Building integration photovoltaic module with reference to Ghana: using triple junction amorphous silicon

This paper assesses the potential for using building integrated photovoltaic (BIPV) roof shingles made from triple-junction amorphous silicon (3a-Si) for electrification and as a roofing material in tropical countries, such as Accra, Ghana. A model roof was constructed using triple-junction amorphous (3a-Si) PV on one section and conventional roofing tiles on the other. The performance of the PV module and tiles were measured, over a range of ambient temperatures and solar irradiance. PVSyst (a computer design software) was used to determine the most appropriate angle of tilt. It was observed that 3a-Si performs well in conditions such as Accra, because it is insensitive to high temperatures. Building integration gives security benefits, and reduces construction costs and embodied energy, compared to freestanding PV systems. Again, it serves as a means of protection from salt spray from the oceans and works well even when shaded. However, compared to conventional roofing materials, 3a-Si would increase the indoor temperature by 1-2 °C depending on the surface area of the roof covered with the PV modules. The results presented in this research enhance the understanding of varying factors involved in the selection of an appropriate method of PV installation to offset the short falls of the conventional roofing material in Ghana.

Adaptive deadbeat control of stochastic systems

This paper considers the use of a discrete-time deadbeat control action on systems affected by noise. Variations on the standard controller form are discussed and comparisons are made with controllers in which noise rejection is a higher priority objective. Both load and random disturbances are considered in the system description, although the aim of the deadbeat design remains as a tailoring of reference input variations. Finally, the use of such a deadbeat action within a self-tuning control framework is shown to satisfy, under certain conditions, the self-tuning property, generally though only when an extended form of least-squares estimation is incorporated.

Microprocessor systems: an introduction

The aim of this book is to provide and introduction to microprocessor systems, their operation and design. It covers those topics needed by engineers and computer scientists who are interested in applying microprocessors in practical situations, namely computer hardware including logic and interfacing, software, in particular high level and assembly language programming, and the design and testing of such systems. The fundamental principles of micrprocessor systems are described and these are illustrated with reference to two microprocessors, the 32-bit MC68020 from Motorola and a single chip microcomputer, the 8051 from Intel; and in addition, interfacing to the general purpose STE bus is described. The details of the processors and the bus are concentrated in three chapters, thus allowing the presentation of the material to be independent of the microprocessors if that is desired, and permitting the specific details to be found easily.

Methods for kinematic modeling of biological and robotic systems

The main objective is to develop methods that automatically generate kinematic models for the movements of biological and robotic systems. Two methods for the identification of the kinematics are presented. The first method requires the elimination of the displacement variables that cannot be measured while the second method attempts to estimate the changes in these variables. The methods were tested using a planar two-revolute-joint linkage. Results show that the model parameters obtained agree with the actual parameters to within 5%. Moreover, the methods were applied to model head and neck movements in the sagittal plane. The results indicate that these movements are well modeled by a two-revolute-joint system. A spatial three-revolute-joint model was also discussed and tested.

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

Re-conceptualising public participation in spatial planning: a view through autopoiesis

This article examines the relevance of the concept of autopoiesis for public participation in spatial planning. The concept of autopoiesis within social science, as advanced by Niklas Luhmann (1995) argues for a systemic, consistent and sophisticated theory of society based on a systems view, as opposed to more familiar action-based theories (for instance as in the case of the Habermasian tradition). By examining the relevance of the concept for public participation in spatial planning, this article highlights specific aspects of public participation that draw attention to dimensions of planning that are not currently explicitly highlighted within mainstream discourses in planning

Stochastic perturbations to dynamical systems: a response theory approach

Using the formalism of the Ruelle response theory, we study how the invariant measure of an Axiom A dynamical system changes as a result of adding noise, and describe how the stochastic perturbation can be used to explore the properties of the underlying deterministic dynamics. We first find the expression for the change in the expectation value of a general observable when a white noise forcing is introduced in the system, both in the additive and in the multiplicative case. We also show that the difference between the expectation value of the power spectrum of an observable in the stochastically perturbed case and of the same observable in the unperturbed case is equal to the variance of the noise times the square of the modulus of the linear susceptibility describing the frequency-dependent response of the system to perturbations with the same spatial patterns as the considered stochastic forcing. This provides a conceptual bridge between the change in the fluctuation properties of the system due to the presence of noise and the response of the unperturbed system to deterministic forcings. Using Kramers-Kronig theory, it is then possible to derive the real and imaginary part of the susceptibility and thus deduce the Green function of the system for any desired observable. We then extend our results to rather general patterns of random forcing, from the case of several white noise forcings, to noise terms with memory, up to the case of a space-time random field. Explicit formulas are provided for each relevant case analysed. As a general result, we find, using an argument of positive-definiteness, that the power spectrum of the stochastically perturbed system is larger at all frequencies than the power spectrum of the unperturbed system. We provide an example of application of our results by considering the spatially extended chaotic Lorenz 96 model. These results clarify the property of stochastic stability of SRB measures in Axiom A flows, provide tools for analysing stochastic parameterisations and related closure ansatz to be implemented in modelling studies, and introduce new ways to study the response of a system to external perturbations. Taking into account the chaotic hypothesis, we expect that our results have practical relevance for a more general class of system than those belonging to Axiom A.

Delays versus performance of visually guided systems

The paper presents an overview of dynamic systems with inherent delays in both feedforward and feedback paths and how the performance of such systems can be affected by such delays. The authors concentrate on visually guided systems, where the behaviour of the system is largely dependent on the results of the vision sensors, with particular reference to active robot heads (real-time gaze control). We show how the performance of such systems can deteriorate substantially with the presence of unknown and/or variable delays. Considered choice of system architecture, however, allows the performance of active vision systems to be optimised with respect to the delays present in the system.

