Modelling a Coupled Thermoelectromechanical Behaviour of Contact Elements via Fractal Surfaces

A three-dimensional coupled thermoelectromechanical model for electrical connectors is here proposed to evaluate local stress and temperature distributions around the contact area of electric connectors under different applied loads. A micromechanical numerical model has been developed by merging together the contact theory approach, which makes use of the so-called roughness parameters obtained from experimental measurements on real contact surfaces, with the topology description of the rough surface via the theory of fractal geometry. Particularly, the variation of asperities has been evaluated via the Weierstrass-Mandelbrot function. In this way the micromechanical model allowed for an upgraded contact algorithm in terms of effective contact area and thermal and electrical contact conductivities. Such an algorithm is subsequently implemented to construct a global model for performing transient thermoelectromechanical analyses without the need of simulating roughness asperities of contact surfaces, so reducing the computational cost. A comparison between numerical and analytical results shows that the adopted procedure is suitable to simulate the transient thermoelectromechanical response of electric connectors

Distance learning training in genetics and genomics testing for Italian health professionals: results of a pre and post-test evaluation

BACKGROUND: Progressive advances in technologies for DNA sequencing and decreasing costs are allowing an easier diffusion of genetic and genomic tests. Physicians' knowledge and confidence on the topic is often low and not suitable for manage this challenge. Tailored educational programs are required to reach a more and more appropriate use of genetic technologies. METHODS: A distance learning course has been created by experts from different Italian medical associations with the support of the Italian Ministry of Health. The course was directed to professional figures involved in prescription and interpretation of genetic tests. A pretest-post-test study design was used to assess knowledge improvement. We analyzed the proportion of correct answers for each question pre and post-test, as well as the mean score difference stratified by gender, age, professional status and medical specialty. RESULTS: We reported an improvement in the proportion of correct answers for 12 over 15 questions of the test. The overall mean score to the questions significantly increased in the post-test, from 9.44 to 12.49 (p-value < 0.0001). In the stratified analysis we reported an improvement in the knowledge of all the groups except for geneticists; the pre-course mean score of this group was already very high and did not improve significantly. CONCLUSION: Distance learning is effective in improving the level of genetic knowledge. In the future, it will be useful to analyze which specialists have more advantage from genetic education, in order to plan more tailored education for medical professionals

Funnel plots and choropleth maps in cancer risk communication: a comparison of tools for disseminating population-based incidence data to stakeholders

Background: Population-based cancer registries provide epidemiological cancer information, but the indicators are often too complex to be interpreted by local authorities and communities, due to numeracy and literacy limitations. The aim of this paper is to compare the commonly used visual formats to funnel plots to enable local public health authorities and communities to access valid and understandable cancer incidence data obtained at the municipal level. Methods: A funnel plot representation of standardised incidence ratio (SIR) was generated for the 82 municipalities of the Palermo Province with the 2003 2011 data from the Palermo Province Cancer Registry (Sicily, Italy). The properties of the funnel plot and choropleth map methodologies were compared within the context of disseminating epidemiological data to stakeholders. Results: The SIRs of all the municipalities remained within the control limits, except for Palermo city area (SIR=1.12), which was sited outside the upper control limit line of 99.8%. The Palermo Province SIRs funnel plot representation was congruent with the choropleth map generated from the same data, but the former resulted more informative as shown by the comparisons of the weaknesses and strengths of the 2 visual formats. Conclusions: Funnel plot should be used as a complementary valuable tool to communicate epidemiological data of cancer registries to communities and local authorities, visually conveying an efficient and simple way to interpret cancer incidence data

Neoadjuvant therapy for breast cancer

Objective: To evaluate the frequency of neoadjuvant therapy (NT) in women with stage I–III breast cancer in Italy and whether it is influenced by biological characteristics, screening history, and geographic area. Methods: Data from the High Resolution Study conducted in 7 Italian cancer registries were used; they are a representative sample of incident cancers in the study period (2009–2013). Included were 3546 women aged &lt;85 years (groups &lt;50, 50–69, 70–64, and 75+) with stage I–III breast cancer at diagnosis who underwent surgery. Women were classified as receiving NT if they received chemotherapy, target therapy, and/or hormone therapy before the first surgical treatment. Logistic models were built to test the association with biological and contextual variables. Results: Only 8.2% of women (290 cases) underwent NT; the treatment decreases with increasing age (14.5% in age &lt;50 and 2.2% in age 75+), is more frequent in women with negative receptors (14.8%), HER2-positive (15.7%), and triple-negative (15.6%). The multivariable analysis showed the probability of receiving NT is higher in stage III (odds ratio [OR] 3.83; 95% confidence interval [CI] 2.83–5.18), luminal B (OR 1.87; 95% CI 1.27–2.76), triple-negatives (OR 1.88; 95% CI 1.15–3.08), and in symptomatic cancers (OR 1.98; 95% CI 1.13–3.48). Use of NT varied among geographic areas: Reggio Emilia had the highest rates (OR 2.29; 95% CI 1.37–3.82) while Palermo had the lowest (OR 0.41; 95% CI 0.24–0.68). Conclusions: The use of NT in Italy is limited and variable. There are no signs of greater use in hospitals with more advanced care

A case of ureteral lesions in a renal transplant recipient with a co-infection of BK virus and JC virus

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Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios

Age-dependent association of white matter abnormality with cognition after TIA or minor stroke

ObjectiveTo investigate if the association between MRI-detectable white matter hyperintensity (WMH) and cognitive status reported in previous studies persists at older ages (&gt;80 years), when some white matter abnormality is almost universally reported in clinical practice.MethodsConsecutive eligible patients from a population-based cohort of all TIA/nondisabling stroke (Oxford Vascular Study) underwent multimodal MRI, including fluid-Attenuated inversion recovery and diffusion-weighted imaging, allowing automated measurement of WMH volume, mean diffusivity (MD), and fractional anisotropy (FA) in normal-Appearing white matter using FSL tools. These measures were related to cognitive status (Montreal Cognitive Assessment) at age 6480 vs &gt;80 years.ResultsOf 566 patients (mean [range] age 66.7 [20-102] years), 107 were aged &gt;80 years. WMH volumes and MD/FA were strongly associated with cognitive status in patients aged 6480 years (all p &lt; 0.001 for WMH, MD, and FA) but not in patients aged &gt;80 years (not significant for WMH, MD, and FA), with age interactions for WMH volume (pinteraction = 0.016) and MD (pinteraction = 0.037). Voxel-wise analyses also showed that lower Montreal Cognitive Assessment scores were associated with frontal WMH in patients 6480 years, but not &gt;80 years.ConclusionMRI markers of white matter damage are strongly related to cognition in patients with TIA/minor stroke at younger ages, but not at age &gt;80 years. Clinicians and patients should not overinterpret the significance of these abnormalities at older ages

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)