Crack Development in Individually Quick Frozen Cut and Peel Carrots

ABSTRACT:  Crack development during freezing (CDF) is one of the major challenges in individually quick frozen (IQF) cut and peel carrot processing. The effects of processing and freezer storage on crack development were examined on the cut and peel carrot variety, Sugarsnax. Carrot samples were removed from the major processing steps, the trans-slicer, the shaper, the blancher, and the dryer, and examined for crack development by measuring percentage cracked, crack morphology, total soluble solids, moisture levels, and membrane injury index immediately after processing. These parameters were also examined following 20 wk of standard freezer storage for cut and peels. Approximately 2% of nonprocessed carrots were cracked compared to 45% of carrots after the initial trans-slicing stage. As the processing continued, cracking decreased due to the removal of the outer epidermis to 16% of the finished product. This suggests that CDF was initiated at the 1st processing stage. Crack width and depth were 2.3 and 2.6 mm, respectively, at the trans-slicer stage and decreased to 1.1 and 1.8 mm at the end of the line. It was found that CDF was further exacerbated by freezer storage due to inefficient water removal at the dryer stage. Crack width and depth increased to 1.5 and 3.0 mm after 20 wk for freezer storage. Root size also played a role in CDF, suggesting that larger pieces are more susceptible to crack development. Total soluble solid concentrations did not play a role in crack formation during cut and peel processing.     

Self assembly of rectangular shapes on concentration programming and probabilistic tile assembly models

Efficient tile sets for self assembling rectilinear shapes is of critical importance in algorithmic self assembly. A lower bound on the tile complexity of any deterministic self assembly system for an n?×?n square is $\Upomega(\frac{\log(n)}{\log(\log(n))})$ (inferred from the Kolmogrov complexity). Deterministic self assembly systems with an optimal tile complexity have been designed for squares and related shapes in the past. However designing $\Uptheta(\frac{\log(n)}{\log(\log(n))})$ unique tiles specific to a shape is still an intensive task in the laboratory. On the other hand copies of a tile can be made rapidly using PCR (polymerase chain reaction) experiments. This led to the study of self assembly on tile concentration programming models. We present two major results in this paper on the concentration programming model. First we show how to self assemble rectangles with a fixed aspect ratio (??:??), with high probability, using $\Uptheta(\alpha+\beta)$ tiles. This result is much stronger than the existing results by Kao et?al. (Randomized self-assembly for approximate shapes, LNCS, vol 5125. Springer, Heidelberg, 2008) and Doty (Randomized self-assembly for exact shapes. In: proceedings of the 50th annual IEEE symposium on foundations of computer science (FOCS), IEEE, Atlanta. pp 85?C94, 2009)??which can only self assembly squares and rely on tiles which perform binary arithmetic. On the other hand, our result is based on a technique called staircase sampling. This technique eliminates the need for sub-tiles which perform binary arithmetic, reduces the constant in the asymptotic bound, and eliminates the need for approximate frames (Kao et?al. Randomized self-assembly for approximate shapes, LNCS, vol 5125. Springer, Heidelberg, 2008) . Our second result applies staircase sampling on the equimolar concentration programming model (The tile complexity of linear assemblies. In: proceedings of the 36th international colloquium automata, languages and programming: Part I on ICALP ??09, Springer-Verlag, pp 235?C253, 2009), to self assemble rectangles (of fixed aspect ratio) with high probability. The tile complexity of our algorithm is $\Uptheta(\log(n))$ and is optimal on the probabilistic tile assembly model (PTAM)??n being an upper bound on the dimensions of a rectangle.     

Ant colony optimisation of spatial steel structures under static and earthquake loading

Optimisation is the process of trying to find out the best possible solution to any problem satisfying constraints. Soft computing is the class of methods which have been inspired by the biological computational methods and nature's problem-solving strategies. Currently, these methods include neural networks, evolutionary computational models such as genetic algorithms, random cost and linguistic models such as fuzzy logic. Ant colony optimisation (ACO) is one such method applied for large engineering combinatorial optimisation problems. A design procedure utilising an ACO technique is developed for discrete optimisation of reticulated steel space trusses. The ACO algorithm is motivated by the analogy with natural phenomena, in particular the ability of a colony of ants to ‘optimise’ their collective endeavours. In this paper, the computational implementation of ACO is presented in a structural design context. The objective function considered is the total weight/cost of the structure subjected to material and performance constraints in the form of stress and deflection limits. In the case of reticulated space trusses, the design variables are the cross-sectional areas of members belonging to various groups. The objective function and constraints are obtained by using structural analysis package FEAST (Anonymous, 1995. FEAST user manual. Trivandrum, India: SEG, SDS Group, ISRO, VSSC) in case of structures subjected to static loading and SAP90 (Anonymous, 1990. SAP90, ETABS, SAFE – computer software for structural and earthquake engineering. Berkeley, CA: Computers and Structures) for earthquake loading for reticulated steel space trusses. The numerical examples presented demonstrate the computational advantage of the ACO for large-scale optimisation problems.     

Application of Sequential Learning Neural Networks to Civil Engineering Modeling Problems

A sequential orthogonal approach to the building and training of a single hidden layer neural network is presented in this paper. The Sequential Learning Neural Network (SLNN) model proposed by Zhang and Morris [1]is used in this paper to tackle the common problem encountered by the conventional Feed Forward Neural Network (FFNN) in determining the network structure in the number of hidden layers and the number of hidden neurons in each layer. The procedure starts with a single hidden neuron and sequentially increases in the number of hidden neurons until the model error is sufficiently small. The classical Gram–Schmidt orthogonalization method is used at each step to form a set of orthogonal bases for the space spanned by output vectors of the hidden neurons. In this approach it is possible to determine the necessary number of hidden neurons required. However, for the problems investigated in this paper, one hidden neuron itself is sufficient to achieve the desired accuracy. The neural network architecture has been trained and tested on two practical civil engineering problems – soil classification, and the prediction o strength and workability of high performance concrete.     

General Thin Rod Model for Preslip Bending Response of Strand

A general discrete thin rod model to study the preslip response of a strand of helical wires having wires-to-core friction contacts under constant curvature free bending has been developed for the case of Coulomb stick friction that considers together (a) all the three wire forces and three wire couples; (b) more appropriate equations for the wire shear forces; (c) the wire normal force as one of the contributors to strand bending moment; (d) wire external equilibrium; and (e) wire rolling with or without zero net shear on the strand cross section. The individual contributions of wire forces and couples to the strand bending moment for 10 models including EPRI model are compared. Several earlier models are shown to be special cases of the present general model.     

Local inhomogeneity and surface degradation of Fe1.15Te and Fe1.03Te0.62Se0.38 single crystals

We have investigated the structural and magnetic properties of Fe_1.15Te and Fe_1.03Te_0.62Se_0.38 single crystals grown by slow cooling method using a specially designed double-walled quartz ampoule. X-ray diffraction reveals significant local inhomogeneity as a result of fractional substitution of Te site by Se in single crystal Fe_1.03Te_0.62Se_0.38. The existence of superconductivity is confirmed for Se substitution to the Te site of Fe_1.03Te_0.62Se_0.38 but with persistent Fe₇Se₈ impurity phase. Raman spectroscopy confirmed the surface structural instability when the samples were exposed to moderate laser irradiation in the atmospheric environment. With high intensity laser irradiation, Fe(Se,Te) compounds can be decomposed into Fe₂O₃ hematite phase.