The effects of temperature and light integral on early vegetative growth and chlorophyll fluorescence of four contrasting genotypes of cacao (Theobroma cacao)

The effect of temperature on early vegetative growth, leaf chlorophyll fluorescence and chlorophyll content was examined on four genotypes of cacao (Amelonado, AMAZ 15–15, SCA 6 and SPEC 54/1). A controlled environment glasshouse was used to simulate the temperature conditions of three cacao-growing regions (Bahia, Brazil; Tafo, Ghana and Lower Perak, Malaysia) over the course of a year. Base temperatures calculated from increments in main stem growth varied from 18.6°C for AMAZ 15/15 to 20.8°C for SPEC 54/1. Temporal variation in Fv/Fm observed for two of the clones (SCA 6 and SPEC 54/1) in two of the compartments were correlated with temperature differences over time. Significant differences were also recorded between genotypes in leaf chlorophyll content. It was shown that variation over time in leaf chlorophyll content could be quantified accurately as a function of temperature and light integral. The results imply that genetic variability exists in cacao in response to temperature stress.

Innovation care pathways and the healthcare built environment: An explanatory framework and research agenda

The built environment in which health and social care is delivered can have an impact on the efficiency and outcomes of care processes. The health-care estate is large and growing and is expensive to build, adapt and maintain. The design of these buildings is a complex, difficult and political process. Better use of care pathways as an input to the design and use of the built environment has the potential to deliver significant benefits. A number of variations on the idea of care pathways are already used in designing health-care buildings but this is under-researched. This paper provides a framework for thinking about care pathways and the health-care built environment. The framework distinguishes between five different pathway ‘types’ defined for the purpose of understanding the relationship between pathways and infrastructure. The five types are: ‘care pathways’, ‘integrated care pathways’, ‘patient pathways’, ‘patient journeys’ and ‘patient flows’. The built environment implications of each type are discussed and recommendations made for those involved in either building development or care pathway projects.

Care pathways and designing the healthcare built environment: an explanatory framework

The built environment in which health and social care is delivered can have an impact on the efficiency and outcomes of care processes. The health-care estate is large and growing and is expensive to build, adapt and maintain. The design of these buildings is a complex, difficult and political process. Better use of care pathways as an input to the design and use of the built environment has the potential to deliver significant benefits. A number of variations on the idea of care pathways are already used in designing health-care buildings but this is under-researched. This paper provides a framework for thinking about care pathways and the health-care built environment. The framework distinguishes between five different pathway ‘types’ defined for the purpose of understanding the relationship between pathways and infrastructure. The five types are: ‘care pathways’, ‘integrated care pathways’, ‘patient pathways’, ‘patient journeys’ and ‘patient flows’. The built environment implications of each type are discussed and recommendations made for those involved in either building development or care pathway projects.

Health informatics: improving patient-centred healthcare pathway

Nowadays the use of information and communication technology is becoming prevalent in many aspects of healthcare services from patient registration, to consultation, treatment and pathology tests request. Manual interface techniques have dominated data-capture activities in primary care and secondary care settings for decades. Despites the improvements made in IT, usability issues still remain over the use of I/O devices like the computer keyboard, touch-sensitive screens, light pen and barcodes. Furthermore, clinicians have to use several computer applications when providing healthcare services to patients. One of the problems faced by medical professionals is the lack of data integrity between the different software applications which in turn can hinder the provision of healthcare services tailored to the needs of the patients. The use of digital pen and paper technology integrated with legacy medical systems hold the promise of improving healthcare quality. This paper discusses the issue of data integrity in e-health systems and proposes the modelling of "Smart Forms" via semiotics to potentially improve integrity between legacy systems, making the work of medical professionals easier and improve the quality of care in primary care practices and hospitals.

The ethical challenges of ubiquitous healthcare

Ubiquitous healthcare is an emerging area of technology that uses a large number of environmental and patient sensors and actuators to monitor and improve patients’ physical and mental condition. Tiny sensors gather data on almost any physiological characteristic that can be used to diagnose health problems. This technology faces some challenging ethical questions, ranging from the small-scale individual issues of trust and efficacy to the societal issues of health and longevity gaps related to economic status. It presents particular problems in combining developing computer/information/media ethics with established medical ethics. This article describes a practice-based ethics approach, considering in particular the areas of privacy, agency, equity and liability. It raises questions that ubiquitous healthcare will force practitioners to face as they develop ubiquitous healthcare systems. Medicine is a controlled profession whose practise is commonly restricted by government-appointed authorities, whereas computer software and hardware development is notoriously lacking in such regimes.

Complexity of Monte Carlo algorithms for a class of integral equations

In this work we study the computational complexity of a class of grid Monte Carlo algorithms for integral equations. The idea of the algorithms consists in an approximation of the integral equation by a system of algebraic equations. Then the Markov chain iterative Monte Carlo is used to solve the system. The assumption here is that the corresponding Neumann series for the iterative matrix does not necessarily converge or converges slowly. We use a special technique to accelerate the convergence. An estimate of the computational complexity of Monte Carlo algorithm using the considered approach is obtained. The estimate of the complexity is compared with the corresponding quantity for the complexity of the grid-free Monte Carlo algorithm. The conditions under which the class of grid Monte Carlo algorithms is more efficient are given.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Condition number estimates for combined potential integral operators in acoustics and their boundary element discretisation

We consider the classical coupled, combined-field integral equation formulations for time-harmonic acoustic scattering by a sound soft bounded obstacle. In recent work, we have proved lower and upper bounds on the $L^2$ condition numbers for these formulations, and also on the norms of the classical acoustic single- and double-layer potential operators. These bounds to some extent make explicit the dependence of condition numbers on the wave number $k$, the geometry of the scatterer, and the coupling parameter. For example, with the usual choice of coupling parameter they show that, while the condition number grows like $k^{1/3}$ as $k\to\infty$, when the scatterer is a circle or sphere, it can grow as fast as $k^{7/5}$ for a class of `trapping' obstacles. In this paper we prove further bounds, sharpening and extending our previous results. In particular we show that there exist trapping obstacles for which the condition numbers grow as fast as $\exp(\gamma k)$, for some $\gamma>0$, as $k\to\infty$ through some sequence. This result depends on exponential localisation bounds on Laplace eigenfunctions in an ellipse that we prove in the appendix. We also clarify the correct choice of coupling parameter in 2D for low $k$. In the second part of the paper we focus on the boundary element discretisation of these operators. We discuss the extent to which the bounds on the continuous operators are also satisfied by their discrete counterparts and, via numerical experiments, we provide supporting evidence for some of the theoretical results, both quantitative and asymptotic, indicating further which of the upper and lower bounds may be sharper.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.