Evaluation of methodologies to determine vegetable oil species present in oil mixtures: Proposition of an approach to meet the EU legislation demands for correct vegetable oils labelling

Refined vegetable oils are widely used in the food industry as ingredients or components in many processed food products in the form of oil blends. To date, the generic term 'vegetable oil' has been used in the labelling of food containing oil blends. With the introduction of new EU Regulation for Food Information (1169/2011) due to take effect in 2014, the oil species used must be clearly identified on the package and there is a need for development of fit for purpose methodology for industry and regulators alike to verify the oil species present in a product. The available methodologies that may be employed to authenticate the botanical origin of a vegetable oil admixture were reviewed and evaluated. The majority of the sources however, described techniques applied to crude vegetable oils such as olive oil due to the lack of refined vegetable oil focused studies. Nevertheless, DNA based typing methods and stable isotopes procedures were found not suitable for this particular purpose due to several issues. Only a small number of specific chromatographic and spectroscopic fingerprinting methods in either targeted or untargeted mode were found to be applicable in potentially providing a solution to this complex authenticity problem. Applied as a single method in isolation, these techniques would be able to give limited information on the oils identity as signals obtained for various oil types may well be overlapping. Therefore, more complex and combined approaches are likely to be needed to identify the oil species present in oil blends employing a stepwise approach in combination with advanced chemometrics. Options to provide such a methodology are outlined in the current study.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Scheduling and Platforming Trains at Busy Complex Stations

We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using ''manual'' methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network.Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.

Child abuse as a complex and wicked problem: Reflecting on policy developments in the United Kingdom in working with children and families with multiple problems

In spite of significant public concern, professional efforts and financial expenditure, there has been a perceived lack of progress in reducing the incidence of child abuse, and in improving the outcomes for children in both the short and longer term. In this article the authors reflect on recent policy developments in the United Kingdom relating to children and families experiencing multiple adversities, and argue that the current conceptualisation of child abuse is flawed. In adopting a rational technical approach to the management of child abuse, there is a tendency to focus on shorter term outcomes for the child, such as immediate safety, that primarily reflect the outputs of the child protection system. However, by viewing child abuse as a wicked problem, that is complex and less amenable to being solved, then child welfare professionals can be supported to focus on achieving longer term outcomes for children that are more likely to meet their needs. The authors argue for an earlier identification of and intervention with children who are experiencing multiple adversity, such as those living with parents misusing substances and exposed to intimate partner violence.

System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful

On the Problems of Mixing RCTs with Qualitative Research: The Case of the MRC Framework for the Evaluation of Complex Healthcare Interventions

In the perceived hierarchy of research designs, the results from randomized controlled trials are considered to provide the highest level of evidence. Indeed these trials have been upheld as the gold standard in research. The benefits and limitations of the randomized controlled trial as a method of evaluating the effectiveness of healthcare interventions are presented. The article then examines the different levels of complexity within healthcare interventions and the problems this poses in determining effectiveness. In an effort to provide a solution to this problem, the Medical Research Council produced a framework to assist investigators to develop and evaluate complex healthcare interventions. The framework is described with reference to an example of implementing and evaluating protocols for weaning patients in the intensive care unit. The framework is critiqued on the basis that it involves an ambiguous or contradictory ontology, which fails to articulate the relationship between the positivism of randomized controlled trials with the relativism of qualitative approaches. It is concluded that the use of realist strategies in combination with randomized controlled trials provides the most coherent solution to this quandary

The Use of Electrospray Mass Spectrometry to Determine Speciation in a Dynamic Combinatorial Library for Anion Recognition

The composition of a dynamic mixture of similar 2,2'-bipyridine complexes of iron(II) bearing either an amide (5-benzylamido-2,2'-bipyridine and 5-(2-methoxyethane)amido-2,2'-bipyridine) or an ester (2,2'-bipyridine-5-carboxylic acid benzylester and 2,2'-bipyridine-5-carboxylic acid 2-methoxyethane ester) side chain have been evaluated by electrospray mass spectroscopy in acetonitrile. The time taken for the complexes to come to equilibrium appears to be dependent on the counteranion, with chloride causing a rapid redistribution of two preformed heteroleptic complexes (of the order of 1 hour), whereas the time it takes in the presence of tetrafluoroborate salts is in excess of 24^^h. Similarly the final distribution of products is dependent on the anion present, with the presence of chloride, and to a lesser extent bromide, preferring three amide-functionalized ligands, and a slight preference for an appended benzyl over a methoxyethyl group. Furthermore, for the first time, this study shows that the distribution of a dynamic library of metal complexes monitored by ESI-MS can adapt following the introduction of a different anion, in this case tetrabutylammonium chloride to give the most favoured heteroleptic complex despite the increasing ionic strength of the solution. 

Combinatorial Frequency Generation in Quasi-Periodic Stacks of Nonlinear Dielectric Layers

Three-wave mixing in quasi-periodic structures (QPSs) composed of nonlinear anisotropic dielectric layers, stacked in Fibonacci and Thue-Morse sequences, has been explored at illumination by a pair of pump waves with dissimilar frequencies and incidence angles. A new formulation of the nonlinear scattering problem has enabled the QPS analysis as a perturbed periodic structure with defects. The obtained solutions have revealed the effects of stack composition and constituent layer parameters, including losses, on the properties of combinatorial frequency generation (CFG). The CFG features illustrated by the simulation results are discussed. It is demonstrated that quasi-periodic stacks can achieve a higher efficiency of CFG than regular periodic multilayers.

Evaluation of creative problem solving abilities in undergraduate structural engineers through interdisciplinary problem based learning

For a structural engineer, effective communication and interaction with architects cannot be underestimated as a key skill to success throughout their professional career. Structural engineers and architects have to share a common language and understanding of each other in order to achieve the most desirable architectural and structural designs. This interaction and engagement develops during their professional career but needs to be nurtured during their undergraduate studies. The objective of this paper is to present the strategies employed to engage higher order thinking in structural engineering students in order to help them solve complex problem-based learning (PBL) design scenarios presented by architecture students. The strategies employed were applied in the experimental setting of an undergraduate module in structural engineering at Queen’s University Belfast in the UK. The strategies employed were active learning to engage with content knowledge, the use of physical conceptual structural models to reinforce key concepts and finally, reinforcing the need for hand sketching of ideas to promote higher order problem-solving. The strategies employed were evaluated through student survey, student feedback and module facilitator (this author) reflection. The strategies were qualitatively perceived by the tutor and quantitatively evaluated by students in a cross-sectional study to help interaction with the architecture students, aid interdisciplinary learning and help students creatively solve problems (through higher order thinking). The students clearly enjoyed this module and in particular interacting with structural engineering tutors and students from another discipline