Increasing Awareness and Allyship for Peers with Nonvisible Disabilities(NVD)

Non-Visible Disabilities (NVD), also known as hidden disabilities, vary across a range of categories, both physical and mental. Lack of awareness and understanding for individuals with NVD, especially from college peers, can have a major effect on students with NVD’s sense of identity and belonging, participation in class and social activities, and ultimately, can influence their decision to limit or stop accessing disability services and accommodations. To increase their awareness and encourage empathy and allyship, I intend to provide a two-hour workshop for college students affiliated with TRIO at CSUMB to engage with and learn from their colleagues with Non-Visible Disabilities

Cytotoxic effects on splenic ultrafiltrates upon leukaemic lymphocytes.

Ultrafiltrates from spleen inhibited both DNA synthesis and the proliferation of normal lymphocytes stimulated inculture from both mouse and man without apparent cytotoxicity. However, the same doses of this spleen ultrafiltrate will kill up to two-thirds of the leukaemic lymphoblasts from both mouse and man after 24 h incubation. This unique lymphocytotoxic effect could also be demonstrated on fresh primary cultures of leukaemic lymphocytes and was highly effective on slowly growing established cell lines under crowd culture conditions. Furthermore. ultrafiltrated thymus extract did not affect the DNA synthesis rates of the viability of NC-37 lymphoblasts, which have B cell characteristic. Thymus extract was cytotoxic to Molt cells, which have T cell characteristics

Coplanar emission near the LHC energy range (observed with XREC in the stratosphere)

The alignment of very high energy secondary cosmic rays was observed at both stratospheric and mountain altitudes by several X-ray emulsion chamber experiments. Extensive simulation with CORSIKA demonstrates that such phenomena can be explained by fluctuations with standard physics. However, in the case of two events observed in the stratosphere, specific features contradicts such explanation. According to the properties of those events with a minimal cascading, we explore the hints of new physics which could explain the alignment in terms of relativistic strings and diquark breaking. One description of the consequent coplanar emission expected in colliders is proposed

A study on the effects of laser shock peening on the microstructure and substructure of Ti–6Al–4V manufactured by Selective Laser Melting

Ti‐6Al‐4V was fabricated by powder-bed fusion using different laser scanning strategies. The microstructure and deformation properties were investigated in the as-built condition, and also after the material had been subjected to a laser-shock-peening (LSP) treatment. The microstructure in each condition was surveyed using 3D optical microscopy, EBSD, and TEM. The post-manufacture residual stresses were determined. The results indicate a correlation between the residual stresses and the substructures observed in TEM: tensile residual stresses from the surface down to 1 mm depth were observed in the as-built material, corresponding to extensive deformation through twinning of the 101̅2 type and wavy slip structures; while after LSP the alloy showed a variety of dislocation arrangements, especially planar and in significantly higher density, along with 112̅2 twins and with the presence of compressive residual stresses. The findings indicate that the deformation capability is mechanistically aided by the peening process, which effectively promotes the replacement of tensile residual stresses by compressive ones, offering routes for potentially improving the mechanical properties of the additively manufactured Ti‐6Al‐4V, as well as its usability.</p

Fluid and particle dynamics in laser powder bed fusion

In this work, we employ a combination of high-speed imaging and schlieren imaging, as well as multiphysics modelling, to elucidate the effects of the interaction between the laser beam and the powder bed. The formation of denuded areas where the powder was removed during single line and island scans over several layers were imaged for the first time. The inclination of the laser plume was shifted from forwards to backwards by changing power and scan speed, resulting in different denudation regimes with implications to the heat, mass and momentum transfer of the process. As the build progressed, denudation became less severe than for a single powder layer, but the occurrence of sintered and fused powder agglomerates, which were affected by the plume, increased. Schlieren imaging enabled the visualisation of the Ar gas flow, which takes place in the atmosphere above the bed due to the plume, in addition to its interaction with affected particles. Numerical modelling was used to understand and quantify the observed flow behaviour, through the hydrodynamic treatment of the laser plume as a multi-component Ar-Fe plasma. These results promote the characterisation of fluid dynamic phenomena during the laser powder-bed fusion (LPBF) process, which constitutes a key factor in the prevention of defects in additively manufactured parts

Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches

Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77–113 J/mm3. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high-energy-density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data