Passion & Purpose: Raising the Fiscal Fitness Bar for Massachusetts Nonprofits

Presents data on and financial analyses of the state's nonprofit sector by organization type, budget, focus area, and location. Recommends better financial stewardship, restructuring, repositioning, and reinvestment to enhance nonprofits' sustainability

M&m's

We consider M theory 5-branes with compact transverse dimensions. In certain limits the theory on the 5-brane decouples and defines ``little string theories'' in 5+1 dimensions. We show that the familiar structure of IIA/IIB,M,F- theory in 10,11,12 dimensions respectively has a perfect parallel in a theory of strings and membranes in 6,7,8 dimensions. We call these theories $a/b,m,f$ theories. They have a coupling constant but no gravity. This construction clarifies some mysteries in F-theory and leads to several speculations about the phase structure of M theory.Comment: harvmac, 24 page

A Biblical Understanding of Making Disciples

One of the many functions of the local church is to make disciples. Unfortunately, thoughtful and consistent efforts have not always proven to yield the results that accompany transformational living. This thesis project seeks to address and remove some of the barriers around discipleship and disciple-making in the local setting. This study provided a discipleship training course targeted toward spiritually mature male leaders. Individuals were invited to attend a six-session training course where they were equipped with a biblical understanding of discipleship, and training in spiritual disciplines, to aid in their personal growth and ability to teach others. Data was collected prior to the intervention through surveys, during the intervention from discussion questions and feedback, and after the intervention via a debrief survey and follow-up conversations. The resulting implications of this project proved the necessity of having a proper biblical understanding to aid in one’s lifelong allegiance to Christ

CSR, Big Data, and Accounting: Firms' Use of Social Media for CSR-Focused Reporting, Accountability, and Reputation Gain

The rise of Big Data, particularly social media, is engendering considerable disruptions in the ways in which firms and stakeholders communicate about firm-relevant issues. The effect of social media appears to be particularly strong in the domain of corporate social responsibility (CSR). This thesis presents three empirical studies on Fortune 200 firms use of social media to engage in CSR-related activities. All three studies rely on original 2014 data related to the 42 CSR-focused Twitter accounts maintained by the US-based Fortune 200 companies comprising 18,722 firm messages and 163,402 messages sent by members of the public. This thesis first examines the outcomes of firms social media-based CSR engagement, building a theoretical argument about the reputational benefits, or reputational capital, acquired by firms through the messages they send on social media. It then turns to an investigation of the publics discussion of the companies CSR activities; this second study relies on inductive analyses to build insights into the nature of the firm-centered CSR messages sent by members of the public, the nature of firms reactions to these public messages, and the relationship between the two. The third and final study refines and then empirically tests the causal model developed in the second study. Collectively, these three studies shed light on the nature of the micro-reporting and micro-accountability behaviors that appear to characterize firms CSR efforts on social media sites. The thesis concludes with a summary of the implications of these new behaviors for the accounting and CSR literatures

Efficient diagnosis of multiprocessor systems under probabilistic models

The problem of fault diagnosis in multiprocessor systems is considered under a probabilistic fault model. The focus is on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. Lower and upper bounds on the number of tests required for regular systems are also presented. A class of regular systems which includes hypercubes is shown to be correctly diagnosable with high probability. In all cases, the number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis techniques