The prediction of seedy grape drying rate using a neural network method

This paper presents an application which uses Feedforward Neural Networks (FNNs) to model the nonlinear behaviour of the drying of seedy grapes. First, a novel type of dryer for experimentally and mathematically evaluating the thin-layer drying kinetics of seedy grapes is developed. In the developed drying system, an expanded-surface solar air collector, a solar air collector with Phase-Change Material (PCM) and drying room with swirl element have been particularly included. Secondly, the drying rate is estimated as an exponential-type equation using non-linear regression analysis. Thirdly, the drying rate of seedy grapes is estimated using an FNN. Finally, the performance of the FNN model is compared with those of nonlinear and linear regression models by means of the root mean square errors, the mean absolute errors, and the correlation coefficient statistics. The results indicate that the FNN is more accurate and performed more consistently than alternative approaches employed in estimating drying rate.     

Modelling population dynamics and response to management options in the poultry red mite Dermanyssus gallinae (Acari: Dermanyssidae)

The poultry red mite Dermanyssus gallinae is a major pest and widespread ectoparasite of laying hens and other domestic and wild birds. Under optimal conditions, D. gallinae can complete its lifecycle in less than 10 days, leading to rapid proliferation of populations in poultry systems. This paper focuses on developing a theoretical model framework to describe the population dynamics of D. gallinae. This model is then used to test the efficacy and residual effect of different control options for managing D. gallinae. As well as allowing comparison between treatment options, the model also allows comparison of treatment efficacies to different D. gallinae life stages. Three different means for controlling D. gallinae populations were subjected to the model using computer simulations: mechanical cleaning (killing once at a given time all accessible population stages), sanitary clearance (starving the mite population for a given duration, e.g. between flocks) and acaricide treatment (killing a proportion of nymphs and adults during the persistence of the treatment). Simulations showed that mechanical cleaning and sanitary clearance alone could not eradicate the model D. gallinae population, although these methods did delay population establishment. In contrast, the complete eradication of the model D. gallinae population was achieved by several successive acaricide treatments in close succession, even when a relatively low treatment level was used.     

Analysing agricultural users’ patterns of behaviour: The case of OPTIRas™, a decision support system for starch crop selection

Redesigning IT systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of this paper is to provide insights into patterns of behaviour of agricultural users, during the usage of a decision support system called OPTIRas™. This system aids agricultural users in their cultivar selection activities. We analyse logs resulting from OPTIRas™, and we get insights into user’s navigational patterns. We claim that the results of our analysis can be used to support the redesign of decision support systems in order to address specific agricultural users’ characteristics.     

Integrating climate change into forest management in South-Central British Columbia: An assessment of landscape vulnerability and development of a climate-smart framework

The achievement of sustainable forest management requires the incorporation of risk and uncertainty into long-term planning. Climatic change will have significant impacts on natural disturbances, species and ecosystems, particularly on landscapes influenced by forest management. Understanding where vulnerabilities lie is important in managing the risks associated directly or indirectly with climatic change. The vulnerability of landscapes to natural disturbances, the resilience of ecosystems and distribution of species are all important components that need to be considered when undertaking forest planning, but climatic change is rarely factored into such planning. In this study, the vulnerability of fire potential, fire regimes, ecosystems and species to climatic change was modelled for a 145,000 ha landscape in the south-central interior of British Columbia, Canada. The results from these analyses were used to guide forest zoning, using the triad zoning framework, and for the development of a “climate-smart” management framework. The use of climate-smart management is advocated as a decision-making framework for managing forested landscapes based on an understanding of landscape vulnerability to future climatic change. From this understanding, the maintenance of ecosystem health and vitality could be achieved.     

Analysis of soil-vegetation systems' sensitivity to changes of climate-dependent carbon turnover parameters

 The nonlinear model of the carbon cycle in soils (NAMSOM) was used to analyze the sensitivity of soil organic matter levels to variations in carbon turnover parameters. We were able to predict the sensitivity of soil organic matter levels to variations of climate-dependent carbon turnover parameters, which allowed us to compare the sensitivity of soil organic matter levels to net primary productivity of plant communities and plant debris decomposition rate constants across the range of soils in the European part of Russia. The results indicate that meadow steppes show the lowest sensitivity to variations of these parameters. In passing from meadow steppes to the northern taiga and to semideserts, the sensitivity increases. In general, soil organic matter levels of boreal forest ecosystems are about 2–3 times more sensitive to input and decomposition of plant debris than to decomposition of humus. In subboreal grassland ecosystems the sensitivity to humus decomposition increases and becomes closer to the degree of sensitivity shown by soil organic matter levels to variations of productivity and decomposition of plant debris. The proposed method may be useful for predicting the response of ecosystems to climatic change. Received: 1 December 1997     

