Journal Impact Factor Versus Eigenfactor and Article Influence

This paper examines the practical usefulness of two new journal performance metrics, namely the Eigenfactor score, which is said to measure “importance”, and Article Influence score, which is said to measure “prestige”, using the most recent ISI data for 2009 for the 200 most highly cited journals in each of the Sciences and Social Sciences, and compares them with two existing ISI metrics, namely Total Citations and the 5-year Impact Factor (5YIF) of a journal. It is shown that the Sciences and Social Sciences are different in terms of the strength of the relationship of journal performance metrics, although the actual relationships are very similar. Moreover, the importance and prestige journal performance metrics are shown to be closely related to the two existing ISI metrics, and hence add little in practical usefulness to what is already known. These empirical results are compared with existing results in the literature.Journal performance metrics; Research assessment measures; Total citations; 5-year impact factor (5YIF); Eigenfactor; Article influence; Importance; Prestige

Scholarly Metrics Baseline: A Survey of Faculty Knowledge, Use, and Opinion About Scholarly Metrics

This article presents the results of a faculty survey conducted at the University of Vermont during academic year 2014-2015. The survey asked faculty about: familiarity with scholarly metrics, metric seeking habits, help seeking habits, and the role of metrics in their department’s tenure and promotion process. The survey also gathered faculty opinions on how well scholarly metrics reflect the importance of scholarly work and how faculty feel about administrators gathering institutional scholarly metric information. Results point to the necessity of understanding the campus landscape of faculty knowledge, opinion, importance, and use of scholarly metrics before engaging faculty in further discussions about quantifying the impact of their scholarly work

High quality indoor environments for sustainable office buildings

The quality of office indoor environments is considered to consist of those factors that impact occupants according to their health and well-being and (by consequence) their productivity. Indoor Environment Quality (IEQ) can be characterized by four indicators: • Indoor air quality indicators • Thermal comfort indicators • Lighting indicators • Noise indicators. Within each indicator, there are specific metrics that can be utilized in determining an acceptable quality of an indoor environment based on existing knowledge and best practice. Examples of these metrics are: indoor air levels of pollutants or odorants; operative temperature and its control; radiant asymmetry; task lighting; glare; ambient noise. The way in which these metrics impact occupants is not fully understood, especially when multiple metrics may interact in their impacts. While the potential cost of lost productivity from poor IEQ has been estimated to exceed building operation costs, the level of impact and the relative significance of the above four indicators are largely unknown. However, they are key factors in the sustainable operation or refurbishment of office buildings. This paper presents a methodology for assessing indoor environment quality (IEQ) in office buildings, and indicators with related metrics for high performance and occupant comfort. These are intended for integration into the specification of sustainable office buildings as key factors to ensure a high degree of occupant habitability, without this being impaired by other sustainability factors. The assessment methodology was applied in a case study on IEQ in Australia’s first ‘six star’ sustainable office building, Council House 2 (CH2), located in the centre of Melbourne. The CH2 building was designed and built with specific focus on sustainability and the provision of a high quality indoor environment for occupants. Actual IEQ performance was assessed in this study by field assessment after construction and occupancy. For comparison, the methodology was applied to a 30 year old conventional building adjacent to CH2 which housed the same or similar occupants and activities. The impact of IEQ on occupant productivity will be reported in a separate future pape

Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management

Daylight performance metrics provide a promising approach for the design and optimization of lighting strategies in buildings and their management. Smart controls for electric lighting can reduce power consumption and promote visual comfort using different control strategies, based on affordable technologies and low building impact. The aim of this research is to assess the energy efficiency of these smart controls by means of dynamic daylight performance metrics, to determine suitable solutions based on the geometry of the architecture and the weather conditions. The analysis considers different room dimensions, with variable window size and two mean surface reflectance values. DaySim 3.1 lighting software provides the simulations for the study, determining the necessary quantification of dynamic metrics to evaluate the usefulness of the proposed smart controls and their impact on energy efficiency. The validation of dynamic metrics is carried out by monitoring a mesh of illuminance-meters in test cells throughout one year. The results showed that, for most rooms more than 3.00 m deep, smart controls achieve worthwhile energy savings and a low payback period, regardless of weather conditions and for worst-case situations. It is also concluded that dimming systems provide a higher net present value and allow the use of smaller window size than other control solutions

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours