Accurately measuring both in-plane and out-of-plane mechanical properties is essential for understanding material behavior at the nanoscale. Conventional atomic force microscopy (AFM) techniques often struggle with the coupling between vertical and lateral forces, which can distort lateral mechanical property measurements. To address this limitation, we propose a novel Peak Force Torsional Resonance (PFTR) method that combines Peak Force Tapping for precise height control with torsional resonance for in-plane property extraction. This approach effectively decouples vertical and lateral forces, significantly reducing the influence of topography and vertical interactions on lateral measurements. Additionally, we introduce a mathematical model to accurately quantify lateral properties, such as shear modulus and lateral viscosity, independent of height variations. Experimental results confirm that the PFTR method achieves high-resolution imaging of both in-plane and out-of-plane mechanical properties, offering a more accurate and reliable solution for nanoscale mechanical characterization compared to conventional AFM-based methods. Note to Practitioners—This work addresses a common challenge in nanoscale mechanical property measurement: the interference from surface topography that affects accurate imaging of in-plane (lateral) properties. Traditional AFM methods struggle with this coupling effect, making it difficult to reliably measure both in-plane and out-of-plane mechanical characteristics—measurements that are vital in fields such as semiconductor manufacturing and materials development. This paper introduces PFTR Microscopy, a new AFM-based approach designed to decouple these forces and accurately resolve nanoscale mechanical properties. PFTR combines PFT for height control with torsional resonance to extract in-plane mechanical data without distortions from surface topography. By providing clear imaging of lateral properties and reducing topographical artifacts, PFTR enables more reliable assessments of materials such as semiconductor coatings, thin films, and composite compounds used in high-precision industries. PFTR is validated through experimental comparisons with conventional AFM methods, demonstrating that it achieves higher resolution and accuracy in in-plane measurements. While this approach provides robust data for characterizing homogeneous materials, further research is needed to adapt it for complex, heterogeneous surfaces and to simplify calibration for broader industrial application. The ability to distinguish mechanical properties at the nanoscale also suggests potential for applications in MEMS design, failure analysis, and the characterization of soft or biological materials.

Summary Deep neural networks for medical image segmentation often face the problem of insufficient clean labeled data. Although non‐expert annotations are more readily accessible, these low‐quality annotations lead to significant performance degradation of existing neural network methods. In this paper, we focus on robust learning of medical image segmentation with noisy annotations and propose a novel noise‐tolerant framework based on dual‐strategy sample selection, which selects the informative samples to provide effective supervision information. First, we propose the first round of sample selection by designing a novel joint loss, which includes conventional supervised loss and regularization loss. To further select information‐rich samples, we propose confidence‐based pseudo‐label sample selection from a novel perspective as the complement. The dual strategies are used in a collaborative manner and the network is optimized with mined informative samples. We conducted extensive experiments on datasets with both simulated noisy labels and real‐world noisy labels. For instance, on a simulated dataset with 25% noise ratio, our method achieves segmentation Dice value with 90.56% 0.03%. Furthermore, increasing the noise ratio to 95%, our method still maintains a high Dice value of 73.85% 0.28% compared to other baselines. Extensive results have demonstrated that our method can weaken the effects of noisy labels on medical image segmentation.

In this paper, we propose an attention-based multi-scale neural network for arrhythmia detection with lead-reversal electrocardiogram data. Electrocardiogram with a set of 12 waveforms(known as 12-lead ECG) measures myocardial electro-physiological activity, which is important in clinical diagnosis of arrhythmia. However, lead reversals caused by electrode interchange may cause great interference to the interpretation, leading to significant accuracy decline of automated algorithms and possible faulty diagnosis by cardiologists. To address this problem, we design a multi-scale neural network using attention mechanism to reduce the influence of lead reversals. The proposed model is evaluated on a dataset which consists of ECG data from 3,658 patients. In experiments, the proposed method shows high performance on both lead-reversal data and normal data, which proves great robustness of the method.

