Structural health monitoring (SHM) techniques often require a large number of sensors to evaluate and monitor the structural health. In this paper, we propose a deep neural network (DNN)-based SHM method for accurate crack detection and localization in real time using a small number of strain gauge sensors and confirm its feasibility based on experimental data. The proposed method combines a DNN model with principal component analysis (PCA) to predict the strain field based on the local strains measured by strain gauge sensors located rather sparsely. We demonstrate the potential of the proposed technique via a cyclic 4-point bending test performed on a composite material specimen without cracks and seven specimens with different lengths of cracks. A dataset containing local strains measured with 12 strain gauge sensors and strain field measured with a digital image correlation (DIC) device was prepared. The strain field dataset from DIC is converted to a smaller dimension latent space with a few eigen basis via PCA, and a DNN model is trained to predict principal component values of each image with 12 strain gauge sensor measurements as input. The proposed method turns out to accurately predict the strain field for all specimens considered in the study.

In recent times, polymer-based flexible pressure sensors have been attracting a lot of attention because of their various applications. A highly sensitive and flexible sensor is suggested, capable of being attached to the human body, based on a three-dimensional dielectric elastomeric structure of polydimethylsiloxane (PDMS) and microsphere composite. This sensor has maximal porosity due to macropores created by sacrificial layer grains and micropores generated by microspheres pre-mixed with PDMS, allowing it to operate at a wider pressure range (~150 kPa) while maintaining a sensitivity (of 0.124 kPa⁻¹ in a range of 0~15 kPa) better than in previous studies. The maximized pores can cause deformation in the structure, allowing for the detection of small changes in pressure. In addition to exhibiting a fast rise time (~167 ms) and fall time (~117 ms), as well as excellent reproducibility, the fabricated pressure sensor exhibits reliability in its response to repeated mechanical stimuli (2.5 kPa, 1000 cycles). As an application, we develop a wearable device for monitoring repeated tiny motions, such as the pulse on the human neck and swallowing at the Adam’s apple. This sensory device is also used to detect movements in the index finger and to monitor an insole system in real-time.

A triboelectric nanogenerator (TENG) is an emerging energy harvesting technology utilizing multi-directional, wasted mechanical energies stemming from vibrations, winds, waves, body movements, etc. In this study, we report a comb-structured TENG (CTENG) capable of effectively scavenging multi-directional motions from human movements, which include walking, jumping, and running. By attaching CTENG to a person’s calf, we obtain a root-mean-square (RMS) power value of 5.28 μW (i.e. 13.12 V and 0.4 μA) for 1 s during mild running action (~5 m/s), which is sufficient for powering 10 light emitting diodes (LEDs). We integrate a CTENG with a simple hand-held pendulum (HHP) system with a natural frequency of 5.5 Hz. The natural frequency and input energy of our HHP system can be easily controlled by changing the holder mass and initial bending displacement, thus producing different output behaviors for the CTENG. Under the optimal HHP-based CTENG system design, the maximum output reaches 116 V at 6.5 μA under 0.1 kg mass and 4 cm bending displacement conditions. The corresponding output energy is 52.7 μJ for an operation time of 10.8 s. Our HHP-CTENG system can sufficiently power 45 LEDs and shows different output performances by varying the driving velocity of a vehicle, thus demonstrating the possibility for a self-powered velocity monitoring system. Graphical abstract

Achieving long-lasting neuronal modulation with low-intensity, low-frequency ultrasound is challenging. Here, we devised theta burst ultrasound stimulation (TBUS) with gamma bursts for brain entrainment and modulation of neuronal plasticity in the mouse motor cortex. We demonstrate that two types of TBUS, intermittent and continuous TBUS, induce bidirectional long-term potentiation or depression-like plasticity, respectively, as evidenced by changes in motor-evoked potentials. These effects depended on molecular pathways associated with long-term plasticity, including N -methyl- d -aspartate receptor and brain-derived neurotrophic factor/tropomyosin receptor kinase B activation, as well as de novo protein synthesis. Notably, bestrophin-1 and transient receptor potential ankyrin 1 play important roles in these enduring effects. Moreover, pretraining TBUS enhances the acquisition of previously unidentified motor skills. Our study unveils a promising protocol for ultrasound neuromodulation, enabling noninvasive and sustained modulation of brain function.

Single-cell analysis is essential to understand the physical and functional characteristics of cells. The basic knowledge of these characteristics is important to elucidate the unique features of various cells and causative factors of diseases and determine the most effective treatments for diseases. Recently, acoustic tweezers based on tightly focused ultrasound microbeam have attracted considerable attention owing to their capability to grab and separate a single cell from a heterogeneous cell sample and to measure its physical cell properties. However, the measurement cannot be performed while trapping the target cell, because the current method uses long ultrasound pulses for grabbing one cell and short pulses for interrogating the target cell. In this paper, we demonstrate that short ultrasound pulses can be used for generating acoustic trapping force comparable to that with long pulses by adjusting the pulse repetition frequency (PRF). This enables us to capture a single cell and measure its physical properties simultaneously. Furthermore, it is shown that short ultrasound pulses at a PRF of 167 kHz can trap and separate either one red blood cell or one prostate cancer cell and facilitate the simultaneous measurement of its integrated backscattering coefficient related to the cell size and mechanical properties.