LOAD CUBING AT R. J. REYNOLDS

R. J. Reynolds Tobacco USA (RJR) is currently implementing a microcomputer-based decision support system to computerize and optimize the selection of patterns for loading cases of finished product into truck trailers at RJR's Central Distribution Center. This system allows for the efficient loading of trucks with less supervision. Total annual savings from reduced personnel and shipping costs is approximately $850,000. In addition to these benefits, the system is a stepping stone for trailer loading automation and the integration of a comprehensive load planning system.     

Roads to American Empire: U.S. Military Public Works and Capitalist Transitions, 1898–1934.

Between 1898 and 1934, in synchronous and successive U.S. military interventions and occupations in Cuba, Puerto Rico, the Philippines, Haiti, and the Dominican Republic, American soldiers made public works, and especially roads, into a global technology of imperial power. This essay examines infrastructure as a factor in state formation and capitalist transition in these five different imperial spaces as a way to study U.S. empire, and its effects on foreign societies, through a comparative, global, and intra‐imperial approach often precluded by the methodological nationalism of historical and sociological literatures. Despite significant differences between these sites of U.S. war and occupation, both prior to American interventions and during them, U.S. military public works expressed and advanced a common political‐economic logic of state centralization and capital accumulation. Colonial and post‐colonial political institutions and political economies, the strength of central governments, the extent of plantation agriculture and rural proletarianization, world commodity markets, and geography and natural events varied, but determined U.S. imperial infrastructure's outcomes. By the 1930s, the U.S. military had elevated infrastructural improvement to a key repertoire of American imperial power in the world, and one which persisted as the United States turned away from formal colonialism in the era of the Cold War and decolonization.     

Understanding foraging flexibility in urban vervet monkeys,Chlorocebus pygerythrus,for the benefit of human-wildlife coexistence

Urban Ecosystems - Generalist wildlife species often thrive in urban environments because of increased anthropogenic resources. However, human-wildlife interactions, especially if negative, raise...     

An elastic-net penalized expectile regression with applications

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.     

Solution Procedures for the Lot-Streaming Problem

Lot streaming is the process of splitting a job into sublots so its operations can be overlapped and its progress accelerated. We present a computationally efficient procedure for solving the m-machine, two-sublot problem, and we discuss the bottleneck insights that emerge from the analysis. We also examine heuristic approaches for more than two sublots and discuss computational results for these procedures.     

Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression

While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks.     

Quantile regression for large-scale data via sparse exponential transform method

In recent decades, quantile regression has received much more attention from academics and practitioners. However, most of existing computational algorithms are only effective for small or moderate size problems. They cannot solve quantile regression with large-scale data reliably and efficiently. To this end, we propose a new algorithm to implement quantile regression on large-scale data using the sparse exponential transform (SET) method. This algorithm mainly constructs a well-conditioned basis and a sampling matrix to reduce the number of observations. It then solves a quantile regression problem on this reduced matrix and obtains an approximate solution. Through simulation studies and empirical analysis of a 5% sample of the US 2000 Census data, we demonstrate efficiency of the SET-based algorithm. Numerical results indicate that our new algorithm is effective in terms of computation time and performs well for large-scale quantile regression.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Participation in Everyday Life Before and After a Housing Adaptation

Housing adaptation aims to enable clients to live independently in their own homes. Studies focusing on participation in everyday life following a housing adaptation are lacking and needed. This study aimed to explore housing adaptation clients' experiences of participation in everyday life before and after a housing adaptation, through the lens of a housing adaptation, using a qualitative follow-up design, with 11 participants. It was found that when the housing adaptation met the participants' needs, performance of activities improved and the housing adaptation opened doors to engagement and participation in everyday life. Thus, focus on performance and engagement in everyday life at the onset of the housing adaptation process, combined with regular follow-ups, may enhance participation.