Cumulative risk models provide a convenient, parsimonious way to identify outcomes associated with multiple, highly correlated risk factors. In this paper, we explored linkages between a cumulative sociodemographic risk index, which included rurality status, and aspects of temperamental difficulty in an early school age sample of 53 school-aged children from Southcentral Appalachia. Cumulative risk was significantly predictive of temperamental difficulty, as defined by high negative affectivity and low effortful control, but post-hoc analyses revealed this association to be driven primarily by two of the eight risk indicators: rural status and income-to-needs risk. Although rurality status was highly correlated with income-to-needs risk, rurality predicted negative affectivity over and above income-to-needs risk and income-to-needs risk predicted effortful control over and above rurality status. Future models of cumulative risk may benefit from including rurality status as a risk indicator, despite high collinearity with income-to-needs risk.

We describe a case study evaluating the ecological impact of Bromus tectorum L. (cheatgrass) invasion following fire disturbance and the effectiveness of revegetation in improving ecological integrity in a degraded semi-arid shrub steppe system. The effectiveness of rehabilitation efforts was assessed from measurements of arthropod richness, vegetation and arthropod community composition, and ground cover characteristics in three habitats: undisturbed, burned and weed-infested (B. tectorum), and burned and rehabilitated with native and non-native vegetation. Arthropods were collected in each habitat using pitfall traps. Differences in arthropod richness were compared using rarefaction curves. Non-metric multidimensional scaling, and non-parametric multivariate statistical procedures including analysis of similarity and similarity percentages routines were used to compare arthropod and vegetation community composition and ground cover characteristics between habitats. Arthropod communities in the rehabilitated habitat were distinct from and intermediate to those observed in the undisturbed and weed-infested habitats. Rehabilitation in this instance resulted in an improvement in ecological integrity and perhaps an intermediate step on the way complete restoration. Arthropod richness, arthropod and vegetation community composition, and ground cover characteristics were all useful indicators of ecological integrity, but returned slightly different results. Assessing multiple variables yielded the most complete understanding of the habitats studied.

Drones in low-altitude air space at a large scale is a new and ongoing field of research and there is a high possibility that we during the coming decades will see a rise in both commercial and state-driven drone activities in this space, especially in urban areas. This will require new ways in which to think and operate all air traffic management around the world. To support the research and realization of this, this master thesis will investigate a way to visualize performance indicators for drones that are related to key performance indicators used in air traffic today. This is done by researching commonly used performance metrics for air traffic and finding a suitable program that can visualize these in different graph types often used in information visualization, to build and evaluate an interactive dashboard that lets a user explore the performance data. The drone data used in this work is retrieved from a drone simulation program where delivery drones act between start, delivery and endpoints. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

The Internet and intranets are viewed as capable of supplying "Anything, Anywhere, Anytime" and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical or if complex and poorly understood interactions of equipment, protocols and application traffic degrade performance creating customer dissatisfaction, another more application-centric, way of managing the network will be needed. This research investigates a new qualitative class of network performance measures derived from the current quantitative metrics known as quality of service (QOS) parameters. The proposed class of qualitative indicators focuses on utilizing current network performance measures (QOS values) to derive abstract quality of experience (QOE) indicators by application class. These measures may provide a more user or application-centric means of assessing network performance even when some individual QOS parameters approach or exceed specified levels. The mathematics of functional analysis suggests treating QOS performance values as a vector, and, by mapping the degradation of the application performance to a characteristic lp-norm curve, a qualitative QOE value (good/poor) can be calculated for each application class. A similar procedure could calculate a QOE node value (satisfactory/unsatisfactory) to represent the service level of the switch or router for the current mix of application traffic. To demonstrate the utility of this approach a discrete event simulation (DES) test-bed, in the OPNET telecommunications simulation environment, was created modeling the topology and traffic of three semi-autonomous networks connected by a backbone. Scenarios, designed to degrade performance by under-provisioning links or nodes, are run to evaluate QOE for an access network. The application classes and traffic load are held constant. Future research would include refinement of the mathematics, many additional simulations and scenarios varying other independent variables. Finally collaboration with researchers in areas as diverse as human computer interaction (HCI), software engineering, teletraffic engineering, and network management will enhance the concepts modeled.

Residual value is defined as the price for which a used piece of equipment can be sold in the market at a particular time. It is an important element of the owning costs of equipment and needs to be estimated by equipment managers for making investment decisions. The purpose of this study is to gain insights into the residual value of selected groups of heavy construction equipment and to develop a mathematical model for its prediction. Auction sales data were collected from two online databases. Manufacturer publications and an online source provided size parameters and manufacturers suggested retail prices matching the auction records. Macroeconomic indicator values were collected from a variety of sources, including government agencies. The data were brought into the same electronic format and were matched by model name and calendar date, respectively. Data from auctions in the U.S. and in Canada were considered for this study. Equipment from four principal manufacturers of up to 15 years of age at the time of sale was included. A total of 35,542 entries were grouped into 11 different equipment types and 28 categories by size as measured by horse power, standard operating weight, or bucket volume. Equipment types considered were track and wheel excavators, wheel and track loaders, backhoe loaders, integrated toolcarriers, rigid frame and articulated trucks, track dozers, motor graders, and wheel tractor scrapers. Multiple linear regression analyses of the 28 datasets were carried out after outliers had been deleted. Explanatory variables for the regression model were age in years, the indicator variables manufacturer, condition rating, and geographic region, and selected macroeconomic indicators. The response variable was residual value percent, defined as auction price divided by manufacturers suggested retail price. Different first, second, and third-order polynomial models and exponential and logarithmic models of age were examined. A second-order polynomial was selected from these functional forms based on the adjusted coefficient of determination. Coefficients for the 28 models and related statistics were tabulated. A spreadsheet tool incorporating the final regression model and its coefficients was developed. It allows performing the residual value prediction in an interactive and intuitive manner. / Ph. D.

<p> This paper attempts to quantify disparate growth in Hamilton's Central Area. The spatial variation over time of three economic indicators was studied to do this. These indicators were property tax assessments, and multi-family and single-family property sales. The Central Area was divided into four geographic zones so that the indicator change could be associated with different parts of the Area. The results indicate that the northeastern sections of the Central Area experienced and are continuing to experience slower economic growth compared to the southwestern sections. </p> / Thesis / Bachelor of Arts (BA)

This dissertation recommends research to investigate the effects inhibitory control and perceptual attention have on the cyber security decision-making process. Understanding the effects that inhibitory control and perceptual attention have on the security decision- making process will allow for better defenses to be developed against social engineering and phishing. A survey and review of previous research in the area of Human Computer- Interaction and Security is presented. An experiment is performed to evaluate inhibitory control, which is composed of prepotent response inhibition, resistance to distractor interference, and resistance to proactive interference (PI). Additionally, the experiment evaluates perceptual attention and the security decision-making process.

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