generation. For this reason, an accurate equipment is needed to inspect and supervise reactors. There are two kind of reactors for research and industry reactors, both of them are needed to inspect. Concern to inspection, regularly check is needed to ensure reactor conditions are always safe to operate. For that reason, an application of advanced technology for detecting leaks is priority. In this research, we are using underwater robot to be implemented. Using the robot is able to detect periodically, continuously with accurate reports to the operator or reactor supervisor. Furthermore, Underwater Robot namely Robaru, with 4 propellers equipped at the robot, Robaru can move on 3 axes x, y and z and rotate. The use of cameras, as well as temperature measurements to obtain data when conducting inspections of the reactor. The survey method used is direct observation to detect leaks. The robot is at a certain distance by following a predetermined connection or path. The robot has successfully performed a good check with an accurate level of positioning accuracy.

Atopic dermatitis (AD) is a chronic inflammatory disorder presenting a dysregulated immune response and derangement of the epidermal barrier lipids. The Th2-type cytokines interleukin IL-4 and IL-13 play a prominent role in AD by activating the Janus Kinase/Signal Transduction and Activator of Transcription (JAK/STAT) intracellular signal axis. This study aimed to investigate the role of JAK/STAT in the lipid perturbations induced by Th2 signaling in 3D-epidermal equivalents. We used tofacitinib, a low-molecular-mass JAK inhibitor, to screen for the JAK/STAT-mediated deregulation of lipid metabolism. Th2 cytokines decreased the expression of elongases 1, 3, and 4, and serine-palmitoyl-transferase and increased that of sphingolipid delta(4)-desaturase, and carbonic anhydrase 2. Th2 cytokines inhibited the synthesis of palmitate and caused a depletion of triglycerides. This depletion was associated with altered PC profiles and fatty acid (FA) metabolism. Overall, the ceramide profiles were minimally affected. Except for most sphingolipids and very long chain FAs, the effects of Th2 on lipid pathways were reversed by co-treatment with tofacitinib. An increase in the mRNA levels of CPT1A and ACAT1, reduced by tofacitinib, suggests that Th2 cytokines promote FA beta-oxidation. In conclusion, pharmacological inhibition of JAK/STAT activation prevents the lipid disruption caused by halted homeostasis of FA metabolism.

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

The reliability of Underwater Wireless Sensor Networks (UWSNs) is measured in terms of energy consumption (EC), end-to-end delay(E2E) and packet delivery ratio (PDR). The adverse effects of channel may cause data loss. Reducing delay up to possible extend improves the reliability of the network, also increasing the number of nodes in a particular network increase reliability. Besides, increasing the number of nodes improve reliability but also increase power consumption. In order to overcome these shortcomings, the two routing protocols are proposed in this paper, namely Delay and Reliability Aware Routing (DRAR) protocol and Cooperative Delay and Reliability Aware Routing (Co-DRAR) protocol for UWSNs. In DRAR protocol, network is divided into two equal regions where two sink nodes(SNs) are positioned at upper region of the network and two SNs are placed at the mid region of the network. The protocol choose the relay node based on residual energy (RE), distance and Bit Error Rate (BER). These parameters protect the data packets from corruption and also provide stable path (where nodes remain active for longer period and do not die quickly). The protocol uses single link and may get worse sometimes while changing channel circumstances. To address this problem, cooperative routing scheme is added to DRAR protocol in order to developed its enhanced version known as Co-DRAR protocol. The protocol works by allowing the destination to receive multiple copies of data packets in order to decide the quality of packets. The proposed protocols DRAR and Co-DRAR perform routing irrespective to geographical position of sensors nodes conversely to the some of conventional routing protocols that's way our propose protocol better perform than the well-known protocol i.e. Depth base routing (DBR) in terms of EC, E2E, PDR, dead nodes, packet drop ratio and number of alive nodes (ANs).

