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Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Recent studies have shown that the occurrence and prevention of osteoporosis often focus on the cellular molecular level, and the mechanism of related signaling pathways is an important way to further understand osteoporosis. At present, traditional Chinese medicine has been proved to play a significant role in alleviating osteoporosis. Kaempferol as an emerging Chinese herbal extract has become the focus of clinical and basic research due to its antiosteoporosis effectiveness and mechanism of action. OBJECTIVE: To further understand the mechanism underlying the anti-osteoporosis effect of kaempferol active monomer through regulation of related signaling pathways by analyzing and collating domestic and foreign literature. METHODS: “Kaempferol, osteoporosis, osteoblasts, osteoclasts, bone marrow mesenchymal stem cells, signaling pathways” were used as Chinese and English search terms to search CNKI, WanFang, VIP, PubMed, Web of Science and Embase databases for relevant literature published from database inception to February 2023. RESULTS AND CONCLUSION: Kaempferol affects the occurrence and progression of osteoporosis to varying degrees by participating in the regulation of differentiation, proliferation and apoptosis of bone marrow mesenchymal stem cells, osteoblasts and osteoclasts. Kaempferol can prevent and treat osteoporosis by regulating various signaling pathways. Kaempferol can promote the proliferation and differentiation of osteoblasts and inhibit the formation of osteoclasts by interfering with the Wnt/β-catenin signaling pathway to regulate β-catenin protein counting and the formation of β-catenin-TCf/LEF complex. Kaempferol interferes with the RANK/RANKL pathway to maintain the dynamic balance of osteoclasts and bone homeostasis. Kaempferol can promote bone formation by intervening with the PI3K/Akt signaling pathway to upregulate the levels of related osteogenic factors Runx2 and Osterix and promote bone cell calcification. Kaempferol interferes with osteoclast differentiation and inhibits reactive oxygen species activity by regulating the ER/ERK pathway. Kaempferol inhibits the expression of ERK, JNK, p38/MAPK and decreases reactive oxygen species production by interfering with the MAPK pathway, thus protecting osteogenesis. Kaempferol enhances the expression of osteogenic factors, bone morphogenetic protein-2, p-Smad1/5/8, β-catenin and Runx2, inhibits the expression of Peroxisome proliferation-activated receptor, and promotes the differentiation and proliferation of osteoblasts through the BMP/Smad pathway. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fire Safety Science is the property of Fire Safety Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: The nuclear factor-κB signaling pathway plays an important role in the pathogenesis of osteoporosis. In recent years, increasing studies have shown that terpenoid herbal monomer compounds can inhibit the activity of bone resorbing cells and promote the differentiation of bone forming cells via the nuclear factor-κB signaling pathway, thus reducing bone resorption and increasing bone formation, which has certain preventive and therapeutic effects on osteoporosis. OBJECTIVE: By analyzing and summarizing the domestic and international literature, to investigate the relationship between nuclear factor-κB signaling pathway and osteoporosis in depth, elucidate the mechanism of terpenoid monomer compounds in regulating the nuclear factor-κB signaling pathway to prevent osteoporosis, and systematically summarize the terpenoid monomer compounds targeting to regulate the nuclear factor-κB signaling pathway to prevent osteoporosis. METHODS: According to the proposed inclusion and exclusion criteria, two researchers searched for relevant articles published from database inception to December 2022 in CNKI and PubMed using the search terms “NF-κB, osteoporosis, osteoblasts, osteoclasts, angiogenesis, traditional Chinese medicine, terpenoid” in Chinese and English, respectively. A third researcher summarized and organized the literature and 75 articles were finally included for a systematic review. RESULTS AND CONCLUSION: The nuclear factor-κB signaling pathway mediates the onset and progression of osteoporosis by regulating the differentiation and proliferation of osteoblasts and osteoclasts, as well as angiogenesis. Activation of the nuclear factor-κB signaling pathway negatively regulates the proliferation and differentiation of osteoblasts. Activation of the nuclear factor-κB signaling pathway enhances osteoclast activity and inhibits osteoblast growth, thereby inhibiting compensatory bone production to maintain bone homeostasis. However, over-activation of the nuclear factor-κB signaling pathway can lead to osteoporosis. The nuclear factor-κB signaling pathway is involved in the “angiogenesis-osteogenesis” coupling by upregulating the expression levels of cytokines such as