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A variety of autonomous oscillations in nature such as heartbeats and some biochemical reactions have been widely studied and utilized for applications in the fields of bioscience and engineering. Here, we report a unique phenomenon of moisture-induced electrical potential oscillations on polymers, poly([2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide-co-acrylic acid), during the diffusion of water molecules. Chemical reactions are modeled by kinetic simulations while system dynamic equations and the stability matrix are analyzed to show the chaotic nature of the system which oscillates with hidden attractors to induce the autonomous surface potential oscillation. Using moisture in the ambient environment as the activation source, this self-excited chemoelectrical reaction could have broad influences and usages in surface-reaction based devices and systems. As a proof-of-concept demonstration, an energy harvester is constructed and achieved the continuous energy production for more than 15,000 seconds with an energy density of 16.8 mJ/cm 2 . A 2-Volts output voltage has been produced to power a liquid crystal display toward practical applications with five energy harvesters connected in series. Moisture-induced energy generation is a potential green energy power source. Here, the authors report a moisture-induced autonomous surface potential oscillation phenomenon and apply it to the demonstration of energy harvesters with long persistence time and good energy density

This paper presents subcutaneous and continuous blood pressure (BP) monitoring using aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducers (PMUTs) in an ambulatory sheep. A 37  ×  45 PMUTs array with a footprint of 5  ×  5 mm 2 has been designed and fabricated as a prototype device. The deep reactive ion etching (DRIE) process to open the backside holes on the silicon substrate has been optimized to create active device diaphragms with a radius of 29 μm. The resulting PMUT unit has a measured resonant frequency of 6.5 MHz in water, an output acoustic pressure of 28 kPa at a distance of 10 mm, and a 6-dB bandwidth of about 33%. The BP monitoring scheme is validated through both in vitro and in vivo experiments to illustrate the correlation between the diameter of the blood vessel and pressure. Simulations indicate that possible issues in misalignment between the device and the blood vessel can result in a 60% reduction in signal strength with only 1 mm in misalignment. This highlights the advantage of subcutaneous implantation in maintaining a stable interface and consistent alignment for reliable long-term BP monitoring, in contrast to similar approaches via wearable system setups. The in vivo testing result shows BP wave fine features such as dicrotic notches and the averaged systolic/diastolic pressure errors are −1.2  ±  2.1 and −2.9 ± 1.4 mmHg, respectively, which meets the clinical standard as calibrated by a gold-standard arterial line pressure sensor. As such, this system highlights the potential applications in silent, continuous, and highly accurate BP monitoring for hypertension patients using this implantable MEMS-based technology.

This work presents air-coupled piezoelectric micromachined ultrasonic transducers (pMUTs) with high sound pressure level (SPL) under low-driving voltages by utilizing sputtered potassium sodium niobate K0.34Na0.66NbO3 (KNN) films. A prototype single KNN pMUT has been tested to show a resonant frequency at 106.3 kHz under 4 Vp-p with outstanding characteristics: (1) a large vibration amplitude of 3.74 μm/V, and (2) a high acoustic root mean square (RMS) sound pressure level of 105.5 dB/V at 10 cm, which is 5–10 times higher than those of AlN-based pMUTs at a similar frequency. There are various potential sensing and actuating applications, such as fingerprint sensing, touch point, and gesture recognition. In this work, we present demonstrations in three fields: haptics, loudspeakers, and rangefinders. For haptics, an array of 15 × 15 KNN pMUTs is used as a non-contact actuator to provide a focal pressure of around 160.3 dB RMS SPL at a distance of 15 mm. This represents the highest output pressure achieved by an airborne pMUT for haptic sensation on human palms. When used as a loudspeaker, a single pMUT element with a resonant frequency close to the audible range at 22.8 kHz is characterized. It is shown to be able to generate a uniform acoustic output with an amplitude modulation scheme. In the rangefinder application, pulse-echo measurements using a single pMUT element demonstrate good transceiving results, capable of detecting objects up to 2.82 m away. As such, this new class of high-SPL and low-driving-voltage pMUTs could be further extended to other applications requiring high acoustic pressure and a small form factor.

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields.

Rock masses are typical inhomogeneous geological materials that contain many fissures and cracks. The coupling effect of the crack propagation and seepage evolution in rocks is very important to the safety of rock engineering. However, hydromechanical coupling behavior during the failure of fissured rocks has rarely been investigated. In this research, hydromechanical coupling tests are performed to fully explore the behaviors in strength, deformation, permeability and failure mode of sandstone samples with two preexisting fissures. The experimental results show that the ratio of crack initiation threshold/peak strength, the ratio of crack damage threshold/peak strength and the value of the elastic modulus decrease by at most 31.8%, 12.2% and 18.4% due to the existence of the two fissures, while the Poisson’s ratio increases by at most 45.6%. Furthermore, the values of permeability before the sudden increase stage range from 2.1% to 17.6% of the maximum permeability value. The influence of bridge length and angle on permeability is more significant under lower confining pressure or higher water pressure. Five failure modes are observed in the double-fissure samples under hydromechanical coupling conditions. Additionally, the “wing cracks + indirect coalescence” failure mode is generated only when the ligament length is shorter than the fissure length. The corresponding strength is lower than that for other failure modes. CT images show that the expansion of cracks inside the samples is more restricted than that at the surface of the samples, especially near the rock bridge region. The effects of failure modes on the mechanical and permeability properties, from greatest to least, are as follows: crack initiation threshold, peak strength, crack damage threshold, elastic modulus, Poisson’s ratio and permeability. This research is contributed to analyze the stability of water-bearing rocks in underground caverns with many preexisting fissures.

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3–7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.

Objective: Walnuts have rich nutritional value and are favored by the majority of consumers. As walnuts are shelled nuts, they are prone to suffer from defects such as mildew during storage. The fullness and mildew of the fruit impose effects on the quality of the walnuts. Therefore, it is of great economic significance to carry out a study on the rapid, non-destructive detection of walnut quality. Methods: Terahertz spectroscopy, with wavelengths between infrared and electromagnetic waves, has unique detection advantages. In this paper, the rapid and nondestructive detection of walnut mildew and fullness based on terahertz spectroscopy is carried out using the emerging terahertz transmission spectroscopy imaging technology. First, the normal walnuts and mildewed walnuts are identified and analyzed. At the same time, the image processing is carried out on the physical samples with different kernel sizes to calculate the fullness of the walnut kernels. The THz image of the walnuts is collected to extract the spectral information in different regions of interest. Four kinds of time domain signals in different regions of interest are extracted, and three qualitative discrimination models are established, including the support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) algorithms. In addition, in order to realize the visual expression of walnut fullness, the terahertz images of the walnut are segmented with a binarization threshold, and the walnut fullness is calculated by the proportion of the shell and kernel pixels. Results: In the frequency domain signal, the amplitude intensity from high to low is the mildew sample, walnut kernel, and walnut shell, and the distinction between walnut kernel, shell samples, and mildew samples is high. The overall identification accuracy of the aforementioned three models is 90.83%, 97.38%, and 97.87%, respectively. Among them, KNN has the best qualitative discrimination effect. In a single category, the recognition accuracy of the model for the walnut kernel, walnut shell, mildew sample, and reference group (background) reaches 94%, 100%, 97.43%, and 100%, respectively. The terahertz transmission images of the five categories of walnut samples with different kernel sizes are processed to visualize the detection of kernel fullness inside walnuts, and the errors are less than 5% compared to the actual fullness of walnuts. Conclusion: This study illustrates that terahertz spectroscopy detection can achieve the detection of walnut mildew, and terahertz imaging technology can realize the visual expression and fullness calculation of walnut kernels. Terahertz spectroscopy and imaging provides a non-destructive detection method for walnut quality, which can provide a reference for the quality detection of other dried nuts with shells, thus having significant practical value.

The fullness of rice seeds is an important factor affecting the growth and yield of rice. Therefore, it is of great significance to detect fullness of rice seeds in nature state. In this paper, the emerging terahertz imaging detection technology is used to carry out the study of rice seed fullness detection. Firstly, the terahertz spectral images of rice seeds with different fullness are acquired. Secondly, the terahertz spectra of sample free region, empty shell seed region, and full seed region are extracted, respectively. To improve the accuracy of the model and reduce the computational effort, competitive adaptive reweighting sampling (CARS), uninformative variable elimination (UVE), Successive projection algorithm (SPA), and their combination are used to extract the features of terahertz spectral information. The corresponding support vector machine (SVM) and K -nearest neighbor (KNN) qualitative discriminant models are established to detect and identify the full and empty regions of rice seeds. In addition, the binarization of terahertz image is carried out to realize the visual expression of rice seeds. The UVE-SPA-KNN model established after band screening is used for classification, and the accuracy of prediction set reaches 98.33%. The UVE-SPA feature extraction method can reduce the amount of imaging data by 97.5% and realizes the visualization detection of kernels in rice seeds. This research verified that the visual detection of seed kernel fullness inside rice seeds can be well achieved by using terahertz imaging and spectrum fusion, which provides a new method for rapid detection of kernel fullness of rice seeds, and also provides a theoretical reference for terahertz imaging technology to detect the fullness of other thin-shell seeds. Highlights In this paper, three different spectra of different sample free region, empty shell seed region and full seed region in 0.5–3.0 THz image are extracted, and five band screening methods of CARS, UVE, SPA, CARS-SPA and UVE-SPA are used to extract the characteristic wavelength of the THz spectral. The discriminant model is established to distinguish between background region, empty shell seed region and full seed region. The research shows that the established UVE-SPA-KNN qualitative discriminant model has a recognition rate of up to 98.33%. The fullness of rice seeds is calculated by the ratio of binarization and shell-kernel pixel points of 0.5–3.0 THz images, the 150 threshold is used for shell in channel B, and the 235 threshold is used for kernel in channel R in the process of image binarization. The error between the detection fullness of rice seeds and the actual fullness is less than 10%. In order to reduce the amount of imaging data and remove redundant information, the UVE-SPA feature extraction method is further used to compress the full spectrum of 329 wavelengths into 8 wavelengths, which reduces the amount of imaging data by 97.5% and can also realize the visualization detection of kernels in rice seeds. Graphical Abstract