Optical Coherence Tomography and Angiography Image Enhancement Using Optical Clearing Agent Tartrazine.

Optical coherence tomography (OCT) provides high-resolution, non-invasive visualization of biological tissues, enabling structural imaging and the visualization of microvascular networks. However, tissue scattering remains a significant limitation for achieving deeper imaging penetration and improved image quality. Optical clearing agents help minimize this scattering, enhancing OCT imaging capabilities.

An Autonomous Localization Vest System Based on Advanced Adaptive PDR with Binocular Vision Assistance.

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors.

How Does AI Trust Foster Innovative Performance Under Paternalistic Leadership? The Roles of AI Crafting and Leader's AI Opportunity Perception.

As artificial intelligence (AI) becomes increasingly embedded in organizational development, understanding how leadership shapes employee responses to AI is critical for fostering workplace innovation. Drawing on trait activation theory, this study develops a theoretical model in which employee AI trust enhances innovative performance through AI crafting. Paternalistic leadership serves as a situational moderator, while the leader's AI opportunity perception functions as a higher-order moderator.

The Dark Side of Employee's Leadership Potential: Its Impact on Leader Jealousy and Ostracism.

In today's rapidly evolving organizations, talent management plays a critical role in driving sustainable growth. Talents, particularly those exhibiting leadership potential, are often seen as essential assets for organizational development. However, the presence of high employee's leadership potential can also generate adverse emotional reactions from leaders, potentially leading to behaviors such as leader jealousy and leader ostracism.

Data Compliance Utilization Method Based on Adaptive Differential Privacy and Federated Learning.

Federated learning (FL), as a method that coordinates multiple clients to train models together without handing over local data, is naturally privacy-preserving for data. However, there is still a risk that malicious attackers can steal intermediate parameters and infer the user's original data during the model training, thereby leaking sensitive data privacy. To address the above problems, we propose an adaptive differential privacy blockchain federated learning (ADP-BCFL) method to accomplish the compliant use of distributed data while ensuring security.

A dual-mode LiDAR system enabled by mechanically tunable hybrid cascaded metasurfaces.

Light detection and ranging (LiDAR) is widely used for active three-dimensional (3D) perception. Beam scanning LiDAR provides high accuracy and long detection range with limited detection efficiency, while flash LiDAR can achieve high-efficiency detection through the snapshot approach at the expense of reduced accuracy and range. With the synergy of these distinct detection approaches, we develop a miniaturized dual-mode, reconfigurable beam forming device by cascading Pancharatnam-Berry phase and propagation phase metasurfaces, integrated with a micro-actuator.

A versatile miniature two-photon microscope enabling multicolor deep-brain imaging.

Here we present FHIRM-TPM 3.0, a 2.6 g miniature two-photon microscope capable of multicolor deep-brain imaging in freely behaving mice.

Chiral petal honeycomb metamaterial structures: Biomimetic design and application in vascular stents.

Auxetic structure has a very good application prospect in biomedical engineering as implant and stent design, but this scenario faces the difficulty of synergistic optimization of multiple performance indexes. This paper aims to break through this dilemma through innovative design concepts and methods. The study integrates bio-morphology and engineering biomimicry to design a chiral petal-type honeycomb structure by extracting the cross symmetry of cruciferous plant petals and the mechanical properties of wind turbine blades.

Research on quality and safety risk identification of import and export toys based on the WOA-BP model.

Within customs risk management, the weak ability to identify quality and safety risks of import and export toys-special products with high safety sensitivity-leads to persistently high recall rates. This study constructs a "data collection-risk classification-risk identification" framework integrating LDA and WOA-BP to address this. First, 2010-2024 toy recall texts from international systems are preprocessed.

Research on Ground Point Cloud Segmentation Algorithm Based on Local Density Plane Fitting in Road Scene.

In road scenes, the collected 3D point cloud data is usually accompanied by a large amount of interference mainly composed of ground point clouds and the property of uneven density distribution, which will bring difficulties to subsequent recognition and prediction. To address these problems, this paper proposes a ground point cloud segmentation algorithm based on local density plane fitting. Firstly, for the uneven density distribution of 3D point clouds, density segmentation is used to obtain several regions with balanced density.

Stimulation intensities influence the effects of spinal cord stimulation in disorders of consciousness: an fNIRS study.

Significance: Disorders of consciousness (DOCs) pose significant challenges for therapeutic intervention. Spinal cord stimulation (SCS) has emerged as a promising neuromodulation technique for treating DOC patients. However, the selection of optimal SCS stimulation parameters, particularly intensity, lacks objective standards, and considerable variations in the configuration of stimulation intensity are evident among different research groups in previous studies.

Energy-landscape analysis of brain network dynamics in a multi-center Alzheimer's disease and mild cognitive impairment cohort.

Convergent dynamic functional connectivity studies have demonstrated their potential as a hallmark for capturing the impairments in brain function associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, understanding whole-brain dynamics patterns remains limited, hampering the understanding of cognitive impairment and symptomatology for AD and MCI. An energy-landscape analysis was conducted to investigate brain dynamics across seven large-scale networks in 516 normal controls (NC), 404 AD patients, and 441 MCI participants from a multi-center cohort.

