Extraction Performance Research Articles

On-site extraction of phenoxycarboxylic acid herbicides in environmental waters utilizing monolith-based in-tip microextraction technique

On-site extraction plays a significant role in the reliable quantification of strong polar phenoxycarboxylic acid herbicides (PCAs) in aqueous samples. In current study, a new technique for the field sample preparation of PCAs was developed by means of three channels in-tip microextraction device (TCIM). To capture PCAs effectively, an extraction phase based on monolith (EPM) using vinylimidazole and divinylbenzene/ethylene dimethacrylate as monomer and cross-linkers, respectively, was in-situ synthesized in pipette tips. The EPM fabricated at optimal conditions were characterized by a series of techniques and employed as the adsorbent of TCIM for the on-site extraction of PCAs. The adsorption isotherm was studied so as to inspect the extraction behaviors of EPM towards PCAs. Results revealed that the proposed EPM/TCIM presented satisfactory extraction performance towards PCAs through multiple interactions. The enrichment factors and adsorption capacity were 74-277 and 20 mg/g, respectively. Under the most beneficial extraction parameters, the developed EPM/TCIM was successfully employed to on-site extract PCAs, and then combining with HPLC equipped with diode array detector to monitor trace PCAs in actual waters. The limits of detection (LODs) towards investigated PCAs varied from 0.071 μg/L to 0.30 μg/L. In addition, the accuracy of established approach was inspected with documented method. Compared with existing lab-based sample preparation approaches, the introduced field sample preparation technique exhibits some merits such as avoidance of transporting large volume of water, prevention of analytes loss during sampling procedure, less usage of organic solvent and achievement of satisfactory efficient in sample preparation.

Study on improving heat extraction capacity of borehole heat exchanger by forced convection of groundwater

Borehole Heat Exchangers (BHEs) has the advantage of "extracting heat without extracting groundwater". Its main drawback is low heat extraction power. Aiming at developing medium-deep hydrothermal geothermal resources, a Branch Well Reinforced Coaxial Borehole Heat Exchanger (BWR-CBHE) is proposed firstly. An assisted circulation loop formed by the wellbore and geothermal reservoir is created through drilling branch well and installing electric submersible pump (ESP). By driving the forced convection of groundwater within the assisted circulation loop, heat extraction capacity of the CBHE is enhanced. Then, a numerical model of the BWR-CBHE is established. The main circulation of the working fluid within the CBHE is coupled with the assisted circulation of groundwater, and the heat extraction performance of the BWR-CBHE is calculated. Under the conditions studied in this paper, the net heat extraction power (after deducting the energy consumption of the ESP) of the BWR-CBHE is 6.09 times higher than that of conventional CBHEs. Finally, impacts of parameters, including working fluid, branch well and ESP, on the heat extraction capacity of the BWR-CBHE are discussed. The heat extraction power of the BWR-CBHE is increased significantly without extracting groundwater, which provides a new approach for the development of BHEs.

Screening deep eutectic solvents as green solvents for efficient extraction of carbazole from model crude anthracene oil

Carbazole is a high value-added component of crude anthracene. Highly efficient and sustainable extraction solvents are extremely significant for carbazole separation. Deep eutectic solvents (DESs), as a new class of green solvents, have gained extensive attention from researchers in the field of separation. However, a large number of DESs are generated due to the enormous combinations of hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs), which makes it challenging to identify the most suitable DES for specific separation process. Therefore, a reliable and effective method for DESs screening was highly desirable. In this work, a DESs screening method was proposed for the separation of carbazole. Initially, the COSMO-RS model was employed to calculate thermodynamic properties at infinite dilution. Subsequently, the liquid–liquid phase equilibrium behavior of prescreened DESs was further predicted by COSMO-RS to eliminate those that were miscible with toluene. Finally, the practical extraction performance of the top DES candidates was evaluated through experimental investigation. The results well validated the reliability of the screening method, identifying tetraethylammonium chloride/propionic acid (1:2) as the most promising DES for carbazole separation. Furthermore, the extraction mechanism was explored using FT-IR analysis and quantum chemical calculations.

Electroextraction of Large Volume Samples Using Paper Points Coupled With Hollow Fiber Membranes: Study of Parameters and Strategies to Enhance Analytical Performance.

Electroextraction (EE) encompasses a range of sample preparation methods whose effectiveness, selectivity, and efficiency are significantly influenced by the physical-chemical characteristics of analytes, samples, and instrumental conditions. This article explores, for the first time, various strategies aimed at enhancing the extraction efficiency of a recent approach of EE utilizing a paper point (PP) combined with a hollow fiber (HF) (abbreviated as PP-HF-EE) to extract various cationic and anionic model compounds from water samples. The study also explores, experimentally, the impact of agitation, organic filter composition, PP diameter, and PP brand on extraction performance, and proves that all these factors are quite important, especially when digital image analysis is utilized for determination. Furthermore, this work demonstrates the ease and feasibility of simultaneously extracting cations and anions using PP-HF-EE and proposes a straightforward method to enhance analyte concentration on the vertex of the PP through a base-to-vertex focusing. Lastly, it is demonstrated, using capillary electrophoresis coupled to a UV-Vis detector, that for PP-HF-EE, the extraction efficiency and pre-concentration factor are less dependent on other parameters when multiple PPs per sample are utilized, with signal enhancement values of up to 111 and 339 for nortriptyline and haloperidol, respectively. All the findings and strategies presented herein constitute significant contributions that can facilitate future research in method development, particularly in the utilization of PP-HF-EE and similar EE approaches.

Federated learning-based wavelength demodulation system for multi-point distributed multi-peak FBG sensors

Machine learning-based demodulation of multi-peak fiber Bragg grating (FBG) sensors has been extensively studied, demonstrating superior performance compared to conventional algorithms because it can neglect potential physical constraints. As the number of real-world monitoring points increases, the volume of fiber-optic sensing data grows exponentially. This necessitates aggregating data from various regions (e.g., via Wi-Fi), unlike traditional single-point monitoring, which challenges server storage capacity and communication efficiency. To address these issues, this paper proposes a federated learning (FL)-based framework for efficient wavelength demodulation of multi-peak FBGs in multipoint monitoring. Specifically, an arrayed waveguide grating (AWG) with multiplexing capability is employed at different monitoring points to convert spectral features into multi-channel transmission intensities, serving as training data for local models. Subsequently, the local model parameters, trained independently at each point, are uploaded to a central server to derive the optimal global model for demodulation across different monitoring points. The proposed demodulation framework is validated through stress demodulation experiments on multi-peak FBG sensors. Experimental results indicate strong multi-peak extraction performance with a demodulation error of ±0.64 pm. Additionally, the method demonstrates excellent applicability for generating high-precision global demodulation models through multi-node cooperative work under various scenarios.

Hierarchical attention for aspect extraction using LSTM in fine-grained sentiment analysis and evaluation

In the current digital world, aspect-based sentiment analysis (ABSA) from a review sentence is a big challenge. In recent works, the aspect term detection from the reviews of the discourse is performed independently, even for contextually interdependent reviews. Further, the baseline techniques could not correlate valid multi-word aspect extraction from different review sentences, which further degrades the aspect extraction performance. Therefore, this paper aims to improve the performance of the aspect term detection subtask using a supervised hierarchical attention-based model. A sentence alignment step is added as preprocessing before ABSA on multi-sentence reviews. This step removes deficient knowledge, captures aspect alignment from different review sentences, and properly relates the aspect to the context domain. Next, the context information is used to extract contextual aspects using word-vector representation. The input sentences and labeled data using sequence tagging are supplied to the proposed supervised hierarchical attention network. The proposed model can capture the semantics of words for different domains, enhancing the extraction of domain-specific relevant aspect terms. The experimental results shown in the SemEval-16 reviews present the effectiveness of the proposed attention-based approach for aspect term detection. The F-score is improved by approximately 3 % as compared to the recent hybrid unsupervised approach for both the laptop and restaurant domains when coreference resolution is not performed. On the other side, F-score is improved by approximately 4 % and 3 % as compared to the recent hybrid unsupervised approach for the laptop and restaurant domains, respectively, when coreference resolution is not performed. When the results of the proposed approach are compared with those of recent supervised approaches, the F-score of the proposed model is improved by 5 % and 9 % for laptops and restaurants, respectively, when coreference resolution is not performed. Whereas the F-score of the proposed model is improved by 9 % and 12 % for laptops and restaurants, respectively, when coreference resolution is performed before the aspect extraction subtask.

A Dual Detection Head YOLO Model With Its Application in Wheat Ear Recognition

The spike number(SN) per unit area is an important indicator for measuring wheat yield. The use of AI to detect and count wheat ears is increasingly valued by researchers. In order to improve the accuracy of wheat ear recognition, the model is deployed on limited mobile devices to achieve accurate estimation of wheat yield. This paper proposes a lightweight hybrid design network Wheat-YoloNet based on improved YOLOv8 for wheat ear recognition. On the basis of the YOLOv8 network structure, we only retain small and large object detection heads, reducing the number of network parameters while ensuring detection performance. We added CBAM attention mechanism module to Backbone to improve the feature extraction performance of the model and introduce Wise-IoU to balance the samples. Compared with the YOLOv8n baseline model, the improved model has reduced parameter count by 33.3%, outperforming other comparison object detection algorithms, and can be deployed and run on devices with limited computing power.

Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)

Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.

A fast solid-phase microextraction scheme for in vivo monitoring of bio-accumulation and bio-transformation of arbidol in living plants

Large quantity of the antiviral drug arbidol is used for resisting virus infection like the Corona Virus Disease 2019 and influenza, resulting in unanticipated environmental pollution. Herein, to investigate the environmental risks of the unanticipated arbidol contamination, a novel in vivo sampling probe was developed based on a bromo-substituted porous organic polymer (Br-POP) and then adopted for tracking the bio-accumulation and bio-transformation of arbidol in living plants by coupling with a nano-electrospray ionization fourier-transform ion cyclotron resonance mass spectrometry (Nano-ESI-FT-ICR-MS) method. The established method showed good extraction performance towards arbidol with limit of detection (LOD) of 0.48 ng g−1, and relative standard deviation (RSD) of single-and multiple- probe of 2.2 and 14 %. Owing to the interactions between the Br-POP and the target analytes, as well as the fast analysis process of Nano-ESI-FT-ICR-MS, <6 min was cost for total sampling and analysis duration, achieving hourly tracking of arbidol and its metabolites in this work. During 21-d in vivo tracking, the concentration of arbidol in living plant stems increased rapidly within 6 h and peaked at 413.93 ± 47.09 ng g−1. Meanwhile, it was found that dissolved organic matters (DOM) had significant effect on arbidol behaviors in living plants, resulting in a decrease of the maximum concentration of arbidol in plant stems (152.70 ± 42.44 ng g−1) and the change of dominant metabolite of arbidol that the S-oxidation rather than N-demethylation product of arbidol was dominant with DOM participation. Additionally, the plant root secretion was found to be significantly altered by arbidol exposure. To summarized, the combination of in vivo SPME and the FT-ICR-MS analysis provide new and important information regarding arbidol contamination and related alternation of plant root metabolism.

Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method

Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm. The experimental results demonstrate that all three thresholding methods exhibit good performance in water body extraction, with the Kittler-Illingworth (KI) thresholding algorithm achieving the highest relative accuracy.

Post-synthetic modification of a MOF via Continuous Flow Methods for Gold E-Waste Recycling.

This study pioneers the use of continuous flow methods to modify the nanoporous metal-organic framework Fe-BTC (MIL-100(Fe)) with redox-active poly-p-phenylenediamine (PpPDA). The Fe-BTC/PpPDA composite, known for its gold extraction capabilities, was synthesized in continuous flow on a 250 g scale. Fe-BTC/PpPDA was then tested in eight industrial leachates (e.g., cyanide, thiourea, aqua regia) exhibiting varying effectiveness, pH, and gold speciation, which led to significant differences in the composite's performance for gold extraction. The composite performed best in solutions containing [AuCl4]- species. Structured into spherical beads using a novel continuous flow microdroplet technique, these adsorbents were tested for gold recovery from real e-waste solutions in a breakthrough epemriment. They achieved a capacity of ~600 mg of gold per gram before breakthrough and ~900 mg per gram at a 60% recovery rate. Selectivity ratios for Au/Ni, Au/Co, and Au/Fe were 972, 262, and 193, respectively. In situ X-ray absorption near edge spectroscopy (XANES) provided evidence of the reduction of Au³⁺ to Au⁰, facilitated by the redox-active oligomers. Outperforming several commercial resins, Fe-BTC/PpPDA shows great promise for scalable, selective metal recovery from waste streams. This study highlights the potential of MOF/polymer composites and continuous flow methods for large-scale production.

Green Technology for Baru Oil Extraction Using Solar Energy: Assessing Extraction Efficiency and Process Parameters.

This study explores the application of solar concentrator technology for Baru oil extraction, a crucial resource in various industries. Through systematic experimental investigations, the effects of particle size and solvent flow rate on extraction efficiency were evaluated. Results demonstrate that smaller particle sizes (1.01 mm) significantly enhance extraction efficiency, achieving yields up to 89.25%, compared to larger particles (2.18 mm) with yields averaging 54.32% (±3.2%). Additionally, the solvent flow rate of 25 mL/min maximized extraction efficiency. These findings highlight the critical influence of particle size and solvent flow rate on extraction performance and underline the potential of solar-assisted leaching as a sustainable and environmentally friendly alternative to conventional extraction methods. The research provides a novel contribution to the field by advancing sustainable oil extraction practices and offering a viable solution for small-scale producers to increase the value of their agricultural products.

Biomedical relation extraction method based on ensemble learning and attention mechanism

BackgroundRelation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale.ResultsThis paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model.ConclusionsSARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical.

BiLSTM-Filt: Neural network for radar word segmentation

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Influence of Structure from Motion Algorithm Parameters on Metrics for Individual Tree Detection Accuracy and Precision

Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools.

Interface-assisted synthesized covalent organic framework film for efficient extraction of microcystins in aquatic organisms

Covalent organic framework (COF) film-based solid-phase extraction (F-SPE) has garnered great attention in sample pretreatment. However, harsh synthesis conditions of COF films have severely hindered their potential applications. In this study, a kind of COF (TPB-DMTP) films were fabricated via a liquid-liquid interfacial synthesis method at a mild condition. The obtained films exhibited excellent extraction performance towards microcystins (MCs, an algal toxin) due to their porous structure, high specific surface area and abundant accessible adsorption sites. Coupled TPB-DMTP films-based F-SPE with high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), a sensitive and environment-friendly analytical method was established for MCs detection. Under the optimal conditions, this method possessed wide linear ranges (2.0–800.0 pg mL−1) with good linearity (R ≥ 0.9991), low limits of detection (0.8–3.0 pg mL−1) and satisfactory precision (RSDs ≤7.1 %), which then successfully applied for MCs detection in actual aquatic organism samples. Trace amounts of MC-RR (42.4 pg mL−1) and MC-YR (14.6 pg mL−1) were detected in the mussels. The results demonstrate the excellent application potential of COF films in sample pretreatment.

Rural Road Extraction in Xiong'an New Area of China Based on the RC-MSFNet Network Model.

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model's ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong'an New Area, China, using high-resolution imagery from China's Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong'an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong'an New Area.

Propulsion and energy extraction performance of wave-powered mechanism considering wave and current

Wave-powered mechanism can extract wave energy as propulsion, enabling long-term ocean observation. The kinematic properties of wave propulsion mechanisms are susceptible to ocean current loads in addition to wave effects, presenting complex dynamic behaviors. In this paper, to understand the effects of ocean currents on the kinematic performances of wave propulsion mechanism, the dynamic performance of a wave propulsion mechanism in the combined condition of wave and current is analyzed based on numerical simulation method for fluid-rigid body coupling and experimental methods. The performance of the propulsion mechanism against the current under counter-current conditions and the contribution of flow to the propulsion performance under co-current conditions are discussed. The intrinsic relationship between wave height, current velocity and current direction is investigated, and the energy extraction performance of wave propulsion mechanism is analyzed. According to the results, the ability of propulsion mechanism to resist counter-current is obtained, and it is found that as the counter-current velocity increases, the hydrofoil will stall and the propulsion force and efficiency decreases significantly. Whilst, the co-current has a promoting effect on the forward of the mechanism, but within a certain range of co-current speed, the mechanism will be negatively affected by the shedding vortex. And the discrimination mechanisms for wave-dominated propulsion and current-dominated propulsion are obtained. This work reveals the combined effect of waves and currents on the motion performance of wave propulsion mechanism, providing a reference for the development of surface vehicles that utilize multiple ocean energies for synergistic propulsion.

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.