Some properties of solutions for a class of metaparabolic equations

In this paper, we study the initial boundary value problem for a class of metaparabolic equations. We establish the existence of solutions by the energy techniques. Some results on the regularity, blow-up and existence of global attractor are obtained

Numerical analysis of aerodynamic features of porosity-optimized wind barriers and running safety of train

A 2-D model-bridge with different porosity barriers is simulated with CFD to explore the windbreak mechanism. The accuracy of simulation is verified by a wind tunnel test. The porosity of the barriers is optimized by analyzing the aerodynamic features of the train-bridge system subjected to cross winds. It is found that wind velocity on the windward track changes greater than that on the leeward track. The train rolls towards the barrier when porosity is lower than 10 % and away from barrier when porosity is higher than 30 %, and the rolling moment is minimized when porosity is 30 %. The dynamic response of running train with and without wind barrier is compared, from which the windbreak effect of barrier is identified

Additive manufacturing techniques and their biomedical applications

Additive manufacturing (AM), also known as three-dimensional (3D) printing, is gaining increasing attention in medical fields, especially in dental and implant areas. Because AM technologies have many advantages in comparison with traditional technologies, such as the ability to manufacture patient-specific complex components, high material utilization, support of tissue growth, and a unique customized service for individual patients, AM is considered to have a large potential market in medical fields. This brief review presents the recent progress of 3D-printed ­biomedical materials for bone applications, mainly for metallic materials, including multifunctional alloys with high strength and low Young’s modulus, shape memory alloys, and their 3D fabrication by AM technologies. It describes the potential of 3D printing techniques in precision medicine and community health

MAPPING LANGUAGE FUNCTION AND PREDICTING CORTICAL STIMULATION RESULTS WITH INTRACRANIAL ELECTROENCEPHALOGRAPHY

To avoid post-operative language impairments after surgery for drug-resistant epilepsy, clinicians rely primarily on electrocortical stimulation mapping (ESM), but this can trigger afterdischarges, clinical seizures, or cause uncomfortable sensations. Moreover, ESM can be time-consuming and the results are usually all-or-none, complicating their interpretation. These practical limitations have long motivated spatial-temporal analysis of passive intracranial electroencephalographic (iEEG) recordings as an alternative or complementary technique that can map cortical function at all sites simultaneously, resulting in significant time savings without adverse side-effects. However, there has not yet been widespread clinical adoption of passive iEEG for pre-operative language mapping, largely because of a failure to realize the potential advantages of iEEG over ESM and other methods for language mapping. The overall goals of this dissertation were to improve and validate passive iEEG as a method for mapping human language function prior to surgical resection for epilepsy and other brain disorders. This was accomplished through three separate aims. First, a spatial-temporal functional mapping (STFM) system was developed and tested for online passive iEEG mapping, providing immediate mapping feedback to both clinicians and researchers. The system output was compared to ESM and to canonical regions of interest in the human language network. In the second aim, the STFM system was used to study the fine temporal dynamics by which Broca’s area is activated and interacts with other areas of language network during a sentence completion task. This study showed that Broca’s area plays a pivotal role in the coordination of language networks responsible for lexical selection. Finally, the third aim sought to reconcile inconsistencies between the results of STFM and ESM. Agreement between these methods has not been as good for language mapping as it has been for motor mapping, which may be due to propagation of ESM effects to cortical areas connected to the site of stimulation. We used cortico-cortical evoked potentials to estimate the effective connectivity of stimulation sites to other sites in the language network. We found that this method improved the accuracy of STFM in predicting ESM results and helped explain similarities and differences between STFM and ESM language maps

Mantle Transition Zone Structure Beneath Northeast Asia From 2‐D Triplicated Waveform Modeling: Implication for a Segmented Stagnant Slab

The structure of the mantle transition zone (MTZ) in subduction zones is essential for understanding subduction dynamics in the deep mantle and its surface responses. We constructed the P (V_p) and SH velocity (V_s) structure images of the MTZ beneath Northeast Asia based on two‐dimensional (2‐D) triplicated waveform modeling. In the upper MTZ, a normal V_p but 2.5% low V_s layer compared with IASP91 are required by the triplication data. In the lower MTZ, our results show a relatively higher‐velocity layer (+2% V_p and −0.5% V_s compared to IASP91) with a thickness of ~140 km and length of ~1,200 km atop the 660‐km discontinuity. Taking this anomaly as the stagnant slab and considering the plate convergence rate of 7–10 cm/year in the western Pacific region during the late Cenozoic, we deduced that the stagnant slab has a subduction age of less than 30 Ma. This suggests that the observed stagnancy of the slab in the MTZ beneath Northeast Asia may have occurred no earlier than the Early Oligocene. From the constraints derived individually on V_p and V_s structures, high V_p/V_s ratios are obtained for the entire MTZ beneath Northeast Asia, which may imply a water‐rich and/or carbonated environment. Within the overall higher‐velocity stagnant slab, a low‐velocity anomaly was further detected, with a width of ~150 km, V_p and V_s reductions of 1% and 3% relative to IASP91. Such a gap may have provided a passage for hot deep mantle materials to penetrate through the thick slab and feed the Changbaishan volcano

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202