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Simple Summary: Adherence to smooth substrates is closely related to the morphology and distribution of adhesive structures on insects' legs, so it is hypothesized that the adhesive structures have been evolved as an adaption to smooth substrates in specific environments. However, the factors that promote the evolution of adhesive structures are still unclear. Using scanning electron microscopy, we compared the microstructure of the tarsi of five tiger beetle species, both male and female, belonging to two tribes living in arboreal and non-arboreal environments. We found that the different types of adhesive setae, including elongated spoon-like setae, elliptical setae, branched setae, filament-like setae, discoidal setae, spatulate setae and tapered setae, varied in different environments and genders. The adaptive evolution of these adhesive structures was probably driven by the selective pressures of both mating behavior and the presence of smooth substrates in the respective environments. The ability of many insects to adhere vertically or even upside down to smooth substrates is closely related to the morphology and distribution of the adhesive structures on their legs. During locomotion, the legs are in direct contact with different substrates, and it is hypothesized that the adhesive structures have been evolved as an adaption to smooth substrates in specific environments. To investigate whether there is a relationship between the presence of adhesive structures and the combined effects of different environments and mating behavior, we compared five species of tiger beetles belonging to two tribes living in arboreal and non-arboreal environments, respectively. In three non-arboreal species, we found a specific type of adhesive structure consisting of elongated spoon-like setae present on the protarsi of males but absent on the male meso- and metatarsi and on females. In Tricondyla pulchripes, an arboreal species living on stems, we found three types of adhesive setae on male protarsi, while only two types of setae were found on male meso- and metatarsi and on females. In Neocollyris linearis, an arboreal species living on leaves, we found three types of adhesive setae on male pro-, meso- and meta-tarsi but only two types of adhesive setae on females. The adaptive evolution of these adhesive structures was probably driven by the selective pressures of both mating behavior and the presence of smooth substrates in the respective environments. It is discussed that the adhesive structures in tiger beetles may be an adaptive evolutionary response to the plant surfaces and may play an important role in species differentiation. [ABSTRACT FROM AUTHOR]

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

Autonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security. [ABSTRACT FROM AUTHOR]