Survivors on the edge: The lived-experience of professional musicians with playing-related injuries

The purpose of this study was to understand the lived-experience of professional instrumental musicians who have experienced playing-related injuries. This study used a hermeneutic phenomenological methodology developed to examine this lived-experience. In-depth interviews were conducted with ten professional musicians. This was followed by a focus group where preliminary findings were presented to participants and their feedback was sought. Other sources of lived-experience included participant-observation by the researcher, who is a musician and has experienced injuries; and biographic and artistic representations of musical performance and its loss, including literature, films and television. The findings were summarized in a visual representation unique to this study. The representation illustrates three roles – musician, worker and teacher – that are participated in, and disrupted by, the experience of being injured. In addition, the experience of a playing-related injury takes place within the context of a healthcare system which was perceived as insufficient to meet their needs. Specialized care was rarely available, and if available, was not local or timely; treatment operated on a fee-for-service model when many musicians had meagre incomes and lacked coverage for these services; and treatment provided often failed to allow musicians to continue to perform at the level they had previously achieved. Finally, the representation illustrated four existentials – lived time, space, body and social relations – that permeated the experience. This study suggests that improvements to healthcare delivery and education of musicians, music teachers and healthcare professionals are needed. It also suggests that occupation and the experience of flow can be detrimental to health, and this impact needs to be considered in future research and in clinical applications

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications

Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem

Evidence identification in heterogeneous data using clustering

Digital forensics faces several challenges in examining and analyzing data due to an increasing range of technologies at people\u27s disposal. The investigators find themselves having to process and analyze many systems manually (e.g. PC, laptop, Smartphone) in a single case. Unfortunately, current tools such as FTK and Encase have a limited ability to achieve the automation in finding evidence. As a result, a heavy burden is placed on the investigator to both find and analyze evidential artifacts in a heterogenous environment. This paper proposed a clustering approach based on Fuzzy C-Means (FCM) and K-means algorithms to identify the evidential files and isolate the non-related files based on their metadata. A series of experiments using heterogenous real-life forensic cases are conducted to evaluate the approach. Within each case, various types of metadata categories were created based on file systems and applications. The results showed that the clustering based on file systems gave the best results of grouping the evidential artifacts within only five clusters. The proportion across the five clusters was 100% using small configurations of both FCM and K-means with less than 16% of the non-evidential artifacts across all cases -- representing a reduction in having to analyze 84% of the benign files. In terms of the applications, the proportion of evidence was more than 97%, but the proportion of benign files was also relatively high based upon small configurations. However, with a large configuration, the proportion of benign files became very low less than 10%. Successfully prioritizing large proportions of evidence and reducing the volume of benign files to be analyzed, reduces the time taken and cognitive load upon the investigator

West Nile Virus Detection in American Crows

A dipstick immunochromatographic assay used for West Nile virus (WNV) detection in mosquitoes was investigated for application to testing of fecal, saliva, and tissue samples from dead American Crows (Corvus brachyrhynchos). Results suggest that VecTest may be an efficient method for WNV detection in field-collected, dead American Crows, although confirmation of results and further investigation are warranted

West Nile Virus Detection in American Crows

A dipstick immunochromatographic assay used for West Nile virus (WNV) detection in mosquitoes was investigated for application to testing of fecal, saliva, and tissue samples from dead American Crows (Corvus brachyrhynchos). Results suggest that VecTest may be an efficient method for WNV detection in field-collected, dead American Crows, although confirmation of results and further investigation are warranted

Early-Season Avian Deaths from West Nile Virus as Warnings of Human Infection

An analysis of 2001 and 2002 West Nile virus (WNV) surveillance data shows that counties that report WNV-infected dead birds early in the transmission season are more likely to report subsequent WNV disease cases in humans than are counties that do not report early WNV-infected dead birds

An adverbial approach for the formal specification of topological constraints involving regions with broad boundaries

Topological integrity constraints control the topological properties of spatial objects and the validity of their topological relationships in spatial databases. These constraints can be specified by using formal languages such as the spatial extension of the Object Constraint Language (OCL). Spatial OCL allows the expression of topological constraints involving crisp spatial objects. However, topological constraints involving spatial objects with vague shapes (e.g., regions with broad boundaries) are not supported by this language. Shape vagueness requires using appropriate topological operators (e.g., strongly Disjoint, fairly Meet) to specify valid relations between these objects; otherwise, the constraints cannot be respected. This paper addresses the problem of the lack of terminology to express topological constraints involving regions with broad boundaries. We propose an extension of Spatial OCL based on a geometric model for objects with vague shapes and an adverbial approach for topological relations between regions with broad boundaries. This extension of Spatial OCL is then tested on an agricultural database