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Phase-Field Sim of Liquid-Vapor Equilibrium and Water loss involving Water Mixtures.
Interestingly, proHp attenuated TGF-β-induced expression of mesenchymal markers and Smad2/3 phosphorylation. It also significantly suppressed cell invasion and migration. Knockdown of Smad1/5 abolished the inhibitory effects of proHp on TGF-β-stimulated Smad2/3 phosphorylation and mesenchymal marker expression. These findings indicate that proHp suppresses the TGF-β-induced EMT and cell invasion in vitro by enhancing Smad1/5 activation via ALK1/2/3 receptors and thus suppressing the Smad2/3 signaling pathway in SK-Hep1 cells. This study suggests that proHp may prevent a de-differentiation of hepatic cells and induce a cell differentiation by regulating the Smad signaling pathway.Adhesively bonded structures are widely used to facilitate the manufacturing process and enhance the performance of critical components in the aerospace, automotive, and energy industries. The assessment of the bond layer using the propagation of ultrasonic guided waves has been extensively investigated in the literature using several different approaches. In this study, a finite element (FE) model was used to simulate the dispersion curves of the modes propagating in an aluminum/adhesive/aluminum bonded structure. The simulated dispersion curves were systematically compared with the experimental measurements to retrieve the shear modulus of the adhesive layer during its curing process. The optimization procedure was able to perform inversion with minimum prior knowledge of the adhesive layer properties. In general, the proposed FE-based forward model was able to match the experimental dispersion curves during curing. Notwithstanding some discrepancies observed in the early to intermediate state of curing, the predicted model parameters were in agreement within 6% of the values obtained by the reference methods. The optimal shear modulus was estimated at 1.55 GPa at the end of the curing, against a reference value of 1.47 GPa.The piezoelectric actuator is a kind of actuation device that acts through the inverse piezoelectric effect. Due to advantages of high precision, low power consumption, compact size, and flexible structure design, they have a wide range of applications in optics, robotics, microelectromechanical systems, and so on. Piezoelectric materials are the core materials for piezoelectric actuators. In this review, recent developments in high-performance piezoelectric materials (HPMs) are introduced, including relaxor ferroelectric crystals, textured ceramics, piezoelectric metamaterials, and so on. The advances of piezoelectric actuators are introduced in this review based on the developments of those piezoelectric materials, where the relationship between the figure of merits of materials and the performance of actuators is also discussed. Finally, we present outlooks and challenges for piezoelectric materials and actuators.In object detection, enhancing feature representation using localization information has been revealed as a crucial procedure to improve detection performance. However, the localization information (i.e., regression feature and regression offset) captured by the regression branch is still not well utilized. In this paper, we propose a simple but effective method called Interactive Regression and Classification (IRC) to better utilize localization information. Specifically, we propose Feature Aggregation Module (FAM) and Localization Attention Module (LAM) to leverage localization information to the classification branch during forward propagation. Furthermore, the classifier also guides the learning of the regression branch during backward propagation, to guarantee that the localization information is beneficial to both regression and classification. Thus, the regression and classification branches are learned in an interactive manner. Our method can be easily integrated into anchor-based and anchor-free object detectors without increasing computation cost. With our method, the performance is significantly improved on many popular dense object detectors, including RetinaNet, FCOS, ATSS, PAA, GFL, GFLV2, OTA, GA-RetinaNet, RepPoints, BorderDet and VFNet. Based on ResNet-101 backbone, IRC achieves 47.2% AP on COCO test-dev, surpassing the previous state-of-the-art PAA (44.8% AP), GFL (45.0% AP) and without sacrificing the efficiency both in training and inference. Moreover, our best model (Res2Net-101-DCN) can achieve a single-model single-scale AP of 51.4%.The emergence of implicit neural representations (INR) has shown the potential to represent images in a continuous form by mapping pixel coordinates to RGB values. Recent work is capable of recovering arbitrary-resolution images from the continuous representations of the input low-resolution (LR) images. However, it can only super-resolve blurry images and lacks the ability to generate perceptual-pleasant details. In this paper, we propose implicit pixel flow (IPF) to model the coordinate dependency between the blurry INR distribution and the sharp real-world distribution. For each pixel near the blurry edges, IPF assigns offsets for the coordinates of the pixel so that the original RGB values can be replaced by the RGB values of a neighboring pixel which are more appropriate to form sharper edges. By modifying the relationship between the INR-domain coordinates and the image-domain pixels via IPF, we convert the original blurry INR distribution to a sharp one. Specifically, we adopt convolutional neural networks to extract continuous flow representations and employ multi-layer perceptrons to build the implicit function for calculating pixel flow. In addition, we propose a new double constraint module to search for more stable and optimal pixel flows during training. To the best of our knowledge, this is the first method to recover perceptually-pleasant details for magnification-arbitrary single image super-resolution. Experimental results on public benchmark datasets demonstrate that we successfully restore shape edges and satisfactory textures from continuous image representations.Hip fracture is one of the most common traumatisms associated with falls in the elderly, severely affecting the patient's mobility and independence. In recent years, the use of robotic technology has proven to be effective in gait rehabilitation, especially for neurological disorders. However, there is a lack of research validating these devices for hip fracture in elderly patients. This paper presents the design and evaluation of a novel assistive platform for hip rehabilitation, SWalker, aimed at improving the rehabilitation of this condition. Functional validation of the SWalker platform was carried out with five healthy elderly subjects and two physiotherapists. Clinical validation was conducted with 34 patients with hip fracture. The control group ( [Formula see text], age = 86.38±6.16 years, 75% female) followed conventional therapy, while the intervention group ( [Formula see text], age = 86.80±6.32 years, 90% female) was rehabilitated using SWalker. The functional validation of the device reported good acceptability (System Usability Scale >85). In the clinical validation, the control group required 68.09±27.38 rehabilitation sessions compared to 22.60±16.75 in the intervention group ( [Formula see text]). Patients in the control group needed 120.33±53.64 days to reach ambulation, while patients rehabilitated with SWalker achieved that stage in 67.11±51.07 days ( [Formula see text]). FAC and Tinetti indexes presented a larger improvement in the intervention group when compared with the control group ( [Formula see text] and [Formula see text], respectively). The SWalker platform can be considered an effective tool to enhance autonomous gait and shorten rehabilitation therapy in elderly hip fracture patients. This result encourages further research on robotic rehabilitation platforms for hip fracture.This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q-Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q-learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.This article presents a novel neural network-based hybrid mode-switching control strategy, which successfully stabilizes the flapping wing aerial vehicle (FWAV) to the desired 3-D position. First, a novel description for the dynamics, resolved in the proposed vertical frame, is proposed to facilitate further position loop controller design. Then, a radial base function neural network (RBFNN)-based adaptive control strategy is proposed, which employs a switching strategy to keep the system away from dangerous flight conditions and achieve efficient flight. Selleckchem Benserazide The learning process of the neural network pauses, resumes, or alternates its update strategy when switching between different modes. Moreover, saturation functions and barrier Lyapunov functions (BLFs) are introduced to constrain the lateral velocity within proper ranges. The closed-loop system is theoretically guaranteed to be semiglobally uniformly ultimately bounded with arbitrarily small bound, based on Lyapunov techniques and hybrid system analysis. Finally, experimental results demonstrate the excellent reliability and efficiency of the proposed controller. Compared to existing works, the innovations are the put forward of the vertical frame and the cooperative switching learning and control strategies.Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly.
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