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

Janus kinase inhibitors: a new tool for the treatment of axial spondyloarthritis

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease involving the spine, peripheral joints, and entheses. This condition causes stiffness, pain, and significant limitation of movement. In recent years, several effective therapies have become available based on the use of biologics that selectively block cytokines involved in the pathogenesis of the disease, such as tumor necrosis factor-α (TNFα), interleukin (IL)-17, and IL-23. However, a significant number of patients show an inadequate response to treatment. Over 10 years ago, small synthetic molecules capable of blocking the activity of Janus kinases (JAK) were introduced in the therapy of rheumatoid arthritis. Subsequently, their indication extended to the treatment of other inflammatory rheumatic diseases. The purpose of this review is to discuss the efficacy and safety of these molecules in axSpA therapy

When autoantibodies are missing. the challenge of seronegative rheumatoid arthritis

Seronegative rheumatoid arthritis (SNRA) is characterized by the absence of both rheumatoid factor (RF) and antibodies against the cyclic citrullinated protein (ACPA) in serum. However, the differences between the two forms of RA are more complex and have not yet been definitively characterized. Several lines of evidences support the idea that there are specific elements of the two forms, including genetic background, epidemiology, pathogenesis, severity of progression over time, and response to therapy. Clinical features that may differentiate SNRA from SPRA are also suggested by data obtained from classical radiology and newer imaging techniques. Although new evidence seems to provide additional help in differentiating the two forms of RA, their distinguishing features remain largely elusive. It should also be emphasized that the distinctive features of RA forms, if not properly recognized, can lead to the underdiagnosis of SNRA, potentially missing the period called the "window of opportunity" that is critical for early diagnosis, timely treatment, and better prognosis. This review aims to summarize the data provided in the scientific literature with the goal of helping clinicians diagnose SNRA as accurately as possible, with emphasis on the most recent findings available

Advances in the pathogenesis and treatment of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a genetically predisposed, female-predominant disease, characterized by multiple organ damage, that in its most severe forms can be life-threatening. The pathogenesis of SLE is complex and involves cells of both innate and adaptive immunity. The distinguishing feature of SLE is the production of autoantibodies, with the formation of immune complexes that precipitate at the vascular level, causing organ damage. Although progress in understanding the pathogenesis of SLE has been slower than in other rheumatic diseases, new knowledge has recently led to the development of effective targeted therapies, that hold out hope for personalized therapy. However, the new drugs available to date are still an adjunct to conventional therapy, which is known to be toxic in the short and long term. The purpose of this review is to summarize recent advances in understanding the pathogenesis of the disease and discuss the results obtained from the use of new targeted drugs, with a look at future therapies that may be used in the absence of the current standard of care or may even cure this serious systemic autoimmune disease

Measurement of the local intrinsic curvature of a l =1 radio-vortex at 30 GHz

We exploit the properties of differential geometry of minimal surfaces to introduce a novel approach for characterizing wavefronts. Since Gaussian and mean curvatures describe global and local properties of any differentiable surface, a method for characterizing wavefronts endowed with non-trivial topological features has been introduced. We provide experimental evidence that the wavefront of an l = 1 radio-vortex at 30 GHz can be fully characterized by exploiting the wavefront phase in the far field of the source, accessing a small portion of the beam only. A particular care is dedicated to distinguish diffraction effects from the intrinsic curvature of the helicoidal wavefront. Results are applicable to the local measurement of the topological charge and to the local detection of orbital angular momentum radiation at the millimetric wavelengths

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

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

Measurement of power spectral density of broad-spectrum visible light with heterodyne near field scattering and its scalability to betatron radiation.

We exploit the speckle field generated by scattering from a colloidal suspension to access both spatial and temporal coherence properties of broadband radiation. By applying the Wiener-Khinchine theorem to the retrieved temporal coherence function, information about the emission spectrum of the source is obtained in good agreement with the results of a grating spectrometer. Experiments have been performed with visible light. We prove more generally that our approach can be considered as a tool for modeling a variety of cases. Here we discuss how to apply such diagnostics to broad-spectrum betatron radiation produced in the laser-driven wakefield accelerator under development at SPARC LAB facility in Frascati