Nickel aluminum shape memory alloys via molecular dynamics

Shape memory materials are an important class of active materials with a wide range of applications in the aerospace, biomedical, and automobile industries. These materials exhibit the two unique properties of shape memory and superelasticity. Shape memory is the ability to recover its original shape by applying heat after undergoing large deformations. Superelasticity is the ability to undergo large, reversible deformations (up to 10%) that revert back when the load is removed. These special properties originate from a reversible, diffusionless solid-solid phase transformation that occurs between a high temperature austenite phase and a low temperature martensite phase. The development of the martensite microstructure is not well understood; this is especially true in regards to the role of size and mechanical constraints that dominate the properties in nanoscale samples. The goals of this research are to use molecular dynamics (MD) to (1) study the effects of simulation size on the martensite transformation to determine the ultimate limit of miniaturization, (2) to investigate the effects of mechanical constraints on the martensite transformation and resulting microstructure, and (3) to explore the effects of grain size in polycrystalline shape memory alloys. MD is well suited to study the transformation, as it shares a similar time scale with the extremely fast, diffusionless transformation.^ An extensive set of cooling and heating simulations were performed on Ni63Al37 disordered shape memory alloys (SMAs) to determine the effect of system size on the transformation. Simulation cell sizes in the range of 4.2 to 20 nm were studied. We discovered that decreasing system size only resulted in a slight increase of both transformation temperatures. However, the variability of the austenite transformation temperature increased considerably with decreasing simulation cell size, reaching 10% of the mean value for a system size of 10 nm. This variability can impose a fundamental limit on the miniaturization of this class of materials, as the reliability of device performance comes into question. Also, mechanical constraints were applied to force the cell angles to remain 90° in order to emulate the environment of a partially transformed polycrystal where grains are constricted by their neighbors. The mechanical constraints caused the austenite transformation temperature to decrease with decreasing size by up to 50%, and resulted in a two-domain microstructure for system sizes above 4.2 nm in order to accommodate the internal stresses. Finally, large scale MD simulations were done on polycrystalline samples with grain sizes ranging from 2.5 to 20 nm. We found that a critical grain size of 7.5 nm resulted in a minimum in the percent transformation to martensite. Below this critical size, martensite forms at the grain boundaries and the grains are able to rotate via grain boundary sliding to relieve internal stresses. In larger grains, martensite can nucleate and grow within the grains more easily. A uniaxial strain of up to 10% was applied to investigate the stress induced martensite transformation. Larger grains showed considerable work hardening when strained beyond about 2%. Plastic recovery was also calculated by unloading and relaxing at 4 and 10% strain. Samples strained to 10% were generally able to recover about 20-30% of the plastic strain, while samples strained to 4% showed varying amounts of recovery that peaked at 66% for a grain size of 7.5 nm

Positive severity feedback between consecutive fires in dry eucalypt forests of southern Australia

Fire regimes have long-term effects on ecosystems which can be subtle, requiring study at a large spatial scale and temporal scale to fully appreciate. The way in which multiple fires interact to create a fire regime is poorly understood, and the relationship between the severities of consecutive fires has not been studied in Australia. By overlaying remotely sensed severity maps, our study investigated how the severity of a fire is influenced by previous fire severity. This was done by sampling points at 500-m spacing across 53 fires in dry eucalypt forests of southeast Australia, over a range of time since fire spanning every major fire season for 30 yr. Generalized additive models were used to determine the influence of previous severity on the probability of crown fire and understory fire, controlling for differences in time since fire, topography, and weather. We found that a crown fire is more than twice as likely after a previous crown fire than previous understory fire, and understory fire is more likely after previous understory fire. Our findings are in line with the results of studies from North America and suggest that severe fire promotes further fire. This may be evidence of a runaway positive feedback, which can drive ecological change, and lead to a mosaic of divergent vegetation, but research into more than two consecutive fires is needed to explore this. Our results also suggest that a low-severity prescribed fire may be a useful management option for breaking a cycle of crown fires

A look back to move ahead: New directions for research on proactive performance and other discretionary work behaviours

Over the last two decades, the multi-dimensional notion of job performance has been fully brought to life. The differentiation between core task performance and various aspects of discretionary work behaviour is now commonly applied. A multitude of empirical studies, enhancing our knowledge of the antecedents and consequences of the different performance aspects, have recently been summarised through various meta-analyses. We use this as an occasion for taking stock in order to identify new areas of theorising and empirical research. Focusing in particular on proactive performance aspects, the present paper identifies three themes that could inspire new research and model development. We suggest taking a new approach to the treatment of time in order to account for the dynamic nature of performance on the one hand, and to consider life-span changes on the other, developing comprehensive models on proactivity-enhancing interventions, and more strongly incorporating a cross- cultural perspective

The touring reader: understanding the bibliophile's experience of literary tourism

This research explores the literary enthusiast’s experience of planning and undertaking literary inspired trips. The research reconceptualises the dominant figure of the literary pilgrim, inspired to visit sites associated with favourite authors, by using detailed results from 30 open-ended surveys distributed to delegates at a literary conference. The findings indicate that these keen readers prefer to plan their own trips and shun organised attractions and mainstream tourist information in favour of employing the texts themselves as source material. Respondents then feed back their experiences into the re-reading of the literary text. These findings are analysed using the concept of concretisation borrowed from literary theory. This concept, which has not been used in previous tourist studies, reflects the experience of these visitors who are using travel to solidify their reading of favourite books. This research therefore highlights the interdependence of texts and travels and emphasises the important role that imagination plays in the experience and recollection of tourist trips

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment