An analytical method reduces noise bias in motor adaptation analysis

When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner’s intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions.

Massive black hole binary systems and the NANOGrav 12.5 yr results

ABSTRACT The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) recently reported evidence for the presence of a common stochastic signal across their array of pulsars. The origin of this signal is still unclear. One possibility is that it is due to a stochastic gravitational-wave background (SGWB) in the ∼1–10 nHz frequency region. Taking the NANOGrav observational result at face value, we show that this signal would be fully consistent with an SGWB produced by an unresolved population of in-spiralling massive black hole binaries (MBHBs) predicted by current theoretical models. Considering an astrophysically agnostic model, the MBHB merger rate is loosely constrained. Including additional constraints from galaxy pairing fraction and MBH–bulge scaling relations, we find that the MBHB merger rate is ${1.2\times 10^{-5}}{\rm -}{4.5\times 10^{-4}}\, \mathrm{Mpc}^{-3}\, \mathrm{Gyr}^{-1}$ , the MBHB merger time-scale is $\le 2.7\, \mathrm{Gyr}$, and the norm of the MBH−Mbulge relation is $\ge 1.2\times 10^{8}\, {\rm M}_\odot$ (all quoted at 90 per cent credible intervals). Regardless of the astrophysical details of MBHB assembly, the NANOGrav result would imply that a sufficiently large population of massive black holes pair up, form binaries and merge within a Hubble time.

Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.

Vehicular Classification Based on Vibration Caused by Uncontrolled Traffic Using Fibre Optic Sensor

Fibre optic vibration sensor (FOVS) converts vibration signal to light signal. Due to its prominent features, distributed fibre optic vibration sensor is preferred to conventional methods. Interest in FOVS has greatly increased over the years in structural health monitoring and vehicular traffic, hence the need to embark on this study. Distributed FOS is employed to measure the frequency of vibration caused by uncontrolled vehicular movement at Oluku By-Pass Bridge. Φ-OTDR is used to obtain millisecond snapshots of the stochastic signal arising thereof. A video shot was also recorded to match the exact timing of each excitation. Traces obtained show several frequency peaks at various corresponding backscatter level for high and low vehicular traffic. Sampled data analyzed with Fiberizer Cloud software indicates high attenuation contribute to low total loss and low attenuation lead to high total loss. Spectral analyses of the data at low and high traffic for the stochastic signal. The corresponding frequency peaks were calculated and the results used to classify vehicles at low and high speed. The FWHM obtained with double Gaussian model for differential trace shows that high traffic gives sharp peaks with standard deviation less than 0.6 and above 0.6 for low traffic. The analyses identified peaks above 4.0x10-3 for traces with trucks and cars at high speed, while peaks less than 4.0x10-3 were obtained for traces with cars.