Today, die design standards are used to design the structure of die components. These standards are usually based on high safety factors. So, the die components are often heavier and larger than required. In this article, a software package is developed which can design an appropriate topology of body structure of stamping die components with a reduced weight. This is done by implementing the evolutionary structural optimization algorithm. The proposed structure can also be modified by the designer to accommodate for a simpler casting method. This software package is developed in Microsoft Visual C# programming environment with a link to Abaqus software to analyze the finite element simulation of the process. The operation of the software is demonstrated by an example where the die for a sheet metal part is studied. The die components are initially designed, analyzed and compared with the standard die (the die which is in general use today). The final results show a reduction of 37% of volume and 8% of maximum displacement, respectively.

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BackgroundProgrammed death-ligand 1 (PD-L1), also called B7 homolog 1 (B7-H1), is a human protein playing an important biomarker role in highly proliferation cells (like cancer cells) which is encoded by the CD274 gene. The interaction between PD-1 and PD-L1 inhibits T cell growth and cytokine secretion. Therefore, PD-L1 negatively regulates immune responses and permits tumor cells to evade immune surveillance.Thus, PD-L1 seems to be a suitable target for designing the immune-therapeutics. ResultsThe predicted structure of this protein indicated that the chains were linked by (Gly4Ser)3 linker. In that line, using different simulation software, the structure of Granzyme B (GrB), a serine protease exists in cytotoxic lymphocytes granules as an apoptosis mediator, was attached to its speci c antibody structure (Atezolizumab) via an adaptor sequence. Evaluation of accuracy, energy minimization and characterization of biological properties of the nal processed structure were done. Our computational outcomes showed that the method of employed structure prediction has been successfully managed to design the immunotoxin structure. ConclusionIt is necessary to mention that the precise and accurate design of the immune-therapeutic agents against cancer cells, can be con rmed by employed in-silico approach. So, using this method, we have designed a capable immunotoxin targeting the PD-L1 in an accurate orientation and cause the cancer cell destruction by its toxin domain.

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Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.