Barriers and facilitators to cooking from 'scratch' using basic or raw ingredients: A qualitative interview study

Background: Previous research has highlighted an ambiguity in understanding cooking related terminology and a number of barriers and facilitators to home meal preparation. However, meals prepared in the home still include convenience products (typically high in sugars, fats and sodium) which can have negative effects on health. Therefore, this study aimed to qualitatively explore: (1) how individuals define cooking from ‘scratch’, and (2) their barriers and facilitators to cooking with basic ingredients.
Methods: 27 semi-structured interviews were conducted with participants (aged 18-58 years) living on the island of Ireland, eliciting definitions of ‘cooking from scratch’ and exploring the reasons participants cook in a particular way. The interviews were professionally transcribed verbatim and Nvivo 10 was used for an inductive thematic analysis.
Results: Our results highlighted that although cooking from ‘scratch’ lacks a single definition, participants viewed it as optimal cooking. Barriers to cooking with raw ingredients included: 1) time pressures; (2) desire to save money; (3) desire for effortless meals; (4) family food preferences; and (5) effect of kitchen disasters. Facilitators included: 1) desire to eat for health and well-being; (2) creative inspiration; (3) ability to plan and prepare meals ahead of time; and (4) greater self-efficacy in one’s cooking ability.
Conclusions: Our findings contribute to understanding how individuals define cooking from ‘scratch’, and barriers and facilitators to cooking with raw ingredients. Interventions should focus on practical sessions to increase cooking self-efficacy; highlight the importance of planning ahead and teach methods such as batch cooking and freezing to facilitate cooking from scratch.

Fast and sensitive aflatoxin B1 and total aflatoxins ELISAs for analysis of peanuts, maize and feed ingredients

Aflatoxins are a group of carcinogenic compounds produced by Aspergillus fungi that can grow on different agricultural crops. Both acute and chronic exposure to these mycotoxins can cause serious illness. Due to the high occurrence of aflatoxins in crops worldwide fast and cost-effective analytical methods are required for the identification of contaminated agricultural commodities before they are processed into final products and placed on the market. In order to provide new tools for aflatoxin screening two prototype fast ELISA methods: one for the detection of aflatoxin B₁ and the other for total aflatoxins were developed. Seven monoclonal antibodies with unique high sensitivity and at the same time good cross-reactivity profiles were produced. The monoclonal antibodies were characterized and two antibodies showing IC₅₀ of 0.037 ng/mL and 0.031 ng/mL for aflatoxin B₁ were applied in simple and fast direct competitive ELISA tests. The methods were validated for peanut matrix as this crop is one of the most affected by aflatoxin contamination. The detection capabilities of aflatoxin B₁ and total aflatoxins ELISAs were 0.4 μg/kg and 0.3 μg/kg for aflatoxin B₁, respectively, which are one of the lowest reported values. Total aflatoxins ELISA was also validated for the detection of aflatoxins B₂, G₁ and G₂. The application of the developed tests was demonstrated by screening 32 peanut samples collected from the UK retailers. Total aflatoxins ELISA was further applied to analyse naturally contaminated maize porridge and distiller's dried grain with solubles samples and the results were correlated with these obtained by UHPLC-MS/MS method.

Density functional and Monte Carlo studies of sulfur. I. Structure and bonding in S-n rings and chains (n=2-18) (vol 118, pg 9257, 2003)

Density functional calculations have been performed for ring isomers of sulfur with up to 18 atoms, and for chains with up to ten atoms. There are many isomers of both types, and the calculations predict the existence of new forms. Larger rings and chains are very flexible, with numerous local energy minima. Apart from a small, but consistent overestimate in the bond lengths, the results reproduce experimental structures where known. Calculations are also performed on the energy surfaces of S8 rings, on the interaction between a pair of such rings, and the reaction between one S8 ring and the triplet diradical S8 chain. The results for potential energies, vibrational frequencies, and reaction mechanisms in sulfur rings and chains provide essential ingredients for Monte Carlo simulations of the liquid–liquid phase transition. The results of these simulations will be presented in Part II.

