Narrow Results

To enable an accurate recognition of neuronal excitability in an epileptic brain for modeling or localization of epileptic zone, here the brain response to single-pulse electrical stimulation (SPES) has been decomposed into its constituent components using adaptive singular spectrum analysis (SSA). Given the response at neuronal level, these components are expected to be the inhibitory and excitatory components. The prime objective is to thoroughly investigate the nature of delayed responses (elicited between 100ms–1 s after SPES) for localization of the epileptic zone. SSA is a powerful subspace signal analysis method for separation of single channel signals into their constituent uncorrelated components. The consistency in the results for both early and delayed brain responses verifies the usability of the approach.

This study provides unique insights into the link between economic policy uncertainty (EPU) and stock market index in Sub-Saharan African (SSA) countries. Basically, the dearth of evidence in this perspective, especially in the context of SSA motivates this new assessment. Thus, on account of the monthly longitudinal series for 11 SSA countries over 2014M1–2023M6, the novel multiple structural break test in panel (MSBP) and cross-sectional ARDL (CS-ARDL) provide the following upshots: first, the high level of EPU worsens the region’s stock index in the short run; second, there is a long-term negative but insignificant effect of EPU on the stock index; third, EPU significantly reduces the stock index in the three identified structural break regimes, but outside it, it is insignificant; fourth, the devastating influence of EPU on stock prices is more pronounced during the COVID-19 era demonstrating that the response of the region’s stock market to EPU is highly sensitive to global shocks. The policy implication from the major findings is for the government authorities to put up plans that can strengthen the stock markets’ resilience to economic policy-induced uncertainty.

Singular spectrum analysis (SSA) is a powerful method that is frequently used in dynamical systems theory and time series analysis. However, the algorithm itself is only partially understood. In this paper, we tackle the problem of a thorough interpretation of the complete basic SSA algorithm. We point out the relationship between SSA and Fourier analysis (FA), linear filtering (LF), principal component analysis (PCA), and power spectrum density estimation (PSDE). The main contribution is a reformulation and comprehensive interpretation of SSA by means of a novel class of data-adaptive circulant frames. Based on this class of frames, we introduce a formalism for time series decomposition and reconstruction. This mathematical framework generalizes principle component analysis and SSA. We show that SSA, PCA, FA, LF, and PSDE can be found from special cases of the proposed frame structures. This way we are able to provide a formal description of SSA and its relationship to the aforementioned methods. Whilst previous approaches to the interpretation of SSA relied on approximations, we provide exact mathematical descriptions of the relation between the above-mentioned techniques.

The fault or mechanical flaw causes several feeble fluctuations to the position signal. The identification of these oscillations by the encoders may help to determine the performance of the machine and the health conditions. In operations, the trend is usually several orders higher than the interested magnitude fluctuations, making it hard to identify feeble swings without signal deformity. Besides, the swings can be intricate, and the amplitude can be changed under a non-stationary operating condition. In order to overcome this problem, the singular spectrum analysis (SSA) is suggested to detect the feeble position oscillations of the rotary encoder signal in this article. It allows the complex signal of the encoder to be reduced to a variety of explainable noise-containing components, a collection of periodic oscillations and a trend. The numerical emulation reveals the achievement of the technique; it demonstrates that the SSA is superior to the empirical mode decomposition (EMD) in terms of accuracy and ability. In addition, rotary encoder signals from the robot arm are evaluated to identify the causes of oscillation at junctions during industrial robot movements. The proposed route for the robotic arm is proven, feasible and reliable.

Generalized parton distributions can be used to obtain information about the dependence of parton distributions on the impact parameter. Potential consequences for T-odd single-spin asymmetries are discussed.

To solve the problem of the lack of representativeness of the training set and the poor prediction accuracy due to the limited number of training samples when the machine learning method is used for the classification and prediction of pharmacokinetic indicators, this paper proposes a 1DCNN-Attention concentration prediction model optimized by the sparrow search algorithm (SSA). First, the SMOTE method is used to expand the small sample experimental data to make the data diverse and representative. Then a one-dimensional convolutional neural network (1DCNN) model is established, and the attention mechanism is introduced to calculate the weight of each variable for dividing the importance of each pharmacokinetic indicator by the output drug concentration. The SSA algorithm was used to optimize the parameters in the model to improve the prediction accuracy after data expansion. Taking the pharmacokinetic model of phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy as an example, the concentration changes of PHB were predicted and the effectiveness of the method was verified. The results show that the proposed model has a better prediction effect than other methods.