The cemented custom femoral stem--a 10 year review

We report a series of 706 patients (759 hip implants) with an average follow up of 10.5 years (range, 10-11 years) following total hip replacement (THR) using a cemented custom-made femoral stem and a cemented HDP acetabular component. The fate of every implant is known. One hundred and seventy-four patients (23%) were deceased at the time of their 10-year review all died with a functioning THR in situ. Four hundred and sixty-two patients (61%) were subsequently reviewed. One hundred and twenty three patients (16%) were assessed by telephone review, as they were too ill or unwilling to attend. Kaplan-Meier survival analysis (all components) demonstrated a median survival at 10 years of 96.05% or 95% Confidence Intervals (CI) for median survival of (94.41% to 97.22%). Revision surgery occurred in 30 cases (3.9%). Seventeen had full revisions (2.2%) and 13 (1.7%) socket revisions only. Twenty-one out of 30 revisions were for infection or dislocation. There were 2 cases (0.3%) of revision for aseptic loosening of the stem. The 10-year results of the custom femoral titanium stem are encouraging and compare well with other cemented systems.

A Significance-Driven Programming Framework for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for energy-con\-strained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.
We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints. Evaluation on a dual-socket 8-core server shows that the proposed
framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.