Scholars of agrarian change have long debated the nature of capitalist transition in the countryside, including whether the deepened interlinking of local, national, and transnational economic activities make past trajectories of agrarian transformation unlikely to reoccur in the present. This essay makes the case that Giovanni Arrighi's work has much to add to our understanding of the agrarian question in global historical perspective. We focus in particular on Arrighi's research on trajectories of change in the Calabrian region of southern Italy, and his essay “Capitalist Development in Hostile Environments.” In this piece, Arrighi and co-author Fortunata Piselli develop two key insights. The first is that the pathways to capitalism are diverse, non-linear, and historically contingent such that within one country—or, in the case of Italy, a single subnational region—multiple trajectories can be found. The second is that the outcomes of capitalist transition vary based on a country's position in the international hierarchy of wealth, meaning that agrarian transformation is compatible with both economic development and underdevelopment. We describe the three methodological principles that enabled Arrighi to develop his analysis of capitalist transition and explain how the papers collected in this special issue reflect and extend the Arrighian approach to agrarian political economy.

The literature disagrees about the statistical distribution of snow avalanche crown depths. Large datasets from Mammoth Mountain, California and the Westwide Avalanche Network show that the three-parameter generalized extreme value distribution provides the most robust fit, followed by a two-parameter variation, the Fréchet distribution. The most parsimonious explanation is neither self-organized criticality nor other complex cascades, but the maximum domain of attraction, implying that distributions of individual avalanche crown depths are scaling. We also show that crown depths do not have a universal tail index. Rather, they range from 2.8 to 4.6 over different avalanche paths, consistent with other geophysical phenomena such as wildfires, which show similar variability. Copyright 2008 by the American Geophysical Union.

The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q.

The objective of our project was to improve the efficiency of the physical examination screening service of a large hospital system. We began with a detailed simulation model to explore the relationships between four performance measures and three decision factors. We then attempted to identify the optimal physician inquiry starting time by solving a goal-programming problem, where the objective function includes multiple goals. One of our simulation results shows that the proposed optimal physician inquiry starting time decreased patient wait times by 50% without increasing overall physician utilization.