3D strip model for continuous roll-forming process simulation

Abstract The paper addresses the complexities for a reliable numerical simulation of the roll forming process. During the process, the material is progressively bent accumulating plastic deformation at each forming step. Strain hardening limits the material formability and may causes flaws of the final shape. A simplified method for the FEM modeling of the process has been developed introducing a narrow-strip 3D model. This approach leads better performance than the classical modeling method, in terms of results reliability and low computational time. In order to verify the proposed model, an experimental campaign of testing, for a specific roll forming production process, was carried out. On the quasi-static regime, the post necking behavior of the sheet metal was characterized. The Vickers hardness and the plastic strain of uniaxial tests were empirically correlated. By the hardness correlation, the plastic strain accumulated at different stages of the process was evaluated and compared with the numerical results. Further possible improvements of the method are highlighted

Large-scale Fully-Unsupervised Re-Identification

Fully-unsupervised Person and Vehicle Re-Identification have received increasing attention due to their broad applicability in surveillance, forensics, event understanding, and smart cities, without requiring any manual annotation. However, most of the prior art has been evaluated in datasets that have just a couple thousand samples. Such small-data setups often allow the use of costly techniques in time and memory footprints, such as Re-Ranking, to improve clustering results. Moreover, some previous work even pre-selects the best clustering hyper-parameters for each dataset, which is unrealistic in a large-scale fully-unsupervised scenario. In this context, this work tackles a more realistic scenario and proposes two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each iteration without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n^2) to O(kn) with k << n. To avoid the pre-selection of specific hyper-parameter values for the clustering algorithm, we also present a novel scheduling algorithm that adjusts the density parameter during training, to leverage the diversity of samples and keep the learning robust to noisy labeling. Finally, due to the complementary knowledge learned by different models, we also introduce a co-training strategy that relies upon the permutation of predicted pseudo-labels, among the backbones, with no need for any hyper-parameters or weighting optimization. The proposed methodology outperforms the state-of-the-art methods in well-known benchmarks and in the challenging large-scale Veri-Wild dataset, with a faster and memory-efficient Re-Ranking strategy, and a large-scale, noisy-robust, and ensemble-based learning approach.Comment: This paper has been submitted for possible publication in an IEEE Transaction

Stress Relaxation Behavior of Additively Manufactured Polylactic Acid (PLA)

In this work, the stress relaxation behavior of 3D printed PLA was experimentally investigated and analytically modeled. First, a quasi-static tensile characterization of additively manufactured samples was conducted by considering the effect of printing parameters like the material infill orientation and the outer wall presence. The effect of two thermal conditioning treatments on the material tensile properties was also investigated. Successively, stress relaxation tests were conducted, on both treated and unconditioned specimens, undergoing three different strains levels. Analytical predictive models of the viscous behavior of additive manufactured material were compared, highlighting and discussing the effects of considered printing parameters

Drop-out rate among patients treated with omalizumab for severe asthma: Literature review and real-life experience

In patients with asthma, particularly severe asthma, poor adherence to inhaled drugs negatively affects the achievement of disease control. A better adherence rate is expected in the case of injected drugs, such as omalizumab, as they are administered only in a hospital setting. However, adherence to omalizumab has never been systematically investigated. The aim of this study was to review the omalizumab drop-out rate in randomized controlled trials (RCTs) and real-life studies. A comparative analysis was performed between published data and the Italian North East Omalizumab Network (NEONet) database

Drop-out rate among patients treated with omalizumab for severe asthma: Literature review and real-life experience

BACKGROUND: In patients with asthma, particularly severe asthma, poor adherence to inhaled drugs negatively affects the achievement of disease control. A better adherence rate is expected in the case of injected drugs, such as omalizumab, as they are administered only in a hospital setting. However, adherence to omalizumab has never been systematically investigated. The aim of this study was to review the omalizumab drop-out rate in randomized controlled trials (RCTs) and real-life studies. A comparative analysis was performed between published data and the Italian North East Omalizumab Network (NEONet) database. RESULTS: In RCTs the drop-out rate ranged from 7.1 to 19.4 %. Although the reasons for withdrawal were only occasionally reported, patient decision and adverse events were the most frequently reported causes. In real-life studies the drop-out rate ranged from 0 to 45.5 %. In most cases lack of efficacy was responsible for treatment discontinuation. According to NEONet data, 32 % of treated patients dropped out, with an increasing number of drop outs observed over time. Patient decision and lack of efficacy accounted for most treatment withdrawals. CONCLUSIONS: Treatment adherence is particularly crucial in patients with severe asthma considering the clinical impact of the disease and the cost of non-adherence. The risk of treatment discontinuation has to be carefully considered both in the experimental and real-life settings. Increased knowledge regarding the main reasons for patient withdrawal is important to improve adherence in clinical practice

BeyondPlanck II. CMB map-making through Gibbs sampling

We present a Gibbs sampling solution to the map-making problem for CMB measurements, building on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps; noise filtering and map binning. Considered as two separate steps, both are computationally much cheaper than solving the combined problem. This provides a huge performance benefit as compared to traditional methods, and allows us for the first time to bring the destriping baseline length to a single sample. We apply the Gibbs procedure to simulated Planck 30 GHz data. We find that gaps in the time-ordered data are handled efficiently by filling them with simulated noise as part of the Gibbs process. The Gibbs procedure yields a chain of map samples, from which we may compute the posterior mean as a best-estimate map. The variation in the chain provides information on the correlated residual noise, without need to construct a full noise covariance matrix. However, if only a single maximum-likelihood frequency map estimate is required, we find that traditional conjugate gradient solvers converge much faster than a Gibbs sampler in terms of total number of iterations. The conceptual advantages of the Gibbs sampling approach lies in statistically well-defined error propagation and systematic error correction, and this methodology forms the conceptual basis for the map-making algorithm employed in the BeyondPlanck framework, which implements the first end-to-end Bayesian analysis pipeline for CMB observations.Comment: 11 pages, 10 figures. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science during the online release conference (November 18-20, 2020). Connection details will be made available at the same website. Registration is mandatory for the online tutorial, but optional for the conferenc

BeyondPlanck VII. Bayesian estimation of gain and absolute calibration for CMB experiments

We present a Bayesian calibration algorithm for CMB observations as implemented within the global end-to-end BeyondPlanck (BP) framework, and apply this to the Planck Low Frequency Instrument (LFI) data. Following the most recent Planck analysis, we decompose the full time-dependent gain into a sum of three orthogonal components: One absolute calibration term, common to all detectors; one time-independent term that can vary between detectors; and one time-dependent component that is allowed to vary between one-hour pointing periods. Each term is then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components are sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole/polarization quadrupole degeneracy and anomalous jumps in the instrumental gain. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of -0.84% (-0.67%) for 30 GHz, -0.14% (0.02%) for 44 GHz and -0.69% (-0.08%) for 70 GHz, compared to Planck 2018 (NPIPE). The deviations we find are within expected error bounds, and we attribute them to differences in data usage and general approach between the pipelines. In particular, the BP calibration is performed globally, resulting in better inter-frequency consistency. Additionally, WMAP observations are used actively in the BP analysis, which breaks degeneracies in the Planck data set and results in better agreement with WMAP. Although our presentation and algorithm are currently oriented toward LFI processing, the procedure is fully generalizable to other experiments.Comment: 18 pages, 15 figures. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science during the online release conference (November 18-20, 2020). Connection details will be made available at the same website. Registration is mandatory for the online tutorial, but optional for the conferenc

Bone health and body composition in transgender adults before gender-affirming hormonal therapy: data from the COMET study

Purpose: Preliminary data suggested that bone mineral density (BMD) in transgender adults before initiating gender-affirming hormone therapy (GAHT) is lower when compared to cisgender controls. In this study, we analyzed bone metabolism in a sample of transgender adults before GAHT, and its possible correlation with biochemical profile, body composition and lifestyle habits (i.e., tobacco smoke and physical activity). Methods: Medical data, smoking habits, phospho-calcic and hormonal blood tests and densitometric parameters were collected in a sample of 125 transgender adults, 78 Assigned Females At Birth (AFAB) and 47 Assigned Males At Birth (AMAB) before GAHT initiation and 146 cisgender controls (57 females and 89 males) matched by sex assigned at birth and age. 55 transgender and 46 cisgender controls also underwent a complete body composition evaluation and assessment of physical activity using the International Physical Activity Questionnaire (IPAQ). Results: 14.3% of transgender and 6.2% of cisgender sample, respectively, had z-score values < -2 (p = 0.04). We observed only lower vitamin D values in transgender sample regarding biochemical/hormonal profile. AFAB transgender people had more total fat mass, while AMAB transgender individuals had reduced total lean mass as compared to cisgender people (53.94 ± 7.74 vs 58.38 ± 6.91, p < 0.05). AFAB transgender adults were more likely to be active smokers and tend to spend more time indoor. Fat Mass Index (FMI) was correlated with lumbar and femur BMD both in transgender individuals, while no correlations were found between lean mass parameters and BMD in AMAB transgender people. Conclusions: Body composition and lifestyle factors could contribute to low BMD in transgender adults before GAHT