angiopoietin-1, platelet-derived growth factor BB and vascular endothelial growth factor, which promote the growth of blood vessels in bone. The terpenoid herbal monomer compounds are used in the field of tissue engineering to promote the proliferation and differentiation of bone cells, thereby promoting the growth and repair of bone tissue. Terpenoid herbal monomer compounds can prevent and treat osteoporosis by inhibiting the degradation of nuclear factor-κB inhibitor, blocking nuclear factor-κB/p65 phosphorylation and nuclear translocation, thereby weakening the nuclear factor-κB signaling pathway, promoting osteoblast differentiation and inhibiting osteoclast formation. Currently, research on the regulation of nuclear factor-κB signaling pathway by monomeric compounds of terpenoids to prevent osteoporosis is mainly based on in vitro cellular experiments and animal models, and there is a lack of research on the complex physiological and pathological processes in humans. In the future, more clinical trials and studies are needed to further clarify the mechanism of action and efficacy of the nuclear factor-κB signaling pathway involved in the intervention of osteoporosis. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ripeness of Camellia oleifera fruits is closely related to their oil yield and tea oil quality. Manual one-time harvesting is the primary harvesting for Camellia oleifera at present. However, the uneven ripeness levels among fruits harvested in the same batch can significantly reduce the overall quality of the fruits. Furthermore, manual harvesting cannot fully meet the large-scale Camellia oleifera industry, such as low efficiency and high costs. Therefore, it is very necessary to implement intelligent harvesting for Camellia oleifera fruits. The maturity of Camellia oleifera fruit can be detected to determine the best maturity using deep learning. The purpose of this study is to establish the ideal ripeness of Camellia oleifera fruits in the natural environment. The harvesting period was also estimated to implement the intelligent harvesting, in order to improve the oil yield and quality of fruits. A dataset was constructed for the ripeness detection. The photographs of Camellia oleifera fruits were captured at different ripening stages in natural environments using a smartphone. The phenotypic characteristics of Camellia oleifera fruits were also determined to follow the industry standards. The ripeness was categorized into three stages: immature, mature, and over-mature. Data augmentation techniques were applied on the dataset, such as brightness adjustment, salt-and-pepper noise addition, and simulating artificial occlusion. After that, the dataset was divided into the training, validation, and testing sets with a ratio of 7:1:2. An improved YOLOv7 model was constructed to deal with the occlusion in the natural environment. A cross-attention module was added in the YOLOv7 feature extraction network. The vertical and horizontal information was calculated for each pixel in Camellia oleifera images using two attention-weighted procedures. The key features were identified to determine the ripeness of Camellia oleifera fruits, thus effectively avoiding the interference from the complex backgrounds, such as the branches and leaves. Additionally, the traditional non-maximum suppression was replaced with the distance- and intersection-based NMS. The normalized distance between the center points of two candidate boxes was selected to calculate the intersection ratio, particularly for the missed detections due to mutual occlusion of Camellia oleifera fruits, in order to detect the overlapping fruits. The YOLOv7 model was trained on 3098 images from the training set, then evaluated using 442 images from the validation set, and finally tested on 885 images from the test set. The better performance was achieved in the precision rate of 93.52%, a recall rate of 90.25%, an F1 score of 91.86%, an average precision of 94.60%, an average detection time of 0.77 s, and a model weight of 82.6 MB in the test set. The ablation experiments demonstrated that the improved model was used to effectively detect the ripeness of Camellia oleifera fruit. Compared with the original YOLOv7 model, the mean average accuracies were improved by 1.10 and 1.81 percentage points, respectively. The overall mean average accuracy was improved by 2.91 percentage points. However, the detection time and model size only increased by 0.015 s and 11.3 MB, respectively. Compared with the Faster R-CNN, EfficientDet, YOLOv3, and YOLOv5l models, the improved YOLOv7 model increased the average accuracy by 7.51, 5.89, 4.21, and 4.21 percentage points, respectively. Additionally, the detection time was reduced by 1.06, 1.12, 0.10, and 0.03 s, respectively. The maturity grade of Camellia oleifera fruits was accurately discriminated, compared with the previous. In summary, the improved YOLOv7 model was achieved in the higher accuracy with only a slight sacrifice in the detection time and model size. This finding can provide a theoretical basis to estimate the optimal harvesting period of Camellia oleifera fruits and intelligent picking under natural conditions. [ABSTRACT FROM AUTHOR]

