In this study, Chinese yam polysaccharides (CYPs) were fermented using Lactobacillus plantarum M616, and changes in the chemical composition, structure, and anti-inflammatory activity of CYPs before and after fermentation were investigated. The carbohydrate content of L. plantarum M616-fermented CYP (CYP-LP) increased from 71.03% ± 2.75 to 76.28% ± 2.37%, whereas protein and polyphenol content were almost unaffected compared with those of the unfermented CYP (CYP-NF). The monosaccharide composition of CYP-NF included rhamnose, arabinose, galactose, glucose, and mannose in a molar ratio of 0.493:0.6695:0.9738:0.7655:12.4365. CYP-LP had the same monosaccharides as CYP-NF, but the molar ratio was 0.3237:0.3457:0.8278:2.5541:10.4995. Meanwhile, the molecular weight and polydispersity of CYP-LP, respectively, increased from 124.774 kDa and 6.58 (CYP-NF) to 376.628 kDa and 17.928, indicating a low homogeneity. In vitro antioxidant analysis showed that L. plantarum M616 fermentation had varying effects on CYP-LP against DPPH, ABTS, hydroxyl, and superoxide radicals. However, CYP-LP had superior anti-inflammatory activity to CYP-NF and is more effective in regulating superoxide dismutase, catalase, glutathione peroxidase, malondialdehyde, nitric oxide, tumor necrosis factor-α, interleukin-1β, and interleukin-6 release in lipopolysaccharide-induced RAW 264.7 macrophages. This study suggested that CYP-LP is a potential anti-inflammatory ingredient in drugs and functional food.

Cigar tobacco (Nicotiana tabacum L.) is an important cash crop in China. Due to suitable ecological conditions, Hainan province is a major area for growing cigar tobacco. Cigar tobacco leaves are b...

As the most popular renewable energy, solar energy could be converted into electricity by photovoltaic (PV) systems directly. To maximize the effectiveness of the conversion, it is critical to find the precise and accurate parameters of the PV model. In this paper, we propose a level‐based learning swarm optimizer with stochastic fractal search (LLSOF) to tackle the parameter estimation of several kinds of solar PV models. The population is separated into multiple levels according to their fitness at first. The individuals at the lower levels evolve through learning from the individuals at the higher levels. Benefiting from the interactive learning among levels, the population could approach the multiple optimal regions rapidly. To enhance the local search ability, stochastic fractal search is introduced to locate the optima accurately. Combination of both, the proposed LLSOF could achieve a good balance on both exploration and exploitation. To evaluate the performance of LLSOF, it is used to obtain the parameters of three PV models and compared with nine well‐established algorithms. Comparative results validate the excellent performance of LLSOF. Moreover, the application manufactory’s data sheets report the superior efficiency and effectiveness of LLSOF for the parameter estimation of PV systems.

Intraspecific variability of Capila omeia (Leech, 1894) from Chongqing is investigated based upon the comparison of the 658 bp COI gene sequences and adult morphology. The morphological characters used for distinguishing C. pseudomeia Fan & Wang, 2004 from C. omeia are proved to be in the range of individual variation of the latter. Therefore, C. pseudomeia syn. n. is treated as a junior subjective synonym of C. omeia. Adults and genitalia of both sexes of C. omeia are illustrated. A distribution map of the species is provided as well.

Feature selection (FS) is a critical task in data science and machine learning, presenting significant challenges in high-dimensional settings due to the complexity and noise inherent in large feature sets. To address these issues, this paper proposes a Dynamic Multi-Level Competition Learning-Based Dual-Task Optimization (DMLC-DTO) method. The approach introduces a dual-task generation strategy that uses the Fisher Score to generate sub-tasks, which aid the primary task in exploring the feature space more effectively and accelerating feature selection. The Dynamic Multi-Level Competition Learning-Based Optimization mechanism enhances population diversity by organizing particles into hierarchical levels, with lower-tier particles learning from those at higher tiers. This hierarchical structure is integrated with traditional Competitive Swarm Optimization (CSO) and with a dynamic factor regulating the balance between exploration and convergence. Furthermore, the Multi-Winner Based Knowledge Transfer method encourages inter-task learning by allowing particles at the same level across tasks to exchange knowledge and facilitate information transfer. Experiments on 13 high-dimensional, real-world datasets confirm the effectiveness and robustness of DMLC-DTO, showcasing its competitive performance in feature selection tasks.

Almost 70 years after its first record from Hainan by Evans in 1949, the second specimen of Halpe sikkima Moore, 1882 was found on the island. The male genitalia of this species are illustrated and re-described. The COI sequence is published for the first time.