Components of the peptidome and transcriptome persist in lin wa pi: the dried skin of the Heilongjiang brown frog Rana amurensis as used in Traditional Chinese Medicine

Although the ancient practice of traditional Chinese medicine (TCM) utilizes predominantly herbal ingredients, many of which are now the subject of intense scientific scrutiny, significant quantities of animal tissue-derived materials are also employed. Here we have used contemporary molecular techniques to study the material known as lin wa pi, the dried skin of the Heilongjiang brown frog, Rana amurensis, that is used commonly as an ingredient of many medicines, as a general tonic and as a topical antimicrobial/wound dressing. Using a simple technology that has been developed and validated over several years, we have demonstrated that components of both the skin granular gland peptidome and transcriptome persist in this material. Interrogation of the cDNA library constructed from the dried skin by entrapment and amplification of polyadenylated mRNA, using a "shotgun" primer approach and 3'-RACE, resulted in the cloning of cDNAs encoding the precursors of five putative antimicrobial peptides. Two (ranatuerin-2AMa and ranatuerin-2AMb) were obvious homologs of a previously described frog skin peptide family, whereas the remaining three were of sufficient structural novelty to be named amurins 1-3. Mature peptides were each identified in reverse phase HPLC fractions of boiling water extracts of skin and their structures confirmed by MS/MS fragmentation sequencing. Components of traditional Chinese medicines of animal tissue origin may thus contain biologically active peptides that survive the preparation procedures and that may contribute to therapeutic efficacy.

Dynamic Knowledge Creation Through Value Management Teams

With the intention of introducing unique and value-added products to the market, organizations have become more conscious of how to best create knowledge as reported by Ganesh Bhatt in 2000 in 'Information dynamics, learning and knowledge creation in organizations'. Knowledge creation is recognized as having an important role in generating and sustaining a competitive advantage as well as in meeting organizational goals, as reported by Aleda Roth and her colleagues in 1994 in 'The knowledge factory for accelerated learning practices.' One of the successful ingredients of value management (VM) is its utilization of diverse knowledge resources, drawing upon different organizational functions, professional disciplines, and stakeholders, in a facilitated team process. Multidisciplinary VM study teams are viewed as having high potential to innovate due to their heterogeneous nature. This paper looks at one of the VM workshop's major benefits, namely, knowledge creation. A case study approach was used to explore the nature, processes, and issues associated with fostering a dynamic knowledge creation capability within VM teams. The results indicate that the dynamic knowledge creating process is embedded in and influenced by managing team constellation, creating shared awareness, developing shared understanding, and producing aligned action. The catalysts that can speed up the processes are open dialogue and discussion among participants. This process is enhanced by the use of facilitators, skilled at extracting knowledge.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Generation of a coherent superposition of a travelling wave field

We propose an experimentally feasible scheme to generate a superposition of travelling field coherent states using an extremely small Kerr effect and an ancilla which could be a single photon or two entangled twin photons. The scheme contains ingredients which are all within the current state of the art and is robust against the main sources of errors which can be identified in our setups.

Generation of entangled coherent states via cross-phase-modulation in a double electromagnetically induced transparency regime

The generation of an entangled coherent state is one of the most important ingredients of quantum information processing using coherent states. Recently, numerous schemes to achieve this task have been proposed. In order to generate travelling-wave entangled coherent states, cross-phase-modulation, optimized by optical Kerr effect enhancement in a dense medium in an electromagnetically induced transparency (EIT) regime, seems to be very promising. In this scenario, we propose a fully quantized model of a double-EIT scheme recently proposed [D. Petrosyan and G. Kurizki, Phys. Rev. A 65, 33 833 (2002)]: the quantization step is performed adopting a fully Hamiltonian approach. This allows us to write effective equations of motion for two interacting quantum fields of light that show how the dynamics of one field depends on the photon-number operator of the other. The preparation of a Schrodinger cat state, which is a superposition of two distinct coherent states, is briefly exposed. This is based on nonlinear interaction via double EIT of two light fields (initially prepared in coherent states) and on a detection step performed using a 50:50 beam splitter and two photodetectors. In order to show the entanglement of an entangled coherent state, we suggest to measure the joint quadrature variance of the field. We show that the entangled coherent states satisfy the sufficient condition for entanglement based on quadrature variance measurement. We also show how robust our scheme is against a low detection efficiency of homodyne detectors.

