Approach to Qualification for Electron Beam Powder Bed Fusion in Ti-6Al-4V

Recent developments in additive manufacturing (AM) show promise for using AM manufactured components in a production setting. However, a crucial step for mass producing AM components is to certify these parts for use. One common method for certifying parts is to manufacture tensile coupons alongside any parts. These coupons are characterized and the results are related to the parts. This causes many researchers to focus on the process-material interactions while neglecting build setup. Another issue related to certification of AM parts is the lack of knowledge in the software calculations for a given process. Original equipment manufacturers (OEM), such as Arcam AB for electron beam powder bed fusion (E-PBF), need secrecy in their software to ensure their scan strategy is protected. Therefore, this practice provides researchers little information or confidence about changes made in process parameters. To provide insight into these areas of variation, the current work can be broken into two parts – (i) understanding how changes in selected process parameters can influence non-selected parameters and (ii) determining the effectiveness of current qualification methods for the E-PBF process.To better understand process parameters, changes in selected process parameters were simulated and compared with the Arcam provided parameter set. Results of these simulations show that speed function variable is only a function of melting time while modifications to the contour passes and surface temperature result in changes to the heat balance. Variations in the heat balance change the cooling rate of as-fabricated material, which causes microstructural evolution in titanium alloys. Preliminary results show that modifying the surface temperature for specific regions can be used to control microstructure.To better understand how build setup can influence parts in a build, build setup variables such as part melt order, build volume, and cross-sectional melt area were modified between two builds. Results of these changes show that performance in test coupons cannot be applied to performance in the other parts since changes in build setup influence each part differently. The current work provide challenges to applying traditional qualification methods to AM fabricated components in hopes that a process-based certification path can be achieved

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Home range estimation within complex restricted environments: importance of method selection in detecting seasonal change

Estimating the home ranges of animals from telemetry data can provide vital information on their spatial behaviour, which can be applied by managers to a wide range of situations including reserve design, habitat management and interactions between native and non-native species. Methods used to estimate home ranges of animals in spatially restricted environments (e.g. rivers) are liable to overestimate areas and underestimate travel distances by including unusable habitat (e.g. river bank). Currently, few studies that collect telemetry data from species in restricted environments maximise the information that can be gathered by using the most appropriate home-range estimation techniques. Simulated location datasets as well as radio-fix data from 23 northern pike (Esox lucius) were used to examine the efficiency of home-range and travel estimators, with and without correction for unusable habitat, for detecting seasonal changes in movements. Cluster analysis most clearly demonstrated changes in range area between seasons for empirical data, also showing changes in patchiness, and was least affected by unusable-environment error. Kernel analysis showed seasonal variation in range area more clearly than peripheral polygons or ellipses. Range span, a linear estimator of home range, had no significant seasonal variation. Results from all range area estimators were smallest in autumn, when cores were least fragmented and interlocation movements smallest. Cluster analysis showed that core ranges were largest and most fragmented in summer, when interlocation distances were most variable, whereas excursion-sensitive methods (e.g. kernels) recorded the largest outlines in spring, when interlocation distances were largest. Our results provide a rationale for a priori selection of home-range estimators in restricted environments. Contours containing 95% of the location density defined by kernel analyses better reflected excursive activity than ellipses or peripheral polygons, whereas cluster analyses better defined range cores in usable habitat and indicate range fragmentation

Retrotransposon Methylation Profiles and Survival in Black Women With High-Grade Serous Ovarian Carcinoma

Introduction: Retrotransposons (REs) constitute nearly half of the genome and include long terminal repeat (LTR) elements, Long INterspersed Elements (LINE), and Short INterspersed Elements (SINE). REs are typically silenced in somatic tissues via DNA methylation but can be reactivated through DNA hypomethylation, potentially impacting gene regulation. Here, we investigate genome-scale profiles of RE methylation in high-grade serous ovarian carcinoma (HGSOC) and associations with survival among Black women. Methods: Methylation levels of LTR, LINE-1, and Alu (type of SINE) in 200 HGSOC tumors were predicted using a random forest approach and clustered using multiple consensus algorithms. Associations between RE methylation clusters and survival were evaluated using Cox proportional hazard regression, adjusting for age, stage, and debulking status. We performed sensitivity analyses restricted to women with late-stage disease and with adjustment for BRCA1/BRCA2 mutations. Results: Two RE methylation clusters were identified. Cluster 1 exhibited a more hypomethylated RE profile ( Active ), while Cluster 2 was more hypermethylated ( Repressed ). No statistically significant differences in patient or clinical characteristics were observed between clusters. Compared to the Active Cluster, the Repressed Cluster was associated with an increased risk of mortality (HR = 2.41; 95% CI 1.04-5.59) and had a lower proportion of T cells. This association was consistent in sensitivity analyses. Conclusion: A more hypermethylated RE profile was linked to worse survival among Black women with HGSOC, highlighting the potential of RE methylation as a prognostic biomarker. Further research is needed to understand the underlying biological mechanisms and their implications in ovarian cancer biology and treatment