First-principles study of the interaction and charge transfer between graphene and metals

Measuring the transport of electrons through a graphene sheet necessarily involves contacting it with metal electrodes. We study the adsorption of graphene on metal substrates using first-principles calculations at the level of density functional theory. The bonding of graphene to Al, Ag, Cu, Au and Pt(111) surfaces is so weak that its unique "ultrarelativistic" electronic structure is preserved. The interaction does, however, lead to a charge transfer that shifts the Fermi level by up to 0.5 eV with respect to the conical points. The crossover from p-type to n-type doping occurs for a metal with a work function ~5.4 eV, a value much larger than the work function of free-standing graphene, 4.5 eV. We develop a simple analytical model that describes the Fermi level shift in graphene in terms of the metal substrate work function. Graphene interacts with and binds more strongly to Co, Ni, Pd and Ti. This chemisorption involves hybridization between graphene $p_z$-states and metal d-states that opens a band gap in graphene. The graphene work function is as a result reduced considerably. In a current-in-plane device geometry this should lead to n-type doping of graphene.Comment: 12 pages, 9 figure

The impact of an educational program in the management of patients with chronic hepatitis C

Introduction: This study was designed to measure the impact of lifestyle changes, involving a diet therapy and physical exercises in patients with chronic hepatitis C (CHC). Methods: The study was conducted during January 2008 - December 2009 at ”Prof. N. Paulescu” National Institute of Diabetes, Nutrition and Metabolic Diseases - Bucharest, Romania. We selected 67 patients (34 men/33 women). We performed anthropometric measurements (weight, height, BMI (body mass index), bioimpedance analysis (BIA) as well as fasting serum lipids (cholesterol, triglycerides, HDL-cholesterol), glucose profile (glucose, HbA1c), liver profile (ALT, AST, GGT, alkaline phosphatase, bilirubin, albumin, total protein), blood count for all patients at baseline. Results: The average age was 53.91±10.19 years. Obesity was present in 32.8% (n=22) of patients at baseline. Total fat mass decreased with weight loss 2.21 kg (p = 0.0001) respectively 3.17 kg (p = 0.0001). Weight loss was accompanied by decreased resting energy expenditure. Triglycerides decreased from 158.11±7.63 mg/dl to 134.88±6.1 mg/dl, cholesterol decreased from 187.3±6.8 mg/dl to 168.65±4.42 mg/dl and HDL-cholesterol increased from 45.13±1.9 mg/dl to 47.2±1.39 mg/dl after 12 months. Aspartaminotransferase, alaninaminotransferese, gamma-glutamil transpeptidase decreased with significant differences. Conclusions: Patients with hepatitis C undergoing an 1-year lifestyle intervention had significant improvements in fasting glucose, fasting insulin, HOMA-IR, lipidic profile, hepatic profile and adipose tissue distribution. The present study establishes the positive impact of an educational program in the management of patients with hepatitis C

Statin therapy in patients with diabetes and hepatitis C

The objective of this study was to determine the effects of statin therapy (atorvastatin) on serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels in patients with type 2 diabetes mellitus (T2DM) and chronic hepatitis C (CHC). A number of 77 patients with T2DM and CHC were selected, treated with atorvastatin, 20 mg, for 6 months, who underwent anthropometric measurements and biochemical tests (including fasting serum glucose, lipid profile, liver profile, cytokines profile) at baseline, after 1 month (clinical and biochemical profile for safety) and after 6 months of treatment. The patients’ average age was 52.53±9.7 years. Plasma low-density lipoprotein cholesterol (LDL-C) (-32.4 mg/dL), triglycerides (-29.7 mg/dL), total cholesterol (-32.8 mg/dL) decreased (p<0.05), and high-density lipoprotein cholesterol (HDL-C) (+3.04 mg/dL) increased (p<0.05), after 6 months. Atorvastatin treatment was associated with decreases of AST, ALT, and also leptin and interleukin-6 (IL-6) levels (all p<0.05) but we did not find any effect on plasma tumor necrosis factor-alpha (TNF-α) (p=0.119). Atorvastatin was an effective and well tolerated treatment for lowering total cholesterol, LDL-C, triglycerides in patients with CHC. Among patients with CHC there was no significant elevation of liver enzymes during statin treatment, and we even noticed an improvement of hepatic profile

A proposal for a new type of thin-film field-emission display by edge breakdown of MIS structure

A new type of field emission display(FED) based on an edge-enhance electron emission from metal-insulator-semiconductor (MIS) thin film structure is proposed. The electrons produced by an avalanche breakdown in the semiconductor near the edge of a top metal electrode are initially injected to the thin film of an insulator with a negative electron affinity (NEA), and then are injected into vacuum in proximity to the top electrode edge. The condition for the deep-depletition breakdown near the edge of the top metal electrode is analytically found in terms of ratio of the insulator thickness to the maximum (breakdown) width of the semiconductor depletition region: this ratio should be less than 2/(3 \pi - 2) = 0.27. The influence of a neighboring metal electrode and an electrode thickness on this condition are analyzed. Different practical schemes of the proposed display with a special reference to M/CaF_2/Si structure are considered.Comment: 11 pages, 5 figure

Level-3 Calorimetric Resolution available for the Level-1 and Level-2 CDF Triggers

As the Tevatron luminosity increases sophisticated selections are required to be efficient in selecting rare events among a very huge background. To cope with this problem, CDF has pushed the offline calorimeter algorithm reconstruction resolution up to Level 2 and, when possible, even up to Level 1, increasing efficiency and, at the same time, keeping under control the rates. The CDF Run II Level 2 calorimeter trigger is implemented in hardware and is based on a simple algorithm that was used in Run I. This system has worked well for Run II at low luminosity. As the Tevatron instantaneous luminosity increases, the limitation due to this simple algorithm starts to become clear: some of the most important jet and MET (Missing ET) related triggers have large growth terms in cross section at higher luminosity. In this paper, we present an upgrade of the Level 2 Calorimeter system which makes the calorimeter trigger tower information available directly to a CPU allowing more sophisticated algorithms to be implemented in software. Both Level 2 jets and MET can be made nearly equivalent to offline quality, thus significantly improving the performance and flexibility of the jet and MET related triggers. However in order to fully take advantage of the new L2 triggering capabilities having at Level 1 the same L2 MET resolution is necessary. The new Level-1 MET resolution is calculated by dedicated hardware. This paper describes the design, the hardware and software implementation and the performance of the upgraded calorimeter trigger system both at Level 2 and Level 1.Comment: 5 pages, 5 figures,34th International Conference on High Energy Physics, Philadelphia, 200

Choosing the nutritional intervention to overweight and obese patients

Weight problems occur in 1.5 billion people and these are a risk factor for type 2 diabetes, cardiovascular, pulmonary and periodontal diseases, cancer and osteoporosis. Our study aimed to evaluate the caloric intake, vitamins and minerals from food before a nutritional intervention to overweight and obese patients

Choosing the nutritional intervention to overweight and obese patients

Weight problems occur in 1.5 billion people and these are a risk factor for type 2 diabetes, cardiovascular, pulmonary and periodontal diseases, cancer and osteoporosis. Our study aimed to evaluate the caloric intake, vitamins and minerals from food before a nutritional intervention to overweight and obese patients

Database Learning: Toward a Database that Becomes Smarter Every Time

In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM SIGMOD conference 201