Search Results (23)

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

The automated localization of the flange interface in LNG tanker loading and unloading imposes stringent requirements for accuracy and illumination robustness. Traditional monocular vision methods are prone to localization failure under extreme illumination conditions, such as intense glare or low light, while LiDAR, despite being unaffected by illumination, suffers from limitations like a lack of texture information. This paper proposes an illumination-robust localization method for LNG tanker flange interfaces by fusing monocular vision and LiDAR, with three scenario-specific innovations beyond generic multi-sensor fusion frameworks. First, an illumination-adaptive fusion framework is designed to dynamically adjust detection parameters via grayscale mean evaluation, addressing extreme illumination (e.g., glare, low light with water film). Second, a multi-constraint flange detection strategy is developed by integrating physical dimension constraints, K-means clustering, and weighted fitting to eliminate background interference and distinguish dual flanges. Third, a customized fusion pipeline (ROI extraction-plane fitting-3D circle center solving) is established to compensate for monocular depth errors and sparse LiDAR point cloud limitations using flange radius prior. High-precision localization is achieved via four key steps: multi-modal data preprocessing, LiDAR-camera spatial projection, fusion-based flange circle detection, and 3D circle center fitting. While basic techniques such as LiDAR-camera spatiotemporal synchronization and K-means clustering are adapted from prior works, their integration with flange-specific constraints and illumination-adaptive design forms the core novelty of this study. Comparative experiments between the proposed fusion method and the monocular vision-only localization method are conducted under four typical illumination scenarios: uniform illumination, local strong illumination, uniform low illumination, and low illumination with water film. The experimental results based on 20 samples per illumination scenario (80 valid data sets in total) show that, compared with the monocular vision method, the proposed fusion method reduces the Mean Absolute Error (MAE) of localization accuracy by 33.08%, 30.57%, and 75.91% in the X, Y, and Z dimensions, respectively, with the overall 3D MAE reduced by 61.69%. Meanwhile, the Root Mean Square Error (RMSE) in the X, Y, and Z dimensions is decreased by 33.65%, 32.71%, and 79.88%, respectively, and the overall 3D RMSE is reduced by 64.79%. The expanded sample size verifies the statistical reliability of the proposed method, which exhibits significantly superior robustness to extreme illumination conditions. Full article

This paper describes an application aimed at discovering cross streets within navigation routes by using the capabilities of the Google Maps API. The application’s core functionality revolves around locating the name of the succeeding cross street in the direction of the user, determined by their current location. To extract this information, the application synergizes the Nearest Roads library with the Geocoding API, utilizing a pair of points situated at an angle within a circular boundary centered on the user’s position, derived through the haversine formula. To ascertain optimal parameters for the circle’s radius and angle, a thorough sampling process encompassing 100 real-world instances from four Greek cities, Athens, Thessaloniki, Patras, and Karystos, was conducted. These cities were selected for their varying urban characteristics, enabling a comprehensive evaluation of the application’s performance across diverse street network complexities. The findings highlight the most effective degrees and radius parameters which exhibit an efficient success rate exceeding 91% in accurately finding the next cross street within navigation routes across the sampled cities. This paper provides a comprehensive account of the solution methodology, the process of sampling locations across diverse urban settings, and the resultant findings. Full article

Coal and gas outburst accident is a significant risk in high-gas outburst coal seams, and effective pressure relief gas extraction plays a crucial role in mitigating these hazards. The core challenge lies in understanding the seepage behavior of the coal rock body in the three zones of the overburden during multiple protective layer mining. This study employed a damaged coal rock body seepage test system to conduct repeated loading and unloading seepage tests on coal rock samples from these zones. The results show that the permeability of the broken coal rock body in the caving zone decreases with increasing stress, while it increases with (a) larger particle sizes of the broken coal rock body and (b) with a higher proportion of rock in the sample. The permeability distribution in the goaf follows an “O”-shaped circle pattern and gradually increases from the center outward. Additionally, When the protected layer is located within the fracture zone of the protective layer mining, and the first protective layer mining has already resulted in significant stress relief and permeability improvement, the effect of stress release and permeability enhancement from the second protective layer mining becomes less pronounced. In contrast, if the first protective layer mining does not sufficiently relieve stress or enhance permeability, the second protective layer mining has a more substantial effect. These findings are significant for analyzing the effects of pressure relief enhancement in multi-protective layer mining of high-gas outburst coal seams and for optimizing gas extraction. Full article

