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The change in Achilles tendon length was significantly lower in the P1 and P2 conditions than the no-AFO condition. Furthermore, changes in the length of the Achilles tendon significantly decreased in the P2 condition when compared with that in the P1 condition. The peak anterior ground reaction force was significantly lower in the P2 condition than the no-AFO condition. CP-91149 solubility dmso suggest that excessive assist provided by an AFO prevents efficient gait by decreasing both the forward-propulsive force and tendon function.Texture is normally represented by aggregating local features based on the assumption of spatial homogeneity. Effective texture features are always the research focus even though both hand-crafted and deep learning approaches have been extensively investigated. Motivated by the success of Bilinear Convolutional Neural Networks (BCNNs) in fine-grained image recognition, we propose to incorporate the BCNN with the Pair-wise Difference Pooling (i.e. BCNN-PDP) for texture classification. The BCNN-PDP is built on top of a set of feature maps extracted at a convolutional layer of the pre-trained CNN. Compared with the outer product used by the original BCNN feature set, the pair-wise difference not only captures the pair-wise relationship between two sets of features but also encodes the difference between each pair of features. Considering the importance of the gradient data to the representation of image structures, we further generalise the BCNN-PDP feature set to two sets of feature maps computed from the original image and its gradient magnitude map respectively, i.e. the Fused BCNN-PDP (F-BCNN-PDP) feature set. In addition, the BCNN-PDP can be applied to two different CNNs and is referred to as the Asymmetric BCNN-PDP (A-BCNN-PDP). The three PDP-based BCNN feature sets can also be extracted at multiple scales. Since the dimensionality of the BCNN feature vectors is very high, we propose a new yet simple Block-wise PCA (BPCA) method in order to derive more compact feature vectors. The proposed methods are tested on seven different datasets along with 21 baseline feature sets. #link# The results show that the proposed feature sets are superior, or at least comparable, to their counterparts across different datasets.In mineral transportations, it is essential to measure the gas hydrate particle concentration to manage the risk of flowline blockage. Traditional single-frequency ultrasonic methods measure the particle concentration by treating the mixtures with an average particle size, which ignores the influence of the particle size distribution, and thus, measurement accuracy is limited. Therefore, this research studies the multifrequency ultrasound attenuation method to measure the particle concentration through the prior estimate of particle size distribution. First, considering the large particle size and low-density contrast characteristics of the hydrate-water dispersion, the influence of multiple scattering among particles cannot be ignored apart from the scattering attenuation caused by each particle, so the ultrasonic scattering attenuation mechanism considering multiple scattering effects is established to solve the attenuation prediction problem of the hydrate-water dispersion. Since the solution of the equation obtained by the ultrasonic attenuation model produces a Fredholm integral equation of the first kind, an inversion algorithm combining simulated annealing with genetic algorithm based on ultrasonic attenuation mechanism is proposed to solve the ill-posed problem in the inversion calculation of particle concentration. Finally, considering the characteristics of hydrate-water dispersion, the experiments were carried out with millimeter-sized acrylic spheres and saltwater as substitute materials of the hydrate-water dispersion. The results show that the method based on the multifrequency attenuation of ultrasound in the range 1-5 MHz has a good discrimination for the particle size, and the measurement error of particle concentration is less than 3% under different particle size distributions.Electric fields are ubiquitous throughout the body, playing important role in a multitude of biological processes including osteo-regeneration, cell signaling, nerve regeneration, cardiac function, and DNA replication. An increased understanding of the role of electric fields in the body has led to the development of devices for biomedical applications that incorporate electromagnetic fields as an intrinsically novel functionality (e.g., bioactuators, biosensors, cardiac/neural electrodes, and tissues scaffolds). However, in the majority of the aforementioned devices, an implanted power supply is necessary for operation, and therefore requires highly invasive procedures. Thus, the ability to apply electric fields in a minimally invasive manner to remote areas of the body remains a critical and unmet need. Here, we report on the potential of magnetoelectric (ME)-based composites to overcome this challenge. ME materials are capable of producing localized electric fields in response to an applied magnetic field, which the body is permeable to. Yet, the use of ME materials for biomedical applications is just beginning to be explored. Here, we present on the potential of ME materials to be utilized in biomedical applications. This will be presented alongside current state-of-the-art for in vitro and in vivo electrical stimulation of cells and tissues. We will discuss key findings in the field, while also identifying challenges, such as the synthesis and characterization of biocompatible ME materials, challenges in experimental design, and opportunities for future research that would lead to the increased development of ME biomaterials and their applications.The emergence of new ultrasound technologies has improved our understanding of the brain functions and offered new opportunities for the treatment of brain diseases. Ultrasound has become a valuable tool in preclinical animal and clinical studies as it not only provides information about the structure and function of brain tissues but can also be used as a therapy alternative for brain diseases. High-resolution cerebral flow images with high sensitivity can be acquired using novel functional ultrasound and super-resolution ultrasound imaging techniques. The noninvasive treatment of essential tremors has been clinically approved and it has been demonstrated that the ultrasound technology can revolutionize the currently existing treatment methods. Microbubble-mediated ultrasound can remotely open the blood-brain barrier enabling targeted drug delivery in the brain. More recently, ultrasound neuromodulation received a great amount of attention due to its noninvasive and deep penetration features and potential therapeutic benefits. This review provides a thorough introduction to the current state-of-the-art research on brain ultrasound and also introduces basic knowledge of brain ultrasound including the acoustic properties of the brain/skull and engineering techniques for ultrasound. Ultrasound is expected to play an increasingly important role in the diagnosis and therapy of brain diseases.In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e., the Fourier slice theorem for SGCT). Based on its Fourier slice theorem and its unique data sampling in the Fourier space, a Linogram-based Fourier reconstruction method is derived for SGCT. We demonstrate that the entire Linogram reconstruction process can be embedded as known operators into an end-to-end neural network. As a learning-based approach, the proposed Linogram-Net has capability of improving CT image quality for non-ideal imaging scenarios, a limited-angle SGCT for instance, through combining weights learning in the projection domain and loss minimization in the image domain. Numerical simulations and physical experiments on an SGCT prototype platform showed that our proposed Linogram-based method can achieve accurate reconstruction from a dual-SGCT scan and can greatly reduce computational complexity when compared with the filtered backprojection type reconstruction. The Linogram-Net achieved accurate reconstruction when projection data are complete and significantly suppressed image artifacts from a limited-angle SGCT scan mimicked by using a clinical CT dataset, with the average CT number error in the selected regions of interest reduced from 67.7 Hounsfield Units (HU) to 28.7 HU, and the average normalized mean square error of overall images reduced from 4.21e-3 to 2.65e-3.Neural architecture search (NAS) is inherently subject to the gap of architectures during searching and validating. To bridge this gap effectively, we develop Differentiable ArchiTecture Approximation (DATA) with Ensemble Gumbel-Softmax (EGS) estimator and Architecture Distribution Constraint (ADC) to automatically approximate architectures during searching and validating in a differentiable manner. link2 Technically, the EGS estimator consists of a group of Gumbel-Softmax estimators, which is capable of converting probability vectors to binary codes and passing gradients reversely, reducing the estimation bias in a differentiable way. To narrow the distribution gap between sampled architectures and supernet, further, the ADC is introduced to reduce the variance of sampling during searching. Benefiting from such modeling, architecture probabilities and network weights in the NAS model can be jointly optimized with the standard back-propagation, yielding an end-to-end learning mechanism for searching deep neural architectures in an extended search space. Conclusively, in the validating process, a high-performance architecture that approaches to the learned one during searching is readily built. Extensive experiments on various tasks including image classification, few-shot learning, unsupervised clustering, semantic segmentation and language modeling strongly demonstrate that DATA is capable of discovering high-performance architectures while guaranteeing the required efficiency.Recently Neural Architecture Search (NAS) has raised great interest in both academia and industry. link3 However, it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method.The motivation behind DSO-NAS is to address the task in the view of model pruning. To achieve this goal, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, sparse regularizations are imposed to prune useless connections in the architecture. Lastly, an efficient and theoretically sound optimization method is derived to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore it can be directly applied to large datasets like ImageNet and tasks beyond classification. Particularly, on the CIFAR-10 dataset, DSO-NAS achieves an average test error 2.74%, while on the ImageNet dataset DSO-NAS achieves 25.