Sensor network optimization for damage detection on aluminium stiffened helicopter panels

Health and Usage Monitoring Systems (HUMS) has received considerable attention from the helicopter community in recent years with the declared aim to increase flight safety, increase mission reliability, extend duration of life limited components and of course reduce the maintenance costs. The latter is about 25 per cent of the direct operating cost of the helicopter, thus playing an important role especially in the case of the ageing aircrafts. In particular, with respect to helicopter fuselages, only some attempts were carried out to monitor directly on-line the damage accumulation and propagation during life. In this field, and in particular in the military applications, an integrated and reliable system for monitoring the damage in the fuselage and for evaluating the time inspections and remaining life (prognosis) is missing. However, because of the presence of many vibratory loads, the diagnosis of helicopter structures is very critical. From one hand, a very large number of sensors would be needed for a robust appreciation of the structural health, from the other hand the industrialization of the product brings the need for a low impact over the existing structures, or toward a reduction in the allowed amount of sensors. As a result, comes the importance for an optimization of the sensor network, with the aim to find out the regions inside the structure which are the most sensible to a damage and at the same time robust to noise. The aim of the present work is to define a methodology for optimising the sensors position inside an helicopter fuselage panel in order to obtain the best compromise between the simplicity and the robustness of a sensor network. In particular, a Finite Element (FE) model will be used to create a database of various damages inside the structure, thus consequently optimising the network sensitivity to any damage. The evaluation of the network performances is provided when some realistic noise [1,2] is added to the FE calculation

MicroRNA-551b expression profile in low and high-grade cervical intraepithelial neoplasia

OBJECTIVE: To evaluate the expression of microRNA (miR)-551b in patients with low and high grade cervical intraepithelial neoplasia (CIN) and to find an association with high-risk Human Papillomavirus (HR-HPV) infection-related prognostic biomarkers. PATIENTS AND METHODS: The expression level of miR-551b was determined in 50 paraffin-embedded cervical specimens (10 normal squamous epithelium, 18 condylomas, 8 CIN1, and 14 CIN2-3) using quantitative Real-time polymerase chain reaction (qRT-PCR). χ2-test compared miR-551b expression in different diagnosis groups. An Ordered Logistic Regression and a Probit correlation were made to correlate miR-551b expression levels with the cervical tissue histological findings. The immunohistochemical distribution of p16 and Ki-67 according to histopathological findings was also assessed. RESULTS: The distribution of the miR-551b expression profile was significantly lower in CIN1-3 samples compared to other histological diagnosis groups (condyloma and negative). The expression levels were inversely correlated to the cervical pathological grade, from negative to CIN2-3. A 1% increase in miR-551b expression level produced an increase of 19% to the probability of a minor histological grade diagnosis in a range from negative to CIN2-3 and an increase of 13% to the probability of a negative histological grade diagnosis. Among the cases with miR-551b expression < 0.02 (considered as cut-off value) a significant statistical correlation was found between p16 and Ki-67 expression and the diagnosis of CIN2-3. CONCLUSIONS: O ur d ata s howed a s ignificant inverse correlation between miR-551b expression and the histological grading of the lesions, suggesting a tumor suppressive function in the different stages of cervical dysplasia

On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios

Confounding influences, such as operational and environmental variations, represent a limitation to the implementation of Structural Health Monitoring (SHM) systems in real structures, potentially leading to damage misclassifications. In this framework, this study considers cointegration as a state of the art method for data normalisation in fatigue crack propagation scenarios, where monitoring is performed by a distributed network of strain sensors. Specifically, the work is aimed at demonstrating the effectiveness of cointegration on real engineering data in a new context, where the damage is continuously growing. Cointegration is applied at first in a controlled scenario consisting of a numerical strain simulation by means of a finite element model, modified in order to take realistic temperature fluctuations and sensor noise into account. Afterwards, detrending and anomaly detection performances are verified in two different experimental programmes on realistic aeronautical structures subjected to fatigue crack growth, including a full-scale fatigue test on a helicopter tail boom. Strain measurements are taken from a network of Fibre Bragg Grating (FBG) sensors, known to be extremely sensitive to temperature variations, hence delivering challenging scenarios for cointegration testing. Results are shown to be in good agreement with the experimental evidence, with the cointegration algorithm capable of detecting the onset of damage propagation within a 4 mm increment from a baseline condition

