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

Testing Verlinde's emergent gravity in early-type galaxies

Verlinde derived gravity as an emergent force from the information flow, through two-dimensional surfaces and recently, by a priori postulating the entanglement of information in 3D space, he derived the effect of the gravitational potential from dark matter (DM) as the entropy displacement of dark energy by baryonic matter. In Emergent Gravity (EG) this apparent DM depends only on the baryonic mass distribution and the present-day value of the Hubble parameter. In this paper we test the EG proposition, formalized by Verlinde for a spherical and isolated mass distribution, using the central velocity dispersion, $\sigma$ and the light distribution in a sample of 4260 massive and local early-type galaxies (ETGs) from the SPIDER sample. Our results remain unaltered if we consider the sample of 807 roundest field galaxies. We derive the predictions by EG for the stellar mass-to-light ratio (M/L) and the Initial Mass Function (IMF), and compare them with the same inferences derived from a) DM-based models, b) MOND and c) stellar population models. We demonstrate that, consistently with a classical Newtonian framework with a DM halo component, or alternative theories of gravity as MOND, the central dynamics can be fitted if the IMF is assumed non-universal. The results can be interpreted with a IMF lighter than a standard Chabrier at low-$\sigma$, and bottom-heavier IMFs at larger $\sigma$. We find lower, but still acceptable, stellar M/L in EG theory, if compared with the DM-based NFW model and with MOND. The results from EG are comparable to what is found if the DM haloes are adiabatically contracted and with expectations from spectral gravity-sensitive features. If the strain caused by the entropy displacement would be not maximal, as adopted in the current formulation, then the dynamics of ETGs could be reproduced with larger M/L. (abridged)Comment: 12 pages, 2 figures, submitted to MNRAS. The updated manuscript presents significantly altered conclusions, after discovering an internal bug in an older version of the Mathematica package, leading to incorrect numerical results when calculating the derivatives of Gamma function

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

The influence of water desalination systems on load levelling of gen-set in small off-grid islands

In minor off-grid islands, energy is typically supplied by diesel generator set, while water, when not shipped with water tankers, is produced by means of desalination systems (typically based on reverse osmosis). The additional demand of desalination related power to the already inefficient diesel power systems may worsen gen-set performance. State-of-the-art remedial strategies advocate the use of renewable energy technologies in order to reduce the impact on diesel generators. Nevertheless, many small islands, due to their environmental value and tourist vocation or due to the quality of the local grid, do suffer of more stringent limitations reducing the potential of penetration of renewable energy. To this end, the present work aims at reporting on the influence of water desalination system on diesel generators load levelling. The study of different matching scenario, using a time-dependent model, demonstrates the possibility of finding management strategy paying-off with improved generator performance

Galaxy evolution within the Kilo-Degree Survey

The ESO Public Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will scan 1500 square degrees in four optical filters (u, g, r, i). Designed to be a weak lensing survey, it is ideal for galaxy evolution studies, thanks to the high spatial resolution of VST, the good seeing and the photometric depth. The surface photometry have provided with structural parameters (e.g. size and S\'ersic index), aperture and total magnitudes have been used to derive photometric redshifts from Machine learning methods and stellar masses/luminositites from stellar population synthesis. Our project aimed at investigating the evolution of the colour and structural properties of galaxies with mass and environment up to redshift $z \sim 0.5$ and more, to put constraints on galaxy evolution processes, as galaxy mergers.Comment: 4 pages, 2 figures, to appear on the refereed Proceeding of the "The Universe of Digital Sky Surveys" conference held at the INAF--OAC, Naples, on 25th-28th november 2014, to be published on Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodic

Robustness of the 0−π0 -\pi transition against compositional and structural ageing in S/F/S heterostructures

We have studied the temperature induced $0 -\pi$ thermodynamic transition in Nb/PdNi/Nb Superconductor/Ferromagnetic/Superconductor (SFS) heterostructures by microwave measurements of the superfluid density. We have observed a shift in the transition temperature with the ageing of the heterostructures, suggesting that structural and/or chemical changes took place. Motivated by the electrodynamics findings, we have extensively studied the local structural properties of the samples by means of X-ray Absorption Spectroscopy (XAS) technique, and the compositional profile by Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). We found that the samples have indeed changed their properties, in particular for what concerns the interfaces and the composition of the ferromagnetic alloy layer. The structural and compositional data are consistent with the shift of the $0-\pi$ transition toward the behaviour of heterostructures with different F layers. An important emerging indication to the physics of SFS is the weak relevance of the ideality of the interfaces: even in aged samples, with less-than-ideal interfaces, the temperature-induced $0-\pi$ transition is still detectable albeit at a different critical F thickness.Comment: 11 pages, 9 figures, accepted for publication on Phys. Rev. B, http://journals.aps.org/prb

Dynamical and gravitational lensing properties of a new phenomenological model of elliptical galaxies

Recent observations of the line of sight velocity profile of elliptical galaxies have furnished controversial results with some works favouring the presence of a large amount of dark matter in the outer regions and others arguing in favour of no dark matter at all. In order to shed new light on this controversy, we propose here a new phenomenological description of the total mass profile of galaxies. Under the hypothesis of spherical symmetry, we assume a double power-law expression for the global M/L ratio Upsilon(r)= Upsilon_0(r/r_0) ^{alpha}(1+r/r_0)^{beta}. In particular, Upsilon propto r^{alpha} for r/r_01 so that alpha1), Upsilon propto r^{alpha+beta} thus showing that models with alpha+beta=0 have an asymptotically constant M/L ratio. A wide range of possibilities is obtained by varying the slope parameters in the range we determine on the basis of physical considerations. Choosing a general expression for the luminosity density profile j(r), we work out an effective galaxy model that accounts for all the phenomenology observed in real elliptical galaxies. We derive the main dynamics and lensing properties of such an effective model. We analyze a general class of models, able to take into account different dynamical trends. We are able to obtain analytical expressions for the main dynamical and lensing quantities. We show that constraining the values of alpha+beta makes it possible to analyze the problem of the dark matter in elliptical galaxies. Indeed, positive values of alpha+beta would be a strong evidence for dark matter. Finally we indicate possible future approaches in order to face the observational data, in particular using velocity dispersion profiles and lensed quasar events

KiDS0239-3211: A new gravitational quadruple lens candidate

We report the discovery of a candidate to quadrupole gravitationally lensed system KiDS0239-3211 based on the public data release 3 of the KiDS survey and machine learning techniques

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA

Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed sources. One ConvNet is trained with single \textit{r}-band galaxy images, hence basing the classification mostly on the morphology. While the other ConvNet is trained on \textit{g-r-i} composite images, relying mostly on colours and morphology. We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample. The new lens-finders achieve a higher accuracy and completeness in identifying gravitational lens candidates, especially the single-band ConvNet. Our analysis indicates that this is mainly due to improved simulations of the lensed sources. In particular, the single-band ConvNet can select a sample of lens candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed gravitational lenses in the LRG sample. With this particular setup and limited human intervention, it will be possible to retrieve, in future surveys such as Euclid, a sample of lenses exceeding in size the total number of currently known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA