Application solutions of highway freight information systems based on quantum communication.

To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data[1]; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.

MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning.

As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there are still deficiencies for effectively mining the discriminant information for enzyme function recognition in existing methods. In this study, we present MVDINET, a novel method for multi-level enzyme function prediction. First, the initial multi-view feature data is extracted by the enzyme sequence. Then, the above initial views are fed into various deep specific network modules to learn the depth-specificity information. Further, a deep view interaction network is designed to extract the interaction information. Finally, the specificity information and interaction information are fed into a multi-view adaptively weighted classification. We compressively evaluate MVDINET on benchmark datasets and demonstrate that MVDINET is superior to existing methods.

Additional social welfare of environmental regulation: The effect of environmental taxes on income inequality.

This study utilizes the environmental tax reform in China as a quasi-natural experiment to investigate the effect of environmental taxes on income inequality. In January 2018, the Environmental Protection Tax Law (EPTL) came into effect in China, provinces began to collect environmental taxes in accordance with the law. We find that the reform contributes to lower within-firm wage inequality. The reform leads to declines in executive compensation and increases in worker wages. We further find that tax enforcement, environmental regulations, fiscal stress and tax competition vary the relationship between the reform and wage inequality. Heterogeneity analyses show that the effect is greater in non-state-owned firms, small firms, and firms with higher board shareholdings. Extensive robustness tests corroborate our inferences. This paper verifies the effectiveness of environmental regulation in enhancing social welfare, and is beneficial for assessing the welfare effects of environmental regulation more accurately. The findings can also help the government reduce obstacles in the implementation of environmental taxes, and further enhance the effectiveness of the EPTL.

Nanoparticles suppress fluid instabilities in the thermal drawing of ultralong nanowires.

Ultra-long metal nanowires and their facile fabrication have been long sought after as they promise to offer substantial improvements of performance in numerous applications. However, ultra-long metal ultrafine/nanowires are beyond the capability of current manufacturing techniques, which impose limitations on their size and aspect ratio. Here we show that the limitations imposed by fluid instabilities with thermally drawn nanowires can be alleviated by adding tungsten carbide nanoparticles to the metal core to arrive at wire lengths more than 30 cm with diameters as low as 170 nm. The nanoparticles support thermal drawing in two ways, by increasing the viscosity of the metal and lowering the interfacial energy between the boron silicate and zinc phase. This mechanism of suppressing fluid instability by nanoparticles not only enables a scalable production of ultralong metal nanowires, but also serves for widespread applications in other fluid-related fields.

MYC Participates in Lipopolysaccharide-Induced Sepsis via Promoting Cell Proliferation and Inhibiting Apoptosis.

OBJECTIVE: This study aimed to explore the potential mechanism of MYC proto-oncogene, BHLH Transcription Factor (MYC) gene, on sepsis. MATERIALS AND METHODS: In this experimental study, rat-derived H9C2 cardiomyocyte cells were cultured in vitro, followed by lipopolysaccharide (LPS) treatment with different concentration gradients. The cholecystokinin octapeptide (CCK-8) assay, enzyme-linked immunoassay (ELISA) assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), cell transfection, Western blot and flow cytometry were used to observe the cellular apoptosis and proliferation of cells in both treated LPS groups and normal control group. RESULTS: The result of CCK-8 assay showed that silencing MYC inhibited cellular proliferation of sepsis in absence or presence of LPS treatment. ELISA assay showed that the expressions of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) were decreased in MYC silenced group, but they were increased after LPS treatment. Moreover, Flow cytometry assay showed that MYC silencing contributed to the apoptosis of sepsis cells. Furthermore, the expression of inflammatory factors showed that MYC silencing elevated the expression of inflammation factors. CONCLUSION: MYC might take part in the process of LPS induced sepsis through suppressing apoptosis and inducing cell proliferation. Moreover, MYC might reduce inflammation during the progression of LPS induced sepsis.

Network-based inference methods for drug repositioning.

Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.

Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.