Linear Learning in Changing Environments

2005 ◽  
Author(s):  
Tilman Klumpp
Keyword(s):  
Changing Environments

scholarly journals An Adaptive Scheduler for Real-Time Operating Systems to Extend WSN Nodes Lifetime

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Roberto Rodriguez-Zurrunero ◽  
Ramiro Utrilla ◽  
Elena Romero ◽  
Alvaro Araujo
Keyword(s):  
Operating System ◽  
Real Time ◽  
Operating Systems ◽  
Research Area ◽  
Rechargeable Batteries ◽  
Wireless Sensor ◽  
Portable Devices ◽  
Wireless Devices ◽  
Changing Environments ◽  
Tasks Scheduling

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource-constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real-time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.

scholarly journals Evolution of bacteria in the human gut in response to changing environments: An invisible player in the game of health

2021 ◽  
Vol 19 ◽  
pp. 752-758
Author(s):  
Aarushi Venkatakrishnan ◽  
Zoie E. Holzknecht ◽  
Rob Holzknecht ◽  
Dawn E. Bowles ◽  
Sanet H. Kotzé ◽  
...  
Keyword(s):  
Human Gut ◽  
Changing Environments

HISTORICAL RETE NETWORKS TO SUPPORT THE DEBUGGING OF FORWARD-CHAINING RULE-BASED PROGRAMS

1993 ◽  
Vol 02 (01) ◽  
pp. 47-70
Author(s):  
SHARON M. TUTTLE ◽  
CHRISTOPH F. EICK
Keyword(s):  
Working Memory ◽  
Data Driven ◽  
Rule Based ◽  
Historical Information ◽  
Forward Chaining ◽  
Changing Environments ◽  
Time Performance ◽  
Inference Network ◽  
One Step ◽  
Explanation System

Forward-chaining rule-based programs, being data-driven, can function in changing environments in which backward-chaining rule-based programs would have problems. But, degugging forward-chaining programs can be tedious; to debug a forward-chaining rule-based program, certain ‘historical’ information about the program run is needed. Programmers should be able to directly request such information, instead of having to rerun the program one step at a time or search a trace of run details. As a first step in designing an explanation system for answering such questions, this paper discusses how a forward-chaining program run’s ‘historical’ details can be stored in its Rete inference network, used to match rule conditions to working memory. This can be done without seriously affecting the network’s run-time performance. We call this generalization of the Rete network a historical Rete network. Various algorithms for maintaining this network are discussed, along with how it can be used during debugging, and a debugging tool, MIRO, that incorporates these techniques is also discussed.

Metabolomics profile responses to changing environments in a common bean (Phaseolus vulgaris L.) germplasm collection

2021 ◽  
pp. 131003
Author(s):  
Elsa Mecha ◽  
Guillaume L. Erny ◽  
Ana C.L. Guerreiro ◽  
Rodrigo P. Feliciano ◽  
Inês Barbosa ◽  
...  
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Germplasm Collection ◽  
Phaseolus Vulgaris L ◽  
Changing Environments

scholarly journals Flexibility in Animal Signals Facilitates Adaptation to Rapidly Changing Environments

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. e25413 ◽  
Author(s):  
Darren S. Proppe ◽  
Christopher B. Sturdy ◽  
Colleen Cassady St. Clair
Keyword(s):  
Changing Environments
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