The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.

Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRT-based improved algorithms still suffer from the problems of difficult to design the hyper-parameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot collaboration, real-time path planning, self-tuning of hyper-parameters, task- or application-scene oriented algorithms and hardware design, and path planning in highly dynamic environments are future trends.

To investigate the impact of a centrifugation method on the surface characteristics, flexural properties, and cytotoxicity of an additively manufactured denture base polymer.
The tested specimens were prepared by digital light processing (DLP). A centrifugation method (CENT) was used to remove the residual uncured resin. In addition, the specimens were post-processed with different post-rinsing solutions: isopropanol (IPA), ethanol (EtOH), and tripropylene glycol monomethyl ether (TPM), respectively. A commercial heat-polymerized polymethyl methacrylate was used as a reference (REF). First, the values of surface topography, arithmetical mean height (Sa), and root mean square height (Sq) were measured. Next, flexural strength (FS) and modulus were evaluated. Finally, cytotoxicity was assessed using an extract test. The data were statistically analyzed using a one-way analysis of variance, followed by Tukey\'s multiple comparison test for post-hoc analysis.
The Sa value in the CENT group was lower than in the IPA, EtOH, TPM, and REF groups (p < 0.001). Moreover, the CENT group had lower Sq values than other groups (p < 0.001). The centrifugation method showed a higher FS value (80.92 ± 8.65 MPa) than the EtOH (61.71 ± 12.25 MPa, p < 0.001) and TPM (67.01 ± 9.751 MPa, p = 0.027), while affecting IPA (72.26 ± 8.80 MPa, p = 0.268) and REF (71.39 ± 10.44 MPa, p = 0.231). Also, the centrifugation method showed no evident cytotoxic effects.
The surfaces treated with a centrifugation method were relatively smooth. Simultaneously, the flexural strength of denture base polymers was enhanced through centrifugation. Finally, no evident cytotoxic effects could be observed from different post-processing procedures.
The centrifugation method could optimize surface quality and flexural strength of DLP-printed denture base polymers without compromising cytocompatibility, offering an alternative to conventional rinsing post-processing.

An effective post-processing algorithm is essential for achieving high rates of secret key generation in quantum key distribution. This work introduces an approach to quantum key distribution post-processing by integrating the three main steps into a unified procedure: syndrome-based error estimation, rate-adaptive reconciliation, and subblock confirmation. The proposed scheme employs low-density parity-check codes to estimate the quantum bit error rate using the syndrome information, and to optimize the channel coding rates based on the Slepian-Wolf coding scheme for the rate-adaptive method. Additionally, this scheme incorporates polynomial-based hash verification in the subblock confirmation process. The numerical results show that the syndrome-based estimation significantly enhances the accuracy and consistency of the estimated quantum bit error rate, enabling effective code rate optimization for rate-adaptive reconciliation. The unified approach, which integrates rate-adaptive reconciliation with syndrome-based estimation and subblock confirmation, exhibits superior efficiency, minimizes practical information leakage, reduces communication rounds, and guarantees convergence to the identical key. Furthermore, the simulations indicate that the secret key throughput of this approach achieves the theoretical limit in the context of a BB84 quantum key distribution system.

This study intends to enhance the mechanical strength of wood-plastic composite selective laser sintering (SLS) parts by using a sustainable composite, peanut husk powder (PHP)/poly ether sulfone (PES) (PHPC). The study aims to address agricultural waste pollution by encouraging the eco-friendly utilization of such waste in SLS technology. To ensure the sintering quality and mechanical properties and prevent deformation and warping during sintering, the thermo-physical properties of PHP and PES powders were analyzed to determine a suitable preheating temperature for PHPC. Single-layer sintering tests were conducted to assess the formability of PHPC specimens with varying PHP particle sizes. The study showed the effects of different PHP particle sizes on the mechanical performance of PHPC parts. The evaluation covered various aspects of PHPC SLS parts, including mechanical strength, density, residual ash content, dimensional accuracy (DA), and surface roughness, with different PHP particle sizes. The mechanical analysis showed that PHPC parts made from PHP particles of ≤0.125 mm were the strongest. Specifically, the density bending strength, residual ash content, tensile, and impact strength were measured as 1.1825 g/cm3, 14.1 MPa, 1.2%, 6.076 MPa, and 2.12 kJ/cm2, respectively. Notably, these parameters showed significant improvement after the wax infiltration treatment. SEM was used to examine the PHP and PES powder particles, PHPC specimen microstructure, and PHPC SLS parts before and after the mechanical tests and waxing. Consequently, SEM analysis wholly confirmed the mechanical test results.