Sustainable manufacturing practices are becoming increasingly necessary due to the growing concerns regarding climate change and resource scarcity. Consequently, material recycling technologies have gradually become preferred over conventional processes. This study aimed to recycle waste polylactic acid (PLA) from household-disposed cups and lids to create 3D-printed parts and promote sustainable manufacturing practices. To achieve this, the current study utilised virgin and post-consumer PLA (sourced from household waste) blends. The PC-PLA wastes were shredded and sorted by size with the aid of a washing step, resulting in a filament with a 1.70 ± 0.5 mm diameter without fragmentation or dissolution. A 50:50 wt.% blend of virgin PLA (vPLA) and PC-PLA was selected as the standard recycling percentage based on previous research and resource conservation goals. The study investigated the impact of three 3D printing parameters (layer height (LH), infill density (I), and nozzle temperature (NT))on the quality of 3D-printed parts using a three-level L9 Taguchi orthogonal array. The findings revealed that blending PC-PLA with vPLA led to significant improvements in the tensile, flexural, and impact strengths by 18.40%, 8%, and 9.15%, respectively, compared to those of recycled PLA. This conclusion was supported by the investigation of the fracture surface area, which revealed fractographic features associated with printing parameters, such as plastic deformation and interfilament debonding. An ANOVA analysis revealed a positive influence of larger layer height and high nozzle temperature on the mechanical properties. Subsequently, the optimal printing parameters (LH: 0.3 mm, I: 100%, and NT: 215 °C) were determined using the S/N ratio, and a confirmation test using the optimum printing parameters exhibited a strong correlation with statistically predicted outcomes. Finally, the study used optimum printing parameters to fabricate 100% post-consumer recycled PLA 3D printed parts, demonstrating their potential for low-strength applications. Based on these findings, it can be concluded that virgin and post-consumer recycled PLA blended filament for fabricating 3D printed components is an effective way to support plastic recycling in the context of a circular economy.

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in underwater confined environments, researchers have studied different underwater positioning methods and selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms, and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have also developed some bio-inspired autonomous positioning methods. This article introduces the applicable robot positioning methods and sensors in underwater confined environments and discusses the research directions of robot positioning methods in such environments.

Underwater acoustic spiral sources are able to generate spiral acoustic fields where the phase depends on the bearing angle. It allows to estimate the bearing angle relatively to a receiver by subtracting the phases of a spiral and a circular wavefront, and finds application in the bearing angle estimate with a single hydrophone, e.g., for unmanned underwater vehicles localization. This paper presents a new prototype for a spiral acoustic source, which is able to generate the required wavefronts, and is composed of four monopoles in the same piezoceramic cylinder. This paper reports the prototyping and the acoustic tests performed in a water tank where the spiral source was characterized in terms o Transmitting Voltage Response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and shows a maximum angle error of 1 degree when the calibration and the operation are carried under the same conditions and a mean angle error up to 6 degrees for frequencies above 25 kHz when the same conditions are not fulfilled.

One of the main issues for underwater robots navigation is represented by the accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this scope are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE, which is able to recover Kalman Filter IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained both in terms of results and performance. In fact, DANAE++is able to denoise the three angles describing the attitude at the same time, and that is verified also on the estimations provided by the more performing Extended KF. Further tests could make this method suitable for real-time applications on navigation tasks.

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the total energy in the nominal bandwidth, the proposed experimental setup is able to accurately measure the mass of individual sediments hitting the aluminum plate. This require a priori knowledge of the vibration trasients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow to accurately localize the transients corresponding to the shocks created by sediment impacts on a steel plate.

Underwater wireless optical communications (UWOC) have attracted considerable attention in recent years as an alternative means for acoustic communication. However, optical path loss of light propagation from attenuation is large due to absorption and scattering in various water conditions. Identification of environmental effects especially tropical storms on underwater optical path loss is the key to the success of using optics for UWOC. Underwater inherent optical properties (IOPs) such as the beam attenuation coefficient for 470 nm light in the western North Pacific Ocean were measured from the U.S. Naval Oceanographic Office sea gliders with being deployed after Super Typhoon Guchol (7-20 June 2012)‘s passage during 25-30 June 2012 and no any typhoon passage during 9 January – 28 February 2014. The observed two sets (with and without super typhoon) of IOPs are taken as input into a recently developed Radiative Transfer Equation solver. The simulated normalized received powers for the two durations show large impact of typhoon passage on UWOC.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

The movement of liquid droplets on the energy gradient surface has attracted extensive attention. Inspired by biological features in nature, such as the periodic spindle-shaped nodes in spider silks and conical-like barbs of cacti, the structure-property-function relationship of multifunctional gradient surfaces. In this study, a series of specific patterns are fabricated by 3D printing technology, followed by modification via the atmospheric pressure plasma treatment and liquid phase chemical deposition, resulting in enhancing the ability of water droplets of 5μL to travel 18.47mm on a horizontal plane and 22.75mm against gravity up to 20° tilting angle. Additionally, analysis techniques have been employed including contact angle analyzer, ESCA and laser confocal microscope to evaluate the sample performance. This work could further be applied to many applications related to microfluidic devices, drug delivery and water/fog collection.

