Rib-cage-movement measurements as a potential new trigger signal in non-invasive mechanical ventilation

Non-invasive ventilation performed through an oronasal mask is a standard in clinical and homecare mechanical ventilation. Besides all its advantages, inevitable leaks through the mask cause errors in the feedback information provided by the airflow sensor and, hence, patient-ventilator asynchrony with multiple negative consequences. Here we investigate a new way to provide a trigger to the ventilator. The method is based on the measurement of rib cage movement at the onset of inspiration and during breathing by fibre-optic sensors. In a series of simultaneous measurements by a long-period fibre grating sensor and pneumotachograph we provide the statistical evidence of the 200 ms lag of the pneumo with respect the fibre-optic signal. The lag is registered consistently across three independent delay metrics. Further, we discuss exceptions from this trend and identify the needed improvements to the proposed fibre-sensing scheme.

A new method for respiratory-volume monitoring based on long-period fibre gratings

Respiratory-volume monitoring is an indispensable part of mechanical ventilation. Here we present a new method of the respiratory-volume measurement based on a single fibre-optical long-period sensor of bending and the correlation between torso curvature and lung volume. Unlike the commonly used air-flow based measurement methods the proposed sensor is drift-free and immune to air-leaks. In the paper, we explain the working principle of sensors, a two-step calibration-test measurement procedure and present results that establish a linear correlation between the change in the local thorax curvature and the change of the lung volume. We also discuss the advantages and limitations of these sensors with respect to the current standards. © 2013 IEEE.

Neuropsychiatric manifestations in inflammatory neuropathies:a systematic review

We conducted a systematic literature review on psychological and behavioral comorbidities in patients with inflammatory neuropathies. In Guillain-Barré syndrome (GBS), psychotic symptoms are reported during early stages in 30% of patients. Typical associations include mechanical ventilation, autonomic dysfunction, inability to communicate, and severe weakness. Anxiety and depression are frequent comorbidities. Anxiety may increase post-hospital admissions and be a predictor of mechanical ventilation. Post-traumatic stress disorder may affect up to 20% of ventilated patients. Sleep disturbances are common in early-stage GBS, affecting up to 50% of patients. In chronic inflammatory demyelinating polyradiculoneuropathy, memory and quality of sleep may be impaired. An independent link between depression and pre-treatment upper limb disability and ascites was reported in POEMS (Polyneuropathy, Organomegaly, Endocrinopathy, M-protein, Skin) syndrome, with an association with early death. Hematological treatment of POEMS appears effective on depression. Published literature on psychological/behavioral manifestations in inflammatory neuropathies remains scarce, and further research is needed. This article is protected by copyright. All rights reserved.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.

A biological-inspired support frame for an artificial cornea

Bilateral corneal blindness represents a quarter of the total blind, world-wide. The artificial cornea in assorted forms, was developed to replace opaque non-functional corneas and to return sight in otherwise hopeless cases that were not amenable to corneal grafts; believed to be 2% of corneal blind. Despite technological advances in materials design and tissue engineering no artificial cornea has provided absolute, long-term success. Formidable problems exist, due to a combination of unpredictable wound healing and unmanageable pathology. To have a solid guarantee of reliable success an artificial cornea must possess three attributes: an optical window to replace the opaque cornea; a strong, long term union to surrounding ocular tissue; and the ability to induce desired host responses. A unique artificial cornea possesses all three functional attributes- the Osteo-odonto-keratoprosthesis (OOKP). The OOKP has a high success rate and can survive for up to twenty years, but it is complicated both in structure and in surgical procedure; it is expensive and not universally available. The aim of this project was to develop a synthetic substitute for the OOKP, based upon key features of the tooth and bone structure. In doing so, surgical complexity and biological complications would be reduced. Analysis of the biological effectiveness of the OOKP showed that the structure of bone was the most crucial component for implant retention. An experimental semi-rigid hydroxyapatite framework was fabricated with a complex bone-like architecture, which could be fused to the optical window. The first method for making such a framework, was pressing and sintering of hydroxyapatite powders; however, it was not possible to fabricate a void architecture with the correct sizes and uniformity of pores. Ceramers were synthesised using alternative pore forming methods, providing for improved mechanical properties and stronger attachment to the plastic optical window. Naturally occurring skeletal structures closely match the structural features of all forms of natural bone. Synthetic casts were fabricated using the replamineform process, of desirable natural artifacts, such as coral and sponges. The final method of construction by-passed ceramic fabrication in favour of pre-formed coral derivatives and focused on methods for polymer infiltration, adhesion and fabrication. Prototypes were constructed and evaluated; a fully penetrative synthetic OOKP analogue was fabricated according to the dimensions of the OOKP. Fabrication of the cornea shaped OOKP synthetic analogue was also attempted.

Polycaprolactone fibres as a potential delivery system for collagen to support bone regeneration

Poly(e-caprolactone) (PCL) is biocompatible, non-immunogenic and non-toxic, and slowly degrades, allowing sufficient time for tissue regeneration. PCL has the potential for application in bone and cartilage repair as it may provide the essential structure required for bone regeneration, however, an ideal scaffold system is still undeveloped. PCL fibres were prepared using the gravity spinning technique, in which collagen was either incorporated into or coated onto the 'as-spun' fibres, in order to develop novel biodegradable polymer fibres which will effectively deliver collagen and support the attachment and proliferation of human osteoblast (HOB) cells for bone regeneration. The physical and mechanical characteristics and cell fibre interactions were analysed. The PCL fibres were found to be highly flexible and inclusion of collagen did not alter the mechanical properties of PCL fibres. Overall, HOB cells were shown to effectively adhere and proliferate on all fibre platforms tested, although proliferation rates were enhanced by surface coating PCL fibres with collagen compared to PCL fibres incorporating collagen and PCL-only fibres. These findings highlight the potential of using gravity spun PCL fibres as a delivery platform for extracellular matrix proteins, such as collagen, in order to enhance cell adherence and proliferation for tissue repair.

Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques.

RELAX:a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self-adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system.

Requirements-aware systems:a research agenda for RE for self-adaptive systems

Requirements are sensitive to the context in which the system-to-be must operate. Where such context is well-understood and is static or evolves slowly, existing RE techniques can be made to work well. Increasingly, however, development projects are being challenged to build systems to operate in contexts that are volatile over short periods in ways that are imperfectly understood. Such systems need to be able to adapt to new environmental contexts dynamically, but the contextual uncertainty that demands this self-adaptive ability makes it hard to formulate, validate and manage their requirements. Different contexts may demand different requirements trade-offs. Unanticipated contexts may even lead to entirely new requirements. To help counter this uncertainty, we argue that requirements for self-adaptive systems should be run-time entities that can be reasoned over in order to understand the extent to which they are being satisfied and to support adaptation decisions that can take advantage of the systems' self-adaptive machinery. We take our inspiration from the fact that explicit, abstract representations of software architectures used to be considered design-time-only entities but computational reflection showed that architectural concerns could be represented at run-time too, helping systems to dynamically reconfigure themselves according to changing context. We propose to use analogous mechanisms to achieve requirements reflection. In this paper we discuss the ideas that support requirements reflection as a means to articulate some of the outstanding research challenges.

RELAX:incorporating uncertainty into the specification of self-adaptive systems

Self-adaptive systems have the capability to autonomously modify their behaviour at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In this paper, we argue that a more rigorous treatment of requirements explicitly relating to self-adaptivity is needed and that, in particular, requirements languages for self-adaptive systems should include explicit constructs for specifying and dealing with the uncertainty inherent in self-adaptive systems. We present RELAX, a new requirements language for selfadaptive systems and illustrate it using examples from the smart home domain. © 2009 IEEE.

Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

QoS guarantee with adaptive transmit power and message rate control for DSRC vehicle network based road safety applications

Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method. © 2011 Springer-Verlag.

Mechanical properties and the evolution of matrix molecules in PTFE upon irradiation with MeV alpha particles

The morphology, chemical composition, and mechanical properties in the surface region of α-irradiated polytetrafluoroethylene (PTFE) have been examined and compared to unirradiated specimens. Samples were irradiated with 5.5 MeV 4He2+ ions from a tandem accelerator to doses between 1 × 106 and 5 × 1010 Rad. Static time-of-flight secondary ion mass spectrometry (ToF-SIMS), using a 20 keV C60+ source, was employed to probe chemical changes as a function of a dose. Chemical images and high resolution spectra were collected and analyzed to reveal the effects of a particle radiation on the chemical structure. Residual gas analysis (RGA) was utilized to monitor the evolution of volatile species during vacuum irradiation of the samples. Scanning electron microscopy (SEM) was used to observe the morphological variation of samples with increasing a particle dose, and nanoindentation was engaged to determine the hardness and elastic modulus as a function of a dose. The data show that PTFE nominally retains its innate chemical structure and morphology at a doses <109 Rad. At α doses ≥109 Rad the polymer matrix experiences increased chemical degradation and morphological roughening which are accompanied by increased hardness and declining elasticity. At  α doses >1010 Rad the polymer matrix suffers severe chemical degradation and material loss. Chemical degradation is observed in ToF-SIMS by detection of ions that are indicative of fragmentation, unsaturation, and functionalization of molecules in the PTFE matrix. The mass spectra also expose the subtle trends of crosslinking within the α-irradiated polymer matrix. ToF-SIMS images support the assertion that chemical degradation is the result of a particle irradiation and show morphological roughening of the sample with increased a dose. High resolution SEM images more clearly illustrate the morphological roughening and the mass loss that accompanies high doses of a particles. RGA confirms the supposition that the outcome of chemical degradation in the PTFE matrix with continuing irradiation is evolution of volatile species resulting in morphological roughening and mass loss. Finally, we reveal and discuss relationships between chemical structure and mechanical properties such as hardness and elastic modulus.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Adaptive control of hybrid battery energy storage systems under capacity fade

There is an increasing call for applications which use a mixture of batteries. These hybrid battery solutions may contain different battery types for example; using second life ex-transportation batteries in grid support applications or a combination of high power, low energy and low power, high energy batteries to meet multiple energy requirements or even the same battery types but under different states of health for example, being able to hot swap out a battery when it has failed in an application without changing all the batteries and ending up with batteries with different performances, capacities and impedances. These types of applications typically use multi-modular converters to allow hot swapping to take place without affecting the overall performance of the system. A key element of the control is how the different battery performance characteristics may be taken into account and the how the power is then shared among the different batteries in line with their performance. This paper proposes a control strategy which allows the power in the batteries to be effectively distributed even under capacity fade conditions using adaptive power sharing strategy. This strategy is then validated against a system of three different battery types connected to a multi-modular converter both with and without capacity fade mechanisms in place.