Use of arc-length method for capturing mode jumping in postbuckling aerostructures

The arc-length method has become a widely established solution technique for studying nonlinear structural behavior. By augmenting the set of nonlinear equilibrium equations with a constraint equation, which is a function of both the displacements and load increment, it is capable of traversing limit points. Numerous investigations have shown that highly nonlinear behavior such as sharp "snap-backs" can still lead to numerical difficulties. Two practical examples are presented to assess the effectiveness of this solution technique in capturing secondary instabilities in postbuckling structures, which present themselves as abrupt mode jumps. Although the first example poses no special difficulties, in the second case the nonlinear procedure fails to converge. An improvement to the method's formulation is suggested, which accounts for the residual forces that are usually neglected, when proceeding to the next increment once convergence is reached on the current increment. The choice of a correct load increment at the first iteration, within a predictor-corrector scheme, is central to the method's effectiveness. Current strategies for a choice of this load increment are discussed and are shown to be no longer consistent with the modified formulation; therefore, a new approach is proposed.

Negative Aspects of Close Relationships as a Predictor of Increased Body Mass Index and Waist Circumference: The Whitehall II Study

Objectives. We investigated whether exposure to negative aspects of close relationships was associated with subsequent increase in body mass index (BMI) and waist circumference.
Methods. Data came from a prospective cohort study (Whitehall II) of 9425 civil servants aged 35 to 55 years at baseline (phase 1: 1985-1988). We assessed negative aspects of close relationships with the Close Persons Questionnaire (range 0-12) at phases 1 and 2 (1989-1990). We measured BMI and waist circumference at phases 3 (1991-1994) and 5 (1997-1999). Covariates at phase 1 included gender, age, marital status, ethnicity, BMI, employment grade, smoking, physical activity, fruit and vegetable consumption, and common mental disorder.
Results. After adjustment for sociodemographic characteristics and health behaviors, participants with higher exposure to negative aspects of close relationships had a higher likelihood of a 10% or greater increase in BMI and waist circumference (odds ratios per 1-unit increase 1.08 [95% confidence interval (CI)=1.02, 1.14; P=.007] and 1.09 [CI=1.04, 1.14; P <= .001], respectively) as well as a transition from the overweight (25 <= BMI <30) to the obese (BMI >= 30) category.
Conclusions. Adverse social relationships may contribute to weight gain.

Burnout as a predictor of all-cause mortality among industrial employees: A 10-year prospective register-linkage study

Objective: Burnout, a psychological consequence of prolonged work stress, has been shown to coexist with physical and mental disorders. The aim of this study was to investigate whether burnout is related to all-cause mortality among employees. Methods: In 1996, of 15,466 Finnish forest industry employees, 9705 participated in the 'Still Working' study and 8371 were subsequently identified from the National Population Register. Those who had been treated in a hospital for the most common causes of death prior to the assessment of burnout were excluded on the basis of the Hospital Discharge Register, resulting in a final study population of 7396 people. Burnout was measured using the Maslach Burnout Inventory-General Survey. Dates of death from 1996 to 2006 were extracted from the National Mortality Register. Mortality was predicted with Cox hazard regression models, controlling for baseline sociodemographic factors and register-based health status according to entitled medical reimbursement and prescribed medication for mental health problems, cardiac risk factors, and pain problems. Results: During the 10-year 10-month follow-up, a total of 199 employees had died. The risk of mortality per one-unit increase in burnout was 35% higher (95% CI 1.07-1.71) for total score and 26% higher (0.99-1.60) for exhaustion, 29% higher for cynicism (1.03-1.62), and 22% higher for diminished professional efficacy (0.96-1.55) in participants who had been under 45 at baseline. After adjustments, only the associations regarding burnout and exhaustion were statistically significant. Burnout was not related to mortality among the older employees. Conclusion: Burnout, especially work-related exhaustion, may be a risk for overall survival. (C) 2010 Elsevier Inc. All rights reserved.

Fetal Umbilical Artery Doppler Pulsatility Index as a Predictor of Cardiovascular Risk Factors in Children - A Long-Term Follow Up Study

Abstract Objective To determine if high umbilical artery Doppler (UAD) pulsatility index (PI) is associated with cardio-vascular (CV) risk-factors in children at age 12 years. Methods We studied 195 children at age 12 years who had had in-utero UAD studies performed at 28 weeks gestation. The children were grouped according to whether their umbilical Doppler PI was high (indicating poor feto-placental circulation) or normal. At age 12 years we assessed CV risk factors, including anthropometric measures, blood pressure, pulse wave velocity (a measure of arterial compliance), cardio-respiratory fitness and homocysteine and cholesterol serum levels. Results Compared with children with a normal UAD PI (N=88), the children (N=107) with high UAD PI had higher resting pulse rate (p=0.04), higher pulse wave velocity (p=0.046), higher serum homocysteine levels (p=0.032) and reduced arterial compliance (7.58 v 8.50 m/sec, p=0.029) using univariate analysis. These differences were not present when adjusting for cofounders was modelled. Conclusion High PI on UAD testing in-utero may be associated with increased likelihood of some cardio-vascular risk factors at age 12-years but confounding variables may be as important. Our study raises possible long-term benefits of in-utero UAD measurements.

