A new carbon nanotube bandage provides a practical, deployable alternative for shape sensing in robots used in surgery. The sensor can be easily installed and offers real-time intraoperative guidance.
Researchers found that frequency-specific transcranial photobiomodulation elicits complementary glial mechanisms for neurovascular protection and amyloid clearance in Alzheimer's disease. CW light primarily targeted astrocytes, while 40-Hz light drove microglial clustering around Aβ plaques.
A novel dynamic modeling approach for a net-membrane capture system accounts for combined deformations, enabling effective capture of stationary and spinning debris. The study identifies optimal shooting parameters, demonstrating excellent despinning capability during capture.
Researchers used a brain vital signs framework to monitor cognitive and neurophysiological function in astronauts on a 17-day mission. The results showed minimal differences in accuracy and reaction time across various cognitive tasks, with no significant neurophysiological decline.
Researchers proposed a novel method for detecting Earth's free oscillations utilizing TianQin, achieving high signal-to-noise ratio of 73 and distinguishing multiple modes. This method leverages frequency splitting effect to circumvent calibration errors, enabling precise probing of Earth's internal structure.
This study proposes a frequency-sweeping interferometry technique to measure intertelescope baselines with high precision and stability. Experimental results demonstrate improved stability by 33.47% and ranging accuracy of 44.30 μm over 10 m range.
A study proposes two detonation defense modes to deal with large-sized near-Earth asteroids. The flyby pre-excavation detonation mode offers stronger energy coupling and can destroy hundred-meter-scale asteroids, making it a preferred option when warning time permits.
A dynamic model of the release mechanism and interfacial adhesion effect is established to evaluate release performance and guide optimization. The improved genetic algorithm-based identification method significantly enhances model approximation, enabling accurate description of dynamic behavior during release.
A new paradigm for brain-computer interfaces uses text sequence stimulation to evoke robust brain responses while being gentler on the eyes. The technology achieved high-speed operation with low discomfort, outperforming conventional brightness flicker in comfort and preference across a wide frequency range.
Untethered micro/nanorobots powered by magnetic, acoustic, or optical fields can disrupt clots and deliver drugs, achieving faster and safer recanalization. Imaging guidance enables real-time feedback on thrombus burden and treatment response.
A dual-mode wireless microsystem simultaneously monitors electrophysiological signals and dopamine-related electrochemical signals, providing a complete view of brain state transitions. The system shows dose-dependent effects of dexmedetomidine on cortical neural activity and neurochemical signals.
A wireless controller was implanted into cockroaches, boosting their mobility and success rates in navigating through obstacles. The implant allowed the insects to use their full behavioral repertoire, including rolling and side-stepping, while backpacked insects struggled with reduced success rates.
Researchers proposed a modular design framework combining rigid and soft-robot-like reconfigurability for next-generation multifunctional magnetic medical robots. The articulated actuator prototypes demonstrated stability, predictability, and cross-scale feasibility in complex biological environments.
The research presents a novel system, RAMRS, that combines robot-assisted osteotomy and augmented reality-guided reconstruction to improve accuracy and efficiency in complex maxillofacial surgeries. The system achieves significant reductions in errors compared to traditional methods, making it a major step forward for the field.
Physical intelligence embeds sensing and actuation into materials, while biological intelligence integrates living cells for biocompatibility and environmental sensing. Computational intelligence enables data-driven perception, planning, and learning. Human oversight remains crucial for safety.
A new AI-based system was trained on 890 cases of nasopharyngeal carcinoma and refined through four iterations, achieving clinically acceptable plans in just 3.5 minutes. The system achieved superior or comparable dosimetric quality with lower doses to organs at risk.
This study proposed a myoelectric prosthetic control framework that integrates tactile, kinesthetic, and EMG information to improve stability and adaptability during dynamic tool handling. The TKE-BGC system achieved stronger stability, accuracy, and generalization in dynamic tool handling.
