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Existing studies provide mixed evidence that the U.S. macroeconomic news impacts international stock prices. We believe this may be related to the fact that economic surprises may not capture how investors interpret macroeconomic releases in various economic conditions. Consequently, we follow Birz and Lott (2011) and use newspaper coverage of economic releases as a measure of news. We argue that in addition to capturing the surprise component of macroeconomic releases, newspaper coverage provide interpretation of these releases similarly to how investors may interpret them in various economic conditions. Out of 15 examined international stock markets, we find that the U.S. macroeconomic news impacts stock returns of 12 countries.

This article details the second cycle of cooperative inquiry undertaken by emerging educators who self-identify as "other" because of gender, language, ethnicity, and/or sexual orientation. The current cycle focuses on the impact participation in cooperative inquiry had on researchers' teaching practices. Data sources include transcripts of group discussions and reflective writing completed six months, eighteen months, and two years after the completion of the first cycle of cooperative inquiry. Findings suggest that as a result of engagement in cooperative inquiry, the teacher/researchers established practices to decrease isolation, build unity, and understand students' backgrounds. Teacher/researchers viewed themselves as advocates for diversity within the classroom, took a collaborative approach to teaching, and came to see research as an essential element of effective teaching.

In this paper, we address the resource and virtual machine instance hour minimization problem for directed-acyclic-graph based deadline constrained applications deployed on computer clouds. The allocated resources and instance hours on computer clouds must: (1) guarantee the satisfaction of a deadline constrained application's end-to-end deadline; (2) ensure that the number of virtual machine (VM) instances allocated to the application is minimized; (3) under the allocated number of VM instances, determine application execution schedule that minimizes the application's makespan; and (4) under the decided application execution schedule, determine a VM operation schedule, i.e., when a VM should be turned on or off, that minimizes total VM instance hours needed to execute the application. We first give lower and upper bounds for the number of VM instances needed to guarantee the satisfaction of a deadline constrained application's end-to-end deadline. Based on the bounds, we develop a heuristic algorithm called minimal slack time and minimal distance (MSMD) algorithm that finds the minimum number of VM instances needed to guarantee the application's deadline and schedules tasks on the allocated VM instances so that the application's makespan is minimized. Once the application execution schedule and the number of VM instances needed are determined, the proposed VM instance hour minimization (IHM) algorithm is applied to further reduce the instance hours needed by VMs to complete the application's execution. Our experimental results show that the MSMD algorithm can guarantee applications' end-to-end deadlines with less resources than the HEFT [32], MOHEFT [16], DBUS [9], QoS-base [40] and Auto-Scaling [25] heuristic scheduling algorithms in the literature. Furthermore, under allocated resources, the MSMD algorithm can, on average, reduce an application's makespan by 3.4 percent of its deadline. In addition, with the IHM algorithm we can effectively reduce the application's execution instance hours compared with when IHM is not applied.

The periodic task set assignment problem in the context of multiple processors has been studied for decades. Different heuristic approaches have been proposed, such as the Best-Fit (BF), the First-Fit (FF), and the Worst-Fit (WF) task assignment algorithms. However, when processors are not dedicated but only periodically available to the task set, whether existing approaches still provide good performance or if there is a better task assignment approach in the new context are research problems which, to our best knowledge, have not been studied by the real-time research community. In this paper, we present the Best-Harmonically-Fit (BHF) task assignment algorithm to assign periodic tasks on multiple periodic resources. By periodic resource we mean that for every fixed time interval, i.e., the period, the resource always provides the same amount of processing capacity to a given task set. Our formal analysis indicates that if a harmonic task set is also harmonic with a resource's period, the resource capacity can be fully utilized by the task set. Based on this analysis, we present the Best-Harmonically-Fit task assignment algorithm. The experimental results show that, on average, the BHF algorithm results in 53.26 , 42.54 , and 27.79 percent higher resource utilization rate than the Best-Fit Decreasing (BFD), the First-Fit Decreasing (FFD), and the Worst-Fit Decreasing (WFD) task assignment algorithms, respectively; but comparing to the optimal resource utilization rate found by exhaustive search, it is about 11.63 percent lower.

Cloud bursting is one of the key research topics in the cloud computing communities. A well designed cloud bursting module enables private clouds to automatically launch virtual machines (VMs) to public clouds when more resources are needed. One of the main challenges in developing a cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on system operational data obtained from FermiCloud, a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows, the VM launching overhead is not a constant. It varies with physical resource utilization, such as CPU and I/O device utilizations, at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launching overhead reference model is needed. In this paper, we first develop a VM launching overhead reference model based on operational data we have obtained on FermiCloud. Second, we apply the developed reference model on FermiCloud and compare calculated VM launching overhead values based on the model with measured overhead values on FermiCloud. Our empirical results on FermiCloud indicate that the developed reference model is accurate. We believe, with the guidance of the developed reference model, efficient resource allocation algorithms can be developed for cloud bursting process to minimize the operational cost and resource waste.

In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM’s parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.

The purpose of this study is to explore the commitment of local governments to environmental programs when fiscal distress is predicted. We hypothesize that commitment to environmental programs diminishes when the local government is experiencing fiscal distress. The regression model results indicate that local governments with high levels of debt were less likely to I mplement environmental programs and that a larger population and higher revenue are factors directly related to the commitment of local government to environmental programs. Communities that are more populous and less fiscally stressed are more likely to benefit from a local government that implements and sustains environmental programs. These results have implications for the stakeholders of local communities and broader implications for the global effort toward environmental protection and sustainable communities.

Disruptive behavior disorders (DBDs) are chronic, impairing, and costly behavioral health conditions that are four times more prevalent among children of color living in impoverished communities as compared to the general population. This disparity is largely due to the increased exposure to stressors related to low socioeconomic status including community violence, unstable housing, under supported schools, substance abuse, and limited support systems. However, despite high rates and greater need, there is a considerably lower rate of mental health service utilization among these youth. Accordingly, the current study aims to describe a unique model of integrated health care for ethnically diverse youth living in a New York City borough. With an emphasis on addressing possible barriers to implementation, integrated models for children have the potential to prevent ongoing mental health problems through early detection and intervention.

NFL Evolution is a Corporate Social Responsibility (CSR) program designed to improve the health and safety of football participants at all levels (NFLEvolution. com). Though sport-based CSR initiatives are generally well-received by the public, the internal focus of this particular initiative may affect consumers differently. Using data provided by a sample of university students via an online survey, regression analysis was used to determine if this program affected their intentions to consume NFL-related products and media. Results showed that this form of CSR initiative may influence consumption behaviors, especially related to media consumption and intentions to discuss the NFL with others. ABSTRACT FROM AUTHOR