Prediction of thermal expansion properties of carbon nanotubes using molecular dynamics simulations

The axial coefficients of thermal expansion (CTE) of various carbon nanotubes (CNTs), i.e., single-wall carbon nanotubes (SWCNTs), and some multi-wall carbon nanotubes (MWCNTs), were predicted using molecular dynamics (MDs) simulations. The effects of two parameters, i.e., temperature and the CNT diameter, on CTE were investigated extensively. For all SWCNTs and MWCNTs, the obtained results clearly revealed that within a wide low temperature range, their axial CTEs are negative. As the diameter of CNTs decreases, this temperature range for negative axial CTEs becomes narrow, and positive axial CTEs appear in high temperature range. It was found that the axial CTEs vary nonlinearly with the temperature, however, they decrease linearly as the CNT diameter increases. Moreover, within a wide temperature range, a set of empirical formulations was proposed for evaluating the axial CTEs of armchair and zigzag SWCNTs using the above two parameters. Finally, it was found that the absolute value of the negative axial CTE of any MWCNT is much smaller than those of its constituent SWCNTs, and the average value of the CTEs of its constituent SWCNTs. The present fundamental study is very important for understanding the thermal behaviors of CNTs in such as nanocomposite temperature sensors, or nanoelectronics devices using CNTs.

Optimisation methods for coupled thermodynamic and 1D design of radial-inflow turbines

Optimisation is a fundamental step in the turbine design process, especially in the development of non-classical designs of radial-inflow turbines working with high-density fluids in low-temperature Organic Rankine Cycles (ORCs). The present work discusses the simultaneous optimisation of the thermodynamic cycle and the one-dimensional design of radial-inflow turbines. In particular, the work describes the integration between a 1D meanline preliminary design code adapted to real gases and the performance estimation approach for radial-inflow turbines in an established ORC cycle analysis procedure. The optimisation approach is split in two distinct loops; the inner operates on the 1D design based on the parameters received from the outer loop, which optimises the thermodynamic cycle. The method uses parameters including brine flow rate, temperature and working fluid, shifting assumptions such as head and flow coefficients into the optimisation routine. The discussed design and optimisation method is then validated against published benchmark cases. Finally, using the same conditions, the coupled optimisation procedure is extended to the preliminary design of a radial-inflow turbine with R143a as working fluid in realistic geothermal conditions and compared against results from commercially-available software RITAL from Concepts-NREC.

The influence of atmospheric conditions on the leakage current of ceramic insulators on the Colombian Caribbean coast

The contamination of electrical insulators is one of the major contributors to the risk of operation outages in electrical substations, especially in coastal zones with high salinity levels and atmospheric pollution. By using the measurement of leakage-currents, which is one of the main indicators of contamination in insulators, this work seeks to the determine the correlation with climatic variables, such as ambient temperature, relative humidity, solar irradiance, atmospheric pressure, and wind speed and direction. The results obtained provide an input to the behaviour of the leakage current under atmospheric conditions that are particular to the Caribbean coast of Colombia. Spearman’s rank correlation coefficients and principal component analysis are utilised to determine the significant relationships among the different variables under consideration. The necessary information for the study was obtained via historical databases of both atmospheric variables and the leakage current measured in over a period of one year in a 220-kV potential transformer insulator. We identified the influencing factors of temperature, humidity, radiation, wind speed and direction on the magnitude of the leakage current as the most relevant.

Building macroscale models from microscale probabilistic models : a general probabilistic approach for nonlinear diffusion and multispecies phenomena

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

Exploring Retinal Markers of Diabetic Neuropathy

Purpose : To investigate the application of retinal nerve fibre layer (RNFL) thickness as a marker for severity of diabetic peripheral neuropathy (DPN) in people with Type 2 diabetes. Methods : This was a cross-sectional study whereby 61 participants (mean age 61 [41-75 years], mean duration of diabetes 14 [1-40 years], 70% male) with Type 2 diabetes and DPN underwent optical coherence tomography (OCT) scans. Global and 4 quadrant (TSNI) RNFL thicknesses were measured at 3.45mm around the optic nerve head of one eye. Neuropathy disability score (NDS) was used to assess the severity of DPN on a 0 to 10 scale. Participants were divided into three age-matched groups representing mild (NDS=3-5), moderate (NDS=6-8) and severe (NDS=9-10) neuropathy. Two regression models were fitted for statistical analysis: 1) NDS scores as co-variate for global and quadrant RNFL thicknesses, 2) NDS groups as a factor for global RNFL thickness only. Results : Mean (SD) RNFL thickness (µm) was 103(9) for mild neuropathy (n=34), 101(10) for moderate neuropathy (n=16) and 95(13) in the group with severe neuropathy (n=11). Global RNFL thickness and NDS scores were statistically significantly related (b=-1.20, p=0.048). When neuropathy was assessed across groups, a trend of thinner mean RNFL thickness was observed with increasing severity of neuropathy; however, this result was not statistically significant (F=2.86, p=0.065). TSNI quadrant analysis showed that mean RNFL thickness reduction in the inferior quadrant was 2.55 µm per 1 unit increase in NDS score (p=0.005). However, the regression coefficients were not statistically significant for RNFL thickness in the superior (b=-1.0, p=0.271), temporal (b=-0.90, p=0.238) and nasal (b=-0.99, p=0.205) quadrants. Conclusions : RNFL thickness was reduced with increasing severity of DPN and the effect was most evident in the inferior quadrant. Measuring RNFL thickness using OCT may prove to be a useful, non-invasive technique for identifying severity of DPN and may also provide additional insight into common mechanisms for peripheral neuropathy and RNFL damage.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Assessing uncertainty in pollutant wash-off modelling via model validation