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

The poly lactic acid (PLA) is one of the most used bio-derived thermoplastic polymers in 3D and 4D printing applications. The determination of PLA surface properties is of capital importance in 3D/4D printing technology. (1) Background: The surface thermodynamic properties of PLA polymer were determined by using the inverse gas chromatography (IGC) technique at infinite dilution. The determination of the retention volume of polar and non-polar molecules adsorbed on the PLA particles filling the column allowed us to obtain the dispersive, polar, and Lewis’s acid-base surface properties at different temperatures from 40 °C to 100 °C. (2) Methods: the applied surface method was based on our recent model that used the London dispersion equation and the new chromatographic parameter function of the deformation polarizability and the harmonic mean of the ionization energies of the PLA polymer and organic molecules. (3) Results: The application of this new method led to the determination of the dispersive and polar free surface energy of adsorption of molecules on the polymeric material, as well as the glass transition and the Lewis acid-base constants. (4) Conclusions: four interval temperatures were distinguished showing four zones of the variations of the surface properties of PLA as a function of the temperature before and after the glass transition.

Passive marine listening, involving the acoustic monitoring of underwater environments, has become increasingly vital for scientific research, environmental monitoring, and defense applications. However, the success of such applications critically depends on the ability to extract meaningful information from the often noisy and dynamic underwater acoustic environment. In this context, Mel-frequency cepstral coefficients (MFCCs) have emerged as an essential tool for feature extraction, capturing the spectral characteristics of marine sounds and making them ideal for species identification and sound event detection. This research presents an innovative approach to enhance the robustness of MFCC-based passive marine listening through adaptive noise reduction techniques. The proposed approach utilizes dynamic spectral subtraction to counter underwater noise, resulting in enhanced signal-to-noise ratios for desired sounds. This adaptive system adjusts to dynamic underwater noise, allowing it to concentrate on target sounds and suppress interference effectively. The experimentation further validates the effectiveness of the proposed approach, with results reaching 99% on the full dataset of 570 mammals and vessels, demonstrating the effectiveness of SVM and Random Forests in the classification of underwater audio.

With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.

Autonomous Underwater Vehicles (AUVs) are emerging as pivotal tools in underwater applications such as seafloor exploration and the inspection of pipelines or cables. Traditional land-based avoidance methods prove insufficient when applied to the complex underwater environment, largely due to the unique constraints presented by marine environments and the specific dynamics of AUVs. In this study, we introduce an improved stream function-based obstacle avoidance algorithm specifically tailored for autonomous underwater vehicles. The proposed algorithm is based on a stream function that is derived from the stream function. The stream function is characterized by a radial histogram for the cost function. In addition, constraints related to the maximum path curvature are discussed to optimize the utility of the path. Our exhaustive simulation cases validate the robustness and adaptability of the proposed algorithm, demonstrating its ability to produce feasible and optimized avoidance paths in a variety of scenarios.

To monitor the detail information in the sea and on the seafloor, sensors are generally installed on Autonomous Underwater Vehicle (AUV). AUV cannot accurately grasp the absolute position and needs to communicate with the sea-surface vehicle. However, the sea-surface vehicle cannot perform observation operations at high speed and with high efficiency due to low mobility and high human and financial costs. From this perspective, Unmanned Aerial Vehicle (UAV) has possibilities as the next-generation platform. In this study, we conducted a demonstration experiment to use UAV as a sea-surface base for AUV’s underwater communication. We additionally verified the capability of UAV, which can land on and separate from the sea surface, as a drifting buoy to monitor underwater data. Experiment results and obtained data suggest that UAV is suitable for a research operation near the coastal region in terms of position keeping ability, performance against sway, and operation speed as a communication base. To carry out more complicated work (transportation of AUV, etc.) with UAV, new research and development such as weight reduction of AUV will be required.

In underwater optical wireless communications (UOWC), scattering of the propagating light beam results in both intensity and phase variations, which limit the transmission link range and channel bandwidth, respectively. Scattering of photons while propagating through the channel is a random process, which results in the channel-dependent scattering noise. In this work, we introduce for the first time an analytical model for this noise and investigate its effect on the bit error rate performance of the UOWC system for three types of waters and a range of transmission link spans. We show that, for a short range of un-clear water or a longer range of clear water, the number of photons experiencing scattering is high, thus leading to the increased scattering noise.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance and overcoming anisotropic mechanical properties. This paper analyzes and compare the machinability of 3D printed PLA, PETG and carbon fiber reinforced PETG focusing on surface roughness and burr formation. A design of experiments (DoE) with a full factorial design was used, considering three factors: rotation speed, feed rate and depth of cut. Each factor had different levels: rotational speed at 3000, 5500 and 8000 rpm; feed rate at 400, 600 and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D printed components made of three different materials PLA, PETG and carbon fiber reinforced PETG, an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

