Can Inflation solve the Hierarchy Problem?

Inflation with tunneling from a false to a true vacuum becomes viable in the presence of a scalar field that slows down the initial de Sitter phase. As a by-product this field also sets dynamically the value of the Newton constant observed today. This can be very large if the tunneling rate (which is exponentially sensitive to the barrier) is small enough. Therefore along with Inflation we also provide a natural dynamical explanation for why gravity is so weak today. Moreover we predict a spectrum of gravity waves peaked at around 0.1 mHz, that will be detectable by the planned space inteferometer LISA. Finally we discuss interesting predictions on cosmological scalar and tensor fluctuations in the light the WMAP 3-year data.Comment: 7 pages. Replaced version with comparison with WMAP 3-year dat

Sex and Age-Related Differences in Neuroticism and Allostatic Load Index in Urban Patients with General Anxiety Disorder Treated with Alprazolam

Introduction: Allostatic Load (AL) index proposes indicators for the functioningof the main potentially stress-affected systems. Sex differences instress response and stress-related diseases susceptibility have been describedfor the general population. In this observational study we describe the effectsof sex and age on allostatic load variables, in a cohort of patients with generalanxiety disorders and neuroticism treated with alprazolam during 12 weeks,before and after treatment. Methods: Patients with general (DSM IV) anxietydisorders with >6 in Hamilton scale, Allostatic load (>1 Crimmins and SeemanAL modified criteria) and neuroticism >18 (NEO-FFI inventory), wereincluded. All patients completed psychiatric assessment, allostatic load indexdetermination before (−1 week) and after 12 weeks of treatment with alprazolam(0.25 - 1 mg/t.i.d). Allostatic load parameters comprised cardiovascular,metabolic and inflammatory variables. Univariate analysis (two-wayANOVA), Student?s t-test (related variables) and Pearson correlations weredetermined. Results: Fifty-four patients, 35 females (48.6 ± 11.7 years) and 19males (44.2 ± 12.8 years) with general anxiety disorder were included; 28 patientswith <50 years (60.7% females), and 26 with ≥50 years (69.2% females).Younger patients (<50 years) (two-way ANOVA, p = 0.02) were significantlyassociated with lower allostatic load index after treatment. However, womenshowed higher anxiety levels in both, before (Two-way ANOVA, p = 0.059)and after treatment (two-way ANOVA, p = 0.005), with a significantly betterprofile than men in many individual AL variables, particularly cardiovascular(systolic blood pressure), obesity (body mass index), and lipids (higher HDLlevels). After treatment a higher reduction of fibrinogen levels was found inmen (two-way ANOVA, p = 0.02). Conclusions: In this preliminary analysiswe described sex and age differences in psychiatry aspects and allostatic loadindexes in patients with general anxiety disorders in the short-term treatmentwith alprazolam. These considerations remark the need of pondering sex andage differences during the use of drugs for protracted periods.Fil: D`Alessio, Luciana. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos A.. Henri Laborit Institute Of Biosciences; ArgentinaFil: Remedi, Carolina. Henri Laborit Institute Of Biosciences; Argentina; ArgentinaFil: Roldán, Emilio J. A.. Instituto de Neurobiología IDNEU; Argentin

Spin fluctuations, susceptibility and the dipole oscillation of a nearly ferromagnetic Fermi gas

We discuss the spin fluctuations and the role played by the magnetic susceptibility in an atomic Fermi gas interacting with positive scattering length. Both thermal and quantum fluctuations are considered. Using a sum rule approach and recent {\it ab initio} Monte Carlo results for the magnetic susceptibility of uniform matter we provide explicit predictions for the frequency of the spin dipole oscillation of a gas trapped by a harmonic potential and discuss the deviations from the behaviour of an ideal gas when the system approaches the ferromagnetic transition. The role of the Landau's parameters in the characterization of the magnetic properties is also discussed.Comment: 5 pages, 2 figure