Stormwater pollution is linked to stream ecosystem degradation. In predicting stormwater pollution, various types of modelling techniques are adopted. The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken. It is well understood that available water quality datasets in urban areas span only relatively short time scales unlike water quantity data, which limits the applicability of the developed models in engineering and ecological assessment of urban waterways. This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset. It was found that the application of MCCV is likely to result in a more realistic measure of model coefficients than LOO. Most importantly, MCCV and LOO were found to be effective in model validation when dealing with a small sample size which hinders detailed model validation and can undermine the effectiveness of stormwater quality management strategies.

Zinc oxide quantum dot nanostructures

Zinc oxide (ZnO) is one of the most intensely studied wide band gap semiconductors due to its many desirable properties. This project established new techniques for investigating the hydrodynamic properties of ZnO nanoparticles, their assembly into useful photonic structures, and their multiphoton absorption coefficients for excitation with visible or infrared light rather than ultraviolet light. The methods developed are also applicable to a wide range of nanoparticle samples.

Hydrolysis of genotoxic methyl-substituted oxiranes : Experimental kinetic and semiempirical studies

The kinetics of acid-catalyzed hydrolysis of seven methylated aliphatic epoxides - R1R2C(O)CR3R4 (A: R1=R2=R3=R4=H; B: R1=R2=R3=H, R4=Me; C: R1=R2=H, R3=R4=Me; D: R1=R3=H, R2=R4=Me(trans); E: R1=R3=H, R2=R4=Me(cis); F: R1=R3=R4=Me, R2=H; G: R1=R2=R3=R4=Me) - has been studied at 36 ± 1.5°C. Compounds with two methyl groups at the same carbon atom of the oxirane ring exhibit highest rate constants (k(eff) in reciprocal molar concentration per second: 11.0 ± 1.3 for C, 10.7 ± 2.1 for F, and 8.7 ± 0.7 for G as opposed to 0.124 ± 0.003 for B, 0.305 ± 0.003 for D, and 0.635 ± 0.036 for E). Ethylene oxide (A) displays the lowest rate of hydrolysis (0.027 M-1 s-1). The results are consistent with literature data available for compounds A, B, and C. To model the reactivities we have employed quantum chemical calculations (MNDO, AM1, PM3, and MINDO/3) of the main reaction species. There is a correlation of the logarithm k(eff) with the total energy of epoxide ring opening. The best correlation coefficients (r) were obtained using the AM1 and MNDO methods (0.966 and 0.957, respectively). However, unlike MNDO, AM1 predicts approximately zero energy barriers for the oxirane ring opening of compounds B, C, E and G, which is not consistent with published kinetic data. Thus, the MNDO method provides a preferential means of modeling the acidic hydrolysis of the series of methylated oxiranes. The general ranking of mutagenicity in vitro, A > B > C, is in line with the concept that this sequence also gradually leaves the expoxide reactivity optimal for genotoxicity toward reactivities leading to higher biological detoxifications.

Food insecurity and physical activity among U.S. populations

Objectives: Examine the association between food insecurity (FI) and physical activity (PA) in the U.S. population. Methods: Accelerometry (PAM) and self-report PA (PAQ) data from NHANES 2003-2006 were used. Those aged less than six years or were older than 65 years, pregnant, with physical limitations, or with family income above 350% of the poverty line were excluded. FI was measured by the USDA Household Food Security Survey Module. Crude and adjusted odd ratios were calculated from logistic regression to identify the association between FI and adherence to the PA recommendation. Crude and adjusted coefficients were calculated from linear regression to identify the association between FI and both sedentary and activity minutes. Results: In children, FI was not associated with adherence to PA recommendation measured via PAM or PAQ (p>0.05) but was significantly associated with sedentary minutes (adjusted coefficient=10.74, one-sided p<0.05). Food-insecure children did less moderate-to-vigorous PA than did food-secure children (adjusted coefficient = -5.31, p = 0.032). In adults, FI was significantly associated with PA (adjusted OR=0.722 for PAM and OR=0.839 for PAQ, one-sided p<0.05) but not associated with sedentary minutes (p>0.05) Conclusions: FI children were more sedentary and FI adults were less likely to adhere to the PA recommendation than those without FI.

