Search Results (177)

Since its introduction into the USA, the spotted lanternfly (SLF), Lycorma delicatula, (White) (Hemiptera: Fulgoridae) has spread across the landscape relatively unchecked. With a wide host range, it is considered a serious pest of native forest species, as well as agricultural crops. Circle traps placed on Ailanthus altissima (Miller) Swingle (Sapindales: Simaroubaceae) are passive traps collecting SLF as they walk up and down the tree trunk. These traps are successful at detecting new populations of SLF, but this can be challenging to implement at a large scale due to costs and host availability. To improve and facilitate SLF trapping practices, we investigated three key trapping components: improved collection containers, placement on alternative hosts, and lure (methyl salicylate) impact. In initial trials comparing collection jars to removable plastic bags, the adult SLF catch was four times higher using the bag design. In a multi-state survey at varying population densities, the bag traps were comparable to the jar traps but were significantly more effective than BugBarrier^® tree bands, especially during the adult stage. Catch and detection in circle traps placed on alternative hosts, Acer spp. L. (Sapindales: Sapindalaceae) and Juglans nigra L. (Fagales: Juglandaceae), were comparable to those placed on the preferred host A. altissima, especially in the earlier life stages. Additionally, detection rates of methyl salicylate-baited traps on all three hosts were comparable to those on non-baited traps. These results suggest that circle traps fitted with bags provide higher trap catch and an improvement in sample quality. In addition, circle traps were equally effective when placed on maple and black walnut, while methyl salicylate lures do not enhance trap catch or detection. Full article

This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between ${20}^{°}$ and ${90}^{°}$, with accuracy decreasing to 3.00% at ${10}^{°}$. The image-based angle estimator maintained a root-mean-square error $\le 3$% across most of the same range, rising to 6.4% at ${10}^{°}$. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article

The electrochemical impedance spectroscopy (EIS) is a measurement method for characterizing bio-recognition events of a sensor, such as field-effect transistor-based biosensors (BioFETs). Due to the lack of portable impedance spectroscopes, EIS applies mainly in laboratories preventing application-oriented use in the field. This work presents a portable impedance analyzer (PIA) providing a 4-channel EIS of BioFETs. It performs the analysis of the recorded spectra by determining the charge transfer resistance ${\mathrm{R}}_{\mathrm{ct}}$ with a power-saving algorithm. Therefore, a circle is fitted into the Nyquist representation of the Randles circuit, from whose zero crossings ${\mathrm{R}}_{\mathrm{ct}}$ can be determined. The introduced algorithm was evaluated on 100 simulated spectra of Randles circuits. To analyze the overall system, an adjustable reference circuit was developed that simulates configurable Randles circuits. Additional measurements with pH-sensitive ion-sensitive field-effect transistors (ISFETs) demonstrate the application of the measurement system with electrochemical sensors. Using simulated spectra, the circular fitting is able to detect ${\mathrm{R}}_{\mathrm{ct}}$ with a median accuracy of $1.2\%$ at an average nominal power of 40 mW and 3054 µs computing time. The PIA with the embedded implementation of the circuit fitting achieves a median error for R_ct of 4.2% using the introduced Randles circuit simulator (RCS). Measurements with ISFETs show deviations of 6.5 ± 2.8% compared to the complex non-linear least squares (CNLS), but is significantly faster and more efficient. The presented system allows a portable, power-saving performance of EIS. Future optimizations for a specific applications can improve the presented system and enable novel low-power and automated measurements of biosensors outside the laboratory. Full article

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows. Full article

Background/Objectives: Semi-active prosthetic feet present a promising solution that enhances adaptability while maintaining modest size, weight, and cost. We propose a semi-active Two-Keel Variable-Stiffness Foot (VSF-2K), the first prosthetic foot where both the hindfoot and forefoot stiffness can be independently and actively modulated. We present a model-based analysis of the effects of different VSF-2K settings on prosthesis characteristics and gait metrics. Methods: The study introduces a simulation model for the VSF-2K: (1) one sub-model to optimize the design of the keels of VSF-2K to maximize compliance, (2) another sub-model to simulate the stance phase of walking with different stiffness setting pairs and ankle alignment angles (dorsiflexion/plantarflexion), and (3) a third sub-model to simulate the keel stiffness of the hindfoot and forefoot keels comparably to typical mechanical testing. We quantitatively analyze how the VSF-2K’s hindfoot and forefoot stiffness settings and ankle alignments affect gait metrics: Roll-over Shape ($ROS$), Effective Foot Length Ratio ($EFLR$), and Dynamic Mean Ankle Moment Arm ($DMAMA$). We also introduce an Equally Spaced Resampling Algorithm (ESRA) to address the unequal-weight issue in the least-squares circle fit of the Roll-over Shape. Results: We show that the optimal-designed VSF-2K successfully achieves controlled stiffness that approximates the stiffness range observed in prior studies of commercial prostheses. Our findings suggest that stiffness modulation significantly affects gait metrics, and it can mimic or counteract ankle angle adjustments, enabling adaptation to sloped terrain. We show that $DMAMA$ is the most promising metric for use as a control parameter in semi-active or variable-stiffness prosthetic feet. We identify the limitations in $ROS$ and $EFLR$, including their nonmonotonic relationship with hindfoot/forefoot stiffness, insensitivity to hindfoot stiffness, and inconsistent trends across ankle alignments. We also validate that the angular stiffness of a two-independent-keel prosthetic foot can be predicted using either keel stiffness from our model or from a standardized test. Conclusions: These findings show that semi-active variation of hindfoot and forefoot stiffness based on single-stride metrics such as $DMAMA$ is a promising control approach to enabling prostheses to adapt to a variety of terrain and alignment challenges. Full article

Spacecraft separation safety is the key characteristic of flight safety. Obtaining the velocity and distance curves of spacecraft and booster at the separation time is at the core of separation safety analysis. In order to solve the separation velocity measurement problem, this paper introduces the YOLOv8_n target detection algorithm and the circle fitting algorithm based on random sample consistency (RANSAC) to measure the separation velocity of space targets according to a space-based video obtained by a monocular camera installed on the spacecraft arrow-shaped body. Firstly, MobileNetV3 network is used to replace the backbone network of YOLOv8_n. Then, the circle fitting algorithm based on RANSAC is improved to improve the anti-interference performance and the adaptability to various light environments. Finally, by analyzing the imaging principle of the monocular camera and the results of circle feature detection, distance information is obtained, and then the measurement results of velocity are obtained. The experimental results based on a space-based video show that the YOLOv8_n target detection algorithm can detect the booster target quickly and accurately, and the improved circle fitting algorithm based on RANSAC can measure the separation speed in real time while maintaining the detection speed. The ground simulation results show that the error of this method is about 1.2%. Full article

The accurate installation position of corrugated pipes is critical for ensuring the quality of prestressed concrete box girders. Given that these pipes can span up to 30 m and are deeply embedded within rebars, manual measurement is both labor-intensive and prone to errors. Meanwhile, automated recognition and measurement methods are hindered by high equipment costs and accuracy issues caused by rebar occlusion. To balance cost effectiveness and measurement accuracy, this paper proposes a method that utilizes an RGB-D camera and deep learning. Firstly, an optimal registration scheme is selected to generate complete point cloud data of pipes from segmented data captured by an RGB-D camera. Next, semantic segmentation is applied to extract the characteristic features of the pipes. Finally, the center points from cross-sectional slices are extracted and curve-fitting is performed to recognize and measure the pipes. A test was conducted in a simulated precast factory environment to validate the proposed method. The results show that under the optimal fitting scheme (BP neural network with circle fitting constraint), the average measurement errors for the three pipes are 2.2 mm, 1.4 mm, and 1.6 mm, with Maximum Errors of −5.8 mm, −4.2 mm, and −5.7 mm, respectively, meeting the standard requirements. The proposed method can accurately locate the pipes, offering a new technical pathway for the automated recognition and measurement of pipes in prefabricated construction. Full article

With the development of the economy and improvements in living standards, public demand for sports activities has continued to increase. However, the supply–demand relationship of urban sports venues remains unbalanced in many cities. Existing theoretical research on the spatial allocation of sports venues predominantly focuses on macro-level functional configuration and the equitable distribution of sports resources, lacking more rigorous and quantitative evaluation frameworks for evaluating spatial allocation. This study innovatively integrates multi-source data into the assessment and sustainable optimization of sports venue allocation in urban planning, using Xi’an as a case study. By analyzing geographic information, road network topology, OpenStreetMap (OSM), population distribution, and social media Points of Interest (POI), and using analytical tools such as ArcGIS 10.8 and Stata 17, the appropriateness of resource distribution of public sports venues in Xi’an’s main urban area is evaluated from three dimensions: accessibility, equity, and spatial activity. The results reveal the appropriateness of venue distribution in urban spatial allocation, the equitable distribution of resources, and imbalances in spatial activity and resource distribution. Finally, the study proposes a series of sustainable optimization strategies, including increasing venue coverage in low-supply areas, adaptive reuse of idle industrial buildings into sports venues guided by green sustainability principles, constructing a “15-min fitness circle” spatial system, optimizing low-carbon mobility networks around venues, enhancing the compatibility of sports venues, and improving commercial operation and management capabilities. These strategies aim to optimize the distribution of public sports venues in Xi’an to improve fairness and operational efficiency in service delivery while promoting sustainable urban development. Full article

The automotive brake piston component is an important part of the automotive brake system, and the concentricity detection of the first piston component is crucial to ensure driving safety. In this paper, an improved Canny algorithm is proposed for non-contact detection of spring concentricity of the first piston component. Firstly, the traditional Canny algorithm is improved by replacing the Gaussian filter with a bilateral filter to fully retain the edge information, and accurate edge detection results are obtained by constructing a multi-scale analysis. After obtaining the edge images, a sub-pixel edge detection method with gray moments is introduced to optimize these edges; secondly, a circle is fitted to the extracted edge points by using the RANSAC algorithm to determine the center position and radius of the circle; and finally, the concentricity of the first piston part is calculated based on the fitting results. The experimental results are compared with those of the CMM and the traditional Canny algorithm, and the results show that the improved Canny algorithm reduces the coaxiality error by 4% and enables effective measurement of the concentricity of the first piston assembly spring. Full article

As resources are extracted from the deeper sections of a mine, the ventilation network becomes increasingly complex. Consequently, determining the optimal installation location for speed-measuring equipment that accurately reflects the average wind speed along the roadway remains a challenging task. In this study, two three-dimensional geometric models, smooth and rough, were developed based on field conditions. The cross-sectional widths, heights, and flow velocities of the model channels were processed dimensionlessly. The dimensionless velocity distributions of the smooth and rough models were then analyzed for different Reynolds numbers. It was observed that the dimensionless average velocity ring distributions for the rough model were smaller than those for the smooth model. Additionally, the maximum values of dimensionless flow velocities were negatively correlated with the flow velocities under laminar flow conditions, whereas they largely overlapped under turbulent flow. The dimensionless distances of the average velocity rings from the top and sidewalls of the channel were studied and determined for both models across different flow regimes. Specifically, the dimensionless distance values $d(-)$ were found to be 0.111 for the smooth model and 0.101 for the rough model under the laminar regime. Under the turbulence regime, the corresponding values were 0.106 and 0.108. Likewise, the values of $h(-)$ were 0.135 and 0.135 for the smooth and rough models in the laminar flow regime, while under turbulent flow, the values were 0.131 and 0.162, respectively. The largest dimensionless velocity value was identified at the center of the velocity distribution circle. For corners that did not maintain simple parallelism with the walls, these regions were incorporated into the circle equation using the Least Squares Method, providing a theoretical basis for the placement of velocity-measuring equipment in practical applications. By using the sidewall as the reference coordinate, an appropriate mathematical model was employed to establish the functional relationship between the centerline velocity of the roadway and the dimensionless horizontal coordinate. The fitting results showed good agreement, and this model can be used to back-calculate and expand the potential installation locations for a mine anemometer. Full article

We have proposed a multi-strategy enhanced particle swarm optimization (PSO) algorithm to optimize the antenna spacing distribution of an optical phased array (OPA). The global search capability is improved by incorporating circle chaotic mapping initialization and an updated strategy based on adaptive inertia weights and dynamic learning factors. We used the peak side-lobe level (PSLL) at different steering angles as the fitness function, which effectively suppresses the rapid degradation of PSLL during scanning. Based on this approach, 32- and 64-channel aperiodic OPAs were designed with a scanning range of ±60°, with improvements of the PSLL of 1.94 and 2.05 dB at 60°, respectively. In addition, the analytical and numerical simulation results are in good agreement. We also analyzed the influence of spacing deviations on PSLL and found that the obtained OPAs exhibit sufficient robustness. Full article

Future mobile communication technology will be used to build an integrated global coverage network. Unmanned aerial vehicles (UAVs) are the first choice for low-altitude networks due to their low cost, flexibility, and ease of operation. The characteristics of UAVs also bring new challenges to communication networks, such as short flight time, complex networking, and unstable communication quality. Therefore, it has become an urgent problem to reasonably plan the location of UAV Base Stations (UAV-BSs), reduce communication power consumption, optimize network performance, and build an efficient and stable UAV communication network (UAVCN). The traditional strategy only pays attention to the signal coverage, and ignores the influence of system transmission power on the network, which reduces the performance of the communication system. In this study, a circular coverage power optimization strategy (CCPO) based on system transmit power is proposed. The mathematical model of the circular coverage problem is used to describe the full coverage process of the UAV base station to ground users, and the deployment optimization is carried out with the goal of minimizing system transmit power, so as to obtain an efficient and reliable site planning scheme. In this paper, the binomial power function is used to continuously fit the discrete solution of the circle covering problem, and the circle covering power optimization formula is rearranged. By analyzing the convexity of the objective function under the circular coverage model, the convex optimization theory is used to solve the objective problem, and the optimal deployment number of UAVs and site planning scheme under the circular coverage power optimization strategy are given. Simulation verifies the superiority of the proposed method. Compared with the traditional hexagon strategy and the minimum power loss strategy, the circular coverage power optimization station location planning strategy can save 14.75% and 6.52% of power resources, providing a valuable reference for the design and optimization of UAV communication systems. It provides a promising solution for further improving the performance and efficiency of UAVCNs. Full article

In this study, we designed the preparation method and application study of chitosan–polyvinyl alcohol–chrysanthemum extract–nanosilver composite gel (CTS/PVA/Ag/CHR), constructed a composite gel system with chitosan/polyvinyl alcohol as the carrier, and utilized chrysanthemum extract within the gel to convert silver nitrate into nanosilver via green reduction. In the bacterial inhibition experiments, the CTS/PVA/Ag/CHR gel showed excellent antibacterial properties, and the diameter of the inhibition circle for Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa by the agar diffusion method was 32.5 mm, 30.5 mm, and 34.0 mm, respectively. In the aqueous bacterial inhibition experiments, the gel’s inhibition rate against the three kinds of bacteria was 100% after 5 h. The abundant hydroxyl groups contained in the polyvinyl alcohol (PVA) formed hydrogen bonds with the amino groups present in chitosan (CTS), which maintained the stability of the gel structure and enhanced the moisturizing and water storage properties of the gel. The adsorption curves of the CTS/PVA/Ag/CHR gel were fitted using a proposed pseudo-second-order kinetic model. Methylene blue, methyl orange, Congo red, and malachite green were discovered to have strong adsorption capacities, with the most significant adsorption effect for methyl orange at 205.65 mg/g. Moreover, the CTS/PVA/Ag/CHR gel showed good freshness preservation in milk simulation experiments. Due to its superior adsorption capability and antibacterial qualities, the CTS/PVA/Ag/CHR gels have great potential for applications in wastewater purification and food preservation. Full article

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM’s adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. Full article

The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based algorithms for tree trunk detection often suffer from spatial information loss, resulting in reduced accuracy, particularly for tilted trees. While cylinder fitting algorithms provide a three-dimensional (3D) solution for trunk detection, their performance in complex forest environments remains limited due to sensitivity to parameters like distance thresholds. To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. Validated in three forest plots with varying complexities in Tianhe District, Guangzhou, the algorithm demonstrated significant advantages in the inlier rate, detection success rate, and robustness for tilted trees. The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (p < 0.05). (2) RANSAC-CyF required only 2 and 8 clusters to achieve a 100% detection success rate in Plot 1 and Plot 2, while the other algorithms needed 26 and 40 clusters. (3) The effective distance threshold range of RANSAC-CyF was more than twice that of the comparison algorithms, maintaining stable inlier rates above 0.9 across all tilt angles. (4) The RANSAC-CyF algorithm still achieved good detection performance in the challenging Plot 3. Together, the other two algorithms failed to detect. The findings highlight the RANSAC-CyF algorithm’s superior accuracy, robustness, and adaptability in complex forest environments, significantly improving the efficiency and precision of individual tree trunk detection for forestry surveys and ecological research. Full article