Energy and Heat Fluctuations in a Temperature Quench

Fluctuations of energy and heat are investigated during the relaxation following the instantaneous temperature quench of an extended system. Results are obtained analytically for the Gaussian model and for the large $N$ model quenched below the critical temperature $T_C$. The main finding is that fluctuations exceeding a critical threshold do condense. Though driven by a mechanism similar to that of Bose-Einstein condensation, this phenomenon is an out-of-equilibrium feature produced by the breaking of energy equipartition occurring in the transient regime. The dynamical nature of the transition is illustrated by phase diagrams extending in the time direction.Comment: To be published in the Proceedings of the Research Program "Small system non equilibrium fluctuations, dynamics and stochastics, and anomalous behavior", Kavli Institute for Theoretical Physics China, July 2013. 40 pages, 9 figure

A Computational Method for the Image Segmentation of Pigmented Skin Lesions

Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College

Repurposing designed mutants: a valuable strategy for computer-aided laccase engineering – the case of POXA1b

The broad specificity of laccases, a direct consequence of their shallow binding site, makes this class of enzymes a suitable template to build specificity toward putative substrates. In this work, a computational methodology that accumulates beneficial interactions between the enzyme and the substrate in productive conformations is applied to oxidize 2,4-diamino-benzenesulfonic acid with POXA1b laccase. Although the experimental validation of two designed variants yielded negative results, most likely due to the hard oxidizability of the target substrate, molecular simulations suggest that a novel polar binding scaffold was designed to anchor negatively charged groups. Consequently, the oxidation of three such molecules, selected as representative of different classes of substances with different industrial applications, significantly improved. According to molecular simulations, the reason behind such an improvement lies in the more productive enzyme–substrate binding achieved thanks to the designed polar scaffold. In the future, mutant repurposing toward other substrates could be first carried out computationally, as done here, testing molecules that share some similarity with the initial target. In this way, repurposing would not be a mere safety net (as it is in the laboratory and as it was here) but rather a powerful approach to transform laccases into more efficient multitasking enzymes.This work was funded by INDOX (KBBE-2013-7-613549) European project and CTQ2013-48287-R Spanish National Project. V. G. and E. M. acknowledge Università degli Studi di Napoli and Generalitat de Catalunya for their respective predoctoral fellowships.Peer ReviewedPostprint (author's final draft

Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules

The pervasive monitoring of HVAC systems through Building Energy Management Systems (BEMSs) is enabling the full exploitation of data-driven based methodologies for performing advanced energy management strategies. In this context, the implementation of Automated Fault Detection and Diagnosis (AFDD) based on collected operational data of Air Handling Units (AHUs) proved to be particularly effective to prevent anomalous running modes which can lead to significant energy waste over time and discomfort conditions in the built environment. The present work proposes a novel methodology for performing AFDD, based on both unsupervised and supervised data-driven methods tailored according to the operation of an AHU during transient and non-transient periods. The whole process is developed and tested on a sample of real data gathered from monitoring campaigns on two identical AHUs in the framework of the Research Project ASHRAE RP-1312. During the start-up period of operation, the methodology exploits Temporal Association Rules Mining (TARM) algorithm for an early detection of faults, while during non-transient period a number of classification models are developed for the identification of the deviation from the normal operation. The proposed methodology, conceived for quasi real-time implementation, proved to be capable of robustly and promptly identifying the presence of typical faults in AHUs

A data analytics-based energy information system (EIS) tool to perform meter-level anomaly detection and diagnosis in buildings

Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus

Online implementation of a soft actor-critic agent to enhance indoor temperature control and energy efficiency in buildings

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively

Automatic Support for Verification of Secure Transactions in Distributed Environment using Symbolic Model Checking

Electronic commerce needs the aid of software tools to check the validity of business processes in order to fully automate the exchange of information through the network. Symbolic model checking has been used to formally verify specifications of secure transactions in a system for business-to-business applications. The fundamental principles behind symbolic model checking are presented along with techniques used to model mutual exclusion of processes and atomic transactions. The computational resources required to check the example process are presented, and the faults are detected through symbolic verification

Phase separation of binary fluids with dynamic temperature

Phase separation of binary fluids quenched by contact with cold external walls is considered. Navier-Stokes, convection-diffusion, and energy equations are solved by lattice Boltzmann method coupled with finite-difference schemes. At high viscosity, different morphologies are observed by varying the thermal diffusivity. In the range of thermal diffusivities with domains growing parallel to the walls, temperature and phase separation fronts propagate towards the inner of the system with power-law behavior. At low viscosity hydrodynamics favors rounded shapes, and complex patterns with different lengthscales appear. Off-symmetrical systems behave similarly but with more ordered configurations.Comment: Accepted for publication in Phys. Rev. E, 11 figures, best quality figures available on reques

Study of a high spatial resolution 10B-based thermal neutron detector for application in neutron reflectometry: the Multi-Blade prototype

Although for large area detectors it is crucial to find an alternative to detect thermal neutrons because of the 3He shortage, this is not the case for small area detectors. Neutron scattering science is still growing its instruments' power and the neutron flux a detector must tolerate is increasing. For small area detectors the main effort is to expand the detectors' performances. At Institut Laue-Langevin (ILL) we developed the Multi-Blade detector which wants to increase the spatial resolution of 3He-based detectors for high flux applications. We developed a high spatial resolution prototype suitable for neutron reflectometry instruments. It exploits solid 10B-films employed in a proportional gas chamber. Two prototypes have been constructed at ILL and the results obtained on our monochromatic test beam line are presented here