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.

Metal additive manufacturing (AM) is capable of producing complex parts, using a wide range of functional metals that are otherwise very difficult to make and involve multiple manufacturing processes. However, because of the involvement of thermal energy in the fabrication of metallic AM parts, residual stress remains one of the major concerns in metal AM. This residual stress has negative effects on part quality, dimensional accuracy, and part performance. This study aims to carry out a comprehensive review and analysis of different aspects of residual stress, including the causes and mechanisms behind the generation of residual stress during metal AM, the state-of-the-art measurement techniques for measuring residual stress, various factors influencing residual stress, its effect on part quality and performance, and ways of minimizing or overcoming residual stress in metal AM parts. Residual stress formation mechanisms vary, based on the layer-by-layer deposition mechanism of the 3D printing process. For example, the residual stress formation for wire-arc additive manufacturing is different from that of selective laser sintering, direct energy deposition, and powder bed fusion processes. Residual stress formation mechanisms also vary based on the scale (i.e., macro, micro, etc.) at which the printing is performed. In addition, there are correlations between printing parameters and the formation of residual stress. For example, the printing direction, layer thickness, internal structure, etc., influence both the formation mechanism and quantitative values of residual stress. The major effect residual stress has on the quality of a printed part is in the distortion of the part. In addition, the dimensional accuracy, surface finish, and fatigue performance of printed parts are influenced by residual stress. This review paper provides a qualitative and quantitative analysis of the formation, distribution, and evolution of residual stress for different metal AM processes. This paper also discusses and analyzes both in situ and ex situ measurement techniques for measuring residual stress. Microstructural evolution and its effect on the formation of residual stress are analyzed. Various pre- and post-processing techniques used to countermeasure residual stress are discussed in detail. Finally, this study aims to present both a qualitative and quantitative analysis of the existing data and techniques in the literature related to residual stress, as well as to provide a critical analysis and guidelines for future research directions, to prevent or overcome residual stress formation in metal AM processes.

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 present systematic review and meta-analysis aimed to assess the characteristics and consequences of post-processing methods after grinding procedures in YSZ ceramics on its surface roughness and flexural strength. The protocol of this review was made prospectively and is available online in the PROSPERO database (link). Literature searches on PubMed/MEDLINE, EMBASE, Lilacs, Web of Science and Scopus were conducted on December 2022 to select in vitro studies written in English, without publishing-date restrictions, that considered surface characteristics and mechanical properties of YSZ ceramics submitted to grinding and subsequent post-processing surface treatments as an attempt to revert the effect induced by grinding. Two authors independently selected the studies, extracted the data and assessed the risk of bias. Mean differences (Rev-Man 5.1, random effects model, α= 0.05) were obtained by comparing flexural strength and surface roughness values of ground surfaces with at least one post-processing surface treatment (global analysis). Subgroup analyses were performed considering the most prevalent categories of post-processing methods. A total of 33 (out of 4032) studies were eligible and included in the analysis. In the global analysis, ground surfaces showed higher flexural strength than when post-processing methodologies were employed (p< 0.0001). The subgroup analysis showed that only polishing was able to enhance the flexural strength after grinding (p= 0.001); however, when other protocols were used, the ground surface was always superior in terms of flexural strength (p< 0.0001). Post-processing techniques in both the global and sub-group analyses were able to reduce the surface roughness after grinding in YSZ ceramics (p< 0.00001). High heterogeneity was found in all the meta-analyses. Concerning the risk of bias analysis, the included studies had mixed scores for the considered factors. In conclusion, in terms of improving flexural strength and restoring surface roughness after grinding, polishing protocols can be considered the best indication as post-processing treatment after YSZ ceramics adjustments/grinding.

The aim of this systematic review was to provide an overview of the technical and clinical outcomes of conventional, speed sintering and high-speed sintering protocols of zirconia in the dental field. Data on precision, mechanical and optical parameters were evaluated and related to the clinical performance of zirconia ceramic. The PICOS search strategy was applied using MEDLINE to search for in vitro and in vivo studies using MeSH Terms by two reviewers. Of 66 potentially relevant studies, 5 full text articles were selected and 10 were further retrieved through a manual search. All 15 studies included in the systematic review were in vitro studies. Mechanical, precision and optical properties (marginal and internal fit, fracture strength and modulus, wear, translucency and opalescence, aging resistance/hydrothermal aging) were evaluated regarding 3-, 4- and 5-YTZP zirconia material and conventional, high- and high-speed sintering protocols. Mechanical and precision results were similar or better when speed or high-speed sintering methods were used for 3-, 4- and 5-YTZP zirconia. Translucency is usually reduced when 3 Y-TZP is used with speed sintering methods. All types of zirconia using the sintering procedures performed mechanically better compared to lithium disilicate glass ceramics but glass ceramics showed better results regarding translucency.

