Abstract Given that type I photosensitizers (PSs) possess a good hypoxic tolerance, developing an innovative tactic to construct type I PSs is crucially important, but remains a challenge. Herein, we present a smart molecular design strategy based on the Förster resonance energy transfer (FRET) mechanism to develop a type I photodynamic therapy (PDT) agent with an encouraging amplification effect for accurate hypoxic tumor therapy. Of note, benefiting from the FRET effect, the obtained nanostructured type I PDT agent (NanoPcSZ) with boosted light‐harvesting ability not only amplifies superoxide radical (O 2 •‐ ) production but also promotes heat generation upon near‐infrared light irradiation. These features facilitate NanoPcSZ to realize excellent phototherapeutic response under both normal and hypoxic environments. As a result, both in vitro and in vivo experiments achieved a remarkable improvement in therapeutic efficacy via the combined effect of photothermal action and type I photoreaction. Notably, NanoPcSZ can be eliminated from organs (including the liver, lung, spleen, and kidney) apart from the tumor site and excreted through urine within 24 h of its systemic administration. In this way, the potential biotoxicity of drug accumulation can be avoided and the biosafety can be further enhanced.
<abstract> <p>The discrete element is an important tool for vibration compaction simulation from the microscopic viewpoint. The irregular particle model was established by the disc filling method, and the linear contact model with anti-rolling was selected to reflect the contact characteristics between the particles, so as to establish the simulation model of subgrade vibratory compaction. Based on this model, the stress characteristics of the area below the center of the vibrating wheel and the surface area of the soil were studied, and the principle of vibratory compaction was discussed. The results show that the distribution of vertical stresses below the center of drum basically presents a decreasing trend in the depth range during vibration, with the stress amplitude of the lower structure increasing and the stress magnitude of the upper structure decreasing. The distribution of horizontal stresses in the area below the center of the vibrating wheel is similar to the stress distribution in the splitting test. The soil at the surface has an obvious pushing and squeezing effect, and the transmission distance of horizontal stresses is larger than that of vertical stresses. The soil at the surface is pushed and the horizontal stresses are transmitted at a greater distance than the vertical stresses, which, together with a certain degree of shear effect, causes a certain uplift deformation of the soil around the vibrating wheel. In general, the vibration compaction process is relatively consistent with the theory of repeated loading and the theory of alternating shear strain.</p> </abstract>
Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion. Traditional methods in TSC, primarily based on transportation engineering and reinforcement learning (RL), often exhibit limitations in generalization across varied traffic scenarios and lack interpretability. This paper presents LLMLight, a novel framework employing Large Language Models (LLMs) as decision-making agents for TSC. Specifically, the framework begins by instructing the LLM with a knowledgeable prompt detailing real-time traffic conditions. Leveraging the advanced generalization capabilities of LLMs, LLMLight engages a reasoning and decision-making process akin to human intuition for effective traffic control. Moreover, we build LightGPT, a specialized backbone LLM tailored for TSC tasks. By learning nuanced traffic patterns and control strategies, LightGPT enhances the LLMLight framework cost-effectively. Extensive experiments on nine real-world and synthetic datasets showcase the remarkable effectiveness, generalization ability, and interpretability of LLMLight against nine transportation-based and RL-based baselines.
As biomechanics research evolves, the significance of motion capture technology in domains like film and television, gaming, and motion training is progressively gaining prominence, with an expanding repertoire of applications. Optical motion capture, employing optical dynamic capture equipment founded on a 3D imaging analysis system within a specified setting, stands as the most mature technique. However, its high cost and constraints pertaining to venue and environmental factors limit its usage. Conversely, inertial motion capture devices, functional under non-optical conditions, can cater to the needs of the broader public. To further enhance affordability, portability, and simplification of the device, this study proposes an embedded system design grounded on a nine-axis inertial sensor. It captures the posture of each joint in the human upper limb and wirelessly transmits the data to the Unreal Engine 3Darmmodel for action replication and motion trajectory delineation. Thus, it generates feedback results facilitating remote motion control.
For crowd counting task, it has been demonstrated that imposing Gaussians to point annotations hurts generalization performance. Several methods attempt to utilize point annotations as supervision directly. And they have made significant improvement compared with density-map based methods. However, these point based methods ignore the inevitable annotation noises and still suffer from low robustness to noisy annotations. To address the problem, we propose a bipartite matching based method for crowd counting with only point supervision (BM-Count). In BM-Count, we select a subset of most similar pixels from the predicted density map to match annotated pixels via bipartite matching. Then loss functions can be defined based on the matching pairs to alleviate the bad effect caused by those annotated dots with incorrect positions. Under the noisy annotations, our method reduces MAE and RMSE by 9% and 11.2% respectively. Moreover, we propose a novel ranking distribution learning framework to address the imbalanced distribution problem of head counts, which encodes the head counts as classification distribution in the ranking domain and refines the estimated count map in the continuous domain. Extensive experiments on four datasets show that our method achieves state-of-the-art performance and performs better crowd localization.
Transport Information Service for the Public (TISP) is one of the important and applicable fields in ITS. This paper describes the latest development of TISP in China. First, the definitions of TISP in Chinese National ITS Architecture are described. Then, the development and application of TISP in China are presented, categorized by the Service Providers, such as Traffic Management Departments, Public Transport Operators, and Highway Operators, and other comprehensive Service Providers. Some relative achievements and current works are introduced, such as Construction and Operation Mechanisms for TISP, Traffic Data Collection and Processing, and Demonstration Project of MOC. Finally, this paper presents some future issues on TISP, including both the bottleneck to hamper the development of TISP and the vision of TISP in China.
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