Beijing City University

Wang S., Geng F., Li Y., Liu H.

Learning high-dimensional chaos is a complex and challenging problem because of its initial value-sensitive dependence. Based on an echo state network (ESN), we introduce homotopy transformation in topological theory to learn high-dimensional chaos. On the premise of maintaining the basic topological properties, our model can obtain the key features of chaos for learning through the continuous transformation between different activation functions, achieving an optimal balance between nonlinearity and linearity to enhance the generalization capability of the model. In the experimental part, we choose the Lorenz system, Mackey–Glass (MG) system, and Kuramoto–Sivashinsky (KS) system as examples, and we verify the superiority of our model by comparing it with other models. For some systems, the prediction error can be reduced by two orders of magnitude. The results show that the addition of homotopy transformation can improve the modeling ability of complex spatiotemporal chaotic systems, and this demonstrates the potential application of the model in dynamic time series analysis.

Zhang Z., Dong Y., Chen X., Zhang G., Liang C., Zhou Y., Huang Z., Shi L., Chen S., Dong L., Wei G.

Plant-associated microbiomes play pivotal roles in promoting plant health, productivity, and resistance to diseases. However, the differential adaptability of microorganisms to different ecological niches in Lonicera japonica remains unclear, which hinders their application in agriculture. Here, the structure and network of bacterial and fungal communities in six compartment niches (bulk soil, rhizosphere soil, root, stem, leaf, and flower) of L. japonica were explored using three host ecotypes. The keystone taxa of each niche were then analyzed, followed by source tracking analysis. Results demonstrated that the structure and assembly of microbiomes along the soil–plant continuum were primarily governed by compartment niches rather than host ecotypes. Bacterial diversity and network complexity gradually decreased from the soil to the root and, ultimately, to the flower; however, fungal diversity and network complexity gradually decreased from the soil to the stem but increased from the stem to the leaf and flower. The bacterial keystone taxa Proteobacteria, Pseudomonas, Pantoea, Kluyvera, and Enterobacter were dominated in endosphere samples. Meanwhile, the core fungal keystone taxa Cryptococcus, Alternaria, and Erysiphe were dominated by endosphere samples. However, Mortierella and Fusarium were dominated by rhizosphere soil. Microbiota was mainly derived from bulk soil and then filtered in each compartment niches. This work provides new insights into the microbial communities of L. japonica, highlighting the potential roles of microbiomes in plant growth, environmental adaptation, and bioactive compound synthesis.

Wang J., Fu L., Huang J., Zhou L.

Wang Y., Jin T., Zhang Y., Zhan L., Cheng C., Zhao J., Zhang W., Fu B., Li P., Li T.

Abstract Objective: Patients with coronavirus disease 2019 (COVID-19) experience various symptoms such as fever, cough, fatigue, and headache. At present, only two traditional over-the-counter medications, acetaminophen, and ibuprofen, are recommended for treating COVID-19 symptoms. However, there is an urgent need to discover safer remedies, potentially found in daily food items, that can be used for long-term prevention of COVID-19 symptoms. This study aimed to explore safe natural products capable of alleviating COVID-19 symptoms. Materials and Methods: We developed a Transcriptome-based Functional Gene Module Reference (TFGMR) approach, utilizing gene expression data from natural products and gene modules associated with COVID-19. Our hypothesis was that candidate natural products would significantly inhibit the expression of genes linked to COVID-19 symptoms without enhancing the expression of genes related to severe acute respiratory syndrome coronavirus 2 invasion. Results: Using the TFGMR approach, we identified 109 natural products with the potential to alleviate COVID-19 symptoms, with 15 of them having experimental evidence supporting their efficacy. These natural products consist of three daily food items – olive oil, nuts, and a mixture of vegetables, fruits, and yogurt; 43 functional foods, such as Fructus Gardeniae and Flos Lonicerae; as well as 63 natural drugs such as Plantamajoside and Safflomin A. These findings suggest that incorporating these three daily food items into one’s diet may contribute to the prevention of COVID-19. Conclusion: The study underscores the importance of recommending functional foods based on robust scientific evidence supporting their efficacy. The integration of diverse technological approaches holds promise in identifying safe remedies that could aid in the prevention and alleviation of COVID-19 symptoms, providing new opportunities for their everyday use.

