Interpersonal brain synchronization during sensorimotor synchronization in people with different aerobic fitness levels: A fNIRS-based hyperscanning study.

Sensorimotor synchronization (SMS) refers to the temporal coordination of individual actions with perceptible external events and rhythms. Previous research has revealed a potential relationship between SMS abilities and physical activity, with proposed links to underlying inner-brain cognitive processes. However, it needs to be explored that whether good aerobic fitness will have a strong SMS ability, its internal mechanism, and the inter-brain mechanism.

Fixed-time optimal time-varying formation control for unmanned surface vehicle systems based on reinforcement learning.

This article proposes the distributed fixed-time optimal time-varying formation control (TVFC) strategy based on reinforcement learning (RL) for unmanned surface vehicle systems (USVSs) with partially unmeasurable states and unknown dynamics. The fixed-time adaptive neural network state observer (FANNSO) is introduced for reconstructing unknown dynamics and unmeasurable states of the system. On this basis, a distributed optimization performance index function containing exponential terms is proposed, and a distributed fixed-time optimal TVFC strategy is developed by combining the actor-critic structure.

Serum LCAT as a Gender-Neutral Biomarker for Early Osteoporosis: A Multicenter Cohort Validation Study.

Purpose: This study aims to identify a novel biomarker for the early detection and prevention of osteoporosis.

Methods: A cross-sectional study (n=138, from 497 screened) was conducted at Nanjing Drum Tower Hospital (Sep 2023-Jun 2024). A second multicenter validation cohort (n=165) was collected (Jun 2024-Jan2025) from three hospitals, including 90 controls, 35 osteopenia, and 40 OP cases.

A Deep Learning Model for Predicting the Cement Soil Deformation Modulus.

Cement, widely used for backfill grouting in shield tunnels, plays a crucial role in maintaining the stability of tunnel structures. To enhance the prediction of cement performance, this study focuses on the elastic modulus () and introduces a novel prediction model based on machine learning─the improved Convolutional Long Short-term Memory (ConvLSTM) model. The model is structured into two key components: differentiating parameter importance and extracting potential spatiotemporal order dependence among features.

Ionic liquid modified MOF-808 for efficient adsorption and stable capture of radioactive iodine.

Radioactive iodine (I) is a potential risk to environmental safety and human health. The design of efficient and stable functional materials for I capture remains a challenge. In this work, a simple in-situ impregnation method is used to load Ionic Liquid (IL)-[Cmim][TfN] into the pores of MOF-808, named IL@MOF.

Nondestructive freshness recognition of chicken breast meat based on deep learning.

Identifying chicken breast freshness is an important component of poultry food safety. Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. In this study, the YOLOv8n_CA_DSC3 algorithm is employed for non-destructive recognition of chicken breast freshness.

Structural Optimization and Performance Analysis of Acoustic Metamaterials with Parallel Unequal Cavities.

Noise reduction for manufacturing enterprises is favorable for workers because it relieves occupational diseases and improves productivity. An acoustic metamaterial with parallel, unequal cavities is proposed and optimized, aiming to achieve an optimal broadband sound absorber in the low-frequency range with a limited total thickness. A theoretical model for the acoustic metamaterial of a hexagonal column with 6 triangular cavities and 12 right-angled trapezoidal cavities was established.

Research on the Optimization of the Electrode Structure and Signal Processing Method of the Field Mill Type Electric Field Sensor.

Aiming at the issues that the field mill type electric field sensor lacks an accurate and complete mathematical model, and its signal is weak and contains a large amount of harmonic noise, on the basis of establishing the mathematical model of the sensor's induction electrode, the finite element method was adopted to analyze the influence laws of parameters such as the thickness of the shielding electrode and the distance between the induction electrode and the shielding electrode on the sensor sensitivity. On this basis, the above parameters were optimized. A signal processing circuit incorporating a pre-integral transformation circuit, a differential amplification circuit, and a bias circuit was investigated, and a completed mathematical model of the input and output of the field mill type electric field sensor was established.

Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission.

This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g.

Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach.

Polymer/graphene (Py/GN) composites under the influence of external electric fields often exhibit unique nonlinear conducting behaviors. However, the underlying mechanism of this field effect at the molecular level is still obscure until now. Herein, the evolution of electrical properties of Py/GN composites induced by electric fields has been explored by combining high-throughput first-principles calculations with machine learning models.

Feasibility of ultrafast DCE-MRI for identifying benign and malignant breast lesions.

Objectives: This study investigates whether the use of ultrafast DCE-MRI immediately after contrast injection is an alternative to conventional DCE-MRI for diagnosing benign and malignant breast lesions.

Methods: A total of 86 female patients were included in this prospective study. Each patient underwent both ultrafast DCE-MRI and conventional DCE-MRI before surgery.

Research on Displacement Monitoring of Key Points in Caverns Based on Distributed Fiber Optic Sensing Technology.

The accurate and real-time monitoring of key-point displacements in cavern structures is crucial for assessing structural safety and stability. However, traditional monitoring methods often fail to meet the high-precision requirements in complex environments. This study explored the potential application of fiber optic sensors in monitoring key-point displacements by leveraging their sensitivity to optical parameters and spectral changes.

Research on the Method of Optimizing the Stress and Improving the Performance for MEMS Gyroscope Based on the Cantilever-Plate Structure.

Thermal stress is one of the most important factors damaging the temperature-dependent performance of MEMS gyroscopes. To reduce thermal stress and improve their performance, this paper deduced the production and effects of thermal stress on a high-precision MEMS butterfly gyroscope theoretically, which provided a basis for optimization and experiments. A novel cantilever plate structure was designed based on the working modes of the MEMS butterfly gyroscope and optimized based on our simulation to achieve stress isolation.

