Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage

The determination of the characteristics of articular cartilage such as thickness, stiffness and swelling, especially in the form that can facilitate real-time decisions and diagnostics is still a matter for research and development. This paper correlates near infrared spectroscopy with mechanically measured cartilage thickness to establish a fast, non-destructive, repeatable and precise protocol for determining this tissue property. Statistical correlation was conducted between the thickness of bovine cartilage specimens (n = 97) and regions of their near infrared spectra. Nine regions were established along the full absorption spectrum of each sample and were correlated with the thickness using partial least squares (PLS) regression multivariate analysis. The coefficient of determination (R2) varied between 53 and 93%, with the most predictive region (R2 = 93.1%, p < 0.0001) for cartilage thickness lying in the region (wavenumber) 5350–8850 cm−1. Our results demonstrate that the thickness of articular cartilage can be measured spectroscopically using NIR light. This protocol is potentially beneficial to clinical practice and surgical procedures in the treatment of joint disease such as osteoarthritis.

Influence of drying conditions on the moisture diffusion and fluidization quality during multi-stage fluidized bed drying of bovine intestine for pet food

In order to establish the influence of the drying air characteristics on the drying performance and fluidization quality of bovine intestine for pet food, several drying tests have been carried out in a laboratory scale heat pump assisted fluid bed dryer. Bovine intestine samples were heat pump fluidized bed dried at atmospheric pressure and at temperatures below and above the materials freezing points, equipped with a continuous monitoring system. The investigation of the drying characteristics have been conducted in the temperature range −10 to 25 ◦C and the airflow in the range 1.5–2.5 m/s. Some experiments were conducted as single temperature drying experiments and others as two stage drying experiments employing two temperatures. An Arrhenius-type equation was used to interpret the influence of the drying air temperature on the effective diffusivity, calculated with the method of slopes in terms of energy activation, and this was found to be sensitive to the temperature. The effective diffusion coefficient of moisture transfer was determined by the Fickian method using uni-dimensional moisture movement in both moisture, removal by evaporation and combined sublimation and evaporation. Correlations expressing the effective moisture diffusivity and drying temperature are reported. Bovine particles were characterized according to the Geldart classification and the minimum fluidization velocity was calculated using the Ergun Equation and generalized equation for all drying conditions at the beginning and end of the trials. Walli’s model was used to categorize stability of the fluidization at the beginning and end of the dryingv for each trial. The determined Walli’s values were positive at the beginning and end of all trials indicating stable fluidization at the beginning and end for each drying condition.

Simulating the effect of acoustic treatment types for residential balconies with road traffic noise

Development of design guides to estimate the difference in speech interference level due to road traffic noise between a reference position and balcony position or façade position is explored. A previously established and validated theoretical model incorporating direct, specular and diffuse reflection paths is used to create a database of results across a large number of scenarios. Nine balcony types with variable acoustic treatments are assessed to provide acoustic design guidance on optimised selection of balcony acoustic treatments based on location and street type. In total, the results database contains 9720 scenarios on which multivariate linear regression is conducted in order to derive an appropriate design guide equation. The best fit regression derived is a multivariable linear equation including modified exponential equations on each of nine deciding variables, (1) diffraction path difference, (2) ratio of total specular energy to direct energy, (3) distance loss between reference position and receiver position, (4) distance from source to balcony façade, (5) height of balcony floor above street, (6) balcony depth, (7) height of opposite buildings, (8) diffusion coefficient of buildings, and; (9) balcony average absorption. Overall, the regression correlation coefficient, R2, is 0.89 with 95% confidence standard error of ±3.4 dB.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Reaction of Aromatic Peroxyl Radicals with Alkynes: A Mass Spectrometric and Computational Study Using the Distonic Radical Ion Approach

