98%
921
2 minutes
20
The impact of time delays on the stability of sampling zeros in sampled-data (SD) control systems is investigated. While delays, arising from communication and computational latencies, are known to critically influence zero-dynamics stability, their specific effect on sampling zeros remains less explored. This work establishes novel conditions for sampling zero stability under time delays, employing the Backward Triangle Sample-and-Hold (BTSH) method for signal reconstruction. In particular, we analyze the asymptotic behavior of sampling zeros with respect to the system's relative degree and delay magnitude using BTSH. Moreover, through this analysis, we derive explicit stability conditions for these zeros, crucial for overall system performance. Finally, we provide a comparative analysis contrasting the stability properties under BTSH with those under the conventional Zero-Order Hold (ZOH) method in delayed settings. The theoretical findings are validated through a detailed numerical example, demonstrating the distinct advantages of BTSH in managing delay-induced zero-dynamics challenges.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307686 | PMC |
http://dx.doi.org/10.1038/s41598-025-13054-8 | DOI Listing |
Ann Appl Stat
September 2025
Department of Statistics and Data Sciences, The University of Texas at Austin.
Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and therefore are not suitable for modeling zero-inflated count data.
View Article and Find Full Text PDFGut Microbes
December 2025
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, HL, China.
The involvement of gut microbiota in host physiological activities is crucial, yet the high sparsity of microbiome data, marked by numerous zeros in count matrices, presents huge analytical challenges. To overcome this, we developed mbSparse, an imputation algorithm that leverages deep learning rather than traditional predefined count distributions. Utilizing a feature autoencoder for learning sample representations and a conditional variational autoencoder (CVAE) for data reconstruction, mbSparse effectively integrates these processes to enhance imputation.
View Article and Find Full Text PDFSci Rep
July 2025
College of Science, Guizhou Institute of Technology, Guiyang, 550005, People's Republic of China.
The impact of time delays on the stability of sampling zeros in sampled-data (SD) control systems is investigated. While delays, arising from communication and computational latencies, are known to critically influence zero-dynamics stability, their specific effect on sampling zeros remains less explored. This work establishes novel conditions for sampling zero stability under time delays, employing the Backward Triangle Sample-and-Hold (BTSH) method for signal reconstruction.
View Article and Find Full Text PDFPopul Health Manag
July 2025
Premier Healthcare Solutions, Inc., Charlotte, North Carolina, USA.
This study explored a large segment of Medicare claims data to evaluate the association between Accountable Care Organization (ACO) attribution and 30-day all-cause hospital readmissions. ACOs deliver value-based care to attributed patient populations, aiming to enhance care coordination and transitional care outcomes. Initiatives such as the Medicare Shared Savings Program (MSSP) incentivize health care systems to reduce readmissions and the total cost of care.
View Article and Find Full Text PDFEur J Investig Health Psychol Educ
July 2025
Department of Educational Technology and Foundations, University of West Georgia, Carrollton, GA 30118, USA.
This study examines the availability and outcomes of Advanced Placement (AP) courses in secondary schools in Georgia (USA) and South Carolina (USA), focusing on how school locale (rurality) and demographic composition influence AP availability and student achievement. The authors analyzed population-level school data from the 2021-22 academic year using a two-step quantitative approach. A zero-inflated negative binomial regression model (ZINB) was employed to assess AP course participation and AP exam performance while addressing overdispersion and excess zeros in the data.
View Article and Find Full Text PDF