Prediction of baking results from farinograph measurements by using stepwise linear regression and artificial neuronal networks

In an effort to extract additional data from farinograph experiments a model was developed to simulate the measurements and correlate the parameters of the model with results from baking tests. This additional information can be used in bakeries to predict the baking properties of the flours and adjust the recipes to maintain a constant product quality. For this eight different flours were characterized with a farinograph and 13 different results from baking experiments. An approach with five nonlinear differential equations was able to model the farinograph measurements very well (average R² = 0.995 ± 0.005). While a stepwise multilinear regression only showed weak correlations in cross validation between a single parameter of the model and the baking volume (R² = 0.745) and the volume yield (R² = 0.796) respectively, the artificial neuronal network was more successful. For the baking weight (R² = 0.926), the dough yield gross (R² = 0.909) and net (R² = 0.913) strong correlations were found. A good correlation for the baking volume (R² = 0.853) was also determined, while the volume yield showed comparable results to the linear regression (R² = 0.792).     

Effect of process conditions and amylose/amylopectin ratio on the pasting behavior of maize starch: A modeling approach

The effects of different process conditions on the pasting behavior of the 14%, w/w suspensions of high amylose, waxy and normal maize starches at mixing speeds of 50, 160 and 250 rpm with the heating rates of 2.5, 5 and 10 °C/min were investigated. In addition, the impact of the starch mixture with an amylose-amylopectin ratio of 0–70% at 160 rpm and a heating rate of 5 °C/min on the pasting parameters was studied. According to the results, when stirring speed decreased from 250 rpm to 50 rpm, the peak viscosity dramatically increased. Furthermore, both heating and stirring rates significantly affected the pasting properties (p < 0.05). The amylose content of maize starch had a negative correlation with peak viscosity, trough viscosity, breakdown viscosity, final viscosity, and setback viscosity. Besides, syneresis values decreased as amylose content decreased from 70% to 0%. According to the kinetic modelling of pasting curves, starch coefficients were found to be higher than 1 for all starches, indicating that the penetration of water into starch granules increased granule swelling rate. The findings of the present study confirmed that both process conditions and amylose/amylopectin ratio can be optimized without necessity of starch modification to obtain the products with the desired quality.     

东莞的节日花坛设计与营造

花坛是将同期开放的多种花卉或不同颜色的同种花卉根据一定的设计主题和图案设计,栽种于特定的苗床内,以表达某些景观主题和烘托节日气氛的花卉布置方式。目前节日花坛在城市的文化生活中越来越重要,该文总结了东莞市自2004年以来节日花坛设计的一般原则和特点,并通过实例分析加以说明。     

Implications derived from a mathematical model for eradication of pseudorabies virus

Simple mathematical models based on experimental and observational data were applied to evaluate the feasibility of eradicating pseudorabies virus (PRV) regionally by vaccination and to determine which factors can jeopardise eradication. As much as possible, the models were uncomplicated and our conclusions were based on mathematical analysis. For complicated situations, Monte-Carlo simulation was used to support the conclusions. For eradication, it is sufficient that the reproduction ratio R (the number of units infected by one infectious unit) is < 1. However, R can be determined at different scales: at one end the region with the herds as units and at the other end compartments with the pigs as units. Results from modelling within herds showed that contacts between groups within a herd is important whenever R between individuals (R_ind) is 1 in one or more groups. This is the case within finishing herds. In addition, if the R_ind is more than 1 within a herd, the size of the herd determines whether PRV can persist in the herd and determines the duration of persistence. Moreover, when reactivation of PRV in well-vaccinated sows is taken into account, R_ind in sow herds is still less than 1. In sow herds with group-housing systems, it is possible that in those groups R_ind is 1. Results from modelling between herds showed that whether or not R_herd is < 1 in a particular region is determined by two factors: (1) the transmission of infection between nucleus herds and rearing herds through transfer of animals and (2) contacts among finishing herds and among rearing herds. The transmission between herds can be reduced by reduction of the contact rate between herds, reduction of the herd size, and reduction of the transmission within herds.