Maps for electron cloud density in Large Hadron Collider dipoles

The generation of a quasistationary electron cloud inside the beam pipe through beam-induced multipacting processes has become an area of intensive study. The analyses performed so far have been based on heavy computer simulations taking into account photoelectron production, secondary emission, electron dynamics, and space charge effects, providing a detailed description of the electron-cloud evolution. Iriso and Peggs [U. Iriso and S. Peggs, Phys. Rev. ST Accel. Beams 8, 024403 (2005)] have shown that, for the typical parameters of RHIC, the bunch-to-bunch evolution of the average electron-cloud density at a point can be represented by a cubic map. Simulations based on this map formalism are orders of magnitude faster compared to those based on standard particle tracking codes. In this communication we show that the map formalism is also applicable to the case of the Large Hadron Collider (LHC), and that, in particular, it reproduces the average electron-cloud densities computed using a reference code to within ∼15% for general LHC bunch filling patterns. We also illustrate the dependence of the polynomial map coefficients on the physical parameters affecting the electron cloud (secondary emission yield, bunch charge, bunch spacing, etc.)

Evaluation of the additional shear demand due to frame-infill interaction: a new capacity model

During earthquakes, masonry infills exert a significant stiffening and strengthening action which can be favourable or adverse to face the earthquake-induced demand. Infills transfer the force increment to the RC frame members as an additional shear force. Because of this, local shear failures at the end of the columns, or at the end of the beam-column joints can occur. This is particularly true in the case of non-seismically conforming frame structures, as also shown by post-earthquake damage revealed by recent and past earthquakes. Assessment of this additional shear demand is not possible using the common equivalent strut model for the infills. On the other hand, 2D inelastic models are not computationally effective to be used for seismic analysis of large and complex buildings. Because of this, the actual shear demand on columns is underestimated in most cases. In order to maintain the simplicity of the equivalent strut approach without losing the information about the actual shear force on the columns, the current paper provides a detailed study about the infill-frame shear transfer mechanism. Refined 2D inelastic models of real experimental tests on infilled frames have been realized in OpenSees with the aid of the STKO pre and post processor platform. Shear demand on the columns is extracted as on output of the simulations and compared to the axial force resulting from the same simulations made with the equivalent strut models. An analytical relationship allowing estimate the additional shear demand as a function of the current axial force on the equivalent struts and the geometrical and mechanical properties of the infilled frames is finally proposed. The formula can be easily used to perform shear safety checks of columns adjacent to the infills in seismic analyses

Lightweight error correction technique in industrial IEEE802.15.4 networks

Industrial Wireless Sensor Networks (IWSNs) are nowadays becoming more and more popular thanks to their flexibility and pervasive monitoring capabilities to support process automation and remote maintenance applications. In such a scenario, channel errors due to the wireless medium can result in data packet losses, and consequently in unreliable IWSN services. To mitigate the above reported problem, this paper presents a lightweight error correction scheme specially developed for IEEE802.15.4-based IWSNs. By adding error correction and detection information inside the IEEE802.15.4 MAC data frame, the proposed FEC scheme is able to guarantee a backward compatibility with the standard while providing advanced capabilities in recovering data packets affected by bit errors. In the paper the benefits of the proposed technique are first evaluated through simulated loss traces, then they are validated in a real environment by considering real loss traces collected in an electricity power plant. The proposed error correction scheme is able to recover around 50% of the data packets that would be lost in case of a standard communication without any error correction capability

Synchronization and variability imbalance underlie cognitive impairment in primary-progressive multiple sclerosis.

We aimed to investigate functional connectivity and variability across multiple frequency bands in brain networks underlying cognitive deficits in primary-progressive multiple sclerosis (PP-MS) and to explore how they are affected by the presence of cortical lesions (CLs). We analyzed functional connectivity and variability (measured as the standard deviation of BOLD signal amplitude) in resting state networks (RSNs) associated with cognitive deficits in different frequency bands in 25 PP-MS patients (12 M, mean age 50.9 ± 10.5 years) and 20 healthy subjects (9 M, mean age 51.0 ± 9.8 years). We confirmed the presence of a widespread cognitive deterioration in PP-MS patients, with main involvement of visuo-spatial and executive domains. Cognitively impaired patients showed increased variability, reduced synchronicity between networks involved in the control of cognitive macro-domains and hyper-synchronicity limited to the connections between networks functionally more segregated. CL volume was higher in patients with cognitive impairment and was correlated with functional connectivity and variability. We demonstrate, for the first time, that a functional reorganization characterized by hypo-synchronicity of functionally-related/hyper-synchronicity of functionally-segregated large scale networks and an abnormal pattern of neural activity underlie cognitive dysfunction in PP-MS, and that CLs possibly play a role in variability and functional connectivity abnormalities

The relationship between cortical lesions and periventricular NAWM abnormalities suggests a shared mechanism of injury in primary-progressive MS.

