The Effects of an Inclusive Badminton Program on Static Postural Control for Individuals with Intellectual and Developmental Disabilities.

The purpose of the study was to examine static postural control/balance in young adults with intellectual and developmental disabilities (IDD) and typically developing (TD) young adults before, during, and after an inclusive badminton intervention. Eight participants (four IDD-BADM and four TD-BADM) participated in a 12-week inclusive badminton intervention, with the other eight participants as matched controls (four IDD-CONTR and four TD-CONTR) (74.19 kg ± 9.8 kg, 171.96 cm ± 5.4 cm; 21.7 ± 1.8 years of age; nine females and seven males; eight with IDD and eight TD). The study followed a repeated measures design (pre, mid, post) before the intervention, at 6 weeks, and after 12 weeks. Static postural sway conditions included: bilateral stance eyes open (20 s), eyes closed (10 s), foam eyes open (20 s), foam eyes closed (10 s), and unilateral stance eyes open (10 s) and foam eyes open (10 s). Sway measurements included: average anterior/posterior (A/P) displacement (in), average medial/lateral (M/L) displacement (in), average 95% ellipsoid area (in2), and average velocity (ft/s). Significant time × group interactions were reported for average velocity (EO) (p = 0.030), average length (EO) (p = 0.030), 95% ellipsoid area (EO) (p = 0.049), and average A/P displacement (1LEO) (p = 0.036) for IDD-BADM. Significant time main effects were reported for average A/P displacement (FEO) (p = 0.040) for IDD groups. Significant time main effects were reported for average M/L displacement (EO) (p = 0.001), (EC) (p = 0.004), (FEO) (p = 0.005), (FEC) (p = 0.004), and average A/P displacement (EO) (p = 0.006) and (FEO) (p = 0.005) for TD groups. An inclusive badminton program indicated evidence of improved static postural control for those with IDD. However, no significant differences were reported for TD peers.

Physiological and Subjective Measures of Anxiety with Repeated Exposure to Virtual Construction Sites at Different Heights.

Background: Occupational workers at altitudes are more prone to falls, leading to catastrophic outcomes. Acrophobia, height-related anxiety, and affected executive functions lead to postural instabilities, causing falls. This study investigated the effects of repeated virtual height exposure and training on cognitive processing and height-related anxiety. Methods: Twenty-eight healthy volunteers (age 20.48 ± 1.26 years; mass 69.52 ± 13.78 kg) were recruited and tested in seven virtual environments (VE) [ground (G), 2-story altitude (A1), 2-story edge (E1), 4-story altitude (A2), 4-story edge (E2), 6-story altitude (A3), and 6-story edge (E3)] over three days. At each VE, participants identified occupational hazards present in the VE and completed an Attitude Towards Heights Questionnaire (ATHQ) and a modified State-Trait Anxiety Inventory Questionnaire (mSTAIQ). The number of hazards identified and the ATHQ and mSTAIQ scores were analyzed using a 7 (VE; G, A1, A2, A3, E1, E2, E3) x 3 (DAY; DAY 1, DAY 2, DAY 3) factorial repeated measures analysis of variance. Results: The participants identified the lowest number of hazards at A3 and E3 VEs and on DAY 1 compared to other VEs and DAYs. ATHQ scores were lowest at G, A1, and E1 VEs. Conclusion: Cognitive processing is negatively affected by virtual altitudes, while it improves with short-term training. The features of virtual reality, such as higher involvement, engagement, and reliability, make it a better training tool to be considered in ergonomic settings. The findings of this study will provide insights into cognitive dual-tasking at altitude and its challenges, which will aid in minimizing occupational falls.

Impact of Sub-Clinical and Clinical Compression Socks on Postural Stability Tasks among Individuals with Ankle Instability.

