This study uses MEA-based graph deviation networks to predict autism syndrome signatures in human forebrain organoids exposed to valproic acid. The results show that VPA exposure induces detectable network-level functional alterations, with session-specific models achieving high accuracy in class separation.
A new hybrid approach combines neural perception and acoustic feature learning to detect acoustic targets with high accuracy. The system leverages neuroanatomical priors, confidence-driven fusion, and streaming-mode validation to achieve robust performance.
This innovative module combines vibration-assisted penetration and real-time force sensing to reduce tissue damage and inflammation risks. The device achieves high precision and accuracy, making it suitable for biomedical applications such as neural probe implantation and single-cell puncture.
This study introduces a carrier-free PDR nanoassembly with a tumor-suppressing peptide, pH-sensitive daunorubicin prodrug, and siRNA targeting the LILRB4 gene, achieving targeted therapy of acute myeloid leukemia. The combination treatment significantly enhances cytotoxicity and anti-leukemia effects.
A new platform integrates magnetic actuation with electrophysiological sensing for high-fidelity, multisite monitoring on dynamic ex vivo tissue surfaces. The soft electrode platform successfully recorded stable signals from cardiac tissue and demonstrated excellent motion control and stretchability.
The study introduces BGMix, which separates task-related components from background EEG to generate valid samples. AETF is designed to process the augmented data and capture EEG's spatiotemporal-frequential features using a Transformer-based attention mechanism. This integrated solution improves the practicality of high-speed SSVEP-bas...
This survey reviews cutting-edge hydrogen tank technologies, exploring how to safely store gaseous or liquid hydrogen in extreme conditions. Key advancements promise to transform aviation, with materials like carbon fiber-reinforced polymers offering exceptional strength-to-weight ratios and reducing the weight of hydrogen tanks.
A new monolithic synaptic device mimics human CT afferents for robot emotional interaction, achieving ultralow threshold sensitivity and high energy efficiency. The device enables real-time processing of gentle touch into emotional signals.
Researchers develop comprehensive model to analyze electrostatic and contact interaction between low-velocity lunar dust and spacecraft. The study focuses on the effects of plasma sheaths and electric charges on dust particles, aiming to improve long-term extravehicular activity and permanent station establishment on the lunar surface.
Legged robots face difficulties in hardware design and control due to impact force and kinematic complexity. Single-leg robots offer a simplified alternative for hopping and biomimetic motion, with various structures, modeling, and control strategies being explored.
A new synchronization method for satellite-ground hopping beam communication is proposed, enabling efficient use of frequency resources and power. The method uses a signaling-assisted fast synchronization technique to match business signals between satellite and ground signal stations.
BioCompNet, a dual-channel 2D U-Net framework, enables automated MRI quantification of multiple tissue compartments, including adipose tissue, muscle, and bone. The model achieves high accuracy and efficiency, with a complete pipeline processing each case in under 1 minute.
This research uses D-STATCOM to dynamically balance loads and supply reactive power at charging stations, improving power quality and reducing energy waste. The study showcases superior reactive power management, stabilizing the low-voltage distribution network and enabling more reliable EV infrastructure.
Researchers develop a game-changing magnetic analysis method to authenticate lithium-ion batteries onboard vehicles, ensuring safety and reliability. The breakthrough enables instant detection of counterfeit or low-quality batteries without invasive checks.
Researchers developed a new P2D-coupled non-ideal double-layer capacitor model to analyze lithium-ion batteries under high-frequency periodic signal excitation. The model considers neglected electric double-layer capacitance and its dispersion effects, enabling more accurate mechanism analysis and performance degradation assessment.
Developed a hybrid sampling technique combining dual-mode distal B-scan imaging measurement to actively locate lesion centers and enable programmable boundary segmentation. The system achieved improved shape fitting accuracy, lowered center error, and enhanced real-time performance.
Researchers propose FedM2CT, a federated metadata-constrained method that enables simultaneous reconstruction of multivendor CT images with different imaging geometries and sampling protocols in one framework. The framework outperforms competing methods on objective metrics, achieving higher PSNR/SSIM and lower RMSE.
