Search

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 [...]

Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system. [ABSTRACT FROM AUTHOR]

Objective.To investigate the influence of pristimerin (Pris) on rat hepatocellular carcinoma (HCC) induced by diethylnitrosamine (DEN) by regulating ROS/ASK1/JNK signal pathway. Methods.Six SD rats were randomly selected as the control group, and the remaining rats were injected with DEN to construct the rat model of HCC. The rats successfully modeled were randomly grouped into HCC group, Pris group (0.8 mg/kg Pris), Vaccarin group (100 mg/kg ROS/ASK1/JNK signal pathway inhibitor Vaccarin), and Pris+Vaccarin group (0.8 mg/kg Pris+100 mg/kg Vaccarin), with 6 rats in each group, and were injected continuously for one week. HCC group and control group were injected with the same amount of normal saline. Body weight, liver weight and liver weight ratio were measured, liver function, inflammatory factors, antioxidant index and ROS level were detected by ELISA, HE staining was applied to detect the pathological changes of liver, western blot was applied to detect the expression of apoptotic markers (Bax, Bcl-2 and cleaved-Caspase-3) and ROS/ASK1/JNK signal path- way protein. Results.The rats in the control group showed normal liver tissue structure. In HCC group, necrosis of some hepatocytes and focal nodular hyperplasia were observed, body weight, Bax, cleaved-Caspase-3, ROS, p- ASK1/ASK1, and p-JNK/JNK level in HCC group were obviously lower than those in the control group (P < 0.05), but liver weight, liver weight ratio, ALT, AST, ALP, LDH contents, IL-6, TNF-, CCL-2 contents, SOD, GR, GPx, CAT contents, and Bcl-2 protein level were obviously higher (P < 0.05). Hepatocyte necrosis and focal nodular hyperplasia in Pris group were improved, and body weight, Bax, cleaved-Caspase-3, ROS, p-ASK1/ ASK1, and p-JNK/JNK level in Pris group were obviously higher than those in HCC group (P<0.05), liver weight, liver weight ratio, ALT, AST, ALP, LDH contents, IL-6, TNF-, CCL-2 contents, SOD, GR, GPx, CAT con- tents, and Bcl-2 protein level were obviously lower (P<0.05). The opposite trend was observed in Vaccarin group, Vaccarin reversed the anticancer effect of Pris on HCC rats. Conclusion.Pris may play a certain role in inhibiting HCC rats by activating ROS/ASK1/JNK signal pathway. [ABSTRACT FROM AUTHOR]