The effect of spatial clustering on stone raw material procurement

Brantingham proposes a neutral model to explain observed data on stone tool raw material procurement. Here we provide the results of investigating how real source locations, and their spatial clustering affect the raw material pattern outcome of the neutral model. Our initial findings are that spatial distributions mimicking empirical data challenge the validity of the neutral model. More specifically, increasing the source clustering increases the amount of time where the forager is without raw materials. In terms of foraging behavior, it is not realistic to expect that foragers go extended periods of time without raw materials to create and repair tools

A Component Approach to Collaborative Scientific Software Development: Tools and Techniques Utilized by the Quantum Chemistry Science Application Partnership

Cutting-edge scientific computing software is complex, increasingly involving the coupling of multiple packages to combine advanced algorithms or simulations at multiple physical scales. Component-based software engineering (CBSE) has been advanced as a technique for managing this complexity, and complex component applications have been created in the quantum chemistry domain, as well as several other simulation areas, using the component model advocated by the Common Component Architecture (CCA) Forum. While programming models do indeed enable sound software engineering practices, the selection of programming model is just one building block in a comprehensive approach to large-scale collaborative development which must also address interface and data standardization, and language and package interoperability. We provide an overview of the development approach utilized within the Quantum Chemistry Science Application Partnership, identifying design challenges, describing the techniques which we have adopted to address these challenges and highlighting the advantages which the CCA approach offers for collaborative development

Tackling component interoperability in quantum chemistry software

The Common Component Architecture (CCA) offers an environment that allows scientific packages to dynamically interact with each other through components. Conceptually, a computation can be constructed with plugand- play components from any componentized scientific package; however, providing such plug-and-play components from scientific packages requires more than componentizing functions/subroutines of interest, especially for large-scale scientific packages with a long development history. In this paper, we present our efforts to construct components for the integral evaluation - a fundamental sub-problem of quantum chemistry computations - that conform to the CCA specification. The goal is to enable fine-grained interoperability between three quantum chemistry packages, GAMESS, NWChem, and MPQC, via CCA integral components. The structures of these packages are quite different and require different approaches to construct and exploit CCA components. We focus on one of the three packages, GAMESS, delineating the structure of the integral computation in GAMESS, followed by our approaches to its component development. Then we use GAMESS as the driver to interoperate with integral components from another package, MPQC, and discuss the possible solutions for interoperability problems along with preliminary results

Pharmacist-led management of chronic pain in primary care:costs and benefits in a pilot randomised controlled trial

To explore differences in mean costs (from a UK National Health Service perspective) and effects of pharmacist-led management of chronic pain in primary care evaluated in a pilot randomised controlled trial (RCT), and to estimate optimal sample size for a definitive RCT

Clustering of 27,525,663 death records from the United States based on health conditions associated with death: an example of big health data exploration

Background: Insight into health conditions associated with death can inform healthcare policy. We aimed to cluster 27,525,663 deceased people based on the health conditions associated with death to study the associations between the health condition clusters, demographics, the recorded underlying cause and place of death. Methods: Data from all deaths in the United States registered between 2006 and 2016 from the National Vital Statistics System of the National Center for Health Statistics were analyzed. A self-organizing map (SOM) was used to create an ordered representation of the mortality data. Results: 16 clusters based on the health conditions associated with death were found showing significant differences in socio-demographics, place, and cause of death. Most people died at old age (73.1 (18.0) years) and had multiple health conditions. Chronic ischemic heart disease was the main cause of death. Most people died in the hospital or at home. Conclusions: The prevalence of multiple health conditions at death requires a shift from disease-oriented towards person-centred palliative care at the end of life, including timely advance care planning. Understanding differences in population-based patterns and clusters of end-of-life experiences is an important step toward developing a strategy for implementing population-based palliative care