Properties of Edible Films from Total Milk Protein

The mechanical properties and water vapor permeability of edible films made from various total milk proteins (TMPs) were investigated. Two TMPs obtained from nonfat dry milk (NDM) by removing lactose and three TMPs obtained from a commercial source were studied. Lactose was extracted from NDM by ultrafiltration or suspension in ethanol followed by filtration. TMP concentrate obtained by ultrafiltration (UF) produced films with the lowest water vapor permeability (WVP) and the highest tensile strength at break. Commercial TMP concentrates produced films more ductile than those from the UF-TMP or retentate from ethanol extraction. Further research is needed to improve mechanical properties of UF-TMP films without increasing the WVP.     

A neural network based system for predicting earthmoving production

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN earth. To engender confidence in the results of neural computation, a validation function is implemented in NN earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN earth to provide default information, such as internal cost rate, fuel cost, and operator's cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN earth are illustrated in a sample application. In practice, NN earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.     

TWO CONCEPTIONS OF RELEVANCE

Courts use complex modes of relevance judgments in regulating the introduction of information and construction of factual narratives. Likewise, common law works both through and around relevance presuppositions in determining doctrine. This study examines different functions of relevance--conceived as different conceptions, at times competing, at times interdependent. The distinctions between these conceptions are arranged on three levels: (1) a normative/"causal" level, arguing for the status of relevance as a requirement for a "meaning-based" conception of entailment and drawing on discussions from relevance logic and modal logic; (2) a pragmatic/metapragmatic level that explores the ways in which law's "factfinding" and other epistemological functions are subjected to normative, practical purposes (under the heading "practical primacy"); and (3) the relevance/metarelevance distinction between the kinds of information admitted to the court's discursive space and the very notion of reliance on information in regulating decision making. All these levels are accommodated primarily by the law of evidence (although not exclusively); in an important sense, they define it. The study claims that although pragmatic and semantic relevance (corresponding to the "fit thesis") are at the center of most studies, it is relevance's metapragmatic function in constituting legal discourse that merits special attention, viz, the constitutive role of relevance in determining what may count as knowledge rather than merely its regulative or derivative function regarding relations between information and presupposed doctrine.     

COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS FOR MODELING PHARMACEUTICAL QSARS

A common step in pharmaceutical development is the formation of a quantitative structure-activity relationship *(QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure (shape) of a compound relates to its biological activity. A comparative study was carried out of six artificial intelligence and traditional algorithms for modeling QSAR's: GOLEM, CART, and MS from symbolic machine learning; back-propagation from neural networks; and linear regression and nearest-neighbor from traditional statistics. Two test case problems were studied: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines, and the inhibition of ratlmouse tumor DHFR by triazines. It was found that there was no significant statistical difference between the methods in terms of their ability to rank unseen compounds by activity. However, symbolic machine learning methods, in particular relational ones, were found to generate rules that provided insight into the stereochemistry of compound receptor interactions.     

Approximating reachable sets for a class of linear control systems†

We present a technique for overestimating the reachable set from the origin for a class of n-dimensional linear control systems. The proposed ‘box’ method is based upon decomposing the system into one and two-dimensional subsystems for which bounds on the new variables can readily be found. Using these bounds enables the construction of a n-dimensional parallelepiped containing the reachable set of the original system. Examples of this procedure are given as well as a comparison to an overapproximation afforded by a Lyapunov approach.     

Parallelizing Imperative Functional Programs: the Vectorization Monad

Traditionally a vectorizing compiler matches the iterative constructs of a program against a set of predefined templates. If a loop contains no dependency cycles then amaptemplate can be used; other simple dependencies can often be expressed in terms offoldorscantemplates. This paper addresses the template matching problem within the context of functional programming. A small collection of program identities are used to specify vectorizable for-loops. By incorporating these program identities within a monad,allwell-typed for-loops in which the body of the loop is expressed using thevectorization monadcan be vectorized. This technique enables the elimination of template matching from a vectorizing compiler, and the proof of the safety of vectorization can be performed by a type inference mechanism.     

USE OF MALIC ACID AND OTHER QUALITY STABILIZING COMPOUNDS TO ASSURE THE SAFETY OF FRESH-CUT "FUJI" APPLES BY INACTIVATION OF LISTERIA MONOCYTOGENES, SALMONELLA ENTERITIDIS AND ESCHERICHIA COLI O157:H7

The effectiveness of malic acid in combination with physicochemical quality stabilizing compounds to inactivate Listeria monocytogenes, Salmonella Enteritidis and Escherichia coli O157:H7 inoculated in fresh-cut "Fuji" apples packaged in air and stored at 5C was evaluated. Apple pieces were immersed for 1 min in solutions containing 1% w/v N-acetyl-L-cysteine, 1% w/v glutathione and 1% w/v calcium lactate with and without 2.5% w/v D-L malic acid to control apple browning, softening and reduce the populations of pathogenic microorganisms. Fresh-cut apples dipped in each solution were then inoculated with L. monocytogenes, S. Enteritidis or E. coli O157:H7 . The use of malic acid in combination with physicochemical quality stabilizing compounds caused more than 5 log₁₀ cfu/g of L. monocytogenes, S. Enteritidis and E. coli O157:H7 counts. The results obtained in this work pointed out the potential use of malic acid in combination with physicochemical quality stabilizing compounds as a good alternative for safety assurance of fresh-cut apples.

PRACTICAL APPLICATIONS

The use of organic acids such as malic acid, as well as physicochemical quality stabilizing compounds such as N-acetyl-L-cysteine, glutathione and calcium lactate on fresh-cut fruits can benefit the fresh-cut products industry, since they can assure the safety and quality of these products. The main reason for their suitability is their natural origin, thus resulting in fruits that are both attractive and healthy for the consumers who demand fresh-like products.     

Drivers of Rising Housing Allowance Claimant Numbers: Evidence from the Irish Private Rented Sector

Recent years have seen growing concern among policy makers about difficulties in controlling the numbers of claimants and cost of housing allowances, which are now the main state housing support for low-income households in most Western European countries. This issue has raised particular concerns in the Republic of Ireland where the numbers of claimants of rent supplement, the principal state housing support for unemployed private renting households, have climbed steadily since the 1980s despite a marked concurrent reduction in unemployment rates. This paper attempts to explain this conundrum using administrative data on all claimants in 2005 and 2004. It reveals that although the traditional drivers of demand for this benefit—rising unemployment and lone-parent families—have become less significant in recent years, they have been replaced by a new driver, rising immigration. In addition, labour market activation measures have proved counterproductive. They have incentivised part-time, rather than full-time, work and consequent continued dependence on rent supplement.     

屋顶公园——曼哈顿楼海中的宁静小岛

在曼哈顿鳞次栉比的高楼中，一个建在屋顶上的公园，虽不扎眼却也不能被人们轻易忽视。如果说纽约是一个瞬息万变的大海，那么沃特街55号的阿克里高架无疑是其中一座令人意外的宁静岛屿。