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Abstract We prove the existence and uniqueness of global smooth solutions of the critical dissipative SQG equation in bounded domains in . We introduce a new methodology of transforming the single nonlocal nonlinear evolution equation in a bounded domain into an interacting system of extended nonlocal nonlinear evolution equations in the whole space. The proof then uses the method of the nonlinear maximum principle for nonlocal operators in the extended system. 

Abstract The configuration of proteins is critical for their biochemical behavior. Mechanical stresses that act on them can affect their behavior leading to the development of decease. The von Willebrand factor (vWF) protein circulating with the blood loses its efficacy when it undergoes non-physiological hemodynamic stresses. While often overlooked, extensional stresses can affect the structure of vWF at much lower stress levels than shear stresses. The statistical distribution of extensional stress as it applies on models of the vWF molecule within turbulent flow was examined here. The stress on the molecules of the protein was calculated with computations that utilized a Lagrangian approach for the determination of the molecule trajectories in the flow filed. The history of the stresses on the proteins was also calculated. Two different flow fields were considered as models of typical flows in cardiovascular mechanical devises, one was a Poiseuille flow and the other was a Poiseuille–Couette flow field. The data showed that the distribution of stresses is important for the design of blood flow devices because the average stress can be below the critical value for protein damage, but tails of the distribution can be outside the critical stress regime. 

The stress distribution along the trajectories of passive particles released in turbulent flow were computed with the use of Lagrangian methods and direct numerical simulations. The flow fields selected were transitional Poiseuille-Couette flow situations found in ventricular assist devices and turbulent flows at conditions found in blood pumps. The passive particle properties were selected to represent molecules of the von Willebrand factor (vWF) protein. Damage to the vWF molecule can cause disease, most often related to hemostasis. The hydrodynamic shear stresses along the trajectories of the particles were calculated and the changes in the distribution of stresses were determined for proteins released in different locations in the flow field and as a function of exposure time. The stress distributions indicated that even when the average applied stress was within a safe operating regime, the proteins spent part of their trajectories in flow areas of damaging stress. Further examination showed that the history of the distribution of stresses applied on the vWF molecules, rather than the average, should be used to evaluate hydrodynamically-induced damage. 

Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complex and user relations can be high-order. Hypergraph provides a natural way to model high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over the current SOTA methods, and the ablation study verifies the effectiveness and rationale of the multi-channel setting and the self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ. 

About three years ago we invited contributions for a Fluids Special Issue on “Flow and Heat or Mass Transfer for the Chemical Process Industry [...] 

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.