Isometric Arm Forces Exerted by Females at Different Levels of Physical Comfort and Their EEG Signatures.

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users' force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

Nonlinear dynamics of EEG responses to unmanned vehicle visual detection with different levels of task difficulty.

The main objective of this study was to examine the presence of chaos in the EEG recordings of brain activity under simulated unmanned ground vehicle visual detection scenarios with different levels of task difficulty. One hundred and fifty people participated in the experiment and completed four visual detection task scenarios: (1) change detection, (2) a threat detection task, (3) a dual-task with different change detection task rates, and (4) a dual-task with different threat detection task rates. We used the largest Lyapunov exponent and correlation dimension of the EEG data and performed 0-1 tests on the EEG data. The results revealed a change in the level of nonlinearity in the EEG data corresponding to different levels of cognitive task difficulty. The differences in EEG nonlinearity measures among the studied levels of task difficulty, as well as between a single task scenario and a dual-task scenario, have also been assessed. The results increase our understanding of the nature of unmanned systems' operational requirements.

The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.

In December 2019, China reported a new virus identified as SARS-CoV-2, causing COVID-19, which soon spread to other countries and led to a global pandemic. Although many countries imposed strict actions to control the spread of the virus, the COVID-19 pandemic resulted in unprecedented economic and social consequences in 2020 and early 2021. To understand the dynamics of the spread of the virus, we evaluated its chaotic behavior in Japan. A 0-1 test was applied to the time-series data of daily COVID-19 cases from January 26, 2020 to August 5, 2021 (3 days before the end of the Tokyo Olympic Games). Additionally, the influence of hosting the Olympic Games in Tokyo was assessed in data including the post-Olympic period until October 8, 2021. Even with these extended time period data, although the time-series data for the daily infections across Japan were not found to be chaotic, more than 76.6% and 55.3% of the prefectures in Japan showed chaotic behavior in the pre- and post-Olympic Games periods, respectively. Notably, Tokyo and Kanagawa, the two most populous cities in Japan, did not show chaotic behavior in their time-series data of daily COVID-19 confirmed cases. Overall, the prefectures with the largest population centers showed non-chaotic behavior, whereas the prefectures with smaller populations showed chaotic behavior. This phenomenon was observed in both of the analyzed time periods (pre- and post-Olympic Games); therefore, more attention should be paid to prefectures with smaller populations, in which controlling and preventing the current pandemic is more difficult.

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach.

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

The Chaotic Behavior of the Spread of Infection During the COVID-19 Pandemic in the United States and Globally.

In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and resulted in a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, COVID-19 resulted in a significant loss of human life in 2020 and early 2021. To better understand the dynamics of the spread of COVID-19, evidence of its chaotic behavior in the US and globally was evaluated. A 0-1 test was used to analyze the time-series data of confirmed daily COVID-19 cases from 1/22/2020 to 12/13/2020. The results show that the behavior of the COVID-19 pandemic was chaotic in 55% of the investigated countries. Although the time-series data for the entire US was not chaotic, 39% of individual states displayed chaotic infection spread behavior based on the reported daily cases. Overall, there is evidence of chaotic behavior of the spread of COVID-19 infection worldwide, which adds to the difficulty in controlling and preventing the current pandemic.

Evidence of Chaos in Human Emotions Expressed in Tweets.

This study explored the chaotic properties of human emotions as expressed in social media and its implications for attainable forecasting horizons. Three human emotional states extracted from Twitter were analyzed using the nonlinear dynamics approach. The greatest positive Lyapunov exponent (LE) and 0-1 test methods were applied to a time series set consisting of over 25,000 data points reflecting the hourly recorded data of over 1.3 million tweets. The results suggest that the examined emotional time series data represent a nonlinear dynamical system with deterministic chaos properties. Therefore, by utilizing traditional linear methods of social media data analysis, one may not be able to fully understand and forecast critical transition trends over time or beyond a limited duration. It was concluded that the nonlinear dynamics approach is useful to determine a feasible forecasting horizon and to assess the prediction accuracy of social media data in general.

The complexity of human performance variability on watch standing task.

The primary objective of this study was to examine the complexity of human temporal variability of topside roving watch task in naval operations concerning the reported times of ship status and to explore the potential presence of chaotic behavior and fractal properties of the reported log times. Topside rover reporting time intervals recorded in the deck logs of the USS Jason Dunham over the 2013-2015 period were analyzed to understand the underlying complexity of the watch standing task that is critical to the success of naval operations. The results on the 0-1 test, analysis of the largest Lyapunov exponents, as well the exploration of the fractal dimension and 1/f spectral analyses, showed that the fluctuation of standing watch time reports data exhibits chaotic and fractal system properties. The critical implications of the study findings for the human-centered design of complex systems were also discussed.

A nonlinear dynamics of trunk kinematics during manual lifting tasks.

BACKGROUND: Human responses at work may exhibit nonlinear properties where small changes in the initial task conditions can lead to large changes in system behavior. Therefore, it is important to study such nonlinearity to gain a better understanding of human performance under a variety of physical, perceptual, and cognitive tasks conditions. OBJECTIVE: The main objective of this study was to investigate whether the human trunk kinematics data during a manual lifting task exhibits nonlinear behavior in terms of determinist chaos. METHODS: Data related to kinematics of the trunk with respect to the pelvis were collected using Industrial Lumbar Motion Monitor (ILMM), and analyzed applying the nonlinear dynamical systems methodology. Nonlinear dynamics quantifiers of Lyapunov exponents and Kaplan-Yorke dimensions were calculated and analyzed under different task conditions. RESULTS: The study showed that human trunk kinematics during manual lifting exhibits chaotic behavior in terms of trunk sagittal angular displacement, velocity and acceleration. CONCLUSIONS: The findings support the importance of accounting for nonlinear dynamical properties of biomechanical responses to lifting tasks.