Resonant Tunneling through double-bended Graphene Nanoribbons

We investigate theoretically resonant tunneling through double-bended graphene nanoribbon structures, i.e., armchair-edged graphene nanoribbons (AGNRs) in between two semi-infinite zigzag graphene nanoribbon (ZGNR) leads. Our numerical results demonstrate that the resonant tunneling can be tuned dramatically by the Fermi energy and the length and/or widths of the AGNR for both the metallic and semiconductor-like AGNRs. The structure can also be use to control the valley polarization of the tunneling currents and could be useful for potential application in valleytronics devices.Comment: 4 pages, 4 figure

The Determinants of Foreign-Direct-Investment (FDI) Inflows in Nigeria

Due to the importance of foreign direct investment (FDI), scholars are always keen to explore FDI determinants and analyze their implications. Nevertheless scholars have proposed mixed viewpoints of FDI and interpreted its determinants differently, which do not contribute to the knowledge advancement and amalgamation of FDI literatures. The current research, therefore, aims to advance the knowledge of FDI determinants in Nigeria through a new investigation on the key determinant factors affecting Nigeria’s inward FDI. Data were collected from UNCTAD (1970-2014) and analyzed by auto-regressive distributed lag tests (ARDL). A comprehensive theory-based model was developed accounting for many variables, such as the interest rate, external debt, oil rents, the Gross Domestic Product (GDP) growth rate, trade and exchange rate volatility. The analysis of FDI determinants in the Nigerian economy yielded reliable, robust, and economically meaningful results thereby offering an insight into the driving factors of inward FDI. Findings indicate that the interest rate, external debt, oil rents, and GDP growth are all important determinants, possessing a long-run effect on FDI. Different from the literature, however, trade and exchange rate volatility are barely important to FDI. Several policy implications flow from the findings. From a policy point of view, regarding the GDP growth rate, there should be concerted efforts to boost the performance of the non-oil sector in Nigeria through more investments in the agricultural and industrial sectors making the growth of the economy spread across other sectors and, in turn, encouraging inward FDI in such areas. Countries such as Nigeria, endowed with natural resources, should pursue policies targeted at full deregulation (privatisation) of their natural resource sector to better utilize the abundance of their natural resources thereby attracting additional FDI. Nigeria should also pursue better debt management practices. When debts are acquired, they should be targeted towards future consumption and longer-term investments. Most importantly, as an import-dependent economy, the Nigerian government should also formulate export-driven and appropriate fiscal policies that will stabilize Nigeria’s trade relationship with other world economies. The Nigerian government should create the necessary environment that will regulate macroeconomic and specifically monetary policy (interest rate) which is essential for the attraction of FDI inflows into the economy. Finally, Nigeria should ensure that the quality of exportable commodities is improved to enhance international competitiveness

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. [Abstract copyright: AJTR Copyright © 2020.

Quantum pumping in graphene nanoribbons at resonant transmission

Adiabatic quantum charge pumping in graphene nanoribbon double barrier structures with armchair and zigzag edges in the resonant transmission regime is analyzed. Using recursive Green's function method we numerically calculate the pumped charge for pumping contours encircling a resonance. We find that for armchair ribbons the whole resonance line contributes to the pumping of a single electron (ignoring double spin degeneracy) per cycle through the device. The case of zigzag ribbons is more interesting due to zero-conductance resonances. These resonances separate the whole resonance line into several parts, each of which corresponds to the pumping of a single electron through the device. Moreover, in contrast to armchair ribbons, one electron can be pumped from the left lead to the right one or backwards. The current direction depends on the particular part of the resonance line encircled by the pumping contour.Comment: 6 pages, 5 figures. This is an author-created, un-copyedited version of an article accepted for publication in EPL. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at 10.1209/0295-5075/92/4701

Can manager's listening behavior benefit employees? Power distance may have the answer

The current research investigated employee’s perception of their manager’s listening behavior (MLB). Drawing on the group-value theory, we examined the role of MLB and analyzed its effect through employee’s power distance orientation. We distributed questionnaires to 219 employees and adopted two-wave data collection to ameliorate the bias of common method variance. Statistical analysis revealed that MLB was related to employees’ well-being and work engagement. For employees with lower power distance orientation, MLB led to more self-esteem. For employees with higher power distance orientation, MLB did not affect their self-esteem. MLB was not always beneficial to the employees, as individuals may interpret MLB positively or negatively. Research findings have brought new insights into the listening literature, particularly from the perspective of manager’s listening behavior. We encourage the organizations to incorporate listening skills into the education programs (for training incumbent managers) and recruitment criterions (for hiring new managers). Implications on the manager-employee relationship are also discussed