Fixing Ground Zero: Race and Religion in Francis Lawrence’s I Am Legend

Francis Lawrence’s I Am Legend is a complex intertext of Matheson’s novel of the same name and its two previous film adaptations. While the film attempts to depict racism as monstrous, the frequent invocation of 9/11 imagery and Christian symbolism throughout the film recodes the vampiric dark-seekers as radical Islamic terrorists. This serves to further enshrine an us/Christians vs. them/Muslim dichotomy present in post-9/11 America, a dichotomy that the film presents as “curable” through the spread of Christianity and the fall of Islam

Sulfate Reduction in Sediments Produces High Levels of Chromophoric Dissolved Organic Matter

Sulfate reduction plays an important role in altering dissolved organic matter (DOM) in estuarine and coastal sediments, although its role in the production of optically active chromophoric DOM (CDOM) and a subset of fluorescent DOM (FDOM) has not been previously investigated in detail. Freshwater sediment slurries were incubated anaerobically with added sulfate and acetate to promote sulfate-reducing bacteria. Ultraviolet visible (UV-Vis) absorbance and 3-dimensional excitation emission matrix (EEM) fluorescence spectra were measured over a five weeks anaerobic dark incubation period. Parallel Factor Analysis (PARAFAC) of FDOM determined components that increased significantly during dark and anaerobic incubation matching three components previously considered of terrestrially-derived or humic-like origin published in the OpenFluor database. The observed FDOM increase was strongly correlated (R2 = 0.96) with the reduction of sulfate. These results show a direct experimental link between sulfate reduction and FDOM production, which impacts our understanding of coastal FDOM sources and early sediment diagenesis. As 3D fluorescence techniques are commonly applied to diverse systems, these results provide increasing support that FDOM can have many diverse sources not consistently captured by common classifications such as “humic-like” fluorescence

Recertification and Reentry to Practice for Nurse Anesthetists: Determining Core Competencies and Evaluating Performance via High-Fidelity Simulation Technology

Introduction The National Board of Certification and Recertification for Nurse Anesthetistsaddressed a barrier to return to practice of uncertified practitioners by replacing required direct patient care experiences with high-fidelity simulation. Objectives The aims of this study were to: (a) validate a set of clinical activities for their relevance to reentry and determine if they could be replicated using simulation, (b) evaluate the content validity of an existing simulation scenario containing the proposed clinical activities and determine its substitutability for a clinical practicum, and (c) evaluate the validity of two methods to assess simulation performance. Methods A modified Delphi method incorporating an autonomous, anonymous, three-round online survey process using three unique expert certified registered nurse anesthetists groups was used to address each study aim. Results Twenty-seven clinical activities gained consensus as necessary to be assessed in the simulation. All 14 survey questions used to determine simulation content validity exceeded the minimum content validity index (CVI) value of 0.78, with a mean CVI of 0.99. The global rating scale CVI and the competency checklist CVI were 0.83 and 1.0, respectively. Conclusion The findings add to the existing literature supporting the utility of simulation for high-stakes provider assessment and certification

New Calabi–Yau manifolds from genetic algorithms

Calabi–Yau manifolds can be obtained as hypersurfaces in toric varieties built from reflexive polytopes. We generate reflexive polytopes in various dimensions using a genetic algorithm. As a proof of principle, we demonstrate that our algorithm reproduces the full set of reflexive polytopes in two and three dimensions, and in four dimensions with a small number of vertices and points. Motivated by this result, we construct five-dimensional reflexive polytopes with the lowest number of vertices and points. By calculating the normal form of the polytopes, we establish that many of these are not in existing datasets and therefore give rise to new Calabi–Yau four-folds. In some instances, the Hodge numbers we compute are new as well

Machine Learning Clifford Invariants of ADE Coxeter Elements

There has been recent interest in novel Clifford geometric invariants of linear transformations. This motivates the investigation of such invariants for a certain type of geometric transformation of interest in the context of root systems, reflection groups, Lie groups and Lie algebras: the Coxeter transformations. We perform exhaustive calculations of all Coxeter transformations for A8, D8 and E8 for a choice of basis of simple roots and compute their invariants, using high-performance computing. This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning. In this paper we focus on neural network classification and principal component analysis. Since the output—the invariants—is fully determined by the choice of simple roots and the permutation order of the corresponding reflections in the Coxeter element, we expect huge degeneracy in the mapping. This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy. This paper is a pump-priming study in experimental mathematics using Clifford algebras, showing that such Clifford algebraic datasets are amenable to machine learning, and shedding light on relationships between these novel and other well-known geometric invariants and also giving rise to analytic results

Equilibrium fluctuations of liquid state static properties in a subvolume by molecular dynamics

Engineering & Physical Science Research Council (EPSRC) EP/G026114/1; Engineering & Physical Science Research Council (EPSRC) EPSRC Fellowship Smith; Engineering & Physical Science Research Council (EPSRC) EP/N025954/1