Kong F., Kou Y., Zhang X., Tian Y., Yang B., Wang W.

The dried rhizome of Notopterygium incisum (NI) from the Umbelliferae family, genuinely produced in Sichuan, China, is a classic traditional Chinese medicinal herb for treating wind-dampness arthralgia. Due to scarce natural resources, wild NI is gradually being replaced by cultivated types. However, knowledge is limited regarding the differences in chemical composition and pharmacological effects between wild and cultivated NI and between Sichuan-grown and other-region-grown NI. In this study, a plant metabolomics strategy, based on GC–MS and UHPLC-Orbitrap MS, was employed to compare metabolic profiles between wild and cultivated NI and between cultivated NI from Sichuan and cultivated NI from Gansu and Qinghai. In total, 195 metabolites were identified, and the biosynthetic pathways of coumarins and phenolic acids, which were the most abundant secondary metabolites in NI, were summarized. Additionally, seven key metabolic intermediates were uncovered, revealing the reasons for the differences in metabolic profiles between wild and cultivated NI. The anti-inflammatory effect of wild and cultivated NI was verified by inflammatory gene expression and neutrophil count using a zebrafish yolk sac inflammation model. Overall, this study presents information on the types and synthesis of pharmacodynamic substances in NI and provides a basis for its cultivation and applications.

Chen T., Ai J., Xiong X., Hu G.

In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement learning (HRL) can be used to effectively alleviate the dimensionality curse in it. However, traditional HRL algorithms are designed for short-term missions with sparse rewards, while existing HRL algorithms proposed for MEC lack delicate a coupling structure and perform poorly. This article introduces a novel HRL-based algorithm, named hierarchical service caching and task offloading (HSCTO), to solve the problem of the cooperative optimization of service caching and task offloading in MEC. The upper layer of HSCTO makes decisions on service caching while the lower layer is in charge of task offloading strategies. The upper-layer module learns policies by directly utilizing the rewards of the lower-layer agent, and the tightly coupled design guarantees algorithm performance. Furthermore, we adopt a fixed multiple time step method in the upper layer, which eliminates the dependence on the semi-Markov decision processes (SMDPs) theory and reduces the cost of frequent service replacement. We conducted numerical evaluations and the experimental results show that HSCTO improves the overall performance by 20%, and reduces the average energy consumption by 13% compared with competitive baselines.

Wang X., Pang L., Miao D., Yan H., Cao X., Wu X.

Collaborative flight formations represent a promising operational model, but the integration of multi-source information in manned interfaces often results in cognitive overload and reduced situation awareness. This study evaluates the effectiveness of intelligent auxiliary information presentation modes in enhancing personnel capabilities. Using a simulation-based collaborative flight formation system, four presentation modes with varying levels of information and dynamics were experimentally tested and evaluated across subjective dimensions (cognitive workload, situation awareness, and interface design) and objective dimensions (design task flow information load and operational task flow information load). The results indicate that Level 3 and Level 4 modes significantly reduced mental workload and improved practical operational ability compared to the original mode. Level 3 achieved the highest interface evaluation scores, while Level 4 demonstrated the lowest design task flow information load. Both modes significantly enhanced situation awareness. Altogether, Level 3 and Level 4 resulted in the most significant improvements in personnel capabilities. These findings provide valuable insights for optimizing interface design and improving situation awareness in collaborative flight formation tasks.

Jia B.

One of the most important aspects of industrial manufacturing is the assessment of product appearance and quality, which frequently occurs manually. A product’s appearance can convey practical qualities, aesthetic value, and a sense of quality to consumers. This study explores the use of computer vision in the automatic generation of product appearance designs. In this study, a dolphin swarm-driven weighted random forest (DS-WRF) design approach is introduced to assist designers in improving the efficiency of product appearance design with automatic generation. In this study, we collected product image data. The data were preprocessed to resize the images. The two primary components of this method are sketched inversion and design scheme creation. The produced sketches are entered into the sketch inversion model combined with the matching color images for training. Sketches by hand are transformed into color design patterns using this approach. The experimental result demonstrates that the proposed method involves the automatic generation of product appearance designs with high quality.

