Motivation is not something that we can take for granted. Some would say that motivation in the workplace is key for optimal performance and production. This master thesis looks into how shift-based operators in industrial work environments can sustain motivation, with the help of Self-Determination Theory, looking deeper into Autonomous motivation and how this can be a change in how operators perform, even in the long shift hours that are common in process control industries. This thesis aims to evaluate the current motivational drivers with operators. What keeps operators motivated in their work today? What type of motivation is lacking in their current workplace? And, how can they keep their motivation for a longer time? The method includes observations, surveying, and interviews. The results showed that there is a lower motivation in three subcategories of intrinsic behavior; Pressure/Tension, Perceived Choice, and Value/Usefulness. This thesis work is part of the research project Interactive Process Control, at Umeå University. By using these insights there is an opportunity to target these motivations in the future development of the IPC interactive tool.

Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.