The microbunching instability has been a long-standing issue for high-brightness free-electron lasers (FELs) and is a significant showstopper to achieve full longitudinal coherence in the x-ray ...regime. This paper reports the first experimental demonstration of microbunching instability mitigation through transverse Landau damping, based on linear optics control in a dispersive region. Analytical predictions for the microbunching content are supported by numerical calculations of the instability gain. The effect is confirmed through the experimental characterization of the spectral brightness of the FERMI FEL under different transverse optics configurations of the transfer line between the linear accelerator and the FEL. Published by the American Physical Society 2024
This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is ...introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in machine learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying machine learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while neural networks are slowly being incorporated in cardiovascular research, other important techniques such as semi-supervised learning and federated learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
Soil organic matter (SOM) is an important factor influencing aggregate stability. Interactions between SOM and soil structure are widely studied, although the subtle relationship between SOM content, ...pore size distribution and aggregate stability is not fully known. Here we investigate such a relationship by means of a long‐term experiment established in 1962 in northeastern Italy, which considers different fertilizer practices (organic, mineral and mixed) applied to a continuous maize crop rotation. We measured wet stability of 1–2 mm aggregates subjected to different pretreatments. Both soil physical properties (such as pore size distribution and hydrophobicity) and chemical properties (soil organic and humic carbon content) affecting aggregate stability were considered. The chemical structure of humic substances was characterized by thermal and spectroscopic analyses (TG‐DTA, DRIFT and 1H HR MAS NMR). The Pore‐Cor network model was then applied to evaluate the contribution of hydrophobicity and porosity to aggregate wetting. Our study suggests that SOM and its humic fraction can affect aggregate wetting and consequently slaking by modifying the pore size distribution with a shift from micropores (5–30 µm) and mesopores (30–75 µm) to ultramicropores (0.1–5 µm); hydrophobicity was also increased as a result of different humic composition. Spectroscopic analysis showed that hydrophobic compounds were mostly associated with complex humic molecules. Models of fast wetting dynamics, however, suggest that the contribution that hydrophobicity makes to aggregate stability, especially to soils with large carbon inputs, may not be the most significant factor.
FERMI is the first user facility based upon an externally seeded free-electron laser (FEL) and was designed to deliver high quality, transversely and longitudinally coherent radiation pulses in the ...extreme ultraviolet and soft x-ray spectral regimes. The FERMI linear accelerator includes a laser heater to control the longitudinal microbunching instability, which otherwise is expected to degrade the quality of the high brightness electron beam sufficiently to reduce the FEL output intensity and spectral brightness. In this paper, we present the results of the FERMI laser heater commissioning. For the first time, we show that optimizing the electron beam heating at an upstream location (beam energy, 100 MeV) leads to a reduction of the incoherent energy spread at the linac exit (beam energy, 1.2 GeV). We also discuss some of the positive effects of such heating upon the emission of coherent optical transition radiation and the FEL output intensity.
Laser-heater systems have been demonstrated to be an important component for the accelerators that drive high gain free electron laser (FEL) facilities. These heater systems suppress longitudinal ...microbunching instabilities by inducing a small and controllable slice energy spread to the electron beam. For transversely uniform heating, the energy spread augmentation is characterized by a non-Gaussian distribution. In this Letter, we demonstrate experimentally that in addition to suppression of the microbunching instability, the laser heater-induced energy distribution can be preserved to the FEL undulator entrance, significantly impacting the performance of high-gain harmonic generation (HGHG) FELs, especially at soft x-ray wavelengths. In particular, we show that the FEL intensity has several local maxima as a function of the induced heating caused by the non-Gaussian energy distribution together with a strong enhancement of the power at high harmonics relative to that expected for an electron beam with an equivalent Gaussian energy spread at an undulator entrance. These results suggest that a single stage HGHG FEL can produce scientifically interesting power levels at harmonic numbers m ≥ 25 and with current seed laser technology could reach output photon energies above 100 eV or greater.
Se evaluó la respuesta de indicadores bioquímicos en función de la reducción de la disponibilidad hídrica a partir de inicio de encañazón en trigo, etapa importante en la definición de componentes de ...rendimiento del cultivo. Se trabajó en invernáculo, en macetas, con dos genotipos de trigo Triticum aestivum L., ACA 315 y Buck Arriero, contrastantes en tolerancia a sequía, sometidos a suspensión del riego durante 0, 5, 10 y 15 días. Por análisis de componentes principales, se determinó que el 71% de la variabilidad total se explicó por los dos primeros componentes. Las variables se separaron en función de los días de suspensión del riego, lo que permitió definir al componente 1 como indicador del contenido hídrico del tejido. El contenido relativo de agua, potencial osmótico y pigmentos estuvieron asociados entre ellos y positivamente con el componente 1. Mientras que proteína, prolina y actividad enzimática antioxidante estuvieron correlacionadas negativamente con el contenido hídrico del tejido. Para el componente 2, se observó que la prolina estuvo asociada negativamente, mientras que la glucosa, fructosa y sacarosa se asociaron positivamente. La superóxido dismutasa estuvo asociada positivamente con el componente 2, la peroxidasa correlacionó negativamente. Las diferencias observadas dependen de la magnitud del estrés y se observó una acumulación diferencial de metabolitos en los genotipos. La concentración de prolina fue el mejor indicador bioquímico del contenido hídrico del tejido y de tolerancia al estrés hídrico.
In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different ...Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.
Artificial intelligence (AI) comprises a wide range of technologies and methods with heterogeneous degrees of complexity, applications, and abilities. In the cardiovascular field, AI holds the ...potential to fulfil many unsolved challenges, eventually translating into improved patient care. In particular, AI appears as the most promising tool to overcome the gap between ever-increasing data-rich technologies and their practical implementation in cardiovascular research, in the cardiologist routine, in the patient daily life and at the healthcare-policy level. A multiplicity of AI technologies is progressively pervading several aspects of precision cardiovascular medicine including early diagnosis, automated imaging processing and interpretation, disease sub-phenotyping, risk prediction and remote monitoring systems. Several methodological, logistical, educational, and ethical challenges are emerging by integrating AI systems at any stage of cardiovascular medicine. This review will discuss the basics of AI methods, the growing body of evidence supporting the role of AI in the cardiovascular field and the challenges to overcome for an effective AI-integrated cardiovascular medicine.
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