by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Transitions away from crime and arrest. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Black-white male probability differences. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Supplementary figures. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by age and subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Transitions toward crime and arrest. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Flow diagram for the 5D model. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by Subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Elasticities of equilibrium proportion in X. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

by age and subgroup. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Derivatives of equilibrium proportion in X. by David McMillon (546087)

“…We examine how marginal increases in flows between states interact to decrease or increase the long-run level of crime, and whether this varies by subgroup. …”

Association of covariates and COPD risk. by Yushan Shi (16440272)

“…Stratified analyses revealed no significant differences or interactions.</p><p>Conclusion</p><p>Our findings suggest a potential link between increased dietary niacin intake and a decreased prevalence of COPD.…”

Prediction of individual spatial patterns of nodal module overlap from structural brain features in children. by Tianyuan Lei (19683861)

The technical route of the study. by Hongliang Zou (20707270)

“…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”