Solving Dirac equations on a 3D lattice with inverse Hamiltonian and spectral methods

A new method to solve the Dirac equation on a 3D lattice is proposed, in which the variational collapse problem is avoided by the inverse Hamiltonian method and the fermion doubling problem is avoided by performing spatial derivatives in momentum space with the help of the discrete Fourier transform, i.e., the spectral method. This method is demonstrated in solving the Dirac equation for a given spherical potential in 3D lattice space. In comparison with the results obtained by the shooting method, the differences in single particle energy are smaller than $10^{-4}$~MeV, and the densities are almost identical, which demonstrates the high accuracy of the present method. The results obtained by applying this method without any modification to solve the Dirac equations for an axial deformed, non-axial deformed, and octupole deformed potential are provided and discussed.Comment: 18 pages, 6 figure

Red Soundscape Index (RSI): An index with the potential to assess soundscape quality

It is not enough to define urban soundscape just using the green soundscape index (GSI), which is the ratio of the perception of natural sounds to the perception of traffic noises. Therefore, in the present study, red soundscape index (RSI), defined as the ratio of perception of natural sounds to perception of human sounds, was introduced. The data for calculating RSI were collected from sound environment measurements and a questionnaire-based survey in seven urban parks in Harbin city, China. The results revealed the following: (1) RSI was correlated with the overall soundscape quality; (2) RSI was correlated with the maximum and minimum instantaneous sound pressure levels and with equivalent sound pressure levels; and (3) The urban sound environment as well as sound quality can be classified by RSI. It was confirmed that RSI could be used as a supplement to GSI in urban soundscape planning

Modulation spectra capture EEG responses to speech signals and drive distinct temporal response functions

Speech signals have a unique shape of long-term modulation spectrum that is distinct from environmental noise, music, and non-speech vocalizations. Does the human auditory system adapt to the speech long-term modulation spectrum and efficiently extract critical information from speech signals? To answer this question, we tested whether neural responses to speech signals can be captured by specific modulation spectra of non-speech acoustic stimuli. We generated amplitude modulated (AM) noise with the speech modulation spectrum and 1/f modulation spectra of different exponents to imitate temporal dynamics of different natural sounds. We presented these AM stimuli and a 10-min piece of natural speech to 19 human participants undergoing electroencephalography (EEG) recording. We derived temporal response functions (TRFs) to the AM stimuli of different spectrum shapes and found distinct neural dynamics for each type of TRFs. We then used the TRFs of AM stimuli to predict neural responses to the speech signals, and found that (1) the TRFs of AM modulation spectra of exponents 1, 1.5, and 2 preferably captured EEG responses to speech signals in the δ band and (2) the θ neural band of speech neural responses can be captured by the AM stimuli of an exponent of 0.75. Our results suggest that the human auditory system shows specificity to the long-term modulation spectrum and is equipped with characteristic neural algorithms tailored to extract critical acoustic information from speech signals

Micromachined membrane particle filters

We report here several particle membrane filters (8 x 8 mm^2) with circular, hexagonal and rectangular through holes. By varying hole dimensions from 6 to 12 pm, opening factors from 4 to 45 % are achieved. In order to improve the filter robustness, a composite silicon nitride/Parylene membrane technology is developed. More importantly, fluid dynamic performance of the filters is also studied by both experiments and numerical simulations. It is found that the gaseous flow through the filters depends strongly on opening factors, and the measured pressure drops are much lower than that from numerical simulation using the Navier-Stokes equation. Interestingly, surface velocity slip can only account for a minor part of the discrepancy. This suggests that a very interesting topic for micro fluid mechanics research is identified

Differences in thermal-acoustic perception in various office behaviors

Thermal-acoustic interaction has vital research value in the development of soundscape prediction models. Previous studies on thermal-acoustic interaction have been focused mainly on perceptual changes. However, the differences in office behaviors warrant attention. We conducted an experimental study to explore the effects of various office behaviors (such as resting, reading, writing, and typing) on the thermal-acoustic interactive perception. The results showed that (at near thermal neutral temperature) (1) sound types affected thermal evaluation, acoustic evaluation, and overall evaluation. The sound of water significantly reduced the score for thermal sensation. (2) Behavior types affected thermal sensation, acoustic comfort, and overall comfort. Reading contributed to significantly lower scores than other behaviors for the three indicators. This indicated that when reading, people are more demanding of the environment. (3) The interaction of sound types and behavior types affected overall annoyance. Therefore, we recommend adjusting the office environment effectively and establishing more effective soundscape prediction models

On the effectiveness of facial expression recognition for evaluation of urban sound perception

Sound perception studies mostly depend on questionnaires with fixed indicators. Therefore, it is desirable to explore methods with dynamic outputs. The present study aims to explore the effects of sound perception in the urban environment on facial expressions using a software named FaceReader based on facial expression recognition (FER). The experiment involved three typical urban sound recordings, namely, traffic noise, natural sound, and community sound. A questionnaire on the evaluation of sound perception was also used, for comparison. The results show that, first, FER is an effective tool for sound perception research, since it is capable of detecting differences in participants' reactions to different sounds and how their facial expressions change over time in response to those sounds, with mean difference of valence between recordings from 0.019 to 0.059 (p < 0.05or p < 0.01). In a natural sound environment, for example, facial expression increased by 0.04 in the first 15 s and then went down steadily at 0.004 every 20 s. Second, the expression indices, namely, happy, sad, and surprised, change significantly under the effect of sound perception. In the traffic sound environment, for example, happy decreased by 0.012, sad increased by 0.032, and surprised decreased by 0.018. Furthermore, social characteristics such as distance from living place to natural environment (r = 0.313), inclination to communicate (r = 0.253), and preference for crowd (r = 0.296) have effects on facial expression. Finally, the comparison of FER and questionnaire survey results showed that in the traffic noise recording, valence in the first 20 s best represents acoustic comfort and eventfulness; for natural sound, valence in the first 40 s best represents pleasantness; and for community sound, valence in the first 20 s of the recording best represents acoustic comfort, subjective loudness, and calmness

Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study

Background Posterior cingulate cortex (PCC) is a key aspect of the default mode network (DMN). Aberrant PCC functional connectivity (FC) is implicated in schizophrenia, but the potential for PCC related changes as biological classifier of schizophrenia has not yet been evaluated. Methods We conducted a data-driven approach using resting-state functional MRI data to explore differences in PCC-based region- and voxel-wise FC patterns, to distinguish between patients with first-episode schizophrenia (FES) and demographically matched healthy controls (HC). Discriminative PCC FCs were selected via false discovery rate estimation. A gradient boosting classifier was trained and validated based on 100 FES vs. 93 HC. Subsequently, classification models were tested in an independent dataset of 87 FES patients and 80 HC using resting-state data acquired on a different MRI scanner. Results Patients with FES had reduced connectivity between PCC and frontal areas, left parahippocampal regions, left anterior cingulate cortex, and right inferior parietal lobule, but hyperconnectivity with left lateral temporal regions. Predictive voxel-wise clusters were similar to region-wise selected brain areas functionally connected with PCC in relation to discriminating FES from HC subject categories. Region-wise analysis of FCs yielded a relatively high predictive level for schizophrenia, with an average accuracy of 72.28% in the independent samples, while selected voxel-wise connectivity yielded an accuracy of 68.72%. Conclusion FES exhibited a pattern of both increased and decreased PCC-based connectivity, but was related to predominant hypoconnectivity between PCC and brain areas associated with DMN, that may be a useful differential feature revealing underpinnings of neuropathophysiology for schizophrenia