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The formation process of gallic acid functionalized graphene hydrogel (GA-GH).

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Feature extraction is an essential part of automatic speech recognition (ASR) to compress raw speech data and enhance features, where conventional implementation methods based on the digital domain have encountered energy consumption and processing speed bottlenecks. Thus, we propose a Mixed-Signal Processing (MSP) architecture to efficiently extract Mel-Frequency Cepstrum Coefficients (MFCC) features. We design MSP-MFCC to pre-process speech signals in the analog domain, which significantly reduces the cost of the analog-to-digital converter (ADC), as well as the computational complexity of the digital backend. Moreover, MSP-MFCC eliminates the time-consuming Fourier transform in the conventional digital realization by improving processing flow. We fabricated the analog part based on 180nm CMOS mixedsignal technology, then measured the chip. The measured results show the energy consumption of MSP-MFCC is 0.72 µJ/frame, and the processing speed is up to 45.79 µs/frame. MSP-MFCC achieves 95% energy saving and about 6.4× speedup than state of the art. Further, by using the features extracted by MSP-MFCC, speech recognition simulation reaches the accuracy of 98.2%, which also keeps the leading performance to its current counterparts. The proposed MFCC extractor is competitive for integration in the ultra-low-power always-on wearable speech recognition applications. INDEX TERMS Mixed signal processing architecture, energy-efficient feature extraction, mel-frequency cepstrum coefficients (MFCC), wearable speech recognition application.

The two Mn2Ln2 complexes can catalyze water oxidation, which may arise from the cooperative catalytic effect of the Mn/Ln ions.

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Purpose The relationship between psychopathic traits and moral judgements has evoked passionate debates among researchers. Psychopathic traits have been characterized as risk factors for immoral behaviours in both non‐forensic and forensic populations; however, whether individuals with elevated psychopathic traits display atypical moral judgements has been controversial. Here, we aim to examine how psychopathic traits are related to moral judgements in five moral foundations (Care, Fairness, Loyalty, Authority, and Sanctity) and further explore how unpleasantness mediates the relationship in non‐forensic and forensic samples. Methods Two hundred and twenty five college students and 219 detainees were recruited in two separate surveys. All the participants were asked to complete the moral judgement task in everyday moral scenarios, the unpleasantness ratings for the immoral behaviours and the Levenson Self‐Report Psychopathy Scale (LSRP). Results Psychopathic traits predicted the binary moral distinction (moral vs. immoral category) in the Care foundation in the non‐forensic sample. Moreover, psychopathic traits predicted moral acceptability ratings (continuous category) in all of the moral foundations in the non‐forensic sample but only for the Care and Loyalty foundations in the forensic sample. Finally, unpleasantness fully mediated the relationship between psychopathic traits and moral judgements in both samples. Conclusions Our findings provide further evidence that individuals with elevated psychopathic traits have atypical moral judgements – emphasizing the role of unpleasantness in contributing to this phenomenon. Our study has implications for understanding and treating various deviant behaviours in psychopathic individuals.