American silk moth, Antheraea polyphemus Cramer 1775 (Lepidoptera: Saturniidae), native to North America, has potential significance in sericulture for food consumption and silk production. To date, the phylogenetic relationship and divergence time of A. polyphemus with its Asian relatives remain unknown. To end these issues, two mitochondrial genomes (mitogenomes) of A. polyphemus from the USA and Canada respectively were determined. The mitogenomes of A. polyphemus from the USA and Canada were 15,346 and 15,345 bp in size, respectively, with only two transitions and five indels. The two mitogenomes both encoded typical mitochondrial 37 genes. No tandem repeat elements were identified in the A+T-rich region of A. polyphemus. The mitogenome-based phylogenetic analyses supported the placement of A. polyphemus within the genus Antheraea, and revealed the presence of two clades for eight Antheraea species used: one included A. polyphemus, A. assamensis Helfer, A. formosana Sonan and the other contained A. mylitta Drury, A. frithi Bouvier, A. yamamai Guérin-Méneville, A. proylei Jolly, and A. pernyi Guérin-Méneville. Mitogenome-based divergence time estimation further suggested that the dispersal of A. polyphemus from Asia into North America might have occurred during the Miocene Epoch (18.18 million years ago) across the Berling land bridge. This study reports the mitogenome of A. polyphemus that provides new insights into the phylogenetic relationship among Antheraea species and the origin of A. polyphemus.

The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is economically one of the most threatening pests in tomato cultivation, which not only causes direct damage but also transmits many viruses. Breeding whitefly-resistant tomato varieties is a promising and environmentally friendly method to control whitefly populations in the field. Accumulating evidence from tomato and other model systems demonstrates that flavonoids contribute to plant resistance to herbivorous insects. Previously, we found that high flavonoid-producing tomato line deterred whitefly oviposition and settling behaviours, and was more resistant to whiteflies compared to the near-isogenic low flavonoid-producing tomato line. The objective of the current work is to describe in detail different aspects of the interaction between the whitefly and two tomato lines, including biochemical processes involved. Electrical penetration graph recordings showed that high flavonoid-producing tomato reduced whitefly probing and phloem-feeding efficiency. We also studied constitutive and induced plant defence responses and found that whitefly induced stronger reactive oxygen species accumulation through NADPH oxidase in high flavonoid-producing tomato than in low flavonoid-producing tomato. Moreover, whitefly feeding induced the expression of callose synthase genes and resulted in callose deposition in the sieve elements in high flavonoid-producing tomato but not in low flavonoid-producing tomato. As a consequence, whitefly feeding on high flavonoid-producing tomato significantly decreased uptake of phloem and reduced its performance when compared to low flavonoid-producing tomato. These results indicate that high flavonoid-producing tomato provides phloem-based resistance against whitefly infestation and that the breeding of such resistance in new varieties could enhance whitefly management.

We undertake a comprehensive investigation into the distribution of in situ stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define in situ stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinise correlation coefficients among internal drivers (e.g. virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio, and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unravelling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations.

The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (PRM) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-PRM) is proposed based on topological discrimination and lazy collision. TL-PRM algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-PRM algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-PRM algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with PRM* and PRM-Astar algorithms. In winding and narrow passage maps, TL-PRM algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-PRM algorithm provides an effective solution for reducing the generation of sampling points in PRM algorithm.

Although active flow control based on deep reinforcement learning (DRL) has been demonstrated extensively in numerical environments, practical implementation of real-time DRL control in experiments remains challenging, largely because of the critical time requirement imposed on data acquisition and neural-network computation. In this study, a high-speed field-programmable gate array (FPGA) -based experimental DRL (FeDRL) control framework is developed, capable of achieving a control frequency of 1–10 kHz, two orders higher than that of the existing CPU-based framework (10 Hz). The feasibility of the FeDRL framework is tested in a rather challenging case of supersonic backward-facing step flow at Mach 2, with an array of plasma synthetic jets and a hot-wire acting as the actuator and sensor, respectively. The closed-loop control law is represented by a radial basis function network and optimised by a classical value-based algorithm (i.e. deep Q-network). Results show that, with only ten seconds of training, the agent is able to find a satisfying control law that increases the mixing in the shear layer by 21.2 %. Such a high training efficiency has never been reported in previous experiments (typical time cost: hours).

Motivated by the impact of emerging technologies on (toll) parks, this paper studies a problem of customers’ strategic behavior, social optimization, and revenue maximization for infinite-server queues. More specifically, we assume that a customer’s utility consists of a positive reward for receiving service minus a cost caused by the other customers in the system. In the observable setting, we show the existence, uniqueness, and expressions of the individual equilibrium threshold, the socially optimal threshold, and the optimal revenue threshold, respectively. Then, we prove that the optimal revenue threshold is smaller than the socially optimal threshold, which is smaller than the individual one. Furthermore, we also extend the cost functions to any finite polynomial function with nonnegative coefficients. In the unobservable setting, we derive the joining probabilities of individual equilibrium and optimal revenue. Finally, using numerical experiments, we complement our results and compare the social welfare and the revenue under these two information levels.

