Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Spectral analysis of Markarian 421 and Markarian 501 with HAWC

The Hight Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory monitors the gamma-ray sky in the energy range from 100 GeV to 100 TeV and has detected two very high energy (VHE) blazars: Markarian 421 (Mrk 421) and Markarian 501 (Mrk 501) in 1.5 years of observations. In this work, we present the spectral analysis above 1 TeV of both sources using a maximum likelihood method and an artificial neural network as an energy estimator. The main objectives are to constrain the spectral curvature of Mrk 421 and Mrk 501 at $\sim$5 TeV using the EBL models from Gilmore et al. (2012) and Franceschini et al. (2008).Comment: Presented at the 35th International Cosmic Ray Conference (ICRC2017), Bexco, Busan, Korea. See arXiv:1708.02572 for all HAWC contribution

Self-Adaptive resource allocation for event monitoring with uncertainty in Sensor Networks

Event monitoring is an important application of sensor networks. Multiple parties, with different surveillance targets, can share the same network, with limited sensing resources, to monitor their events of interest simultaneously. Such a system achieves profit by allocating sensing resources to missions to collect event related information (e.g., videos, photos, electromagnetic signals). We address the problem of dynamically assigning resources to missions so as to achieve maximum profit with uncertainty in event occurrence. We consider timevarying resource demands and profits, and multiple concurrent surveillance missions. We model each mission as a sequence of monitoring attempts, each being allocated with a certain amount of resources, on a specific set of events that occurs as a Markov process. We propose a Self-Adaptive Resource Allocation algorithm (SARA) to adaptively and efficiently allocate resources according to the results of previous observations. By means of simulations we compare SARA to previous solutions and show SARA’s potential in finding higher profit in both static and dynamic scenarios

Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)

We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and l21 minimization problems promoting low rankness and joint average sparsity of the wideband model cube. On the one hand, enforcing low rankness enhances the overall resolution of the reconstructed model cube by exploiting the correlation between the different channels. On the other hand, promoting joint average sparsity improves the overall sensitivity by rejecting artefacts present on the different channels. An adaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem. The algorithmic structure is highly scalable to large data sets and allows for imaging in the presence of unknown noise levels and calibration errors. We showcase the superior performance of the proposed approach, reflected in high-resolution images on simulations and real VLA observations with respect to single channel imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software. Our MATLAB code is available online on GITHUB

Assessment of a diagnostic procedure for the monitoring and control of industrial processes

The definition of “energy efficiency” entails programming, planning and implementation of operational tools and strategies leading to the reduction of energy demand for the same offered services. Among the typical industrial energy uses, the production of compressed air represents certainly an important segment of potential saving. The present work studies the monitoring of the compressed air used for blow moulding of a packaging solution company. The study addresses the monitoring of compressed air line in term of operational and energy variables. The available measured data are used to evaluate the energy performance evolution during a year time. The work tackles the problem with two different approaches based on univariate and multivariate methods. The first method aims at finding a key performance index and a new univariate control chart related to energy/operational parameters to better monitor the performance of the compressed air plant. Besides, the multivariate analysis of the production process is applied in order to analyse the energy efficiency by also considering the multiple variables influencing the whole process itself. Final purposes are identify a new methodology for the production process analysis and evaluate flaws and strengths of these models

Sustainability and Food: a Text Analysis of the Scientific Literature

The paper analyses the evolution of the research debate related to sustainability and to the relation between food and sustainability. A number of text analysis techniques were combined for the investigation of scientific papers. The results stress how discourse analysis of sustainability in the pre-Rio period is mostly associated with agriculture and with a vision where the ecological and environmental aspects are dominant. In the post-Rio phase, the discussion about sustainability, though still strongly linked to environmental issues, enters a holistic dimension that includes social elements. The themes of energy and the sustainability of urban areas become central, and the scientific debate stresses the importance of indicators within an assessment approach linked to the relevance of planning and intervention aspects. The focus on the role of food within the debate on sustainability highlights a food security oriented approach in the pre-Rio phase, with a particular attention towards agriculture and third world Countries. In the post-Rio period, the focus of the analysis moves towards developed Countries. Even though food security remains a strongly significant element of the debate, the attention shifts towards consumers and food choices

Brown Mid-Rib Corn Population Trial

Brown mid-rib (BMR) corn hybrids are of interest to many growers in the Northeast who would like to maximize milk production on homegrown forage. BMR corn has a naturally-occurring genetic mutation that leads to less lignin in the stalk and makes corn silage more digestible. Corn yields can be highly dependent on population, and it is generally recommended to plant BMR corn at lower populations than conventional silage corn. BMR corn has always been considered to be more prone to lodging due to its lower lignin content, and lower populations allow for less stress on each individual plant. However, optimal populations for the Northeast have yet to be developed. With this in mind, University of Vermont Extension Northwest Crops & Soils Program conducted a field experiment in 2014 to evaluate the yield and quality performance of four BMR corn hybrids at three different populations. The data presented are only representative of one year, but this information can be combined with other research to aid in making agronomic decisions for BMR corn in the Northeast