Flux creep in Bi2Sr2CaCu2O8(sub +x) single crystals

The results of a magnetic study on a Bi2Sr2CaCu2O(8+x) single crystal are reported. Low field susceptibility (dc and ac), magnetization cycles and time dependent measurements were performed. With increasing the temperature the irreversible regime of the magnetization cycles is rapidly restricted to low fields, showing that the critical current J(sub c) becomes strongly field dependent well below T(sub c). At 2.4 K the critical current in zero field, determined from the remanent magnetization by using the Bean formula for the critical state, is J(sub c) = 2 10(exp 5) A/sq cm. The temperature dependence of J(sub c) is satisfactorily described by the phenomenological law J(sub c) = J(sub c) (0) (1 - T/T(sub c) (sup n), with n = 8. The time decay of the zero field cooled magnetization and of the remanent magnetization was studied at different temperatures for different magnetic fields. The time decay was found to be logarithmic in both cases, at least at low temperatures. At T = 4.2 K for a field of 10 kOe applied parallel to the c axis, the average pinning energy, determined by using the flux creep model, is U(sub o) = 0.010 eV

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of nonlinear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are nonlinearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions without the need for time-consuming machine learning algorithms. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware

Excitation of the l=3 diocotron mode in a pure electron plasma by means of a rotating electric field

The l=3 diocotron mode in an electron plasma confined in a Malmberg–Penning trap has been resonantly excited by means of a rotating electric field applied on an azimuthally four-sectored electrode. The experimental observations are interpreted with a theory based on the linearization of the drift-Poisson equations and by means of two-dimensional particle-in-cell simulations. The experimental technique presented in this paper is able to selectively excite different diocotron perturbations and can be efficiently used for electron or positron plasma control and manipulation

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

Diocotron modulation in an electron plasma through continuous radio-frequency excitation

The application of a radio-frequency (RF) excitation to any electrode of a Penning-Malmberg trap may result in significant electron heating and ionization of the residual gas with the formation of a plasma column when the RF frequency is of the order or larger than the typical axial bounce frequencies of few-eV electrons. The use of a quadrupolar excitation can induce additional phenomena, like formation of dense, narrow-cross section columns which exhibit an m\u3b8 = 1 diocotron mode, i.e., a rotation of their center around the trap axis. A series of experiments is presented and discussed showing that the continuous application of such excitation causes a dramatic perturbation of the plasma equilibrium also involving continuous production and loss of particles in the trapping region. In particular, the growth of the first diocotron mode is suppressed even in the presence of ion resonance and resistive instability and the mode exhibits steady-state or underdamped amplitude and frequency modulations, typically in the Hertz range

Electron Beam Size Measurements Using the Heterodyne Near Field Speckles at ALBA

Experiments using the heterodyne near field speckle method (HNFS) have been performed at ALBA to characterize the spatial coherence of the synchrotron radiation, with the ultimate goal of measuring both the horizontal and vertical electron beam sizes. The HNFS technique consists on the analysis of the interference between the radiation scattered by a colloidal suspension of nanoparticles and the synchrotron radiation, which in this case corresponds to the hard x-rays (12keV) produced by the in-vacuum undulator of the NCD-Sweet beamline. This paper describes the fundamentals of the technique, possible limitations, and shows the first experimental results changing the beam coupling of the storage ring

Progress on transverse beam profile measurement using the heterodyne near field speckles method at Alba

We present the recent developments of a study aimed at measuring the transverse beam profile using the Heterodyne Near Field Speckles (HNFS) method. The HNFS technique works by illuminating a suspension of Brownian nanoparticles with synchrotron radiation and studying the resulting interference pattern. The transverse coherence of the source, and therefore, under the conditions of validity of the Van Cittert and Zernike theorem, the transverse electron beam size is retrieved from the interference between the transmitted beam and the spherical waves scattered by each nanoparticle. We here describe the fundamentals of this technique, as well as the recent experimental results obtained with 12 keV undulator radiation at the NCD beamline at the ALBA synchrotron. The applicability of such a technique for future accelerators (e.g. CLIC or FCC) is also discussed

About Distress in Chronic Pain Conditions: A Pre–Post Study on the Effectiveness of a Mindfulness-Based Intervention for Fibromyalgia and Low Back Pain Patients

Chronic pain (CP) affects about 30% of the global population and poses significant challenges to individuals and healthcare systems worldwide. The interactions between physiological, psychological, and social factors are crucial in the onset and development of CP conditions. This study aimed to evaluate the effectiveness of mindfulness-based intervention, examining its impact on perceived stress (PSS), depression and anxiety (BDI-II, PGWBI/DEP, SAS, STAI Y), sleep quality (PSQI), and mindfulness abilities (MAAS) in individuals with CP. Participants (N = 89, 84.3% female) underwent one of two diagnoses [fibromyalgia (FM) or low back pain (LBP)] and took part in an MBSR intervention. The mindfulness program proved effective in reducing PSQI scores (F = 11.97; p < 0.01) over time, independently of the type of diagnosis. There was also a marginal increase in trait mindfulness as measured by MAAS (F = 3.25; p = 0.07) in both groups. A significant difference between the two groups was found for the effect on PSS: F (1,87) = 6.46; p < 0.05. Mindfulness practice also reduced anxiety in FM and depressive symptoms in LBP, indicating a reduction in psychological distress among participants. Our findings suggest that mindfulness-based interventions may offer promising avenues for personalized pain management in clinical settings

Excitation of the l=2 diocotron mode with a resistive load

The resistive wall instability of the l=2 diocotron mode in a pure electron plasma has been investigated with a systematic variation of the parameters of the external impedance connected to a pair of sectored electrodes. The measured growth rate is well described by a linear perturbation theory of the two-dimensional drift-Poisson system