Ferromagnetism of a Repulsive Atomic Fermi Gas in an Optical Lattice: a Quantum Monte Carlo Study

Using continuous-space quantum Monte Carlo methods we investigate the zero-temperature ferromagnetic behavior of a two-component repulsive Fermi gas under the influence of periodic potentials that describe the effect of a simple-cubic optical lattice. Simulations are performed with balanced and with imbalanced components, including the case of a single impurity immersed in a polarized Fermi sea (repulsive polaron). For an intermediate density below half filling, we locate the transitions between the paramagnetic, and the partially and the fully ferromagnetic phases. As the intensity of the optical lattice increases, the ferromagnetic instability takes place at weaker interactions, indicating a possible route to observe ferromagnetism in experiments performed with ultracold atoms. We compare our findings with previous predictions based on the standard computational method used in material science, namely density functional theory, and with results based on tight-binding models.Comment: Published version with Supplemental Material. Added comparison with Hubbard model result

Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks

The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionalities is that, after they have learned a probability distribution from a dataset, they allow exact and efficient sampling of typical system configurations. Here we employ a neural autoregressive distribution estimator (NADE) to boost Markov chain Monte Carlo (MCMC) simulations of a paradigmatic classical model of spin-glass theory, namely the two-dimensional Edwards-Anderson Hamiltonian. We show that a NADE can be trained to accurately mimic the Boltzmann distribution using unsupervised learning from system configurations generated using standard MCMC algorithms. The trained NADE is then employed as smart proposal distribution for the Metropolis-Hastings algorithm. This allows us to perform efficient MCMC simulations, which provide unbiased results even if the expectation value corresponding to the probability distribution learned by the NADE is not exact. Notably, we implement a sequential tempering procedure, whereby a NADE trained at a higher temperature is iteratively employed as proposal distribution in a MCMC simulation run at a slightly lower temperature. This allows one to efficiently simulate the spin-glass model even in the low-temperature regime, avoiding the divergent correlation times that plague MCMC simulations driven by local-update algorithms. Furthermore, we show that the NADE-driven simulations quickly sample ground-state configurations, paving the way to their future utilization to tackle binary optimization problems.Comment: 13 pages, 14 figure

Coordination networks incorporating halogen-bond donor sites and azobenzene groups

Two Zn coordination networks, [Zn(1)(Py)2]2(2-propanol)n (3) and [Zn(1)2(Bipy)2](DMF)2n (4), incorporating halogen-bond (XB) donor sites and azobenzene groups have been synthesized and fully characterized. Obtaining 3 and 4 confirms that it is possible to use a ligand wherein its coordination bond acceptor sites and XB donor sites are on the same molecular scaffold (i.e., an aromatic ring) without interfering with each other. We demonstrate that XBs play a fundamental role in the architectures and properties of the obtained coordination networks. In 3, XBs promote the formation of 2D supramolecular layers, which, by overlapping each other, allow the incorporation of 2-propanol as a guest molecule. In 4, XBs support the connection of the layers and are essential to firmly pin DMF solvent molecules through I⋯O contacts, thus increasing the stability of the solvated systems

Incidence of mild cognitive impairment and dementia in Parkinson's disease: The Parkinson's disease cognitive impairment study

Background: Cognitive impairment in Parkinson's disease (PD) includes a spectrum varying from Mild Cognitive Impairment (PD-MCI) to PD Dementia (PDD). The main aim of the present study is to evaluate the incidence of PD-MCI, its rate of progression to dementia, and to identify demographic and clinical characteristics which predict cognitive impairment in PD patients. Methods: PD patients from a large hospital-based cohort who underwent at least two comprehensive neuropsychological evaluations were retrospectively enrolled in the study. PD-MCI and PDD were diagnosed according to the Movement Disorder Society criteria. Incidence rates of PD-MCI and PDD were estimated. Clinical and demographic factors predicting PD-MCI and dementia were evaluated using Cox proportional hazard model. Results: Out of 139 enrolled PD patients, 84 were classified with normal cognition (PD-NC), while 55 (39.6%) fulfilled the diagnosis of PD-MCI at baseline. At follow-up (mean follow-up 23.5 ± 10.3 months) 28 (33.3%) of the 84 PD-NC at baseline developed MCI and 4 (4.8%) converted to PDD. The incidence rate of PD-MCI was 184.0/1000 pyar (95% CI 124.7-262.3). At multivariate analysis a negative association between education and MCI development at follow-up was observed (HR 0.37, 95% CI 0.15-0.89; p = 0.03). The incidence rate of dementia was 24.3/1000 pyar (95% CI 7.7-58.5). Out of 55 PD-MCI patients at baseline, 14 (25.4%) converted to PDD, giving an incidence rate of 123.5/1000 pyar (95% CI 70.3-202.2). A five time increased risk of PDD was found in PD patients with MCI at baseline (RR 5.09, 95% CI 1.60-21.4). Conclusion: Our study supports the relevant role of PD-MCI in predicting PDD and underlines the importance of education in reducing the risk of cognitive impairment

Density functional theory versus quantum Monte Carlo simulations of Fermi gases in the optical-lattice arena