Percutaneous coronary intervention (PCI) with stent implantation is one of the most effective treatments for cardiovascular diseases (CVDs). However, there are still many complications after stent implantation. As a medical device with a complex structure and small size, the manufacture and post-processing technology greatly impact the mechanical and medical performances of stents. In this paper, the development history, material, manufacturing method, and post-processing technology of vascular stents are introduced. In particular, this paper focuses on the existing manufacturing technology and post-processing technology of vascular stents and the impact of these technologies on stent performance is described and discussed. Moreover, the future development of vascular stent manufacturing technology will be prospected and proposed.

A number of processes for post-production treatment of \"raw\" biochars, including leaching, aeration, grinding or sieving to reduce particle size, and chemical or steam activation, have been suggested as means to enhance biochar effectiveness in agriculture, forestry, and environmental restoration. Here, I review studies on post-production processing methods and their effects on biochar physio-chemical properties and present a meta-analysis of plant growth and yield responses to post-processed vs. \"raw\" biochars. Data from 23 studies provide a total of 112 comparisons of responses to processed vs. unprocessed biochars, and 103 comparisons allowing assessment of effects relative to biochar particle size; additional 8 published studies involving 32 comparisons provide data on effects of biochar leachates. Overall, post-processed biochars resulted in significantly increased average plant growth responses 14% above those observed with unprocessed biochar. This overall effect was driven by plant growth responses to reduced biochar particle size, and heating/aeration treatments. The assessment of biochar effects by particle size indicates a peak at a particle size of 0.5-1.0 mm. Biochar leachate treatments showed very high heterogeneity among studies and no average growth benefit. I conclude that physiochemical post-processing of biochar offers substantial additional agronomic benefits compared to the use of unprocessed biochar. Further research on post-production treatments effects will be important for biochar utilization to maximize benefits to carbon sequestration and system productivity in agriculture, forestry, and environmental restoration.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s42773-021-00115-0.

Additive manufacturing (AM) has attracted much attention due to its capability in building parts with complex geometries. Unfortunately, AM metals suffer from three major drawbacks, including high porosity, poor surface finish, and tensile residual stresses, all of which will significantly compromise the fatigue performance. These drawbacks present a major obstacle to the application of AM metals in industries that produce fatigue-sensitive components. Many post-processing methods, including heat treatment, hot isotropic pressing, laser shock peening, ultrasonic nanocrystal surface modification, advanced finishing and machining, and laser polishing, have been used to treat AM metals to decrease their porosity, improve the surface finish, and eliminate tensile residual stresses. As a result, significant improvement in fatigue performance has been observed. In this paper, the state of the art in utilizing post-processing techniques to treat AM metals and the effects of these treatments on the porosity, surface finish, and residual stresses of metal components and their resultant fatigue performance are reviewed.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model\'s performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field.

Bionanotechnology, the use of biological resources to produce novel, valuable nanomaterials, has witnessed tremendous developments over the past two decades. This eco-friendly and sustainable approach enables the synthesis of numerous, diverse types of useful nanomaterials for many medical, commercial, and scientific applications. Countless reviews describing the biosynthesis of nanomaterials have been published. However, to the best of our knowledge, no review has been exclusively focused on the in vivo biosynthesis of inorganic nanomaterials. Therefore, the present review is dedicated to filling this gap by describing the many different facets of the in vivo biosynthesis of nanoparticles (NPs) using living eukaryotic cells and organisms-more specifically, live plants and living biomass of several species of microalgae, yeast, fungus, mammalian cells, and animals. It also highlights the strengths and weaknesses of the synthesis methodologies and the NP characteristics, bio-applications, and proposed synthesis mechanisms. This comprehensive review also brings attention to enabling a better understanding between the living organisms themselves and the synthesis conditions that allow their exploitation as nanobiotechnological production platforms as these might serve as a robust resource to boost and expand the bio-production and use of desirable, functional inorganic nanomaterials.