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Giovanni Canestrini’s models of Leonardo da Vinci’s friction experiments
IM Hutchings
Feb 16, 2017
Among the exhibits at the 2016 Leonardo da Vinci exhibition at the Science Museum, London, was one that purported to illustrate Leonardo’s experiments on friction. The models involved were the work of Giovanni Canestrini...
Dual mode imaging in mid infrared with thermal signal reconstruction for innovative diagnostics of the "Monocromo" by Leonardo da Vinci.
Dual mode imaging in the mid infrared band, a joint use of thermography and quasi-thermal reflectography, was recently proposed as a full field diagnostic tool in cultural heritage. Here we discuss for the first time, to the...
Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant...
Published by: IGI Global Scientific Publishing
Management Support Model for Information Technology Outsourcing
This study deals with the problem of outsourcing management, presenting a structure to support decision-making on effective information-technology outsourcing and highlighting the advantages and disadvantages that must be...
Published by: IGI Global Scientific Publishing
Leonardo da Vinci's studies of friction
IM Hutchings
Apr 28, 2016
Oriented Type I Collagen - A Review on Artificial Alignment Strategies
Collagen is the most abundant protein in the human body and serves many functions, from mechanical stability and elasticity in tendons and bone, to optical properties, such as transparency and a fine tuned refractive index in...
Published by: IGI Global Scientific Publishing
Estimates of running ground reaction force parameters from motion analysis
We compared running mechanics parameters determined from ground reaction force (GRF) measurements with estimated forces obtained from double differentiation of kinematic (K) data from motion analysis in a broad spectrum of...
Focus U-Net
BACKGROUND: Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity...
Sphingolipid, fatty acid and phospholipid metabolites are associated with disease severity and mTOR inhibition in lymphangioleiomyomatosis.
BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease almost exclusively affecting women which causes loss of lung function, lymphatic abnormalities and angiomyolipomas. LAM occurs sporadically and in people...
Introduction to the Special Issue on the Utility of Transdiagnostic Approaches for Developing Novel Interventions for Substance and Behavioural Addictions.
It is widely established that certain psychoactive substances (e.g., alcohol, cocaine) affect brain reward pathways including dopamine, opioid, and glutamatergic neurochemical systems (Kalivas & Volkow, 2005). These biological...
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.
The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have...
A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery
Leonardo Bottolo, Marco Banterle, Sylvia Richardson, Mika Ala‐Korpela, Marjo‐Riitta Järvelin, Alex Lewin
May 09, 2021
Estimates of running ground reaction force parameters from motion analysis
We compared running mechanics parameters determined from ground reaction force (GRF) measurements with estimated forces obtained from double differentiation of kinematic (K) data from motion analysis in a broad spectrum of...
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation.
Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang
Mar 25, 2023
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated...
Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle
Lorena Escudero Sanchez, Leonardo Rundo, Andrew B. Gill, Matthew Hoare, Eva Mendes Serrao, Evis Sala
Apr 15, 2021
Abstract: Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its...
Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.
Erick Costa de Farias, Christian di Noia, Changhee Han, Evis Sala, Mauro Castelli, Leonardo Rundo
Oct 18, 2021
Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their...
Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle.
Lorena Escudero Sanchez, Leonardo Rundo, Andrew B Gill, Matthew Hoare, Eva Mendes Serrao, Evis Sala
Mar 31, 2021
MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
Nikita Sushentsev, Leonardo Rundo, Oleg Blyuss, Vincent J. Gnanapragasam, Evis Sala, Tristan Barrett
Jun 21, 2021
Abstract: Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years...
Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
Erick Costa de Farias, Christian di Noia, Changhee Han, Evis Sala, Mauro Castelli, Leonardo Rundo
Nov 01, 2021
Abstract: Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered...
Phase editing as a signal pre-processing step for automated bearing fault detection
Leonardo Barbini, Agusmian P. Ompusunggu, Andrei Bartic, Andrew Hillis, Jonathan Du Bois
Jul 01, 2017
Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit...
A quantum-inspired classifier for clonogenic assay evaluations
Giuseppe Sergioli, Carmelo Militello, Leonardo Rundo, Luigi Minafra, Filippo Torrisi, Giorgio Russo, Keng Loon Chow, Roberto Giuntini
Feb 02, 2021
Abstract: Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with...
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