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Rheumatoid arthritis (RA) is an inflammatory autoimmune disease characterized primarily by the synovial infiltration of inflammatory cells. Macrophage infiltration in the joint synovium is one of the early hallmarks of RA disease activity. Cortex periplocae, which has been widely employed in traditional Chinese medicine (TCM) to alleviate RA, harbors a bioactive compound known as Periploca sepium periplosides (PePs). In this study, collagen antibody-induced arthritis (CAIA) was established in mice through the administration of collagen antibodies and lipopolysaccharide (LPS), followed by treatment with PePs. The therapeutic effects of PePs were evaluated by measuring paw thickness, clinical arthritis scores, and histological changes in joint tissues. Flow cytometry and qRT-PCR were used to assess macrophage polarization in vivo and in vitro. The findings indicate that PePs effectively attenuated CAIA by suppressing the polarization of RAW264.7 cells towards the M1 phenotype while promoting their polarization towards the M2 phenotype. These results provide valuable insights into the scientific significance of PePs as a potential therapeutic agent for RA.

Laiwu black goats comprise an excellent local germplasm resource; however, a shortage of feed resources has led to the application of unconventional feed. Ginger straw feed has good physiological effects, but research on this feed source for ruminant animals is lacking. The aim of this study was to determine the effects of enzymatic silage ginger straw on Laiwu black goat performance. The experiment used an independent sample t-test analysis method; 24 healthy Laiwu black goats with a body weight of 20.05 ± 1.15 kg and age of 5.67 ± 0.25 months were randomly divided into two groups with three replicates (bars) per group and four goats per replicate. The experimental diet was composed of mixed concentrate, silage, and garlic peel at a 2:7:1 ratio. The silage used in the two groups was whole corn silage (CON group) and 60% whole corn silage plus 40% enzymatic silage ginger straw (SG group), and the other components were identical. Daily feed intake/daily gain (F/G) was significantly higher in the SG group than in the CON group (p < 0.05), but there were no significant differences in dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) digestibility between the groups. The shear force, cooking loss, centrifugal loss, and pressure loss of the longissimus dorsi muscle group were significantly lower in the SG than in the CON group (p < 0.05). Compared with those in the CON group, the serum and liver total antioxidant capacity was significantly increased in the SG group, and in the liver, the O2·−, malondialdehyde, and OH· contents were significantly decreased. Collectively, the rumen fluid microbial diversity was changed in the SG group. It was concluded that enzymatic silage ginger straw usage instead of 40% whole silage corn as feed for Laiwu black goats can significantly improve the muscle quality, antioxidant capacity, and intestinal flora, with no adverse effects on production performance. In conclusion, our study provides a basis for ginger straw processing and storage and its rational application in the Laiwu black goat diet.

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, a self-attention mechanism called GD-Attention is proposed, which considers global pixel value distribution information and guide the deep learning model to give more concern on the leaf disease area. Based on data augmentation, the proposed method inputs both the image and its pixel value distribution information to the model. The GD-Attention mechanism guides the model to extract features related to pixel value distribution information, thereby increasing attention towards the disease area. The model is trained and tested on the Plant Village (PV) dataset, and by analyzing the generated attention heatmaps, it is observed that the disease area obtains greater weight. The results achieve an accuracy of 99.97% and 27 MB parameters only. Compared to classical and state-of-the-art models, our model showcases competitive performance. As a next step, we are committed to further research and application, aiming to address real-world, complex scenarios.

The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model.

The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework.