GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Modelling sugar beet water use in Spain

Crop production has a great contribution to water use and abstraction. Sugar beet is an important crop in irrigated land in Spain and covers 70.000 Ha. Crop and resources management are key factors for a sustainable agriculture. The aim of this work is to mode the sugar beet crop growth and water consumption in order to quantify crop water use and virtual water content in different growing conditions.

Analyzing energy-efficient configurations in hexapod robots for demining applications

Purpose – Reducing energy consumption in walking robots is an issue of great importance in field applications such as humanitarian demining so as to increase mission time for a given power supply. The purpose of this paper is to address the problem of improving energy efficiency in statically stable walking machines by comparing two leg, insect and mammal, configurations on the hexapod robotic platform SILO6. Design/methodology/approach – Dynamic simulation of this hexapod is used to develop a set of rules that optimize energy expenditure in both configurations. Later, through a theoretical analysis of energy consumption and experimental measurements in the real platform SILO6, a configuration is chosen. Findings – It is widely accepted that the mammal configuration in statically stable walking machines is better for supporting high loads, while the insect configuration is considered to be better for improving mobility. However, taking into account the leg dynamics and not only the body weight, different results are obtained. In a mammal configuration, supporting body weight accounts for 5 per cent of power consumption while leg dynamics accounts for 31 per cent. Originality/value – As this paper demonstrates, the energy expended when the robot walks along a straight and horizontal line is the same for both insect and mammal configurations, while power consumption during crab walking in an insect configuration exceeds power consumption in the mammal configuration.

Energy consumption of passenger land transport modes

This paper shows how the methodologies used in current practice might lead to an underestimation of energy consumption by different passenger transport modes, and also offers recommendations for improvements to these methodologies. The first recommendation is related to energy consumption rates. The studies reviewed use traditional energy consumption rates based on transportation demand, such as kilowatts-hour per vehicle-kilometre or kilowatts-hour per passenger-kilometre, and include other rates based on transportation supply which might prove useful. Second, energy consumption rates are dependent on factors, and the introduction of homogeneous units which are independent of these factors therefore offers a significant improvement when comparing transport modes. Third, the use of a vehicle energy consumption equation will improve the quality of the assessments. Fourth, we propose that the coefficients which define the energy consumption equation should be broken down to determine market niches and sources for improvements in energy consumption in the vehicle categories.

Influence of denitrifiers abundance on N2O emissions in long term tillage system under a rainfed legume crop

Current studies about nitrous oxide (N2O) emissions from legume crops have raised considerable doubt, observing a high variability between sites (0.03-7.09 kg N2O–N ha−1 y -1) [1]. This high variability has been associated to climate and soil conditions, legume species and soil management practices (e.g. conservation or conventional tillage). Conservation tillage (i.e. no tillage (NT) and minimum tillage (MT)) has spread during the last decades because promotes several positive effects (increase of soil organic content, reduction of soil erosion and enhancement of carbon (C) sequestration). However, these benefits could be partly counterbalanced by negative effects on the release of N2O emissions. Among processes responsible for N2O production and consumption in soils, denitrification plays an importantrole both in tilled and no-tilled ropping systems [2]. Recently, amplification of functional bacterial genes involved in denitrification is being used to examine denitrifiers abundance and evaluate their influence on N2O emissions. NirK and nirS are functional genes encoding the cytochrome cd1 and copper nitrite reductase, which is the key enzyme regulating the denitrification process.

Heat losses in a CVD reactor for polysilicon: comprehensive model and experimental validation

This work addresses heat losses in a CVD reactor for polysilicon production. Contributions to the energy consumption of the so-called Siemens process are evaluated, and a comprehensive model for heat loss is presented. A previously-developed model for radiative heat loss is combined with conductive heat loss theory and a new model for convective heat loss. Theoretical calculations are developed and theoretical energy consumption of the polysilicon deposition process is obtained. The model is validated by comparison with experimental results obtained using a laboratory-scale CVD reactor. Finally, the model is used to calculate heat consumption in a 36-rod industrial reactor; the energy consumption due to convective heat loss per kilogram of polysilicon produced is calculated to be 22-30 kWh/kg along a deposition process.

