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.
Researchers developed a rapid battery-capacity estimation method using early voltage response during the first discharge cycle. The approach extracts electrochemical signatures related to battery condition and enhances features to improve prediction accuracy, reducing testing time by over 80%.
Researchers proposed a model-based diagnostic framework for electric vertical take-off and landing aircraft battery systems, improving fault detection and isolation under demanding aviation conditions. The approach achieves high detection rates, even in concurrent-fault scenarios, making it suitable for certifying eVTOL systems.
Researchers propose a personalized longitudinal motion planning policy combining reinforcement learning and imitation learning for intelligent vehicles. The approach adapts driving style to target drivers while meeting performance requirements, promoting human-like behavior and increasing acceptance.
A novel active equalization scheme uses path planning to address cell inconsistency in battery packs, improving equalization speed, accuracy, and robustness. The approach combines flexible topology with graph-based energy-transfer modeling and adaptive battery grouping to reduce energy loss and improve overall pack performance.
The study developed a ferrocene-modified nanoscale COF-based sonodynamic platform that integrates ultrasound-triggered ROS generation with Fenton-like catalysis, ferroptosis induction, and immune microenvironment remodeling. This approach showed potent antitumor activity against primary breast tumors and bone metastasis.
A novel bioengineered platform enhances MSC potency through 3D culture and localized delivery, preserving therapeutic phenotype and boosting paracrine activity. The system demonstrates significant therapeutic outcomes in a murine ALI model, addressing unmet needs in ALI therapy.
A new humanoid robotic bioreactor delivers human-like multiaxial mechanical stimulation to engineered tendon constructs, enhancing cell alignment and mechanotransduction-related responses. This study shows that biomimetic multiaxial loading can reshape cellular mechanosensing and promote early tendon-related biological responses.
Researchers developed a hybrid BCI framework integrating frequency, phase, and spatial information to unlock full potential of visual spatiotemporal neural signals. The system achieved record-breaking performance with high-density EEG recording, expanding command set and reducing stimulus size.
Researchers developed an uncertainty-aware AI framework for predicting proton exchange membrane fuel cell degradation trends. The framework provides both point estimates and interval estimates with probability density information, improving the reliability of fuel-cell prognosis under realistic operating conditions.
Researchers have developed a fast prediction and suppression method for transient piston displacement overshoot in free piston Stirling generators. The new approach detects dangerous overshoot without relying on displacement sensors and suppresses the fault response early enough to maintain safe operation and continuous power delivery.
Researchers developed a dynamic range compression dual-domain attention network to tackle extreme exposure conditions in tunnels. The DRC-DFANet model optimizes global illumination coordination and local detail restoration, preserving fine details while adjusting brightness intelligently.
The integrated framework combines incremental capacity analysis with image feature transformation and a hybrid machine-learning pipeline to improve SOH estimation accuracy. It achieves an RMSE of 1.76% on the NASA dataset and shows robustness when operating conditions shift, suggesting better generalization across different datasets.
Researchers developed a predictive energy management framework for megawatt-class parallel hybrid-electric regional aircraft, showing improved environmental and operational performance. Simulation results show reduced fuel consumption, CO2 emissions, NOx emissions, and energy-specific air range.
Researchers have developed a multi-fidelity framework combining coupled degradation mechanisms with machine learning to predict battery lifespan. The framework addresses the challenge of making reliable forecasts before long-term aging data are available, enabling safer operation and better-informed decision-making.
Researchers developed a lightweight fault-diagnosis framework for high-speed train bogies using selective knowledge distillation-based domain adaptation. The approach improves cross-domain diagnostic accuracy by at least 2.1% while keeping the final model size to 28.5 kB.
Researchers developed a two-step diagnostic strategy to detect subtle abnormal behavior in lithium-ion batteries. The method combines Hellinger distance with an Inverse Markov Method to identify micro short circuits that can lead to serious safety failures and thermal runaway.
Researchers developed a three-dimensional electro-thermo-mechanical model to quantify the swelling force generated by lithium-ion batteries during charging. The model accurately identifies and quantifies swelling force, offering a new tool for improving battery safety.
Researchers developed a nonlinear galloping-driven triboelectric-electromagnetic hybrid generator to harvest low-speed wind energy. The system can work over a wide wind-speed range and produce enough power to support practical electronics.
A new soft multiaxial strain mapping interface combines with AI processing to capture complex throat muscle movements and decode silent speech. The system achieved 85.8% recognition accuracy for NATO phonetic words in controlled tests and real-world noisy environments.
RST2G automates tumor segmentation, reducing radiologist workload and improving accuracy. It captures complex tumor morphologies and boundaries, outperforming traditional methods in visual assessments.
Researchers from Sun Yat-sen University and TianQin Research Center propose a novel method for detecting Earth's free oscillations using the TianQin space-borne gravitational wave detector. Through numerical simulation and Bayesian parameter estimation, they demonstrate clear detection of seismic events with high signal-to-noise ratios...
This study introduces a novel method that couples buoyancy-driven convection with localized acoustic microstreaming to enhance macroscopic mixing and microscopic mass transfer. The results show significant advantages in high-viscosity media, including increased effective coverage area and mean flow velocity amplitude.
A new UAV perching system utilizes a magnetic tensegrity-enabled robotic gripper with an adaptive energy barrier, enabling low-power and stable aerial operations. The system demonstrates rapid closure and high holding capability, with improved performance on rough surfaces.
Burst stimulation effectively mitigates decline in turning response decay, preserving stable frequency-response relationship and improving closed-loop locomotion control. The proposed navigation system achieves reliable path following with a success rate of 73% and average tracking error of approximately 12 mm.
A new study uses swarm intelligence to optimize the integration of distributed generation and fast Electric Vehicle Charging Stations in power distribution networks. The approach reduces active power losses by up to 68% and enhances voltage profiles across the network.
A new fusion model combining CNN, GRU, and PF techniques achieves remarkable improvements in lithium-ion battery RUL prediction accuracy. This enhances safety and longevity in electric vehicles and grid applications.
Researchers developed an accurate detection approach for lithium plating in lithium-ion capacitors, enabling safe exploitation of their full potential. The study reveals that lithium plating initiates at a charging rate of 20 C and can be reversed under certain conditions, but above 50 C, irreversible 'dead' lithium accumulates.
The brain extracellular space plays a crucial role in molecular diffusion, metabolic waste clearance, and post-blood–brain barrier drug transport. Recent advances have improved ECS characterization, enabling its integration into studies of CNS disease mechanisms, therapeutic design, and regulatory evaluation.