In subjects with multiple sclerosis (MS), pathology is more frequent near the inner and outer surfaces of the brain. Here, we sought to explore if in subjects with primary progressive MS (PPMS) cortical lesion load is selectively associated with the severity of periventricular normal appearing white matter (NAWM) damage, as assessed with diffusion weighted imaging. To this aim, twenty-four subjects with PPMS and twenty healthy controls were included in the study. Using diffusion data, skeletonized mean diffusivity (MD) NAWM maps were computed excluding WM lesions and a 2 mm-thick peri-lesional rim. The supra-tentorial voxels between 2 and 6 mm of distance from the lateral ventricles were included in the periventricular NAWM mask while the voxels between 6 and 10 mm from the lateral ventricles were included in the deep NAWM mask; mean MD values were then computed separately for these two masks. Lastly, cortical lesions were assessed on phase-sensitive inversion recovery (PSIR) images and cortical thickness was quantified on volumetric T1 images. Our main result was the observation in the PPMS group of a significant correlation between periventricular NAWM MD values and cortical lesion load, with a greater cortical lesion burden being associated with more abnormal periventricular NAWM MD. Conversely, there was no correlation between cortical lesion load and deep NAWM MD values or periventricular WM lesions. Our data thus suggest that a common - and relatively selective - factor plays a role in the development of both cortical lesion and periventricular NAWM abnormalities in PPMS

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

The accurate automatic volcanic cloud detection by means of satellite data is a challenging task and of great concern for both scientific community and stakeholder due to the well-known issues generated by a strong eruption event in relation to aviation safety and health impact. In this context, machine learning techniques applied to recent spaceborne sensors acquired data have shown promising results in the last years. This work focuses on the application of a neural network based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. The classification of the clouds and of the other surfaces composing the scene is also carried out. The neural network has been trained with MODIS (MODerate resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallaj&ouml;kull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the events comparable latitudes foster the robustness of the approach, which allows overcoming the lack in SLSTR products collected in previous mid-high latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared with RGB visual inspection and BTD (Brightness Temperature Difference) procedure. Moreover, the comparison between the ash cloud obtained by neural network and a plume mask manually generated for the specific SLSTR considered images, shows significant agreement. Thus, the proposed approach allows an automatic image classification during eruption events, which it is also considerably faster than time-consuming manually algorithms (e.g. find the best BTD product-specific threshold). Furthermore, the whole image classification indicates an overall reliability of the algorithm, in particular for meteo-clouds recognition and discrimination from volcanic clouds. Finally, the results show that the NN developed for the SLSTR nadir view is able to properly classify also the SLSTR oblique view images.</p

A retrospective exploratory analysis on cardiovascular risk and cognitive dysfunction in multiple sclerosis

Background. Cardiovascular comorbidities have been associated with cognitive decline in the general population. Objectives. To evaluate the associations between cardiovascular risk and neuropsychological performances in MS. Methods. This is a retrospective study, including 69 MS patients. For all patients, we calculated the Framingham risk score, which provides the 10-year probability of developing macrovascular disease, using age, sex, diabetes, smoking, systolic blood pressure, and cholesterol levels as input variables. Cognitive function was examined with the Brief International Cognitive Assessment for MS (BICAMS), including the Symbol Digit Modalities Test (SDMT), the California Verbal Learning Test-II (CVLT-II), and the Brief Visuospatial Memory Test-Revised (BVMT-R). Results. Each point increase of the Framingham risk score corresponded to 0.21 lower CVLT-II score. Looking at Framingham risk score components, male sex and higher total cholesterol levels corresponded to lower CVLT scores (Coeff = −8.54; 95%CI = −15.51, −1.57; and Coeff = −0.11; 95%CI = −0.20, −0.02, respectively). No associations were found between cardiovascular risk and SDMT or BVMT-R. Conclusions. In our exploratory analyses, cardiovascular risk was associated with verbal learning dysfunction in MS. Lifestyle and pharmacological interventions on cardiovascular risk factors should be considered carefully in the management of MS, given the possible effects on cognitive function