Valuable knowledge acquired by small manufacturing enterprises (SMEs) over time can be lost through the exit of staÚ. The diÝculty of retaining knowledge lies in the fact that part of the knowledge involved is tacit in nature and is highly, if not totally, inarticulable. This paper analyzes the explicit/tacit nature of the design knowledge involved in projects undertaken by an SME. It is analyzed to provide a model of knowledge application and a schema for eliciting the mental process during knowledge application and the types of knowledge used respectively. The tacit characteristics associated with the application of design knowledge are explored. The paper also discusses the tactics that can be employed to tackle the knowledge retention problem as experienced by the SME.

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As Cloud Computing adoption by enterprise customers grows, so too the need for optimal utilisation of their virtual resources. Likewise, cost pressures on cloud providers with a utility business model e.g. Amazon Web Services, would also need to optimise the utilisation of their physical infrastructure. Clearly, the ability to predict demand would be valuable. We introduce BoostPred, an automatic demand predictor for the cloud. BoostPred's design goals are to require no human expert intervention in making accurate predictions from noisy realworld demand signals. We evaluate the accuracy of BoostPred using noisy real-world signals which reveal its potential and current shortcomings.

Funding Acknowledgements Type of funding sources: None. Background Diabetes is a well-known risk factor for cardiovascular diseases and continues to be a global public health challenge. In Australia, prevalence rates of diabetes increase with age with almost 1 in 5 older people reported being diagnosed with diabetes. Older persons with diabetes are also more likely to have multiple comorbidities adding to their complexity. A better understanding of comorbidity patterns and their associated factors in older people with diabetes is instrumental to identify groups of individuals that differ in healthcare needs, resources utilized, and health trajectories. Purpose To identify comorbidity patterns in community-dwelling older adults with type 2 diabetes attending general practice settings in Australia. Methods This is a cross-sectional study based on the Bettering the Evaluation And Care of Health (BEACH) data. The BEACH program was a continuous, national study of the state of general practice clinical activity in Australia from 1998 to 2016. As part of the program, General Practitioners (GPs) would collect additional patient information during patient visits through structured paper-based recording sheets. For the purposes of this sub-study, a random sample of 1800 participating GPs were invited to record all diagnosed chronic conditions for 30 consecutive patients over twelve five-weeks recording periods between November 2012 and March 2016. The dataset was analyzed with descriptive analysis and exploratory factor analyses were applied to examine comorbidity patterns. Result From the dataset, there were 14 042 patients aged ≥65 with recorded chronic conditions. Of these, 2688 reported to have other comorbidities in addition to the diagnosis of diabetes. Hypertension was present in 67.33% (95% CI: 64.62 – 70.04) of these participants, followed by hyperlipidemia, 44.85% (95% CI: 41.80 –47.90), ischemic heart disease, 22.81% (95% CI: 20.74 – 24.87), atrial fibrillation, 10.25% (95% CI: 8.86 – 11.63), congestive heart failure, 7.03% (95% CI: 5.99 – 8.09), stroke/cerebrovascular accident, 6.76% (95% CI:5.36 -8.16) and peripheral vascular disease 5.26% (4.36 – 6.15). Top non-cardiovascular co-morbidities included arthritis, 51.78% (95% CI: 48.80–54.77) and depression, 15.52% (95% CI 13.78 –17.27). We identified two comorbidity patterns among older people with diabetes. The first were primarily psychological and musculoskeletal (Depression, Anxiety, Insomnia, Chronic Back Pain, Arthritis, Gastroesophageal Reflux Disease, Osteoporosis) and the second were cardiovascular conditions (Congestive Heart Failure, Ischaemic Heart Disease, Atrial Fibrillation, Peripheral Vascular Disease) and Chronic Renal Failure. Conclusion The prevalence of cardiovascular and non-cardiovascular comorbidities in older patients with diabetes was high. These findings highlight the need for elaborating primary care strategies to reduce cardiovascular risk and improve long-term care for this population.

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