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Salvia leucantha is a perennial herb of the genus Salvia in the family Labiatae, which has a wide range of biological activities, mainly including inhibition of acetylcholinesterase, antibacterial, and anti-inflammatory activity. To explore the protective effects and mechanism of action of S. leucantha on Alzheimer's disease (AD), the anti-AD activity of SLE (extracts of S. leucantha) was determined by using a transgenic Caenorhabditis elegans (C. elegans) model (CL4176). Analyses included paralysis assay, phenotypic experiments, transcriptome sequencing, RNA interference (RNAi), heat shock assays, and gas chromatography-mass spectrometry (GC-MS). SLPE (S. leucantha petroleum ether extract) could significantly delay CL4176 paralysis and extend the longevity of C. elegans N2 without harmful effects. A total of 927 genes were significantly changed by SLPE treatment in C. elegans, mainly involving longevity regulatory pathways—nematodes, drug metabolism—cytochrome P450, and glutathione metabolic pathways. RNAi showed that SLPE exerted its anti-AD activity through up-regulation of the gene gst-5; the most abundant compound in SLPE analyzed by GC-MS was 2,4-Di-tert-butylphenol (2,4-DTBP), and the compound delayed nematode paralysis. The present study suggests that active components in S. leucantha may serve as new-type anti-AD candidates and provide some insights into their biological functions. [ABSTRACT FROM AUTHOR]

The width of natural fractures is an important parameter in the leak prevention and plugging operations for oil and gas drilling. To accurately predict the fracture width of the leaking formation when well leakage occurs during oilfield drilling, based on the mud logging data of adjacent oil wells during the loss process, the Spearman correlation analysis and data normalization methods were used to preprocess the data. A new method was then established to predict the natural fracture width based on FDCNN (Fluid Dynamics Constrained Neural Network), which is a neural network algorithm constrained by the prior knowledge of fluid dynamics. In this method, first, based on the fluid dynamics prior knowledge that there is a strict positive partial derivative relationship between the natural fracture width and the leakage volume and pressure difference in the fluid dynamics model, the constraint conditions of the neural network were optimized. Second, the augmented Lagrange multiplier method was used to establish the performance index of the neural network through a multiplier and a penalty factor. Finally, the model was trained using the backpropagation learning rule and gradient descent training methods. The results indicate that this method, utilizing FDCNN, can train the model with a small sample training set, demonstrating superior generalization ability and prediction accuracy compared to traditional fluid dynamics models and Data-Driven Neural Network (which refers to an algorithm training a neural network solely on data samples without using fluid dynamics prior knowledge). This approach can effectively reduce the prediction error of fracture width, providing valuable reference for the development of field plugging programs. [ABSTRACT FROM AUTHOR]

The health of mobile software ecosystems is closely related to the interests of software developers, end‐users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems. [ABSTRACT FROM AUTHOR]

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper's improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. [ABSTRACT FROM AUTHOR]

Mixed traffic is a common phenomenon in urban environment. For the mixed traffic situation, the detection of traffic obstacles, including motor vehicle, non-motor vehicle, and pedestrian, is an essential task for intelligent and connected vehicles (ICVs). In this paper, an improved YOLO model is proposed for traffic obstacle detection and classification. The YOLO network is used to accurately detect the traffic obstacles, while the Wasserstein distance-based loss is used to improve the misclassification in the detection that may cause serious consequences. A new established dataset containing four types of traffic obstacles including vehicles, bikes, riders, and pedestrians is collected under different time periods and different weather conditions in urban environment in Wuhan, China. Experiments are performed on the established dataset on Windows PC and NVIDIA TX2, respectively. From the experimental results, the improved YOLO model has higher mean average precision than the original YOLO model and can effectively reduce intolerable misclassifications. In addition, the improved YOLOv4-tiny model has a detection speed of 22.5928fps on NVIDIA TX2, which can basically realize the real-time detection of traffic obstacles., Author(s): Luyao Du [1]; Xiongjie Chen [2]; Zhonghui Pei [3]; Donghua Zhang [4]; Bo Liu [4]; Wei Chen (corresponding author) [1] 1. Introduction Traffic conditions in urban societies can be [...]