Efficient two-step procedure for parameter identification and uncertainty assessment in model updating problems

The model updating procedures employed in vibration-based health monitoring need to be reliable and computationally efficient. The computational time is a fundamental task if the results are used to evaluate, in quasi-real-time, the safe or the unsafe state of strategic and relevant structures. The paper presents an efficient two-step procedure for the identification of the mechanical parameters and for the assessment of the corresponding uncertainty in model updating problems. The first step solves a least squares problem, providing a first estimate of the unknown parameters. The second (iterative) step produces a refinement of the solution. Moreover, by exploiting the error propagation theory, this article presents a direct (non-iterative) procedure to assess the uncertainty affecting the unknown parameters starting from the experimental data covariance matrix. To test the reliability of the procedure as well as to prove its applicability to structural problems, the methodology has been applied to two test-bed case studies. Finally, the procedure has been used for the damage assessment in an existing building. The results provided in this article indicate that the procedure can accurately identify the unknown parameters and properly localize and quantify the damage

Parametric and numerical modeling tools to forecast hydrogeological impacts of a tunnel

The project of interest involving a hydroelectrical diversion tunnel through a crystalline rock massif in the Alps required a detailed hydrogeological study to forecast the magnitude of water inflows within the tunnel and possible effects on groundwater flow The tunnel exhibits a length of 9.5 km and is located on the right side of the Toce River in Crevoladossola (Verbania Province, Piedmont region, northern Italy). Under the geological framework of the Alps, the tunnel is located within the Lower Penninic Frappes in the footwall of the Simplon Normal Fault, and the geological succession is mostly represented by Antigorio gneiss (metagranites) and Baceno metasediments (metacarbonates). Due to the presence of important mineralized springs for commercial mineral water purposes, the above mentioned hydrogeological study focused on both quantity and quality aspects via rainfall data analysis, monitoring of major spring flow rates, monitoring of hydraulic heads and pumping rates of existing wells/boreholes, hydrochemical and isotopic analysis of springs and boreholes and hydraulic tests (Lefranc and Lugeon). The resulting conceptual model indicated dominant low-permeability (aquitard) behavior of the gneissic rock masses, except under conditions of intense fracturing due to tectonization, and aquifer behavior of the metasedimentary rocks, particularly when interested by dissolution. Groundwater flow systems are mainly controlled by gravity. The springs located near the Toce River were characterized by high mineralization and isotopic ratios, indicating long groundwater flow paths. Based on all the data collected and analyzed, two parametric methods were applied: 1) the Dematteis method, slightly adapted to the case study and the available data, which allows assessment of both potential inflows within the tunnel and potential impacts on springs (codified as the drawdown hazard index; DHI); 2) the Cesano method, which only allow assessment of potential inflows within the tunnel, thereby discriminating between major and minor inflows. Contemporarily, a groundwater flow model was implemented with the equivalent porous medium (EPM) approach in MODFLOW-2000. This model was calibrated under steady-state conditions against the available data (groundwater levels inside wells/piezometers and elevation and flow rate of springs). The Dematteis method was demonstrated to be more reliable and suitable for the site than was the Cesano method. This method was validated considering a tunnel through gneissic rock masses, and this approach considered intrinsic parameters of rock masses more notably than morphological and geomorphological factors were considered. The Cesano method relatively overestimated tunnel inflows, considering variations in the topography and overburden above the tunnel. Sensitivity analysis revealed a low sensitivity of these parametric methods to parameter values, except for the rock quality designation (RQD) employed to represent the fracturing degree. The numerical model was calibrated under ante-operam conditions, and sensitivity analysis evaluated the influence of uncertainties in the hydraulic conductivity (K) values of the different hydrogeological units.The hydraulic head distribution after tunnel excavation was forecasted considering three scenarios, namely, a draining tunnel, tunnel as a eater loss source, and tunnel sealed along its aquifer sectors, considering 3 levels of K reduction. Tunnel impermeabilization was very effective, thus lowering the drainage rate and impact on springs. The model quantitatively defined tunnel inflows and the effects on spring flow at the surface in terms of flow rate decrease. The Dematteis method and numerical model were combined to obtain a final risk of impact on the springs. This study likely overestimated the risk because all the values assigned to the parameters were chosen in a conservative way, and the steady-state numerical simulations were also very conservative (the transient state in this hydrogeological setting supposedly lasts 1-3 years). Monitoring of the tunnel and springs during tunnel boring could facilitate the feedback process

A statistical approach for modeling individual vertical walking forces

This paper proposes a statistical approach for modeling vertical walking forces induced by single pedestrians. To account for the random nature of human walking, the individual vertical walking force is modeled as a series of steps and the gait parameters are assumed to vary at each step. Walking parameters are statistically calibrated with respect to the results of experimental tests performed with a force plate system. Results showed that the walking parameters change during walking and are correlated with each other. The force model proposed in this paper is a step-by-step model based on the description of the multivariate distribution of the walking features through a Gaussian Mixture model. The performance of the proposed model is compared to that of a simplified load model and of two force models proposed in the literature in a numerical case study. Results demonstrate the importance of an accurate modeling of both the single step force and the variability of the individual walking force

Can the plasma PD-1 levels predict the presence and efficiency of tumor-infiltrating lymphocytes in patients with metastatic melanoma?

