Real effects of inflation uncertainty in the US

We empirically investigate the effects of inflation uncertainty on output growth for the US using both monthly and quarterly data over 1985-2009. Employing a Markov regime switching approach to model output dynamics, we show that inflation uncertainty obtained from a Markov regime switching GARCH model exerts a negative and regime dependant impact on output growth. In particular, we show that the negative impact of inflation uncertainty on output growth is almost 4.5 times higher during the low growth regime than that during the high growth regime. We verify the robustness of our findings using quarterly data

The impact of inflation uncertainty on economic growth: a MRS-IV approach

We empirically investigate inflation uncertainty effects on output growth for the US by implementing a Markov regime switching model as we account for endogeneity problems. We show that inflation uncertainty -obtained from a Markov regime switching GARCH model - has a negative and regime dependent impact on output growth. Moreover, we find that the smooth probability of high growth regime falls long before the recent financial crisis was imminent. This might be driven by a regime dependent causality, an issue which has been left unexplored

Using data compression for increasing memory system utilization

Cataloged from PDF version of article.The memory system presents one of the critical challenges in embedded system design and optimization. This is mainly due to the ever-increasing code complexity of embedded applications and the exponential increase seen in the amount of data they manipulate. The memory bottleneck is even more important for multiprocessor-system-on-a-chip (MPSoC) architectures due to the high cost of off-chip memory accesses in terms of both energy and performance. As a result, reducing the memory-space occupancy of embedded applications is very important and will be even more important in the next decade. While it is true that the on-chip memory capacity of embedded systems is continuously increasing, the increases in the complexity of embedded applications and the sizes of the data sets they process are far greater. Motivated by this observation, this paper presents and evaluates a compiler-driven approach to data compression for reducing memory-space occupancy. Our goal is to study how automated compiler support can help in deciding the set of data elements to compress/decompress and the points during execution at which these compressions/decompressions should be performed. We first study this problem in the context of single-core systems and then extend it to MPSoCs where we schedule compressions and decompressions intelligently such that they do not conflict with application execution as much as possible. Particularly, in MPSoCs, one needs to decide which processors should participate in the compression and decompression activities at any given point during the course of execution. We propose both static and dynamic algorithms for this purpose. In the static scheme, the processors are divided into two groups: those performing compression/decompression and those executing the application, and this grouping is maintained throughout the execution of the application. In the dynamic scheme, on the other hand, the execution starts with some grouping but this grouping can change during the course of execution, depending on the dynamic variations in the data access pattern. Our experimental results show that, in a single-core system, the proposed approach reduces maximum memory occupancy by 47.9% and average memory occupancy by 48.3% when averaged over all the benchmarks. Our results also indicate that, in an MPSoC, the average energy saving is 12.7% when all eight benchmarks are considered. While compressions and decompressions and related bookkeeping activities take extra cycles and memory space and consume additional energy, we found that the improvements they bring from the memory space, execution cycles, and energy perspectives are much higher than these overheads

Compiler-directed energy reduction using dynamic voltage scaling and voltage Islands for embedded systems

Cataloged from PDF version of article.Addressing power and energy consumption related issues early in the system design flow ensures good design and minimizes iterations for faster turnaround time. In particular, optimizations at software level, e.g., those supported by compilers, are very important for minimizing energy consumption of embedded applications. Recent research demonstrates that voltage islands provide the flexibility to reduce power by selectively shutting down the different regions of the chip and/or running the select parts of the chip at different voltage/frequency levels. As against most of the prior work on voltage islands that mainly focused on the architecture design and IP placement related issues, this paper studies the necessary software compiler support for voltage islands. Specifically, we focus on an embedded multiprocessor architecture that supports both voltage islands and control domains within these islands, and determine how an optimizing compiler can automatically map an embedded application onto this architecture. Such an automated support is critical since it is unrealistic to expect an application programmer to reach a good mapping correlating multiple factors such as performance and energy at the same time. Our experiments with the proposed compiler support show that our approach is very effective in reducing energy consumption. The experiments also show that the energy savings we achieve are consistent across a wide range of values of our major simulation parameters

Assessment of plasma nitric oxide concentration and erythrocyte arginase activity in dairy cows with traumatic reticuloperitonitis

ΔΕΝ ΔΙΑΤΙΘΕΤΑΙ ΠΕΡΙΛΗΨΗThe aim of this study was to evaluate plasma nitric oxide (NO) concentrations, erythrocyte arginase (ARG) activity, plasma fibrinogen (Fb) and serum iron (Fe) levels and some biochemical parameters in dairy cows with traumatic reticuloperitonitis (TRP). The animal material of the study consisted of 14 Swiss Brown cows diagnosed with TRP (TRP group) between 4-8 years old brought to Firat University Animal Hospital Clinics and 14 healthy Swiss Brown cows (control group) aged 4-8 years obtained from dairy farms in different regions. Blood samples were taken from the vena jugularis of the animals. Concentrations of plasma NO, Fb, erythrocyte ARG activity, and some biochemical markers were determined after the serum and plasma of the receiving blood were separated. While the NO (318.9±5.8 vs. 270.3±9.6 μmol/L) concentrations of the TRP group were found to be significantly higher than the control group (P<0.001), the erythrocyte ARG activity (29.5±0.5 vs. 35.2±1.0 U/hb) was found to be higher in the control group (P<0.001). It was also observed that total protein (TP) (6.6±0.5 vs. 7.8±0.1 g/dL) (P<0.05) and Fb (914.3±68.6 vs. 265.4±19.8 mg/dL) (P<0.001) concentrations were higher in the TRP group, compared to the control group, while albumin (ALB) (1.9±0.2 vs. 3.1±0.1 g/dL) and Fe (47.00±5.29 vs.106.79±9.44 μg/dL) concentrations were significantly lower than the control group (P<0.001). In addition, a positive correlation was found between NO and Fb concentrations and between erythrocyte ARG activity and Fe concentrations. As a result, it was determined that NO concentrations were increased and erythrocyte ARG activity was not significant in dairy cows with TRP. In addition, increased plasma Fb concentration and decreased serum Fe concentration were determined in dairy cows with TRP. This study demonstrated that plasma NO, Fb and serum Fe concentrations in dairy cows with TRP may be useful markers for prognosis