The reaction of the aromatic distonic peroxyl radical cations N-methyl pyridinium-4-peroxyl (PyrOO center dot+) and 4-(N,N,N-trimethyl ammonium)-phenyl peroxyl (AnOO center dot+), with symmetrical dialkyl alkynes 10?ac was studied in the gas phase by mass spectrometry. PyrOO center dot+ and AnOO center dot+ were produced through reaction of the respective distonic aryl radical cations Pyr center dot+ and An center dot+ with oxygen, O2. For the reaction of Pyr center dot+ with O2 an absolute rate coefficient of k1=7.1X10-12 cm3 molecule-1 s-1 and a collision efficiency of 1.2?% was determined at 298 K. The strongly electrophilic PyrOO center dot+ reacts with 3-hexyne and 4-octyne with absolute rate coefficients of khexyne=1.5X10-10 cm3 molecule-1 s-1 and koctyne=2.8X10-10 cm3 molecule-1 s-1, respectively, at 298 K. The reaction of both PyrOO center dot+ and AnOO center dot+ proceeds by radical addition to the alkyne, whereas propargylic hydrogen abstraction was observed as a very minor pathway only in the reactions involving PyrOO center dot+. A major reaction pathway of the vinyl radicals 11 formed upon PyrOO center dot+ addition to the alkynes involves gamma-fragmentation of the peroxy O?O bond and formation of PyrO center dot+. The PyrO center dot+ is rapidly trapped by intermolecular hydrogen abstraction, presumably from a propargylic methylene group in the alkyne. The reaction of the less electrophilic AnOO center dot+ with alkynes is considerably slower and resulted in formation of AnO center dot+ as the only charged product. These findings suggest that electrophilic aromatic peroxyl radicals act as oxygen atom donors, which can be used to generate alpha-oxo carbenes 13 (or isomeric species) from alkynes in a single step. Besides gamma-fragmentation, a number of competing unimolecular dissociative reactions also occur in vinyl radicals 11. The potential energy diagrams of these reactions were explored with density functional theory and ab initio methods, which enabled identification of the chemical structures of the most important products.

Analysis of temporary traffic control cost items in transportation construction bidding process

Temporary Traffic Control Plans (TCP’s), which provide construction phasing to maintain traffic during construction operations, are integral component of highway construction project design. Using the initial design, designers develop estimated quantities for the required TCP devices that become the basis for bids submitted by highway contractors. However, actual as-built quantities are often significantly different from the engineer’s original estimate. The total cost of TCP phasing on highway construction projects amounts to 6–10% of the total construction cost. Variations between engineer estimated quantities and final quantities contribute to reduced cost control, increased chances of cost related litigations, and bid rankings and selection. Statistical analyses of over 2000 highway construction projects were performed to determine the sources of variation, which later were used as the basis of development for an automated-hybrid prediction model that uses multiple regressions and heuristic rules to provide accurate TCP quantities and costs. The predictive accuracy of the model developed was demonstrated through several case studies.

Experimental study on creep and shrinkage of high-performance ultra-lightweight cement composite of 60 MPa

Creep and shrinkage behaviour of an ultra lightweight cement composite (ULCC) up to 450 days was evaluated in comparison with those of a normal weight aggregate concrete (NWAC) and a lightweight aggregate concrete (LWAC) with similar 28-day compressive strength. The ULCC is characterized by low density < 1500 kg/m3 and high compressive strength about 60 MPa. Autogenous shrinkage increased rapidly in the ULCC at early-age and almost 95% occurred prior to the start of creep test at 28 days. Hence, majority of shrinkage of the ULCC during creep test was drying shrinkage. Total shrinkage of the ULCC during the 450-day creep test was the lowest compared to the NWAC and LWAC. However, corresponding total creep in the ULCC was the highest with high proportion attributed to basic creep (≥ ~90%) and limited drying creep. The high creep of the ULCC is likely due to its low elastic modulus. Specific creep of the ULCC was similar to that of the NWAC, but more than 80% higher than the LWAC. Creep coefficient of the ULCC was about 47% lower than that of the NWAC but about 18% higher than that of the LWAC. Among five creep models evaluated which tend to over-estimate the creep coefficient of the ULCC, EC2 model gives acceptable prediction within +25% deviations. The EC2 model may be used as a first approximate for the creep of ULCC in the designs of steel-concrete composites or sandwich structures in the absence of other relevant creep data.

