The end of Dennard scaling has made all systems energy-constrained. For data-intensive applications with limited temporal locality, the major energy bottleneck is data movement between processor chips and main memory modules. For such workloads, the best way to optimize energy is to place processing near the data in main memory. Advances in 3D integration provide an opportunity to implement near-data processing (NDP) without the technology problems that similar efforts had in the past. This paper develops the hardware and software of an NDP architecture for in-memory analytics frameworks, including MapReduce, graph processing, and deep neural networks. We develop simple but scalable hardware support for coherence, communication, and synchronization, and a runtime system that is sufficient to support analytics frameworks with complex data patterns while hiding all the details of the NDP hardware. Our NDP architecture provides up to 16x performance and energy advantage over conventional approaches, and 2.5x over recently-proposed NDP systems. We also investigate the balance between processing and memory throughput, as well as the scalability and physical and logical organization of the memory system. Finally, we show that it is critical to optimize software frameworks for spatial locality as it leads to 2.9x efficiency improvements for NDP.

These results suggested that we successfully established a mouse model of ARDS with H5N1 viral infection, which may benefit further investigation into the pathogenesis of human ARDS induced by H5N1 virus.

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Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.

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Magnesium deficiency and oxidative stress have been identified as correlative factors in many diseases. The origin of free radicals correlated with oxidative damage resulting from Mg-deficiency is unclear at the cellular level. To investigate whether hydrogen peroxide (H2O2) is associated in the oxidative stress induced by Mg-deficiency, the effect of Mg2+ deficiency (0, 0.4, 0.7 mM) on the metabolism of H2O2 was investigated in cultured chick embryo hepatocytes. After being cultured in the media with various concentrations of Mg2+ for 1, 2, 4, 6 and 10 days, parameters of H2O2 production, catalase activity, lipid peroxidation, intracellular total Mg and cell viability were analyzed. Results demonstrated that long-term incubation of chick embryo hepatocyte in extracellular Mg2+-deprivative and Mg2+-deficient (0.4 mM) states significantly enhanced the production of H2O2 (approximately twofold, respectively) and lipid peroxidation in the cell cultures, while decreasing the cell viability. Additionally, the reversing action of Mg2+ re-added to 1.0 mM and the partial reversing action of dimethylthiourea suggested that (i) [Mg2+]e deficiency induced the increase of H2O2 production, (ii) [Mg2+]e deficiency decreased catalase activity in chick embryo hepatocyte in vitro, subsequently causing oxidative stress and cell peroxidative damage.

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