A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. Evaluated on real-world data based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations

A new strategy for controlling invasive weeds: selecting valuable native plants to defeat them

To explore replacement control of the invasive weed Ipomoea cairica, we studied the competitive effects of two valuable natives, Pueraria lobata and Paederia scandens, on growth and photosynthetic characteristics of I. cairica, in pot and field experiments. When I. cairica was planted in pots with P. lobata or P. scandens, its total biomass decreased by 68.7% and 45.8%, and its stem length by 33.3% and 34.1%, respectively. The two natives depressed growth of the weed by their strong effects on its photosynthetic characteristics, including suppression of leaf biomass and the abundance of the CO 2 -fixing enzyme RUBISCO. The field experiment demonstrated that sowing seeds of P. lobata or P. scandens in plots where the weed had been largely cleared produced 11.8-fold or 2.5-fold as much leaf biomass of the two natives, respectively, as the weed. Replacement control by valuable native species is potentially a feasible and sustainable means of suppressing I. cairica

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

PanoDetNet: Multi-Resolution Panoramic Object Detection With Adaptive Feature Attention

Panoramic image object detection has significant applications in autonomous driving, robotic navigation, and security monitoring. However, most current object detection algorithms are trained on pinhole images and cannot be directly applied to panoramic images, which have a large field-of-view (FOV) and distortion. Additionally, research on panoramic image object detection lacks dedicated dataset support, and these images face challenges such as target distortion, occlusion, and multi-scale variations. Existing methods for panoramic image object detection have not yielded satisfactory performance. To address these issues, we propose PanoDetNet, an object detection model based on YOLOv7. We introduce two new modules: the Multi-Scale Feature Fusion (MSFF) module and the Adaptive Panoramic Feature Attention (APFA) module. The MSFF module enhances detection precision for targets of different scales by fusing feature maps of various sizes, while also reducing the number of parameters and simplifying the model structure. The APFA module adaptively addresses the distortion in panoramic images, improving the model’s ability to locate and recognize objects in complex backgrounds and under occlusion. We trained PanoDetNet on our self-built panoramic image object detection dataset, PanoDet. This dataset was collected using a self-developed panoramic camera and manually annotated with the Labelme tool. Experimental results show that PanoDetNet achieves [email protected], [email protected]:.95, and accuracy scores of 95.3%, 75.2%, and 94.1%, respectively. These results represent improvements of 1.6%, 2.5%, and 3.3% over YOLOv7. Our code is available at https://github.com/github98317/PanoDetNet

Photosynthetic characteristics and light energy conversions under different light environments in five tree species occupying dominant status at different stages of subtropical forest succession

In order to reveal the mechanism of succession in subtropical forest along a light gradient, we investigated photosynthetic physiological responses to three light environments in five tree species including a pioneer species Pinus massoniana Lamb., two mid-successional species Schima superba Gardn. et Champ. and Castanopsis fissa (Champ. ex Benth.) Rehd. et Wils., and two late-successional species Cryptocarya concinna Hance. and Acmena acuminatissima (BI.) Merr et Perry) that were selected from Dinghu Mountain subtropical forest, South China. Results showed that, among the three kinds of species in all light conditions (100%, 30% and 12% of full sunlight), the pioneer species had the highest photosynthetic capacity (Amax), light saturation point (LSP), carboxylation efficiency (CE) and maximum utilisation rate of triose phosphate (TPU) that characterised a strong photosynthetic capacity and high carbon dioxide uptake efficiency. However, a higher light compensation point (LCP) and dark respiration (Rd) as well as lower apparent quantum yield (AQY) indicated that the pioneer specie cannot adapt to low light conditions. Mid-successional species had photosynthetic characteristics in between pioneer and late-successional species, but had the greatest effective quantum yield of PSII (ΦPSII) and light use efficiency (LUE, expressed in terms of photosynthesis). In contrast to pioneer and mid-successional species, late-successional species had lower photosynthetic capacity and carbon uptake efficiency, but higher shade tolerance and high-light heat dissipation capacity, as characterised by higher levels of total xanthophyll cycle pigments (VAZ) and de-epoxidation state of xanthophyll cycle (DEPs). These results indicate that photosynthetic capacity decreases along the successional axis and that late-successional species have more responsive heat dissipation capability to compensate for their inferior photosynthetic capacity

Anthocyanins function as a light attenuator to compensate for insufficient photoprotection mediated by nonphotochemical quenching in young leaves of Acmena acuminatissima in winter

Anthocyanins and nonphotochemical quenching (NPQ) are two important tools that provide photoprotection in plant leaves. In order to understand how plants use these tools for acclimation to changing seasonal conditions, we investigated pigments, antioxidative capacity, and photosynthesis in leaves of an evergreen tree (Acmena acuminatissima) in two contrasting seasons. Young leaves of A. acuminatissima appeared in distinct colors, being light green in summer and red in winter due to the presence of anthocyanins. In the winter young leaves, anthocyanins contributed less than 2% to the antioxidant pool. In the summer, young leaves had higher NPQ than that of mature leaves, but in the winter, they did not derive any NPQ-related advantage over mature leaves. These results suggest that the accumulation of anthocyanins in young leaves in the winter may compensate for the insufficient photoprotection afforded by NPQ and that anthocyanins function as a light attenuator to protect the photochemical apparatus against excess light.This work was funded by the National Natural Science Foundation of China (31570398, 31270287). The study was also supported by the key program of Guangdong Province Natural Science Foundation (2014A070713039; 2015A030311023; 2016A030303063