The five-parameter grain boundary character and energy distributions of a fully austenitic high-manganese steel using three dimensional data

The three-dimensional interfacial grain boundary network in a fully austenitic high-manganese steel was studied as a function of all five macroscopic crystallographic parameters (i.e. lattice misorientation and grain boundary plane normal) using electron backscattering diffraction mapping in conjunction with focused ion beam serial sectioning. The relative grain boundary area and energy distributions were strongly influenced by both the grain boundary plane orientation and the lattice misorientation. Grain boundaries terminated by (1 1 1) plane orientations revealed relatively higher populations and lower energies compared with other boundaries. The most frequently observed grain boundaries were {1 1 1} symmetric twist boundaries with the Σ3 misorientation, which also had the lowest energy. On average, the relative areas of different grain boundary types were inversely correlated to their energies. A comparison between the current result and previously reported observations (e.g. high-purity Ni) revealed that polycrystals with the same atomic structure (e.g. face-centered cubic) have very similar grain boundary character and energy distributions. © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Typology of alcohol mixed with energy drink consumers: motivations for use

Background: Previous research on alcohol mixed with energy drinks (AmED) has shown that use is typically driven by hedonistic, social, functional, and intoxication-related motives, with differential associations with alcohol-related harm across these constructs. There has been no research looking at whether there are subgroups of consumers based on patterns of motivations. Consequently, the aims were to determine the typology of motivations for AmED use among a community sample and to identify correlates of subgroup membership. In addition, we aimed to determine whether this structure of motivations applied to a university student sample. Methods: Data were used from an Australian community sample (n = 731) and an Australian university student sample (n = 594) who were identified as AmED consumers when completing an online survey about their alcohol and ED use. Participants reported their level of agreement with 14 motivations for AmED use; latent classes of AmED consumers were identified based on patterns of motivation endorsement using latent class analysis. Results: A 4-class model was selected using data from the community sample: (i) taste consumers (31%): endorsed pleasurable taste; (ii) energy-seeking consumers (24%): endorsed functional and taste motives; (iii) hedonistic consumers (33%): endorse pleasure and sensation-seeking motives, as well as functional and taste motives; and (iv) intoxication-related consumers (12%): endorsed motives related to feeling in control of intoxication, as well as hedonistic, functional, and taste motives. The consumer subgroups typically did not differ on demographics, other drug use, alcohol and ED use, and AmED risk taking. The patterns of motivations for the 4-class model were similar for the university student sample. Conclusions: This study indicated the existence of 4 subgroups of AmED consumers based on their patterns of motivations for AmED use consistently structured across the community and university student sample. These findings lend support to the growing conceptualization of AmED consumers as a heterogeneous group in regard to motivations for use, with a hierarchical and cumulative class order in regard to the number of types of motivation for AmED use. Prospective research may endeavor to link session-specific motives and outcomes, as it is apparent that primary consumption motives may be fluid between sessions.

A study and a directory of energy consumption data sets of buildings

Energy consumption data are required to perform analysis, modelling, evaluation, and optimisation of energy usage in buildings. While a variety of energy consumption data sets have been examined and reported in the literature, there is a lack of a comprehensive categorisation and analysis of the available data sets. In this study, an overview of energy consumption data of buildings is provided. Three common strategies for generating energy consumption data, i.e., measurement, survey, and simulation, are described. A number of important characteristics pertaining to each strategy and the resulting data sets are discussed. In addition, a directory of energy consumption data sets of buildings is developed. The data sets are collected from either published papers or energy related organisations. The main contributions of this study include establishing a resource pertaining to energy consumption data sets and providing information related to the characteristics and availability of the respective data sets; therefore facilitating and promoting research activities in energy consumption data analysis.

Fast and energy efficient data storage for information discovery in multi-dimensional WSNs

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). An efficient information dis-covery for multi-dimensional WSNs deployed in mission–critical environments has become an essential research consideration. Timely and energy efficient information discovery is very impor-tant to maintain the QoS of such mission critical applications. An inefficient information discovery mechanism will result in high transmission of data packets over the network creating bottlenecks leading to unbalanced energy consumption over the network. High latency and inefficient energy consumption will have a direct effect on the QoS of mission-critical applications of particular importance in this regard is the minimization of hotspots.

Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. Theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.

An energy-aware QoS enhanced method for service computing across clouds and data centers

QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Performance evaluation of 3D printed miniature electromagnetic energy harvesters driven by air flow

As a renewable and non-polluting energy source, wind is used to produce electricity via large-diameter horizontal or vertical axis wind turbines. Such large wind turbines have been well designed and widely applied in industry. However, little attention has been paid to the design and development of miniature wind energy harvesters, which have great potential to be applied to the HVAC (heating, ventilating and air conditions) ventilation exhaust systems and household personal properties. In this work, 10 air-driven electromagnetic energy harvesters are fabricated using 3D printing technology. Parametric measurements are then conducted to study the effects of (1) the blade number, (2) its geometric size, (3) aspect ratio, presence or absence of (4) solid central shaft, (5) end plates, and (6) blade orientation. The maximum electrical power is 0.305 W. To demonstrate its practical application, the electricity generated is used to power 4 LED (light-emitting diode) lights. The maximum overall efficiency ηmax is approximately 6.59%. The cut-in and minimum operating Reynolds numbers are measured. The present study reveals that the 3D printed miniature energy harvesters provide a more efficient platform for harnessing ‘wind power’.