Modeling of Calcium Chloride and Pectin Methylesterase Prefreezing Treatments of Strawberries and Jams

ABSTRACT: :
Different calcium chloride (CaCl₂) and pectin methylesterase (PME) prefreezing treatments in a vacuum were used to clarify the most effective prefreezing factors for strawberries and traditional jams. Fractional factorial design and modeling were used. CaCl₂ had the greatest effect on the majority of the 15 studied responses ( p < 0.001). The pretreatment time should be short (about 5 to 10 min), the temperature low (less than 20 °C), the vacuum level high (pressure less than 10 kPa), the CaCl₂ concentration moderate (about 1%) and the dosage of PME comparatively low (about 50 to 100 nkat/g) in order to yield high quality frozen strawberries for jam making.     

Why is software late? An empirical study of reasons for delay insoftware development

A study of the reasons for delay in software development is described. The aim of the study was to gain an insight into the reasons for differences between plans and reality in development activities in order to be able to take actions for improvement. A classification was used to determine the reasons. 160 activities, comprising over 15000 hours of work, have been analyzed. The results and interpretations of the results are presented. Insight into the predominant reasons for delay enabled actions for improvements to be taken in the department concerned. Because the distribution of reasons for delay varied widely from one department to another, it is recommended that every department should gain an insight into its reasons for delay in order to be able to take adequate actions for improvement     

Molecular correlations in sheared thermotropic copolyesters showing banded textures

Banded textures produced in a thermotropic liquid crystal polymer by shearing between glass slides are examined by using both transmission electron and polarized light microscopy. The periodic variation in director orientation about the shear axis, as measured by light microscopy, is shown to be distinctly different from that indicated by electron diffraction. Measurements of birefringence and observation of Zernicke phase contrast indicate periodic variations in optical properties of the polymer, in step with the bands. Such effects are accounted for in terms of a synchronous rotation of the planar aromatic groups about the molecular chain axes. Evidence for an out-of-plane component of molecular orientation is also presented.     

Reconstruction of polygonal sets by constrained and unconstrained double probing

The problem of recovering the shape of planar objects arises in robotics. This work deals with this problem under the assumption that composite double probings are made. Two kinds of double probes are considered and their use for reconstructing convex planar polygons is investigated. For both kinds of probes, lower bounds on the number of probings required for reconstruction under any strategy are obtained and specific strategies which are proven to be almost optimal are provided.     

A Lie group approach to a neural system for three-dimensionalinterpretation of visual motion

A novel approach is presented to neural network computation of three-dimensional rigid motion from noisy two-dimensional image flow. It is shown that the process of 3-D interpretation of image flow can be viewed as a linear signal transform. The elementary signals of this linear transform are the 2-D vector fields of the six infinitesimal generators of the 3-D Euclidean group. This transform can be performed by a neural network. Results are also reported of neural network simulations for the 3-D interpretation of image flow and a comparison of the performance of this approach with that using conventional methods. Computer simulation results verify the Lie-group-based neural network approach to three-dimensional motion perception.     

Recurrent correlation associative memories

A model for a class of high-capacity associative memories is presented. Since they are based on two-layer recurrent neural networks and their operations depend on the correlation measure, these associative memories are called recurrent correlation associative memories (RCAMs). The RCAMs are shown to be asymptotically stable in both synchronous and asynchronous (sequential) update modes as long as their weighting functions are continuous and monotone nondecreasing. In particular, a high-capacity RCAM named the exponential correlation associative memory (ECAM) is proposed. The asymptotic storage capacity of the ECAM scales exponentially with the length of memory patterns, and it meets the ultimate upper bound for the capacity of associative memories. The asymptotic storage capacity of the ECAM with limited dynamic range in its exponentiation nodes is found to be proportional to that dynamic range. Design and fabrication of a 3-mm CMOS ECAM chip is reported. The prototype chip can store 32 24-bit memory patterns, and its speed is higher than one associative recall operation every 3 mus. An application of the ECAM chip to vector quantization is also described.     

A real-time neural system for color constancy

A neural network approach to the problem of color constancy is presented. Various algorithms based on Land's retinex theory are discussed with respect to neurobiological parallels, computational efficiency, and suitability for VLSI implementation. The efficiency of one algorithm is improved by the application of resistive grids and is tested in computer simulations; the simulations make clear the strengths and weaknesses of the algorithm. A novel extension to the algorithm is developed to address its weaknesses. An electronic system that is based on the original algorithm and that operates at video rates was built using subthreshold analog CMOS VLSI resistive grids. The system displays color constancy abilities and qualitatively mimics aspects of human color perception.     

Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics.