CALCULATION PROGRAM FOR THE PROCESSING OF LOW-CONCENTRATED TRITIUM AND DEUTERIUM WASTE THROUGH THE CECE ISOTOPIC SEPARATION PROCESS

Within the research conducted at our Institute of Cryogenic and Isotopic Technologies (ICSI), is developed a project entitled "Innovative CECE process solution to promote a new technology for decontamination of liquid waste, tritium low concentrated and deuterium recovery”. The main objective of the project carried out within our team is to promote an innovative solution of CECE isotopic separation process (Combined Electrolysis and Catalytic Exchange), part of a new technology for decontamination of liquid waste, poorly concentrated in tritium, generated by nuclear reactors, ensuring increased recovery of the isotope deuterium and tritium. This paper presents the current stage of an innovative CECE isotopic separation process solution, and also the mathematical model developed for the simulation of hydrogen isotope separation processes through the CECE process and a theoretical analysis based on numerical data resulting from the simulation of two CECE plant operating mode.

Effect of artificial aging on the tribological properties of an Al-25Zn-1Mg alloy

The tribological properties of aluminum based Al-25Zn-1Mg alloy was experimentally investigated in an MSc thesis, and the results are presented in this study. To this purpose, Al-25Zn-1Mg was commercially produced by casting. The prepared samples were divided into seven groups: no-process, solution treatment, and artificial aging for 1, 3, 6, 12 and 24 hours. Aged and untreated samples were subjected to tribological experiments under conditions of dry friction under 5 N and 10 N loads at speed of 200 rpm, 400 rpm and 800 rpm for a distance of 500 m. As a result of the tests, it was found that hardness increases in all samples depending on the aging process. It was also found that when the test speed was kept constant, the coefficient of friction increased with increasing load.

Assembly Wirebond Process Solution for Mitigating Leadframe Bouncing on Multi-Die QFN Device

Modification and improvement of an existing tooling design in semiconductor packaging industry has been a usual practice, to enhance the current setup and to provide a solution to a specific assembly problem. This paper discusses the solution in eliminating the smashed ball defect occurrence observed after wirebond process. Smashed ball is usually encountered if the unit is unstable and creates a bouncing effect during wirebond process. It is therefore important to mitigate this micro-bouncing effect by analyzing the package design and the window clamp and top plate (WCTP). The objective is to increase the stability of the unit during wirebonding, especially for quad-flat no-leads (QFN) package with no tape. To achieve this, the solution is to alter the vacuum hole design of the top plate from single hole per unit to multiple holes of varied sizes per unit. Ultimately, after changing the design of the top plate, the micro-bouncing encountered during wirebond process was significantly reduced. This in turn created a consistent ball formation in all bonded wires. The comparative data presented in this paper confirmed the effectivity of the redesigned WCTP. For future works and studies, the improvement and learnings could be used on devices with comparable configuration.

An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer

Background and PurposeTo develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy.Materials and MethodsA full-process solution that integrates autosegmentation and automatic treatment planning was developed under a single deep-learning framework. A convolutional neural network (CNN) was used to generate segmentations of the target and the organs at risk (OAR) as well as dose distribution. A script in Pinnacle that simulates the treatment planning process was used to execute plan optimization. A total of 172 rectal cancer patients were used for model training, and 18 patients were used for model validation. Another 40 rectal cancer patients were used for an end-to-end evaluation for both autosegmentation and treatment planning. The PTV and OAR segmentation was compared with manual segmentation. The planning results was evaluated by both objective and subjective assessment.ResultsThe total time for full-process planning without contour modification was 7 min, and an additional 15 min may require for contour modification and re-optimization. The PTV DICE similarity coefficient was greater than 0.85 for all 40 patients in the evaluation dataset while the DICE indices of the OARs also indicated good performance. There were no significant differences between the auto plans and manual plans. The physician accepted 80% of the auto plans without any further operation.ConclusionWe developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.

Application of Innovative Processes for Gold Recovery from Romanian Mining Wastes

The application of a new hydrometallurgical process for gold extraction by thiosulphate leaching from Romanian mining wastes, coming from Balan and Deva deposits, was studied. There was obtained 85% of Au extraction after leaching; moreover, an integrated flow-sheet, including recycling of process solution and carbon, was outlined, based on results obtained at a laboratory scale, using a schematic chemical circuit of treatment. Global recovery of the process (leaching-adsorption-desorption-electrodeposition) of about 75-80% of Au was achieved. The developed integrated flow-sheet, allows to recycle the reagents during the process, with a loss of only 5-10%, in particular thiosulphate and alcohol, for each complete circuit of treatment.

The identification problem for BSDEs driven by possibly non-quasi-left-continuous random measures

In this paper, we focus on the so-called identification problem for a BSDE driven by a continuous local martingale and a possibly non-quasi-left-continuous random measure. Supposing that a solution [Formula: see text] of a BSDE is such that [Formula: see text] where [Formula: see text] is an underlying process and [Formula: see text] is a deterministic function, solving the identification problem consists in determining [Formula: see text] and [Formula: see text] in terms of [Formula: see text]. We study the over-mentioned identification problem under various sets of assumptions and we provide a family of examples including the case when [Formula: see text] is a non-semimartingale jump process solution of an SDE with singular coefficients.