Search Results (107,323)

A Fe²⁺-EGTA(ethylene glycol-bis (β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid)-H₂O₂ system emits photons, and quenching this chemiluminescence can be used for determination of anti-hydroxyl radical (•OH) activity of various compounds. The generation of •OH and light emission due to oxidative damage to EGTA may depend on the buffer and pH of the reaction milieu. In this study, we evaluated the effect of pH from 6.0 to 7.4 (that may occur in human cells) stabilized with 10 mM phosphate buffer (main intracellular buffer) on a chemiluminescence signal and the ratio of this signal to noise (light emission from medium alone). The highest signal (4698 ± 583 RLU) and signal-to-noise ratio (9.7 ± 1.5) were noted for pH 6.6. Lower and higher pH caused suppression of these variables to 2696 ± 292 RLU, 4.0 ± 0.8 at pH 6.2 and to 3946 ± 558 RLU, 5.0 ± 1.5 at pH 7.4, respectively. The following processes may explain these observations: enhancement and inhibition of •OH production in lower and higher pH; formation of insoluble Fe(OH)₃ at neutral and alkaline environments; augmentation of •OH production by phosphates at weakly acidic and neutral environments; and decreased regeneration of Fe²⁺-EGTA in an acidic environment. Fe²⁺-EGTA-H₂O₂ system in 10 mM phosphate buffer pH 6.6 seems optimal for the determination of anti-•OH activity. Full article

Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise where silhouette images are smaller than 64 × 64, leading to a loss in detail information and significantly affecting model accuracy. To address these challenges, we propose a gait recognition system named Multi-scale Feature Cross-Fusion Gait (MFCF-Gait). At the input stage of the model, we employ super-resolution algorithms to preprocess the data. During this process, we observed that different super-resolution algorithms applied to larger silhouette images also affect training outcomes. Improved super-resolution algorithms contribute to enhancing model performance. In terms of model architecture, we introduce a multi-scale feature cross-fusion network model. By integrating low-level feature information from higher-resolution images with high-level feature information from lower-resolution images, the model emphasizes smaller-scale details, thereby improving recognition accuracy for smaller silhouette images. The experimental results on the CASIA-B dataset demonstrate significant improvements. On 64 × 64 silhouette images, the accuracies for NM, BG, and CL states reached 96.49%, 91.42%, and 78.24%, respectively. On 32 × 32 silhouette images, the accuracies were 94.23%, 87.68%, and 71.57%, respectively, showing notable enhancements. Full article

In the realm of urban planning and environmental evaluation, the delineation and categorization of land types are pivotal. This study introduces a convolutional neural network-based image semantic segmentation approach to delineate parcel data in remote sensing imagery. The initial phase involved a comparative analysis of various CNN architectures. ResNet and VGG serve as the foundational networks for training, followed by a comparative assessment of the experimental outcomes. Subsequently, the VGG+U-Net model, which demonstrated superior efficacy, was chosen as the primary network. Enhancements to this model were made by integrating attention mechanisms. Specifically, three distinct attention mechanisms—spatial, SE, and channel—were incorporated into the VGG+U-Net framework, and various loss functions were evaluated and selected. The impact of these attention mechanisms, in conjunction with different loss functions, was scrutinized. This study proposes a novel network model, designated VGG+U-Net+Channel, that leverages the VGG architecture as the backbone network in conjunction with the U-Net structure and augments it with the channel attention mechanism to refine the model’s performance. This refinement resulted in a 1.14% enhancement in the network’s overall precision and marked improvements in MPA and MioU. A comparative analysis of the detection capabilities between the enhanced and original models was conducted, including a pixel count for each category to ascertain the extent of various semantic information. The experimental validation confirms the viability and efficacy of the proposed methodology. Full article

Drones are typically built with integrated cameras and inertial measurement units (IMUs). It is crucial to achieve drone attitude control through relative pose estimation using cameras. IMU drift can be ignored over short periods. Based on this premise, in this paper, four methods are proposed for estimating relative pose and focal length across various application scenarios: for scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be computed from four-point correspondences; for planar motion scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be determined from three-point correspondences; for instances of planar motion where the camera’s focal length is equal between adjacent moments and is unknown, the relative pose and focal length can be calculated from two-point correspondences; finally, for scenarios where multiple cameras are employed for image acquisition but only one is calibrated, a method proposed for estimating the pose and focal length of uncalibrated cameras can be used. The numerical stability and performance of these methods are compared and analyzed under various noise conditions using simulated datasets. We also assessed the performance of these methods on real datasets captured by a drone in various scenes. The experimental results demonstrate that the method proposed in this paper achieves superior accuracy and stability to classical methods. Full article