This article develops algorithms for the characterization and the visualization of micro-scale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. We implement the algorithms to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) surface. Such images are highly dense; therefore, traditional image processing techniques might be computationally expensive. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

The present study drops the line for the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers undergoing the extrusion process during three-dimensional printing. Applying to the Herschel-Bulkley rheological model the research is ready with the sound theoretical basis which is used to understand the flow behavior of these materials under various conditions. A combination of experimental and numerical techniques that are based on the Finite Element Method (FEM) in COMSOL has been used in this study. The results of both the experimental and simulation approaches are then compared and the behavior of the material during extrusion is then examined. The experimental results proved PP fiber content has significant effects on rheological properties. The experiments on mixes which are either devoid of fibers or possess a high content of fiber, encountered problems like a high static yield stress and material segregation. Mixes with fibers of moderate content showed smoother extrusion processes, which means that a suitable range of fiber addition exists for the fibers that optimally balance the mechanical properties and extrudability. Numerical simulations typically agreed with experimental data for fiber contents up until it reached a certain level but not above, suggesting the existence of more complex interactions inside the fibers and the necessity for the subsequent model refinements. The investigation outlines a 0.25% and 0.5% fiber content range that leads to enhanced performance without bringing the extrusion process to greater complications and further making the production of proper, architecturally printed structures possible.

Water covers a greater part of the earth's surface. Even though we know very little about the underwater world as most parts of it remain unexplored. Oceans including other water bodies hold huge natural resources and also the aquatic lives. These are mostly unexplored and very few of those are known due to unsuited and hazardous environments for the human to explore. This vast underwater world can be monitored remotely from a distant location with much ease and less risk. To monitor water-bodies remotely in real-time, sensor networking has been playing a great role. It is needed to deploy a wireless sensor network over the volume which we want to surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from the different heights of the water level which is sent to the surface sink. Unlike terrestrial communication, radio waves and other conventional mediums can't serve the purpose of underwater communication as they pose high attenuation and very reduced transmission range. Rather an acoustic medium can transmit data more efficiently and reliably in comparison to other mediums. To transmit data reliably from the bottom of the sea to the sinks at the surface, multi-hop communication is needed which must involve a certain scheme. For seabed to surface sink communication, leading researchers have proposed different routing protocols. The goal of these routing protocols is to make underwater communication more reliable, energy-efficient and delay efficient thus to improve the performance of the overall communication. This paper surveys the advancement and applications of the routing protocols which eventually helps in finding the most efficient routing protocol for the Underwater Wireless Sensor Network (UWSN).

Existing implants used with Total Knee Arthroplasty (TKA), Total Hip Arthroplasty (THA), and other joint reconstruction treatments, have displayed premature failures and frequent needs for revision surgery in recent years, particularly with young active patients who represent more than 55% of all joint reconstruction patients. Bone cement and stress shielding have been identified as the major reasons for premature joint failures. A breakdown of the cement may happen, and revision surgery may be needed because of the aseptic loosening. The significant mismatch of stiffness properties of patient trabecular bones and metallic implant materials in joint reconstruction surgery results in the stress shielding phenomenon. This could lead to significant bone resorption and increased risk of bone fracture and the aseptic loosening of implants. The present project introduces an approach for development of customized cellular structures to match the mechanical properties and architecture of human trabecular bone. It consists of four modules:1- characterization of geometry, microarchitecture, and stiffness properties of patient’s bones; 2- developing customized lattice structures tailored to mimic properties established in module 1; 3 - integration of developed lattice structure into full scale implant design and manufacturing; 4- implementation of the developed technology for use in implants that would overcome most of the issues related to aseptic loosening phenomenon. The present work aims at fulfilling the objectives of module 2 by exploring new designs of customized lattice structures and texture tailored to mimic closely patients’ bone anisotropic properties and that can incorporate an engineered biological press-fit fixation technique. The effects of various lattice design variables on mechanical performance of the structure are examined through systematic experimental plan using statistical design of experiments technique and analysis of variance method. All tested lattice designs were explored under realistic geometrical, biological, and manufacturing constraints. Of the four design factors examined in this study, strut thickness was found to have the highest percent contribution (41%) regarding the structure stiffness, followed by unit cell type, and cell size. Srut shape was found to have the lowest effect with only 11% contribution. The introduced solution offers lattice structure designs that can be adjusted to match bone stiffness distribution and promote bone ingrowth and hence eliminating the phenomenon of stress shielding while incorporating biological press-fit fixation technique.