We benchmark the ground state energies and the density profiles of atomic repulsive Fermi gases in optical lattices computed via Density Functional Theory (DFT) against the results of diffusion Monte Carlo (DMC) simulations. The main focus is on a half-filled one-dimensional optical lattices, for which the DMC simulations performed within the fixed-node approach provide unbiased results. This allows us to demonstrate that the local spin-density approximation (LSDA) to the exchange-correlation functional of DFT is very accurate in the weak and intermediate interactions regime, and also to underline its limitations close to the strongly-interacting Tonks-Girardeau limit and in very deep optical lattices. We also consider a three dimensional optical lattice at quarter filling, showing also in this case the high accuracy of the LSDA in the moderate interaction regime. The one-dimensional data provided in this study may represent a useful benchmark to further develop DFT methods beyond the LSDA and they will hopefully motivate experimental studies to accurately measure the equation of state of Fermi gases in higher-dimensional geometries.Comment: 8 pages, 7 figures, plus supplemental material (1 page). Typos correcte

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

Dilute Bose gas with correlated disorder: A Path Integral Monte Carlo study

We investigate the thermodynamic properties of a dilute Bose gas in a correlated random potential using exact path integral Monte Carlo methods. The study is carried out in continuous space and disorder is produced in the simulations by a 3D speckle pattern with tunable intensity and correlation length. We calculate the shift of the superfluid transition temperature due to disorder and we highlight the role of quantum localization by comparing the critical chemical potential with the classical percolation threshold. The equation of state of the gas is determined in the regime of strong disorder, where superfluidity is suppressed and the normal phase exists down to very low temperatures. We find a $T^2$ dependence of the energy in agreement with the expected behavior in the Bose glass phase. We also discuss the major role played by the disorder correlation length and we make contact with a Hartree-Fock mean-field approach that holds valid if the correlation length is very large. The density profiles are analyzed as a function of temperature and interaction strength. Effects of localization and the depletion of the order parameter are emphasized in the comparison between local condensate and total density. At very low temperature we find that the energy and the particle distribution of the gas are very well described by the T=0 Gross-Pitaevskii theory even in the regime of very strong disorder.Comment: 27 pages, 20 figure

The Impact of Specific Viruses on Clinical Outcome in Children Presenting with Acute Heart Failure

Abstract: The presence and type of viral genomes have been suggested as the main etiology for inflammatory dilated cardiomyopathy. Information on the clinical implication of this finding in a large population of children is lacking. We evaluated the prevalence, type, and clinical impact of specific viral genomes in endomyocardial biopsies (EMB) collected between 2001 and 2013 among 63 children admitted to our hospital for acute heart failure (median age 2.8 years). Viral genome was searched by polymerase chain reaction (PCR). Patients underwent a complete two-dimensional echocardiographic examination at hospital admission and at discharge and were followed-up for 10 years. Twenty-seven adverse events (7 deaths and 20 cardiac transplantations) occurred during the follow-up. Viral genome was amplified in 19/63 biopsies (35%); PVB19 was the most commonly isolated virus. Presence of specific viral genome was associated with a significant recovery in ejection fraction, compared to patients without viral evidence (p < 0.05). In Cox-regression analysis, higher survival rate was related to virus-positive biopsies (p < 0.05). When comparing long-term prognosis among different viral groups, a trend towards better prognosis was observed in the presence of isolated Parvovirus B19 (PVB19) (p = 0.07). In our series, presence of a virus-positive EMB (mainly PVB19) was associated with improvement over time in cardiac function and better long-term prognosis

2-Iodo-imidazolium receptor binds oxoanions via charge-assisted halogen bonding.

A detailed (1)H-NMR study of the anion binding properties of the 2-iodo-imidazolium receptor 1 in DMSO allows to fully attribute the observed affinities to strong charge-assisted C-IX(-) halogen bonding (XB). Stronger binding was observed for oxoanions over halides. Phosphate, in particular, binds to 1 with an association constant of ca. 10(3) M(-1), which is particularly high for a single X-bond. A remarkably short C-IO(-) contact is observed in the structure of the salt 1·H(2)PO(4)(-)

Enhancing the scratch resistance of polycarbonate with poly(ethylene oxide)-silica hybrid coatings

Scratch-resistant coatings for bisphenol-A polycarbonate sheets were obtained by the sol–gel synthesis of an organic–inorganic hybrid system based on poly(ethylene oxide) and silica. The organic–inorganic hybrids were thermally cured into hard transparent coatings by using conventional and microwave (MW) ovens. Both techniques proved to be equally efficient in promoting the system’s crosslinking, as evaluated by 29Si MAS-NMR. The MW-assisted curing, however, was much faster. Photoelasticity analysis showed that MW-assisted curing causes localized overheating of the samples, inducing a state of residual plane stresses that bring about dimensional instability of the coated material. Instrumented scratch tests for the coated samples revealed an increase of 1 order of magnitude in the minimal load at which a scratch track appears on the sample surface. However, the friction coefficient values for samples with thermally cured coatings were lower than those produced by MW-assisted curing