Prediction of worst case migration from packaging to food using mathematical models

Prediction of migration from packaging to food is often made using equations which are not always designed specifically for the problem. At least, these equations should overestimate migration, in order to be on the safe side. Integration of Fick's equation under the assumption of 'infinite packaging' provides an equation which is very practical since it requires only a few experimental data. It is shown here that, unfortunately, the use of this equation leads to a systematic underestimation of the diffusivity, by the square of the percentage of migration at steady state. In contrast to widely accepted opinion, this model is not conservative. A conservative approach requires that the diffusivity is determined under 'finite packaging' assumptions, associated with very large volumes of food and with long term experiments. These equations are applied to the migration of a phenolic antioxidant from polypropylene.     

Premembranes for porogen-derived membranes and thermal decomposition of porogens

Various t-butyl and di-t-butyl esters, N-boc and di-N-boc amines, metal salts of N-boc amino acid, and glycerol–formic acid esters are potential porogens for porogen-derived membranes. Introduction of more than 20 such compounds into polysulfone or poly(vinyl chloride) films was studied. Transparency, IR spectra, changes in glass transition temperature, and differential scanning calorimetry were used to differentiate between molecular dispersion and formation of micro phases. The results show that for many of the studied additives more than 10% by weight can be practically molecularly dispersed in the polymers. The upper limit of porogen concentration in premembranes depends on characteristics of the polymer and of the porogen, on casting solvent composition, and on drying procedure. Metal salts of N-boc amino acids were found to be suitable heat-decomposable porogens. Their decomposition in premembranes proceeds in two stages, in the first of which 2 C₄H₈ + 2CO₂ are released per salt molecule. The metal complex formed goes through decarboxylation at a higher temperature. A heating procedure for completion of the first decomposition stage and for avoiding the second was determined.     

The criminalization of the mentally ill.

Comments on the contribution of L. A. Teplin (see record 1985-12217-001) who discussed issues relating to the incarceration of the mentally ill. Although the present author agrees with Teplin's conclusions and recommendations, he asserts that the logic of the argument presented was not convincing. Teplin does not include the role of the magistrate in discussing the process through which mentally ill people end up in jail, and she thus lacks a comprehensive viewpoint. No direct data were presented to support the conclusion that the criminalization of the mentally ill is a result of inadequate funding for the mental health system. (1 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The effect of goal-subgoal conflict on planning ability after frontal- and temporal-lobe lesions in humans

Twenty-one patients with unilateral prefrontal cortical neurosurgical lesions (11 left and 10 right) and 38 patients with unilateral temporal lobectomy (19 left and 19 right) were compared to 44 matched control subjects on their performance on the 3-D Computerized Tower of Hanoi (3-D CTOH) test. The problems were split into those with or without a significant goal-subgoal conflict determined by whether the correct first move in each problem took the subject apparently away or towards the final goal state. The left frontal lesion and right temporal lobectomy groups were significantly impaired on problems with goal-subgoal conflicts. In the left frontal group, this deficit was confined to earlier four-move problems, whereas the right temporal group showed a more general deficit on later five-move problems. The left frontal lesion deficit is explained in terms of an inability to inhibit the response compatible with achieving a final goal, whereas the impairment in the right lesion group was related to a specific impairment in spatial memory.     

Molecular mobility in starchy materials studied by electron spin resonance

Phase transitions in hydrated starch–sorbitol system were investigated both by molecular (electron spin resonance, ESR) and macroscopic (differential scanning calorimetry, DSC) methods. In rapid‐tumbling region, one did not observe the same phenomena by DSC and by ESR. The transitions observed by ESR, which seemed to reflect more the interactions probe–matrix than plasticization, probably corresponded to the interactions probe–sorbitol. For system concentrated in plasticizer, it was conceivable to admit that a demixion of sorbitol occurred. In slow‐tumbling region, a correspondence in temperature mobility changes measured by mechanical spectroscopy and ESR was observed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 990–997, 2003     