Copyright of Art Panorama is the property of Art Panorama Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Immunology is the property of Medical Periodical Society of Jilin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To explore the effect of thrombolytic capsule combined with butylphthalide soft capsule on hemorheology, cerebral hemodynamics and nerve factors in patients with cerebral infarction in the recovery period. Methods: 100 patients with cerebral infarction in the recovery period admitted to Heping Hospital Affiliated to Changzhi Medical College from January 2021 to January 2022 were divided into the study group (n=50) and the control group (n=50) according to the random number table method.The control group received treatment with butylphthalide soft capsules, while the study group received treatment with thrombolytic capsules combined with butylphthalide soft capsules. The curative effect, hemorheology, cerebral hemodynamics, nerve factors and adverse reactions of the two groups were compared. Results: The clinical total effective rate of 90.00% in the study group was higher than 72.00% in the control group (P<0.05). After 20 days of treatment, the National Institutes of Health Neurological Impairment Scale (NIHSS) score decreased and the Barthel Index (BI) score increased in both groups, and the improvement in the study group was greater than that in the control group (P<0.05). After 20 days of treatment, the whole blood viscosity, plasma viscosity, erythrocyte sedimentation rate (ESR), fibrinogen (FIB), and hematocrit (HCT) of both groups decreased, and the study group was lower than the control group (P<0.05). After 20 days of treatment, the average blood flow velocity (Vm) increased, while the pulsatile index (PI) and resistance index (RI) decreased in both groups, and the improvement in the study group was greater than that in the control group (P<0.05). After 20 days of treatment, neuron specific enolase (NSE) decreased, nerve growth factor (NGF) and neurotrophin (BDNF) increased in both groups, and the improvement in the study group was greater than that in the control group (P<0.05). There was no difference in the incidence of adverse reactions between the two groups (P>0.05). Conclusion: Thrombolytic capsule combined with butylphthalide soft capsule can effectively improve the hemorheology and cerebral hemodynamics of patients with cerebral infarction in the recovery period, promote the recovery of neural function, and improve the ability of life activities. The effect is significant. [ABSTRACT FROM AUTHOR]

The traditional manual control measures for apple disease primarily rely on subjective experience, which can lead to arbitrariness and bias. This approach is also prone to pesticide waste and misuse. As apple leaves are one of the high-incidence areas of diseases, achieving natural environment-based identification of apple leaf diseases can provide effective guidance for disease prevention and control. To address the challenge of accurate identification of apple leaf diseases in natural settings, a DenseNet121+EfficientNet model with Focal Loss and Label Smoothing (DEFL) is designed. Given that apple leaf disease lesions are small and exhibit varying spatial characteristics for different diseases, accurately localizing and identifying disease regions is challenging. The DEFL model utilizes two parallel networks: EfficientNet-B0 and DenseNet121 for feature extraction. It combines the semantic and positional information extracted from these networks to enhance the model's finegrained feature extraction capability. To overcome difficulties in identifying apple leaf samples due to the similarity in features among different diseases, a Focal Loss function, combined with label smoothing strategies, is introduced. This prevents the model from being overly confident about the class of apple leaf samples, focusing on those samples with challenging decision boundaries during training, thereby improving the model's ability to recognize boundary cases and enhancing its robustness and generalization. The DEFL model proposed in this study was tested with 3906 apple leaf disease images in natural scenes. The results show that the overall recognition accuracy of the model proposed in this study is 99.13%, and the mean average accuracy is 98.47%. The results of ablation experiments show that the mean average precision of the model is improved by 7.99% after adding DenseNet121 feature extraction branch to EfficientNet-B0. And, after introducing the focus loss function combined with label smoothing strategy to EfficientNet-B0, the mean average precision of the model is improved by 3.15%. After the combination of the two improvements, the mean average precision of the model increased by 12.29%. Visualization results using Gradient-weighted class activation mapping (Grad-CAM) heatmaps show that the model accurately localizes and focuses on disease regions. The image feature information used by the model for apple leaf disease recognition is reliable. Uniform manifold approximation and projection (UMAP) feature dimension reduction visualization results indicate that the Focal Loss function with label smoothing effectively enhances the model's feature extraction capability, resulting in more distinctive feature information and improved disease recognition. The comparative experimental results show that compared with mainstream recognition models such as ResNet50, Inception V3, and ResNeXt, as well as EfficientNet-B0 models that fuse these three models, the mean average precision of the proposed model has increased by 14.53, 13.17, 14.61, 6.4, 7.71, and 8.91 percentage points, respectively, and the model size has decreased by 18.73, 7.7, 12.2, 83.62, 69.6, and 60.09 Mb, respectively. Comparative experiments have shown that the proposed model has the best recognition performance. In practical applications, the DEFL model achieved an overall recognition accuracy of 97.73% and a mean average precision of 95.82% when tested with 1501 apple leaf images collected from apple plantations. In conclusion, the DEFL model proposed in this study enables accurate and rapid identification of apple leaf diseases, providing valuable guidance for apple disease prevention and control. [ABSTRACT FROM AUTHOR]

The number of trees is essential information for modern forestry management, which affects managers' formulation of development strategies. At present, tree inventory mainly relies on manual counting, which is costly, time-consuming and labor-intensive. Using unmanned aerial vehicle (UAV) to count targets has become increasingly popular in agriculture and forestry due to its low cost, ease of operation, and flexibility of use. This study focused on using UAV images to count spruce numbers. A total of 603 images with an average canopy density of 81% and an average plant density of 6 667 plants/hm² were selected for the dataset, each containing an average of 354 spruce trees. Among these, 205 images had interference factors such as weeds. The images were divided into a training set and a test set at a ratio of 7:3. The training set was expanded by randomly flipping the images to improve the robustness to different flight attitudes of UAV. After data augmentation, 844 training images and 181 test images were obtained. Aiming at the problem of dense spruces in natural environments including severe adhesion and background interference such as weeds with similar characteristics to spruces, we selected IntegrateNet, a model known for its strong performance in dense target counting tasks as baseline model for spruces. This study then worked to improve the IntegrateNet model to achieve a more accurate counting of dense spruce that is closer to real-world conditions. First of all, this paper used the self-calibrated convolutions (SCConv) to replace the ordinary convolutional layers at 1/8, 1/16 feature maps and density maps in the baseline model to expand the convolution receptive field. Secondly, in order to deal with background interference problems such as weeds and the serious adhesion of the target, this paper added the criss-cross attention mechanism (CCA) to the feature fusion of the IntegrateNet model. It can consider the horizontal and vertical context information of each pixel to generate richer semantic features to improve the contextual information extraction ability of the model. The mean counting accuracy(MCA), mean absolute error(MAE), root mean square error(RMSE), and the coefficient of determination R² are used as evaluation indicators. The ablation experiments and comparative experiments are designed to verify the performance of the proposed model. The ablation experiments show that the improved methods proposed in this paper can effectively improve the counting accuracy of the model. The comparative experiments show that the improved IntegrateNet model proposed in this study has MCA, MAE and RMSE reached 98.32%, 8.99 plants and 13.79 plants respectively, and the R² was 0.99.Compared with TasselNetv3_lite, TasselNetv3_seg, and IntegrateNet models, the MCA of the improved IntegrateNet model increased by 16.44, 10.55, and 9.26 percentage points, the MAE of the improved IntegrateNet model decreased by 25.62, 10.45, and 6.99 plants, and the RMSE of the improved IntegrateNet model decreased by 48.25, 13.84, and 12.52 plants. In summary, the improved IntegrateNet model proposed in this study demonstrated a significant increase in accuracy for counting dense spruce in natural environments. Moreover, our research is not limited to spruce, as the methodology can be applied to other tree species as well. It can providing technical support for subsequent forestry intelligent statistics. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Our previous research found that Panax notoginseng can repair the morphological structure of bone cells, which has a good application prospect in the treatment of osteoarthritis, but the specific mechanism of Panax notoginseng is still unclear. OBJECTIVE: To identify the main components of Panax notoginseng using ultra-high performance liquid chromatography-Q exactive-mass spectrometry (UHPLC-QE-MS), and to explore the mechanism of Panax notoginseng in the treatment of osteoarthritis by combining network pharmacology, molecular docking and molecular dynamics simulation. METHODS: After identifying the main components of Panax notoginseng by UHPLC-QE-MS technology, the active components were screened by TCMSP database, and the targets of active components were found by TCMSP and Uniprot database. Osteoarthritis targets were screened out through disease databases. After the intersection of drug targets and disease targets, the protein-protein interaction network was constructed by importing STRING database and Cytoscape software, and the “active ingredient-action target” network was constructed to screen key active ingredients. Then the key targets were enriched and analyzed, and the key active components and key targets were verified by molecular docking. Finally, the results with the lowest binding energy were selected for molecular dynamics simulation. RESULTS AND CONCLUSION: A total of 57 active components were identified in the solution of Panax Notoginseng, including 50 intersection targets of components and disease targets, 5 key active components (quercetin, ursodeoxycholic acid, kaempferol, naringenin and erythrocyanine), and 5 key targets (interleukin 6, matrix metalloproteinase 9, interleukin 1β, albumin and recombinant chemokine c-motif ligand 2). Gene ontology enriched 642 entries, among which 620 entries represent biological processes, 21 entries represent molecular functions, and 1 entry represents cellular components. Kyoto encyclopedia of genes and genomes analysis indicated 63 pathways, mainly including estrogen signaling pathway, interleukin 17 signaling pathway and hyperglycosylation end product-hyperglycosylation end product receptor signaling pathway. Molecular docking showed good binding activity of key active components and key targets. Molecular dynamics simulation indicated that the stable interaction between quercetin and matrix metalloproteinase 9. The composition of Panax notoginseng was comprehensively studied, and the material basis of its efficacy was preliminarily clarified. It was predicted that Panax notoginseng could play an antiinflammatory, cartilage-protective, and immunomodulatory role in treating osteoarthritis through multiple components, targets, approaches and pathways. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Science Research & Application is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Engineering Science Research & Application is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The extensive range of olive cultivars available in the market exhibit minor differences in their phenotypic traits. Nonetheless, their quality attributes, particularly the oil content and fatty acid composition, significantly differ among distinct cultivars, resulting in the emergence of use iinferior products as superior products in the market. The accurate and quick identification of olive cultivars holds significant importance in enhancing the production and quality of olives. As such, delving into the study of olive cultivar identification is crucial for the advancement of the olive industry. This study presents a novel approach to address the challenge of identifying olive cultivars in natural conditions. Specifically, a bilinear attentional EfficientNet model is proposed, which incorporates the bilinear network design concept and attention mechanism. The model is trained and evaluated using four commonly planted olive cultivars (i.e., Frantoio, Leccino, Picholine, and Ezhi 8) in Longnan, Gansu. The experimental results demonstrate the effectiveness of the proposed model in accurately and quickly identifying different olive cultivars. A bilinear network has been suggested to comprehensively extract feature information from olive images, for the limited phenotypic differences across different olive cultivars. In light of this, the selection of a feature extraction network has been made with consideration for both speed and accuracy, leading to the selection of the EfficientNetB0 network. To tackle the challenge of identifying olive cultivars under natural conditions which are prone to intricate background interferences, a novel approach combining convolutional block attention module (CBAM) with bilinear network has been proposed. This approach facilitates the model in selectively focusing on the salient features responsible for cultivar identification during the feature extraction process of olive images. Upon conducting experiments, the bilinear attention EfficientNet model presented in this study has exhibited an overall accuracy of 90.28% and an inference time of 9.15 ms