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The purpose of this paper is to evaluate the health‐states of unit under test (UUT) in aerospace systems by means of unreliable test outcomes, and the evaluation results can provide a guide for engineers to carry out proper maintenance prior to total failure.

In this paper, the authors formulate the health‐state evaluation (HSE) problem with unreliable test outcomes based on Bayes rule, and develop the Lagrangian relaxation and adaptive genetic algorithm (LRAGA) to solve it. The solution scheme can be viewed as a two‐level coordinated solution framework for the HSE problem. At the top level, the Lagrange multipliers are updated by using AGA. At the bottom level, each of the sub‐problems is solved by using AGA.

The experimental results show that the HSE model appears promising and the LRAGA can obtain the higher quality solution and converge to it at a faster rate than conventional methods (i.e. Lagrangian relaxation (LR), genetic algorithm (GA), simulated annealing (SA) and Lagrangian relaxation and genetic algorithm (LRGA).

The proposed method for the HSE problem of large‐scale systems which include thousands of faults and tests needs to be verified further.

The HSE results for aerospace systems can help engineers to carry out a schedule for prompt maintenance prior to UUTs' failure, to avoid the consequences of total failure. It is important to improve aerospace systems' safety, reliability, maintainability, affordability, and reduce life cycle cost.

This paper constructs the HSE model with unreliable test outcomes based on the Bayes rule and proposes a method based on LRAGA to solve the HSE problem.

Purpose – This chapter offers an integrative review of psychological and neurobiological differences between younger and older adults that might impact economic behavior. Focusing on key health economic challenges facing the elderly, it offers perspectives on how these psychological and neurobiological factors may influence decision-making over the life course and considers future interdisciplinary research directions.

Methodology/approach – We review relevant literature from three domains that are essential for developing a comprehensive science of decision-making and economic behavior in aging (psychology, neuroscience, and economics), consider implications for prescription drug coverage and long-term care (LTC) insurance, and highlight future research directions.

Findings – Older adults face many complex economic decisions that directly affect their health and well-being, including LTC insurance, prescription drug plans, and end of life care. Economic research suggests that many older Americans are not making cost-effective and economically rational decisions. While economic models provide insight into some of the financial incentives associated with these decisions, they typically do not consider the roles of cognition and affect in decision-making. Research has established that older age is associated with predictable declines in many cognitive functions and evidence is accumulating that distinct social motives and affect-processing profiles emerge in older age. It is unknown how these age differences impact the economic behaviors of older people and implies opportunities for path-breaking interdisciplinary research.

Originality/value of the chapter – Our chapter looks to develop interdisciplinary research to better understand the causes and consequences of age-related changes in economic decision-making and guide interventions to improve public programs and overall social welfare.

This paper aims to report findings from the first year of the Vanguard Laundry evaluation study, which explores the impacts of a work integration social enterprise (WISE) on health.

Drawing on social determinants of health (SDH) conceptual framework, a mixed-methods longitudinal case study is used to evaluate the health impacts of a WISE employing people with lived experience of mental illness. In stage one of this study, 31 participants including staff and managers completed semi-structured interviews, validated health and well-being measures and a questionnaire based on the organisation’s theory of change. Quantitative data were analysed descriptively, and qualitative data are analysed thematically.

Data provided further evidence of the conceptualised WISE pathways for impact on SDH through employment, social integration, increased income, improved social position and living conditions. While social enterprise (SE) beneficiary staff had lower well-being scores than other staff members and management, the majority reported improved health, increased social supports and enhanced well-being through improved material conditions and increased sense of purpose, confidence and social connection.

Stage one of this study relied on self-reported data. Future stages will incorporate income support and health data extracted from government agencies with participant consent.

This paper responds to calls for longitudinal studies that include a range of participants and use health outcome measures to further explore how a WISE impacts health through acting on SDH.

The measurement of the costs of crime is an increasingly important topic in established industrial economies. Such costs imply a substantial loss in both tangible and intangible productivity, opportunity cost, resource use and quality of life. Here, we summarise the results of the latest research in the UK, and show that researchers are using increasingly accurate costs and indices analyses to allow the calculation of the costs of criminal activity.

This study aims to explore the use and relevance of WALYs (well-being-adjusted life years) in light of the utilitarian premises of neoclassical economics that continue to dominate health outcomes evaluation. QALYs (quality-adjusted life years) and DALYs (disability-adjusted life years) measure longevity and quality of life in terms of purely health-related aspects and outcomes of medical interventions. However, evaluative questions of subjective well-being may be equally important in comparing outcomes and cost-effectiveness of these interventions.

A two-phase online search strategy for refereed research on dry eye treatment with omega-3 fatty acids (omega-3s) was adopted. Phase I aimed to identify and contrast clinical parameters of efficacy in omega-3 dietary supplementation. Phase II aimed to find a preference-based, multi-attribute utility instrument specific and sensitive enough to dry eye and its consequences on patients’ subjective well-being. We then illustrate how WALYs can be conceptualized and calculated based on the search results.

Empiric therapies like omega-3s can be assessed in terms of reducing or relieving symptomatic discomfort and pain, and enabling the patient to enjoy life and derive satisfaction from daily activities. We find in VisQoL (Vision and Quality of Life Index) a viable alternative to conventional multi-attribute utility instruments, including those typically used in QALY and DALY calculations. Clinical efficacy indices of dry eye can be linked to VisQoL’s quality of life dimensions. Differently weighted outcomes can be aggregated. And WALYs per patient per year can be computed by scaling aggregated outcomes to match the WALY rating scale. The implications of subjective well-being for both patient and society can thus be approached from a broader and richer perspective.

