Service discovery requests' messages have a vital role in sharing and locating resources in many of service discovery protocols. Sending more messages than a link can handle may cause congestion and loss of messages which dramatically influences the performance of these protocols. Re-send the lost messages result in latency and inefficiency in performing the tasks which user(s) require from the connected nodes. This issue become a serious problem in two cases: first, when the number of clients which performs a service discovery request is increasing, as this result in increasing in the number of sent discovery messages; second, when the network resources such as bandwidth capacity are consumed by other applications. These two cases lead to network congestion and loss of messages. This paper propose an algorithm to improve the services discovery protocols performance by separating each consecutive burst of messages with a specific period of time which calculated regarding the available network resources. It was tested when the routers were connected in two configurations; decentralised and centralised .In addition, this paper explains the impact of increasing the number of clients and the consumed network resources on the proposed algorithm.
Zayas, Cilia E. (University of Florida) | He, Zhe (Florida State University) | Yuan, Jiawei ( Embry-Riddle Aeronautical University ) | Maldonado-Molina, Mildred (University of Florida) | Hogan, William (University of Florida) | Modave, François (University of Florida) | Guo, Yi (University of Florida) | Bian, Jiang (University of Florida)
Elderly patients, aged 65 or older, make up 13.5% of the U.S. population, but represent 45.2% of the top 10% of healthcare utilizers, in terms of expenditures. Middle-aged Americans, aged 45 to 64 make up another 37.0% of that category. Given the high demand for healthcare services by the aforementioned population, it is important to identify high-cost users of healthcare systems and, more importantly, ineffective utilization patterns to highlight where targeted interventions could be placed to improve care delivery. In this work, we present a novel multi-level framework applying machine learning (ML) methods (i.e., random forest regression and hierarchical clustering) to group patients with similar utilization profiles into clusters. We use a vector space model to characterize a patient's utilization profile as the number of visits to different care providers and prescribed medications. We applied the proposed methods using the 2013 Medical Expenditures Panel Survey (MEPS) dataset. We identified clusters of healthcare utilization patterns of elderly and middle-aged adults in the United States, and assessed the general and clinical characteristics associated with these utilization patterns. Our results demonstrate the effectiveness of the proposed framework to model healthcare utilization patterns. Understanding of these patterns can be used to guide healthcare policy-making and practice.
This graph shows Capacity Utilization. This series is up 8.8 percentage points from the record low set in June 2009 (the series starts in 1967). Capacity utilization at 75.5% is 4.5% below the average from 1972 to 2015 and below the pre-recession level of 80.8% in December 2007. Note: y-axis doesn't start at zero to better show the change. The second graph shows industrial production since 1967.
CORE Pathways provides unprecedented insight into individual patient diagnostics and utilization to enable clinical and financial analysts to identify, report, and benchmark cost trends, treatment patterns, condition severity, and health outcomes. CORE Pathways is built on top of our proprietary DL&M Pipeline and organizes disparate claim-line data into individualized events, or COREs, allowing analysts and actuaries to review program performance, explain risk, and report on costs and utilization across billions of records.