Compression socks are used by a very diverse group of individuals and may potentially have a greater impact on physically diminished or impaired individuals as opposed to healthy individuals. The purpose of this study was to compare the effects of sub-clinical (SC) and clinical (CL) compression socks among healthy (CON), copers (COP), and individuals with chronic ankle instability (CAI). Postural stability was evaluated in 20 participants (11 males and 9 females) using Balance Tracking System Balance platform (BTrackS™) during the modified clinical test of sensory integration in balance (mCTSIB) and limits of stability (LOS) tests. Postural sway parameters were analyzed using a mixed model repeated measures analysis of variance 3 (group: CON, COP, and CAI) by 3 (compression condition: BF, SC, and CL) × 4 (balance condition: EO, EC, EOF, and ECF) for mCTSIB and a 3 (group: CON, COP, and CAI) by 3 (compression condition: BF, SC, CL) × 4 (balance condition: FL, BL, BR, FR) for LOS. Results revealed significantly greater postural stability with both SC and CL compression socks when compared to barefoot conditions. However, no significant differences were observed among groups for compression socks grades. Both SC and CL compression socks may be effective in increasing postural stability.

Symmetry-aware reservoir computing.

We demonstrate that matching the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increases its processing power. We apply our method to the parity task, a challenging benchmark problem that highlights inversion and permutation symmetries, and to a chaotic system inference task that presents an inversion symmetry rule. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming artificial neural networks. When both symmetries are respected, we find that the network size N necessary to obtain zero error for 50 different RC instances scales linearly with the parity-order n. Moreover, some symmetry-aware RC instances perform a zero error classification with only N=1 for n≤7. Furthermore, we show that a symmetry-aware RC only needs a training data set with size on the order of (n+n/2) to obtain such a performance, an exponential reduction in comparison to a regular RC which requires a training data set with size on the order of n2^{n} to contain all 2^{n} possible n-bit-long sequences. For the inference task, we show that a symmetry-aware RC presents a normalized root-mean-square error three orders-of-magnitude smaller than regular RCs. For both tasks, our RC approach respects the symmetries by adjusting only the input and the output layers, and not by problem-based modifications to the neural network. We anticipate that the generalizations of our procedure can be applied in information processing for problems with known symmetries.

Next generation reservoir computing.

Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.

Forecasting chaotic systems with very low connectivity reservoir computers.

We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the forecasted system rather than short-term prediction. We find that optimizing over this measure more quickly excludes reservoirs that fail to reproduce the climate. The results of optimization are surprising: the optimized parameters often specify a reservoir network with very low connectivity. Inspired by this observation, we explore reservoir designs with even simpler structure and find well-performing reservoirs that have zero spectral radius and no recurrence. These simple reservoirs provide counterexamples to widely used heuristics in the field and may be useful for hardware implementations of reservoir computers.

Rapid time series prediction with a hardware-based reservoir computer.

Reservoir computing is a neural network approach for processing time-dependent signals that has seen rapid development in recent years. Physical implementations of the technique using optical reservoirs have demonstrated remarkable accuracy and processing speed at benchmark tasks. However, these approaches require an electronic output layer to maintain high performance, which limits their use in tasks such as time-series prediction, where the output is fed back into the reservoir. We present here a reservoir computing scheme that has rapid processing speed both by the reservoir and the output layer. The reservoir is realized by an autonomous, time-delay, Boolean network configured on a field-programmable gate array. We investigate the dynamical properties of the network and observe the fading memory property that is critical for successful reservoir computing. We demonstrate the utility of the technique by training a reservoir to learn the short- and long-term behavior of a chaotic system. We find accuracy comparable to state-of-the-art software approaches of a similar network size, but with a superior real-time prediction rate up to 160 MHz.

Identification of a TBHQ-Interfering Peak in Crude Canola Oil Using AOCS Official Method Ce 6-86 and its Chromatographic Resolution.

AOCS Official Method Ce 6-86 "Antioxidants, Liquid Chromatographic Method" was originally developed to confirm the correct antioxidant was added at the specified concentration to refined oils. Today, this method is increasingly utilized to validate that antioxidants are absent from oil products. False positive results can have a significant impact on the ability to sell products in specific markets and can impart additional business expenditures for conclusive secondary analyses. In the current work, quantification of tert-butylhydroquinone (TBHQ) in crude canola/rapeseed oil using liquid chromatography (LC) with ultraviolet (UV) detection was compromised by an interfering peak. Analyses using liquid chromatography-mass spectrometry (GC-MS) and high-resolution accurate mass LC-MS identified the interferent as 2,6-dimethoxy-4-vinylphenol (canolol), an endogenous compound present in crude canola/rapeseed oil. Resolution of canolol and TBHQ using LC-UV can be achieved via minor modification of the chromatographic conditions.