Studies of Efficiency of the LHCb Muon Detector Using Cosmic Rays

We study the efficiency of the muon detector using the cosmic ray events collected in the summer and autumn 2008. We find that the efficiencies in all stations are consistent with 100% for cosmic tracks coming from the LHCb interaction point, without any restriction on time. We calculate the efficiencies also per station and region and per station and quadrant, finding consistent results

Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field

Ellipsoidal classification via semidefinite programming

We propose a classification approach exploiting relationships between ellipsoidal separation and Support-vector Machine (SVM) with quadratic kernel. By adding a (Semidefinite Programming) SDP constraint to SVM model we ensure that the chosen hyperplane in feature space represents a non-degenerate ellipsoid in input space. This allows us to exploit SDP techniques within Support-vector Regression (SVR) approaches, yielding better results in case ellipsoid-shaped separators are appropriate for classification tasks. We compare our approach with spherical separation and SVM on some classification problems

Exciton states in monolayer MoSe2 and MoTe2 probed by upconversion spectroscopy

Transitions metal dichalcogenides (TMDs) are direct semiconductors in the atomic monolayer (ML) limit with fascinating optical and spin-valley properties. The strong optical absorption of up to 20 % for a single ML is governed by excitons, electron-hole pairs bound by Coulomb attraction. Excited exciton states in MoSe$_2$ and MoTe$_2$ monolayers have so far been elusive due to their low oscillator strength and strong inhomogeneous broadening. Here we show that encapsulation in hexagonal boron nitride results in emission line width of the A:1$s$ exciton below 1.5 meV and 3 meV in our MoSe$_2$ and MoTe$_2$ monolayer samples, respectively. This allows us to investigate the excited exciton states by photoluminescence upconversion spectroscopy for both monolayer materials. The excitation laser is tuned into resonance with the A:1$s$ transition and we observe emission of excited exciton states up to 200 meV above the laser energy. We demonstrate bias control of the efficiency of this non-linear optical process. At the origin of upconversion our model calculations suggest an exciton-exciton (Auger) scattering mechanism specific to TMD MLs involving an excited conduction band thus generating high energy excitons with small wave-vectors. The optical transitions are further investigated by white light reflectivity, photoluminescence excitation and resonant Raman scattering confirming their origin as excited excitonic states in monolayer thin semiconductors.Comment: 14 pages, 7 figures, main text and appendi

Optimization of extraction of drugs containing polyphenols using an innovative technique

The role of polyphenols in human health nowadays is well established and these natural products, found in many plant species, are the active ingredients of drugs, food supplements and cosmetics. Extraction procedure is pivotal to obtain high quality herbal products but paradoxically this factor is often underrated and obsolete techniques are used. In this work we compared the classic and most used method of maceration and an innovative and standardized technique of extraction, Estrattore Naviglio((R)), processing ten common medicinal plants containing polyphenols and for each analysing specific biological markers such as flavonoids, anthocyanosides and caffeic derivatives in addition to total polyphenols content. Estrattore Naviglio((R)) guaranteed a significant improvement of the chemical quality of extracts combining effectiveness with rapidity and reproducibility. In this work we further investigated the optimization of drug extractions by replicating operations varying parameters setting on Estrattore Naviglio((R)) instrument

Deterministic and stochastic P systems for modelling cellular processes

This paper presents two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological sys- tems and illustrates their use through two case studies.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/

Cognitive speed and white matter integrity in secondary progressive multiple sclerosis

BACKGROUND: Processing speed (PS) deficits have been consistently observed in secondary progressive multiple sclerosis (SPMS). However, the underlying neural correlates have not been clarified yet. The present study aimed to investigate the relationship between macrostructural and microstructural white matter (WM) integrity and performance on different cognitive measures with prominent PS load. METHODS: Thirty-one patients with SPMS were recruited and underwent neurological, neuropsychological, and MRI assessments. The associations between a composite index of PS abilities and scores on various tests with prominent PS load and T1-weighted and diffusion tensor image parameters were tested. Analyses were carried out using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS). RESULTS: VBM results showed that only the semantic fluency task correlated with grey matter (GM) volume in a range of cortical and subcortical areas bilaterally as well as the corpus callosum and the superior longitudinal fasciculus. TBSS analysis revealed consistent results across all the cognitive measures investigated, showing a prominent role of commissural and frontal associative WM tracts in supporting PS-demanding cognitive operations. CONCLUSIONS: In patients with SPMS, PS abilities are mainly dependent on the degree of both macrostructural and microstructural WM integrity. Preservation of associative WM tracts that support information integration seems crucial to sustain performance in tasks requiring fast cognitive processes