Power calculation for gravitational radiation: oversimplification and the importance of time scale

A simplified formula for gravitational-radiation power is examined. It is shown to give completely erroneous answers in three situations, making it useless even for rough estimates. It is emphasized that short timescales, as well as fast speeds, make classical approximations to relativistic calculations untenable.Comment: Three pages, no figures, accepted for publication in Astronomische Nachrichte

Family Networks and School Enrolment: Evidence from a Randomized Social Experiment

We present evidence on whether and how a household's behavior is influenced by the presence and characteristics of its extended family. Using data from the PROGRESA program in Mexico, we exploit information on the paternal and maternal surnames of heads and spouses in conjunction with the Spanish naming convention to identify the inter and intra generational family links of each household to others in the same village. We then exploit the randomized research design of the PROGRESA evaluation data to identify whether the treatment effects of PROGRESA transfers on secondary school enrolment vary according to the characteristics of extended family. We find PROGRESA only raises secondary enrolment among households that are embedded in a family network. Eligible but isolated households do not respond. The mechanism through which the extended family influences household schooling choices is the redistribution of resources within the family network from eligibles that receive de facto unconditional cash transfers from PROGRESA, towards eligibles on the margin of enrolling children into secondary school.extended family network, PROGRESA, resource sharing, schooling

Extended Family Networks in Rural Mexico: A Descriptive Analysis

We provide descriptive evidence on the characteristics of a household’s extended family network using data from the Progresa social assistance program in rural Mexico. We exploit information on the paternal and maternal surnames of household heads and their spouses and the patronymic naming convention to identify the inter and intra generational family links of each household to others in the village. This provides an almost complete mapping of extended family networks structures across 506 Mexican villages, covering 22,000 households and over 130,000 individuals. We then provide evidence on – (i) whether husbands and wives differ in the extent to which members of their extended family are located in geographic proximity; (ii) the characteristics that predict the existence of extended family links; (iii) the similarity of households within the same family network in terms of their poverty, and how this differs within and between generations of the extended family.extended family network, Progresa

Village Economies and the Structure of Extended Family Networks

This paper documents how the structure of extended family networks in rural Mexico relates to the poverty and inequality of the village of residence. Using the Hispanic naming convention, we construct within-village extended family networks in 504 poor rural villages. Family networks are larger (both in the number of members and as a share of the village population) and out-migration is lower the poorer and the less unequal the village of residence. Our results are consistent with the extended family being a source of informal insurance to its members.extended family network, migration, village inequality, village marginality

Mesoscopic continuous and discrete channels for quantum information transfer

We study the possibility of realizing perfect quantum state transfer in mesoscopic devices. We discuss the case of the Fano-Anderson model extended to two impurities. For a channel with an infinite number of degrees of freedom, we obtain coherent behavior in the case of strong coupling or in weak coupling off-resonance. For a finite number of degrees of freedom, coherent behavior is associated to weak coupling and resonance conditions

Droplet minimizers for the Gates-Lebowitz-Penrose free energy functional

We study the structure of the constrained minimizers of the Gates-Lebowitz-Penrose free-energy functional ${\mathcal F}_{\rm GLP}(m)$, non-local functional of a density field $m(x)$, $x\in {\mathcal T}_L$, a $d$-dimensional torus of side length $L$. At low temperatures, ${\mathcal F}_{\rm GLP}$ is not convex, and has two distinct global minimizers, corresponding to two equilibrium states. Here we constrain the average density L^{-d}\int_{{\cal T}_L}m(x)\dd x to be a fixed value $n$ between the densities in the two equilibrium states, but close to the low density equilibrium value. In this case, a "droplet" of the high density phase may or may not form in a background of the low density phase, depending on the values $n$ and $L$. We determine the critical density for droplet formation, and the nature of the droplet, as a function of $n$ and $L$. The relation between the free energy and the large deviations functional for a particle model with long-range Kac potentials, proven in some cases, and expected to be true in general, then provides information on the structure of typical microscopic configurations of the Gibbs measure when the range of the Kac potential is large enough

Particle simulation of granular flows in electrostatic separation processes

In waste processing technology, the recent Corona Electrostatic Separation (CES) method is used to separate conductive from non-conductive particles in recycling streams. This paper proposes an innovative simulation approach based on non-smooth dynamics. In this context, a differential-variational formulation is used to implement a scalable and efficient time integrator that allows the large-scale simulation of trajectories of particles with different properties under the effect of particle-particle interactions and frictional contacts. Issues related to performance optimization, fast collision detection and parallelization of the code are discussed

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques

A NEURAL NETWORK APPROACH TO ANALYSE CAVITATING FLOW REGIME IN AN INTERNAL ORIFICE

none3The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor - liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.Paper ESDA2012-82205M.G. De Giorgi; D. Bello; A.FicarellaDE GIORGI, Maria Grazia; Bello, Daniela; Ficarella, Antoni