In this paper, the sufficient conditions for the global exponential stability of the switched genetic regulatory networks with mixed time delays are obtained. The proposed method does not need the construction of Lyapunov-Krasovskii functional, but is directly proceeded by the definition of global exponential stability. The derived sufficient conditions can easily be verified by checking the eigenvalues of a constant matrix or solving several simple linear matrix inequalities. Finally, two numerical examples are presented to illustrate that the obtained global exponential stability criteria are available.University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments. Often these factors result in long-term issues with daytime sleepiness, emotional instability, and mental exhaustion, which may lead to difficulties in the educational process. This study introduces the Daily Sampling System (DSS) implemented as a smartphone application, which combines a set of self-assessment scales for evaluating variations in the emotional state and sleep quality throughout a full academic term. In addition to submitting the daily sampling scores, the participants regularly filled in the Depression, Anxiety, and Stress Scales (DASS) reports and took part in resting-state EEG data recording immediately after report completion. learn more In total, this study collected 1835 daily samples and 94 combined DASS with EEG datasets from 18 university students (aged 23-27 years), with 79.3±15.3% response ratio in submitting the daily reports during an academic semester. The results of pairwise testing and multiple regression analysis demonstrate that the daily level of self-perceived fatigue correlates positively with stress, daytime sleepiness, and negatively with alertness on awakening, self-evaluated sleep quality, and sleep duration. The spectral analysis of the EEG data reveals a significant increase in the resting-state spectral power density across the theta and low-alpha frequency bands associated with increased levels of anxiety and stress. Additionally, the state of depression was accompanied by an intensification of high-frequency EEG activity over the temporal regions. No significant differences in prefrontal alpha power asymmetry were observed under the described experimental conditions while comparing the states of calmness and emotional arousal of the participants for the three conditions of depression, anxiety, and stress.Functional near infrared spectroscopy (fNIRS) is a noninvasive optics-based neuroimaging modality successfully applied to real-life settings. The technology uses light in the near infrared range (650-950nm) to track changes in oxygenated (HbO2) and deoxygenated hemoglobin (Hb) obtained from measured light intensity using light-tissue interaction principles. fNIRS data processing involves artifact removal and hemodynamic signal conversion using modified Beer-Lambert law (MBLL) to obtain Hb and HbO2, reliably. fNIRS signals can get contaminated by various noise sources of physiological and non-physiological origins. Various algorithms have been proposed for the elimination of artifacts from frequency selective filters to blind source separation methods. Hemodynamic signal extraction using raw intensity measurements at multiple wavelengths based on MBLL usually involves apriori knowledge of certain conversion parameters such as molar extinction coefficients ( ε ) and differential path length factor (DPF). Different sets of conversion parameters dependent upon wavelength, chromophores, and age have been reported. Variation in processing algorithms and parameters can cause differences in Hb and HbO2 extraction which can in turn change study outcomes. Using fNIRS, we have previously shown significant increases in oxygenation in the prefrontal cortex from Single-Task-Walking (STW) to Dual-task-Walking (DTW) conditions in older adults due to greater cognitive demands inherent in the latter condition. In the current study, we re-analyzed our data and determined that although using different conversion parameters i.e. ε and age dependent DPF and filter cut-off frequencies at 0.14 and 0.08Hz including those designed to remove confounding effects of Mayer waves had caused some linear increases or decreases on the extracted Hb and HbO2 signals, those effects were minimal in task related comparisons and hence, the overall study outcomes.The measurement of handrim wheelchair propulsion characteristics and performance in the field is complicated due to the non-stationary nature of wheelchair driving. In contrast, the laboratory provides a constrained and standardisable environment to conduct measurements and experiments. Apart from wheelchair treadmills, dynamometers or ergometers for handrim wheelchairs are often custom-made, one-of-a-kind, expensive, and sparsely documented in the research literature. To facilitate standardised and comparable lab-based measurements in research, as well as in clinical settings and adapted sports, a new wheelchair ergometer was developed. The ergometer with instrumented dual rollers allows for the performance analysis of individuals in their personal handrim wheelchair and facilitates capacity assessment, training and skill acquisition in rehabilitation or adapted sports. The ergometer contains two servomotors, one for each rear wheel roller, that allow for the simulation of translational inertia and resistive forces as encountered during wheelchair propulsion based on force input and a simple mechanical model of wheelchair propulsion. A load cell configuration for left and right roller enables the measurement of effective user-generated torque and force on the handrim and the concomitant timing patterns. Preliminary results are discussed.The visualization of hierarchically structured data over time is an ongoing challenge and several approaches exist trying to solve it. Techniques such as animated or juxtaposed tree visualizations are not capable of providing a good overview of the time series and lack expressiveness in conveying changes over time. Nested streamgraphs provide a better understanding of the data evolution, but lack the clear outline of hierarchical structures at a given timestep. Furthermore, these approaches are often limited to static hierarchies or exclude complex hierarchical changes in the data, limiting their use cases. We propose a novel visual metaphor capable of providing a static overview of all hierarchical changes over time, as well as clearly outlining the hierarchical structure at each individual time step. Our method allows for smooth transitions between tree maps and nested streamgraphs, enabling the exploration of the trade-off between dynamic behavior and hierarchical structure. As our technique handles topological changes of all types, it is suitable for a wide range of applications.