Leakage of nitrous oxide emissions within the Spanish agro-food system in 1961-2009

Abstract In this paper we examine the trends of nitrous oxide (N2O) emissions of the Spanish agricultural sector related to national production and consumption in the 1961?2009 period.The comparison between production- and consumption-based emissions at the national level provides a complete overview of the actual impact resulting from the dietary choices of a given country and allows the evaluation of potential emission leakages. On average, 1.5 % of the new reactive nitrogen that enters Spain every year is emitted as N2O. Production- and consumption-based emissions have both significantly increased in the period studied and nowadays consumption-based emissions are 45 % higher than production-based emissions. A large proportion of the net N2O emissions associated with imported agricultural godos comes from countries that are not committers for the United Nations Framework Convention on Climate Change Kyoto Protocol Annex I. An increase in feed consumption is the main driver of the changes observed, leading to a arkable emission leakage in the Spanish agricultural sector. The complementary approach used here is essential to achieve an effective mitigation of Spanish greenhouse gas emissions.

Modeling GHG emissions, N and C dynamics in Spanish agricultural soils.

To date, only few initiatives have been carried out in Spain in order to use mathematical models (e.g. DNDC, DayCent, FASSET y SIMSNIC) to estimate nitrogen (N) and carbon (C) dynamics as well as greenhouse gases (GHG) in Spanish agrosystems. Modeling at this level may allow to gain insight on both the complex relationships between biological and physicochemical processes, controlling the processes leading to GHG production and consumption in soils (e.g. nitrification, denitrification, decomposing, etc.), and the interactions between C and N cycles within the different components of the continuum plant-soil-environment. Additionally, these models can simulate the processes behind production, consumition and transport of GHG (e.g. nitrous oxide, N2O, and carbon dioxide, CO2) in the short and medium term and at different scales. Other sources of potential pollution from soils can be identified and quantified using these process-based models (e.g. NO3 y NH3).

Post-­‐occupancy monitoring of two flats in Madrid: development and assessment of a mixed methods methodology

Recent research has shown large differences between the expected and the actual energy consumption in buildings. The differences have been attributed partially, to the assumptions made during the design phase of buildings when simulation methods are employed. More accurate occupancy profiles on building operation could help to carry out more precise building performance calculations. This study focuses on the post-occupancy evaluation of two apartments, one renovated and one non renovated, in Madrid within the same building complex. The aim of this paper is to present an application of the mixed-methods methodology (Creswell, 2007) to assess thermal comfort and occupancy practices used in the case studies, and to discuss the shortcomings and opportunities associated with it. The mixed-methods methodology offers strategies for integrating qualitative and quantitative methods to investigate complex phenomena. This approach is expected to contribute to the growing knowledge of occupants’ behaviour and building performance by explaining the differences observed between energy consumption and thermal comfort in relation to people’s saving and comfort practices and the related experiences, preferences and values.

Development of a fuzzy control based self-adaptive system for energy saving in constrained networks for the Internet of Things

Las redes del futuro, incluyendo las redes de próxima generación, tienen entre sus objetivos de diseño el control sobre el consumo de energía y la conectividad de la red. Estos objetivos cobran especial relevancia cuando hablamos de redes con capacidades limitadas, como es el caso de las redes de sensores inalámbricos (WSN por sus siglas en inglés). Estas redes se caracterizan por estar formadas por dispositivos de baja o muy baja capacidad de proceso y por depender de baterías para su alimentación. Por tanto la optimización de la energía consumida se hace muy importante. Son muchas las propuestas que se han realizado para optimizar el consumo de energía en este tipo de redes. Quizás las más conocidas son las que se basan en la planificación coordinada de periodos de actividad e inactividad, siendo una de las formas más eficaces para extender el tiempo de vida de las baterías. La propuesta que se presenta en este trabajo se basa en el control de la conectividad mediante una aproximación probabilística. La idea subyacente es que se puede esperar que una red mantenga la conectividad si todos sus nodos tienen al menos un número determinado de vecinos. Empleando algún mecanismo que mantenga ese número, se espera que se pueda mantener la conectividad con un consumo energético menor que si se empleara una potencia de transmisión fija que garantizara una conectividad similar. Para que el mecanismo sea eficiente debe tener la menor huella posible en los dispositivos donde se vaya a emplear. Por eso se propone el uso de un sistema auto-adaptativo basado en control mediante lógica borrosa. En este trabajo se ha diseñado e implementado el sistema descrito, y se ha probado en un despliegue real confirmando que efectivamente existen configuraciones posibles que permiten mantener la conectividad ahorrando energía con respecto al uso de una potencia de transmisión fija. ABSTRACT. Among the design goals for future networks, including next generation networks, we can find the energy consumption and the connectivity. These two goals are of special relevance when dealing with constrained networks. That is the case of Wireless Sensors Networks (WSN). These networks consist of devices with low or very low processing capabilities. They also depend on batteries for their operation. Thus energy optimization becomes a very important issue. Several proposals have been made for optimizing the energy consumption in this kind of networks. Perhaps the best known are those based on the coordinated planning of active and sleep intervals. They are indeed one of the most effective ways to extend the lifetime of the batteries. The proposal presented in this work uses a probabilistic approach to control the connectivity of a network. The underlying idea is that it is highly probable that the network will have a good connectivity if all the nodes have a minimum number of neighbors. By using some mechanism to reach that number, we hope that we can preserve the connectivity with a lower energy consumption compared to the required one if a fixed transmission power is used to achieve a similar connectivity. The mechanism must have the smallest footprint possible on the devices being used in order to be efficient. Therefore a fuzzy control based self-adaptive system is proposed. This work includes the design and implementation of the described system. It also has been validated in a real scenario deployment. We have obtained results supporting that there exist configurations where it is possible to get a good connectivity saving energy when compared to the use of a fixed transmission power for a similar connectivity.

Microvascular and tissue oxygen gradients in the rat mesentery

One of the most important functions of the blood circulation is O2 delivery to the tissue. This process occurs primarily in microvessels that also regulate blood flow and are the site of many metabolic processes that require O2. We measured the intraluminal and perivascular pO2 in rat mesenteric arterioles in vivo by using noninvasive phosphorescence quenching microscopy. From these measurements, we calculated the rate at which O2 diffuses out of microvessels from the blood. The rate of O2 efflux and the O2 gradients found in the immediate vicinity of arterioles indicate the presence of a large O2 sink at the interface between blood and tissue, a region that includes smooth muscle and endothelium. Mass balance analyses show that the loss of O2 from the arterioles in this vascular bed primarily is caused by O2 consumption in the microvascular wall. The high metabolic rate of the vessel wall relative to parenchymal tissue in the rat mesentery suggests that in addition to serving as a conduit for the delivery of O2 the microvasculature has other functions that require a significant amount of O2.

Autoradiographic characterization of [3H]-5-HT-moduline binding sites in rodent brain and their relationship to 5-HT1B receptors

