The effect of feeding modified soyabean oil enriched with C18 : 4 n-3 to broilers on the deposition of n-3 fatty acids in chicken meat

Supplementing broiler diets with conventional vegetable oils has little effect on the long-chain n-3 PUFA (LC n-3 PUFA) content of the meat. The present study investigated the effect on fatty acid composition and sensory characteristics of chicken meat when broilers were fed oil extracted from soyabeans (SDASOY) that had been genetically engineered to produce C18 : 4n-3 (stearidonic acid (SDA), 240 mg/g oil). Three diets were fed to 120 birds (eight replicate pens of five birds) from 15 d to slaughter (41–50 d). Diets were identical apart from the oil added to them (45 and 50 g/kg as fed in the grower and finisher phases, respectively), which was either SDASOY, near-isogenic soya (CON) or fish oil (FISH). The LC n-3 PUFA content of the meat increased in the order CON, SDASOY and FISH. In breast meat with skin, the SDA concentration was 522, 13 and 37 (sem 14·4) mg/100 g meat for SDASOY, CON and FISH, respectively. Equivalent values for C20 : 5n-3 (EPA) were 53, 13 and 140 (sem 8·4); for C22 : 5n-3 (docosapentaenoic acid (DPA)) 65, 15 and 101 (sem 3·5); for C22 : 6n-3 (DHA) 19, 9 and 181 (sem 4·4). Leg meat (with skin) values for SDA were 861, 23 and 68 (sem 30·1); for EPA 87, 9 and 258 (sem 7·5); for DPA 95, 20 and 165 (sem 5·0); for DHA 29, 10 and 278 (sem 8·4). Aroma, taste and aftertaste of freshly cooked breast meat were not affected. Fishy aromas, tastes and aftertastes were associated with LC n-3 PUFA content of the meat, being most noticeable in the FISH leg meat (both freshly cooked and reheated) and in the reheated SDASOY leg meat.

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Givens rotation based fast backward elimination algorithm for RBF neural network pruning

A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

Blind equalization of nonminimum phase channels: higher order cumulant based algorithm

Higher order cumulant analysis is applied to the blind equalization of linear time-invariant (LTI) nonminimum-phase channels. The channel model is moving-average based. To identify the moving average parameters of channels, a higher-order cumulant fitting approach is adopted in which a novel relay algorithm is proposed to obtain the global solution. In addition, the technique incorporates model order determination. The transmitted data are considered as independently identically distributed random variables over some discrete finite set (e.g., set {±1, ±3}). A transformation scheme is suggested so that third-order cumulant analysis can be applied to this type of data. Simulation examples verify the feasibility and potential of the algorithm. Performance is compared with that of the noncumulant-based Sato scheme in terms of the steady state MSE and convergence rate.

Robust blind deconvolution algorithm: variance approximation and series decoupling

An alternative blind deconvolution algorithm for white-noise driven minimum phase systems is presented and verified by computer simulation. This algorithm uses a cost function based on a novel idea: variance approximation and series decoupling (VASD), and suggests that not all autocorrelation function values are necessary to implement blind deconvolution.

Time diversity decorrelator (TDD); a near-far resistant detector for asynchronous CDMA system

Greater attention has been focused on the use of CDMA for future cellular mobile communications. CA near-far resistant detector for asynchronous code-division multiple-access (CDMA) systems operating in additive white Gaussian noise (AWGN) channels is presented. The multiuser interference caused by K users transmitting simultaneously, each with a specific signature sequence, is completely removed at the receiver. The complexity of this detector grows only linearly with the number of users, as compared to the optimum multiuser detector which requires exponential complexity in the number of users. A modified algorithm based on time diversity is described. It performs detection on a bit-by-bit basis and overcomes the complexity of using a sequence detector. The performance of this detector is shown to be superior to that of the conventional receiver.

Simple adaptive momentum: new algorithm for training multilayer perceptrons

The speed of convergence while training is an important consideration in the use of neural nets. The authors outline a new training algorithm which reduces both the number of iterations and training time required for convergence of multilayer perceptrons, compared to standard back-propagation and conjugate gradient descent algorithms.

Second meal effect: modified sham feeding does not provoke the release of stored triacylglycerol from a previous high-fat meal

The present study was carried out to determine whether cephalic stimulation, associated with eating a meal, was sufficient stimulus to provoke the release of stored triacylglycerol (TAG) from a previous high-fat meal. Ten subjects were studied on three separate occasions. Following a 12 h overnight fast, subjects were given a standard mixed test meal which contained 56 g fat. Blood samples were taken before the meal and for 5 h after the meal when the subjects were randomly allocated to receive either water (control) or were modified sham fed a low-fat (6 g fat) or moderate-fat (38 g fat) meal. Blood samples were collected for a further 3 h. Compared with the control, modified sham feeding a low- or moderate-fat meal did not provoke an early entry of TAG, analysed in either plasma or TAG-rich lipoprotein (TRL) fraction (density ,1´006 kg/l). The TRL-retinyl ester data showed similar findings. A cephalic phase secretion of pancreatic polypeptide, without a significant increase in cholecystokinin levels, was observed on modified sham feeding. Although these data indicate that modified sham feeding was carried out successfully, analysis of the fat content of the expectorant showed that our subjects may have accidentally ingested a small amount of fat (0´7 g for the low-fat meal and 2´4 g for the moderate-fat meal). Nevertheless, an early TAG peak following modified sham feeding was not demonstrated in the present study, suggesting that significant ingestion of food, and not just orosensory stimulation, is necessary to provoke the release of any TAG stored from a previous meal.

Prolonged effects of modified sham feeding on energy substrate mobilization

Background: Vagal stimulation in response to nutrients is reported to elicit an array of digestive and endocrine responses, including an alteration in postprandial lipid metabolism. Objective: The objective of this study was to assess whether neural stimulation could alter hormone and substrate metabolism during the late postprandial phase, with implications for body fat mobilization. Design: Vagal stimulation was achieved by using the modified sham feeding (MSF) technique, in which nutrients are chewed and tasted but not swallowed. Ten healthy subjects were studied on 3 separate occasions, 4 wk apart. Five hours after a high-fat breakfast (56 g fat), the subjects were given 1 of 3 test meals allocated in random order: water, a lunch containing a modest amount of fat (38 g), or MSF (38 g fat). Blood was collected for 3 h poststimulus for hormone and metabolite analyses. Results: Plasma insulin and pancreatic polypeptide concentrations peaked at 250% and 209% of baseline concentrations within 15 min of MSF. The plasma glucose concentration increased significantly (P = 0.038) in parallel with the changes observed in the plasma insulin concentration. The nonesterified fatty acid concentration was significantly suppressed (P = 0.006); maximum suppression occurred at a mean time of 114 min after MSF. This fall in nonesterified fatty acid was accompanied by a fall in the plasma glucagon concentration from 122 to 85 pmol/L (P = 0.018) at a mean time of 113 min after MSF. Conclusions: Effects on substrate metabolism after MSF in the postprandial state differ from those usually reported in the postabsorptive state. The effects of MSF were prolonged beyond the period of the cephalic response and these may be relevant for longer-term metabolic regulation.

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.

Application of a novel optimal control algorithm to a benchmark fed-batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.