To investigate dynamic fracture characteristics and failure behavior of different sections of tunnel surrounding rock mass, six kinds of model tunnels were fabricated using green sandstone, and impact tests were performed using a split Hopkinson pressure bar system. The dynamic compressive strength and energy change behaviors of samples comprising different-shaped tunnels were assessed, and crack propagation paths were analyzed employing a digital image correlation method. Numerical calculations were carried out using the software LS-DYNA (v. 2021R1), and the dynamic stress concentration factors of different model tunnel samples were determined. The results of the research indicated that the shape of the tunnel affected the dynamic compressive strength. The elliptical tunnel had the smallest percentage of dissipated energy, and the three-centered circular tunnel had the largest percentage of dissipated energy. The maximum tensile stress concentration factor in the model tunnels consistently occurred at the top or bottom; so, the locations of initiation were most commonly at the bottoms and tops of the tunnels. Sample failure resulted from a combination of tensile and shear cracks, with the failure mode being primarily tensile-dominated. Finally, the inverted arch had an obvious alleviating action on the stress concentration phenomenon at the bottom of the three-centered circle. Full article

Recently, aerial manipulations are becoming more and more important for the practical applications of unmanned aerial vehicles (UAV) to choose, transport, and place objects in global space. In this paper, an aerial manipulation system consisting of a UAV, two onboard cameras, and a multi-fingered robotic hand with proximity sensors is developed. To achieve self-contained autonomous navigation to a targeted object, onboard tracking and depth cameras are used to detect the targeted object and to control the UAV to reach the target object, even in a Global Positioning System-denied environment. The robotic hand can perform proximity sensor-based grasping stably for an object that is within a position error tolerance (a circle with a radius of 50 mm) from the center of the hand. Therefore, to successfully grasp the object, a requirement for the position error of the hand (=UAV) during hovering after reaching the targeted object should be less than the tolerance. To meet this requirement, an object detection algorithm to support accurate target localization by combining information from both cameras was developed. In addition, camera mount orientation and UAV attitude sampling rate were determined by experiments, and it is confirmed that these implementations improved the UAV position error to within the grasping tolerance of the robot hand. Finally, the experiments on aerial manipulations using the developed system demonstrated the successful grasping of the targeted object. Full article

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is $64.1\%$ higher than the Hough transform and $86.4\%$ higher than the circle fitting algorithm. Full article

We re-examine the internal structure of bimerons, which are stabilized in easy-plane chiral magnets and represent coupled states of two merons with the same topological charge $|1/2|$ but with opposite vorticity and the polarity. We find that, in addition to the vortices and antivortices, bimerons feature circular regions which are located behind the anti-vortices and bear the rotational sense opposite to the rotational sense chosen by the Dzyaloshinskii–Moriya interaction. In an attempt to eliminate these wrong-twist regions with an excess of positive energy density, bimerons assemble into chains, and as such exhibit an attracting interaction potential. As an alternative to chains, we demonstrate the existence of ring-shaped bimeron clusters of several varieties. In some rings, bimeron dipoles are oriented along the circle and swirl clockwise and/or counterclockwise (dubbed “roundabouts”). Moreover, a central meron encircled by the outer bimerons may possess either positive or negative polarity. In other rings, the bimeron dipoles point towards the center of a ring and consequently couple to the central meron (dubbed “crossings”). We point out that the ringlike solutions for baryons obtained within the Skyrme model of pions, although driven by the same tendency of the energy reduction, yield only one type of bimeron rings. The conditions of stability applied to the described bimeron rings are additionally extended to bimeron networks when bimerons fill the whole space of two-dimensional samples and exhibit combinations of rings and chains dispersed with different spatial density (dubbed bimeron “polymers”). In particular, bimeron crystals with hexagonal and the square bimeron orderings are possible when the sides of the unit cells represent chains of bimerons joined in intersections with three or four bimerons, respectively; otherwise, bimeron networks represent disordered bimeron structures. Moreover, we scrutinize the inter-transformations between hexagonal Skyrmion lattices and disordered bimeron polymers occuring via nucleation and mutual annihilation of merons within the cell boundaries. Our theory provides clear directions for experimental studies of bimeron orderings in different condensed-matter systems with quasi-two-dimensional geometries. Full article