Background: The immune response in melanoma patients is locally affected by presence of tumor-infiltrating lymphocytes (TILs), generally divided into brisk, nonbrisk, and absent. Several studies have shown that a greater presence of TILs, especially brisk, in primary melanoma is associated with a better prognosis and higher survival rate. Patients and Methods: We investigated by enzyme-linked immunosorbent assay (ELISA) the correlation between PD-1 levels in plasma and the presence/absence of TILs in 28 patients with metastatic melanoma. Results: Low plasma PD-1 levels were correlated with brisk TILs in primary melanoma, whereas intermediate values correlated with the nonbrisk TILs, and high PD-1 levels with absent TILs. Although the low number of samples did not allow us to obtain a statistically significant correlation between the plasma PD-1 levels and the patients' overall survival depending on the absence/presence of TILs, the median survival of patients having brisk type TILs was 5 months higher than that of patients with absent and nonbrisk TILs. Conclusions: This work highlights the ability of measuring the plasma PD-1 levels in order to predict the prognosis of patients with untreated metastatic melanoma without a BRAF mutation at the time of diagnosis

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring

Evaluation of carcass and meat traits of Muscovy duck fed with black soldier fly partially defatted meal

The aim of this study was to evaluate the carcass characteristics and breast meat quality in Muscovy duck (Cairina moschata domestica) fed different inclusion levels of a partially defatted black soldier fly larva (BSF) meal. A total of 256 Muscovy ducklings (average live weight, LW: 71.32\ub12.70 g) were reared from day 3 to day 48 and randomly allotted in 32 pens (8 replicates/treatment). Four different diets were formulated with increasing substitution level of corn gluten meal with BSF larva meal (0, 3, 6 and 9%; BSF0, BSF3, BSF6 and BSF9, respectively) and divided in 3 feeding phases: starter (1-14 days), grower (14-35 days) and finisher (35-48 days). At day 48, 2 animals/replicate were slaughtered and dissected to determine their carcass yields. The weights of spleen, bursa of Fabricius, liver, heart and abdominal fat were recorded. Breast and thigh muscles were then excised from 16 ducks/treatment and weighted. Ultimate pH (pHu) and L*, a*, b* colour values were then measured on breast muscle. The collected data were tested by means of oneway ANOVA evaluating the effect of dietary BSF inclusion level by polynomial contrasts. Significance was declared at P<0.05. The inclusion of BSF did not affect final LW (2,515.68\ub192.42 g on average). Hot and cold carcass weights showed a quadratic response (P<0.05) to increasing BSF larva meal, with a minimum corresponding to BSF6; however, refrigeration losses were not affected by treatments. Weight of spleen, bursa of Fabricius, liver and heart did not differ among treatments. The weight of abdominal fat showed a quadratic response to increasing BSF meal with a minimum corresponding to BSF6 group (P<0.05). Breast and thigh yields, pHu and L*, a*, b* colour values did not differ among groups. With the exception of BSF6, the inclusion of BSF meal did not affect meat traits and carcass characteristics, confirming the potential use of BSF meal in Muscovy duck diets

Emergence of chaos in a viscous solution of rods

It is shown that the addition of small amounts of microscopic rods in a viscous fluid at low Reynolds number causes a significant increase of the flow resistance. Numerical simulations of the dynamics of the solution reveal that this phenomenon is associated to a transition from laminar to chaotic flow. Polymer stresses give rise to flow instabilities which, in turn, perturb the alignment of the rods. This coupled dynamics results in the activation of a wide range of scales, which enhances the mixing efficiency of viscous flows.Comment: 4 pages, 5 figures, 1 tabl

Laboratory implementation of edge illumination X-ray phase-contrast imaging with energy-resolved detectors

Edge illumination (EI) X-ray phase-contrast imaging (XPCI) has potential for applications in different fields of research, including materials science, non-destructive industrial testing, small-animal imaging, and medical imaging. One of its main advantages is the compatibility with laboratory equipment, in particular with conventional non-microfocal sources, which makes its exploitation in normal research laboratories possible. In this work, we demonstrate that the signal in laboratory implementations of EI can be correctly described with the use of the simplified geometrical optics. Besides enabling the derivation of simple expressions for the sensitivity and spatial resolution of a given EI setup, this model also highlights the EI’s achromaticity. With the aim of improving image quality, as well as to take advantage of the fact that all energies in the spectrum contribute to the image contrast, we carried out EI acquisitions using a photon-counting energy-resolved detector. The obtained results demonstrate that this approach has great potential for future laboratory implementations of EI. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only