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IntroductionBrain perfusion-weighted images obtained through dynamic contrast studies play a critical and clinical role in diagnosis and treatment decisions. However, due to the patient's limited exposure to radiation, computed magnetic resonance imaging (MRI) suffers from low contrast-to-noise ratios (CNRs). Denoising MRI images is a critical task in many e-health applications for disease detection. The challenge in this research field is to define novel algorithms and strategies capable of improving accuracy and performance in terms of image vision quality and computational cost to process data. Using MRI statistical information, the authors present a method for improving image quality by combining a total variation-based denoising algorithm with histogram matching (HM) techniques.MethodsThe total variation is the Rudin–Osher–Fatemi total variation (TV-ROF) minimization approach, TV-L2, using the isotropic TV setting for the bounded variation (BV) component. The dual-stage approach is tested against two implementations of the TV-L2: the split Bregman (SB) algorithm and a fixed-point (FP) iterations scheme. In terms of HM, the study explores approximate matching and the exact histogram matching from Coltuc.ResultsAs measured by the structural similarity index (SIMM), the results indicate that in the more realistic study scenarios, the FP with an HM pairing is one of the best options, with an improvement of up to 12.2% over the one without an HM.DiscussionThe findings can be used to evaluate and investigate more advanced machine learning-based approaches for developing novel denoising algorithms that infer information from ad hoc MRI histograms. The proposed methods are adapted to medical image denoising since they account for the preference of the medical expert: a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction.

In the world, Italy, and Tuscany in particular, is the country with the highest number and concentration of UNESCO Heritage sites. To fully understand, preserve and promote the richness of these areas, different research perspectives must be kept together and a proper educational approach to the places is needed to fully valorise this asset, which also has the highest economic value in the tourism industry. The T-PLACE project, funded by the PNR 2021-2027 and currently running at the University of Florence, aims at creating an inclusive and participatory model of cultural education focused on place and its assets, also exploiting new technologies such as the X-Reality and 3D modelling. T-PLACE is grounded on a strong multidisciplinary approach, ranging from Educational Technology to Historical Geography to Geomatics, that will be implemented with a full-range pilot in the surrounding of Florence, in the area of Sesto Fiorentino, of particular interest for its diffused cultural heritage and social testimonies. T-PLACE will actively involve the citizenship in training, documenting, and exploring activities with the objective of encouraging the digital transition in the interpretation of tangible and intangible cultural heritage and contributing to the formation of resilient, inclusive and cohesive societies.

Alkaline electrolyzed water (REW) is known for its cleaning action. The aim of this work was to assess REW effectiveness in reducing microbial load on surfaces intended for contact with food. Stainlesssteel surfaces were experimentally contaminated, bacterial inactivation was tested before and after treatment with REW. Treatment with REW was operated spraying it on the contaminated plates until drying. Tests were conducted for Salmonella spp., Listeria spp., Staphylococcus aureus and Escherichia coli. The treatment revealed different degrees of sanitizing activity of REW on different bacterial species, with higher efficacy on E. coli and Salmonella spp. than S. aureus, Listeria spp.. Statistical analysis revealed a significant microbial load reduction (p

Artificial Neuronal Networks are models widely used for many scientific tasks. One of the well-known field of application is the approximation of high-dimensional problems via Deep Learning. In the present paper we investigate the Deep Learning techniques applied to Shape Functionals, and we start from the so--called Torsional Rigidity. Our aim is to feed the Neuronal Network with digital approximations of the planar domains where the Torsion problem (a partial differential equation problem) is defined, and look for a prediction of the value of Torsion. Dealing with images, our choice fell on Convolutional Neural Network (CNN), and we train such a network using reference solutions obtained via Finite Element Method. Then, we tested the network against some well-known properties involving the Torsion as well as an old standing conjecture. In all cases, good approximation properties and accuracies occurred.

In this paper we propose a numerical scheme to estimate the price of a barrier option in a general framework. More precisely, we extend a classical Sequential Monte Carlo approach, developed under the hypothesis of deter-ministic volatility, to Stochastic Volatility models, in order to improve the efficiency of Standard Monte Carlo techniques in the case of barrier options whose underlying approaches the barriers. The paper concludes with the application of our procedure to two case studies under a SABR model.

The Laplace transform inversion is a well-known ill-conditioned problem and many numerical schemes in literature have investigated how to solve it. In this paper, we revise the Gaver-Stehfest method by using polyharmonic splines augmented with polynomials to approximate the Laplace transform into the numerical inversion formula. Theoretical accuracy bounds for the fitting model are given. Discussions on the effectiveness of the inversion algorithm are produced and confirmed by numerical experiments about approximation errors and inversion results. Comparisons with an existing model are also presented.