BackgroundLimb amputation is often an inevitable procedure in the advanced condition of various diseases and poses a dramatic impact on a patient's life. The aim of the present study is to analyze the impact of lower-limb amputations on aesthetic factors such as body image and self-esteem as well as quality of life (QoL).Methods298 patients (149 uni- or bilateral lower-limb amputees and 149 controls) were included in this cross-sectional study in three centers. Demographic data was collected and patients received a 118-item questionnaire including the Multidimensional Body-Self Relations Questionnaire (MBSRQ), the Rosenberg Self-esteem (RSE) scale and the SF-36 Health Survey (QoL). ANOVA and student's t-test were used for statistical analysis.ResultsUnilateral lower-limb amputees showed a significant lower MBSRQ score of 3.07±0.54 compared with 3.41±0.34 in controls (p<0.001) and a lower score in the RSE compared to controls (21.63±4.72 vs. 21.46±5.86). However, differences were not statistically significant (p = 0.36). Patients with phantom pain sensation had a significantly reduced RSE (p = 0.01). The SF-36 health survey was significantly lower in patients with lower-limb amputation compared to controls (42.17±14.47 vs. 64.05±12.39) (p<0.001).ConclusionThis study showed that lower-limb amputations significantly influence patients' body image and QoL. Self-esteem seems to be an independent aspect, which is not affected by lower-limb amputation. However, self-esteem is influenced significantly by phantom pain sensation.

No abstract

In this article, Bayesian networks are used to model semiconductor lifetime data obtained from a cyclic stress test system. The data of interest are a mixture of log-normal distributions, representing two dominant physical failure mechanisms. Moreover, the data can be censored due to limited test resources. For a better understanding of the complex lifetime behavior, interactions between test settings, geometric designs, material properties, and physical parameters of the semiconductor device are modeled by a Bayesian network. Statistical toolboxes in MATLAB® have been extended and applied to find the best structure of the Bayesian network and to perform parameter learning. Due to censored observations Markov chain Monte Carlo (MCMC) simulations are employed to determine the posterior distributions. For model selection the automatic relevance determination (ARD) algorithm and goodness-of-fit criteria such as marginal likelihoods, Bayes factors, posterior predictive density distributions, and sum of squared errors of prediction (SSEP) are applied and evaluated. The results indicate that the application of Bayesian networks to semiconductor reliability provides useful information about the interactions between the significant covariates and serves as a reliable alternative to currently applied methods.

Reliable semiconductor devices are of paramount importance as they are used in safety relevant applications. To guarantee the functionality of the devices, various electrical measurements are analyzed and devices outside pre-defined specification limits are scrapped. Despite numerous verification tests, risk devices (Mavericks) remain undetected. To counteract this, remedial actions are given by statistical screening methods, such as Part Average Testing and Good Die in Bad Neighborhood. For new semiconductor technologies it is expected that, due to the continuous miniaturization of devices, the performance of the currently applied screening methods to detect Mavericks will lack accuracy. To meet this challenge, new screening approaches are required. Therefore, we propose to use a data transformation which analyzes information sources instead of raw data. First results confirm that Independent Component Analysis extracts meaningful measurement information in a compact representation to enhance the detection of Mavericks.

No abstract