In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of robots. However, the transplanted ridges often experience missing seedling rows. Additionally, due to the limited computational resources of field agricultural robots, a more lightweight navigation line fitting algorithm is required. To address these issues, this study focuses on mid-to-high ridges planted with double-row vegetables and develops a seedling band-based navigation line extraction model, a Seedling Navigation Convolutional Neural Network (SN-CNN). Firstly, we proposed the C2f_UIB module, which effectively reduces redundant computations by integrating Network Architecture Search (NAS) technologies, thus improving the model’s efficiency. Additionally, the model incorporates the Simplified Attention Mechanism (SimAM) in the neck section, enhancing the focus on hard-to-recognize samples. The experimental results demonstrate that the proposed SN-CNN model outperforms YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s in terms of the model parameters and accuracy. The SN-CNN model has a parameter count of only 2.37 M and achieves an [email protected] of 94.6%. Compared to the baseline model, the parameter count is reduced by 28.4%, and the accuracy is improved by 2%. Finally, for practical deployment, the SN-CNN algorithm was implemented on the NVIDIA Jetson AGX Xavier, an embedded computing platform, to evaluate its real-time performance in navigation line fitting. We compared two fitting methods: Random Sample Consensus (RANSAC) and least squares (LS), using 100 images (50 test images and 50 field-collected images) to assess the accuracy and processing speed. The RANSAC method achieved a root mean square error (RMSE) of 5.7 pixels and a processing time of 25 milliseconds per image, demonstrating a superior fitting accuracy, while meeting the real-time requirements for navigation line detection. This performance highlights the potential of the SN-CNN model as an effective solution for autonomous navigation in field cross-ridge walking robots. Full article

To fully leverage the advantages of both hot air drying and radio frequency vacuum drying, a segmented combination drying technique was applied to post-harvest Cistanche. This new drying method involves using hot air drying in the initial stage to remove the majority of free water, followed by radio frequency vacuum drying in the later stage to remove the remaining small amount of free water and bound water. During the radio frequency vacuum drying (RFV) phase, the effects of temperature (45, 55, and 65 °C), vacuum pressure (0.020, 0.030, and 0.040 MPa), plate spacing (65, 75, and 85 mm), and slice thickness (4, 5, and 6 mm) on the drying characteristics, quality, and microstructure of Cistanche slices were investigated. Additionally, infrared thermal imaging technology was used to examine the surface temperature distribution of the material during the drying process. The results showed that compared to radio frequency vacuum drying alone, the hot air–radio frequency combined drying significantly shortened the drying time. Under conditions of lower vacuum pressure (0.020 MPa), plate spacing (65 mm), and higher temperature (65 °C), the drying time was reduced and the drying rate increased. Infrared thermal imaging revealed that in the early stages of hot air–radio frequency vacuum combined drying, the center temperature of Cistanche was higher than the edge temperature. As drying progressed, the internal moisture of the material diffused from the inside out, resulting in higher edge temperatures compared to the center and the formation of overheating zones. Compared to natural air drying, the hot air–radio frequency vacuum combined drying effectively preserved the content of active components such as polysaccharides (275.56 mg/g), total phenols (38.62 mg/g), total flavonoids (70.35 mg/g), phenylethanoid glycosides, and iridoids. Scanning electron microscopy observed that this combined drying method reduced surface collapse and cracking of the material. This study provides theoretical references for future drying processes of Cistanche. Full article

