Gender-specific barriers and facilitators to heart failure self-care: a mixed methods study

A systematic approach to chronic heart failure care : a consensus statement

The National Heart Foundation of Australia assembled an expert panel to provide guidance on policy and system changes to improve the quality of care for people with chronic heart failure (CHF). The recommendations have the potential to reduce emergency presentations, hospitalisations and premature death among patients with CHF. Best-practice management of CHF involves evidencebased, multidisciplinary, patient-centred care, which leads to better health outcomes. A CHF care model is required to achieve this. Although CHF management programs exist, ensuring access for everyone remains a challenge. This is particularly so for Aboriginal and Torres Strait Islander peoples, those from non-metropolitan areas and lower socioeconomic backgrounds, and culturally and linguistically diverse populations. Lack of data and inadequate identifi cation of people with CHF prevents effi cient patient monitoring, limiting information to improve or optimise care. This leads to ineff ectiveness in measuring outcomes and evaluating the CHF care provided. Expanding current cardiac registries to include patients with CHF and developing mechanisms to promote data linkage across care transitions are essential. As the prevalence of CHF rises, the demand for multidisciplinary workforce support will increase. Workforce planning should provide access to services outside of large cities, one of the main challenges it is currently facing. To enhance community-based management of CHF, general practitioners should be empowered to lead care. Incentive arrangements should favour provision of care for Aboriginal and Torres Strait Islander peoples, those from lower socioeconomic backgrounds and rural areas, and culturally and linguistically diverse populations. Ongoing research is vital to improving systems of care for people with CHF. Future research activity needs to ensure the translation of valuable knowledge and high quality evidence into practice.

Abnormal mitochondrial L-arginine transport contributes to the pathogenesis of heart failure and rexoygenation injury

Impaired mitochondrial function is fundamental feature of heart failure (HF) and myocardial ischemia. In addition to the effects of heightened oxidative stress, altered nitric oxide (NO) metabolism, generated by a mitochondrial NO synthase, has also been proposed to impact upon mitochondrial function. However, the mechanism responsible for arginine transport into mitochondria and the effect of HF on such a process is unknown. We therefore aimed to characterize mitochondrial L-arginine transport and to investigate the hypothesis that impaired mitochondrial L-arginine transport plays a key role in the pathogenesis of heart failure and myocardial injury.

The effects of mechanical stress, environment and cathodic protection on the degradation and failure of coatings: An overview

Organic coatings have been used in conjunction with cathodic protection as the most economical method of corrosion protection by the oil and gas pipeline industry. In a bid to prolong the life of the pipelines, the degradation and failure of pipeline coatings under the effects of major influencing factors including mechanical stress, the environmental corrosivity and cathodic protection have been extensively investigated over the past decades. This paper provides an overview of recent research for understanding coating degradation under the effect of these factors, either individually or in combination. Electrochemical impedance spectroscopy remains the primary and the most commonly used technique of studying the degradation of organic coatings, although there have been attempts to use other techniques such as electrochemical polarization (both dynamic and static), electrochemical noise, Scanning Kelvin Probe, Fourier Transform Infrared Spectroscopy, Differential Scanning Calorimetry and Dynamic Mechanical Analyser. Major knowledge and technological gaps in the investigation of the combined effects of mechanical stress, environmental corrosivity and cathodic protection on coating degradation have been identified.

Stress-based predictions of formability and failure in incremental sheet forming

 This research investigates the deformation mechanism in incremental sheet forming (ISF) with relation to necking and failure. A strain-based forming limit criterion is widely used in sheet-metal forming industry to predict necking. However, this criterion is strictly valid only when the strain path is linear throughout the deformation process. Where the strain path in ISF is often found to be severely nonlinear throughout the deformation history. Therefore, the practice of using a strain-based forming limit criterion often leads to erroneous assessments of formability and failure prediction. On the other hands, stress-based forming limit is insensitive against any changes in the strain path and hence it is used to model the necking and fracture limits. Simulation model is evaluated for a single point incremental forming using AA 6022-T4E32 and checked the accuracy against experiments.

Application of the embedded element technique to predict interlaminar failure

In this study a modelling technique, namely, the embedded element method is assessed to evaluate its ability to predict Mode I interlaminar failure. The embedded element technique takes advantage of the embedded constraint in ABAQUS and allows the two constituents, fibre and matrix, to be meshed independently. Since the two constituents can be meshed independently a contiguous mesh is not required and the time taken to create an acceptable mesh is significantly reduced. The embedded element technique has been used to model fibre-reinforced composite structures, however, to date no studies have been conducted which combine the embedded element technique with an interlaminar damage model. The work described herein evaluates the ability of the embedded element technique to predict mode I interlaminar failure. DCB specimens were modelled using the embedded element method and a traditional 3D solid FE modelling approach with the predictions compared against experimental data. Both modelling approaches provided good agreement with experimental results. The good agreement demonstrates that the embedded element technique is capable of providing a response that is equivalent to a traditional 3D solid FE models and is particularly suited to modelling thick composite structures with complex geometry.

A new application of an evolving tree to failure mode and effect analysis methodology

Failure Mode and Effect Analysis (FMEA) is a popular safety and reliability analysis methodology for examining potential failure modes of products, process, designs, or services, in a wide range of industries. Despite its popularity, there are a number of limitations of FMEA, and two highlighted issues are the bulky FMEA form and its intricacy of use. To overcome these shortcomings, we introduce the idea of visualisation pertaining to the failure modes or control actions in FMEA. A visualisation model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes or control actions in FMEA to be clustered and visualized. The failure modes or control actions are grouped and visualized with consideration of their Severity, Occurrence, and Detection scores. Our proposed approach allows the failure modes or control actions to be mapped into a tree structure for visualisation. The devised approach is evaluated with a benchmark problem. The experiments show that the control actions of FMEA can be visualised through the tree structure, which provides a quick and easily understandable platform of the FMEA spreadsheet to facilitate decision making tasks.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups. © 2014 The Natural Computing Applications Forum.

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.

Patient self-management in chronic heart failure – establishing concordance between guidelines and practice

 Chronic heart failure (CHF) is a progressive and debilitating disease with a broad symptom profile, intermittently marked by periods of acute decompensation. CHF patients are encouraged to self-manage their illness, such as adhering to medical regimens and monitoring symptoms, to optimise health outcomes and quality of life. In so doing, patients are asked to collaborate with their health service providers with regard to their care. However, patients generally do not self-manage well, even with specialist support. Moreover, self- management interventions are yet to demonstrate morbidity or mortality benefits. Social network approaches to self-management consider the availability and mobilisation of all resources, beyond those of only the patient and healthcare providers. Used in conjunction with e-health platforms, social network approaches may offer a means by which to optimise self-management programmes of the future.

A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis

This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA.