Introduction: Alzheimer’s disease (AD) is a common neurological disorder. Based on clinical characteristics, it can be categorized into normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia (AD). Once the condition begins to progress, the process is usually irreversible. Therefore, early identification and intervention are crucial for patients. This study aims to explore the sensitivity of fNIRS in distinguishing between SCD and MCI.Methods: An in-depth analysis of the Functional Connectivity (FC) and oxygenated hemoglobin (HbO) characteristics during resting state and different memory cognitive tasks is conducted on two patient groups to search for potential biomarkers. The 33 participants were divided into two groups: SCD and MCI.Results: Functional connectivity strength during the resting state and hemodynamic changes during the execution of Verbal Fluency Tasks (VFT) and MemTrax tasks were measured using fNIRS. The results showed that compared to individuals with MCI, patients with SCD exhibited higher average FC levels between different channels in the frontal lobe during resting state, with two channels’ FC demonstrating significant ability to distinguish between SCD and MCI. During the VFT task, the overall average HbO concentration in the frontal lobe of SCD patients was higher than that of MCI patients from 5 experimental paradigm. Receiver operating characteristic analysis indicated that the accuracy of the above features in distinguishing SCD from MCI was 78.8%, 72.7%, 75.8%, and 66.7%, respectively.Discussion: fNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia.

The natural interaction between robots and pedestrians in the process of autonomous navigation is crucial for the intelligent development of mobile robots, which requires robots to fully consider social rules and guarantee the psychological comfort of pedestrians. Learning-based socially adaptive algorithms have shown promise in specific humanrobot interaction environments, yet generalization of robot path planning requires improvement. This work combines the generative adversarial network (GAN) with the Optimal Rapidly-exploring Random Tree (RRT*) navigation algorithm. It proposes a GAN model with strong generalization performance and a GAN-RRT* algorithm for path generation in human-robot interaction environments. Additionally, a socially adaptive path planning framework named GAN-RTIRL is presented, which combines GAN with Rapidly-exploring random Trees Inverse Reinforcement Learning (RTIRL) to enhance the homotopy rate between planned and demonstration paths. The GAN-RRT* path planner can update the GAN model from the demonstration path, enabling the robot to generate more anthropomorphic paths and have stronger generalization in complex environments. Experimental results indicate that the proposed method effectively improves the anthropomorphic degree of robot motion planning and the homotopy rate

Numerous plants evolve ingeniously microcantilever‐based hairs to ultra‐sensitively detect out‐of‐plane quasi‐static tactile loads, providing a natural blueprint for upgrading the industrial static mode microcantilever sensors, but how do the biological sensory hairs work mechanically? Here, the action potential‐producing trigger hairs of carnivorous Venus flytraps (Dionaea muscipula) are investigated in detail from biomechanical perspective. Under tiny mechanical stimulation, the deformable trigger hair, composed of distal stiff lever and proximal flexible podium, will lead to rapid trap closure and prey capture. The multiple features determining the sensitivity such as conical morphology, multi‐scale functional structures, kidney‐shaped sensory cells, and combined deformation under tiny mechanical stimulation are comprehensively researched. Based on materials mechanics, finite element simulation, and bio‐inspired original artificial sensors, it is verified that the omnidirectional ultra‐sensitivity of trigger hair is attributed to the stiff‐flexible coupling of material, the double stress concentration, the circular distribution of sensory cells, and the positive local buckling. Also, the balance strategy of slender hair between sensitivity and structural stability (i.e., avoiding disastrous collapse) is detailed revealed. The unique basic biomechanical mechanism underlying trigger hairs is essential for significantly enhancing the performance of the traditional industrial static mode microcantilever sensors, and ensure the stability of arbitrary load perception.