Performance of Port Facilities During the Northridge Earthquake

During the January 17, 1994, Northridge earthquake, two of the Port of Los Angeles\u27 facilities called Berths 121-126 and Pier 300 sustained moderate damage. Lateral displacement of dikes up to five inches and liquefaction of hydraulic fills were observed. Several geotechnical analyses from simplified SPT -based method to sophisticated fully-coupled analyses are presented. Observed lateral displacements are predicted reasonably well by the fully-coupled analysis procedure and an intermediate analysis procedure which incorporates some results from a fully-coupled analysis in to a simplified Newmark-type deformation analysis. The potential for higher pore pressure generation underneath the dike compared to a level ground is also discussed

Exercise interventions for preventing dementia or delaying cognitive decline in people with mild cognitive impairment

This is the protocol for a review and there is no abstract. The objectives are as follows: To evaluate the effects of exercise interventions for preventing dementia in people with mild cognitive impairment. We refer to Forbes 2015b and Forbes 2015c for the review protocols on exercise interventions for maintaining cognitive function in cognitively healthy people in mid and late life

Exercise interventions for maintaining cognitive function in cognitively healthy people in mid life

This is the protocol for a review and there is no abstract. The objectives are as follows: To evaluate the effects ofexercise interventions on cognitive function in cognitively healthy people in mid life. We refer to Forbes 2015b for the review protocol on Exercise interventions for maintaining cognitive function in cognitively healthy people in late life and to Forbes 2015c for the review protocol on Exercise interventions for prevention of dementia in people with mild cognitive impairment

Today’s older adults are cognitively fitter than older adults were 20 years ago, but when and how they decline is no different than in the past

History-graded increases in older adults' levels of cognitive performance are well documented, but little is known about historical shifts in within-person change: cognitive decline and onset of decline. We combined harmonized perceptual-motor speed data from independent samples recruited in 1990 and 2010 to obtain 2,008 age-matched longitudinal observations (M = 78 years, 50% women) from 228 participants in the Berlin Aging Study (BASE) and 583 participants in the Berlin Aging Study II (BASE-II). We used nonlinear growth models that orthogonalized within- and between-person age effects and controlled for retest effects. At age 78, the later-born BASE-II cohort substantially outperformed the earlier-born BASE cohort (d = 1.20; 25 years of age difference). Age trajectories, however, were parallel, and there was no evidence of cohort differences in the amount or rate of decline and the onset of decline. Cognitive functioning has shifted to higher levels, but cognitive decline in old age appears to proceed similarly as it did two decades ago

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p