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

To address the problem of insufficient available modeling data for glucose prediction, as well as modeling burden, a model migration method was developed in a previous work to quickly transfer an old model to a new subject using a simple parameter adjustment. However, this method, which is referred to as first‐order model migration (FOMM), only considers a single order for each exogenous input, and may not produce an optimal model structure for accurate prediction. To overcome this problem, a multiple order model migration (MOMM) algorithm is proposed in this study. For different numbers of modeling samples, including glucose and two exogenous inputs (meal and insulin), the optimal modeling method may be different, and therefore, must be properly determined for each modeling scenario. First, the optimal model order is determined for each input and a multiple order prediction model is used. Then, a MOMM algorithm is developed based on particle swarm optimization (PSO) to simultaneously revise multiple parameters. The multiple order parameters of each input in the old model are quickly customized so that the revised model can be used for new subjects with desirable prediction accuracy. In particular, the influence of the number of modeling samples is analysed to check the applicability of different methods; this analysis determines the appropriate selection guidelines for the optimal model in response to different data sizes. The proposed method was evaluated using thirty in silico subjects and clinical data from seven individuals with Type 1 diabetes mellitus (DM). Overall, the MOMM algorithm presented superior results when the time period for collecting the samples was larger than 10 h (50 samples). In particular, the size of the modeling samples was separated into three different regions by evaluating the glucose prediction performance and the comparison between different algorithms for both in silico and clinical subjects. In Region I, the FOMM method achieves the best performance. In Region II, the MOMM method should be used and the prediction accuracy is superior in Region II in general. With enough samples (Region III), the subject‐dependent model (SM) algorithm can be chosen. The MOMM algorithm is demonstrated to be able to transfer models for new subjects with improved model structure. © 2017 American Institute of Chemical Engineers AIChE J, 64: 822–834, 2018     

Online Learning versus Offline Learning

We present an off-line variant of the mistake-bound model of learning. This is an intermediate model between the on-line learning model (Littlestone, 1988, Littlestone, 1989) and the self-directed learning model (Goldman, Rivest & Schapire, 1993, Goldman & Sloan, 1994). Just like in the other two models, a learner in the off-line model has to learn an unknown concept from a sequence of elements of the instance space on which it makes guess and test trials. In all models, the aim of the learner is to make as few mistakes as possible. The difference between the models is that, while in the on-line model only the set of possible elements is known, in the off-line model the sequence of elements (i.e., the identity of the elements as well as the order in which they are to be presented) is known to the learner in advance. On the other hand, the learner is weaker than the self-directed learner, which is allowed to choose adaptively the sequence of elements presented to him.We study some of the fundamental properties of the off-line model. In particular, we compare the number of mistakes made by the off-line learner on certain concept classes to those made by the on-line and self-directed learners. We give bounds on the possible gaps between the various models and show examples that prove that our bounds are tight.Another contribution of this paper is the extension of the combinatorial tool of labeled trees to a unified approach that captures the various mistake bound measures of all the models discussed. We believe that this tool will prove to be useful for further study of models of incremental learning.     

Connectors, splices, and couplers for infrared transmitting optical fibers

Connectors, splices, and couplers are widely used in silica optical fiber systems. Until now they have not been developed for mid-infrared fibers, in spite of the recognized need for such elements in many applications such as spectroscopy, radiometry, and heterodyne detection. We describe the construction and the optical and mechanical properties of such optical components for infrared transmitting silver halide optical fibers.     

The languages FCP(:) and FCP(:,?)

The language FCP(:,?) is the outcome of attempts to integrate the best of several flat concurrent logic programming languages, including Flat GHC, FCP (↓, |) and Flat Concurrent Prolog, in a single consistent framework. FCP(:) is a subset of FCP(:, ?), which is a variant of FPP(↓, |) and employs concepts of the concurrent constraint framework of cc(↓, |). FCP(:, ?) is a language which is strong enough to accommodate all useful concurrent logic programming techniques, including those which rely on atomic test unification and read-only variables, yet incorporates the weaker languages mentioned as subsets. This allows the programmer to remain within a simple subset of the language such as Flat GHC when the full power of atomic unification or read-only variables is not needed.