UNASSIGNED: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons.
UNASSIGNED: For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting.
UNASSIGNED: Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

Under the background of green and low-carbon era, efficiently utilization of renewable biomass materials is one of the important choices to promote ecologically sustainable development. Accordingly, 3D printing is an advanced manufacturing technology with low energy consumption, high efficiency, and easy customization. Biomass 3D printing technology has attracted more and more attentions recently in materials area. This paper mainly reviewed six common 3D printing technologies for biomass additive manufacturing, including Fused Filament Fabrication (FFF), Direct Ink Writing (DIW), Stereo Lithography Appearance (SLA), Selective Laser Sintering (SLS), Laminated Object Manufacturing (LOM) and Liquid Deposition Molding (LDM). A systematic summary and detailed discussion were conducted on the printing principles, common materials, technical progress, post-processing and related applications of typical biomass 3D printing technologies. Expanding the availability of biomass resources, enriching the printing technology and promoting its application was proposed to be the main developing directions of biomass 3D printing in the future. It is believed that the combination of abundant biomass feedstocks and advanced 3D printing technology will provide a green, low-carbon and efficient way for the sustainable development of materials manufacturing industry.

Computed Tomography (CT) imaging is an effective non-invasive examination. It is widely used in the diagnosis of fractures, arthritis, tumor, and some anatomical characteristics of patients. The density value (Hounsfield unit, HU) of a material in computed tomography can be the same for materials with varying elemental compositions. This value depends on the mass density of the material and the degree of X-ray attenuation. Computed Tomography Osteoabsorptiometry (CTOAM) imaging technology is developed on the basis of CT imaging technology. By applying pseudo-color image processing to the articular surface, it is used to analyze the distribution of bone mineralization under the articular cartilage, evaluate the position of prosthesis implantation, track the progression of osteoarthritis, and determine the joint injury prognosis. Furthermore, this technique was combined with indentation testing to discuss the relationship between the high bone density area of the articular surface, the mechanical strength of the bone, and the anchorage stability of the implant, in addition to the study of the relationship between mechanical strength and bone density. This narrative study discusses the pre- and postoperative evaluation of medical device implantation position, orthopedic surgery, and the clinical treatment of bone injury and degeneration. It also discusses the research status of CTOAM technology in image post-processing engineering and the relationship between bone material and mechanical strength.

The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.

UNASSIGNED: The proposed algorithm could support accurate localization of lung disease. To develop and validate an automated deep learning model combined with a post-processing algorithm to segment six pulmonary anatomical regions in chest computed tomography (CT) images acquired during positron emission tomography/computed tomography (PET/CT) scans. The pulmonary regions have five pulmonary lobes and airway trees.
UNASSIGNED: Patients who underwent both PET/CT imaging with an extra chest CT scan were retrospectively enrolled. The pulmonary segmentation of six regions in CT was performed via a convolutional neural network (CNN) of DenseVNet architecture with some post-processing algorithms. Three evaluation metrics were used to assess the performance of this method, which combined deep learning and the post-processing method. The agreement between the combined model and ground truth segmentations in the test set was analyzed.
UNASSIGNED: A total of 640 cases were enrolled. The combined model, which involved deep learning and post-processing methods, had a higher performance than the single deep learning model. In the test set, the all-lobes overall Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.972, 12.025 mm, and 0.948, respectively. The airway-tree Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.849, 32.076 mm, and 0.815, respectively. A good agreement was observed between our segmentation in every plot.
UNASSIGNED: The proposed model combining two methods can automatically segment five pulmonary lobes and airway trees on chest CT imaging in PET/CT. The performance of the combined model was higher than the single deep learning model in each region in the test set.