Deciphering the complexity of sainfoin (Onobrychis viciifolia) proanthocyanidins by MALDI-TOF mass spectrometry with a judicious choice of isotope patterns and matrixes

Use of superdihydroxybenzoic acid as the matrix enabled the analysis of highly complex mixtures of proanthocyanidins from sainfoin (Onobrychis viciifolia) by MALDI-TOF mass spectrometry. Proanthocyanidins contained predominantly B-type homopolymers and heteropolymers up to 12- mers (3400 Da). Use of another matrix, 2,6-dihydroxyacetophenone, revealed the presence of A-type glycosylated dimers. In addition, we report here how a comparison of the isotopic adduct patterns, which resulted from Li and Na salts as MALDI matrix additives, could be used to confirm the presence of A-type linkages in complex proanthocyanidin mixtures. Preliminary evidence suggested the presence of A-type dimers in glycosylated prodelphinidins and in tetrameric procyanidins and prodelphinidins.

Land re-use, complexity and actor-networks: a framework for research

This paper will present a conceptual framework for the examination of land redevelopment based on a complex systems/networks approach. As Alvin Toffler insightfully noted, modern scientific enquiry has become exceptionally good at splitting problems into pieces but has forgotten how to put the pieces back together. Twenty-five years after his remarks, governments and corporations faced with the requirements of sustainability are struggling to promote an ‘integrated’ or ‘holistic’ approach to tackling problems. Despite the talk, both practice and research provide few platforms that allow for ‘joined up’ thinking and action. With socio-economic phenomena, such as land redevelopment, promising prospects open up when we assume that their constituents can make up complex systems whose emergent properties are more than the sum of the parts and whose behaviour is inherently difficult to predict. A review of previous research shows that it has mainly focused on idealised, ‘mechanical’ views of property development processes that fail to recognise in full the relationships between actors, the structures created and their emergent qualities. When reality failed to live up to the expectations of these theoretical constructs then somebody had to be blamed for it: planners, developers, politicians. However, from a ‘synthetic’ point of view the agents and networks involved in property development can be seen as constituents of structures that perform complex processes. These structures interact, forming new more complex structures and networks. Redevelopment then can be conceptualised as a process of transformation: a complex system, a ‘dissipative’ structure involving developers, planners, landowners, state agencies etc., unlocks the potential of previously used sites, transforms space towards a higher order of complexity and ‘consumes’ but also ‘creates’ different forms of capital in the process. Analysis of network relations point toward the ‘dualism’ of structure and agency in these processes of system transformation and change. Insights from actor network theory can be conjoined with notions of complexity and chaos to build an understanding of the ways in which actors actively seek to shape these structures and systems, whilst at the same time are recursively shaped by them in their strategies and actions. This approach transcends the blame game and allows for inter-disciplinary inputs to be placed within a broader explanatory framework that does away with many past dichotomies. Better understanding of the interactions between actors and the emergent qualities of the networks they form can improve our comprehension of the complex socio-spatial phenomena that redevelopment comprises. The insights that this framework provides when applied in UK institutional investment into redevelopment are considered to be significant.

Dealing with complexity: an overview of the neural network approach

The problem of complexity is particularly relevant to the field of control engineering, since many engineering problems are inherently complex. The inherent complexity is such that straightforward computational problem solutions often produce very poor results. Although parallel processing can alleviate the problem to some extent, it is artificial neural networks (in various forms) which have recently proved particularly effective, even in dealing with the causes of the problem itself. This paper presents an overview of the current neural network research being undertaken. Such research aims to solve the complex problems found in many areas of science and engineering today.

Reduced complexity set-point following in self-tuning control

An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.

Effect of phonetic complexity on word reading and repetition in deep dyslexia

This investigation moves beyond the traditional studies of word reading to identify how the production complexity of words affects reading accuracy in an individual with deep dyslexia (JO). We examined JO’s ability to read words aloud while manipulating both the production complexity of the words and the semantic context. The classification of words as either phonetically simple or complex was based on the Index of Phonetic Complexity. The semantic context was varied using a semantic blocking paradigm (i.e., semantically blocked and unblocked conditions). In the semantically blocked condition words were grouped by semantic categories (e.g., table, sit, seat, couch,), whereas in the unblocked condition the same words were presented in a random order. JO’s performance on reading aloud was also compared to her performance on a repetition task using the same items. Results revealed a strong interaction between word complexity and semantic blocking for reading aloud but not for repetition. JO produced the greatest number of errors for phonetically complex words in semantically blocked condition. This interaction suggests that semantic processes are constrained by output production processes which are exaggerated when derived from visual rather than auditory targets. This complex relationship between orthographic, semantic, and phonetic processes highlights the need for word recognition models to explicitly account for production processes.

Dealing with complexity: an autopoietic view of the People's Planning Campaign, Kerala

Complexity is integral to planning today. Everyone and everything seem to be interconnected, causality appears ambiguous, unintended consequences are ubiquitous, and information overload is a constant challenge. The nature of complexity, the consequences of it for society, and the ways in which one might confront it, understand it and deal with it in order to allow for the possibility of planning, are issues increasingly demanding analytical attention. One theoretical framework that can potentially assist planners in this regard is Luhmann's theory of autopoiesis. This article uses insights from Luhmann's ideas to understand the nature of complexity and its reduction, thereby redefining issues in planning, and explores the ways in which management of these issues might be observed in actual planning practice via a reinterpreted case study of the People's Planning Campaign in Kerala, India. Overall, this reinterpretation leads to a different understanding of the scope of planning and planning practice, telling a story about complexity and systemic response. It allows the reinterpretation of otherwise familiar phenomena, both highlighting the empirical relevance of the theory and providing new and original insight into particular dynamics of the case study. This not only provides a greater understanding of the dynamics of complexity, but also produces advice to help planners implement structures and processes that can cope with complexity in practice.

Time complexity analysis of the stochastic diffusion search

The Stochastic Diffusion Search algorithm -an integral part of Stochastic Search Networks is investigated. Stochastic Diffusion Search is an alternative solution for invariant pattern recognition and focus of attention. It has been shown that the algorithm can be modelled as an ergodic, finite state Markov Chain under some non-restrictive assumptions. Sub-linear time complexity for some settings of parameters has been formulated and proved. Some properties of the algorithm are then characterised and numerical examples illustrating some features of the algorithm are presented.

Family hardship and cultural values: child labor in Malian artisanal gold mining communities

This paper contributes to the debate on child labor in small-scale mining communities, focusing specifically on the situation in sub-Saharan Africa. It argues that the child labor now widespread in many of the region’s small-scale mining communities is a product of a combination of cultural issues, household-level poverty and rural livelihood diversification. Experiences from Komana West, a subsistence gold panning area in Southern Mali, are drawn upon to make this case. The findings suggest that the sector’s child labor “problem” is far more nuanced than international organizations and policymakers have diagnosed.