Application of neuroergonomics in the industrial design of mining equipment.

Neuroergonomics is an interdisciplinary field merging neuroscience and ergonomics to optimize performance. In order to design an optimal user interface, we must understand the cognitive processing involved. Traditional methodology incorporates self-assessment from the user. This dissertation examines the use of neurophysiological techniques in quantifying the cognitive processing involved in allocating cognitive resources. Attentional resources, cognitive processing, memory and visual scanning are examined to test the ecological validity of theoretical laboratory settings and how they translate to real life settings. By incorporating a non-invasive measurement technique, such as the quantitative electroencephalogram (QEEG), we are able to examine connectivity patterns in the brain during operation and discern whether or not a user has obtained expert status. Understanding the activation patterns during each phase of design will allow us to gauge whether our design has balanced the cognitive requirements of the user.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Haptic Choice Reaction Time in Elite Judo Competitors

Rapid reactions are an essential part of performance in most sports. In Judo the main route of information input is somatosensory, yet reaction studies have historically utilised visual or audio prompts. We have addressed the gap in knowledge pertaining to judoka’s cognitive performance on a suitable reaction test. We have designed a Judo-specific reaction device with a sensory signal that is consistent with the stream of tactile feedback in Judo. We set up a study to evaluate this novel haptic choice reaction test device and found it to be valid and reliable. We found mean reaction time to haptic signals to be shorter compared to visual ones, which is consistent with findings reported elsewhere in scientific literature. We also found evidence of judoka having achieved consistently shorter mean reaction times to haptic signals than people with experience in sports where the dominant sensory input is visual. We then used the haptic device to collect reaction time data from a cohort of elite judoka on multiple occasions. In order to sustain the judoka’s attention during the tests we introduced competition in the testing procedure. Our approach has added to the ecological validity of the method used due to the Judo-specific sensory modality of the reaction task and because the tests took place at the judoka’s training environment, during regular training sessions, and under competition pressure. We collected data under three conditions of physical intensity: Baseline (at rest), Moderate Intensity (post warm up), and Severe Intensity (post maximum effort tests). Our results show that the mean reaction time improved from Baseline by a considerable margin in a group of elite judoka at Moderate Intensity with no difference in accuracy. We found no significant difference between the mean reaction time at Baseline and Severe Intensity but there was a significant deterioration in accuracy