The impact of a congestion charging exemption on the demand for new low‐emission vehicles

Numerous initiatives have been employed around the world in order to address rising greenhouse gas (GHG) emissions originating from the transport sector. These measures include: travel demand management (congestion‐charging), increased fuel taxes, alternative fuel subsidies and low‐emission vehicle (LEV) rebates. Incentivizing the purchase of LEVs has been one of the more prevalent approaches in attempting to tackle this global issue. LEVs, whilst having the advantage of lower emissions and, in some cases, more efficient fuel consumption, also bring the downsides of increased purchase cost, reduced convenience of vehicle fuelling, and operational uncertainty. To stimulate demand in the face of these challenges, various incentive‐based policies, such as toll exemptions, have been used by national and local governments to encourage the purchase of these types of vehicles. In order to address rising GHG emissions in Stockholm, and in line with the Swedish Government’s ambition to operate a fossil free fleet by 2030, a number of policies were implemented targeting the transport sector. Foremost amongst these was the combination of a congestion charge – initiated to discourage emissions‐intensive travel – and an exemption from this charge for some LEVs, established to encourage a transition towards a ‘green’ vehicle fleet. Although both policies shared the aim of reducing GHG emissions, the exemption for LEVs carried the risk of diminishing the effectiveness of the congestion charging scheme. As the number of vehicle owners choosing to transition to an eligible LEV increased, the congestion‐reduction effectiveness of the charging scheme weakened. In fact, policy makers quickly recognized this potential issue and consequently phased out the LEV exemption less than 18 months after its introduction (1). Several studies have investigated the demand for LEVs through stated‐preference (SP) surveys across multiple countries, including: Denmark (2), Germany (3, 4), UK (5), Canada (6), USA (7, 8) and Australia (9). Although each of these studies differed in approach, all involved SP surveys where differing characteristics between various types of vehicles, including LEVs, were presented to respondents and these respondents in turn made hypothetical decisions about which vehicle they would be most likely to purchase. Although these studies revealed a number of interesting findings in regards to the potential demand for LEVs, they relied on SP data. In contrast, this paper employs an approach where LEV choice is modelled by taking a retrospective view and by using revealed preference (RP) data. By examining the revealed preferences of vehicle owners in Stockholm, this study overcomes one of the principal limitations of SP data, namely that stated preferences may not in fact reflect individuals’ actual choices, such as when cost, time, and inconvenience factors are real rather than hypothetical. This paper’s RP approach involves modelling the characteristics of individuals who purchased new LEVs, whilst estimating the effect of the congestion charging exemption upon choice probabilities and subsequent aggregate demand. The paper contributes to the current literature by examining the effectiveness of a toll exemption under revealed preference conditions, and by assessing the total effect of the policy based on key indicators for policy makers, including: vehicle owner home location, commuting patterns, number of children, age, gender and income. Extended Abstract Submission for Kuhmo Nectar Conference 2014 2 The two main research questions motivating this study were:  Which individuals chose to purchase a new LEV in Stockholm in 2008?; and,  How did the congestion charging exemption affect the aggregate demand for new LEVs in Stockholm in 2008? In order to answer these research questions the analysis was split into two stages. Firstly, a multinomial logit (MNL) model was used to identify which demographic characteristics were most significantly related to the purchase of an LEV over a conventional vehicle. The three most significant variables were found to be: intra‐cordon residency (positive); commuting across the cordon (positive); and distance of residence from the cordon (negative). In order to estimate the effect of the exemption policy on vehicle purchase choice, the model included variables to control for geographic differences in preferences, based on the location of the vehicle owners’ homes and workplaces in relation to the congestion‐charging cordon boundary. These variables included one indicator representing commutes across the cordon and another indicator representing intra‐cordon residency. The effect of the exemption policy on the probability of purchasing LEVs was estimated in the second stage of the analysis by focusing on the groups of vehicle owners that were most likely to have been affected by the policy i.e. those commuting across the cordon boundary (in both directions). Given the inclusion of the indicator variable representing commutes across the cordon, it is assumed that the estimated coefficient of this variable captures the effect of the exemption policy on the utility of choosing to purchase an exempt LEV for these two groups of vehicle owners. The intra‐cordon residency indicator variable also controls for differences between the two groups, based upon direction of travel across the cordon boundary. A counter‐hypothesis to this assumption is that the coefficient of the variable representing commuting across the cordon boundary instead only captures geo‐demographic differences that lead to variations in LEV ownership across the different groups of vehicle owners in relation to the cordon boundary. In order to address this counter‐hypothesis, an additional analysis was performed on data from a city with a similar geodemographic pattern to Stockholm, Gothenburg ‐ Sweden’s second largest city. The results of this analysis provided evidence to support the argument that the coefficient of the variable representing commutes across the cordon was capturing the effect of the exemption policy. Based upon this framework, the predicted vehicle type shares were calculated using the estimated coefficients of the MNL model and compared with predicted vehicle type shares from a simulated scenario where the exemption policy was inactive. This simulated scenario was constructed by setting the coefficient for the variable representing commutes across the cordon boundary to zero for all observations to remove the utility benefit of the exemption policy. Overall, the procedure of this second stage of the analysis led to results showing that the exemption had a substantial effect upon the probability of purchasing and aggregate demand for exempt LEVs in Stockholm during 2008. By making use of unique evidence of revealed preferences of LEV owners, this study identifies the common characteristics of new LEV owners and estimates the effect of Stockholm's congestion charging exemption upon the demand for new LEVs during 2008. It was found that the variables that had the greatest effect upon the choice of purchasing an exempt LEV included intra‐cordon residency (positive), distance of home from the cordon (negative), and commuting across the cordon (positive). It was also determined that owners under the age of 30 years preferred non‐exempt LEVs (low CO2 LEVs), whilst those over the age of 30 years preferred electric vehicles. In terms of electric vehicles, it was apparent that those individuals living within the city had the highest propensity towards purchasing this vehicle type. A negative relationship between choosing an electric vehicle and the distance of an individuals’ residency from the cordon was also evident. Overall, the congestion charging exemption was found to have increased the share of exempt LEVs in Stockholm by 1.9%, with, as expected, a much stronger effect on those commuting across the boundary, with those living inside the cordon having a 13.1% increase, and those owners living outside the cordon having a 5.0% increase. This increase in demand corresponded to an additional 538 (+/‐ 93; 95% C.I.) new exempt LEVs purchased in Stockholm during 2008 (out of a total of 5 427; 9.9%). Policy makers can take note that an incentive‐based policy can increase the demand for LEVs and appears to be an appropriate approach to adopt when attempting to reduce transport emissions through encouraging a transition towards a ‘green’ vehicle fleet.

