Decomposing variance in co-rumination using dyadic daily diary data

Co-rumination is the process of perseverating on problems, negative thoughts, or feelings with another person. Still unknown is how co-rumination unfolds within the daily lives of romantic couples. Using a variance decomposition procedure on data from a 14-day dyadic daily diary, we assess how much co-rumination varies over time and whether it is a couple- or individual-level process. Results revealed that within-person fluctuations in co-rumination contributed most (~33%) to the total variance and that these fluctuations could be reliably assessed using multi-item summary scores. Although time-invariant between-couple differences account significantly for the total variance (~14%) and can be reliably assessed, there is little within-couple agreement on the extent to which co-rumination fluctuates on a daily level. More research is needed to understand when and why perceptions of daily co-rumination diverge within couples, and how this informs theory on co-rumination and similar ostensibly dyadic constructs.

Manufacturing, Control, and Automation

This chapter identifies the common needs for process controls and automation that include methodologies to enable in-situ-level process controls, optimization at the plant or industry level, open-architecture software tools, adaptive control systems, methods and diagnostic tools for condition-based maintenance of process equipment in a manufacturing industry.

IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM

This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator

Modeling and Learning Constraints for Creative Tool Use

Improvisation is a hallmark of human creativity and serves a functional purpose in completing everyday tasks with novel resources. This is particularly exhibited in tool-using tasks: When the expected tool for a task is unavailable, humans often are able to replace the expected tool with an atypical one. As robots become more commonplace in human society, we will also expect them to become more skilled at using tools in order to accommodate unexpected variations of tool-using tasks. In order for robots to creatively adapt their use of tools to task variations in a manner similar to humans, they must identify tools that fulfill a set of task constraints that are essential to completing the task successfully yet are initially unknown to the robot. In this paper, we present a high-level process for tool improvisation (tool identification, evaluation, and adaptation), highlight the importance of tooltips in considering tool-task pairings, and describe a method of learning by correction in which the robot learns the constraints from feedback from a human teacher. We demonstrate the efficacy of the learning by correction method for both within-task and across-task transfer on a physical robot.