Household food insecurity is associated with less physical activity among children and adults in the U.S. population

Background Household food insecurity and physical activity are each important public-health concerns in the United States, but the relation between them was not investigated thoroughly. Objective We wanted to examine the association between food insecurity and physical activity in the U.S. population. Methods Physical activity measured by accelerometry (PAM) and physical activity measured by questionnaire (PAQ) data from the NHANES 2003–2006 were used. Individuals aged <6 y or >65 y, pregnant, with physical limitations, or with family income >350% of the poverty line were excluded. Food insecurity was measured by the USDA Household Food Security Survey Module. Adjusted ORs were calculated from logistic regression to identify the association between food insecurity and adherence to the physical-activity guidelines. Adjusted coefficients were obtained from linear regression to identify the association between food insecurity with sedentary/physical-activity minutes. Results In children, food insecurity was not associated with adherence to physical-activity guidelines measured via PAM or PAQ and with sedentary minutes (P > 0.05). Food-insecure children did less moderate to vigorous physical activity than food-secure children (adjusted coefficient = −5.24, P = 0.02). In adults, food insecurity was significantly associated with adherence to physical-activity guidelines (adjusted OR = 0.72, P = 0.03 for PAM; and OR = 0.84, P < 0.01 for PAQ) but was not associated with sedentary minutes (P > 0.05). Conclusion Food-insecure children did less moderate to vigorous physical activity, and food-insecure adults were less likely to adhere to the physical-activity guidelines than those without food insecurity.

Online estimation of Allan variance parameters

A new online method is presented for estimation of the angular randomwalk and rate randomwalk coefficients of inertial measurement unit gyros and accelerometers. In the online method, a state-space model is proposed, and recursive parameter estimators are proposed for quantities previously measured from offline data techniques such as the Allan variance method. The Allan variance method has large offline computational effort and data storage requirements. The technique proposed here requires no data storage and computational effort of approximately 100 calculations per data sample.

On-line estimation of Allan variance parameters

A new online method is presented for estimation of the angular random walk and rate random walk coefficients of IMU (inertial measurement unit) gyros and accelerometers. The online method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph. Allan variance graphs have large off-line computational effort and data storage requirements. The technique proposed here requires no data storage and computational effort of O(100) calculations per data sample.

A novel manoeuvering model based on low-aspect-ratio lift theory and Lagrangian mechanics

This paper proposes a physically motivated reappraisal of manoeuvring models for ships and presents a new model developed from first principles by application of low aspect-ratio aerodynamic theory and Lagrangian mechanics. The coefficients of the model are shown to be related to physical processes, and validation is presented using the results from a planar motion mechanism dataset.

A constitutive model for mechanical response characterization of pumpkin peel and flesh tissues under tensile and compressive loadings

Enhancing quality of food products and reducing volume of waste during mechanical operations of food industry requires a comprehensive knowledge of material response under loadings. While research has focused on mechanical response of food material, the volume of waste after harvesting and during processing stages is still considerably high in both developing and developed countries. This research aims to develop and evaluate a constitutive model of mechanical response of tough skinned vegetables under postharvest and processing operations. The model focuses on both tensile and compressive properties of pumpkin flesh and peel tissues where the behaviours of these tissues vary depending on various factors such as rheological response and cellular structure. Both elastic and plastic response of tissue were considered in the modelling process and finite elasticity combined with pseudo elasticity theory was applied to generate the model. The outcomes were then validated using the published results of experimental work on pumpkin flesh and peel under uniaxial tensile and compression. The constitutive coefficients for peel under tensile test was α = 25.66 and β = −18.48 Mpa and for flesh α = −5.29 and β = 5.27 Mpa. under compression the constitutive coefficients were α = 4.74 and β = −1.71 Mpa for peel and α = 0.76 and β = −1.86 Mpa for flesh samples. Constitutive curves predicted the values of force precisely and close to the experimental values. The curves were fit for whole stress versus strain curve as well as a section of curve up to bio yield point. The modelling outputs had presented good agreement with the empirical values and the constructive curves exhibited a very similar pattern to the experimental curves. The presented constitutive model can be applied next to other agricultural materials under loading in future.