Surface roughness genomics in contact mechanics : a new method enabling roughness design towards surface prototyping

In actual age of Industry 4.0, the miniaturization of mechanical components is becoming extremely sophisticated, thanks to enhancing techniques such as additive manufacturing technologies. This requires an efficient description of multi-scale roughness to properly characterize the interface contact problem. In this dissertation, a new approach called surface roughness genomics is proposed to uniquely characterize surfaces at different length scales, from the topological point of view. Similar to biological systems, where the biological information is encoded in DNA base pairs, surface roughness is decomposed in elementary waves, whose unique ensemble is the surface genome. The identification process of the real surfaces genome, the sequencing procedure, is based on the solution of a constrained convex optimization problem. A rough profile (chromosome), collecting the features of roughness at a fixed length-scale is isolated from the surface genome So, a rough profile is reconstructed by summing up subsequent chromosomes. The top-down and bottom-up approaches are pursued to reconstruct a rough profile, to quantify the role of specific multi-scale features in the frictional normal contact problem. New algorithms are then proposed to generate roughness morphology achieving a target mechanical response, enabling surface prototyping towards morphology real time control.Beside the mechanical contact problem, the fluid sealing between contacting bodiesis herein investigated by proposing a simple algorithm and applying it to a set of fractal rough surfaces. This algorithm evaluates the free networks involved in leakage process, considering different normal contact indentations at various surface resolutions

A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard

Yield prediction is a key factor to optimize vineyard management and achieve the desired grape quality. Classical yield estimation methods, which consist of manual sampling within the field on a limited number of plants before harvest, are time-consuming and frequently insufficient to obtain representative yield data. Non-invasive machine vision methods are therefore being investigated to assess and implement a rapid grape yield estimate tool. This study aimed at an automated estimation of yield in terms of cluster number and size from high resolution RGB images (20 MP) taken with a low-cost UAV platform in representative zones of the vigor variability within an experimental vineyard. The flight campaigns were conducted in different light conditions and canopy cover levels for 2017 and 2018 crop seasons. An unsupervised recognition algorithm was applied to derive cluster number and size, which was used for estimating yield per vine. The results related to the number of clusters detected in different conditions, and the weight estimation for each vigor zone are presented. The segmentation results in cluster detection showed a performance of over 85% in partially leaf removal and full ripe condition, and allowed grapevine yield to be estimated with more than 84% of accuracy several weeks before harvest. The application of innovative technologies in field-phenotyping such as UAV, high-resolution cameras and visual computing algorithms enabled a new methodology to assess yield, which can save time and provide an accurate estimate compared to the manual method

Sediment transport models for Shallow Water equations

The numerical simulation of sediment transport problems is considered. The physical problem is modeled through the shallow-water equations coupled with the Exner equation to describe the time evolution of the bed profile. Three different models of solid transport discharge are considered. The spatial discretization of the governing equations is carried out by a finite-volume method and a modified Roe scheme designed for non-conservative systems. Linearized implicit schemes for time advancing are built through a recently proposed strategy, based on automatic differentiation to compute the flux Jacobians and on the defect correction approach to reach second-order accuracy. Explicit schemes for time advancing are compared with implicit ones in one-dimensional sediment transport problems, characterized by different time scales for the evolution of the bed. It is shown that, independently of the model used for the solid transport discharge, for slow and intermediate speeds of interaction between the bedload and the water flow, for which the use of large time steps is compatible with the capture of the bed evolution, implicit time advancing is far more efficient than explicit one with a CPU reduction up to more than four orders of magnitude. In the last part, a realistic test is proposed, concerning the sediment transport in Tunis Lake. This lake is divided into two main parts, the North Lake and South Lake. The simulations are performed setting input values to be consistent with reality, in order to have a good representation of sediment transport in Tunis Lake

Percolation properties of the free volume generated by two rough surfaces in contact

The mechanism of fluid leakage trough the free volume between rough surfaces in contact is relevant in physics and in many engineering applications. In the present study, the normal contact problem between randomly generated fractal rough surfaces is solved using the boundary element method. Then, an algorithm for the evaluation of the network involved in the percolation of fluid is proposed. Numerical results are synthetically collected in diagrams relating the free volume involved in the percolation to the dimensionless statistical parameters of the rough surfac