A new deep learning framework uses diffusion tensor imaging to identify subcortical vascular cognitive impairment (SVCI) with high accuracy. The model stratifies patients into low-, moderate-, and high-risk subgroups for each cognitive domain, offering a more objective and scalable tool for clinicians.
A new multimodal controller for myoelectric prosthetic hands uses tactile, kinesthetic, and EMG signals to improve tool handling and reduce tool drops. The system significantly reduced the number of tool drops and shortened completion times in both able-bodied participants and amputees.
Researchers developed a deep learning framework to classify and profile subcortical vascular cognitive impairment using DTI volumes. The model achieved high accuracy and correlated with neuropsychological scores, enabling stratification of patients by predicted risk of impairment in specific cognitive functions.
Researchers developed a novel bioinspired nanocomposite for targeted immunoengineering and improved tendon regeneration. The KGN@PB@CM nanoparticles effectively modulated macrophage polarization, reduced inflammation, and promoted tenocyte differentiation, leading to enhanced tissue repair.
Researchers developed nanomotors cloaked in breast cancer cell membrane that utilize NIR light to target and kill cancer cells. The nanomotors release chemically generated reactive nitrogen species, leading to synergistic tumor treatment.
This study investigates the effects of different spinal cord stimulation frequencies on brain networks in disorders of consciousness. It found that 5 Hz and 70 Hz triggered distinct network responses, with 5 Hz promoting rapid electrophysiological integration and 70 Hz favoring delayed hemodynamic and long-range network modulation.
A new framework models tumor, stroma, and immune-related compartments separately to predict treatment response in HER2+ breast cancer. The stromal compartment achieved the best performance, outperforming other methods.
The J-MSR soft robot achieves record-breaking speeds of 14.85 BL/s, showcasing multimodal maneuverability and functionality versatility. It can carry payloads, navigate confined environments, and even perform wireless power and signaling tasks.
A new generation of wearable technologies is being developed to mitigate the effects of microgravity on the human body. These systems, including passive suits and active exoskeletons, use advanced materials and sensors to provide continuous, adaptive loading during astronaut activities.
The new device mimics an octopus's multimodal grasping strategy, achieving the fastest stiffness-switching performance reported to date. It delivers stunning speed, softening in 1.3 seconds and rigidifying in 0.8 seconds, while maintaining cooperative grasping forces exceeding 4 newtons.
A team of researchers has developed a wearable fabric electrotactile system that provides more precise and localized tactile feedback in virtual reality. The system uses a novel 'stimulation-inhibition' design to cancel lateral current spread, resulting in improved recognition accuracy and reaction times for users.
The review systematically maps cyborg animal research, covering key components such as brain-computer interfaces and stimulation strategies. It highlights the importance of achieving a balance among adaptability, biocompatibility, control accuracy, system complexity, and real-world deployability in future progress.
A pinecone-inspired self-curling adhesive conduit was developed to provide adaptive wrapping for peripheral nerve repair, achieving faster curling speed and higher bending curvature than existing conduits. The material demonstrated good biocompatibility, promoting cell migration and creating a favorable repair microenvironment.
The review maps the most promising routes for recycling spent LiFePO4 batteries, focusing on pretreatment, impurity control, direct regeneration, hydrometallurgy, and selective auxiliary processes. It highlights hydrometallurgy as a promising strategy for large-scale recovery needs.
Researchers developed a novel adhesive conduit inspired by pinecones that can adapt to natural nerve geometries without suturing. The conduit achieved robust nerve regeneration, functional recovery, and minimized secondary complications in animal models.
Researchers have developed cyborg animals that integrate machine and biological intelligence, enabling them to execute human commands while retaining natural advantages. Key findings include advancements in control paradigms, miniaturized electronic backpacks, and self-sustaining energy harvesting technologies.
The article reviews bidirectional capacitive power transfer, conversion topologies, resonant networks, and power control strategies. Key findings highlight the need for coordination between power electronics and coupling structures in bidirectional operation.
