Establishing and maintaining a system of quality control for a CPA firm\u27s accounting and auditing practice

https://egrove.olemiss.edu/aicpa_guides/1204/thumbnail.jp

Effect of Leisure Activity on Quality of Life in Post-Stroke Patients

Background: Stroke can cause impairment and disability so that the patient will have difficulty in doing daily activities, including self-care, productivity, and leisure time usage. This condition can decrease the quality of life of the post-stroke patient. This study aimed to determine the effect of leisure activity on the quality of life of post-stroke patients. Subjects and Method: This was a quasi experiment with pre and post design conducted in Ngemplak, Boyolali, Central Java. A sample of 25 post stroke patients was selected for this study by purposive sampling. The dependent variable was quality of life including physical health, psychological health, social relation, and environment. The quality of life was measured before and after intervention. The independent variable was leisure activity. The data were analyzed by Wilcoxon. Results: 11 people (44%) of the study subjects were elderly, 16 (65%) were male, 20 (80%) had married / had spouse, 8 (32%) did not work, and 10 (40%) suffered stroke over 24 months. Physical health after intervention (mean= 3.36, SD= 0.76) was higher than before intervention (mean= 2.56, SD= 0.77) with p= 0.002. The psychological condition after intervention (mean= 3.44; SD= 0.58) was higher than before intervention (mean= 2.44, SD= 0.82) with p= 0.001. The social relationship after intervention (mean= 3.16; SD= 0.69) was higher than before intervention (mean= 1.92; SD= 0.81) with p= 0.001. The environment after intervention (mean= 3.00; SD= 0.58) was higher than before intervention (mean = 2.40; SD= 0.58) with p<0.001. Conclusion: Leisure activity seems effective to improve the quality life of post stroke patients. A randomized control trial is recommended to provide stronger evidence on this effectiveness. Keywords: leisure activity, quality of life, post stroke, patien

Audio impairment recognition using a correlation-based feature representation

Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, hand-crafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy

Where should patient safety be installed?

The structure of an organization is important, and structure has a profound influence on the way people work and what gets done. Where work units and individuals in an organization are placed, to whom they report, and with whom they are grouped signals power, prestige, and privilege. It also divides workers into groups with common interests and motivations. The question is, where should patient safety be placed in a health care organization? Such a question can be answered only within a framework of understanding that gives a clear definition to patient safety. We define safety, as do safety professionals from other industries, as the reduction of risk. This definition is also in concert with the risk management model that identifies claims management, risk financing, and loss control as its foundational triad.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141505/1/jhrm21285_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141505/2/jhrm21285.pd