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications

Adjuvant vemurafenib in resected, BRAF V600 mutation-positive melanoma (BRIM8): a randomised, double-blind, placebo-controlled, multicentre, phase 3 trial

BACKGROUND: Systemic adjuvant treatment might mitigate the high risk of disease recurrence in patients with resected stage IIC-III melanoma. The BRIM8 study evaluated adjuvant vemurafenib monotherapy in patients with resected, BRAFV600 mutation-positive melanoma. METHODS: BRIM8 was a phase 3, international, double-blind, randomised, placebo-controlled study that enrolled 498 adults (aged ≥18 years) with histologically confirmed stage IIC-IIIA-IIIB (cohort 1) or stage IIIC (cohort 2) BRAFV600 mutation-positive melanoma that was fully resected. Patients were randomly assigned (1:1) by an interactive voice or web response system to receive twice-daily adjuvant oral vemurafenib 960 mg tablets or matching placebo for 52 weeks (13 × 28-day cycles). Randomisation was done by permuted blocks (block size 6) and was stratified by pathological stage and region in cohort 1 and by region in cohort 2. The investigators, patients, and sponsor were masked to treatment assignment. The primary endpoint was disease-free survival in the intention-to-treat population, evaluated separately in each cohort. Hierarchical analysis of cohort 2 before cohort 1 was prespecified. This trial is registered with ClinicalTrials.gov, number NCT01667419. FINDINGS: The study enrolled 184 patients in cohort 2 (93 were assigned to vemurafenib and 91 to placebo) and 314 patients in cohort 1 (157 were assigned to vemurafenib and 157 to placebo). At the time of data cutoff (April 17, 2017), median study follow-up was 33·5 months (IQR 25·9-41·6) in cohort 2 and 30·8 months (25·5-40·7) in cohort 1. In cohort 2 (patients with stage IIIC disease), median disease-free survival was 23·1 months (95% CI 18·6-26·5) in the vemurafenib group versus 15·4 months (11·1-35·9) in the placebo group (hazard ratio [HR] 0·80, 95% CI 0·54-1·18; log-rank p=0·026). In cohort 1 (patients with stage IIC-IIIA-IIIB disease) median disease-free survival was not reached (95% CI not estimable) in the vemurafenib group versus 36·9 months (21·4-not estimable) in the placebo group (HR 0·54 [95% CI 0·37-0·78]; log-rank p=0·0010); however, the result was not significant because of the prespecified hierarchical prerequisite for the primary disease-free survival analysis of cohort 2 to show a significant disease-free survival benefit. Grade 3-4 adverse events occurred in 141 (57%) of 247 patients in the vemurafenib group and 37 (15%) of 247 patients in the placebo group. The most common grade 3-4 adverse events in the vemurafenib group were keratoacanthoma (24 [10%] of 247 patients), arthralgia (17 [7%]), squamous cell carcinoma (17 [7%]), rash (14 [6%]), and elevated alanine aminotransferase (14 [6%]), although all keratoacanthoma events and most squamous cell carcinoma events were by default graded as grade 3. In the placebo group, grade 3-4 adverse events did not exceed 2% for any of the reported terms. Serious adverse events were reported in 40 (16%) of 247 patients in the vemurafenib group and 25 (10%) of 247 patients in the placebo group. The most common serious adverse event was basal cell carcinoma, which was reported in eight (3%) patients in each group. One patient in the vemurafenib group of cohort 2 died 2 months after admission to hospital for grade 3 hypertension; however, this death was not considered to be related to the study drug. INTERPRETATION: The primary endpoint of disease-free survival was not met in cohort 2, and therefore the analysis of cohort 1 showing a numerical benefit in disease-free survival with vemurafenib versus placebo in patients with resected stage IIC-IIIA-IIIB BRAFV600 mutation-positive melanoma must be considered exploratory only. 1 year of adjuvant vemurafenib was well tolerated, but might not be an optimal treatment regimen in this patient population