Socio-cultural dimensions of Active Futures : Importance of studying migrants’ perspectives of wellness

In this paper we provide a migrant perspective on how women and men from a different culture perceive wellness while settling down in a new country. We are discussing the texts of research interviews with Indian migrant women and men that illuminate their perception of lifestyle enhancement in their adopted country Australia. Our purpose is to show how socio-cultural factors influence the migrants‟ perspective of lifestyle enhancement, and to what extent they direct their wellness. Personal development, both in theory and practice, is a huge concept in Australia. Concerted efforts are made towards increasing public awareness about health literacy leading to a better understanding and practice of wellness. However, as research studies have pointed out, lifestyle enhancement leading to holistic wellness is not void of socio-cultural factors. The number of women and men migrating to Australia from India has increased greatly in the present decade. As migrants their participation in developing Australian society is significant. So what is their socio-cultural perception of wellness including nutrition and physical exercises as active citizens? How do young Indian migrants participate in lifestyle enhancement programmes? As parents what are their socio-cultural beliefs, attitudes, practices and values, and how do they influence their children‟s participation in personal development and PE progammes? To what extent gender differences exist in such participation levels? What is the space available in State school curriculum to learn from the migrants‟ cultures towards enhancing lifestyles including nutrition and personal development?The findings may sensitise Australian researchers, academics, school teachers and practitioners of wellness therapies. Long term research studies may inform the governments and HPE practitioners of the changes occurring in such values, beliefs and practices as they incorporate nutrition and lifestyles of Australian society.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Structural health monitoring of bridges using acoustic emission technology

Bridges are an important part of society's infrastructure and reliable methods are necessary to monitor them and ensure their safety and efficiency. Bridges deteriorate with age and early detection of damage helps in prolonging the lives and prevent catastrophic failures. Most bridges still in used today were built decades ago and are now subjected to changes in load patterns, which can cause localized distress and if not corrected can result in bridge failure. In the past, monitoring of structures was usually done by means of visual inspection and tapping of the structures using a small hammer. Recent advancements of sensors and information technologies have resulted in new ways of monitoring the performance of structures. This paper briefly describes the current technologies used in bridge structures condition monitoring with its prime focus in the application of acoustic emission (AE) technology in the monitoring of bridge structures and its challenges.

Optimal monitoring in family-owned companies? Evidence from Asia

Manuscript Type: Empirical Research Issue: We propose that high levels of monitoring are not always in the best interests of minority shareholders. In family-owned companies the optimal level of board monitoring required by minority shareholders is expected to be lower than that of other companies. This is because the relative benefits and costs of monitoring are different in family-owned companies. Research Findings: At moderate levels of board monitoring, we find concave relationships between board monitoring variables and firm performance for family-owned companies but not for other companies. The optimal level of board monitoring for our sample of Asian family-owned companies equates to board independence of 38%, separation of the Chairman and CEO positions and establishment of audit and remuneration committees. Additional testing shows that the optimal level of board monitoring is sensitive to the magnitude of the agency conflict between the family group and minority shareholders and the presence of substitute monitoring. Practitioner/Policy Implications: For policymakers, the results show that more monitoring is not always in the best interests of minority shareholders. Therefore, it may be inappropriate for regulators to advise all companies to follow the same set of corporate governance guidelines. However, our results also indicate that the board governance practices of family-owned companies are still well below the identified optimal levels. Keywords: Corporate Governance, Board Independence, Board of Directors, Family Firms, Monitoring.

Apparatus for monitoring load bearing rehabilitation exercises of a transfemoral amputee fitted with an osseointegrated fixation : a proof-of-concept study

The purpose of this proof-of-concept study was to determine the relevance of direct measurements to monitor the load applied on the osseointegrated fixation of transfemoral amputees during static load bearing exercises. The objectives were (A) to introduce an apparatus using a three-dimensional load transducer, (B) to present a range of derived information relevant to clinicians, (C) to report on the outcomes of a pilot study and (D) to compare the measurements from the transducer with those from the current method using a weighing scale. One transfemoral amputee fitted with an osseointegrated implant was asked to apply 10 kg, 20 kg, 40 kg and 80 kg on the fixation, using self-monitoring with the weighing scale. The loading was directly measured with a portable kinetic system including a six-channel transducer, external interface circuitry and a laptop. As the load prescribed increased from 10 kg to 80 kg, the forces and moments applied on and around the antero-posterior axis increased by 4 fold anteriorly and 14 fold medially, respectively. The forces and moments applied on and around the medio-lateral axis increased by 9 fold laterally and 16 fold from anterior to posterior, respectively. The long axis of the fixation was overloaded and underloaded in 17 % and 83 % of the trials, respectively, by up to ±10 %. This proof-of-concept study presents an apparatus that can be used by clinicians facing the challenge of improving basic knowledge on osseointegration, for the design of equipment for load bearing exercises and for rehabilitation programs.

Rover autonomous integrity monitoring of GNSS RTK positioning solutions with multi-constellations

This paper studies receiver autonomous integrity monitoring (RAIM) algorithms and performance benefits of RTK solutions with multiple-constellations. The proposed method is generally known as Multi-constellation RAIM -McRAIM. The McRAIM algorithms take advantage of the ambiguity invariant character to assist fast identification of multiple satellite faults in the context of multiple constellations, and then detect faulty satellites in the follow-up ambiguity search and position estimation processes. The concept of Virtual Galileo Constellation (VGC) is used to generate useful data sets of dual-constellations for performance analysis. Experimental results from a 24-h data set demonstrate that with GPS&VGC constellations, McRAIM can significantly enhance the detection and exclusion probabilities of two simultaneous faulty satellites in RTK solutions.