Optimal design of solidification processes

An optimal design algorithm is presented for the analysis of general solidification processes, and is demonstrated for the growth of GaAs crystals in a Bridgman furnace. The system is optimal in the sense that the prespecified temperature distribution in the solidifying materials is obtained to maximize product quality. The optimization uses traditional numerical programming techniques which require the evaluation of cost and constraint functions and their sensitivities. The finite element method is incorporated to analyze the crystal solidification problem, evaluate the cost and constraint functions, and compute the sensitivities. These techniques are demonstrated in the crystal growth application by determining an optimal furnace wall temperature distribution to obtain the desired temperature profile in the crystal, and hence to maximize the crystal's quality. Several numerical optimization algorithms are studied to determine the proper convergence criteria, effective 1-D search strategies, appropriate forms of the cost and constraint functions, etc. In particular, we incorporate the conjugate gradient and quasi-Newton methods for unconstrained problems. The efficiency and effectiveness of each algorithm is presented in the example problem

Optimal Control of Industrial Assembly Lines

This paper discusses the problem of assembly line control and introduces an optimal control formulation that can be used to improve the performance of the assembly line, in terms of cycle time minimization, resources' utilization, etc. A deterministic formulation of the problem is introduced, based on mixed-integer linear programming. A simple numerical simulation provides a first proof of the proposed concept

The effect of various oxide dispersions on the oxidation resistance of Fe{sub 3}Al

Oxide-dispersed Fe-28at.%Al-2%Cr alloys were produced by a powder metallurgy technique followed by hot extrusion. Yttria and ceria were added to the base alloy to assess the effect of these dopants on the oxidation behavior. The amount of dopant was varied from 0.05-0.5 at.% Y in a series of Y{sub 2}O{sub 3}-dispersed alloys. isothermal and cyclic oxidation testing was conducted at temperatures from 800{degrees} to 1300{degrees}C. A CeO{sub 2} addition was detrimental to the oxidation behavior. The Y{sub 2}O{sub 3} improved the {alpha}-Al{sub 2}O{sub 3} scale adhesion relative to an undoped alloy, but was not as effective as similar additions to an oxide-dispersed FeCrAl alloy

Taste intensity and hedonic responses to simple beverages in gastrointestinal cancer patients

Changes in the taste of food have been implicated as a potential cause of reduced dietary intake among cancer patients. However, data on intensity and hedonic responses to the four basic tastes in cancer are scanty and contradictory. The present study aimed at evaluating taste intensity and hedonic responses to simple beverages in 47 anorectic patients affected by gastrointestinal cancer and in 55 healthy subjects. Five suprathreshold concentrations of each of the four test substances (sucrose in black current drinks, citric acid in lemonade, NaCl in unsalted tomato juice, and urea in tonic water) were used. Patients were invited to express a judgment of intensity and pleasantness ranging from 0 to 10. Mean intensity scores directly correlated with concentrations of sour, salty, bitter, and sweet stimuli, in both normals and those with cancer. Intensity judgments were higher in cancer patients with respect to sweet (for median and high concentrations, P < 0.05), salty (for all concentrations, P < 0.05), and bitter tastes (for median concentration, P < 0.01). Hedonic function increased with the increase of the stimuli only for the sweet taste. A negative linear correlation was found between sour, bitter, and salty concentrations and hedonic score. Both in cancer patients and in healthy subjects, hedonic judgments increased with the increase of the stimulus for the sweet taste (r 1/4 0.978 and r 1/4 0.985, P 1/4 0.004 and P 1/4 0.002, respectively), and decreased for the salty (r 1/4 ??0.827 and r 1/4 ??0.884, P 1/4 0.084 and P 1/4 0.047, respectively) and bitter tastes (r 1/4 ??0.990 and r 1/4 ??0.962, P 1/4 0.009 and P 1/4 0.001, respectively). For the sour taste, the hedonic scores remained stable with the increase of the stimulus in noncancer controls (r 1/4 ??0.785, P 1/4 0.115) and decreased in cancer patients (r 1/4 ??0.996, P 1/4 0.0001). The hedonic scores for the sweet taste and the bitter taste were similar in cancer patients and healthy subjects, and these scores were significantly higher in cancer patients than in healthy subjects for most of the concentrations of the salty taste and all the concentrations of the sour taste. The present study suggests that cancer patients, compared to healthy individuals, have a normal sensitivity, a normal likingfor pleasant stimuli, and a decreased dislike for unpleasant stimuli. Moreover, when compared to controls, they show higher hedonic scores for middle and high concentrations of the salty taste and for all concentrations of the sour taste. Further studies are needed to evaluate whether these changes observed in cancer patients translate into any alteration in dietary behavior and/or food preferences

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403 and MACS J1149.5+2223

The wavelength dependence of the Kormendy relation (KR) is well characterised at low-redshift, but poorly studied at intermediate redshifts. The KR provides information on the evolution of the population of early-type galaxies (ETGs), therefore, by studying it, we may shed light on the assembly processes of these objects and their size evolution. Since studies at different redshifts are generally conducted in different rest-frame wavebands, investigating whether there is a wavelength dependence of the KR is fundamental to interpret the conclusions we might draw from it. We analyse the KRs of the three Hubble Frontier Fields clusters, Abell S1063 (z = 0.348), MACS J0416.1-2403 (z = 0.396), and MACS J1149.5+2223 (z = 0.542), as a function of wavelength. This is the first time the KR of ETGs has been explored consistently in such a large range of wavelength at intermediate redshifts. We exploit very deep HST photometry, ranging from the observed B-band to the H-band, and VLT/MUSE integral field spectroscopy. We improve the structural parameters estimation we performed in a previous work (Tortorelli et al. 2018) by means of a newly developed Python package called morphofit (Tortorelli&Mercurio 2023). With its use on cluster ETGs, we find that the KR slopes smoothly increase with wavelength from the optical to the near-infrared bands in all three clusters, with the intercepts getting fainter at lower redshifts due to the passivisation of the ETGs population. The slope trend is consistent with previous findings at lower redshifts. The slope increase with wavelength implies that smaller size ETGs are more centrally concentrated than larger size ETGs in the near-infrared with respect to the optical regime. Since different bands probe different stellar populations in galaxies, the slope increase also implies that smaller ETGs have stronger internal gradients with respect to larger ETGs.Comment: Submitted to Astronomy and Astrophysics in the form of letter to the Editor, 5 pages, 1 figure, 1 tabl

Searching for galaxy-scale strong-lenses in galaxy clusters with deep networks -- I: methodology and network performance

Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological simulations. We search for galaxy-galaxy strong-lensing systems in HST multi-band imaging of galaxy cluster cores from the CLASH and HFF programs by exploring the classification capabilities of deep learning techniques. Convolutional neural networks are trained utilising highly-realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members. To this aim, we take advantage of extensive spectroscopic information on member galaxies in 16 clusters and the accurate knowledge of the deflection fields in half of these from high-precision strong lensing models. Using observationally-based distributions, we sample magnitudes, redshifts and sizes of the background galaxy population. By placing these sources within the secondary caustics associated with cluster galaxies, we build a sample of ~3000 galaxy-galaxy strong lenses which preserve the full complexity of real multi-colour data and produce a wide diversity of strong lensing configurations. We study two deep learning networks processing a large sample of image cutouts in three HST/ACS bands, and we quantify their classification performance using several standard metrics. We find that both networks achieve a very good trade-off between purity and completeness (85%-95%), as well as good stability with fluctuations within 2%-4%. We characterise the limited number of false negatives and false positives in terms of the physical properties of the background sources and cluster members. We also demonstrate the neural networks' high degree of generalisation by applying our method to HST observations of 12 clusters with previously known galaxy-scale lensing systems.Comment: 17 pages, 13 figures, to be published on A&