Dirac Fermion in Strongly-Bound Graphene Systems

It is highly desirable to integrate graphene into existing semiconductor technology, where the combined system is thermodynamically stable yet maintain a Dirac cone at the Fermi level. Firstprinciples calculations reveal that a certain transition metal (TM) intercalated graphene/SiC(0001), such as the strongly-bound graphene/intercalated-Mn/SiC, could be such a system. Different from free-standing graphene, the hybridization between graphene and Mn/SiC leads to the formation of a dispersive Dirac cone of primarily TM d characters. The corresponding Dirac spectrum is still isotropic, and the transport behavior is nearly identical to that of free-standing graphene for a bias as large as 0.6 V, except that the Fermi velocity is half that of graphene. A simple model Hamiltonian is developed to qualitatively account for the physics of the transfer of the Dirac cone from a dispersive system (e.g., graphene) to an originally non-dispersive system (e.g., TM).Comment: Apr 25th, 2012 submitte

Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications

Optimal Facility Location Model Based on Genetic Simulated Annealing Algorithm for Siting Urban Refueling Stations

This paper analyzes the impact factors and principles of siting urban refueling stations and proposes a three-stage method. The main objective of the method is to minimize refueling vehicles’ detour time. The first stage aims at identifying the most frequently traveled road segments for siting refueling stations. The second stage focuses on adding additional refueling stations to serve vehicles whose demands are not directly satisfied by the refueling stations identified in the first stage. The last stage further adjusts and optimizes the refueling station plan generated by the first two stages. A genetic simulated annealing algorithm is proposed to solve the optimization problem in the second stage and the results are compared to those from the genetic algorithm. A case study is also conducted to demonstrate the effectiveness of the proposed method and algorithm. The results indicate the proposed method can provide practical and effective solutions that help planners and government agencies make informed refueling station location decisions

Knowledge-Driven Semantic Segmentation for Waterway Scene Perception

Semantic segmentation as one of the most popular scene perception techniques has been studied for autonomous vehicles. However, deep learning-based solutions rely on the volume and quality of data and knowledge from specific scene might not be incorporated. A novel knowledge-driven semantic segmentation method is proposed for waterway scene perception. Based on the knowledge that water is irregular and dynamically changing, a Life Time of Feature (LToF) detector is designed to distinguish water region from surrounding scene. Using a Bayesian framework, the detector as the likelihood function is combined with U-Net based semantic segmentation to achieve an optimized solution. Finally, two public datasets and typical semantic segmentation networks, FlowNet, DeepLab and DVSNet are selected to evaluate the proposed method. Also, the sensitivity of these methods and ours to dataset is discussed

Evaluating the role of serum uric acid in the risk stratification and therapeutic response of patients with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD)

Background: Pulmonary arterial hypertension (PAH) is a malignant pulmonary vascular disease that negatively impacts quality of life, exercise capacity, and mortality. This study sought to investigate the relationship between serum uric acid (UA) level and the disease severity and treatment response of patients with PAH and congenital heart disease (PAH-CHD).Methods: This study included 225 CHD patients and 40 healthy subjects. Serum UA was measured in all patients, and UA levels and haemodynamic parameters were re-evaluated in 20 patients who had received PAH-specific drug treatment for at least 7 ± 1 month.Results: Serum UA levels were significantly higher in PAH-CHD patients than in CHD patients with a normal pulmonary artery pressure and normal subjects (347.7 ± 105.7 μmol/L vs. 278.3 ± 84.6 μmol/L; 347.7 ± 105.7 μmol/L vs. 255.7 ± 44.5 μmol/L, p < 0.05). UA levels in the intermediate and high risk groups were significantly higher than those in the low-risk group (365.6 ± 107.8 μmol/L vs. 311.2 ± 82.8 μmol/L; 451.6 ± 117.6 μmol/L vs. 311.2 ± 82.8 μmol/L, p < 0.05). Serum UA levels positively correlated with mean pulmonary arterial pressure, WHO functional class, pulmonary vascular resistance, and NT-proBNP (r = 0.343, 0.357, 0.406, 0.398; p < 0.001), and negatively with mixed venous oxygen saturation (SvO2) and arterial oxygen saturation (SaO2) (r = −0.293, −0.329; p < 0.001). UA significantly decreased from 352.7 ± 97.5 to 294.4 ± 56.8 μmol/L (p = 0.001) after PAH-specific drug treatment for at least 6 months, along with significant decreases in mean pulmonary arterial pressure and pulmonary vascular resistance and increases in cardiac index and mixed SvO2.Conclusion: Serum UA can be used as a practical and economic biomarker for risk stratification and the evaluation of PAH-specific drug treatment effects for patients with PAH-CHD

Strategic Planning for Connected and Automated Vehicles In Massachusetts

ISA#92312Connected and Automated Vehicle (CAV) technologies are evolving at a fast pace, and new developments are occurring on a daily basis. These technologies have potential to significantly change transportation and travel, to make them safer, more accessible, and more efficient than they are today. There are many issues that will have to be investigated, such as the pace and extent to which CAV technologies become pervasive, the socioeconomic impacts of CAVs, their implication on privacy and security; to what extent will these technologies replace human drivers, and the legal and regulatory responsibilities for the safe operation of CAVs. State departments of transportation (DOTs) will need to plan to prepare for and accommodate CAV technologies. The purpose of this study is to provide baseline information pertaining to strategic planning for CAV technologies in Massachusetts. This information may be used to assist the Massachusetts Department of Transportation (MassDOT) to develop a strategic plan to accommodate the deployment of such technologies and for related infrastructure investment decisions