Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRT-based improved algorithms still suffer from the problems of difficult to design the hyper-parameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot collaboration, real-time path planning, self-tuning of hyper-parameters, task- or application-scene oriented algorithms and hardware design, and path planning in highly dynamic environments are future trends.

UNASSIGNED: Spontaneous Adverse Event Reporting (SAER) databases play a crucial role in post-marketing drug surveillance. However, the traditional model-free disproportionality analysis has been challenged by the insufficiency in investigating subgroup and confounders. These issues result in significant low-precision and biases in data mining for SAER.
UNASSIGNED: The Model-Driven Reporting Odds Ratio (MD-ROR) was proposed to bridge the gap between SAER database and explainable models for exploring individual and confounding effects. MD-ROR is grounded in a well-designed model, rather than a 2 × 2 cross table, for estimating AE-drug signals. Consequently, individual and confounding effects can be parameterized based on these models. We employed simulation data and the FDA Adverse Event Reporting System (FAERS) database.
UNASSIGNED: The simulated data indicated the subgroup effects estimated by MD-ROR were unbiased and efficient. Moreover, the adjusted-MD-ROR demonstrated greater robustness against confounding biases than the crude ROR. Applying our method to the FAERS database suggested higher occurrences of drug interactions and cardiac adverse events induced by Midazolam in females compared to males.
UNASSIGNED: The study underscored that MD-ROR holds promise as a method for investigating individual and confounding effects in SAER databases.

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the \"beam squint\" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the \"beam squint\" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Cyber-physical systems (CPSs) based on space-ground integrated networks (SGINs) enable CPSs to break through geographical restrictions in space. Therefore, providing a test platform is necessary for new technical verification and network security strategy evaluations of SGINs. User behavior emulation technology can effectively support the construction of a test platform. Given the inherent dynamic changes, diverse behaviors, and large-scale characteristics of SGIN users, we propose user behavior emulation technology based on a cloud platform. First, the dynamic emulation architecture for user behavior for SGINs is designed. Then, normal user behavior emulation strategy driven by the group user behavior model in real time is proposed, which can improve the fidelity of emulation. Moreover, rogue user behavior emulation technology is adopted, based on traffic replay, to perform the security evaluation. Specifically, virtual Internet Protocol (IP) technology and the epoll model are effectively integrated in this investigation to resolve the contradiction between large-scale emulation and computational overhead. The experimental results demonstrate that the strategy meets the requirement of a diverse and high-fidelity dynamic user behavior emulation and reaches the emulation scale of 100,000-level concurrent communication for normal users and 100,000-level concurrent attacks for rogue users.