Toward sustainable data centers: a comprehensive energy management strategy

Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

Statistical evaluation of research performance of young university scholars: A case study

The research performance of a small group of 49 young scholars, such as doctoral students, postdoctoral and junior researchers, working in different technical and scientific fields, was evaluated based on 11 types of research outputs. The scholars worked at a technical university in the fields of Civil Engineering, Ecology, Economics, Informatics, Materials Engineering, Mechanical Engineering, and Safety Engineering. Principal Component Analysis was used to statistically analyze the research outputs and its results were compared with factor and cluster analysis. The metrics of research productivity describing the types of research outputs included the number of papers, books and chapters published in books, the number of patents, utility models and function samples, and the number of research projects conducted. The metrics of citation impact included the number of citations and h-index. From these metrics -the variables -the principal component analysis extracted 4 main principal components. The 1st principal component characterized the cited publications in high-impact journals indexed by the Web of Science. The 2nd principal component represented the outputs of applied research and the 3rd and 4th principal components represented other kinds of publications. The results of the principal component analysis were compared with the hierarchical clustering using Ward's method. The scatter plots of the principal component analysis and the Mahalanobis distances were calculated from the 4 main principal component scores, which allowed us to statistically evaluate the research performance of individual scholars. Using variance analysis, no influence of the field of research on the overall research performance was found. Unlike the statistical analysis of individual research metrics, the approach based on the principal component analysis can provide a complex view of the research systems.Web of Science30217716

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US