To the best of our knowledge, this is the first study of its kind in pharmaceutical outcomes valuation and marketing. It offers a framework for analyzing life satisfaction and well-being among dry eye patients under treatment. It is also the first to use and adapt a multi-attribute utility measure to treatment outcomes of omega-3s in ocular diseases, from which this study suggests WALYs may be computed. However, it does not suggest that WALYs should supplant QALYs and DALYs in evaluating health outcomes. Medical economics is enriched if alternative methods of outcomes evaluations can help fill in the gaps in existing paradigms and do so by accounting for other effects of condition-specific interventions. Costs and benefits of interventions to the individual and society can then be valued not just more effectively, but also more equitably.

This paper aims to optimize the maintenance scheduling of emergency rescue wagons in railway companies using a genetic algorithm (GA). It offers an integrated model for simultaneously solving maintenance planning, preventive maintenance, prognostic information and resource planning from which optimal levels of the system performance in terms of cost and repair time could be determined.

This study initially evaluates the previous types of research in presenting the optimal model of the rescue train wagon for maintenance and repair planning and lists the identified criteria based on experts' opinions using fuzzy analytic hierarchy process (FAHP) and stepwise weight assessment ratio analysis (SWARA) techniques. Then, the final weight of the desired criteria is calculated. Later, the final decision matrix is evaluated by the experts. The final normal decision matrix is formed to select the optimal maintenance and repair planning plan based on the GA. Finally, two strategies including joint optimization strategy of preventive maintenance planning are compared with the independent preventive maintenance planning strategy.

According to the primary results, three primary parameters, including technology, human damage caused by negligence and the average failure rate, should be considered for launching the GA model. Based on the results of the second part; by comparing the preventive maintenance planning strategy independently, the joint optimization strategy reduces the total cost of production up to 8.25%. Comparison results show that the total cost of joint optimization production is less than independent preventive maintenance and repair planning. Moreover, the value of total process time in joint optimization strategy was reduced by 1.2% compared to independent preventive maintenance (PM) planning (from 137.90 to 136.20 h).

The novelty of this paper lies on the application of the GAs to develop an optimized PM scheduling to localize the maintenance planning for maximizing productivity, avoid train accidents, reduce costs and increase efficiency and capacity.

Health outcomes are often described according to two dimensions: quality of life and quantity of life. We analyze the measurement of inequality of health distributions referring to these two dimensions. Our analysis relies on a novel treatment of the quality-of-life dimension, which might not have a standard mathematical structure. We single out two families of (absolute and relative) multidimensional health inequality indices, inspired by the classical normative approach to income inequality measurement. We also discuss how to extend the analysis to deal with the related problem of health deprivation measurement in this setting.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The purpose of this paper is the development of an empirically based typology of condition based maintenance (CBM) approaches, including the relevant characteristics and requirements.

An exploratory case study was conducted in a major gas production facility. The CBM typology that resulted from this case study was subsequently tested against a large set of CBM literature.

In the literature, CBM is usually presented as a single theory or practice. The paper finds that CBM in fact includes several different approaches and that each of the approaches is only suitable in situations where the specific characteristics of the approach match the situational characteristics. Aided by these findings, a new typology for CBM was developed. The typology is based on the method for obtaining the expected value, or trend (through statistical vs analytical modeling) and the type of data used (process vs failure data). A subsequent literature survey reveals that the proposed typology is applicable for the categorization of a large number of CBM cases found in the literature.

One of the most important requirements in selecting and using a CBM approach is the availability and integration of various types of knowledge, in particular process engineering and maintenance engineering knowledge. Practitioners can use these insights to assess current CBM cases, and identify the key characteristics of current and future use of various CBM types.

This paper presents a novel and empirically based framework for the classification of the different CBM types. Such frameworks were lacking in the current literature. The paper adds to maintenance engineering literature by identifying the key dimensions of the various types along with their key requirements.

This research aims to study the materials that compose older reinforced concrete bridges which are damaged and degrading to explain the mechanisms and origins of various disorders. Therefore, this work will contribute to providing answers on the capacity of nondestructive evaluation method during the diagnosis. In addition to the characterization of affected structures, it will aim to provide effective solutions for different serious pathologies.

In this context, two bridges located on NH16 and NH21, respectively, were studied in Annaba city (north-east Algeria), specifically in El-Hadjar municipality located in the central industrial zone of Pont-Bouchet. This study makes it possible to make conclusions from the in-depth diagnosis based on disorders exposition causes and mechanical characteristics evolution by non-destructive testing (NDT) tools. Furthermore, solutions are proposed, including conservation maintenance of these degraded structures.

All degradations can be the result of several factors: either human (poor design) or chemical (surface water, wastewater and groundwater quality (acidic or basic)). In addition to other natural causes (geological formations, flood phenomena or climate), NDT tools play a major role in the evaluating mechanical performance of degraded structures (resistance and hardness).

The NDT techniques can be transmitted to civil engineering experts because their training is limited regarding mechanical and structural construction.

NDT tools are the most suitable for in-situ assessing, and the concrete constructions health state, so far from financial problems.

Degraded bridge diagnosis by NDT testing is necessary for a thorough safety evaluation (mechanical performance, strength and deformability), to protect human lives and design durability.

This is an original paper which contains new information at different scales and from special fields, based on an evaluation using NDT tools on real degraded structures. It can be used to improve the knowledge of materials employed in a bridge without performing expensive direct tests or the need for destroying it.