Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Recent advances in swine wastewater treatment technologies for resource recovery: A comprehensive review.

Swine wastewater (SW), characterized by highly complex organic and nutrient substances, poses serious impacts on aquatic environment and public health. Furthermore, SW harbors valuable resources that possess substantial economic potential. As such, SW treatment technologies place increased emphasis on resource recycling, while progressively advancing towards energy saving, sustainability, and circular economy principles. This review comprehensively encapsulates the state-of-the-art knowledge for treating SW, including conventional (i.e., constructed wetlands, air stripping and aerobic system) and resource-utilization-based (i.e., anaerobic digestion, membrane separation, anaerobic ammonium oxidation, microbial fuel cells, and microalgal-based system) technologies. Furthermore, this research also elaborates the key factors influencing the SW treatment performance, such as pH, temperature, dissolved oxygen, hydraulic retention time and organic loading rate. The potentials for reutilizing energy, biomass and digestate produced during the SW treatment processes are also summarized. Moreover, the obstacles associated with full-scale implementation, long-term treatment, energy-efficient design, and nutrient recovery of various resource-utilization-based SW treatment technologies are emphasized. In addition, future research prospective, such as prioritization of process optimization, in-depth exploration of microbial mechanisms, enhancement of energy conversion efficiency, and integration of diverse technologies, are highlighted to expand engineering applications and establish a sustainable SW treatment system.

Adaptive Clustering for Point Cloud.

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

Rapid determination of geographical authenticity and pungency intensity of the red Sichuan pepper (Zanthoxylum bungeanum) using differential pulse voltammetry and machine learning algorithms.

The development of an analytical method for assessing pungency intensity and determining geographical origins is crucial for evaluating the quality of visually similar Zanthoxylum bungeanum pericarp (PZB). This study analyzed 210 PZB samples from 14 origins across China, focusing on origin adulteration identification and pungency intensity using a combination of differential pulse voltammetry (DPV) and machine learning algorithms. The artificial neural network (ANN) and K-nearest neighbor (KNN) algorithms provided the highest accuracy in origin identification (100 %) and adulteration detection (97.9 %) respectively. Moreover, the ANN excelled in predicting pungency intensity (R2 = 0.918). Assessment via feature importance analysis of DPV features revealed that segments of polyphenols (0.34-0.52 V and 1.0-1.2 V) and alkylamides (1.0-1.2 V) contributed significantly to the PZB pungency intensity. These findings highlight the potential of DPV as a reliable method for assessing the quality of PZB, and offer a promising solution for ensuring the geographical authenticity of this important crop.

Method to Solve Underwater Laser Weak Waves and Superimposed Waves.

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

SERS determination of hydroxy-α-sanshool in spicy hotpot seasoning: The strategy to restrain the interference of capsaicin and its mechanism.

Hydroxy-α-sanshool (α-SOH) is the principal ingredient responsible for the numbing sensation in spicy hotpot. However, utilizing surface-enhanced Raman scattering (SERS) to analyze the α-SOH in hotpot seasoning is challenging due to the significant interference of capsaicin (CAP). Therefore, two schemes were proposed to address CAP interference in hotpot seasoning, namely laccase-catalyzed conversion and metal-organic framework (MOF) interaction. Among them, Fe-BTC MOF exhibited significant anti-interference effect and the underlying mechanism is elucidated. The motion of CAP aromatic ring was constrained by steric hindrance and electrostatic interactions of Fe-BTC. Additionally, the interaction between CAP aromatic ring and conjugated triene group in α-SOH was quenched, enhancing the α-SOH SERS signal. The proposed method had a significant anti-interference effect on α-SOH quantification in the presence of CAP, significantly enhancing the α-SOH SERS signal in a range of 0.85 to 4.00 × 107. The linearity and reproducibility of the proposed hotpot seasoning testing method were also validated.

The Effect of Molten Phosphate on Corrosion of 316 Stainless Steel, Alloy 625, and Titanium TA8 in Supercritical Water Oxidation Conditions.

The early formation of phosphate oxide formed on 316 stainless steel (316 SS), nickel-based Alloy 625, and titanium alloy TA8 exposed in supercritical water (400 °C, 25 MPa) containing phosphate, chloride, and oxygen was investigated. Phosphate corrosion products of austenitic stainless steel displayed the severest spallation. Stable phosphates oxide films were inclined to form on Alloy 625. TiO2 and Ti2O3 are the two main components of oxide films on TA8. There is a strong synergistic effect between phosphates, oxygen, and supercritical water, leading to severe corrosion. The corrosion behavior of the three alloys at the top and bottom of the reaction tube was compared. Both at the top of the reaction tube and at the bottom of the reaction tube, TA8 showed an increase in mass. 316 SS and alloy 625 showed mass gain at the top and mass loss at the bottom. The alloys' detailed molten corrosion mechanism after exposure to supercritical water is discussed.

Intestinal toxicity and resistance gene threat assessment of multidrug-resistant Shigella: A novel biotype pollutant.

Multidrug-resistant bacteria, especially pathogens, pose a serious threat to disease treatment and recovery, but their potential toxicity to animal development is not entirely clear. As the most important site for nutrient absorption, we studied the intestinal microbiome of Xenopus tropicalis by analyzing the effect of multidrug-resistant Shigella on its intestinal health. Unlike in the control, Shigella intake promoted the secretion of neutral mucus and inhibited intestinal development and weight gain. Following 60 days of exposure, intestinal crypt atrophy, intestinal villus shortening, internal cavity enlargement, and external mucosal muscle disintegration were observed. The circular and longitudinal intestinal muscles became thinner with increasing pathogen exposure. In addition, the presence of Shigella altered the expression of multiple cytokines and classic antioxidant enzyme activities in the gut, which may have caused the intestinal lesions that we observed. 16 S rDNA sequencing analysis of intestinal samples showed that exposure to Shigella destroyed the normal gut microbial abundance and diversity and increased the functional bacterial ratio. Notably, the increased abundance of intestinal antibiotic resistance genes (ARGs) may imply that the resistance genes carried by Shigella easily migrate and transmit within the intestine. Our results expand existing knowledge concerning multidrug-resistant Shigella-induced intestinal toxicity in X. tropicalis and provide new insights for the threat assessment of resistance genes carried by drug-resistant pathogens.

Improving the noise immunity of 3D computational ghost imaging.

Computational ghost imaging (CGI) can build a three-dimensional (3D) image using the reconstructed shading images. However, it could be easily affected by the noise accumulated in the 3D reconstruction. More importantly, the selection of initial growing position will also affect the quality of the formed 3D image significantly. In this paper, we apply the technique of sub-pixel displacement to achieve smooth shading images in noisy environments and propose a method for selecting the optimal initial growing position to preserve the stereo feature of the object. We demonstrate that the surfaces of the reconstructed 3D images are more accurate using our proposed method as compared to the ones achieved by previously used methods. Our research would promote the development of 3D imaging using CGI in noisy environments.