First-principles Analysis of Photo-current in Graphene PN Junctions

We report a first principles investigation of photocurrent generation by graphene PN junctions. The junctions are formed by either chemically doping with nitrogen and boron atoms, or by controlling gate voltages. Non-equilibrium Green's function (NEGF) formalism combined with density functional theory (DFT) is applied to calculate the photo-response function. The graphene PN junctions show a broad band photo-response including the terahertz range. The dependence of the response on the angle between the light polarization vector and the PN interface is determined. Its variation against photon energy $E_{ph}$ is calculated in the visible range. The essential properties of chemically doped and gate-controlled PN junctions are similar, but the former shows fingerprints of dopant distribution.Comment: 7 pages, 6 figure

Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Recent Progress on the Molecular Mechanisms of Anti-invasive and Metastatic Chinese Medicines for Cancer Therapy

Despite of the recent advances in diagnostic and therapeutic approaches, cancer remains as the leading cause of death worldly with diverse causal factors regarding genes and environment. Invasion and metastasis, as one of the most important hallmarks for cancer, have restrained the successful clinical therapy and are the primary causes of death among cancer patients. So far, most chemotherapeutic drugs are not effective for metastatic cancer due to drug resistance and serious side effects. Therefore, it is urgently essential to develop more effective therapeutic methods. Owing to their diverse biological activities and low toxicity, naturally active compounds derived from Chinese medicines, as a complementary and alternative approach, are reported to promote the therapeutic index and provoked as an excellent source for candidates of anti-metastatic drugs. With the rapid development of molecular biology techniques, the molecular mechanisms of the effects of potential anti-invasive and metastatic Chinese medicines are gradually elucidated. This chapter reviews the potential anti-invasive and metastatic mechanisms of naturally active compounds from Chinese medicines, including suppression of EMT, proteases and cancer-induced angiogenesis, anoikis regulation of circulating tumor cells and regulation of miRNA-mediated gene expression, providing scientific evidence for clinically using Chinese medicines in the field of cancer therapy

Chinese Medicines for Cancer Treatment from the Metabolomics Perspective

Cancer is one of the most prevalent diseases all over the world with poor prognosis and the development of novel therapeutic strategies is still urgently needed. The large amount of successful experiences in fighting against cancer-like diseases with Chinese medicine has suggested it as a great source of alternative treatments to human cancers. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation. Metabolic reprogramming is a remarkable hallmark of cancer and therapies targeting cancer metabolism can be highly specific and effective. Based on the sophisticated study of small molecule metabolites, metabolomics can provide us valuable information on dynamically metabolic responses of living systems to certain environmental condition. In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Overall, the elucidation of the underlying molecular mechanism of metabolism-targeting pharmacologic therapies will provide us a new insight to develop novel therapeutics for cancer treatment

Thermomechanical fatigue life prediction for a marine diesel engine piston considering ring dynamics

A newly designed marine diesel engine piston was modeled using a precise finite element analysis (FEA). The high cycle fatigue (HCF) safety factor prediction procedure designed in this study incorporated lubrication, thermal, and structure analysis. The piston ring dynamics calculation determined the predicted thickness of lubrication oil film. The film thickness influenced the calculated magnitude of the heat transfer coefficient (HTC) used in the thermal loads analysis. Moreover, the gas pressure of ring lands and ring grooves used in mechanical analysis is predicted based on the piston ring dynamics model