A manipulation technique based on optothermally generated surface bubbles is proposed in this paper. The manipulation and assembly of microstructures are completed by using bubbles. In addition, the hydrogel microstructures are also used as microrobots driven by the bubble to operate and pattern the microspheres. Considering that many materials and lasers with different wavelength have been used for generating bubbles by optothermal effects, absorptivity and transmissivity are used as indicators of selections. Besides, the size of the bubble can be controlled by the frequency and time of the laser. This technique is supposed to be applied for manipulation of cells, microparticles and microstructures.

Bio-syncretic robots made up of living biological systems and electromechanical systems may have the potential excellent performance of natural biological entities. Therefore, the study of the bio-syncretic robots has got lots of attention in recent years. The 3D skeletal muscles have been used widely, due to the considerable contraction force and the controllability. However, the low differentiation quality of the C2C12 in the tissues hinders the broad application in the development of the skeleton muscle actuated bio-syncretic robots. In this work, an approach based on circular mould and rotary electrical stimulation to build high-quality muscle rings, which can be used to actuate various bio-syncretic robots, has been proposed. Firstly, the advantage of the proposed circular mould for the muscle rings culture has been shown by simulation. Then, the muscle rings have been fabricated with different moulds using the experiment-optimized compositions of the biological mixture. After that, the muscle rings in the circular moulds with different electrical stimulations have been cultured, to show the superiority of the proposed rotary electrical stimulation. Moreover, the contractility of the muscle rings have been measured under the different electrical pulses stimulation, for the study of the control property of the muscle rings. This work may be meaningful not only the development of bio-syncretic robots actuated by 3D muscle tissues but also the muscle tissue engineering.

This paper proposed a phase-closed-loop nanomachining control method to realize the directly control of machining depth based on ultrasonic vibration-assisted AFM. By using applied force to control the machining depth, conventional AFM machining approaches unable to machining a nanostructure with specified machined depth. With the proposed method, the vibration phase of micro-cantilever has a specific relationship with machining depth. Therefore, the nano-grooves with desired depth can be machined by using phase value as feedback of PID control. In this paper, the theoretical analysis and simulation are carried out, and the experiments of phase-closed-loop control method are conducted. The experimental results verify the primary feasibility of the proposed method. The present method also demonstrates the potential on the fabrication of three-dimension nanostructures and nanoelectronic device.

Cellular mechanical properties as the main physical performance characteristics have been actively studied in the past years for the study of cytobiology and the development of medicine. In this study, by combining Hertz model, a novel strategy is proposed to simultaneously measure the cellular mechanical properties including cellular mass, elasticity and viscosity, based on the principle of forced vibration stimulated by simple harmonic force, with piezoelectric transducer (PZT) as vibrator and Atomic Force Microscope (AFM) as detector. The corresponding theoretical model was derived and the simulation was realized based on the proposed model. The experiments of indentations and vibrations with myoblasts and myotubes were implemented to calculate the three mechanical parameters of cells according to the proposed strategy. The results validated the proposed approach. This work would be useful for the development of cytology, medicine, previously diagnose, specific therapy and so on.

Nanoscale vibration, a critical nanomechanical property of cell membranes/walls, is a crucial aspect of cell physiology. However, limitations of current nanoscale vibration detecting methods remain the major obstacle for scientific study and cell vibration experiments. Due to the absence of effective method of feeble nanoscale vibration detecting, most sorts of quantitative and dynamic cell vibrations cannot be observed. Therefore, a real-time tracking detection method is vital for the study of cell physiology. In this paper, a real-time tracking detection of nanoscale vibrations based on sweep frequency (SF) - amplitude modulation (AM) method using cantilever sweep frequency as a carrier frequency was proposed. Furthermore, the process of tip-sample vibration coupling is analyzed by using the idea of amplitude modulation model. The nanoscle vibration detecting experiments were carried out on a piezoceramic disc, which can mimic cell vibrations. The experiment results show that the SF-AM AFM real-time vibration tracking and detecting approach can accurately detect and track feeble sample vibration within few nanometers amplitude.