In the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

Skin carotenoid status (SCS) measured by resonance Raman spectroscopy (RRS) may serve as an emerging alternative measurement for dietary carotenoid, fruit and vegetable (FV) intake although its application had not been assessed in a middle-aged and older population in Asia. This cross-sectional study aims to concurrently examine the use of SCS and plasma carotenoids to measure FV and carotenoid intake in a middle-aged and older population, taking into consideration potential socio-demographic and nutritional confounders. The study recruited 103 middle-aged and older adults (mean age: 58 years) in Singapore. Dietary carotenoids and FV, plasma carotenoid concentration and SCS were measured using 3-d food records, HPLC and a biophotonic scanner which utilised RRS, respectively. Adjusted for statistically defined socio-demographic covariates sex, age, BMI, prescription medication and cigarette smoking, plasma carotenoids and SCS showed positive associations with dietary total carotenoids (β plasma: 0·020 (95 % CI 0·000, 0·040) µmol/l/mg, P = 0·05; β skin: 265 (95 % CI 23, 506) arbitrary units/mg, P = 0·03) and FV (β plasma: 0·076 (95 % CI 0·021, 0·132) µmol/l per FV serving, P = 0·008; β skin: 1036 (95 % CI 363, 1708) arbitrary units/FV serving, P = 0·003). The associations of SCS with dietary carotenoid and FV intake were null with the inclusion of dietary PUFA, fibre and vitamin C as nutritional covariates (P > 0·05). This suggests a potential influence of these nutritional factors on carotenoid circulation and deposition in the skin. In conclusion, SCS, similar to plasma carotenoids, may serve as a biomarker for both dietary carotenoid and FV intake in a middle-aged and older Singaporean population.

It is well known that Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regression model. A central limit theorem is established for the integration error if the number of control variates tends to infinity. The integration error is scaled by the standard deviation of the error term in the regression model. If the linear span of the control variates is dense in a function space that contains the integrand, the integration error tends to zero at a rate which is faster than the square root of the number of Monte Carlo replicates. Depending on the situation, increasing the number of control variates may or may not be computationally more efficient than increasing the Monte Carlo sample size.

In most developing countries, there has been a long-standing conflict of interest between using land for agriculture and the conservation of biodiversity. This paper reports on a study of factors influencing farmers’ decisions to integrate trees into their agricultural practice. We also discuss the possibility of protecting and managing planted and naturally regenerating trees on farmland in order to restore degraded land and improve biodiversity. Data were collected from interviews with farmers in the Center-West region of Burkina Faso and analyzed using Principal Component Analysis, multiple linear regression and binary logistic regression. The results show that farmers’ decisions to incorporate trees into their farmland were mainly influenced by silvicultural knowledge and skills, participation in farmers’ groups or other social organizations with an interest in tree conservation, the social value of biodiversity in the rural landscape, and the perceived economic benefits of trees on farmland. The most important factors associated with variation in levels of motivation to conserve trees on farms included household wealth, gender, age, education level, marital status, residence status, farmland size, household size and technical support. We conclude that an agroforestry project will be more successful if the local biophysical conditions and diversity of smallholder socio-economic characteristics and their perceptions, needs and preferences are considered in its design. There is also an immediate need for coordinated development of information and training to raise local community awareness of the potential of agroforestry as well as to disseminate information about adding value to tree products in order to encourage farmers to protect on-farm trees.

Raw ewes' milk semi-soft cheeses (RESS-cheeses) are important products in Portugal and in several European regions. Creamy texture is an essential attribute of these cheeses, which results from structural properties that are not always well characterized. Here, the structural changes occurring during the ripening period of a traditional RESS-cheese, known as Serpa cheese, were analysed through small amplitude oscillatory shear (SAOS). Rheological data was complemented with other physical and chemical parameters, that were monitored during ripening, in order to estimate Serpa cheese ripening time using multiple linear regression (MLR). Mechanical spectra indicated a relatively strong structure, comparable to a gel, with a low dependence on frequency at the beginning of ripening and a weak structure, comparable to a concentrated suspension, with a crossing point (G″=G′) at the left of the graphic and with both moduli highly dependent on frequency, at the end of ripening. Good correlations (P<0·05) were obtained between structural (hardness and storage modulus) and proteolysis indicators. Using a combination of chemical, colour and rheological parameters we were able to obtain a multiple linear regression (MLR) which allows the estimation of Serpa cheese ripening time with an estimation error of 1·7 d (adjusted R2=0·98, P<0·0001).