Combined Effects of Impervious Surface and Vegetation Cover on Air Temperature Variations in a Rapidly Expanding Desert City

The goal of this study is to improve our understanding of the interac- tive function of impervious and vegetation covers at different levels of the local and intra-urban spatial scales in relation to air temperatures in an urban environment. A multiple regression model was developed using impervious and vegetation frac- tions at different scales to predict maximum air temperature for the entire Phoenix metropolitan area in Arizona, USA. This study demonstrates that a small amount of impervious cover in a desert city can still increase maximum air temperature despite abundant vegetation cover.

Retrieval-induced forgetting of emotional traits and its effect on implicit attitudes

Memory is fundamental for multitudinous cognitive functions. The function of current interest is retrieval and the effect it has on the accessibility of stored information. Besides facilitating recall of previously retrieved items, retrieval may also inhibit information that is associated to the same cue as the retrieved items. This study examines the effect of retrieval practice on person memory for neutral and emotional traits. The study also investigates if forgetting a person's traits changes the implicit attitude towards that person. The results showed retrieval-induced forgetting for negative traits but not for neutral and positive traits. When the recall performance was assessed with focus on emotional accuracy rather than on the recall of specific words, the forgetting effect for negative traits diminished, suggesting that the general concepts of good and bad were not as susceptible to retrieval inhibition as the specific traits. Further, retrieval-induced forgetting of negative traits did not lead to changes in implicit attitudes, thus, suggesting that implicit attitudes are not dependent of the explicit accessibility of information that they were originally based on

Per-Pixel Versus Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per- pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Do Different Data Analytics Impact Auditors\u27 Decisions?

Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying auditrelevant information, auditors may not use this information to its full potential, resulting in a missed opportunity for possible improvements to audit quality. This article summarizes a study by Koreff (2022) that examines whether conclusions from different types of data analytical models (anomaly vs. predictive) and data analyzed (financial vs. non-financial), result in different auditor decisions. Findings suggest that when predictive models are used and identify a risk of misstatement, auditors increase budgeted audit hours more when financial data is analyzed than when non-financial data is analyzed. However, when anomaly models are used and identify a risk of misstatement, auditors’ budgeted hours do not differ based on the type of data analyzed. These findings provide evidence that different data analytics do not uniformly impact auditors’ decisions

The theory of critical distances applied to problems in fracture andfatigue of bone

The theory of critical distances (TCD) has been applied to predict notch-based fracture and fatigue in a wide range of materials and components. The present paper describes a series of projects in which we applied this approach to human bone. Using experimental data from the literature, combined with finite element analysis, we showed that the TCD was able to predict the effect of notches and holes on the strength of bone failing in brittle fracture due to monotonic loading, in different loading regimes. Bone also displays short crack effects, leading to R-curve data for both fracture toughness and fatigue crack propagation thresholds; we showed that the TCD could predict this data. This analysis raised a number of questions for discussion, such as the significance of the L value itself in this and other materials. Finally, we applied the TCD to a practical problem in orthopaedic surgery: the management of bone defects, showing that predictions could be made which would enable surgeons to decide on whether a bone graft material would be needed to repair a defect, and to specify what mechanical properties this material should have

A model equation for the optical tunnelling problem using parabolic cylinder functions

The fundamental purpose of this thesis is to estimate the exponentially small imaginary part of the eigenvalue of a second order ordinary differential equation subject to certain stated boundary conditions. This problem is modelled on a partial differential equation which arises when examining wave losses m bent fibre optic waveguides. In Chapter 1 we provide an overview of the thesis and introduce the area of mathematics known as exponential asymptotics. In Chapter 2 we investigate the physical background to the problem of energy losses due to optical tunnelling in fibre optic waveguides. We then derive the partial differential equation upon which we base our model. In Chapter 3 we commence by manipulating the partial differential equation into a more convenient form. We then outline the model problem we shall consider and obtain a preliminary estimate for the eigenvalue of this problem. In Chapter 4 we introduce the special function known as the parabolic cylinder function and derive its asymptotic behaviour. We also examine its connection with Stokes phenomenon and deduce its Stokes and anti-Stokes lines. In Chapter 5 , we finally solve the *model problem by transforming it into one form of Weber's parabolic cylinder equation. We then use the boundary conditions of the problem together with properties of parabolic cylinder functions to obtain a valid estimate for the imaginary part of the eigenvalue. In Chapter 6 we conclude the thesis by commenting on this result and indicating future developments in this area