Influence Mechanism of Smart City Innovation on Green Supply Chain Network Efficiency

The traditional logistics industry faces increasingly prominent problems like high energy consumption, high pollution, and high emissions. The improvement of green supply chain network efficiency (GSCNE) has become the development direction of this industry. Focusing on the panel data of 225 prefectures in China during 2012-2021, this paper uses the difference in differences (DID) method to explore the influence mechanism of smart city construction on GSCNE. The results show that smart city construction can enhance GSCNE via three mediators: information and communication technology (ICT), sustainable development, and technological innovation. Finally, some managerial implications were summarized according to the research conclusions

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201

深層強化学習による東京湾フェリーの避航方法について

東京海洋大学修士学位論文 2022年度(2022年9月) 海運ロジスティクス 修士 第3881号指導教員: 田丸人意全文公表年月日: 2022-12-22東京海洋大学202

Crystal Structure Transformation and Dielectric Properties of Polymer Composites Incorporating Zinc Oxide Nanorods

Zinc oxide (ZnO) nanorods were synthesized using a modified wet chemical method. Poly(vinylidene fluoride-co-hexafluoropropylene), P(VDF-HFP), nanocomposites with different ZnO nanorods loadings were prepared via a solution blend route. Field emission scanning electron microscopic (FE-SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) were used to investigate the structure and morphology of the nanocomposites. XRD and FTIR data indicate that the incorporation of ZnO nanorods promote the crystalline structure transformation of P(VDF-HFP). As the content of ZnO nanorods increases, the β phase structure increases while the α phase decreases. In addition, the dielectric properties of the P(VDF-HFP) and its composites were systematically studied

Investigation of Haemophilus parasuis from healthy pigs in China

Haemophilus parasuis is a common colonizer of the upper respiratory tract of swine and frequently causes disease, especially in weaner pigs. To date, limited epidemiological data was available for H. parasuis from healthy pigs, which might be carriers of potential pathogenic strains. In this study, from September 2016 to October 2017, we investigated the prevalence and characteristics of H. parasuis from healthy pigs in China. Totally, we obtained 244 isolates from 1675 nasal samples from 6 provinces. H. parasuis isolation was more successful in weaner pigs (22.6%, 192/849), followed by finisher pigs (9.3%, 43/463), and sows (2.5%, 9/363). The most prevalent serovars were 7 (20.1%, 49/244), followed by 3 (14.8%, 36/244), 2 (14.3%, 35/244), 11 (12.7%, 31/244), 5/12 (5.7%, 14/244) and 4 (2.5%, 6/244). Bimodal or multimodal distributions of MICs were observed for most of the tested drugs, which suggested the presence of non-wild type populations. It was noted that the MIC90 values of tilmicosin (64 μg/ml) was relatively higher than that reported in previous studies. Our results suggest that: 1) potentially pathogenic serovars of H. parasuis are identified in healthy pigs, and 2) elevated MICs and presence of mechanisms of resistance not yet described for clinically important antimicrobial agents would increase the burden of disease caused by H. parasuis.info:eu-repo/semantics/acceptedVersio

A single dose of DNA vaccine based on conserved H5N1 subtype proteins provides protection against lethal H5N1 challenge in mice pre-exposed to H1N1 influenza virus

<p>Abstract</p> <p>Background</p> <p>Highly pathogenic avian influenza virus subtype H5N1 infects humans with a high fatality rate and has pandemic potential. Vaccination is the preferred approach for prevention of H5N1 infection. Seasonal influenza virus infection has been reported to provide heterosubtypic immunity against influenza A virus infection to some extend. In this study, we used a mouse model pre-exposed to an H1N1 influenza virus and evaluated the protective ability provided by a single dose of DNA vaccines encoding conserved H5N1 proteins.</p> <p>Results</p> <p>SPF BALB/c mice were intranasally infected with A/PR8 (H1N1) virus beforehand. Six weeks later, the mice were immunized with plasmid DNA expressing H5N1 virus NP or M1, or with combination of the two plasmids. Both serum specific Ab titers and IFN-γ secretion by spleen cells in vitro were determined. Six weeks after the vaccination, the mice were challenged with a lethal dose of H5N1 influenza virus. The protective efficacy was judged by survival rate, body weight loss and residue virus titer in lungs after the challenge. The results showed that pre-exposure to H1N1 virus could offer mice partial protection against lethal H5N1 challenge and that single-dose injection with NP DNA or NP + M1 DNAs provided significantly improved protection against lethal H5N1 challenge in mice pre-exposed to H1N1 virus, as compared with those in unexposed mice.</p> <p>Conclusions</p> <p>Pre-existing immunity against seasonal influenza viruses is useful in offering protection against H5N1 infection. DNA vaccination may be a quick and effective strategy for persons innaive to influenza A virus during H5N1 pandemic.</p

State Space Model with hidden variables for reconstruction of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.</p> <p>Method</p> <p>True GRNs and synthetic gene expression datasets were generated by using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.</p> <p>Results</p> <p>Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.</p> <p>Conclusion</p> <p>This study provides useful information in handling the hidden variables and improving the inference precision.</p