Intube two-phase flow probabilities based on capacitance signal clustering

To study the objectivity in flow pattern mapping of horizontal two-phase flow in macroscale tubes, a capacitance sensor is developed for use with refrigerants. Sensor signals are gathered with R410A in an 8mm I.D. smooth tube at a saturation temperature of 15°C in the mass velocity range of 200 to 500kg/m²s and vapour quality range from 0 to 1 in steps of 0.025. A visual classification based on high speed camera images is made for comparison reasons. A statistical analysis of the sensor signals shows that the average and the variance are suitable for flow regime classification into slug flow, intermittent flow and annular flow by using a the fuzzy c-means clustering algorithm. This soft clustering algorithm perfectly predicts the slug/intermittent flow transition compared to our visual observations. The intermittent/annular flow transition is found at higher vapour qualities, but with the same trend compared to our observations and the prediction of [Barbieri et al., 2008, Flow patterns in convective boiling of refrigerant R-134a in smooth tubes of several diameters, 5th European Thermal-Sciences Conference, The Netherlands]. The intermittent/annular flow transition is very gradual. A probability approach can therefore better describe such a transition. The membership grades of the cluster algorithm can be interpreted as flow probabilities. These probabilities are further compared to time fraction functions of [Jassim et al., 2008, Prediction of refrigerant void fraction in horizontal tubes using probabilistic flow regime maps

deepNEC: A Novel Alignment-Free Tool for the Characterization of Nitrification-Related Enzymes Using Deep Learning, A Step Towards Comprehensive Understanding of the Nitrogen Cycle

Abstract: Nitrification is an important microbial two-step transformation in the global nitrogen cycle, as it is the only natural process that produces nitrate within a system. The functional annotation of nitrification-related enzymes has a broad range of applications in metagenomics, agriculture, industrial biotechnology, etc. The time and resources needed for determining the function of enzymes experimentally are restrictively costly. Therefore, an accurate genome-scale computational prediction of the nitrification-related enzymes has become much more important.In this study, we developed an alignment-free computational approach to determine the nitrification-related enzymes from the sequence itself. We propose deepNEC, a novel end-to-end feature selection and classification model training approach for nitrification-related enzyme prediction. The algorithm has been developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input data. The raw protein sequences encoding is used as an input, extracting sequential and convolutional features from raw encoded protein sequences based on classification rather than using traditional alignment-based methods for nitrification-related enzyme prediction. Two large datasets of protein sequences, enzymes, and non-enzymes were used to train the models with protein sequence features like amino acid composition, dipeptide composition, conformation transition and distribution (CTD), NMBroto, conjoint, quasi order, etc. The K-fold cross-validation and independent testing were performed to validate our model training. The models were further implemented as web server (deepNEC) which is publicly available. deepNEC uses a two-tier approach for prediction; in the first phase, it will predict a query sequence as enzyme or non-enzyme; in the second phase, it will further predict and classify enzymes into various nitrification-related enzyme classes. Among all, the DPC+NMBrot hybrid feature gave the best prediction performance (accuracy of 96.15% in k-fold training and 93.43% in independent testing) with an MCC (0.92 training and 0.87 independent testing) in phase I; in phase-II, the DPC feature gave the best prediction performance for 13 nitrification-related enzyme classes. We have also implemented a homology-based method to remove false negatives. The tool can be accessed freely at http://bioinfo.usu.edu/deepNEC/. Presentation Time: Thursday, 12-1 p.m.Zoom link: https://usu-edu.zoom.us/j/83738417563?pwd=SHlRcGdaaTdmVzVUOENqTnVHQ3UzZz0

Mapping of horizontal refrigerant two-phase flow patterns based on clustering of capacitive sensor signals

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Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations

Determining phase diagrams and phase transitions semi-automatically using machine learning has received a lot of attention recently, with results in good agreement with more conventional approaches in most cases. When it comes to more quantitative predictions, such as the identification of universality class or precise determination of critical points, the task is more challenging. As an exacting test-bed, we study the Heisenberg spin-1/2 chain in a random external field that is known to display a transition from a many-body localized to a thermalizing regime, which nature is not entirely characterized. We introduce different neural network structures and dataset setups to achieve a finite-size scaling analysis with the least possible physical bias (no assumed knowledge on the phase transition and directly inputing wave-function coefficients), using state-of-the-art input data simulating chains of sizes up to L=24. In particular, we use domain adversarial techniques to ensure that the network learns scale-invariant features. We find a variability of the output results with respect to network and training parameters, resulting in relatively large uncertainties on final estimates of critical point and correlation length exponent which tend to be larger than the values obtained from conventional approaches. We put the emphasis on interpretability throughout the paper and discuss what the network appears to learn for the various used architectures. Our findings show that a it quantitative analysis of phase transitions of unknown nature remains a difficult task with neural networks when using the minimally engineered physical input.Comment: v2: published versio

Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach