A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

DEVELOPMENT AND APPLICATION OF THE ENVIRONMENTAL HYDRODYNAMIC 3D MODEL FOR COMPUTATION AND FORECASTING OF OIL POLLUTIONS IN COASTAL MARINE ENVIRONMENT

Joint Research on Environmental Science and Technology for the Eart

Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.Comment: 18 pages, 7 figures, 5 table

Synthesis of ZnO nanorod for immunosensor application

This paper reported a facile method to synthesize ZnO nanorods for immunosensor application. The ZnO nanorods were synthesized by hydrothermal reaction. Synthesis time affecting on morphology of nanorods was also studied. The immobilization of anti-rotavirus onto ZnO nanorod-deposited sensor was performed via absorption method. The electrochemical responses of the immunosensor were studied by cyclic voltammetry (C-V) method with [Fe(CN)6]3−/4− as redox probe. A linear decreased response in C-V for cell of rotavirus concentration was found in the range of 7.8×105 CFU/mL to 7.8×108 CFU/mL. The detection limit of the immunosensor was 7.8×105 CFU/mL. The results indicated application of ZnO nanorod sensor for label-free real-time detection of a wide dynamics range of biological species

Corporate governance and firm performance: Evidence from Vietnamese listed companies

The research aims to provide empirical evidence on the relationship between corporate governance and firm performance in Vietnam – a developing economy in Asia. It focuses on the corporate governance of Vietnamese listed companies with a data-set of the five-year period from 2011 to 2015. Vietnamese listed companies are governed and controlled by two boards, Board of Directors and Supervisory Board. The research investigates the impacts of directors’ and supervisors’ characteristics and ownership structure on firm performance. The outcomes reveal that most governance mechanisms employed by Vietnamese listed companies were not effective and had no effect on the companies’ performance, except for managerial ownership and Supervisory Board size. Specifically, management ownership and firm performance were negatively correlated. Additional analyses show a positive relationship between the number of supervisors and firm performance, which was measured by market-based measurement