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Intersecting Attractors

We apply the entropy formalism to the study of the near-horizon geometry of extremal black p-brane intersections in D>5 dimensional supergravities. The scalar flow towards the horizon is described in terms an effective potential given by the superposition of the kinetic energies of all the forms under which the brane is charged. At the horizon active scalars get fixed to the minima of the effective potential and the entropy function is given in terms of U-duality invariants built entirely out of the black p-brane charges. The resulting entropy function reproduces the central charges of the dual boundary CFT and gives rise to a Bekenstein-Hawking like area law. The results are illustrated in the case of black holes and black string intersections in D=6, 7, 8 supergravities where the effective potentials, attractor equations, moduli spaces and entropy/central charges are worked out in full detail.Comment: 1+41 pages, 2 Table

Effect of different good solvents in flash nano-precipitation via multi-scale population balance modeling-CFD coupling approach

A computational and modeling approach is used to highlight the key factors that affect polymer nanoparticles (NP) formation in flash nano-precipitation (FNP), when the good solvent, e.g., acetone, is replaced by acetonitrile, tetrahydrofuran and tert-butanol. A population balance model is coupled with computational fluid dynamics to study the kinetics effects on FNP. The mean NP size is predicted in terms of mean radius of gyration via the Flory law of real polymers. The effect of different good solvents is modeled in terms of solute–solvent interactions, using the Flory–Huggins theory and Hansen solubility parameters. Promising results show how the proposed methodology is able to investigate the role played by different good solvents, analyzing single factors at the time. A deep insight into both the dynamics of mixing and the dynamics of aggregation is therefore reached and the main mechanisms involved are pointed out, showing a good agreement with experimental data

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

Detrended fluctuation analysis (DFA) and detrended moving average (DMA) are two scaling analysis methods designed to quantify correlations in noisy non-stationary signals. We systematically study the performance of different variants of the DMA method when applied to artificially generated long-range power-law correlated signals with an {\it a-priori} known scaling exponent $\alpha_{0}$ and compare them with the DFA method. We find that the scaling results obtained from different variants of the DMA method strongly depend on the type of the moving average filter. Further, we investigate the optimal scaling regime where the DFA and DMA methods accurately quantify the scaling exponent $\alpha_{0}$, and how this regime depends on the correlations in the signal. Finally, we develop a three-dimensional representation to determine how the stability of the scaling curves obtained from the DFA and DMA methods depends on the scale of analysis, the order of detrending, and the order of the moving average we use, as well as on the type of correlations in the signal.Comment: 15 pages, 16 figure

Cell cultures harbouring constructs of different pig promoter polymorphisms show different transcriptional efficiency in gene reporter systems

AbstractProduction traits variability among and within breeds, differences among developmental stages or the response to different environments are in part due to genetic factors that affect gene expression. Within the context of an Italian FIRB project, whose objective is to identify genes and molecular mechanisms affecting meat quality and production traits in pig, we studied the promoter regions of candidate genes selected on the basis of their physiological role in animal tissue development or composition. Genomic DNA was isolated from liver or muscle tissue of individuals belonging to Large White and Casertana breed. PCR primers were designed to amplify 5' upstream region of SCD (Stearoyl-CoA Desaturase), LDLR (Low Density Lipoprotein Receptor), LEP (Leptin), MSTN (Myostatin), ACTA1 (Alpha-actin) and HFABP (Heart Fatty Acid Binding Protein) genes using sequences available at NCBI. A total of 19 single nucleotide polymorphisms (SNPs) not previously described were characterised. Some haplotypes, harbou..

Exploiting Chordality in Optimization Algorithms for Model Predictive Control

In this chapter we show that chordal structure can be used to devise efficient optimization methods for many common model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.Comment: arXiv admin note: text overlap with arXiv:1502.0638

Non-local density correlations as signal of Hawking radiation in BEC acoustic black holes

We have used the analogy between gravitational systems and non-homogeneous fluid flows to calculate the density-density correlation function of an atomic Bose-Einstein condensate in the presence of an acoustic black hole. The emission of correlated pairs of phonons by Hawking-like process results into a peculiar long-range density correlation. Quantitative estimations of the effect are provided for realistic experimental configurations.Comment: Strongly revised version. 5 pages, 3 eps figure