Experimental analysis and modeling of the recrystallization behaviour of a AA6060 extruded profile

The microstructure of Al-Mg-Si alloys is gaining nowadays an increasing industrial interest because it influences the strength, crash, corrosion and esthetic properties of the extruded profiles. In order to investigate and predict the recrystallization behaviour in the extrusion of 6XXX aluminum alloys, experimental and numerical activities are still needed. In this work, the extrusion of an industrial-scale AA6060 aluminum alloy hollow profile was carried out. An innovative recrystallization model was developed and optimized by comparing the microstructural data experimentally acquired with the outputs of the simulation performed using the Finite Element commercial code Qform Extrusion. A good correlation between numerical prediction and experimental data was found, thus proving the reliability of the proposed AA6060 recrystallization model

Investigation on the topological optimization of cooling channels for extrusion dies

During the extrusion process, high temperatures are generated, due to friction and deformation works, potentially leading to profile and die defects. Among the suggested solutions aimed at controlling the thermal field of the process, the most accredited one involves the manufacturing of cooling channels at the mating face between the die and a third plate. Despite the proven efficiency of well-designed channels, the main drawback lies in the managing of the many variables involved that strongly affect the cooling efficiency and balancing. In this frame, aim of the work is to investigate the applicability of the topological optimization tool, proposed by COMSOL Multiphysics software, for the design of cooling channels in extrusion dies. To validate the tool, an industrial case study has been selected and results compared between not optimized and optimized cooling solutions

Advancements in extrusion and drawing: a review of the contributes by the ESAFORM community

The present review paper would celebrate the 25 years anniversary of the ESAFORM association by summarizing the studies performed by the delegates of the ESAFORM conference series within mini-symposium “Extrusion and Drawing” and of the papers published in the International Journal of Material Forming in the same fields. The 160 analyzed papers have been divided in four main categories corresponding to the paper main chapters (Hot Metal Extrusion, Cold Metal Extrusion, Polymer Extrusion and Drawing) then further divided in sub-chapters in order to group them in more specific research subjects. The aim of this review paper is then to provide to the reader a complete overview of the investigated topics and of the research trends over the years within the ESAFORM associate researchers

Multiple cues produced by a robotic fish modulate aggressive behaviour in Siamese fighting fishes

The use of robotics to establish social interactions between animals and robots, represents an elegant and innovative method to investigate animal behaviour. However, robots are still underused to investigate high complex and flexible behaviours, such as aggression. Here, Betta splendens was tested as model system to shed light on the effect of a robotic fish eliciting aggression. We evaluated how multiple signal systems, including a light stimulus, affect aggressive responses in B. splendens. Furthermore, we conducted experiments to estimate if aggressive responses were triggered by the biomimetic shape of fish replica, or whether any intruder object was effective as well. Male fishes showed longer and higher aggressive displays as puzzled stimuli from the fish replica increased. When the fish replica emitted its full sequence of cues, the intensity of aggression exceeded even that produced by real fish opponents. Fish replica shape was necessary for conspecific opponent perception, evoking significant aggressive responses. Overall, this study highlights that the efficacy of an artificial opponent eliciting aggressive behaviour in fish can be boosted by exposure to multiple signals. Optimizing the cue combination delivered by the robotic fish replica may be helpful to predict escalating levels of aggression

Toward A Stable Alpha-cycloalkyl Amino Acid With A Photoswitchable Cationic Side Chain

The N-alkylated indanylidenepyrroline (NAIP) Schiff base 3 is an unnatural a-amino acid precursor potentially useful for the preparation of semisynthetic peptides and proteins incorporating charged side chains whose structure can be modulated via Z/E photoisomerization. Here we report that the heteroallylic protons of 3 led to partial loss of ethanol accompanied by formation of the novel heterocyclic system 4 during attempted deprotection. We also show that the same protons catalyze the thermal isomerization of 3, making the light-driven conformational control concept ineffective for times longer than a few hours. These problems are not present in the previously unreported compound 5 where the acidic methyl group is replaced by an H atom. Therefore, 5, rather than 3, constitutes a promising prototype for the design of building blocks capable to modulate the electrostatic potential of a protein in specific locations via light irradiation

Investigation of the skin contamination predictability by means of QForm UK extrusion code

The paper presents an innovative approach implemented in QForm UK Extrusion FEM software to analyse one of the core defects encountered in profile extrusion known as billet skin defect. The validation of the algorithm has been performed based on a number of experimental case studies taken from the literature [1,2]. Additionally, the sensitivity of the accuracy of the results to the variation in initial parameters has been analysed for both types of profile shapes: solid and hollow. Based on this, practical recommendations have been formalised for the successful industrial use of the presented algorithm

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity. Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I-M) and erythema (I-E) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of >= 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes. ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG >= 2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I-M,I-T0 and I-E,I-T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I-M,I-T0 >= 99 to be associated with RTOG >= 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959. ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG >= 2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

The SURF (Italian observational study for renal insufficiency evaluation in liver transplant recipients): A post-hoc between-sex analysis

Background: Female sex has been reported as an independent predictor of severe post-liver transplantation (LT) chronic kidney disease. We performed a by sex post-hoc analysis of the SURF study, that investigated the prevalence of renal impairment following LT, aimed at exploring possible differences between sexes in the prevalence and course of post-LT renal damage. Methods: All patients enrolled in the SURF study were considered evaluable for this sex-based analysis, whose primary objective was to evaluate by sex the proportion of patients with estimated Glomerular Filtration Rate (eGFR) < 60 ml/min/1.73m2 at inclusion and follow-up visit. Results: Seven hundred thirty-eight patients were included in our analysis, 76% males. The proportion of patients with eGFR < 60 mL/min/1.73 m2 was significantly higher in females at initial study visit (33.3 vs 22.8%; p = 0.005), but also before, at time of transplantation (22.9 vs 14.7%; p = 0.0159), as analyzed retrospectively. At follow-up, such proportion increased more in males than in females (33.9 vs 26.0%, p = 0.04). Mean eGFR values decreased over the study in both sexes, with no significant differences. Statistically significant M/F differences in patient distribution by O'Riordan eGFR levels were observed at time of transplant and study initial visit (p = 0.0005 and 0.0299 respectively), but not at follow-up. Conclusions: Though the limitation of being performed post-hoc, this analysis suggests potential sex differences in the prevalence of renal impairment before and after LT, encouraging further clinical research to explore such differences more in depth

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient’s dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Measurement of χ c1 and χ c2 production with s√ = 7 TeV pp collisions at ATLAS

The prompt and non-prompt production cross-sections for the χ c1 and χ c2 charmonium states are measured in pp collisions at s√ = 7 TeV with the ATLAS detector at the LHC using 4.5 fb−1 of integrated luminosity. The χ c states are reconstructed through the radiative decay χ c → J/ψγ (with J/ψ → μ + μ −) where photons are reconstructed from γ → e + e − conversions. The production rate of the χ c2 state relative to the χ c1 state is measured for prompt and non-prompt χ c as a function of J/ψ transverse momentum. The prompt χ c cross-sections are combined with existing measurements of prompt J/ψ production to derive the fraction of prompt J/ψ produced in feed-down from χ c decays. The fractions of χ c1 and χ c2 produced in b-hadron decays are also measured