Er:CaF2 crystals are crucial gain media for producing 3 μm mid-infrared (MIR) lasers pumped by 976 nm continuous-wave (CW) lasers owing to their low phonon energy and high conversion efficiency. This study investigated the damage characteristics and mechanism of Er:CaF2 crystals irradiated with a 976 nm CW laser. The laser-induced damage threshold of Er:CaF2 crystals with different Er3+ doping levels was tested; the damage morphology consists of a series of regular 70° cracks related to the angle of the crystal slip system on the surface. A finite-element model was used to calculate the temperature and stress fields of the crystals. The results indicated that the damage can be attributed to surface tensile stresses caused by the temperature gradient, and crystals with higher doping concentrations were more susceptible to damage owing to stronger light absorption. These findings provide valuable insights into the development of high-power MIR lasers.

Revealing the impact of forest succession processes on changes in plant diversity is crucial for understanding the mechanisms that maintain plant diversity across various succession stages. While previous research has predominantly focused on the influence of environmental factors or management strategies on plant diversity within rubber plantation understories, there is a scarcity of studies examining the effects of forest succession processes on plant diversity. This study focuses on the plant diversity of the understory herbaceous layer within the rubber forest of the Yinggeling area, located in National Park of Hainan Tropical Rainforest. It employs a spatial analysis approach, rather than a temporal one, to examine the characteristics of the understory herbaceous community. The findings revealed that (1) The understory of Yinggeling rubber plantations harbors 175 plant species from 149 genera and 75 families, with Gramineae and Rubiaceae representing 46.45% of total species. And the dominant families are Rubiaceae, Gramineae, and Moraceae, with Ficus and Pteris being the dominant genera. (2) The dominant species vary with succession duration, with Tetrastigma pachyphyllum dominating in 0-year succession, Paspalum conjugatum in 3-year succession, and Microstegium fasciculatum in 7-year succession. (3) Diversity indices such as the Shannon–Wiener index, Simpson index, and Pielou index peak at 7 years of natural succession, while the species richness is highest at 3 years. (4) The similarity coefficient between understory herbaceous plant communities in rubber plantations undergoing 0 and 3 years of natural succession is highest 0.56, indicating a significant similarity, while similarity is lowest between 0 and 7 years of succession. This research shows that natural restoration helps increase species diversity in the understory herb layer of rubber forests. Succession leads to changes in the dominant families, genera, and species of the herbaceous layer. This change can be attributed to the intraspecific competition and ecological competition that occur during the succession process, leading to changes in biological and resource allocation.

Newborn calf diarrhea has led to widespread overuse of antibiotics. Therefore, it is crucial to find effective solutions for calf diarrhea. In this study, we aimed to evaluate the impact of the synthetic organic zinc-chelating-peptide glycine-glutamine-Zn (GQ-Zn) on the microbiota and metabolites in the gut of calves with diarrhea. The results showed that GQ-Zn alleviated diarrhea in calves. Additionally, 16S rDNA sequencing and metabolomic analysis revealed that GQ-Zn improved antioxidant capacity, relieved inflammation, altered the gut microbiota by decreasing the number of harmful bacteria Prevotella denticola, Fusobacterium necrophorum and influenced metabolomic profiles via the linoleic acid metabolic pathway in calves. In conclusion, GQ-Zn supplementation alleviated diarrhea through regulating the gut microbiota and metabolites in pre-weaning Holstein calves.

Neural tuning for visual words is essential for fluent reading across various scripts. This study investigated the emergence and development of N170 tuning for Chinese characters and its cognitive–linguistic correlates. Electroencephalogram data from 48 adult L2 learners and 23 native Chinese readers were collected using a color detection task. The N170 for real characters, pseudo-characters, false characters, stroke combinations and line drawings were recorded. We found beginner adult L2 learners showed larger N170 Chinese characters compared to stroke combinations (coarse neural tuning). The intermediate-level L2 Chinese learners demonstrated fine-tuning for Chinese orthographic regularities. Importantly, a clear shift from bilateral to left-lateralized coarse and fine-tuning for print was observed from beginner to intermediate L2 learners as their Chinese reading experience increased. Moreover, individual differences in neural print tuning moderately correlated with word-reading fluency, Chinese vocabulary knowledge and morphological awareness.

Recent years have seen the emergence of new technologies that exploit nanoscale evaporation, ranging from nanoporous membranes for distillation to evaporative cooling in electronics. Despite the increasing depth of fundamental knowledge, there is still a lack of simulation tools capable of capturing the underlying non-equilibrium liquid–vapour phase changes that are critical to these and other such technologies. This work presents a molecular kinetic theory model capable of describing the entire flow field, i.e. the liquid and vapour phases and their interface, while striking a balance between accuracy and computational efficiency. In particular, unlike previous kinetic models based on the isothermal assumption, the proposed model can capture the temperature variations that occur during the evaporation process, yet does not require the computational resources of more complicated mean-field kinetic approaches. We assess the present kinetic model in three test cases: liquid–vapour equilibrium, evaporation into near-vacuum condition, and evaporation into vapour. The results agree well with benchmark solutions, while reducing the simulation time by almost two orders of magnitude on average in the cases studied. The results therefore suggest that this work is a stepping stone towards the development of an accurate and efficient computational approach to optimising the next generation of nanotechnologies based on nanoscale evaporation.