Hierarchical porous TiO2 single crystals templated from partly glassified polystyrene

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.

Specific emitter identification based on one-dimensional complex-valued residual networks with an attention mechanism

Specific emitter identification (SEI) can distinguish single-radio transmitters using the subtle features of the received waveform. Therefore, it is used extensively in both military and civilian fields. However, the traditional identification method requires extensive prior knowledge and is time-consuming. Furthermore, it imposes various effects associated with identifying the communication radiation source signal in complex environments. To solve the problem of the weak robustness of the hand-crafted feature method, many scholars at home and abroad have used deep learning for image identification in the field of radiation source identification. However, the classification method based on a real-numbered neural network cannot extract In-phase/Quadrature (I/Q)-related information from electromagnetic signals. To address these shortcomings, this paper proposes a new SEI framework for deep learning structures. In the proposed framework, a complex-valued residual network structure is first used to mine the relevant information between the in-phase and orthogonal components of the radio frequency baseband signal. Then, a one-dimensional convolution layer is used to a) directly extract the features of a specific one-dimensional time-domain signal sequence, b) use the attention mechanism unit to identify the extracted features, and c) weight them according to their importance. Experiments show that the proposed framework having complex-valued residual networks with attention mechanism has the advantages of high accuracy and superior performance in identifying communication radiation source signals

IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs

Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current studies pay scant attention to the following two aspects. First, the interpretability of current models is manifested in providing reasoning paths, which is an essential property of path-based models. However, the comparison of reasoning paths in these models is operated in a black-box fashion. Moreover, contemporary models utilize separate networks to evaluate paths at different hops. Although the network for each hop has the same architecture, each network achieves different parameters for better performance. Different parameters cause identical semantics to have different scores, so models cannot measure identical semantics at different hops equally. Inspired by the observation that reasoning based on multi-hop paths is akin to answering questions step by step, this paper designs an Interpretable Multi-Hop Reasoning (IMR) framework based on consistent basic models for temporal KG forecasting. IMR transforms reasoning based on path searching into stepwise question answering. In addition, IMR develops three indicators according to the characteristics of temporal KGs and reasoning paths: the question matching degree, answer completion level, and path confidence. IMR can uniformly integrate paths of different hops according to the same criteria; IMR can provide the reasoning paths similarly to other interpretable models and further explain the basis for path comparison. We instantiate the framework based on common embedding models such as TransE, RotatE, and ComplEx. While being more explainable, these instantiated models achieve state-of-the-art performance against previous models on four baseline datasets

The Spatio-Temporal Pattern and Transition Mode of Recessive Cultivated Land Use Morphology in the Huaibei Region of the Jiangsu Province

Examining land use transition is a new way of building on the comprehensive research on Land Use/Cover Change (LUCC). Research on transition law and characteristics is important for improving the theory of land use transition and the practice of land resource management, and for being able to provide a basis and reference for promoting socio-economic transformation. Based on the relevant statistical data concerning cultivated land use in the Huaibei area of the Jiangsu Province from 1995 to 2020, and by understanding the county as a unit to be measured, this paper constructed a multi-dimensional (economic–social–ecological) functional index system of recessive morphology, analyzed the spatio-temporal pattern of the transition of cultivated land use, identified transition point mutations, and established the transition mode by adopting multi-dimensional time series point mutation detection and the piecewise linear regression method. The findings suggest that the index of recessive cultivated land use morphology in the Huaibei region of the Jiangsu Province presents a trend of “slow decline to significant growth to stable growth”. Moreover, the index presented evolutionary characteristics such as “high in the middle and east while low in the west”, as well as “the relatively balanced distribution between counties”, thus indicating that the degree of transition deepened, it showed a homogeneous development trend, and the transition process presented obvious “ladder” stage characteristics; therefore, the authors suggest making scientific use of cultivated land resources, in accordance with local conditions, in order to make the land use transition of cultivated land efficient, green, and sustainable

SeAttE: An Embedding Model Based on Separating Attribute Space for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models

Similar Impacts of the Interaural Delay and Interaural Correlation on Binaural Gap Detection

<div><p>The subjective representation of the sounds delivered to the two ears of a human listener is closely associated with the interaural delay and correlation of these two-ear sounds. When the two-ear sounds, e.g., arbitrary noises, arrive simultaneously, the single auditory image of the binaurally identical noises becomes increasingly diffuse, and eventually separates into two auditory images as the interaural correlation decreases. When the interaural delay increases from zero to several milliseconds, the auditory image of the binaurally identical noises also changes from a single image to two distinct images. However, measuring the effect of these two factors on an identical group of participants has not been investigated. This study examined the impacts of interaural correlation and delay on detecting a binaurally uncorrelated fragment (interaural correlation = 0) embedded in the binaurally correlated noises (i.e., binaural gap or break in interaural correlation). We found that the minimum duration of the binaural gap for its detection (i.e., duration threshold) increased exponentially as the interaural delay between the binaurally identical noises increased linearly from 0 to 8 ms. When no interaural delay was introduced, the duration threshold also increased exponentially as the interaural correlation of the binaurally correlated noises decreased linearly from 1 to 0.4. A linear relationship between the effect of interaural delay and that of interaural correlation was described for listeners participating in this study: a 1 ms increase in interaural delay appeared to correspond to a 0.07 decrease in interaural correlation specific to raising the duration threshold. Our results imply that a tradeoff may exist between the impacts of interaural correlation and interaural delay on the subjective representation of sounds delivered to two human ears.</p></div

Crystal Structure of the FGFR4/LY2874455 Complex Reveals Insights into the Pan-FGFR Selectivity of LY2874455.

Aberrant FGFR4 signaling has been documented abundantly in various human cancers. The majority of FGFR inhibitors display significantly reduced potency toward FGFR4 compared to FGFR1-3. However, LY2874455 has similar inhibition potency for FGFR1-4 with IC50 less than 6.4 nM. To date, there is no published crystal structure of LY2874455 in complex with any kinase. To better understand the pan-FGFR selectivity of LY2874455, we have determined the crystal structure of the FGFR4 kinase domain bound to LY2874455 at a resolution of 2.35 Å. LY2874455, a type I inhibitor for FGFR4, binds to the ATP-binding pocket of FGFR4 in a DFG-in active conformation with three hydrogen bonds and a number of van der Waals contacts. After alignment of the kinase domain sequence of 4 FGFRs, and superposition of the ATP binding pocket of 4 FGFRs, our structural analyses reveal that the interactions of LY2874455 to FGFR4 are largely conserved in 4 FGFRs, explaining at least partly, the broad inhibitory activity of LY2874455 toward 4 FGFRs. Consequently, our studies reveal new insights into the pan-FGFR selectivity of LY2874455 and provide a structural basis for developing novel FGFR inhibitors that target FGFR1-4 broadly

Data-driven design of brake pad composites for high-speed trains

Brake pads play a vital role in controlling the operation of high-speed trains with over 300 km/h. Currently the copper-based composites produced by powder metallurgy techniques have been proved as one of the ideal materials. However，the braking performance is strongly influenced by the chemical composition, production, microstructure as well as the working conditions. The intrinsic mechanism of the thermal fade and wear loss under thermal and mechanical coupled influence has not yet been fully revealed. As of late, machine learning algorithms have attracted widespread attention and extraordinary results has been accomplished in materials science and engineering. In this study the algorithms for predicting the braking performance of copper-based brake pads are developed. A new PM copper-based alloy, was designed via the models of the coefficient of friction (COF) and wear loss. The braking test results of this alloy show that the average COF was 0.37 at stable stage I and thermal recession decrease only 0.012 at stage II when under 350 km/h/0.48 MPa continuous braking for 10 times. And the total wear loss is only 1.30 g when under braking from 200 km/h to 350 km/h with pressure from 0.31 MPa to 0.48 MPa. Compared with the commercial brake pads, our designed alloy exhibits excellent braking performance with COF for thermal fade reduced to 21% and the total wear loss reduced to 20%

Imaging-Guided Drug Release from Glutathione-Responsive Supramolecular Porphysome Nanovesicles

Drug delivery systems that can be employed to load anticancer drugs and release them triggered by a specific stimulus, such as glutathione, are of great importance in cancer therapy. In this study, supramolecular porphysome nanovesicles that were self-assembled by amphiphilic porphyrin derivatives were successfully constructed, mainly driven by the π–π stacking, hydrogen bonding, and hydrophobic interactions, and were used as carriers of anticancer drugs. The nanovesicles are monodispersed in shape and uniform in size. The drug loading and in vitro drug release investigations indicate that these nanovesicles are able to encapsulate doxorubicin (DOX) to achieve DOX-loaded nanovesicles, and the nanovesicles could particularly release the loaded drug triggered by a high concentration of glutathione (GSH). More importantly, the drug release in cancer cells could be monitored by fluorescent recovery of the porphyrin derivative. Cytotoxicity experiments show that the DOX-loaded nanovesicles possess comparable therapeutic effect to cancer cells as free DOX. This study presents a new strategy in the fabrication of versatile anticancer drug nanocarriers with stimuli-responsive properties. Thus, the porphysome nanovesicles demonstrated here might offer an opportunity to bridge the gap between intelligent drug delivery systems and imaging-guided drug release