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This study aims to identify how objective indicators of destination country risk differentiate business study abroad programs from those in other academic disciplines.

The authors trained a neural network model on six years of student-initiated inquiries about study abroad programs at a large US university. The model classified business versus nonbusiness study abroad programs using objective measures of destination country risk as the primary inputs.

The model correctly classifies business and nonbusiness study abroad programs with over 70% accuracy. Business programs were found to be 20% less likely to include destinations where the Centers for Disease Control and Prevention recommend nonroutine vaccinations and favor countries with higher Global Peace Index scores.

These results underscore the need to consider destination country risk in the design and administration of study abroad programs. An understanding of student preferences for lower risk destinations can contribute to improved planning, execution and student experiences in these programs.

Better planning and management of study abroad programs based on understanding of destination country risk can lead to enhanced student safety and experiences.

This study offers a unique perspective on understanding study abroad programs by focusing on objective measures of destination country risk rather than risk perceptions. It also is, to the best of the authors’ knowledge, the first to use a neural network to classify study abroad programs as business versus nonbusiness using objective measures of country-specify risk indicators.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

From the 1960s onwards, students and members of the academic community on growing numbers of college and university campuses in the United States chose to confront the issue of apartheid by advocating divestment from corporations or financial institutions with any sort of presence in or relationship with South Africa. Student divestment advocates faced serious opposition from university administrators as well as opponents of institutional divestiture both at home and abroad. Despite these challenges, the academic community in the United States was one of the first arenas where anti-apartheid activism coalesced. This chapter examines the campaigns of students and educators who participated in the debate over divestment – to engage with the South African government and apartheid through dialogue and communication or to disengage completely from the country through withdrawal of financial investments. The anti-apartheid efforts of the academic community at Michigan State University, one of the first large research universities in the United States to confront the issue of apartheid and divestment at the university level and beyond, serves as a window to view academic activism against apartheid. The Southern Africa Liberation Committee (SALC), a consortium of students, faculty, and community members dedicated to aiding the liberation struggle of Southern Africa, led the efforts at Michigan State and collaborated with allies across Michigan and the United States. SALC focused most of its efforts on South Africa, though the organization also confronted the issue of South Africa's controversial occupation of South West Africa and the ongoing civil war in Angola.