Explore the vast range of titles available.

With the increasing popularity of low-cost, clean, and environmentally friendly new energy sources, the proportion of grid-connected new energy units has increased significantly. However, since these units are frequency decoupled from the grid through a power electronic interface, they are unable to provide inertia support during active power perturbations, which leads to a decrease in system inertia and reduced frequency stability. In this study, the urgent need to accurately assess inertia is addressed by developing an energy-function-based inertia identification technique that eliminates the effect of damping terms. By integrating vibration mechanics, the proposed method calculates the inertia value after a perturbation using port measurements (active power, voltage phase, and frequency). Simulation results of the Western System Coordinating Council (WSCC) 9-bus system show that the inertia estimation error of the method is less than 1%, which is superior to conventional methods such as rate-of-change-of-frequency (RoCoF) and least squares methods. Notably, the technique accurately evaluates the inertia of synchronous generators and doubly fed induction generators (DFIGs) under virtual inertia control, providing a robust inertia evaluation framework for low-inertia power systems with high renewable energy penetration. This research deepens the understanding of inertial dynamics and contributes to practical applications in grid stability analysis and control strategy optimalization.

Structured illumination microscopy (SIM) has become a widely used tool for insight into biomedical challenges due to its rapid, long-term, and super-resolution (SR) imaging. However, artifacts that often appear in SIM images have long brought into question its fidelity, and might cause misinterpretation of biological structures. We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background. In particular, HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters; hence, it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment, thus promoting SIM as a daily imaging tool.

Background As a kind of potential probiotic, Akkermansia muciniphila abundance in human body is directly causally related to obesity, diabetes, inflammation and abnormal metabolism. In this study, A. muciniphila dynamic cultures using five different media were implemented in an in vitro bionic intestinal reactor for the first time instead of the traditional static culture using brain heart infusion broth (BHI) or BHI + porcine mucin (BPM). Results The biomass under dynamic culture using BPM reached 1.92 g/L, which improved 44.36% compared with the value under static culture using BPM. The biomass under dynamic culture using human mucin (HM) further increased to the highest level of 2.89 g/L. Under dynamic culture using porcine mucin (PM) and HM, the main metabolites were short-chain fatty acids (acetic acid and butyric acid), while using other media, a considerable amount of branched-chain fatty acids (isobutyric and isovaleric acids) were produced. Under dynamic culture Using HM, the cell diameters reached 999 nm, and the outer membrane protein concentration reached the highest level of 26.26 μg/mg. Conclusions This study provided a preliminary theoretical basis for the development of A. muciniphila as the next generation probiotic.

Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.

In natural orifice transluminal endoscopic surgery (NOTES), an ideal endoscope platform should be flexible and dexterous enough to go through the natural orifices to access the lesion site inside the human body, and meanwhile provide sufficient rigidity to serve as a base for the end-effectors to operate during the surgical tasks. However, the conventional endoscope has limited ability for maintaining high rigidity over the length of the body. This paper presents a novel design of a variable stiffness endoscopic manipulator. By using a new bioplastic named FORMcard, whose stiffness can be thermally adjusted, water at different temperatures is employed to switch the manipulator between rigid mode and flexible mode. A biocompatible microencapsulated phase change material (MEPCM) with latent heat storage properties is adopted as the thermal insulation for better safety. Experiments are conducted to test the concept design, and the validated advantages of our proposed variable stiffness endoscopic manipulator include: shorter mode activation time (25 s), significantly improved stiffness in rigid mode (547.9–926.3 N·cm2) and larger stiffness-adjusting ratio (23.9–25.1 times).

The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed. Tactile sensors have developed rapidly, which increases the need for data processing. While machine learning, rather than conventional approaches, provides new support for tactile data analysis, thanks to its strong capacity for processing massive data quickly, the machine learning methods for tactile data processing and their applications are reviewed. The current issues and future directions are then discussed.

Over the past decade, soft robot research has expanded to diverse fields, including biomedicine, bionics, service robots, human–robot interaction, and artificial intelligence. Much work has been done in modeling the kinematics and dynamics of soft robots, but closed‐loop control is still in its early stages due to limited sensory feedback. Thanks to the advancement in functional materials, structures, and manufacturing techniques for flexible electronics, flexible and stretchable sensors are developing rapidly. These sensors provide feedback for closed‐loop control tasks and enable soft robots to effectively explore the unknown and safely interact with humans and the environment. Herein, recent advances in perceptive soft robots that utilize flexible/stretchable sensors and functional materials are outlined. The perceptive functions of soft robots from two different aspects, that is, proprioception and exteroception, are summarized. Furthermore, the constructions of autonomous soft robots by integrating both proprioceptive and exteroceptive capabilities for closed‐loop control tasks and other challenging tasks in the real world are discussed. Soft robots have shown potential in bionics, human–robot interaction, and artificial intelligence. To improve their interactivity and adaptability, developing closed‐loop control systems is essential. Herein, a review of the proprioceptive and exteroceptive functions and closed‐loop control systems is presented to provide readers with a better understanding of recent advances in perceptive intelligence for soft robotics.

Telekinesis, as commonly portrayed in science fiction literature and cinema, is a super power wherein users control and manipulate objects absent in physical interaction. In real world, enhancing human–robot interaction needs the synthesis of human intuitive processes with robotic arm. This paper introduces a robotic teleoperation system achieving the essence of telekinetic operations, combining the profound capabilities of augmented reality (AR) with the robotic arm operations. Utilizing AR, the proposed methodology offers operators with a visual feedback, facilitating a level of control surpassing the capacities of natural interfaces. By using AR-driven visual recognition, this system achieves operations in a virtual environment, subsequently actualized in the real world through the robotic arm. Through multiple experiments, we found that the system has a small margin of error in telekinesis operations, meeting the needs of remote operation. Furthermore, our system can operate on objects in the real world. These experiments underscore the capability of the remote control system to assist humans in accomplishing a wider range of tasks through the integration of AR and robotic arms, providing a natural human–robot interaction approach.

The application of stretchable strain sensors in human movement recognition, health monitoring, and soft robotics has attracted wide attention. Compared with traditional electronic conductors, stretchable ionic hydrogels are more attractive to organization‐like soft electronic devices yet suffer poor sensitivity due to limited ion conduction modulation caused by their intrinsic soft chain network. This paper proposes a strategy to modulate ion transport behavior by geometry‐induced strain concentration to adjust and improve the sensitivity of ionic hydrogel‐based strain sensors (IHSS). Inspired by the phenomenon of vehicles slowing down and changing lanes when the road narrows, the strain redistribution of ionic hydrogel is optimized by structural and mechanical parameters to produce a strain‐induced resistance boost. As a result, the gauge factor of the IHSS is continuously tunable from 1.31 to 9.21 in the strain range of 0–100%, which breaks through the theoretical limit of homogeneous strain‐distributed ionic hydrogels and ensures a linear electromechanical response simultaneously. Overall, this study offers a universal route to modulate the ion transport behavior of ionic hydrogels mechanically, resulting in a tunable sensitivity for IHSS to better serve different application scenarios, such as health monitoring and human–machine interface.

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm −1 using the proposed algorithm compared to DQN.