Willingness to use functional breads. Applying the Health Belief Model across four European countries

The present study focused on the role of the Health Belief Model (HBM) in predicting willingness to use functional breads, across four European countries: UK (N = 552), Italy (N = 504), Germany (N = 525) and Finland (N = 513). The behavioural evaluation components of the HBM (the perceived benefits and barriers conceptualized respectively as perceived healthiness and pleasantness) and the health motivation component were good predictors of willingness to use functional breads whereas threat perception components (perceived susceptibility and perceived anticipated severity) failed as predictors. This result was common in all four countries and across products. The role of 'cue to action' was marginal. On the whole the HBM fit was similar across the countries and products in terms of significant predictors (the perceived benefits, barriers and health motivation) with the exception of self-efficacy which was significant only in Finland. Young consumers seemed more interested in the functional bread with a health claim promoting health rather than in reducing risk of disease, whereas the opposite was true for older people. However, functional staple foods, such as bread in this European study, are still perceived as common foods rather than as a means of avoiding diseases. Consumers seek these foods for their healthiness (the perceived benefits) as they expect them to be healthier than regular foods and for the pleasantness (the perceived barriers) as they do not expect any change in the sensory characteristics due to the addition of the functional ingredients. The importance of health motivation in willingness to use products with health claims implies that there is an opening for developing better models for explaining health-promoting food choices that take into account both food and health-related factors without making a reference to disease-related outcome. (C) 2008 Elsevier Ltd. All rights reserved.

Textural properties of whey based edible films

The objective of this work was to study the textural properties of edible films made from sour (acid) whey for food wrapping application. Acid whey is often regarded as a waste product, obtained as a watery effluent in the manufacturing of cottage cheese. In general, owing to its high nutritional value, whey has gained importance as an additive in food manufacturing processes and in health drink formulations. In this work, fresh sour whey was used to make edible films. The proteins in the whey were concentrated by ultrafiltration to reduce the water content. Only natural ingredients such as acid whey and agar were used to form the film under controlled heating (650 W) in a microwave oven. The structural and surface characteristics of the films were tested by a texture analyser and scanning electron micrographs.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

FACTORIAL EXPERIMENTAL-DESIGN FOR CONCRETE DURABILITY RESEARCH

It is widely accepted that concrete designed to perform satisfactorily in adverse environmental conditions must have a high cement content and a low water-cement ratio. In addition, in order to enhance its durability, many types of additive and admixture such as super-plasticizers, fly ash, silica fume, ggbfs, etc., have been used in the past. However, a close study of the published literature indicates that the effect of mix variables on the durability and the interaction between the various ingredients are not fully understood. Some of these apparent contradictions are due to the limitations in the design of the experimental programme. For instance, it is evident that relatively higher concentrations of aggregates increase the tortuosity of the flow path and hence reduce the permeability, which results in an improvement in the durability. Therefore, an increase in cement content without a proportional decrease in water-cement ratio may reduce the durability. In such cases, the interactive effects of factors can be established by resorting to a properly designed experimental programme, such as the factorial experimental design.

Automatic Parallelization in the Paralax Compiler

The efficient development of multi-threaded software has, for many years, been an unsolved problem in computer science. Finding a solution to this problem has become urgent with the advent of multi-core processors. Furthermore, the problem has become more complicated because multi-cores are everywhere (desktop, laptop, embedded system). As such, they execute generic programs which exhibit very different characteristics than the scientific applications that have been the focus of parallel computing in the past.
Implicitly parallel programming is an approach to parallel pro- gramming that promises high productivity and efficiency and rules out synchronization errors and race conditions by design. There are two main ingredients to implicitly parallel programming: (i) a con- ventional sequential programming language that is extended with annotations that describe the semantics of the program and (ii) an automatic parallelizing compiler that uses the annotations to in- crease the degree of parallelization.
It is extremely important that the annotations and the automatic parallelizing compiler are designed with the target application do- main in mind. In this paper, we discuss the Paralax approach to im- plicitly parallel programming and we review how the annotations and the compiler design help to successfully parallelize generic programs. We evaluate Paralax on SPECint benchmarks, which are a model for such programs, and demonstrate scalable speedups, up to a factor of 6 on 8 cores.