Evaluation of the Stress State in Aluminium Foam Sandwiches

In this paper a discussion about the determination of the stress state corresponding to the application of four-points bending load on a sandwich panel having a core made of closed cell aluminium foam is reported. An analytical model based on laminated plate classical theory is compared to a more complex FEM model, considering the effect of geometric parameters of panels, like core and plate thickness, and of loading mode, like span length. The results show the difficulties to define a reliable model to calculate stress state in this kind of composite material

Mechanical and microstructural behaviour of 2024–7075 aluminium alloy sheets joined by friction stir welding

The aim of the present work is to investigate on the mechanical and microstructural properties of dissimilar 2024 and 7075 aluminium sheets joined by friction stir welding (FSW). The two sheets, aligned with perpendicular rolling directions, have been successfully welded; successively, the welded sheets have been tested under tension at room temperature in order to analyse the mechanical response with respect to the parent materials. The fatigue endurance (S–N) curves of the welded joints have been achieved, since the fatigue behaviour of light welded sheets is the best performance indicator for a large part of industrial applications; a resonant electro-mechanical testing machine load and a constant load ratio RZsmin/smaxZ0.1 have been used at a load frequency of about 75 Hz. The resulted microstructure due to the FSW process has been studied by employing optical and scanning electron microscopy either on ‘as welded’ specimens and on tested specimen after rupture occurred

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized

real time monitoring of damage evolution by nonlinear ultrasonic technique

Abstract In this work, the ultrasound technique was used to monitor the damage of material subjected to fatigue loads. Prediction of structural damage is critical for safe and reliable operation of engineered complex systems. In these measurements, conventional ultrasonic probes (transmitter and receiver) were stably fixed to the tested samples with steel brackets, in order to eliminate ever possible variability associated with the coupling of probes. The transmitted and received ultrasonic signals were recorded and analyzed using a digital oscilloscope. The data were converted into the frequency domain using an algorithm developed in Matlab based on Fast Fourier Transform (FFT) for received signal in dependence of the applied stress level and the accumulated fatigue damage was deeply studied in order to recognize quantitative effects, suitable for an experimental prediction of the integrity of the material. The acquired data were compared with the reference signal, at the beginning of the fatigue tests. Particular care has been paid to UT signal attenuation and to the study of the frequency spectrum as the number of load cycles varies. The applied experimental technique has proved efficient for detecting damage induced by mechanical stress

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices

Study of damage of t-joint components by using different non-destructive techniques

The present research is focused on the use of different non-destructive techniques for detecting damage in CFRP composite structures obtained by an innovative technological process: Automated Fiber Placement. The component was a T-joint stringer adhesively bonded to a skin panel. The aim of the present work is to show the capability of these techniques to provide complementary information for detecting the damage in composites. Automated Fibre Placement consists in an automatic deposing of prepeg or dry plies on a specific mould. The innovation lies in the possibility to reduce the time of the manufacturing process of large and complex structures by using a robotic arm that contemporary deposes fibre tows and pre-polymerizes them. The resulting products present higher quality in terms of surface finish, internal flaws absent and higher mechanical properties. The T-joint component tested in the present research was addressed to both static and cyclic tests. After the damage was induced in the material it was performed a qualitative and quantitative study of the damage by using different nondestructive techniques: Thermoelastic stress analysis (TSA), Ultrasound tests (UT) and displacement/strain measurements provided by strain gages. Processing and post-processing procedures were developed to analyze the data from each tests and finally the comparison of the results allowed a complete characterization and an overview of damage in the component by observing specifically where and when it occurred and how many regions of the component were interested. Finally, dimension, shape and depth where assessed

Assessment of immunostimulatory responses to the antimiR-22 oligonucleotide compound RES-010 in human peripheral blood mononuclear cells

microRNA-22 (miR-22) is a key regulator of lipid and energy homeostasis and represents a promising therapeutic target for NAFLD and obesity. We have previously identified a locked nucleic acid (LNA)-modified antisense oligonucleotide compound complementary to miR-22, designated as RES-010 that mediated robust inhibition of miR-22 function in cultured cells and in vivo. In this study we investigated the immune potential of RES-010 in human peripheral blood mononuclear cells (PBMCs). We treated fresh human peripheral blood mononuclear cells isolated from six healthy volunteers with different concentrations of the RES-010 compound and assessed its proinflammatory effects by quantifying IL-1β, IL-6, IFN-γ, TNF-α, IFN-α2a, IFN-β, IL-10, and IL-17A in the supernatants collected 24 h of treatment with RES-010. The T-cell activation markers, CD69, HLA-DR, and CD25 were evaluated by flow cytometry after 24 and 144 h of treatment, respectively, whereas cell viability was assessed after 24 h of treatment with RES-010. Our results show that RES-010 compound does not induce any significant immunostimulatory responses in human PBMCs in vitro compared to controls, implying that the proinflammatory potential of RES-010 is low.</p

Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy

The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.info:eu-repo/semantics/publishedVersio

A 20-year H2O maser monitoring program with the Medicina 32-m telescope

The Arcetri/Bologna H2O maser group has been monitoring the 1.3-cm water maser emission from a sample of 43 star-forming regions (SFRs) and 22 late-type stars for about 20 years at a sampling rate of 4-5 observations each year, using the 32-m Medicina Radio Telescope (HPBW 1.‧9 at 22 GHz). For the late-type stars we observe representative samples of OH/IR-stars, Mira's, semi-regular variables, and supergiants. The SFR-sample spans a large interval in FIR luminosity of the associated Young Stellar Object (YSO), from 20 L to 1.5 × 106 L, and offers a unique data base for the study of the long-term (years) variability of the maser emission in regions of star formation. This presentation concerns only the masers in SFRs. The information obtained from single-dish monitoring is complementary to what is extracted from higher-resolution (VLA and VLBI) observations, and can better explore the velocity domain and the long-term variability therein. We characterize the variability of the sources in various ways and we study how it depends on the luminosity and other properties of the associated YSO and its environment