Estimating the post-mortem interval is still one of the most complex challenges in forensics. In fact, the main tools currently used are burdened by numerous limitations, which sometimes allow the time of death to be placed only within too large time intervals. In recent years, researchers have tried to identify new tools to try to narrow down the interval within which to place the time of death; among these, the analysis of microRNAs seems to be promising. An evidence-based systematic review of the literature has been conducted to evaluate the state of the art of knowledge, focusing on the potential correlation between miRNA degradation and PMI estimation. The research has been performed using the electronic databases PubMed, Scopus, and WOS. The results allowed us to highlight the usefulness of miRNAs both as markers for PMI estimation and for normalization, especially due to their stability. In fact, some miRNAs remain particularly stable for long periods and in different tissues, while others degrade faster. Furthermore, there are numerous factors capable of influencing the behavior of these molecules, among which the type of tissue, the cause of death, and the circadian rhythm appear to be the most relevant. Despite the promising results of the few articles present in the literature, because of the numerous limitations they are burdened by, further research is still necessary to achieve more solid and shareable results. Full article

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot. Full article

Cryofixation by ultra-rapid freezing is widely regarded as the gold standard for preserving cell structure without artefacts for electron microscopy. However, conventional cryofixation technologies are not compatible with live imaging, making it difficult to capture dynamic cellular processes at a precise time. To overcome this limitation, we recently introduced a new technology, called microfluidic cryofixation. The principle is based on micro-hotplates counter-cooled with liquid nitrogen. While the power is on, the sample inside a foil-embedded microchannel on top of the micro-hotplate is kept warm. When the heater is turned off, the thermal energy is drained rapidly and the sample freezes. While this principle has been demonstrated experimentally with small samples (<0.5 mm${}^2$), there is an important trade-off between the attainable cooling rate, sample size, and heater power. Here, we elucidate these connections by theoretical modeling and by measurements. Our findings show that cooling rates of 10⁶ K s⁻¹, which are required for the vitrification of pure water, can theoretically be attained in samples up to ∼1 mm wide and $5\mathrm{m}$ thick by using diamond substrates. If a heat sink made of silicon or copper is used, the maximum thickness for the same cooling rate is reduced to ∼3 $\mathsf{\mu }$m. Importantly, cooling rates of 10⁴ K s⁻¹ to 10⁵ K s⁻¹ can theoretically be attained for samples of arbitrary area. Such rates are sufficient for many real biological samples due to the natural cryoprotective effect of the cytosol. Thus, we expect that the vitrification of millimeter-scale specimens with thicknesses in the $10\mathrm{m}$ range should be possible using micro-hotplate-based microfluidic cryofixation technology. Full article

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of $0.1094$ and a mean square error (MSE) of $0.0711$, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing. Full article

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network’s ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model’s capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model’s ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP⁵⁰ of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18. Full article

This review provides insights into mapping and ablation strategies for VT, offering a comprehensive overview of contemporary approaches and future perspectives in the field. The strengths and limitations of classical mapping strategies, namely activation mapping, pace mapping, entrainment mapping, and substrate mapping, are deeply discussed. The increasing pivotal relevance of CMR and MDCT in substrate definition is highlighted, particularly in defining the border zone, tissue channels, and fat. The integration of CMR and MDCT images with EAM is explored, with a special focus on their role in enhancing effectiveness and procedure safety. The abstract concludes by illustrating the Pisa workflow for the VT ablation procedure. Full article

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future. Full article

Due to growing public concern over environmental preservation and the growing development of Internet information communication platforms, media coverage of corporate environmental issues can exert certain environmental public opinion pressure (EPOP) on enterprises and influence their behaviors. However, the current study of EPOP on the influence mechanism of corporate green innovation (CGI) has not yet formed a systematic and comprehensive theoretical analysis framework. Therefore, based on legitimacy theory and stakeholder theory, this paper explores the impact mechanism and role boundary between EPOP and CGI based on the data from 328 valid questionnaires of construction enterprises of the Chengdu–Chongqing Dual City Economic Circle using hierarchical regression analysis. The findings of the research indicate that EPOP can affect construction company green innovations positively, green corporate image (GCI) plays a partial mediating effect in the relationship between EPOP on CGI; market competition (MC) negatively moderates the relationship between EPOP and CGI, in addition, MC negatively regulates the intermediary effect of GCI in the relationship between EPOP and CGI. The findings of the study serve as theoretical support and decision-making reference to promote Chinese construction enterprise’s transition to green innovation and improve environmental governance level. Full article

Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80–200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image–satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80–200 m, and the relative error rate of height is 18.1%. Full article