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Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstacle to training the AI models through supervised learning. To overcome this obstacle, this research proposes the RailGAN model, which enhances the basic CycleGAN model by introducing a pre-sampling stage for railway tracks. Two pre-sampling techniques are tested for the RailGAN model: image-filtration, and U-Net. By applying both techniques to 20 real-time railway images, it is demonstrated that U-Net produces more consistent results in image segmentation across all images and is less affected by the pixel intensity values of the railway track. Comparison of the RailGAN model with U-Net and the original CycleGAN model on real-time railway images reveals that the original CycleGAN model generates defects in the irrelevant background, while the RailGAN model produces synthetic defect patterns exclusively on the railway surface. The artificial images generated by the RailGAN model closely resemble real cracks on railway tracks and are suitable for training neural-network-based defect identification algorithms. The effectiveness of the RailGAN model can be evaluated by training a defect identification algorithm with the generated dataset and applying it to real defect images. The proposed RailGAN model has the potential to improve the accuracy of NDT for railway defects, which can ultimately lead to increased safety and reduced economic losses. The method is currently performed offline, but further study is planned to achieve real-time defect detection in the future. [ABSTRACT FROM AUTHOR]

Objective: High intensity focused ultrasound (HIFU) is a new noninvasive therapeutics that allows local treatment of solid tumors through a hyperthermal mechanism using ultrasonic energy. One promising strategy to increase the thermal efficiency of HIFU is to employ nanoparticles (NPs) as ultrasound agents for the hyperthermia procedure. However, the interaction mechanism between NPs and ultrasonic waves has not been well understood. Methods: In an effort to investigate the heating process of NPs-enhanced HIFU, we derived a set of HIFU equations governing the temperature variation during the thermal ablation based on the principle of conservation of energy for heat transfer mechanism. A numerical model was developed to solve the HIFU equations to simulate the absorption mechanism of HIFU in the presence of NPs, the consequent heat transfer process, and the temperature rise profile during the sonication period. The accuracy of numerical model was verified by performing a series of experiments on tissue-mimicking phantoms embedded with magnetic NPs (MNPs). Results: The transport processes taking place at the boundaries between NPs and surrounding medium played the major role in the temperature rise during HIFU sonication. Besides, the effects of MNPs on rising temperature were improved by amplifying the ultrasonic power and frequency as well as by increasing the MNP concentration. Conclusion: A quantitative comparison with experimental results demonstrated the potential of the numerical model to accurately predict the heating mechanism of HIFU mediated by NPs. Significance: The proposed method can help with simulation of HIFU when NPs are employed as ultrasound agents. [ABSTRACT FROM AUTHOR]

Focused ultrasound (FUS) therapy has emerged as a promising noninvasive solution for tumor ablation. Accurate monitoring and guidance of ultrasound energy is crucial for effective FUS treatment. Although ultrasound (US) imaging is a well-suited modality for FUS monitoring, US-guided FUS (USgFUS) faces challenges in achieving precise monitoring, leading to unpredictable ablation shapes and a lack of quantitative monitoring. The demand for precise FUS monitoring heightens when complete tumor ablation involves controlling multiple sonication procedures. To address these challenges, we propose an artificial intelligence (AI)-assisted USgFUS framework, incorporating an AI segmentation model with B-mode ultrasound imaging. This method labels the ablated regions distinguished by the hyperechogenicity effect, potentially bolstering FUS guidance. We evaluated our proposed method using the Swin-Unet AI architecture, conducting experiments with a USgFUS setup on chicken breast tissue. Our results showed a 93% accuracy in identifying ablated areas marked by the hyperechogenicity effect in B-mode imaging. Our findings suggest that AI-assisted ultrasound monitoring can significantly improve the precision and control of FUS treatments, suggesting a crucial advancement toward the development of more effective FUS treatment strategies. [ABSTRACT FROM AUTHOR]

High-intensity focused ultrasound (HIFU) is a non-invasive medical procedure, which is mainly used to ablate tumors externally by focusing on them with high-frequency ultrasound. Because a single ablation can process only a small volume of tissue, a succession of ablations is required to treat a large volume of cancerous tissue. In order to maximize the therapeutic effect and reduce side effects such as skin burns, careful preoperative treatment planning must be performed to determine the focal location and sonication time for each ablation. This paper proposes a novel optimization algorithm, called the thermodynamic equilibrium algorithm (TEA), inspired by the behavior of thermodynamic systems reaching their equilibrium states. Like other evolutionary algorithms, TEA starts with an initial population. Gas chambers at various thermodynamic states are employed as representatives of the population individuals, and the equilibrium state is regarded as the global minimum. The movement of thermodynamic parameters in the direction of reducing the temperature gradient forms the basis of the proposed evolutionary algorithm. During this movement, the second law of thermodynamics is checked to ensure that entropy will increase in each process. This movement leads to the state where most of the systems are at equilibrium. In this state, the systems are localized at the same position and have the same cost as the global minimum. The TEA was applied to several well-known unconstrained and constrained benchmark cost functions, and the performance was compared with other well-known optimization algorithms. The results showed that the TEA has high potential to handle various types of optimization problems with a good convergence rate and high precision. Finally, the suggested evolutionary approach is applied to HIFU treatment regimens adopting a map of patient-specific material properties and an accurate thermal model. High-quality treatment plans could be created using the suggested method, and the average amount of tissue that is over- or under-treated was less than 0.08 percent. [ABSTRACT FROM AUTHOR]

Regularization is an essential aspect in the context of deep learning as it mitigates the risk of overfitting in deep neural networks. This study presents a novel deep learning regularization method, referred to DL-Reg, which effectively reduces the nonlinearity of deep networks by enforcing linearity to a certain extent. The proposed method is based on the incorporation of a linear constraint into the objective function of deep neural networks, which is defined as the error of a linear mapping from the inputs to the outputs of the model. Specifically, DL-Reg imposes a linear constraint on the network, which is further adjusted by a regularization factor, thereby preventing the network from overfitting. The effectiveness of DL-Reg is evaluated by training state-of-the-art deep network models on several benchmark datasets. The results of the experiments demonstrate that the proposed regularization method provides significant improvements over existing regularization techniques and enhances the performance of deep neural networks, particularly when dealing with small-sized training datasets. The main code of DL-Reg written in PyTorch is also available here: https://github.com/m2dgithub/DL-Reg.git. • Regularizing deep networks using a linear regression model. • Allowing supervised deep networks to be trained using the semi-supervised learning. • Increasing the performance of deep networks on the small-sized dataset. • Conducting extensive experiments to certify the significance of the proposed method. [ABSTRACT FROM AUTHOR]

Micro and nanotubes have found major application in fluidic systems as channels for conveying fluid. In some micro and nanofluidic applications such as drug delivery, a transverse magnetic field can be used to guide the fluid flow by generating an axial force in the flow direction. An important issue in the design of micro and nanofluidic systems is the structural vibration caused by the fluid flow. In the current study, we investigate the effect of transverse magnetic field on the vibration of cantilever micro and nanotubes conveying fluid by considering the small size effects. We couple the nonlocal Euler–Bernoulli beam model with Navier–Stokes theory to determine a fluid structure interaction (FSI) model for the vibration analysis of the system. We modify the FSI governing equation by driving a velocity correction factor to consider the effect of transverse magnetic field on the fluid flow's pattern through the tube. Then, we use the Galerkin's method to obtain the frequency diagrams for the instability analysis of the system. We show that the transverse magnetic field can have a substantial effect on the dynamics of tube conveying fluid by increasing the system's natural frequencies and critical flow velocity which contributes to the flutter instability. We also discover that although the transverse magnetic field plays a crucial role on dynamics of microstructures, its effect on the dynamics of nanotubes is not significant and can be ignored. [ABSTRACT FROM AUTHOR]