Carbon Nanotubes Doped Vanadium Oxide Thin Film

Vanadium oxide films were prepared by chemical coating techniques on glass and ITO coated glass substrates. Vanadium oxide films were doped with carbon nanotubes and examined their optical band gap and structures. The effects of carbon nanotubes on visible light absorbance and band gap energies were observed. Electrochemical, structural and optical studies on vanadium oxide coatings were reported in the liquid electrolyte. Optical constant such as refractive index, extinction coefficient, and band gap calculation was done by NKD analyzer. Effect of CNT into vanadium oxide films structural properties were examined by atomic force microscopy (AFM) When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3500

Cu-Au type orderings in the staggered quadrupolar region of the fcc Blume Emery Griffiths model

The spin-1 Ising (BEG) model has been simulated using a cellular automaton (CA) algorithm improved from the Creutz cellular automaton (CCA) for a face-centered cubic (fcc) lattice. The ground state diagram ($k$, $d$) of the fcc BEG model has ferromagnetic ($F$), quadrupolar ($Q$) and staggered quadrupolar ($SQ$) ordering regions. The simulations have been made in the staggered quadrupolar region for the parameter values in the intervals $-24\leq d=D/J<0$ and $-3\leq k=K/J\leq 0$ . The phase diagrams on the ($kT_{C}/J$, $d$) and the ($kT_{C}/J$, $k$) planes have been obtained through $k=-3$ and $d=-4$ lines, respectively. The staggered quadrupolar ordering region separates into five ordering regions ($A_{3}B(a)$, $A_{3}B(f)$, $AB$ (type-I), $AB$(type-II) and $AB_{3}(f)$) which have the different stoichiometric Cu-Au type structures.Comment: 24 pages, 11 figure

Evaluation of the factors associated with sublingual varices: a descriptive clinical study

Background: Age is considered as an important factor for the development of sublingual varices (SV). It has been suggested that some other conditions such as gender, systemic diseases, smoking, denture wearing may play a role in aetiology. However, there are a limited number of studies investigating these associations. This study was perform to evaluate association between SV and the conditions which are described as possible risk factors previously.  Materials and methods: A total of 691 patients (470 females, 221 males) who attended for comprehensive clinical examination were included in the study. Age, gender, systemic health, venous varix of the lower extremities, smoking status, denture wearing were recorded during the history taking. SV were classified into two categories: stage 0 (few or none visible) and stage 1 (moderate or severe). Tongue photographs were taken from a group of these patients. For the evalu- ation of intra-observer reliability, 60 photographs of tongue were re-evaluated by the same observer. Intra-observer reliability was evaluated using Kappa statistics. Pearson c2 test and Fisher’s exact test were used to assess SV in relation to each variable, and variables showing associations with p &lt; 0.05 were selected for the multivariable analysis, then logistic regression analysis was applied.  Results: Kappa value of intra-observer reliability was 0.91. SV were significantly associated with age (odds ratio [OR] = 1.08, p = 0.000), hypertension (OR = 2.3, p = 0.007) and denture wearing (OR = 2.17, p = 0.02). Conclusions: The presence of SV is associated with hypertension and denture wearing as well as aging. More detailed studies are needed to prove causative relations between SV and systemic diseases.

First Order Phase Transition in the 3-dimensional Blume-Capel Model on a Cellular Automaton

The first order phase transition of the three-dimensional Blume Capel are investigated using cooling algorithm which improved from Creutz Cellular Automaton for the $D/J=2.9$ parameter value in the first order phase transition region. The analysis of the data using the finite-size effect and the histogram technique indicate that the magnetic susceptibility maxima and the specific heat maxima increase with the system volume ($L^{d}$) at $$.Comment: 13 pages, 4 figure

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production?

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools

Social media addiction and poor mental health: examining the mediating roles of internet addiction and phubbing

Many researchers have examined the potential detrimental role of problematic social media use (often referred as ‘social media addiction’) on mental health. The present study investigated how social media addiction is associated with three components of mental health: depression, anxiety, and stress. In addition, structural equation modeling was used to test the mediating roles of internet addiction and phubbing among a sample of young adults (N = 603). Results showed that social media addiction was associated with poorer mental health via internet addiction and phubbing. More specifically, associations between social media addiction and stress, and social media addiction and anxiety were explained by both internet addiction and phubbing. The association between social media addiction and depression was explained by internet addiction only. These results remained consistent after controlling for gender, age, frequency of internet use, frequency of social media use, and frequency of smartphone use. These findings extend the extant literature by providing evidence for the dual roles of internet addiction and phubbing in explaining the relationship between social media addiction and poor mental health. Social media addiction did not directly influence poorer mental health but did via internet addiction and phubbing. Therefore, greater awareness of the inter-relationships between technology-based behaviors and their impact on mental health is needed among a wide range of stakeholders, and these inter-relationships need considering in the prevention and treatment of technology-based disorders