A method for determining the distribution of carbon nanotubes in nanocomposites by electric conductivity

Carbon nanotube (CNT) polymer nanocomposites are one of the most promising materials due to their remarkable mechanical properties as well as the electrical conductivity, which offers the capability of monitoring the deformation and damage of composite structures by measuring the related conductivity variations. However, quantifying the distribution of CNTs inside the material remains a challenge with respects to both experimental and numerical works. In the current study, the electrical conductivity was used to determine the microstructure of CNT-reinforced polymer. By introducing a modified parameter related to the polar angle of CNTs, the mechanical properties as well as the electrical conductivity change with respect to deformation of nanocomposites can be replicated. After validation by experimental data from the multi-walled CNT/polymer nanocompo sites under tensile loading, the capability of the current method was then studied for composites with various weight fractions of nanotubes. (C) 2022 The Authors. Published by Elsevier B.V

Heterodyne near-field scattering: A technique for complex fluids

The heterodyne near-field scattering (HNFS) technique for studying complex fluids such as colloidal systems was discussed. A different data reduction scheme was adopted which allowed the improvement in performance of the technique, at levels of sensitivity and accuracy much higher than those achievable with classical low-angle light scattering instrumentation. It was observed that this method also relaxes the requirements on the optical/mechanical stability of the experimental setup and allows for a real time analysis. Nonstationary samples, such as aggregating colloidal solutions, were also investigated, and their kinetics quantitatively characterized

Calibration of the Prompt L0 Trigger of the Silicon Pixel Detector for the ALICE Experiment

The ALICE Silicon Pixel Detector (SPD) is the innermost detector of the ALICE experiment at LHC. It includes 1200 front-end chips, with a total of ~107 pixel channels. The pixel size is 50 x 425 μm2. Each front-end chip transmits a Fast-OR signal upon registration of at least one hit in its pixel matrix. The signals are extracted every 100 ns and processed by the Pixel Trigger (PIT) system, to generate trigger primitives. Results are then sent within a latency of 800 ns to the Central Trigger Processor (CTP) to be included in the first Level 0 trigger decision. This paper describes the commissioning of the PIT, the tuning procedure of the front-end chips Fast-OR circuit, and the results of operation with cosmic muons and in tests with LHC beam

Advanced Glycation End Products are Increased in the Skin and Blood of Patients with Severe Psoriasis

Psoriasis is frequently associated with metabolic comorbidities. Advanced glycation end products (AGEs) are highly oxidant, biologically active compounds that accumulate in tissues in association with hyperglycaemia, hyperlipidaemia and oxidative stress. This is a cross-sectional case-control study involving 80 patients with mild/severe psoriasis and 80 controls matched for age, sex and body mass index (40 with severe eczema, 40 healthy individuals). Patients and healthy individuals with a smoking habit, diabetes, dyslipidaemia, hypercholesterolaemia, hypertension or who were under systemic treatment were excluded from the study. Skin AGEs were measured in normal-appearing skin by a standard fluorescence technique, and blood AGEs (total AGEs, pentosidine and AGEs receptor) by enzyme-linked immunosorbent assay. Levels of cutaneous AGEs (p < 0.04), serum AGEs (p < 0.03) and pentosidine (p < 0.05) were higher in patients with severe psoriasis. Cutaneous AGEs correlated well with serum AGEs (r = 0.93, p < 0.0001) and with Psoriasis Area and Severity Index score (r = 0.91, p < 0.0001). Receptor levels were lower (p < 0.001) in severe psoriasis, and inversely correlated with disease severity (r = –0.71, p < 0.0002). Patients with severe psoriasis have accumulation of skin and serum AGEs, independent of associated metabolic disorders