Researchers developed a feature selection-based solar irradiance forecasting method to improve stand-alone photovoltaic system operation. The approach forecasts solar irradiance using a bidirectional long short-term memory hybrid network, then estimates the optimum tilt angle to increase PV output power.
Researchers developed an energy-saving control strategy for intelligent connected plug-in hybrid electric vehicles that incorporates driving-intention identification of the vehicle ahead. The approach optimizes speed, improves energy economy, and maintains comfort and safety by anticipating front-vehicle behavior.
Researchers propose an integrated eco-driving framework using deep reinforcement learning to optimize motion trajectory planning and energy management. The framework achieves substantial improvements in transverse-longitudinal comfort, energy economy, and power system health, while reducing hydrogen consumption and driving costs.
A novel energy management system (EMS) reduces converter count, battery stress, and hydrogen use in stand-alone hybrid photovoltaic and proton exchange membrane fuel cell microgrids. The EMS maintains DC-link stability while adapting to changing renewable generation and load conditions.
Researchers have developed an electrochemical impedance spectroscopy (EIS) identification algorithm to reconstruct EIS at low frequencies using short-duration sine-wave current pulses. The approach enables accurate state-of-charge estimation for LiFePO4 batteries, which is essential for battery management systems.
Researchers designed a high-efficiency bidirectional wireless power transfer system for multiple electric vehicles, supporting both grid-to-battery and battery-to-grid operations. The system achieved high efficiency under various operating conditions, with demonstrated interoperability between different vehicle-side equipment.
Researchers propose a novel SENet-CNN-Transformer model to predict electric vehicle charging duration, outperforming existing models in accuracy and reducing training time. The approach combines data enhancement, channel attention, convolutional neural networks, Transformer modeling, and transfer learning to address real-world data sca...
Researchers developed a health-aware V2G strategy using reinforcement learning to optimize charging and discharging times, resulting in significant lifecycle cost savings ($1,539) and extended battery life (21 months). The study suggests electric bus charging stations can be promising platforms for scalable V2G services.
Researchers evaluate the effectiveness of Vision Transformers and convolutional neural networks for faster and more accurate defect detection in railway track fasteners. The study finds that transformer-based models outperform traditional CNNs, suggesting their potential value for predictive health management in rail networks.
Researchers propose a Fourier graph neural network to estimate lithium-ion battery state of health, capturing spatial and temporal feature relationships. The model achieves significant reductions in error compared to existing methods, suggesting improved accuracy and transferability.
Battery performance is critical to electrified transportation and green energy systems. Real-world diagnostics are challenging due to complex environments and varying data quality. The review emphasizes the need for adaptive models and AI integration to improve battery status prediction.
A review article highlights a deep learning-driven CNN approach for detecting and classifying dynamic road obstacles, achieving high accuracy in obstacle identification and classification. The proposed architecture shows strong performance, but real-world deployment requires continued evaluation across larger and more varied scenarios.
A new study investigates sulfide-based and oxide-based solid electrolyte systems for next-generation lithium-ion solid-state batteries. The researchers found that the oxide-based hybrid approach offered notable advantages in performance, with improved lifespan and capacity retention compared to all-solid-state sulfide cells.
A comprehensive review reorganizes the design space for electrified propulsion systems in three-dimensional transportation, proposing six design stages to guide future research. The review addresses challenges specific to TDT propulsion design, including balancing energy efficiency, weight, reliability, and emissions.
Researchers propose an efficient feature search approach for estimating lithium-ion battery state of health, reducing reliance on manually selected aging features. The method combines Bayesian optimization and ensemble regression to improve accuracy and robustness.
A new planning framework proposes integrating bidirectional electric vehicle battery networks into sustainable communities, evaluating how EVs can support local energy systems. The framework models EVs as active participants in the neighborhood energy system, simulating grid interaction and energy exchange characteristics.