MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

Llam-Mdcnet for Detecting Remote Sensing Images of Dead Tree Clusters

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network\u27s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network\u27s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images

New pyrrole derivatives with potent tubulin polymerization inhibiting activity as anticancer agents including hedgehog-dependent cancer

We synthesized 3-aroyl-1-arylpyrrole (ARAP) derivatives as potential anticancer agents having different substituents at the pendant 1-phenyl ring. Both the 1-phenyl ring and 3-(3,4,5-trimethoxyphenyl)carbonyl moieties were mandatory to achieve potent inhibition of tubulin polymerization, binding of colchicine to tubulin, and cancer cell growth. ARAP 22 showed strong inhibition of the P-glycoprotein-overexpressing NCI-ADR-RES and Messa/Dx5MDR cell lines. Compounds 22 and 27 suppressed in vitro the Hedgehog signaling pathway, strongly reducing luciferase activity in SAG treated NIH3T3 Shh-Light II cells, and inhibited the growth of medulloblastoma D283 cells at nanomolar concentrations. ARAPs 22 and 27 represent a new potent class of tubulin polymerization and cancer cell growth inhibitors with the potential to inhibit the Hedgehog signaling pathway

STUDY ON FREE VIBRATION ANALYSIS FOR HONEYCOMB SANDWICH STRUCTURE WITH DOUBLE CORES

The free vibration problem of the honeycomb sandwich structure with double cores was studied based on the Layerwise/Solid-Elements（ LW/SE） and Fixed-interface Modal Synthesis Technique（ FMST） of the dynamic substructure method. The governing equation and the total modal space of the honeycomb sandwich structure were assembled based on LW/SE and FMST,respectively,and the final governing equation on the basis of the modal spaces was assembled based on the governing equation and the total modal space. This method obtains the natural frequency of the honeycomb sandwich structure with double cores accurately and reduces memory requirement. The numerical results of the method are compared with those obtained by the 3D solid finite element,and good agreements are achieved

Fruit Target Detection Based on BCo-YOLOv5 Model

After the birth of deep learning, artificial intelligence has entered a vigorous period of rapid development. In this process of rising and growing, we have made one achievement after another. When deep learning is applied to fruit target detection, due to the complex recognition background, large similarity between models, serious texture interference, and partial occlusion of fruits, the fruit target detection rate based on traditional methods is low. In order to solve these problems, a BCo-YOLOv5 network model is proposed to recognize and detect fruit targets in orchards. We use YOLOv5s as the basic model for feature image extraction and target detection. This paper introduces BCAM (bidirectional cross attention mechanism) into the network and adds BCAM between the backbone network and the neck network of the YOLOv5s basic model. BCAM uses weight multiplication strategy and maximum weight strategy to build a deeper position feature relationship, which can better assist the network in detecting fruit targets in fruit images. After training and testing the network, the map BCo-YOLOv5 network model reaches 97.70%. In order to verify the detection ability of the BCo-YOLOv5 network to citrus, apple, grape, and other fruit targets, we conducted a large number of experiments BCo-YOLOv5 network. The experimental results of the BCo-YOLOv5 network show that this method can effectively detect citrus, apple, and grape targets in fruit images, and the fruit target detection method based on BCo-YOLOv5 network is better than most orchard fruit detection methods

Passive Mechanical Properties of Human Medial Gastrocnemius and Soleus Musculotendinous Unit.

The in vivo characterization of the passive mechanical properties of the human triceps surae musculotendinous unit is important for gaining a deeper understanding of the interactive responses of the tendon and muscle tissues to loading during passive stretching. This study sought to quantify a comprehensive set of passive muscle-tendon properties such as slack length, stiffness, and the stress-strain relationship using a combination of ultrasound imaging and a three-dimensional motion capture system in healthy adults. By measuring tendon length, the cross-section areas of the Achilles tendon subcompartments (i.e., medial gastrocnemius and soleus aspects), and the ankle torque simultaneously, the mechanical properties of each individual compartment can be specifically identified. We found that the medial gastrocnemius (GM) and soleus (SOL) aspects of the Achilles tendon have similar mechanical properties in terms of slack angle (GM: -10.96° ± 3.48°; SOL: -8.50° ± 4.03°), moment arm at 0° of ankle angle (GM: 30.35 ± 6.42 mm; SOL: 31.39 ± 6.42 mm), and stiffness (GM: 23.18 ± 13.46 Nmm-1; SOL: 31.57 ± 13.26 Nmm-1). However, maximal tendon stress in the GM was significantly less than that in SOL (GM: 2.96 ± 1.50 MPa; SOL: 4.90 ± 1.88 MPa, p = 0.024), largely due to the higher passive force observed in the soleus compartment (GM: 99.89 ± 39.50 N; SOL: 174.59 ± 79.54 N, p = 0.020). Moreover, the tendon contributed to more than half of the total muscle-tendon unit lengthening during the passive stretch. This unequal passive stress between the medial gastrocnemius and the soleus tendon might contribute to the asymmetrical loading and deformation of the Achilles tendon during motion reported in the literature. Such information is relevant to understanding the Achilles tendon function and loading profile in pathological populations in the future

Ligand Engineering of Gold Nanoclusters for NIR-II Imaging

Near-infrared-II (NIR-II) imaging has shown great potential in medical diagnosis and surgical navigation, but developing safe, photostable, high-brightness molecular probes remains a great challenge. Due to their ultrasmall size resulting in highly efficient renal clearance, gold nanoclusters have shown great clinical potential. In this work, we systematically explored the effect of different ligands on the luminescence and bioactivity of gold nanoclusters. Our results show that gold nanoclusters protected by thioglycolic acid (TGA) and 6-mercaptohexanoic acid (MHA) exhibit the strongest fluorescence, while 3-mercaptopropionic acid (MPA)- and sodium sulfide (Na2S)-protected gold nanoclusters show the weakest NIR-II signal. Further doping showed that the Cd-doped MPA and MHA-protected gold nanoclusters exhibited enhanced fluorescence, but the cysteine (Cys)-, glutathione (GSH)-, and Na2S-protected gold nanoclusters showed fluorescence quenching after doping, indicating significant ligand selectivity. Because of the unique multi-energy structure and the large number of electronic states at the highest occupied molecular orbital energy level, MPA- and MHA-protected gold nanoclusters exhibit high stability and photostability. In addition, gold nanoclusters with different ligands exhibited different selective enzyme-mimicking activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT). Imaging in vivo showed that gold nanoclusters could accomplish reliable imaging of cerebral vasculature, hindlimb vessels, and spine as well as monitor renal clearance. The stable nanoclusters allow the time window of imaging to reach 125 min for hindlimb imaging and 270 min for spinal imaging. The gold nanoclusters exhibit a high signal-to-noise ratio of up to 11 for whole-body imaging and show efficient renal clearance and low toxicity at an injected dose of 50 mg/kg