Partitioning the variation in stream fish assemblages within a spatio-temporal hierarchy

This paper describes the relative influence of: (i) landscape scale environmental and hydrological factors; (ii) local scale environmental conditions including recent flow history, and; (iii) spatial effects (proximity of sites to one another) on the spatial and temporal variation in local freshwater fish assemblages in the Mary River, south-eastern Queensland, Australia. Using canonical correspondence analysis, each of the three sets of variables explained similar amounts of variation in fish assemblages (ranging from 44 to 52%). Variation in fish assemblages was partitioned into eight unique components: pure environmental, pure spatial, pure temporal, spatially structured environmental variation, temporally structured environmental variation, spatially structured temporal variation, the combined spatial/temporal component of environmental variation and unexplained variation. The total variation explained by these components was 65%. The combined spatial/temporal/environmental component explained the largest component (30%) of the total variation in fish assemblages, whereas pure environmental (6%), temporal (9%) and spatial (2%) effects were relatively unimportant. The high degree of intercorrelation between the three different groups of explanatory variables indicates that our understanding of the importance to fish assemblages of hydrological variation (often highlighted as the major structuring force in river systems) is dependent on the environmental context in which this role is examined.

Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.

Patterns of diversity in fatty acid composition in the Australian groundnut germplasm collection

Knowledge of the amounts and types of fatty acids in groundnut oil is beneficial, particularly from a nutritional standpoint. Germplasm evaluation data for fatty acid composition on 819 accessions of groundnut (Arachis hypogaea L.) from the Australian Tropical Field Crops Genetic Resource Centre, Biloela, Queensland were examined. Data for eight quantitative fatty acid descriptors have been documented. Statistical assessment, via methods of pattern analysis, summarised and described the patterns of variation in fatty acid composition of the groundnut accessions in the Australian germplasm collection. Presentation of the results from principal components analysis and hierarchical cluster analysis using a biplot was shown to be a very useful interpretative tool. Such a biplot enables a simultaneous examination of the relationships among all the accessions and the fatty acids. Unlike that information available via database searches, the results from contribution analysis together with the biplot provide a global picture of the diversity available for use in plant breeding programs. The use of standardised data for eight fatty acids, compared to using three specific fatty acids, provided a better description of the total diversity available because it remains relevant with possible changes in the nutritional preferences for fatty acids. Fatty acid composition was found to vary in relation to the branching pattern of the accessions. This pattern is generally indicative of the botanical types of groundnuts; Virginia (alternate) compared to Spanish and Valencia (sequential) botanical types.