Ru-RuO Coordinated with Si-Supported GaN Nanowires for Light-Driven Simultaneous Activation of CO and H to Achieve Exceptional Reverse Water-Gas Shift Reaction.

The reverse water-gas shift (RWGS) reaction presents an industrial promise for carbon dioxide valorization. In this work, a dual-site Ru-RuO cocatalyst anchored on Si-supported GaN nanowires (NWs) is developed for solar-powered CO-to-CO conversion. The assembled Si-supported GaN NWs/Ru-RuO delivers a considerable CO rate of 57.

MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization.

Cross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately.

Measurement Techniques for Highly Dynamic and Weak Space Targets Using Event Cameras.

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras-drawing inspiration from biological vision-can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications.

Influence of Flow Field on the Imaging Quality of Star Sensors for Hypersonic Vehicles in near Space.

When hypersonic vehicles fly in near space, the flow field near the optical window leads to light displacement, jitter, blurring, and energy attenuation of the star sensor. This ultimately affects the imaging quality and navigation accuracy. In order to investigate the impact of aerodynamic optical effects on imaging, the fourth-order Runge-Kutta and the fourth-order Adams-Bashforth-Moulton (ABM) predictor-corrector methods are used for ray tracing on the density data.

A Review of Three-Dimensional Electric Field Sensors.

Three-dimensional electric field sensors (3D EFSs) can simultaneously measure electric field components in three mutually orthogonal directions and comprehensively capture the spatial distribution and dynamic changes of the electric field. They can be widely used in atmospheric science, smart grids, aerospace, target detection, and other fields. This paper deeply analyzes the latest progress in 3D EFSs, focusing on four major types of sensors: DC field mill, electro-optic effect, capacitive sensing, and microelectromechanical system (MEMS).

Altitude aware trajectory prediction methods for non towered terminal airspace.

In non-towered terminal airspace, the lack of real-time air traffic control makes accurate trajectory prediction a critical task for aviation safety. To address the challenges of trajectory prediction caused by frequent altitude changes in this airspace, we propose ACTrajNet, an altitude-aware trajectory prediction model. The model independently extracts altitude features using temporal convolutional networks(TCN), it then incorporates a channel attention fusion mechanism to dynamically fuse altitude features into the trajectory representation across different channels.

Exploring 400 Gbps/λ and beyond with AI-accelerated silicon photonic slow-light technology.

Silicon photonics is a promising platform for the extensive deployment of optical interconnections, with the feasibility of low-cost and large-scale production at the wafer level. However, the intrinsic efficiency-bandwidth trade-off and nonlinear distortions of pure silicon modulators result in the transmission limits, which raises concerns about the prospects of silicon photonics for ultrahigh-speed scenarios. Here, we propose an artificial intelligence (AI)-accelerated silicon photonic slow-light technology to explore 400 Gbps/λ and beyond transmission.

An Image-Free Single-Pixel Detection System for Adaptive Multi-Target Tracking.

Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes a single-pixel detection system for adaptive multi-target tracking based on the geometric moment and the exponentially weighted moving average (EWMA).

Single-Step Hydrothermal Synthesis of Two-Dimensional Ammonium Tungsten Bronze and Its Properties.

Two-dimensional (2D) ammonium tungsten bronze as a doped degenerate semiconductor material has attracted extensive attention. Due to its special channel structure, large specific surface area, and high activity, it is fully utilized in various types of optoelectronic devices. The one-step hydrothermal synthesis method is not only simple to operate but also highly repeatable.

Multi-Stage BiSTU Network Combining BiLSTM and Transformer for ABP Waveform Prediction from PPG Signals.

Purpose: Cardiovascular disease (CVD) remains a global health issue, and arterial blood pressure (ABP) waveforms provide critical physiological data that aid in the early diagnosis of CVD. However, existing pulse waveform evaluation methods are insufficient for accurately predicting ABP. This study aims to propose a novel U-net joint network architecture, the BiSTU Sequential Network, to predict high-quality ABP waveforms.

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Metabolic molecules are highly correlated with various physiological indicators and diseases, so it is particularly important to monitor the levels of multiple metabolites in the body. Due to the similar electrochemical properties of uric acid (UA), dopamine (DA), and ascorbic acid (AA), multi-component detection of these substances is challenging. When establishing relationships between electrochemical characteristics and concentrations of the respective components, there will be issues such as overlapping peaks and other difficulties.

EFCNet for small object detection in remote sensing images.

Object detection, as a crucial component of remote sensing image processing, has become one of the primary methods with the maturation of deep learning technologies. Nonetheless, detecting small objects in remote sensing images remains a significant challenge. Addressing this issue, this study proposes an enhanced network model based on You Only Look Once YOLOv5, aimed at improving the detection capabilities for small objects in remote sensing images.