A Parallel Dynamic Binary Translator for Efficient Multi-Core Simulation

In recent years multi-core processors have seen broad adoption in application domains ranging from embedded systems through general-purpose computing to large-scale data centres. Simulation technology for multi-core systems, however, lags behind and does not provide the simulation speed required to effectively support design space exploration and parallel software development. While state-of-the-art instruction set simulators (Iss) for single-core machines reach or exceed the performance levels of speed-optimised silicon implementations of embedded processors, the same does not hold for multi-core simulators where large performance penalties are to be paid. In this paper we develop a fast and scalable simulation methodology for multi-core platforms based on parallel and just-in-time (Jit) dynamic binary translation (Dbt). Our approach can model large-scale multi-core configurations, does not rely on prior profiling, instrumentation, or compilation, and works for all binaries targeting a state-of-the-art embedded multi-core platform implementing the ARCompact instruction set architecture (Isa). We have evaluated our parallel simulation methodology against the industry standard Splash-2 and Eembc MultiBench benchmarks and demonstrate simulation speeds up to 25,307 Mips on a 32-core x86 host machine for as many as 2,048 target processors whilst exhibiting minimal and near constant overhead, including memory considerations.     

An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills,Sudan

The effectiveness of spectral and textural information in the identification of surface rock types in an arid region, the Red Sea Hills of Sudan, is evaluated using spectral information from the six Landsat TM optical bands and textural features derived from Shuttle Imaging Radar-C (SIR-C) C-band HH polarization data. An initial classification is derived from Landsat TM data alone using three classification algorithms, Gaussian maximum likelihood, a multi-layer feed-forward neural network and a Kohonen self-organizing feature map (SOM), to generate lithological maps, with classification accuracy being measured using a confusion matrix approach. The feed-forward neural net produced the highest overall classification accuracy of 57 per cent and was, therefore, selected for the second experiment, in which texture measures from SIR-C C-band HH-polarized synthetic aperture radar (SAR) data are added to selected TM spectral features. Four methods of measuring texture are employed, based on the Fourier power spectrum, grey level co-occurrence matrix (GLCM), multi-fractal measures, and the multiplicative autoregressive random field (MAR) model. The use of textural information together with a subset of the TM spectral features leads to an increase in classification accuracy to almost 70 per cent. Both the MAR model and the GLCM matrix approach perform better than Fourier and multi-fractal based methods of texture characterization.     

An optimal approach to the connection admission control problem

The paper considers the connection admission control (CAC), which is a key resource management procedure, and proposes a solution to the problem based on modelling and control methodologies. The CAC problem will be formulated as an optimal control problem subject to a set of constraints. As a matter of fact, the proposed controller, modelling the CAC mechanism, computes the above-mentioned control variables so that (i) a set of proper constraints, which model the quality of service (QoS) requirements (link availability, blocking probability and dropping probability), are respected and (ii) a proper performance index, which models the exploitation degree of the available bandwidth, is maximized. The proposed CAC successfully compares with other CACs proposed in the literature, and in particular significantly extends the upper limit of the accepted traffic rate.     

Exercise capacity and mid-term survival in patients with tricuspid atresia and complex congenital cardiac malformations after modified Fontan-operation

A 68-year-old man experienced systemic pruritus since he was 63 years old, and systemic sclerosis and skin pigmentation were observed when he was 64. When he developed dyspnea the same year, he was admitted and SSc was diagnosed on the basis of the clinical and skin biopsy findings, lung fibrosis on X-P and TBLB findings. At 65, his dyspnea reappeared along with elevated blood pressure, acute renal failure and lung congestion, and he was diagnosed as having a scleroderma renal crisis (SRC) from the clinical and renal biopsy findings. Hemodialysis was started because he showed mental disturbance, and this and other acute symptoms were subsequently reduced. As he showed no recovery from his renal failure, the patient has been maintained on hemodialysis for over four years now. In the meantime, his sclerosis has improved and antinuclear antibody almost disappeared. Hemodialysis appears to be the most likely reason for his improvement, although spontaneous remission, D-penicillamine and angiotensin converting enzyme (ACE) inhibitor therapy may also have contributed, considering the short period and the small amount of drugs given until improvement.     

It is time to move from the nursing process to critical thinking

As we reflect on the concept of critical thinking in perioperative nursing practice, we should ask ourselves whether we think critically. If not, we must learn the principles of critical thinking and apply them in our clinical practice settings. Our perioperative nurse managers and directors must demonstrate critical thinking in leading other nurses in the delivery of quality, cost-effective, service-oriented patient care. They also must identify peers who use critical thinking skills to support and sustain them in their quest.