Effect of Coulomb blockade, gold resistance, and thermal expansion on the electrical resistance of ultrathin gold films

Controlling the electrical resistance of granular thin films is of great importance for many applications, yet a full understanding of electron transport in such films remains a major challenge. We have studied experimentally and by model calculations the temperature dependence of the electrical resistance of ultrathin gold films at temperatures between 2 K and 300 K. Using sputter deposition, the film morphology was varied from a discontinuous film of weakly coupled meandering islands to a continuous film of strongly coupled coalesced islands. In the weak-coupling regime, we compare the regular island array model, the cotunneling model, and the conduction percolation model with our experimental data. We show that the tunnel barriers and the Coulomb blockade energies are important at low temperatures and that the thermal expansion of the substrate and the island resistance affect the resistance at high temperatures. At low temperatures our experimental data show evidence for a transition from electron cotunneling to sequential tunneling but the data can also be interpreted in terms of conduction percolation. The resistivity and temperature coefficient of resistance of the meandering gold islands are found to resemble those of gold nanowires. We derive a simple expression for the temperature at which the resistance changes from non-metal-like behavior into metal-like behavior. In the case of strong island coupling, the total resistance is solely determined by the Ohmic island resistance.

Minimizing the Gibbs–Thomson effect in the low-temperature plasma synthesis of thin Si nanowires

An advanced combination of numerical models, including plasma sheath, ion- and radical-induced species creation and plasma heating effects on the surface and within a Au catalyst nanoparticle, is used to describe the catalyzed growth of Si nanowires in the sheath of a low-temperature and low-pressure plasma. These models have been used to explain the higher nanowire growth rates, low-energy barriers, much thinner Si nanowire nucleation and the less effective Gibbs–Thomson effect in reactive plasma processes, compared with those of neutral gas thermal processes. The effects of variation in the plasma sheath parameters and substrate potential on Si nanowire nucleation and growth have also been investigated. It is shown that increasing the plasma-related effects leads to decreases in the nucleation energy barrier and the critical nanoparticle radius, with the Gibbs–Thomson effect diminished, even at low temperatures. The results obtained are consistent with available experimental results and open a path toward the energy- and matter-efficient nucleation and growth of a broad range of one-dimensional quantum structures.

Desalination of seawater using solar, ambient energy and waste heat from air conditioning

Desalination is considered one of the most suitable areas for the utilization of solar energy, as there are many places in the world where abundant supply of solar energy is available and also there is a great demand for fresh water. An integrated solar heat pump desalination system has been developed at the National University of Singapore. The system also offers the opportunity of water heating and drying utilizing solar, ambient energy and waste heat from air conditioning system, which is conventionally dumped into the environment causing global warming. Desalination is carried out by making use of a single effect of Multi-Effect Distillation (MED) system. Within the desalination chamber, both fl ashing and evaporation of saline water take place. The maximum Coefficient of Performance (COP) of the heat pump system was around 5.8. In the integrated system, the maximum fresh water production rate was 9.6 l h−1 and a Performance Ratio (PR) of 1.2. For only desalination, the system has the potential to produce a maximum of 30 l h−1 of fresh water.

Modelling of a magnetocaloric system for cooling in the kilowatt range

A numerical time-dependent model of an active magnetic regenerator (AMR) was developed for cooling in the kilowatt range. Earlier numerical models have been mostly developed for cooling power in the 0.4 kW range. In contrast, this paper reports the applicability of magnetic refrigeration to the 50 kW range. A packed bed active magnetic regenerator was modelled and the influence of parameters such as geometry and operating parameters were studied for different geometries. The pressure drop for AMR bed length and particle diameter was also studied. High cooling power and coefficient of performance (COP) were achieved by optimization of the diameter of the magnetocaloric powder particles and operating frequency. The optimum operating conditions of the AMR for a cooling capacity of 50 kW was determined for a temperature span of 15 K. The predicted coefficient of performance (COP) was found to be ∼6, making it an attractive alternative to vapour compression systems.