5-HT-moduline is an endogenous tetrapeptide [Leu-Ser-Ala-Leu (LSAL)] that was first isolated from bovine brain tissue. To understand the physiological role of this tetrapeptide, we studied the localization of 5-HT-moduline binding sites in rat and mouse brains. Quantitative data obtained with a gaseous detector of β-particles (β-imager) indicated that [3H]-5-HT-moduline bound specifically to rat brain sections with high affinity (Kd = 0.77 nM and Bmax = 0.26 dpm/mm2). Using film autoradiography in parallel, we found that 5-HT-moduline binding sites were expressed in a variety of rat and mouse brain structures. In 5-HT1B receptor knock-out mice, the specific binding of [3H]-5-HT-moduline was not different from background labeling, indicating that 5-HT-moduline targets are exclusively located on the 5-HT1B receptors. Although the distribution of 5-HT-moduline binding sites was similar to that of 5-HT1B receptors, they did not overlap totally. Differences in distribution patterns were found in regions containing either high levels of 5-HT1B receptors such as globus pallidus and subiculum that were poorly labeled or in other regions such as dentate gyrus of hippocampus and cortex where the relative density of 5-HT-moduline binding sites was higher than that of 5-HT1B receptors. In conclusion, our data, based on autoradiographic localization, indicate that 5-HT-moduline targets are located on 5-HT1B receptors present both on 5-HT afferents and postsynaptic neurons. By interacting specifically with 5-HT1B receptors, this tetrapeptide may play a pivotal role in pathological states such as stress that involves the dysfunction of 5-HT neurotransmission.

Regulation of sterol regulatory element binding proteins in livers of fasted and refed mice

Hepatic lipid synthesis is known to be regulated by food consumption. In rodents fasting decreases the synthesis of cholesterol as well as fatty acids. Refeeding a high carbohydrate/low fat diet enhances fatty acid synthesis by 5- to 20-fold above the fed state, whereas cholesterol synthesis returns only to the prefasted level. Sterol regulatory element binding proteins (SREBPs) are transcription factors that regulate genes involved in cholesterol and fatty acid synthesis. Here, we show that fasting markedly reduces the amounts of SREBP-1 and -2 in mouse liver nuclei, with corresponding decreases in the mRNAs for SREBP-activated target genes. Refeeding a high carbohydrate/low fat diet resulted in a 4- to 5-fold increase of nuclear SREBP-1 above nonfasted levels, whereas nuclear SREBP-2 protein returned only to the nonfasted level. The hepatic mRNAs for fatty acid biosynthetic enzymes increased 5- to 10-fold above nonfasted levels, a pattern that paralleled the changes in nuclear SREBP-1. The hepatic mRNAs for enzymes involved in cholesterol synthesis returned to the nonfasted level, closely following the pattern of nuclear SREBP-2 regulation. Transgenic mice that overproduce nuclear SREBP-1c failed to show the normal decrease in hepatic mRNA levels for cholesterol and fatty acid synthetic enzymes upon fasting. We conclude that SREBPs are regulated by food consumption in the mouse liver and that the decline in nuclear SREBP-1c upon fasting may explain in part the decrease in mRNAs encoding enzymes of the fatty acid biosynthetic pathway.

Fusicoccin Counteracts the n-Ethylmaleimide and Silver-Induced Stimulation of Oxygen Uptake in Egeria densa Leaves1

It was previously shown that a number of sulfhydryl [SH] group reagents (N-ethylmaleimide [NEM], iodoacetate, Ag+, HgCl2, etc.) can induce a marked, transitory stimulation of O2 uptake (QO2) in Egeria densa leaves, insensitive to CN− and salicylhydroxamic acid and inhibited by diphenylene iodonium and quinacrine. The phytotoxin fusicoccin (FC) also induces a marked increase in O2 consumption in E. densa leaves, apparently independent of the recognized stimulating action on the H+-ATPase. In this investigation we compared the FC-induced increase in O2 consumption with those induced by NEM and Ag+, and we tested for a possible interaction between FC and the two SH blockers in the activation of QO2. The results show (a) the different nature of the FC- and NEM- or Ag+-induced increases of QO2; (b) that FC counteracts the NEM- (and Ag+)-induced respiratory burst; and (c) that FC strongly reduces the damaging effects on plasma membrane permeability observed in E. densa leaves treated with the two SH reagents. Two alternative models of interpretation of the action of FC, in activating a CN−-sensitive respiratory pathway and in suppressing the SH blocker-induced respiratory burst, are proposed.