Study on the influence of topological structures and soft magnetic materials on the performance of high-speed motors.

The high-speed permanent magnet synchronous motor is one of the crucial components of air compressors, where motor efficiency and noise-vibration issues are the main challenges in the design of high-speed motors. Topology and soft magnetic materials commonly constitute the key factors behind the suitable design of high-speed motors that directly affect their performance. This study designs and analyzes four different 25 kW, 95 000 rpm air compressors with two types of topological structures, namely 6-slot 2-pole and 12-slot 4-pole (12S4P), using two different soft magnetic materials, namely thin 15SW1200 silicon sheet and amorphous magnetic alloy AYFA_Z.

Arthroscopic Suture Bridge Combined All-Inside Fixation for Delaminated Rotator Cuff Tears.

The anatomic repair of delaminated rotator cuff tears presents a surgical challenge, necessitating innovative solutions for optimal outcomes. This Technical Note describes the arthroscopic suture bridge combined with all-inside fixation, a novel approach tailored for small yet deep-layer dominant delaminated tears. The method involves addressing the upper-layer tear beneath the acromion and employing all-inside suturing in the glenohumeral joint for the lower-layer tear, decoupling the outer tear size from limiting the treatment of the deeper tear.

Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage.

Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. In this context, spiking neural networks show the ability to resist Gaussian noise, spike interference, and AC electric field interference by adjusting synaptic plasticity. The anti-interference ability to spike neural networks has become an important direction of electromagnetic protection bionics research.

Slot Allocation Protocol for UAV Swarm Ad Hoc Networks: A Distributed Coalition Formation Game Approach.

With the rapid development of unmanned aerial vehicle (UAV) manufacturing technology, large-scale UAV swarm ad hoc networks are becoming widely used in military and civilian spheres. UAV swarms equipped with ad hoc networks and satellite networks are being developed for 6G heterogeneous networks, especially in offshore and remote areas. A key operational aspect in large-scale UAV swarm networks is slot allocation for large capacity and a low probability of conflict.

A wearable obstacle avoidance device for visually impaired individuals with cross-modal learning.

It is challenging for wearable obstacle avoidance devices to simultaneously meet practical demands of high reliability, rapid response, long-lasting duration, and usable design. Here we report a wearable obstacle avoidance device, comprising a set of self-developed glasses (weighing  ~400 grams, including an  ~80 grams battery) and a common smartphone. Specifically, the glasses collect the multi-modal data for comprehensive environmental perception, including video and depth modalities, and implement a depth-aided video compression module.

Accentuated Eccentric Loading in Countermovement Jumps Vs. Drop Jumps: Effects on Jump Performance and Strength in A Randomized Controlled Trial.

This study examined the effects of Accentuated Eccentric Loading Countermovement Jump (AEL CMJ) training on jump performance, lower body strength, sprint performance, and change of direction ability, compared to drop jump (DJ) training. This study used a randomized controlled trial (RCT) with a parallel design. Forty men physical education students (Mean ± SD: age 22.

Intelligent anti-jamming decision algorithm for wireless communication under limited channel state information conditions.

Deep reinforcement learning has been widely applied to solve the anti-jamming problems in wireless communications, achieving good results. However, most research assumes that the communication system can obtain complete Channel State Information (CSI). Under limited CSI conditions, this paper models the system using Partially Observable Markov Decision Processes (POMDPs).

Interlayer Parallel Connection of Multiple Helmholtz Resonators for Optional Broadband Low Frequency Sound Absorption.

The Helmholtz resonance acoustic metamaterial is an effective sound absorber in the field of noise reduction, especially in the low-frequency domain. To overcome the conflict between the number of Helmholtz resonators and the volume of the rear cavity for each chamber with a given front area of single-layer metamaterial, a novel acoustic metamaterial of interlayer parallel connection of multiple Helmholtz resonators (IPC-MHR) is proposed in this study. The developed IPC-MHR consists of several layers, and the Helmholtz resonators among different layers are connected in parallel.

Offline prompt reinforcement learning method based on feature extraction.

Recent studies have shown that combining Transformer and conditional strategies to deal with offline reinforcement learning can bring better results. However, in a conventional reinforcement learning scenario, the agent can receive a single frame of observations one by one according to its natural chronological sequence, but in Transformer, a series of observations are received at each step. Individual features cannot be extracted efficiently to make more accurate decisions, and it is still difficult to generalize effectively for data outside the distribution.

Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy.

The rapid proliferation of mobile social networks has significantly accelerated the dissemination of misinformation, posing serious risks to social stability, public health, and democratic processes. Early detection of misinformation is essential yet challenging, particularly in contexts where initial content propagation lacks user feedback and engagement data. This study presents a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the detection of misinformation using only textual content.

On the Properties of New Polyurethane Fast-Curing Polymer Materials.

A sequences of unconfined compressive strength tests and flexural tests were conducted in this study to evaluate the curing performance of a new type of polyurethane sand fast-curing polymer material. The mechanical properties of the material were investigated under different curing temperatures (-10 °C to 60 °C), particle sizes (10-15 mesh, 60-80 mesh, 100-120 mesh, and 325 mesh), and material proportions (20% to 60%). Additionally, SEM analysis was employed to further reveal the reinforcement mechanism.

Generation of Multiple-Depth 3D Computer-Generated Holograms from 2D-Image-Datasets Trained CNN.

Generating computer-generated holograms (CGHs) for 3D scenes by learning-based methods can reconstruct arbitrary 3D scenes with higher quality and faster speed. However, the homogenization and difficulty of obtaining 3D high-resolution datasets seriously limit the generalization ability of the model. A novel approach is proposed to train 3D encoding models based on convolutional neural networks (CNNs) using 2D image datasets.