Liu Y., Li X., Cheng R., Wang C., Dai D., Li C.

Wu Y., Xie L., Jiang Y., He A., Li D., Yang L., Xu Y., Liu K., Ozaki Y., Noda I.

The structures of complexes of alditols where μ2-bridge-relevant-deprotonations occur are described and the structural features of the complexes are discussed.

Jiang L., Hao Y., Wang Y., Chen Q., Xin G., Li P., Hui Y., Gao T., Li Q., Wang X.

The evidence for an association between early menarche and depression is inconsistent. We therefore performed a meta-analysis to examine this association.

Zhou X., Li J., Chen L., Guo M., Liang R., Pan Y.

Rice, as one of the most important staple crops, its genetic improvement plays a crucial role in agricultural production and food security. Although extensive research has utilized single nucleotide polymorphisms (SNPs) data to explore the genetic basis of important agronomic traits in rice improvement, reports on the role of other types of variations, such as insertions and deletions (INDELs), are still limited. In this study, we extracted INDELs from resequencing data of 148 rice improved varieties. We identified 938,585 INDELs and found that as the length of the variation increases, the number of variations decreases, with 89.0% of INDELs being 2–10 bp. The highest number of INDELs was found on chromosome 1, while the least was on chromosome 10. INDELs were unevenly distributed across the genome, generating a total of 33 hotspot regions. 47.0% of INDELs were located within 2 kb upstream and downstream of genes. Using phenotypic data from five agronomic traits (heading date, flag leaf length, flag leaf width, panicle number, and plant height) along with INDEL data to perform genome-wide association study (GWAS), we identified 6,331 significant loci involving 157 cloned genes. Haplotype analysis of candidate genes revealed INDELs affecting important functional genes, such as OsMED25 and OsRRMh related to heading date, and MOC2 related to plant height. Our work analyzed the variation patterns of INDELs in rice improvement and identified INDELs associated with agronomic traits. These results will provide valuable genetic and material resources for the genetic improvement of rice.

Bai Y., Usman O., Ozkan O.

Tang Y., Zhao Y., Li Z., Zhou J., Wang H.

The rational method (RM) adopting steady uniform flow assumption is a simple and mainstream approach for the design of urban stormwater-drainage systems (USDSs). However, when designing large-scale USDSs, the RM significantly deviates from the actual flow regime due to inappropriate assumptions. To improve the accuracy and reliability of the design method, a dynamic-wave-simulation-based method (DWSBM) is proposed. Firstly, a numerical model which is equivalent to the surface runoff yield process of RM is established and validated. Then the dynamic wave module is adopted for pipe flow calculations. This method integrates hydraulic models for the whole design process. Both DWSBM and RM are used for USDS design of two areas, and design comparison demonstrates that the design flow rates computed using DWSBM are greater than obtained by RM. With the increase in pipeline length, the design flow differences between the two methods in the two areas increased from 3.09% to 28.97% and from 16.01% to 45.40% respectively. Adopting the DWSBM for design flow rate calculation can effectively improve the design reliability and drainage capacity of USDSs.

Zhang S., Li Q., Wang H., Xi Y.

In this paper, an event-triggered sliding-mode output feedback control (SMOFC) strategy with adaptive fault-tolerance is proposed to achieve path tracking for networked autonomous vehicles, considering the challenges posed by network communication and electric steering systems. Initially, signal quantization, external disturbances, and actuator faults are incorporated into the vehicle model. This incorporation enhances the designed controller’s robustness against a broader and more demanding range of driving scenarios. Subsequently, in situations where only output feedback is available, a dynamic output compensator is designed to reconstruct the unmeasurable vehicle state. Utilizing the reconstructed vehicle state, an event-triggered strategy is devised to alleviate the network burden and determine the minimum time between triggering events to prevent Zeno behavior. Furthermore, an adaptive mechanism is employed to estimate the actuator fault boundaries. The performance of the designed controller is evaluated through simulation instances.