Cancer cells activate telomere maintenance mechanisms (TMMs) to overcome senescence and thus are targets for TMM-specific therapies. Telomerase-independent alternative lengthening of telomeres (ALT) is frequently utilized as a TMM in human sarcoma subtypes. Histiocytic sarcoma (HS) is a rare but aggressive tumor of hematopoietic origin with unknown ALT incidence in humans. ALT has been identified in canine HS, a tumor type comparable to human HS that occurs with high rates in certain canine breeds such as Bernese mountain dogs (BMDs). This retrospective study characterized the frequency of ALT in BMD and non-BMD patients diagnosed with HS as surrogates for humans. Formalin-fixed paraffin-embedded tumor samples from 63 dogs at two centers, including 47 BMDs, were evaluated for their ALT activity and relative telomere content (TC) using a radiolabel C-circle assay (CCA). Known ALT-positive samples served as controls. CCA-positive cases were validated via FISH. Two BMD samples showed ALT activity of 1–14% compared to controls. All other samples were ALT-negative. The TC did not correlate with the CCA results. ALT positivity was validated by the appearance of ultrabright telomere foci. Low ALT activity was present in 4% of BMDs with HS and therefore does not appear to be a common target for therapeutic approaches but can have diagnostic value. Full article

Meter reading is an important part of intelligent inspection, and the current meter reading method based on target detection has problems of low accuracy and large error. In order to improve the accuracy of automatic meter reading, this paper proposes an automatic reading method for pointer-type meters based on the YOLOv5-Meter Reading (YOLOv5-MR) model. Firstly, in order to improve the detection performance of small targets in YOLOv5 framework, a multi-scale target detection layer is added to the YOLOv5 framework, and a set of Anchors is designed based on the lightning rod dial data set; secondly, the loss function and up-sampling method are improved to enhance the model training convergence speed and obtain the optimal up-sampling parameters; Finally, a new external circle fitting method of the dial is proposed, and the dial reading is calculated by the center angle algorithm. The experimental results on the self-built dataset show that the Mean Average Precision (mAP) of the YOLOv5-MR target detection model reaches 79%, which is 3% better than the YOLOv5 model, and outperforms other advanced pointer-type meter reading models. Full article

This paper aims to obtain the best shape accuracy evaluation algorithm for silicon nitride ceramic balls after lapping, and to extract the initial signal of the ball surface to improve the accuracy and reliability of the algorithm. The research methods of this paper are as follows: Firstly, an analysis of the uniform envelope of the lapping trajectory of ceramic balls is carried out to verify whether the lapping trajectory after processing can achieve a consistent envelope on the balls’ surface. On this basis, it is found through experiments that the standard deviation SD between the roundness deviations of different contour sections is small. The value is maintained at approximately 0.03 μm, and the roundness deviation can approximately replace the spherical deviation. Then the different contour sections of the sphere are sampled by the Taylor roundness instrument. Considering the uncertainty, the sampling points of different contour sections are averaged and used as the original signal of the sphere surface. Then the EMD method is used to process the signal to be detected on the sphere surface. The initial signal of the sphere surface is extracted by judging whether the number of ripples Kc obtained by decomposition is greater than the critical value. Then the initial signal is used as the input value of the approximation algorithm. Through the roundness deviation approximation algorithm based on the least square method, the given minimum approximation domain range is finely processed. The divided fine points are used as the center of the circle to intersect with the initial signal. The maximum, minimum, and range of each circle are calculated to obtain the roundness error based on the minimum circumscribed circle, the maximum inscribed circle, and the minimum region method. Finally, the calculated values are compared with those obtained by the traditional algorithm. The experimental results of this paper show that the algorithm is consistent with the roundness error measured by the instrument, compared with the mainstream evaluation criteria. In summary, the conclusions can be drawn as follows: Through a large number of experimental cases and comparative experiments, the algorithm has high accuracy and reliability. The research results of this paper have essential reference significance for accurately evaluating the shape accuracy of ceramic balls in actual production. Full article

Cone-shaped volcanoes have important research significance and application value due to their typical cone shape and unique structural features. The existing methods for recognizing volcanoes are mainly morphological feature matching and machine learning. In general, the former has low recognition accuracy, while the latter requires a large number of training samples. The contour lines of cone-shaped volcanoes are distributed in concentric circles. Furthermore, from the center outwards, the elevation of the contour lines increases first and then decreases. Based on the morphological characteristics of cone-shaped volcanoes and the Hough transform algorithm, the main algorithm includes (1) preliminary filtering of contour lines, (2) filtering circular contour lines based on random Hough transform, (3) grouping contour lines based on contour trees, (4) recognizing cone-shaped volcanoes based on concentric-circle contour lines, and (5) automatically mapping cone-shaped volcanoes. Case studies demonstrate the effectiveness of this method for detecting cone-shaped volcanoes in the Western Galapagos shield volcanoes and the Mariana Trench submarine volcano group. The proposed algorithm has low missed and false alarm rates, which is basically consistent with the manual recognition results. This method can effectively automatically recognize cone-shaped volcanoes and cone-shaped landscapes and is a powerful means to support deep-space and deep-sea exploration. Full article

Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the input eye image; however, iris images that have been captured in user non-cooperative and visible illumination environments often suffer from adverse noise (e.g., light reflection, blurring, and glasses occlusion), which challenges many existing segmentation-based parameter-fitting localization methods. To address this problem, we propose a novel double-center-based end-to-end iris localization and segmentation network. Different from many previous iris localization methods, which use massive post-process methods (e.g., integro-differential operator-based or circular Hough transforms-based) on iris or contour mask to fit the inner and outer circles, our method directly predicts the inner and outer circles of the iris on the feature map. In our method, an anchor-free center-based double-circle iris-localization network and an iris mask segmentation module are designed to directly detect the circle boundary of the pupil and iris, and segment the iris region in an end-to-end framework. To facilitate efficient training, we propose a concentric sampling strategy according to the center distribution of the inner and outer iris circles. Extensive experiments on the four challenging iris data sets show that our method achieves excellent iris-localization performance; in particular, it achieves 84.02% box IoU and 89.15% mask IoU on NICE-II. On the three sub-datasets of MICHE, our method achieves 74.06% average box IoU, surpassing the existing methods by 4.64%. Full article

Wastewater surveillance for monitoring the spread of SARS-CoV-2 remains important even in the current endemic stage of the COVID-19 outbreak. This approach has already demonstrated its value by providing early warnings of coronavirus spread in different communities. The aim of the present publication is to share relevant experience from the Center of Competence “Clean&Circle”, obtained in the development of an effective strategy for SARS-CoV-2 detection in the wastewater of Sofia, Bulgaria. Using four different RNA concentration/extraction methods, we revealed that the key hindering factor for successful viral detection was the presence of PCR inhibitors in the wastewater. The most efficient way to overcome their presence turned out to be the application of a specialized polymerase in the RT-PCR detection setup. Our data showed that using such an enzyme increases the detection efficiency from 1.9% to 70.5% in samples with a spiked control virus. We also evaluated the recovery rates of viral particles by using silica columns (71%), PEG precipitation (23%), ultrafiltration (15%), and MCE filtration (10%). These results support the international effort to unify and standardize the various techniques used for SARS-CoV-2 monitoring in wastewater. Full article

Oudemansiella raphanipies has gradually gained more and more popularity in the market for its delicious taste, while enhancing human immunity and regulating human body functions as well. To achieve the high-throughput and automatic monitoring of the phenotypes of Oudemansiella raphanipies, a novel method, based on YOLO v4 and Distance Filter (DF), was proposed for high-precision diameter estimation of Oudemansiella raphanipies caps. To begin with, a dataset of Oudemansiella raphanipies was established by the laboratory cultivation and collection of factory samples. The improved YOLO v4 target detection model with added CBAM modules to each convolution block in the backbone was trained to locate the caps and, thus, obtain an approximate bounding box. Secondly, the approximate contour of the cap was gained through the H component, canny edge detection operators, and distance filtering to conduct the noise elimination. Finally, the center of the fitted circle and its accurate contour of the cap could be obtained by the constrained least square method, and the diameter of the fitted circle was estimated by the calibration data. The results of practical tests showed that this method achieved an accuracy of 95.36% in recognizing Oudemansiella raphanipies caps in the growing bed, and the fitting effect of caps was superior to Circle Hough Transform (CHT), the least square method (LS), and Ransac, with no manual adjustment on parameters. Compared with the manual measurement, the mean absolute error (MAE) of this method was 0.77 mm, the coefficient of determination (R²) was 0.95, and the root mean square error (RMSE) was 0.96 mm. Therefore, the model had high-cost performance and could meet the needs of continuous and long-term tracking of the cap shape of Oudemansiella raphanipies, providing the basis for future high-throughput breeding and machine picking. Full article