Non-invasive medical nanofiber technology, characterized by its high specific surface area, biocompatibility, and porosity, holds significant potential in various medical domains, including tissue repair and biosensing. It is increasingly becoming central to healthcare by offering safer and more efficient treatment options for contemporary medicine. Numerous studies have explored non-invasive medical nanofibers in recent years, yet a comprehensive overview of the field remains lacking. In this paper, we provide a comprehensive summary of the applications of electrospun nanofibers in non-invasive medical fields, considering multiple aspects and perspectives. Initially, we introduce electrospinning nanofibers. Subsequently, we detail their applications in non-invasive health, including health monitoring, personal protection, thermal regulation, and wound care, highlighting their critical role in improving human health. Lastly, this paper discusses the current challenges associated with electrospun nanofibers and offers insights into potential future development trajectories.

Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model’s ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.

Triply periodic minimal surfaces (TPMS) became an effective method to design porous scaffolds in recent years due to their superior mechanical and other engineering properties. Since the advent of additive manufacturing (AM), different TPMS-based scaffolds are designed and fabricated for a wide range of applications. In this study, Schwarz Primitive triply periodic minimal surface (P-TPMS) is adopted to design a novel porous scaffold according to the distribution of the scaffold stress under a fixed load with optimized thickness to tune both the mechanical and biological properties. The designed scaffolds are then additively manufactured through selective laser melting (SLM). The micro-features of the scaffolds are studied through scanning electron microscopy (SEM) and micro-computed tomography (CT) images, and the results confirm that morphological features of printed samples are identical to the designed ones. Afterwards, the quasi-static uniaxial compression tests are carried out to observe the stress–strain curves and the deformation behavior. The results indicate that the mechanical properties of the porous scaffolds with optimized thickness were significantly improved. Since the mass transport capability is important for the transport of nutrients within the bone scaffolds, computational fluid dynamics (CFD) are used to calculate the permeability under laminar flow conditions. The results reveal that the scaffolds with optimized structures possess lower permeability due to the rougher inner surface. In summary, the proposed method is effective to tailor both the mechanical properties and permeability, and thus offers a means for the selection and design of porous scaffolds in biomedical fields.

A water lubricating axial piston pump (WLPP) is the core power component of a green and environmentally friendly water hydraulic system. The friction and wear of the friction pairs of a WLPP are the key factors that restrict its development. In order to explore the friction and wear mechanism of materials, the tribological properties of CFRPEEK against 316L and 1Cr17Ni2 under water lubrication were investigated in a friction testing machine and an axial piston pump, respectively. An environmental scanning electron microscope (ESEM), confocal laser scanning microscopy and a surface profiler were used to analyze the morphology of the samples. In a friction testing machine, two different metals are paired with CFRPEEK, and the friction coefficient and wear rate barely show any differences. The wear rate of CFRPEEK is two orders of magnitude higher than that of metal. In the WLPP, 316L can hardly be paired with CFRPEEK, while 1Cr17Ni2 works well. The wear of 1Cr17Ni2 in the WLPP is greater than that of CFRPEEK. The high-pressure water film lubrication friction pairs cause the wear of the metal and show the difference in these two test methods. The wear mechanism is mainly abrasive wear. Improving the wear resistance of metals is very important for the development of WLPP.

The morphology size of laser cladding is a crucial parameter that significantly impacts the quality and performance of the cladding layer. This study proposes a predictive model for the cladding morphology size based on the Least Squares Support Vector Regression (LSSVR) and the Crowned Porcupine Optimization (CPO) algorithm. Specifically, the proposed model takes three key parameters as inputs: laser power, scanning speed, and powder feeding rate, with the width and height of the cladding layer as outputs. To further enhance the predictive accuracy of the LSSVR model, a CPO-based optimization strategy is applied to adjust the penalty factor and kernel parameters. Consequently, the CPO-LSSVR model is established and evaluated against the LSSVR model and the Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP) model in terms of relative error metrics. The experimental results demonstrate that the CPO-LSSVR model can achieve a significantly improved relative error of no more than 2.5%, indicating a substantial enhancement in predictive accuracy compared to other methods and showcasing its superior predictive performance. The high accuracy of the CPO-LSSVR model can effectively guide the selection of laser cladding process parameters and thereby enhance the quality and efficiency of the cladding process.