Structural Performance-Based Design Optimisation of a Secondary Mirror for a Concentrated Solar Power (CSP) Plant

Concentrated Solar Power (CSP) plants use mirrors to reflect and concentrate sunlight onto a receiver, to heat a fluid and store thermal energy, at high temperature and energy density, to produce dispatchable heat and/or electricity. The secondary mirror is a critical component in the optical system of certain Solar Power Tower plants (SPT), as it redirects the concentrated sunlight from the primary mirror onto the receiver, which can be arranged at ground level. In this study, we propose a design optimisation for the secondary mirror of a CSP plant. The design optimisation method consists of two steps. The first step involves the use of the finite element simulation software Abaqus 2022 to analyse the structural performance of the secondary mirror under thermal loads and wind. The second step consists of the use of simulation results to identify the combination of design parameters and best performances, with respect to both design constraints and structural safety. This is carried out by developing an algorithm that selects those configurations which satisfy the constraints by using safety coefficients. The proposed optimisation method is applied to the design of a potential configuration of a secondary mirror for the beam-down of the CSP Magaldi STEM® technology, although the methodology can be extended to other components of CSP plants, such as primary mirrors and receivers, to further enhance the structural performance of these systems

Serum Vitamin D as a Biomarker in Autoimmune, Psychiatric and Neurodegenerative Diseases

Vitamin D is a steroid hormone regulating calcium-phosphorus homeostasis, immune response and brain function. In the past thirty years, an increasing number of cohort studies, meta-analyses and randomized controlled trials (RTCs) evaluated the serum levels of 25-hydroxyvitamin D [25(OH)D], which is considered the Vitamin D status biomarker, in patients affected by neurological, psychiatric and autoimmune diseases. Although an association between low 25(OH)D serum levels and the prevalence of these diseases has been found, it is still unclear whether the serum 25(OH)D measurement can be clinically useful as a biomarker for diagnosis, prognosis and predicting treatment response in neurodegeneration, mental illness and immune-mediated disorders. The lack of standardized data, as well as discrepancies among the studies (in the analytical methods, cut-offs, endpoints and study sets), weakened the findings achieved, hindered pooling data, and, consequently, hampered drawing conclusions. This narrative review summarizes the main findings from the studies performed on serum 25(OH)D in neurological, psychiatric and autoimmune diseases, and clarifies whether or not serum 25(OH)D can be used as a reliable biomarker in these diseases

A theoretical-experimental framework for the analysis of the dynamic response of a QEPAS tuning fork device immersed in a fluid medium

Quartz-enhanced photoacoustic spectroscopy (QEPAS) is a trace gas sensing technique that employs a designed high-quality factor quartz tuning fork (QTF) as acousto-electric transducer. The first in-plane skew-symmetric flexural mode of the QTF is excited when weak resonant sound waves are generated between the QTF prongs. Thus, the performance of a QEPAS sensor strongly depends on the resonance properties of the QTF, namely the determination of flexural eigenfrequencies and air damping loss. In this work, we present a mixed theoretical-experimental framework to study the dynamic response of a QTF while vibrating in a fluid environment. Due to the system linearity, the dynamic response of the resonator immersed in a fluid medium is obtained by employing a Boundary Element formulation based on an ad hoc calculated Green's function. In particular, the QTF is modelled as constituted by a pair of two Euler-Bernoulli cantilevers partially coupled by a distributed linear spring. As for the forces exerted by the fluid on QTF structure, the fluid inertia and viscosity as well as an additional diffusivity term, whose influence is crucial for the correct evaluation of the system response, have been taken into account. By corroborating the theoretical analysis with the experimental outcomes obtained by means of a vibro-acoustic setup, the fluid response coefficients and the dynamics of the QTF immersed in a fluid environment are fully determined