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Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines were created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak-seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data are extracted by the statistics of the wave number locations with high frequency. The attention mechanism was used for the peak data, and the attention mechanism was weighted to the feature map of the feature extraction block. The training set, validation set and prediction set were divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, the CWT-AM-CNN proposed in this paper was compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets was as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets. [ABSTRACT FROM AUTHOR]

Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph's stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future. [ABSTRACT FROM AUTHOR]

As a natural cationic peptide, Nisin is capable of widely inhibiting the growth of Gram-positive bacteria. However, it also has drawbacks such as its antimicrobial activity being susceptible to environmental factors. Nano-encapsulation can improve the defects of nisin in food applications. In this study, nisin-loaded egg white protein nanoparticles (AH-NEn) were prepared in fixed ultrasound-mediated under pH 3.0 and 90 °C. Compared with the controls, AH-NEn exhibited smaller particle size (112.5 ± 2.85 nm), smaller PDI (0.25 ± 0.01), larger Zeta potential (24 ± 1.18 mV), and higher encapsulation efficiency (91.82%) and loading capacity (45.91%). The turbidity and Fourier transform infrared spectroscopy (FTIR) results indicated that there are other non-covalent bonding interactions between the molecules of AH-NEn besides the electrostatic forces, which accounts for the fact that it is structurally more stable than the controls. In addition, by the results of fluorescence intensity, differential scanning calorimetry (DSC), and X-ray diffraction (XRD), it was shown that thermal induction could improve the solubility, heat resistance, and encapsulation of nisin in the samples. In terms of antimicrobial function, acid–heat induction did not recede the antimicrobial activity of nisin encapsulated in egg white protein (EWP). Compared with free nisin, the loss rate of bactericidal activity of AH-NEn was reduced by 75.0% and 14.0% following treatment with trypsin or a thermal treatment at 90 °C for 30 min, respectively. [ABSTRACT FROM AUTHOR]

Based on the tuna swarm optimization-based support vector machine (TSO-SVM) and the multi-objective sparrow search algorithm (MOSSA), this paper proposes a multi-objective optimization approach for injection molding of thin-walled plastic components, addressing the issues of warpage deformation and volume shrinkage that compromise molding quality. Firstly, data samples are obtained based on the Box–Behnken experimental design and computer-aided engineering (CAE) simulation. Subsequently, SVM is employed to build a predictive model between the experimental factors and quality objectives. Additionally, the TSO is applied to optimize the hyperparameters of SVM, aiming to enhance its regression performance and prediction accuracy. Finally, the MOSSA is employed for multi-objective optimization, combined with the CRITIC scoring method for decision-making, to obtain the optimal combination of process parameters. The obtained parameters are then validated through simulation in Moldflow software. After optimization, the warpage deformation is reduced to 0.5085 mm, and the volume shrinkage rate is decreased to 7.573 %, representing a significant reduction of 40.9 % and 18.1 %, respectively, compared to the pre-optimized results. The remarkable improvement demonstrates the effectiveness of the method based on TSO-SVM and MOSSA for the efficient monitoring of the injection molding process. [ABSTRACT FROM AUTHOR]

Flexible strain sensors have important applications in wearable electronics, human motion monitoring, and healthcare. Soft, compatible, and conductive hydrogels are considered promising stretchable materials. However, hydrogel-based sensors still face challenges with the combined advantages of good electrical conductivity, low strain limit (less than 5%), mechanical strength, and anti-interference. As a result, most hydrogel-based sensors cannot stably recognize human tiny movements (such as pulse, throat, etc.). Herein, we constructed a double network (DN) hydrogel consisting of SA–Zn and P(AA-AM) to boost the mechanical performance and prepare the graphene oxide (GO) modified by polydopamine@Ag nanoparticles (PDA@Ag) via a one-step redox reaction method to enhance the sensitivity. The prepared GO/PDA@Ag/SA–Zn hydrogel strain sensor has an ultralow strain detection limit (0.1%), good gauge factor (GF = 8.29), and high stretchability (600%). It is proven that the hydrogel strain sensor can work as a wearable device to continuously monitor full-scale human motions. Furthermore, it works as a speech interface to detect subtle throat speech movements and which is further classified by a transfer learning algorithm based on the Resnet50 neural network, demonstrating that our sensor can recognize ten kinds of common words expressing physical demands with a precise accuracy of 100%. It provides the possibility to help patients or weak people with voice disorders in the hospital express their requirements. This work provides new insights into fabricating flexible hydrogel strain sensors with promising applications in next-generation intelligent wearable devices for healthcare, medical treatments, diagnosis, therapy, and rehabilitation. [ABSTRACT FROM AUTHOR]

Respiration is an important physiological process of the human body and can assess an individual's health status. Humidity detection is an effective method for real‐time breath monitoring. Herein, the α‐Co(OH)2‐based quartz crystal microbalance humidity sensor (Co‐QCM sensor) is prepared via the hydrothermal method. The Co‐QCM sensor is characterized by scanning electron microscopy, Fourier‐transform infrared, and X‐ray diffraction. The α‐Co(OH)2 with hierarchical nanostructure and hydroxy functional groups leads to good humidity‐sensing performance. The Co‐QCM sensor exhibits high sensitivity (23.1 Hz/% relative humidity), fast response and recovery times (within 3 and 4 s, respectively), high linearity (R2 = 0.994), and long‐term stability (≈30 days) in the humidity range of 4–73.3%. In addition, different human breathing signals, including free breathing, shallow breathing, and deep breathing, can be detected and distinguished by this humidity sensor, showing the potential application in human respiratory detection. [ABSTRACT FROM AUTHOR]