The Affordable Care Act (ACA) is a federal statute enacted with a goal of increasing the quality and affordability of health insurance. Through a web service, CMS sends applicant information to SSA. SSA matches applicant data to various SSA data sources and provides a response back to CMS, based on the results of the matches. The results of these matches help CMS and states determine an applicant's eligibility and cost for health insurance. SSA provides results to CMS for matches of SSN, Name, and DOB against the Numident. SSA may also provide incarceration data from PUPS, Title II income from the MBR, and quarters of coverage data from the MEF.

The Medicaid Managed Care Enrollment Report profiles enrollment statistics on Medicaid managed care programs on a plan-specific level. The managed care enrollment statistics include enrollees receiving comprehensive benefits and limited benefits and are point-in-time counts.

1. Total Medicaid Enrollees represents an unduplicated count of all beneficiaries in FFS and any type of managed care, including Medicaid-only and Medicare-Medicaid ("dual") enrollees.
2. Total Medicaid enrollment in Any Type of Managed Care represents an unduplicated count of beneficiaries enrolled in any Medicaid managed care program, including comprehensive MCOs, limited benefit MCOs, and PCCMs.
3. The “Medicaid Enrollment in Comprehensive Managed Care” column represents an unduplicated count of Medicaid beneficiaries enrolled in a managed care plan that provides comprehensive benefits (acute, primary care, specialty, and any other), or PACE program. It excludes beneficiaries who are enrolled in a Financial Alignment Demonstration Medicare-Medicaid Plan as their only form of managed care.
4. The “Medicaid Enrollment in Comprehensive MCOs Under ACA Section VIII Expansion” column is a subset of the total reported in column C and includes individuals who are enrolled in comprehensive MCOs and are low-income adults, with or without dependent children, eligible for Medicaid under ACA Section VIII.
5. n/a" indicates that a state or territory was either not able to report data or does not operate a managed care program.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

Capture Feeding Area (ACA) for the PEA. In order to achieve the objective of regaining the quality of the water resource with regard to diffuse pollution, in particular that used for the production of drinking water, the State has committed itself with the communities to regain the quality of 1000 abstractions following the “Environmental Conference” of September 2014. In accordance with Article R 114-6 of the Rural Code, measures to be promoted to owners and operators may be financed by specific financial aid from Europe, the State, the Regional Council, the Departmental Council and the Water Agency. In the Artois Picardie Basin, the Agence de l’Eau initiated ORQUEs on strategic abstractions. Based on the volunteering of drinking water communities, these operations follow the same methodology as Grenelle’s problem. Some ORQUEs aim to preserve water quality when it is in compliance with the regulations.