Hasil untuk "United States"

A. Young, Ruth S. Klap, C. Sherbourne et al.

R. Brownson, E. Baker, R. Housemann et al.

James D. Williams, M. L. Warren, Kevin S. Cummings et al.

A. Jha, E. Orav, Jie Zheng et al.

During the 20th century, the health and life expectancy of persons residing in the United States improved dramatically. Since 1900, the average lifespan of persons in the United States has lengthened by >30 years; 25 years of this gain are attributable to advances in public health. To highlight these advances, MMWR will profile 10 public health achievements (see box) in a series of reports published through December 1999.

K. Chung, Sandra V. Spilson

D. C. Edwards

A. Cherlin

W. Mosher, Jo Jones

Stuart L. Gillan, Laura T. Starks

A. Stone, J. Schwartz, J. Broderick et al.

Lara J. Akinbami, J. Moorman, P. Garbe et al.

Stephen D. Persell

Pierfederico Asdrubali, Bent E. Sørensen, Oved Yosha

C. Bern, Sonia A. Kjos, M. Yabsley et al.

I. Allen, J. Seaman

M. Thigpen, C. Whitney, Nancy E. Messonnier et al.

J. Leigh

Dipesh Singh Chuphal, Vimal Mishra

Abstract Reliable streamflow projections are essential for effective water‐resource management and climate adaptation. However, streamflow projections are associated with large uncertainties due to divergent precipitation projections from climate models, which directly propagate into hydrological estimates. Observation‐constrained approaches that condition future projections on past observations have been shown to reduce such uncertainties; however, they have not been applied to streamflow projections across the Indian rivers. Using long‐term streamflow and global mean surface temperature observations, climate model projections, hydrological modeling, and a Bayesian detection–attribution framework, we developed observational constrained streamflow projections for nine major Indian rivers. The method reduces the 5–95% confidence interval of future streamflow projections by nearly one‐third compared to raw multimodel ensembles, with constraint strength controlled by internal streamflow variability and inter‐model spread in the unconstrained ensemble. Projection uncertainty is further reduced to ∼20% when considering projections based only on skillful climate models. Constrained projections indicate significant increases in streamflow in the near‐, mid‐, and far‐future periods, except for the Cauvery basin, which shows a near‐term decline. Applying the method to raw precipitation projections reveals comparable constraint strength and increases confidence in the results, given the strong dependence of Indian river flows on precipitation. Our findings underscore the importance of combining skillful climate models with post‐processing constraint methods to substantially reduce model‐based uncertainty. Overall, our results provide critical insights into future streamflow changes across Indian rivers, supporting long‐term water‐resource planning and climate‐resilient management.

Manuel Cuerno, Luis Guijarro, Rosa María Arnaldo Valdés et al.

Analyzing flight trajectory data sets poses challenges due to the intricate interconnections among various factors and the high dimensionality of the data. Topological Data Analysis (TDA) is a way of analyzing big data sets focusing on the topological features this data sets have as point clouds in some metric space. Techniques as the ones that TDA provides are suitable for dealing with high dimensionality and intricate interconnections. This paper introduces TDA and its tools and methods as a way to derive meaningful insights from ATM data. Our focus is on employing TDA to extract valuable information related to airports. Specifically, by utilizing persistence landscapes (a potent TDA tool) we generate footprints for each airport. These footprints, obtained by averaging over a specific time period, are based on the deviation of trajectories and delays. We apply this method to the set of Spanish' airports in the Summer Season of 2018. Remarkably, our results align with the established Spanish airport classification and raise intriguing questions for further exploration. This analysis serves as a proof of concept, showcasing the potential application of TDA in the ATM field. While previous works have outlined the general applicability of TDA in aviation, this paper marks the first comprehensive application of TDA to a substantial volume of ATM data. Finally, we present conclusions and guidelines to address future challenges in the ATM domain.