Negative Energy Modes and Gravitational Instability of Interpenetrating Fluids

We study the longitudinal instabilities of two interpenetrating fluids interacting only through gravity. When one of the constituents is of relatively low density, it is possible to have a band of unstable wave numbers well separated from those involved in the usual Jeans instability. If the initial streaming is large enough, and there is no linear instability, the indefinite sign of the free energy has the possible consequence of explosive interactions between positive and negative energy modes in the nonlinear regime. The effect of dissipation on the negative energy modes is also examined

Experimental and numerical research activity on a packed bed TES system

This paper presents the results of experimental and numerical research activities on a packed bed sensible thermal energy storage (TES) system. The TES consists of a cylindrical steel tank filled with small alumina beads and crossed by air used as the heat transfer fluid. Experimental tests were carried out hile varying some operating parameters such as the mass flow rate, the inlet–outlet temperature thresholds and the aspect ratio (length over diameter). Numerical simulations were carried out using a one-dimensional model, specifically developed in the Matlab-Simulink environment and a 2D axisymmetric model based on the ANSYS-Fluent platform. Both models are based on a two-equation transient approach to calculate fluid and solid phase temperatures. Thermodynamic properties were considered to be temperature-dependent and, in the Computational Fluid Dynamics (CFD) model, variable porosity of the bed in the radial direction, thermal losses and the effective conductivity of the alumina beads were also considered. The simulation results of both models were compared to the experimental ones, showing good agreement. The one-dimensional model has the advantage of predicting the axial temperature distribution with a very low computational cost, but it does not allow calculation of the correct energy stored when the temperature distribution is strongly influenced by the wall. To overcome this problem a 2D CFD model was used in this work

Numerical Solutions of Matrix Differential Models using Cubic Matrix Splines II

This paper presents the non-linear generalization of a previous work on matrix differential models. It focusses on the construction of approximate solutions of first-order matrix differential equations Y'(x)=f(x,Y(x)) using matrix-cubic splines. An estimation of the approximation error, an algorithm for its implementation and illustrative examples for Sylvester and Riccati matrix differential equations are given.Comment: 14 pages; submitted to Math. Comp. Modellin

Detection of Solar Coronal Mass Ejections from Raw Images with Deep Convolutional Neural Networks

Coronal Mass Ejections (CMEs) are massive releases of plasma from the solar corona. When the charged material is ejected towards the Earth, it can cause geomagnetic storms and severely damage electronic equipment and power grids. Early detection of CMEs is therefore crucial for damage containment. In this paper, we study detection of CMEs from sequential images of the solar corona acquired by a satellite. A low-complexity deep neural network is trained to process the raw images, ideally directly on the satellite, in order to provide early alerts

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager, is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities

Skeleton based cage generation guided by harmonic fields

International audienceWe propose a novel user-assisted cage generation tool. We start from a digital character and its skeleton, and create a coarse control cage for its animation. Our method requires minimal interaction to select bending points on the skeleton, and computes the corresponding cage automatically. The key contribution is a volumetric field defined in the interior of the character and embedding the skeleton. The integral lines of such field are used to propagate cutting surfaces from the interior of the character to its skin, and allow us to robustly trace non-planar cross sections that adapt to the local shape of the character. Our method overcomes previous approaches that rely on the popular (but tedious and limiting) cutting planes. We validated our software on a variety of digital characters. Our final cages are coarse yet entirely compliant with the structure induced by the underlying skeleton, enriched with the semantics provided by the bending points selected by the user. Automatic placement of bending nodes for a fully automatic caging pipeline is also supported

Designing Research

The aim of this chapter is to set out a process that researchers can follow to design a robust quantitative research study of occupant behavior in buildings. Central to this approach is an emphasis on intellectual clarity around what is being measured and why. To help achieve this clarity, researchers are encouraged to literally draw these relationships out in the form of a concept map capturing the theoretical model of the cause and effect between occupant motivations and energy use. Having captured diagrammatically how the system is thought to work, the next step is to formulate research questions or hypotheses capturing the relationship between variables in the theoretical model, and to start to augment the diagram with the measurands (things that can actually be measured) that are good proxies for each concept. Once these are identified, the diagram can be further augmented with one or more methods of measuring each measurand. The chapter argues that it is necessary to carefully define concepts and their presumed relationships, and to clearly state research questions and identify what the researcher intends to measure before starting data collection. The chapter also explains the ideas of reliability, validity, and uncertainty, and why knowledge about them is essential for any researcher

Computing the Noncomputable

We explore in the framework of Quantum Computation the notion of computability, which holds a central position in Mathematics and Theoretical Computer Science. A quantum algorithm that exploits the quantum adiabatic processes is considered for the Hilbert's tenth problem, which is equivalent to the Turing halting problem and known to be mathematically noncomputable. Generalised quantum algorithms are also considered for some other mathematical noncomputables in the same and of different noncomputability classes. The key element of all these algorithms is the measurability of both the values of physical observables and of the quantum-mechanical probability distributions for these values. It is argued that computability, and thus the limits of Mathematics, ought to be determined not solely by Mathematics itself but also by physical principles.Comment: Extensively revised and enlarged with: 2 new subsections, 4 new figures, 1 new reference, and a short biography as requested by the journal edito

Towards model-based control of Parkinson's disease

Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson’s disease is gaining increasing acceptance. Thus, the confluence of these three developments—control theory, computational neuroscience and deep brain stimulation—offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson’s disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development