PET-CT as a predictor of outcome in resectable colorectal liver metastases

Introduction
PET-computed tomography (PET-CT) is a useful staging imaging modality in colorectal liver metastases (CRLM). This study aimed to determine whether PET-CT parameters, standardized uptake value (SUV) and reconstructed tumour volume (RTV), are predictors of prognosis and survival.

Methods
A study of all resectable CRLM patients in the regional HPB unit from 2007–2009 was performed. Preoperative PET-CT scans were retrospectively reviewed; SUV, diameter and RTV for each lesion was recorded. Correlation analysis was performed with other pathological and biochemical parameters, by Pearson’s correlation analysis. Survival analysis was performed using Cox regression hazard model. A P value of less than 0.05 was considered statistically significant.

Results
A total of 79 patients were included. SUV moderately correlated with tumour diameter, both PET-CT (r=0.4927; P<0.0001) and histology (r=0.4513; P=0.0003); RTV (r=0.4489; P<0.001), preoperative carcinoembryonic antigen (CEA) (r=0.4977; P=0.0001), and postoperative CEA (r=0.3727; P=0.004). Multivariate analysis found that an independent predictor of SUVmax was preoperative CEA (P=0.03). RTV strongly correlated with preoperative CEA (r=0.9389; P<0.0001). SUV and RTV had a negative effect on survival.

Conclusion
PET-CT, in the setting of CRLM, may have a prognostic role in assessing survival. Although no definite conclusions can be drawn regarding the prognostic role of SUV and RTV, it acts to reinforce the need for further prospective studies to validate these findings.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Estimated Glomerular Filtration Rate Decline as a Predictor of Dialysis in Kidney Transplant Recipients

BACKGROUND: It is now common for individuals to require dialysis following the failure of a kidney transplant. Management of complications and preparation for dialysis are suboptimal in this group. To aid planning, it is desirable to estimate the time to dialysis requirement. The rate of decline in the estimated glomerular filtration rate (eGFR) may be used to this end.

METHODS: This study compared the rate of eGFR decline prior to dialysis commencement between individuals with failing transplants and transplant-naïve patients. The rate of eGFR decline was also compared between transplant recipients with and without graft failure. eGFR was calculated using the four-variable MDRD equation with rate of decline calculated by least squares linear regression.

RESULTS: The annual rate of eGFR decline in incident dialysis patients with graft failure exceeded that of the transplant-naïve incident dialysis patients. In the transplant cohort, the mean annual rate of eGFR decline prior to graft failure was 7.3 ml/min/1.73 m(2) compared to 4.8 ml/min/1.73 m(2) in the transplant-naïve group (p < 0.001) and 0.35 ml/min/1.73 m(2) in recipients without graft failure (p < 0.001). Factors associated with eGFR decline were recipient age, decade of transplantation, HLA mismatch and histological evidence of chronic immunological injury.

CONCLUSIONS: Individuals with graft failure have a rapid decline in eGFR prior to dialysis commencement. To improve outcomes, dialysis planning and management of chronic kidney disease complications should be initiated earlier than in the transplant-naïve population.

An HMM-based spectrum occupancy predictor for energy efficient cognitive radio

Spectrum sensing is the cornerstone of cognitive radio technology and refers to the process of obtaining awareness of the radio spectrum usage in order to detect the presence of other users. Spectrum sensing algorithms consume considerable energy and time. Prediction methods for inferring the channel occupancy of future time instants have been proposed as a means of improving performance in terms of energy and time consumption. This paper studies the performance of a hidden Markov model (HMM) spectrum occupancy predictor as well as the improvement in sensing energy and time consumption based on real occupancy data obtained in the 2.4GHz ISM band. Experimental results show that the HMM-based occupancy predictor outperforms a kth order Markov and a 1-nearest neighbour (1NN) predictor. Our study also suggests that by employing such a predictive scheme in spectrum sensing, an improvement of up to 66% can be achieved in the required sensing energy and time.

Education as a Predictor of Antidepressant and Anxiolytic medication use after Bereavement: a population based record linkage study.

Purpose: Educational attainment has been shown to be positively associated with mental health and a potential buffer to stressful events. One stressful life event likely to affect everyone in their lifetime is bereavement. This paper assesses the effect of educational attainment on mental health post bereavement.
Methods: By utilising large administrative datasets, linking Census returns to death records and prescribed medication data, we analysed the bereavement exposure of 208,332 individuals aged 25-74 years. Two-level multi-level logistic regression models were constructed to determine the likelihood of antidepressant medication use (a proxy of mental ill-health) post bereavement given level of educational attainment.
Results: Individuals who are bereaved have greater antidepressant use than those who are not bereaved, with over a quarter (26.5%) of those bereaved by suicide in receipt of antidepressant medication compared to just 12.4% of those not bereaved. Within individuals bereaved by a sudden death those with a University Degree or higher qualifications are 73% less likely to be in receipt of antidepressant medication compared to those with no qualifications, after full adjustment for demographic, socio-economic and area factors (OR=0.27, 95% CI 0.09,0.75). Higher educational attainment and no qualifications have an equivalent effect for those bereaved by suicide.
Conclusions: Education may protect against poor mental health, as measured by the use of antidepressant medication, post bereavement, except in those bereaved by suicide. This is likely due to the improved cognitive, personal and psychological skills gained from time spent in education.