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In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human-robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

The cystic cavity that develops following spinal cord injury is a major obstacle for repairing spinal cord injury (SCI). The injectable self-healing biomaterials treatment is a promising strategy to enhance tissue repair after traumatic spinal cord injury. Herein, a natural extracellular matrix (ECM) biopolymer hyaluronic acid-based hydrogel was developed based on multiple dynamic covalent bonds. The hydrogels exhibited excellent injectable and self-healing properties, could be effectively injected into the injury site, and filled the lesion cavity to accelerate the tissue repair of traumatic SCI. Moreover, the hydrogels were compatible with cells and various tissues and possessed proper stiffness matched with nervous tissue. Additionally, when implanted into the injured spinal cord site, the hyaluronic acid-based hydrogel promoted axonal regeneration and functional recovery by accelerating remyelination, axon regeneration, and angiogenesis. Overall, the injectable self-healing hyaluronic acid-based hydrogels are ideal biomaterials for treating traumatic SCI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Sulfides possess either high toxicity or play crucial physiological role such as gas transmitter dependent upon dosage, hence the significant for their rapid sensitive and selective concentration determination. Herein, a machine learning enhanced ratiometric fluorescence sensor was engineered for sulfide determination by incorporating the nanometal-organic framework (UiO-66-NH 2 ) along with protoporphyrin IX (Ppix). The blue fluorescence at 431 nm originated from the moiety of UiO-66-NH 2 by 365 nm excitation serves as an internal calibration reference signal, while the red fluorescence at 629 nm from the moiety of Ppix serves as the analytical signal, and the intensity is correlated to the amount of sulfides. The fluorescence color of the sensor gradually varies from blue to red upon sequential addition of copper and sulfide ions, resulting in RGB (Red, Green, Blue) feature values for corresponding sulfide concentrations, which facilities the advanced data processing techniques using machine learning algorithms. On the basis of fluorescence image fingerprint extraction and machine learning algorithms, an online data analysis model was developed to improve the precision and accuracy of sulfide determination. The established model employed Linear Discriminant Analysis (LDA) and was subjected to rigorous cross-validation to ensure its robustness. By analyzing the correlation between RGB feature values and sulfide concentrations, the study highlighted a significant positive relationship between the red feature values and sulfide concentrations. The application of machine learning techniques on the ratiometric fluorescence signal of the UiO-66-NH 2 /Ppix probe demonstrated its potential for intelligent quantitative determination of sulfides, offering a valuable and efficient tool for pollution detection and real-time rapid environmental monitoring., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Chitosan hydrogel have presented great potential in biomedical applications due to its biocompatibility, biodegradability and characteristic similarity to native extracellular matrix. However, 3D printing of chitosan hydrogel often suffers from weak formability and poor mechanical property, limiting its further utilization. In this study, a novel chiotsan hydrogel is prepared from maleic chitosan (MCS) with high acrylate group substitution (i.e. 1.67) and thiol-terminated poly (ethylene glycol) (TPEG) via step-chain growth photopolymerization approach, which can overcome significantly the oxygen inhibition effect. Rheological property, microstructure, mechanical properties and in vitro degradation can be regulated by changing the thiol/acrylate molar ratio. There is strong intermolecular action between MCS and TPEG. Notably, photopolymerized MCS/TPEG hydrogel exhibited ~2-fold and ~ 10-fold increase in gelling rate and compressive strength, respectively, compared to pure chitosan hydrogel. Based on these results, 3D printing of chitosan hydrogel fabricated by simultaneous extrusion deposition and thiol-acrylate photopolymerization, demonstrates printing accuracy and improved scaffold stability. This 3D printing of chitosan hydrogel shows no cytotoxicity and can support adherence of L929 cells, suggesting its potential in biomedical applications such as tissue engineering and drug delivery., (Copyright © 2021 Elsevier B.V. All rights reserved.)