A methodology for full-system power modeling in heterogeneous data centers

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft

Identifying specific prefrontal neurons that contribute to autism-associated abnormalities in physiology and social behavior.

Functional imaging and gene expression studies both implicate the medial prefrontal cortex (mPFC), particularly deep-layer projection neurons, as a potential locus for autism pathology. Here, we explored how specific deep-layer prefrontal neurons contribute to abnormal physiology and behavior in mouse models of autism. First, we find that across three etiologically distinct models-in utero valproic acid (VPA) exposure, CNTNAP2 knockout and FMR1 knockout-layer 5 subcortically projecting (SC) neurons consistently exhibit reduced input resistance and action potential firing. To explore how altered SC neuron physiology might impact behavior, we took advantage of the fact that in deep layers of the mPFC, dopamine D2 receptors (D2Rs) are mainly expressed by SC neurons, and used D2-Cre mice to label D2R+ neurons for calcium imaging or optogenetics. We found that social exploration preferentially recruits mPFC D2R+ cells, but that this recruitment is attenuated in VPA-exposed mice. Stimulating mPFC D2R+ neurons disrupts normal social interaction. Conversely, inhibiting these cells enhances social behavior in VPA-exposed mice. Importantly, this effect was not reproduced by nonspecifically inhibiting mPFC neurons in VPA-exposed mice, or by inhibiting D2R+ neurons in wild-type mice. These findings suggest that multiple forms of autism may alter the physiology of specific deep-layer prefrontal neurons that project to subcortical targets. Furthermore, a highly overlapping population-prefrontal D2R+ neurons-plays an important role in both normal and abnormal social behavior, such that targeting these cells can elicit potentially therapeutic effects

A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes

Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws, but current models do not account for the uptake of compound sources of carbon and nitrogen. Here, we formulate a C-GRAM with nitrogen and carbon pathways converging on biomass production, with parametrizations accounting for respirofermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximized the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state

A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes

Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws, but current models do not account for the uptake of compound sources of carbon and nitrogen. Here, we formulate a C-GRAM with nitrogen and carbon pathways converging on biomass production, with parametrizations accounting for respirofermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximized the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state

Game Perakitan Komputer Berbasis Mobile Menggunakan Metode Finite State Machines (Fsm)

Games are entertainment media in the form of multimedia that is made as attractive as possible that is played using electronic media to provide satisfaction to its users. Aside from being a means of entertainment, games can also be used as a means of learning to increase knowledge. But the development of games that contain elements of education is very difficult to find. Various alternative and programming innovations were carried out to make educational media to facilitate the learning process, including Computer Assembling Games. Computer assembly is to assemble all computer components to become a PC that is ready to use. However, the assembly process is difficult because it requires adequate equipment and space. For that you need a media that can facilitate the process of learning to assemble a computer. This game is made based on Android so it makes it easier for users to play it. The method used in making this game is Finite State Machines (FSM). With the existence of this game is expected to increase knowledge and facilitate the learning process of assembling computers

Ligninolytic Activity of Fungi Isolated from Empty Fruit Bunch of Oil Palm (Elaesis guineensis Jacq.)

Lignin is a natural polymer and plays an important role as a compound of plant cell wall constituent. A study about the degradation of lignin in the environment has been receiving considerable attention because the complex structure and difficult to be degraded compared to the degradation of others plant cell wall constituent. A study to determine the activity of the ligninolytic enzyme (lignin peroxidase, manganese peroxidase and laccase) of fungi isolated from oil palm empty fruit bunch. This study has been done with a screening of ligninolytic activity using potato dextrose agar supplemented with tannic acid (0,1%), showed that two out of five fungal isolates have ligninolytic activity. The highest activity of lignin peroxidase was produced by SN2 isolatesi.e. 9.677U ml-1, whereas the highest activity of manganese peroxidase and laccase was produced by SN3isolates i.e.1.942 U ml-1 and 1.846 U m-1 respectively

The alternating least-squares algorithm for CDPCA

Clustering and Disjoint Principal Component Analysis (CDP CA) is a constrained principal component analysis recently proposed for clustering of objects and partitioning of variables, simultaneously, which we have implemented in R language. In this paper, we deal in detail with the alternating least-squares algorithm for CDPCA and highlight its algebraic features for constructing both interpretable principal components and clusters of objects. Two applications are given to illustrate the capabilities of this new methodology

Evaluating the Thermal Performance of Retrofitted Lightweight Green Roofs and Walls in Sydney and Rio de Janeiro

© 2017 The Authors. With increasing densification in urban settlements, environmental issues are a challenge in the sustainable development of all cities globally. Considering that the built environment releases almost half of the total greenhouse gas emissions, an effective solution to mitigating the impacts of increasing temperatures can be the improved performance of existing buildings. Furthermore 87% of the buildings we will have in 2050 are already built. Retrofitting roofs and walls with a living vegetated system such as green roofs and walls could be an upgrade option, increasing sustainable construction. The benefits are improved thermal performance but also improved air quality, stormwater attenuation, increased bio-diversity and lower heating and / or cooling energy consumption. No empirical data exists for Sydney and Rio de Janeiro and the question is; what is the extent of thermal improvement with retrofitted green walls and roof in timber framed and blockwork structures? This study analyses both effects and benefits of the green roofs and walls through an experiment in two countries: one in Sydney, Australia; a timber framed construction, and another one in Rio de Janeiro, Brazil; with blockwork construction. This difference in the material choice was made according to the most common type of construction for housing in each country. In each site, the walls and the roof of one of the prototypes were covered with plants and compared to the performance of an unplanted but otherwise identical prototype. The thermal performance was analysed by observing the temperature variation simultaneously in a non-vegetated and vegetated structure. The initial findings show that the combination of green roof and green walls have a relevant role in temperature attenuation. These results indicate, that this lightweight retrofit green technology could not only represent an important advance on sustainable development, but can that it also lead to more comfortable internal conditions for humans living in dense urban environments