The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function.

Platelets in the circulation are triggered by vascular damage to activate, aggregate and form a thrombus that prevents excessive blood loss. Platelet activation is stringently regulated by intracellular signalling cascades, which when activated inappropriately lead to myocardial infarction and stroke. Strategies to address platelet dysfunction have included proteomics approaches which have lead to the discovery of a number of novel regulatory proteins of potential therapeutic value. Global analysis of platelet proteomes may enhance the outcome of these studies by arranging this information in a contextual manner that recapitulates established signalling complexes and predicts novel regulatory processes. Platelet signalling networks have already begun to be exploited with interrogation of protein datasets using in silico methodologies that locate functionally feasible protein clusters for subsequent biochemical validation. Characterization of these biological systems through analysis of spatial and temporal organization of component proteins is developing alongside advances in the proteomics field. This focused review highlights advances in platelet proteomics data mining approaches that complement the emerging systems biology field. We have also highlighted nucleated cell types as key examples that can inform platelet research. Therapeutic translation of these modern approaches to understanding platelet regulatory mechanisms will enable the development of novel anti-thrombotic strategies.

Examining the large-scale spatial coherence of European drought using regional indicators of precipitation and streamflow deficit

Droughts tend to evolve slowly and affect large areas simultaneously, which suggests that improved understanding of spatial coherence of drought would enable better mitigation of drought impacts through enhanced monitoring and forecasting strategies. This study employs an up-to-date dataset of over 500 river flow time series from 11 European countries, along with a gridded precipitation dataset, to examine the spatial coherence of drought in Europe using regional indicators of precipitation and streamflow deficit. The drought indicators were generated for 24 homogeneous regions and, for selected regions, historical drought characteristics were corroborated with previous work. The spatial coherence of drought characteristics was then examined at a European scale. Historical droughts generally have distinctive signatures in their spatio-temporal development, so there was limited scope for using the evolution of historical events to inform forecasting. Rather, relationships were explored in time series of drought indicators between regions. Correlations were generally low, but multivariate analyses revealed broad continental-scale patterns, which appear to be related to large-scale atmospheric circulation indices (in particular, the North Atlantic Oscillation and the East Atlantic West Russia pattern). A novel methodology for forecasting was developed (and demonstrated with reference to the United Kingdom), which predicts drought from drought i.e. uses spatial coherence of drought to facilitate early warning of drought in a target region, from drought which is developing elsewhere in Europe.Whilst the skill of the methodology is relatively modest at present, this approach presents a potential new avenue for forecasting, which offers significant advantages in that it allows prediction for all seasons, and also shows some potential for forecasting the termination of drought conditions.

Nutrimetabonomics: applications for nutritional sciences, with specific reference to gut microbial interactions

Understanding the role of the diet in determining human health and disease is one major objective of modern nutrition. Mammalian biocomplexity necessitates the incorporation of systems biology technologies into contemporary nutritional research. Metabonomics is a powerful approach that simultaneously measures the low-molecular-weight compounds in a biological sample, enabling the metabolic status of a biological system to be characterized. Such biochemical profiles contain latent information relating to inherent parameters, such as the genotype, and environmental factors, including the diet and gut microbiota. Nutritional metabonomics, or nutrimetabonomics, is being increasingly applied to study molecular interactions between the diet and the global metabolic system. This review discusses three primary areas in which nutrimetabonomics has enjoyed successful application in nutritional research: the illumination of molecular relationships between nutrition and biochemical processes; elucidation of biomarker signatures of food components for use in dietary surveillance; and the study of complex trans-genomic interactions between the mammalian host and its resident gut microbiome. Finally, this review illustrates the potential for nutrimetabonomics in nutritional science as an indispensable tool to achieve personalized nutrition.

Healable Polymer Systems

Polymers are used in many everyday technologies and their degradation due to environmental exposure has lead to great interest in materials which can heal and repair themselves. In order to design new self healing polymers it's important to understand the fundamental healing mechanisms behind the material.Healable Polymer Systems will outline the key concepts and mechanisms underpinning the design and processing of healable polymers, and indicate potential directions for progress in the future development and applications of these fascinating and potentially valuable materials. The book covers the different techniques developed successfully to date for both autonomous healable materials (those which do not require an external stimulus to promote healing) and rehealable or remendable materials (those which only recover their original physical properties if a specific stimulus is applied). These include the encapsulated-monomer approach, reversible covalent bond formation, irreversible covalent bond formation and supramolecular self-assembly providing detailed insights into their chemistry.Written by leading experts, the book provides polymer scientists with a compact and readily accessible source of reference for healable polymer systems.

Mobile-to-mobile MIMO transmit-receive diversity systems: analysis and performance in three-dimensional double-correlated channels

Mobile-to-mobile (M-to-M) communications are expected to play a crucial role in future wireless systems and networks. In this paper, we consider M-to-M multiple-input multiple-output (MIMO) maximal ratio combining system and assess its performance in spatially correlated channels. The analysis assumes double-correlated Rayleigh-and-Lognormal fading channels and is performed in terms of average symbol error probability, outage probability, and ergodic capacity. To obtain the receive and transmit spatial correlation functions needed for the performance analysis, we used a three-dimensional (3D) M-to-M MIMO channel model, which takes into account the effects of fast fading and shadowing. The expressions for the considered metrics are derived as a function of the average signal-to-noise ratio per receive antenna in closed-form and are further approximated using the recursive adaptive Simpson quadrature method. Numerical results are provided to show the effects of system parameters, such as distance between antenna elements, maximum elevation angle of scatterers, orientation angle of antenna array in the x–y plane, angle between the x–y plane and the antenna array orientation, and degree of scattering in the x–y plane, on the system performance. Copyright © 2011 John Wiley & Sons, Ltd.