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Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

The gut damage and microflora disturbance caused by NP intake significantly depended on their surface functional groups.

Formaldehyde oxidation at room temperature with low-cost catalysts is one of the main development directions of environmental governance.

Circulating tumor cells (CTCs) acquire enhanced anti-anoikis abilities after experiencing flow shear stress in the circulatory system. Our previous study demonstrated that low shear stress (LSS) promotes anoikis resistance of human breast carcinoma cells via caveolin-1 (Cav-1)-dependent extrinsic and intrinsic apoptotic pathways. However, the underlying mechanism how LSS enhanced Cav-1 expression in suspended cancer cells remains unclear. Herein, we found that LSS induced redox signaling was involved in the regulation of Cav-1 level and anoikis resistance in suspension cultured cancer cells. Exposure of human breast carcinoma MDA-MB-231 cells to LSS (2 dyn/cm

Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test.

Background: Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for septic shock. Methods: GSE26440 dataset was downloaded from Gene Expression Omnibus (GEO), including 32 normal controls and 98 children with septic shock RNA samples from whole blood. The pathways and functional annotations of differentially expressed genes (DEGs) in the two types of samples were examined by GO and KEGG pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Protein-protein interactions (PPI) of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING) and Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. Results: A total of 140 genes were identified as DEGs, of which 98 genes were up-regulated and 42 genes were down-regulated. GO function analysis showed that DEGs were significantly enriched in biological processes, including immune response, leukocyte activation involved in immune response, and so on. The top hub genes, namely MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, S100A12, GPR97, and TRAT1 were recognized from the protein-protein interaction (PPI) network. Furthermore, qRT-PCR results demonstrated that the mRNA level of MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, and S100A12 was elevated while GPR97 was decreased in involved mouse and human models. However, TRAT1 expression is species-dependent which was decreased in the mouse septic shock model but elevated in the human LPS-treated macrophages model. Conclusion: Taken together, the identification and validation of several novel hub genes, especially GPR97 and TRAT1, deepen our comprehension of the molecular mechanisms of septic shock progression. These genes may be therapeutic molecular targets or diagnostic biomarkers in patients with septic shock.

Animal welfare plays the importance role in protecting the ecological environment, developing the animal industry, and regulating the human-animal relationship and maintaining the balance of species and ecosystems. Unfortunately, captive animals often suffer from poor welfare due to their long-term confinement in small and narrow environment, resulting in depressive and stereotyped behaviors such as repetitive running or pacing. Early detection and intervention of these behaviors contribute to improve the captive animal welfare. However, the traditional methods on manually observing and analyzing animal behaviors are labor-intensive and require specialized personnel. In recent years, many zoos and animal reserves are equipped with video surveillance systems, which can be conveniently used for animal behaviour analysis, but most research on the animal behaviour is reported for domestic animal. In this paper, we propose the novel method utilizing the videos from surveillance systems to automatically track and recognize animals’ stereotyped behaviors. Our approach employs a novel target tracking method based on Siamese relation network, which can accurately track target animals in complex environments and distinguish different individuals. We then identify the stereotyped behaviors by counting the cycle number that an animal repeats similar motion trajectories, such as repeatedly running around a similar circular trajectory. The experiments are conducted on the video datasets including three kinds of moving trajectories. The experimental results show that can accurately detect the cycle number of the repeated trajectories to recognized wild animal stereotypes.

This paper presents a dynamic neural network based adaptive impedance control method for redundant robots under multiple physical constraints. In order to provide optimal contact performance without an accurate environment model, an adaptive impedance learning method is proposed to establish the optimal interaction between robot and environment. In the inner loop, a theoretical framework of constraint optimization is constructed, and then a dynamic neural network is established to compensate the nonlinear dynamics, and compliance to physical limitations is also satisfied. These limitations include joint angle restriction, angular velocity restriction, angular acceleration restriction, and torque restriction. Theoretical analysis proves the stability of the closed loop system. Numerical results show the effectiveness of the proposed control scheme.

Motivated by applications to root-cause identification of faults in high-dimensional data streams that may have very limited samples after faults are detected, we consider multiple testing in models for multivariate statistical process control (SPC). With quick fault detection, only small portion of data streams being out-of-control (OC) can be assumed. It is a long standing problem to identify those OC data streams while controlling the number of false discoveries. It is challenging due to the limited number of OC samples after the termination of the process when faults are detected. Although several false discovery rate (FDR) controlling methods have been proposed, people may prefer other methods for quick detection. With a recently developed method called Knockoff filtering, we propose a knockoff procedure that can combine with other fault detection methods in the sense that the knockoff procedure does not change the stopping time, but may identify another set of faults to control FDR. A theorem for the FDR control of the proposed procedure is provided. Simulation studies show that the proposed procedure can control FDR while maintaining high power. We also illustrate the performance in an application to semiconductor manufacturing processes that motivated this development.

The integration of two-dimensional (2D) materials with photonic structures has catalyzed a wide spectrum of optical and optoelectronic applications. Conventional nanophotonic structures generally lack efficient reconfigurability and multifunctionality. The atomically thin 2D van der Waals materials can thus infuse new functionality and reconfigurability to the well-established library of photonic structures such as integrated waveguides, optical fibers, photonic crystals, micro-cavities, and metasurface, to name a few. Thanks to the handiness of van der Waals interfaces, the 2D materials can be easily transferred and mixed with other prefabricated photonic templates with high degrees of freedom, and can act as the optical gain, modulation, sensing, or plasmonic media for diverse applications. Here we review recent advents on combining 2D materials to nanophotonic structures for new functionality development or performance enhancements. Challenges and emerging opportunities in integrating van der Waals building blocks beyond 2D materials are also discussed., Comment: 23 pages