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In recent years, dynamic voltage and frequency scaling (DVFS) has been considered as one of the most efficient techniques to decrease energy consumption, especially for battery-powered portable devices. However, many DVFS algorithms discuss the issue from the perspective of the processors only. Some researches have started to study the effects of memories in the DVFS algorithms. In this paper, an approximation equation (called MAR-CSE) based on the correlation of the memory access rate and the critical speed for the minimum energy consumption is conducted for frequency and voltage prediction. The memory access information is obtained from the performance monitoring unit (PMU) provided on an Intel XScale platform which we used in this study. With MAR-CSE, an MA-DVFS (Memory-aware DVFS) algorithm is proposed. The algorithm has been realized in the Linux kernel. Experiment results show that the energy consumption of the memory bound benchmarks can be reduced from 50% to 65%, much better than the result of 19% to 53% energy saving for the On-demand mechanism which is already supported by the Linux Kernel.

Background: Numerous studies have examined the association between heavy metal contamination (including arsenic [As] [Pb], and zinc [Zn]) and lung cancer. However, data from previous studies on pathological cell types are limited, particularly regarding exposure to low-dose soil heavy metal contamination. The purpose of this study was to explore the association between soil heavy metal contamination and lung cancer incidence by specific cell type in Taiwan. Methods: We conducted an ecological study and calculated the annual averages of eight soil heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) by using data from the Taiwan Environmental Protection Administration from1982 to 1986. The age-standardized incidence rates of lung cancer according to two major pathological types (adenocarcinoma [AC] and squamous cell carcinoma [SCC]) were obtained from the National Cancer Registry Program conducted in Taiwan from 2001 to 2005. A geographical information system was used to plot the maps of soil heavy metal concentration and lung cancer incidence rates. Poisson regression models were used to obtain the adjusted relative ratios (RR) and 95% confidence intervals (CI) for the lung cancer incidence associated with soil heavy metals.