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Energy demand of soil tillage implements has been reported based on different operating conditions and the chemical and physical soil properties. However, tillage operations cannot only be evaluated according to their energy consumption; the soil structure improvement and the consequent agronomic benefits must also be considered. Power harrows can adjust soil clod size by varying the velocity ratio (λ) of the machinery which is calculated from the ratio of the peripheral speed of the tine rotors and the vehicle's advancing speed. This paper aims to gain deeper insight into controlling the soil structure and find correlations with the power harrow's energy requirement in different setups. Field tests were conducted at the experimental farm of the University of Bologna on a 3-m working width power harrow coupled with a tractor with 107 kW of rated engine power. Field tests were performed by varying λ from 1.46 to 7.90 while tractor parameters, such as speed, engine power, fuel rate consumption, draught, and power take-off (PTO) speed and torque, were acquired with a datalogger. After harrowing, soil samples were sieved and significant granulometric parameters were calculated and correlated to data acquired from the tractor–power harrow system. The results show that the optimum conditions for a high-quality seedbed are obtained with high values of implement–soil impact speed, whilst λ should be kept as low as possible to minimise the fuel consumption per ha. • Efficiency and seedbed quality must be considered during tillage operation. • A 25% efficiency improvement was obtained at high power harrow absorbed power. • High implement–soil impact speeds reduce the soil aggregate size. [ABSTRACT FROM AUTHOR]

This paper examines the effects of extreme temperatures on mortality rates, using random year-to-year variation in temperature based on county-level panel data from China. The analysis finds a robust, U-shaped relationship between temperature and mortality rates, indicating that extremely cold or hot temperatures lead to excess deaths. The heat-related (cold-related) effect is 3.5 times (3.2 times) as large as previous findings that used U.S. data, and it is especially large for the elderly population, mainly due to excess deaths caused by cardiovascular diseases. Applying these results to climate change predictions from Hadley Centre Global Environmental Model shows that by 2061–2080 the annual mortality rate is likely to increase by 14.2% if global greenhouse gas emissions continue to rise throughout the 21st century, the estimated health cost of which is around 0.98 trillion Chinese Yuan per year. The paper also explores households' adaptation behaviors to extreme temperatures. It finds that although urban households adaptively increase energy consumption when they are exposed to cold temperatures and purchase more air conditioners on hot and cold days, rural households are unresponsive to temperature fluctuations. This finding implies that rural people may be more resource constrained and suffer more when extreme temperatures occur. [ABSTRACT FROM AUTHOR]

Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and significant saving potentials. Currently, energy cost models are generally generated using logarithmic, exponential or linear functions that could produce not accurate results when the relationship between variables is highly complex and non-linear. In order to overcome this issue, this paper proposes a new methodology based on machine-learning algorithms that perform better with complex datasets. In this paper, machine learning was used to generate high-performing energy cost models for wastewater treatment plants, using a database of 317 wastewater treatment plants located in north-west Europe. The most important variables in energy cost modelling were identified and for the first time, the energy price was used as model parameter and its importance evaluated. [ABSTRACT FROM AUTHOR]

This paper documents the perennial role of energy in geographic thought. The science of thermodynamics initially challenged geography's evolutionary and geological precepts. This incommensurability was overcome by the development of organismic theories of statehood. Energy went on to provide a means to make determinate statements about the relation between climate and society. With the rejection of such determinism, energy slowly regained eminence, particularly in post-war French geography. This work encouraged economic geographers to draw on new national records of energy use to describe and analyse growing industrial economies. Concurrently, physical geographers showed renewed interest in energy as means of explaning the natural world. However, in both branches, a nascent energy geography was subsumed by a more general systems approach. It took the energy crises of the nineteen-seventies to affirm the need for applied energy geography. Today, it is clear fossil energy use has transformed Earth's atmospheric and biogeochemical cycles, requiring studies which address new and complex relations between energy and society. While encouraging such work, it is argued that new geographies of energy have much to learn from the old. • Surveys a number of roles energy has played in geographic thought. • Historicises 'new geographies of energy and the 'energy turn' more widely. • Traces threads of energy determinist thought and their present significance. • Points to new energy geography research avenues in the Anthropocene. [ABSTRACT FROM AUTHOR]

The computing power of supercomputers and data centers has noticeably grown during the last decades at the cost of an ever increasing energy demand. The need for energy (and power) of these facilities has finally limited the evolution of high performance computing, making that many researchers are concerned not only about performance but also about energy efficiency. However, despite the many concerns about energy consumption, the search for computing power continues. In this regard, the research on exascale systems, able to deliver 1018 floating point operations per second, has reached a widely consensus that these systems should operate within a maximum power budget of 20 megawatts. Many efficiency improvements are necessary for achieving this goal. One of these improvements is the usage of ARM low-power processors, as the Mont-Blanc project proposes. In this paper we analyze the combined use of ARM processors with the rCUDA remote GPU virtualization middleware as a way to improve efficiency even more. Results show that it is possible to speed up applications by almost 8x while reducing energy consumption up to 35% when rCUDA is used to access high-end GPUs. These improvements are achieved while maintaining a feasible average power consumption level for future exascale systems. • Suitability of rCUDA remote GPU virtualization middleware for future exascale. • Performance, power and energy consumption measurements. • Almost 8x application speed up while reducing energy consumption up to 35% • Maintaining a feasible average power consumption level for future exascale systems. [ABSTRACT FROM AUTHOR]

Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

In the steel industry, about 0.86 ton of oily sludge is produced for every 1000 tons of rolling steel. Due to the adverse impact on human health and the environment, oily sludge is designated as a hazardous waste in the Resource Conservation and Recovery Act (RCRT). In this paper, the pyrolysis treatment of oily sludge is studied in a fluidized bed reactor at a temperature range of 400–600 °C. During oily sludge pyrolysis, a maximum oil yield of 59.2% and a minimum energy loss of 19.0% are achieved at 500 °C. The energy consumption of treating 1 kg oily sludge is only 2.4–2.9 MJ. At the same time, the energy of produced oil, gas and solid residue are 20.8, 6.32, and 0.83 MJ, respectively. In particular, it is found that the solid residue contains more than 42% iron oxide, which can be used as the raw material for iron production. Thus, the simultaneous recovery of energy and iron from oil sludge by pyrolysis is feasible. [ABSTRACT FROM AUTHOR]

Most recently, an important metric called “energy proportional” is presented as a guideline for energy efficiency systems (Barroso and Hölzle, 2007), which advocates that energy consumption should be in proportion to system performance/utilization. However, this tradeoff metric is only defined for normal mode where the system is functioning normally without node failures. When node failure occurs, the system enters degradation mode during which node reconstruction is initiated. This very process needs to wake/spin up a number of disks and takes a substantial amount of I/O bandwidth, which will not only compromise energy efficiency but also performance. Moreover, as in replication-based storage such as Google File System (Sanjay Ghemawat, Gobioff, 2003 [10] ) and Hadoop Distributed File System (Borthakur, 2007), systems are adopting a recovery policy that defines a deadline for recovery rather than simply recovering the data as soon as possible. Given the flexibility of the recovery time, this makes it possible to reduce energy consumption with respect to the performance and recovery requirements. This raises a natural problem: how to balance the performance, energy, and recovery in degradation mode for an energy efficient storage system? Without considering the I/O bandwidth contention between recovery and performance, we find that the current energy proportional solutions cannot answer this question accurately. This paper presents a mathematical model named Perfect Energy, Recovery and Performance (PERP) which provides guidelines for provisioning the number of active nodes as well as the assigned recovery bandwidth at each time slot with respect to the performance and recovery constraints. To utilize PERP in storage systems, we take data layouts into consideration and propose a node selection algorithm named “Gradual Increase/decrease” Algorithm (GIA) to select the active nodes based on PERP results. We apply PERP and GIA to current popular power proportional layouts and test their effectiveness on a 25 nodes in-house CASS cluster. Experimental results validate that while meeting both performance and recovery constraints, PERP helps realize 25% power savings compared with maximum recovery policy from Sierra (Thereska et al., 2011) and 20% power savings compared with recovery group policy from Rabbit (Amur et al., 2010). [ABSTRACT FROM AUTHOR]

Abstract: Reliable energy-aware application mapping, task scheduling, and voltage–frequency island partitioning so as to minimize the energy consumption while preserving the required bandwidth and latency is considered as a challenging problem in the designing of Multi-Processor System-on-Chip. To achieve modular design and low power consumption, Globally Asynchronous Locally Synchronous (GALS) design paradigm is a promising approach which fits very well with the voltage–frequency islands concept. In this paper, we formulate mapping problem of a real-time application with stochastic execution times onto multicore systems, scheduling tasks on processors, and assigning voltage–frequency levels to Processing Elements (PEs) as a Mixed Integer Linear Programming (MILP) in GALS-based Network-on-Chip. Furthermore, owing to the importance of reliability issue, we address the effects of transient faults in our proposed MILP formulation such that the reliability of the whole system incorporating several heterogeneous PEs is guaranteed to be better than a given threshold. Due to the NP-hardness of such a problem, a rounding by sampling-based heuristic algorithm is provided. Experimental results based on E3S benchmark suite and some real applications show the effectiveness of our proposed heuristic in achieving a near-optimal solution in a small fractional of time needed to find the optimal solution. Experimental results also show that, our formulation preserves the required reliability and increases the energy consumption by 70% in some cases. [Copyright &y& Elsevier]

Abstract: Previous research has tended to focus on adults or households as a whole when investigating attitudes and behaviours towards energy use. This study focussed on teenagers, ‘the adults of tomorrow’, and their attitudes towards energy consumption. 114 Teenagers aged 10–19 years took part and multiple data collection methods were used to investigate this topic including: diaries, stories and focus groups. Data was analysed using thematic analysis. Themes that emerged from the data were: energy use, impact of energy use, sources of information, location, barriers to saving energy and green teens. This paper discusses the findings in relation to future research and reflects on the methods used. [Copyright &y& Elsevier]

Abstract: Several countries are considering using daylight saving time (DST) as a tool for energy conservation and reduction of greenhouse gas emissions, and the United States extended DST in 2007 with the goal of reducing electricity consumption. This paper assesses DST''s impact on electricity demand by examining a quasi-experiment in which parts of Australia extended DST in 2000 to facilitate the Sydney Olympics. Using detailed panel data and a difference-in-difference-in-difference framework, we show that the extension did not reduce overall electricity consumption, but did cause a substantial intraday shift in demand consistent with activity patterns that are tied to the clock rather than sunrise and sunset. [Copyright &y& Elsevier]