in identifying four distinct olive cultivars. These experiment results demonstrate that the proposed model better achieved better rapaid and accurate identification of olive cultivars under natural conditions. The present study proposes a bilinear attention EfficientNet model for identifying olive cultivars and utilizes gradient-weighted class activation mapping (Grad-CAM) to analyse its performance. The results demonstrate that the proposed model exhibits a greater attention towards fruit regions, as well as some leaf regions, within olive images. These findings are in agreement with the expert knowledge and experience of human practitioners. The analysis of heat maps generated from misidentified olive images revealed that inadequate focus on the fruit and leaf regions, which are pivotal for successful identification of cultivars, was the primary contributing factor to misidentification. The outcomes of the ablation experiments indicated that the bilinear network and the CBAM exhibited a positive impact on the precision of olive variety recognition. To ascertain the efficacy of the method elucidated in this manuscript, a set of comparative experiments has been formulated. The primary objective of these experiments is to juxtapose the proposed bilinear attention EfficientNet model against the conventional cultivar identification models, including bilinear ResNet34, EfficientNet-SE attention, bilinear ResNet18, bilinear VGG16, and bilinear GoogLeNet. The experimental results obtained from the comparison analysis provide that the proposed bilinear attention EfficientNet model exhibits superior performance in terms of overall accuracy for the identification of olive cultivars. The bilinear attention EfficientNet model's accuracy for the identification of olive cultivars exceeded that of bilinear ResNet34, EfficientNet-SE attention, bilinear ResNet18, bilinear VGG16, and bilinear GoogLeNet models by 12.78%, 11.53%, 11.11%, 10.70%, and 5.00%, respectively. The present study establishes a foundation for resolving the challenge of accurately identifying olive cultivars in natural conditions. Moreover, it offers a valuable point of reference for the identification of cultivars of diverse crops. [ABSTRACT FROM AUTHOR]

High voltage direct current (HVDC) transmission lines have an attenuation effect on most frequency bands of fault transient voltage signals, while the physical boundary of HVDC will have a strong attenuation effect on high-frequency components and amplify low-frequency components. According to the above characteristics, the paper proposes a single-ended protection method for HVDC transmission lines based on variational mode decomposition (VMD)-sparrow search algorithm ( SSA) and Multi-band Hilbert energy amplitude. The VMD parameters was optimized by using SSA to decompose the fault transient voltage signal. Then, the Hilbert transform of each intrinsic mode function (IMF) after decomposition was performed to obtain the Hilbert energy of 200 ~ 1 200 Hz low-frequency band and 8 ~ 12 kHz high-frequency band, and the fault segment was identified by using the ratio of low-band and high-frequency Hilbert energy, and the fault pole was judged by the ratio of the sum of the Hilbert energy of the high and low frequency bands in the positive and negative poles. Simulation experiments show that the proposed method can effectively avoid the phenomenon of modal aliasing when using empirical mode decomposition (EMD) fault signals, accurately identify fault areas and fault poles, have good transition resistance, and effectively protect the full length of the line. [ABSTRACT FROM AUTHOR]

Copyright of Urban Architecture & Development is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Osteoclasts are the only known type of bone resorption cells, and its life activities are essential to the normal development of bones and the repair of bone damage. Osteoclasts show abnormal proliferation and differentiation and increased bone resorption activity in most bone diseases, while receptor activator of nuclear factor-kappa B (RANK) is a key signal pathway that regulates osteoclastogenesis. OBJECTIVE: To summarize the latest research progress on the downstream targets of osteoclast RANK signal and DNA transcription factors at home and abroad, and provide a basis for the research and treatment of related diseases. METHODS: A computer-based retrieval was conducted in CNKI, WanFang, VIP, PubMed, Embase and Web of Science databases. The search terms were “osteoclasts, osteoclast precursor cells, osteoporosis, bone metabolism, pathogenesis, epigenetics, signaling pathway, signal transduction, transcription factors, tissue engineering” in Chinese and English. The retrieval time was set to 2017-2021. Finally 52 articles were included according to the inclusion and exclusion criteria. RESULTS AND CONCLUSION: The special structure of RANK determines that its signal transduction needs to be combined with tumor necrosis factor receptorassociated factor 6 to recruit a variety of proteins, active enzymes and cytokines to form a RANK complex with intrinsic enzyme activity. This complex then activates nuclear factor-κB, mitogen-activated protein kinase and other signal pathways, ultimately regulating osteoclast differentiation, proliferation, and bone resorption. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The picking maturity of olive fruit varies greatly in different uses. However, the commonly-used manual picking cannot fully meet the picked fruit in the best picking period, due to the low picking efficiency and high cost during olive fruit picking. The purpose of this study was to determine the distribution areas of fruits in the different maturity under the natural environment, in order to realize the automatic picking of olive fruits. Firstly, the phenotypic characteristics of olive fruit at maturity were determined to develop the new standard of ripeness, according to the recommendations of the International Olive Council and China's forestry industry standards. Secondly, mobile phones were utilized to capture the olive images at the different maturity periods under natural conditions. An olive image dataset was then constructed. A series of operations were implemented to adjust the contrast, the image size, salt-pepper noise, and artificial occlusion, in order to expand the olive images to four times the original ones. The training and test set was built at the ratio of 9:1. Thirdly, an improved EfficientDet was proposed to detect the ripeness of olive fruits, the inconspicuous changes of the adjacent ripeness features, and the mutual occlusion between the fruits. The main advantages of this model were as follows: 1) A module of convolution block attention was introduced into the feature extraction network of the EfficientDet model. The weight distribution of features was then calculated in the channel and spatial domain. The improved model was then used to accurately locate the target area, thereby eliminating the interference of background information. The phenotypic characteristics of olive fruit were fully extracted for the key features to distinguish maturity categories. 2) A cross-level data flow was added to fuse the features of the bottom nodes into the high-level nodes for common learning. As such, the weighted bidirectional feature pyramid network of the EfficientDet model fully utilized the semantic relationship and location information of the high-level and bottom layers. Finally, the improved EfficientDet model was verified with the 623 olive test images. The experimental results showed that the precision, recall, and mean average precision of the improved EfficientDet model under the test set were 92.89%, 93.59%, and 94.60%, respectively, where the average detection time was only 0.337s, and the model size was only 32.4M. The mean average precision was improved by 4.77 percentage points, but the average detection time and model size were reduced by 0.028 s and 14 M, respectively, compared with the original. The mean average precision of the improved EfficientDet model increased by 7.85, 3.73, and 1.04 percentage points, respectively, compared with the SSD, YOLOv3, and Faster R-CNN model. Correspondingly, the model sizes were reduced by 60.8, 202.6, and 75.6 M, respectively. The mean average precision increased by 1.15 percentage points, while the model size increased by 4.9M, compared with the YOLOv5s model. Therefore, the improved EfficientDet model presented a higher detection accuracy with a smaller memory size, fully meeting the actual needs of promotion and application under natural conditions. [ABSTRACT FROM AUTHOR]

In order to solve the problem that continuous commutation failure in high voltage direct current ( HVDC) transmission system has a serious impact on the operation of HVDC system, the mechanism of commutation failure of HVDC system was analyzed, and the concept of virtual capacitor was introduced. The virtual capacitor was used as the carrier of DC voltage change rate, and by re-ducing the current command value and compensating the DC voltage during the fault, the continuous commutation failure of HVDC transmission system can be restrained. The method of introducing virtual capacitor can reflect the dynamic characteristics of DC voltage during fault and recovery process. Through the electromagnetic transient model of HVDC system, it is verified that the proposed method can effectively suppress the continuous commutation failure of HVDC system under single-phase fault and three-phase fault, and accelerate the recovery of the system after fault. [ABSTRACT FROM AUTHOR]

我国的许多公路都存在着防水路面的基层问题,当前的公路设施中或多或少存在着开裂和其他的破损问 题,这些有部分由于在公路施工中防水路面基层的施工技术不够成熟或是出现了其他问题,在公路工程的施工中进行防 水路面基层的施工技术的加强和管理是十分重要的。本文对公路工程施工中防水路面基层的施工技术进行研究。 [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Liaoning Technical University (Natural Science Edition) / Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of Liaoning Technical University (Natural Science) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)