Aiming at the reduction in penetration depth caused by the deformation and fracture of conventional 90W-Ni-Fe rod penetrators during ultra-high-velocity penetration, a lightweight tungsten-based ceramic alloy was fabricated in this study. Ballistics tests were conducted to verify the penetration performance of the novel lightweight alloy against concrete targets, and the existing theoretical penetration model was modified accordingly. The results indicate that, within the impact velocity range of 1400~1750 m/s, the penetration depth of both rod penetrators presents an initial increasing and subsequent decreasing trend, with an extreme value near 1625 m/s and a dimensionless penetration depth X_p/L close to 4. Compared with the traditional 90W-Ni-Fe alloy, the lightweight tungsten-based ceramic alloy penetrator achieves a mass reduction of 5.7%. In the impact velocity range from 1408.0 m/s to 1743.5 m/s, its penetration depth increases by 6.22%~10.58%, and the improvement becomes more significant with the increase in impact velocity. For the modified theoretical model, the predicted ultimate penetration depth of the lightweight alloy rod penetrator increases by 8.85%, while the average mass loss rate and average erosion rate decrease by 12.09% and 17.65%, respectively. The error between theoretical calculations and experimental data is within 5%.

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research.