Researchers developed wearable components for cyborg insects, preserving natural functions and enabling stable neural responses. The design uses multimaterial 3D printing and surface stimulators to control motion and navigation.
Researchers developed advanced imaging strategies using intelligent micro/nanomotors, enabling enhanced detection sensitivity and real-time tracking of subcellular events. The proposed approach integrates MNMs as dynamic contrast agents for multimodal diagnostics, providing a theoretical framework for bioimaging technologies.
A new SnS₂-based in-sensor reservoir computing device introduces tunable multi-timescale optoelectronic dynamics to address high-speed and low-speed movement recognition challenges. The device achieves 100% accuracy in motion recognition tasks using the Weizmann dataset, surpassing conventional networks like LSTM.
Developed a novel EEG transformer model, Augmenting Electroencephalogram Transformer (AETF), to enhance steady-state visually evoked potential-based brain–computer interface (BCI) systems. The AETF model improves decoding efficiency by capturing temporal, spatial, and frequency features of dynamic EEG signals.
This study evaluated the combined effects of epidural electrical stimulation (EES) and physical therapy (PT) on sensory, motor, and autonomic functions in SCI patients. EES+PT significantly enhanced long-term sensory function, muscle strength, spasticity, and urinary function compared to PT alone.
A novel centimeter-scale quadruped piezoelectric robot demonstrates key advancements in miniaturization, actuation, control, communication, and power supply. The robot achieves fast locomotion, exceptional robustness, and real-time image sensing capabilities.
Researchers developed an acoustofluidic device for efficient isolation and biomarker-specific detection of small extracellular vesicles. The device achieves 6-fold signal enhancement for EGFR-positive sEVs in just 20 minutes, offering a portable and low-cost alternative to Western blotting.
A bioinspired soft robotic system combines a steerable, elongating soft arm with a leech-inspired three-finger grasper to overcome limitations of existing ESD robots. The system achieves high precision, force and endurance, but requires improvements in fine force control, haptic feedback and sterilization for clinical translation.
The multimodal limbless crawling soft robot features a foldable, multistable kirigami skin that maintains stable frictional anisotropy under large deformations. It achieves straight crawling, in-place rotation, and lateral turning, resolving the trade-off among large deformation, controllable friction, and agile steering.
A comprehensive study reveals critical vulnerabilities in smart railways, from data breaches to operational sabotage. Key findings propose comprehensive mitigation strategies, emphasizing human factors and staff training in building cyber-resilient systems.
Artificial neural networks offer superior predictive accuracy in predicting biodiesel properties and enable rapid assessment of diverse feedstock options. Hybrid models combining generative and discriminative approaches achieve significant yield improvements and optimize biodiesel production from waste cooking oil.
Researchers developed a groundbreaking solution to tackle both energy efficiency and ride comfort challenges in electric vehicles. The innovative Teamwork Optimization Algorithm (TOA) dynamically adjusts the motor's magnetic flux for optimal performance, resulting in reduced energy consumption by up to 15%, smoother acceleration with 4...
A groundbreaking study explores how temperature swings, vibrations, humidity, and salt spray affect lithium-ion battery performance in marine environments. Key findings reveal environmental impact on batteries and advanced state estimation methods for real-time optimization.
The proposed method achieves exceptional accuracy of up to 1.6% SOC error under normal conditions and corrects itself within 5 seconds when faced with initial errors, outperforming conventional approaches.
Researchers develop hybrid energy storage system to address solar intermittency challenges. The dual-level design combines lithium-ion batteries with supercapacitors to extend battery lifespan and optimize costs.
The proposed helical microswimmer system achieves real-time obstacle evasion using radar-based navigation fused with a neural-fuzzy motion controller. The integrated control scheme minimizes computational load while ensuring collision-free navigation, enabling a 2.6 Hz update frequency for motion direction.
The robot features a soft electrohydraulic actuator that allows it to adapt to different environments, including land crawling, underwater crawling, and swimming. It achieves high speeds of up to 5.9 cm/s in water and demonstrates excellent temperature adaptability.
A new dual-task learning framework was developed for lateral walking gait recognition and continuous hip joint angle estimation, achieving superior performance compared to existing methods. The framework combined CNN, LSTM, and SEAM for gait phase recognition and a regression network for hip angle estimation.
Researchers developed a self-healing bioadhesive interface to overcome foreign body response and signal instability. The material architecture integrates multiple components, including conductive hydrogels and MXene-silk fibroin composite coatings.
Researchers developed an amplification-free biosensing platform for ultrasensitive detection of F. nucleatum using a tetrahedral DNA nanostructure and electrochemiluminescence. The platform showed improved sensitivity and specificity, enabling detection down to 1 colony-forming unit/ml.
The Second International Conference on Space Science and Technology was held in Suzhou, bringing together over 300 experts from around the world to discuss cutting-edge research and innovative applications in the aerospace field. The conference focused on key technologies and explored solutions to global aerospace research challenges.
A new two-layer active balancing strategy improves energy transfer efficiency and speed by redistributing energy among cells. The layered structure integrates inductor and transformer circuits to balance both within and between battery cell groups, achieving faster equalization and increased energy efficiency.
The proposed framework addresses challenges in autonomous robotic surgery by integrating multiple soft tissue manipulation modes and adapting to dynamic environments. The ID-SAC algorithm outperforms traditional SAC algorithms, achieving faster task completion times and smoother trajectories.
The magnetic shaftless propeller-like millirobot (MSPM) integrates multimodal motion and fluid manipulation, breaking through limitations of existing magnetic miniature robots. It achieves multiple adaptive motion modes, including rolling, propelling, and tumbling, in different terrains.
Researchers at Beijing Institute of Technology developed an efficient method to represent the environment as a hybrid of feasible planar regions and heightmaps. The method accelerates planar region extraction and ensures walking safety for biped robots, completing the perception process in 0.16 s per frame.
A new study presents a learning-free method for hip exoskeleton control, achieving seamless adaptation to continuous locomotion modes without user-specific data training. The proposed three-layer control framework ensures smooth transitions by predicting terrain changes before the end of transition periods.
Researchers developed a simplified CFD model to evaluate the impact of ported shrouds on centrifugal compressor performance. The findings reveal that the ported shroud extends the operational range by approximately 10% and improves pressure ratio near surge limits, enhancing overall system efficiency.
A new study develops advanced machine learning models tailored to Canadian data, offering precise predictions for e-bus energy use under varying climates and heating systems. The research reveals that tree-based models deliver the highest accuracy in predicting energy consumption, with a mean absolute error of just 0.09–0.1 kWh/km.
The research paper proposes a novel motion coordination framework based on multi-task prioritization and null-space projection for physically constrained quadruped manipulators. The framework adapts to different tasks by executing optimal motion while meeting physical constraints and enhancing manipulability.
A novel needle-free reagent injection method has been developed using electrically induced microbubbles to improve the depth of reagent injection. The system reflects shock waves through microbubble dynamics, resulting in increased perforation ability and deeper wound expansion. This technique holds promise for future optimization and ...
A new vision-based tactile sensor family, CrystalTac, has been developed using rapid monolithic manufacturing techniques. The sensors feature unique sensing mechanisms and demonstrate good performance in terms of cost-effectiveness and design flexibility.
Researchers propose a novel approach using fluorescent soft robots labeled with Cy5 to accurately locate tumors within the stomach. The technique demonstrates excellent movement ability, strong adhesion, and long-lasting fluorescence, making it a promising alternative for efficient and accurate tumor localization in laparoscopic surgery.
Scientists from Tianjin University developed a novel method for noninvasive intracranial source signal localization and decoding, achieving high spatial and temporal resolution. The approach uses transcranial focused ultrasound modulated EEG technology and a real skull structure to accurately identify brain signals.