Real Time RF Simulator (RTS) and control

The multi-cavity RTS allows LLRF algorithm development and lab testing prior to commissioning with real cavities and cryomodules. The RTS is a valuable tool since it models the functions, errors and disturbances of real RF systems. The advantage of a RTS over an off-line simulator is that it can be implemented on the actual LLRF hardware, on the same FPGA and processor, and run at the same speed of the LLRF control loop. Additionally the RTS can be shared by collaborators who do not have access to RF systems or when the systems are not available to LLRF engineers. The RTS simulator incorporates hardware, firmware and software errors and limitations of a real implementation, which would be hard to identify and time consuming to model in off-line simulations

Electrify Italy

This study explores a possible pathway to implement a new energy paradigm in Italy based on electrification. The objectives are: • To build a forward-looking vision of possible scenarios at 2022, 2030 and 2050 by integrating a multi-focus perspective on the penetration of renewables and the electrification potential of the residential, industrial and transport sectors. • To estimate the potential benefits of further electrification through the calculation of Key Performance Indicators in four different areas: energy, economy, environment and society. The study shows how the electricity triangle, a paradigm based on clean generation by renewable sources, electrification of final uses, and electricity exchange through efficient smart grids, closes the loop of clean energy and efficient consumption. This leads to improvements in energy, environment, economy and social performances, and boosts the share of renewables in final consumption

Global hybrids from the semiclassical atom theory satisfying the local density linear response

We propose global hybrid approximations of the exchange-correlation (XC) energy functional which reproduce well the modified fourth-order gradient expansion of the exchange energy in the semiclassical limit of many-electron neutral atoms and recover the full local density approximation (LDA) linear response. These XC functionals represent the hybrid versions of the APBE functional [Phys. Rev. Lett. 106, 186406, (2011)] yet employing an additional correlation functional which uses the localization concept of the correlation energy density to improve the compatibility with the Hartree-Fock exchange as well as the coupling-constant-resolved XC potential energy. Broad energetical and structural testings, including thermochemistry and geometry, transition metal complexes, non-covalent interactions, gold clusters and small gold-molecule interfaces, as well as an analysis of the hybrid parameters, show that our construction is quite robust. In particular, our testing shows that the resulting hybrid, including 20\% of Hartree-Fock exchange and named hAPBE, performs remarkably well for a broad palette of systems and properties, being generally better than popular hybrids (PBE0 and B3LYP). Semi-empirical dispersion corrections are also provided.Comment: 12 pages, 4 figure

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

Semimetallic carbon allotrope with topological nodal line in mixed sp2sp^2-sp3sp^3 bonding networks

Graphene is known as a two-dimensional Dirac semimetal, in which electron states are described by the Dirac equation of relativistic quantum mechanics. Three-dimensional analogues of graphene are characterized by Dirac points or lines in momentum space, which are protected by symmetry. Here, we report a novel 3D carbon allotrope belonging to a class of topological nodal line semimetals, discovered by using an evolutionary structure search method. The new carbon phase in monoclinic $C$2$/m$ space group, termed $m$-$C_8$, consists of five-membered rings with $sp^3$ bonding interconnected by $sp^2$-bonded carbon networks. Enthalpy calculations reveal that $m$-$C_8$ is more favorable over recently reported topological semimetallic carbon allotropes, and the dynamical stability of $m$-$C_8$ is verified by phonon spectra and molecular dynamics simulations. Simulated x-ray diffraction spectra propose that $m$-$C_8$ would be one of the unidentified carbon phases observed in detonation shoot. The analysis of electronic properties indicates that $m$-$C_8$ exhibits the nodal line protected by both inversion and time-reversal symmetries in the absence of spin-orbit coupling and the surface band connecting the projected nodal points. Our results may help design new carbon allotropes with exotic electronic properties.Comment: 18 pages, 5 figure

A prognostic model for patients with lymphoma and COVID-19: a multicentre cohort study

Lymphoma represents a heterogeneous hematological malignancy (HM), which is characterized by severe immunosuppression. Patients diagnosed of coronavirus disease 2019 (COVID-19) during the course of HM have been described to have poor outcome, with only few reports specifically addressing lymphoma patients. Here, we investigated the clinical behavior and clinical parameters of a large multicenter cohort of adult patients with different lymphoma subtypes, with the aim of identifying predictors of death. The study included 856 patients, of whom 619 were enrolled prospectively in a 1-year frame and were followed-up for a median of 66 days (range 1-395). Patients were managed as outpatient (not-admitted cohort, n = 388) or required hospitalization (n = 468), and median age was 63 years (range 19-94). Overall, the 30-and 100-days mortality was 13% (95% confi-dence interval (CI), 11% to 15%) and 23% (95% CI, 20% to 27%), respectively. Antilymphoma treatment, including anti-CD20 containing regimens, did not impact survival. Patients with Hodgkin's lymphoma had the more favorable survival, but this was partly related to signifi-cantly younger age. The time interval between lymphoma diagnosis and COVID-19 was inversely related to mortality. Multivariable analysis recognized 4 easy-to-use factors (age, gender, lymphocyte, and platelet count) that were associated with risk of death, both in the admitted and in the not-admitted cohort (HR 3.79 and 8.85 for the intermediate-and high risk group, respectively). Overall, our study shows that patients should not be deprived of the best available treatment of their underlying disease and indicates which patients are at higher risk of death. This study was registered with ClinicalTrials.gov, NCT04352556