Lessons learned in effective community-university-industry collaboration models for smart and connected communities research

In 2017, the Boston University Hariri Institute for Computing and the Initiative on Cities co-hosted two workshops on “Effective Community-University-Industry Collaboration Models for Smart and Connected Communities Research,” with the support of the National Science Foundation (NSF). These efforts brought together over one hundred principal investigators and research directors from universities across the country, as well as city officials, community partners, NSF program managers and other federal agency representatives, MetroLab Network representatives and industry experts. The focus was on transdisciplinary “smart city” projects that bring technical fields such as engineering and computer science together with social scientists and community stakeholders to tackle community-sourced problems. Presentations, panel discussions, working sessions and participant white papers surfaced operational models as well as barriers and levers to enabling effective research partnerships. To capture the perspectives and beliefs of all participants, in addition to the presenters, attendees were asked to synthesize lessons on each panel topic. This white paper summarizes the opportunities and recommendations that emerged from these sessions, and provides guidance to communities and researchers interested in engaging in these types of partnerships as well as universities and funders that endeavor to nurture them. It draws on the collective wisdom of the assembled participants and the authors. While many of the examples noted are drawn from medium and large cities, the lessons may still be applicable to communities of various sizes.National Science Foundatio

An analysis of the regional impact of the Kapuni ammonia / urea plant : a research report constituting two 14.499 Research Reports, in partial fulfilment of the requirements for the degree of Master of Agricultural Business and Administration, Massey University

Successive post war governments in New Zealand have emphasised industrial development as the basis of economic growth. During this period national-level planning has become institutionalised. Central to the choice of planning policy for industrial development is the balance of payments problem. This has tended to mask other considerations which can be identified as pertinent to national and regional level decision making by private and public organisations . A major difficulty for the researcher is establishing which questions are relevant in a New Zealand context. This research exercise pursues the perspectives and questions which might be brought to bear on specific national development projects. The research focuses on the Kapuni Amronia/urea plant and analyses the regional impact of that project

Conifer-angiosperm interactions: Physiological ecology and life history.

Worldwide, conifers are most successful on sites subject to chronic stresses that limit productivity (low temperatures, nutrient poverty, poor drainage). They are poorly represented in the lowland tropics but are often important in Montane tropical forests. Here I explore some functional differences between leaf and xylem traits of conifer and angiosperm trees and their implications for the distributions of these two groups on environmental gradients. Analysis of a global data set shows that compared with angiosperm trees, conifers tend to have longer-lived leaves with greater mass per area (LMA) and lower mass-based photosynthetic capacity. As leaf life span is thought to be the main determinant of nutrient retention time, the prominence of conifers on infertile soils worldwide is at least partly attributable to thrifty use of nutrients through long leaf life spans. Furthermore, because leaf life span correlates with litter decomposition rates, these leaf trait differences could potentially influence the competitive balance between conifers and angiosperms via positive feedbacks on nutrient cycling. Although scaling of leaf life span with LMA is similar in the two groups, angiosperms achieve slightly longer leaf life spans than conifers of similar photosynthetic capacity. This might be caused by less-efficient leaf display in conifers, resulting in the useful life span of leaves being curtailed by self-shading. Representatives of both lineages have narrower conduits in the temperate zone than in the lowland tropics/subtropics, reflecting selection for resistance to freeze-thaw embolism in cold climates. However, conduit diameters of conifers and angiosperm trees differ more in tropical and subtropical forests than at higher latitudes. This probably reflects mechanical constraints on maximum tracheid diameters in the homoxylous wood of conifers, preventing this group from producing the highly conductive wood typical of fast-growing angiosperm pioneers in tropical forests. This pattern might explain why coexistence of conifers and angiosperms is more common in temperate forests and on tropical mountains than in the lowland tropics. Impairment of angiosperm carbon gain by freeze-thaw embolism during cold weather may further narrow performance differences between the two lineages on temperate sites. Differences in canopy residence time probably deserve more attention as a determinant of conifer-angiosperm coexistence in many temperate forests, the longer life span of conifers compensating for infrequent recruitment

Diverse and widespread contamination evident in the unmapped depths of high throughput sequencing data

Background: Trace quantities of contaminating DNA are widespread in the laboratory environment, but their presence has received little attention in the context of high throughput sequencing. This issue is highlighted by recent works that have rested controversial claims upon sequencing data that appear to support the presence of unexpected exogenous species. Results: I used reads that preferentially aligned to alternate genomes to infer the distribution of potential contaminant species in a set of independent sequencing experiments. I confirmed that dilute samples are more exposed to contaminating DNA, and, focusing on four single-cell sequencing experiments, found that these contaminants appear to originate from a wide diversity of clades. Although negative control libraries prepared from "blank" samples recovered the highest-frequency contaminants, low-frequency contaminants, which appeared to make heterogeneous contributions to samples prepared in parallel within a single experiment, were not well controlled for. I used these results to show that, despite heavy replication and plausible controls, contamination can explain all of the observations used to support a recent claim that complete genes pass from food to human blood. Conclusions: Contamination must be considered a potential source of signals of exogenous species in sequencing data, even if these signals are replicated in independent experiments, vary across conditions, or indicate a species which seems a priori unlikely to contaminate. Negative control libraries processed in parallel are essential to control for contaminant DNAs, but their limited ability to recover low-frequency contaminants must be recognized

Six days that shook the world: how the Easter Rising changed everything

No abstract available

Commemorating Connolly: contexts, comparisons and Celtic connections

No abstract available

Rule Based Forecasting [RBF] - Improving Efficacy of Judgmental Forecasts Using Simplified Expert Rules

Rule-based Forecasting (RBF) has emerged to be an effective forecasting model compared to well-accepted benchmarks. However, the original RBF model, introduced in1992, incorporates 99 production rules and is, therefore, difficult to apply judgmentally. In this research study, we present a core rule-set from RBF that can be used to inform both judgmental forecasting practice and pedagogy. The simplified rule-set, called coreRBF, is validated by asking forecasters to judgmentally apply the rules to time series forecasting tasks. Results demonstrate that forecasting accuracy from judgmental use of coreRBF is not statistically different from that reported from similar applications of RBF. Further, we benchmarked these coreRBF forecasts against forecasts from (a) untrained forecasters, (b) an expert system based on RBF, and (c) the original 1992 RBF study. Forecast accuracies were in the hypothesized direction, arguing for the generalizability and validity of the coreRBF rules

Development and Validation of a Rule-based Time Series Complexity Scoring Technique to Support Design of Adaptive Forecasting DSS

Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity