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.

Pebrine disease, caused by Nosema bombycis (Nb) infection in silkworms, is a severe and long-standing disease that threatens sericulture. As parasitic pathogens, a complex relationship exists between microsporidia and their hosts at the mitochondrial level. Previous studies have found that the translocator protein (TSPO) is involved in various biological functions, such as membrane potential regulation, mitochondrial autophagy, immune responses, calcium ion channel regulation, and cell apoptosis. In the present study, we found that TSPO expression in silkworms (BmTSPO) was upregulated following Nb infection, leading to an increase in cytoplasmic calcium, adenosine triphosphate, and reactive oxygen species levels. Knockdown and overexpression of BmTSPO resulted in the promotion and inhibition of Nb proliferation, respectively. We also demonstrated that the overexpression of BmTSPO promotes host cell apoptosis and significantly increases the expression of genes involved in the immune deficiency and Janus kinase-signal transducer and the activator of the transcription pathways. These findings suggest that BmTSPO activates the innate immune signalling pathway in silkworms to regulate Nb proliferation. Targeting TSPO represents a promising approach for the development of new treatments for microsporidian infections.

The definition of subshifts of finite symbolic rank is motivated by the finite rank measure-preserving transformations which have been extensively studied in ergodic theory. In this paper, we study subshifts of finite symbolic rank as essentially minimal Cantor systems. We show that minimal subshifts of finite symbolic rank have finite topological rank, and conversely, every minimal Cantor system of finite topological rank is either an odometer or conjugate to a minimal subshift of finite symbolic rank. We characterize the class of all minimal Cantor systems conjugate to a rank-$1$ subshift and show that it is dense but not generic in the Polish space of all minimal Cantor systems. Within some different Polish coding spaces of subshifts, we also show that the rank-1 subshifts are dense but not generic. Finally, we study topological factors of minimal subshifts of finite symbolic rank. We show that every infinite odometer and every irrational rotation is the maximal equicontinuous factor of a minimal subshift of symbolic rank $2$, and that a subshift factor of a minimal subshift of finite symbolic rank has finite symbolic rank.

The turbulent boundary layer (TBL) is a widely existing flow phenomenon in nature and engineering applications. Its strong mixing effect can achieve more sufficient material mixing, heat transport, etc. The understanding of the entrainment process and mechanism of irrotational fluids entering the turbulent region can be promoted by studying the geometric and dynamic characteristics of turbulent${/}$non-turbulent interfaces (TNTI). In compressible flow, it is unclear whether the properties of TNTI will change and whether the entrainment will show different features due to the influence of compressibility. Based on the direct numerical simulation results of supersonic compressible plate TBLs with Mach number of 2.9, the geometric and dynamic characteristics of TNTI are investigated in this paper. The interface is identified by the enstrophy method, and the height, thickness, fractal dimension, enstrophy transportation and entrainment characteristics of the interface are investigated. It is found that for the enstrophy transportation in a TBL, the contribution of compressibility-related terms accounts for approximately 13.4 % of the total enstrophy transportation, which tends to transfer the enstrophy of turbulence near the interface to both directions vertical to the interface. This promotes the expansion of the turbulent region towards the non-turbulent region, and the mean height, thickness and entrainment velocity are increased by approximately 3.7 %, 7.0 % and 8.5 %, respectively, while the fractal dimension is basically unaffected. Different from the incompressible flow, the contribution of the compressibility-related terms to the entrainment velocity is independent of the local curvature, and the intense entrainment process is more likely to occur on a highly curved concave surface.

Gradual typing integrates static and dynamic typing by introducing a dynamic type and a consistency relation. A problem of gradual type systems is that dynamic types can easily hide erroneous data flows since consistency relations are not transitive. Therefore, a more rigorous static check is required to reveal these hidden data flows statically. However, in order to preserve the expressiveness of gradually typed languages, static checks for gradually typed languages cannot simply reject programs with potentially erroneous data flows. By contrast, a more reasonable request is to show how these data flows can affect the execution of the program. In this paper, we propose and formalize Static Blame, a framework that can reveal hidden data flows for gradually typed programs and establish the correspondence between static-time data flows and runtime behavior. With this correspondence, we build a classification of potential errors detected from hidden data flows and formally characterize the possible impact of potential errors in each category on program execution, without simply rejecting the whole program. We implemented Static Blame on Grift, an academic gradually typed language, and evaluated the effectiveness of Static Blame by mutation analysis to verify our theoretical results. Our findings revealed that Static Blame exhibits a notable level of precision and recall in detecting type-related bugs. Furthermore, we conducted a manual classification to elucidate the reasons behind instances of failure. We also evaluated the performance of Static Blame, showing a quadratic growth in run time as program size increases.