INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

A checklist for choosing between R packages in ecology and evolution

The open source and free programming language R is a phenomenal mechanism to address a multiplicity of challenges in ecology and evolution. It is also a complex ecosystem because of the diversity of solutions available to the analyst. Packages for R enhance and specialize the capacity to explore both niche data/experiments and more common needs. However, the paradox of choice or how we select between many seemingly similar options can be overwhelming and lead to different potential outcomes. There is extensive choice in ecology and evolution between packages for both fundamental statistics and for more specialized domain‐level analyses. Here, we provide a checklist to inform these decisions based on the principles of resilience, need, and integration with scientific workflows for evidence. It is important to explore choices in any analytical coding environment—not just R—for solutions to challenges in ecology and evolution, and document this process because it advances reproducible science, promotes a deeper understand of the scientific evidence, and ensures that the outcomes are correct, representative, and robust.York University Librarie

Developing research capacity in the social sciences: a professionality-based model

It is argued in this article that the shortcomings of social science research stem fundamentally from the lack of a developmentalist culture, which manifests itself by researchers’ inadequate interest and participation in continuing professional development. Yet institutional research leaders also have a key role in increasing research capacity. They need to be specific about precisely what sort of development they want to occur: what specific skills need to be developed, and what kinds of output they are encouraging. These will be incorporated into their visions of institutional research activity and achievement, which must be communicated clearly to those for whose development they are responsible. What is proposed is a model of institutionally-based professional development centred around the notion of ‘extended’ professionality, and from which developmentalist research cultures are likely to emerge

Near-optimal loop tiling by means of cache miss equations and genetic algorithms

The effectiveness of the memory hierarchy is critical for the performance of current processors. The performance of the memory hierarchy can be improved by means of program transformations such as loop tiling, which is a code transformation targeted to reduce capacity misses. This paper presents a novel systematic approach to perform near-optimal loop tiling based on an accurate data locality analysis (cache miss equations) and a powerful technique to search the solution space that is based on a genetic algorithm. The results show that this approach can remove practically all capacity misses for all considered benchmarks. The reduction of replacement misses results in a decrease of the miss ratio that can be as significant as a factor of 7 for the matrix multiply kernel.Peer ReviewedPostprint (published version

Segregated prisoners: nature imagery project in prisons as a program option

Master's Project (M.A.) University of Alaska Fairbanks, 2018Solitary confinement can be summarized as the state of being alone in a prison cell for 22 to 24 hours a day with minimal human interaction, little to no natural light, property restrictions, visitation constraints, and the inability to participate in group activities and communal meals. Solitary confinement can go by many names; it can be referred to as lockdown, Security or Special Housing Units (SHU), Special Management Units (SMU), administrative segregation, disciplinary or punitive segregation, restrictive housing, or "the hole". Solitary confinement is utilized for many purposes, primarily for the health and safety of themselves and others. It was first intended as a means of rehabilitation. However, instead, it has contributed to negative psychological and physiological effects on prisoners. There is argument for and against the use of solitary confinement and reformation efforts are being made to reduce solitary confinement. In an attempt to provide programming to segregated prisoners and reduce the amount of time that prisoners are in their cells, various correctional institutions have implemented nature imagery programs to reduce violent behavior and physiological states. Nature Imagery in Prisons Project (NIPP) was the first program of its kind and has laid the groundwork for other correctional institutions to follow. Programs such as this are designed for segregated prisoners and are used as a means of rehabilitation for these individuals as they prepare for their return to the community or to general prison population

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded