RESUMEN
A compact wireless near-field hydrogen gas sensor is proposed, which detects leaking hydrogen near its source to achieve fast responses and high reliability. A semiconductor-type sensing element is implemented in the sensor, which can provide a significant response in 100 ms when stimulated by pure hydrogen. The overall response time is shortened by orders of magnitude compared to conventional sensors according to simulation results, which will be within 200 ms, compared with over 25 s for spatial concentration sensors under the worst conditions. Over 1 year maintenance intervals are enabled by wireless design based on the Bluetooth low energy protocol. The average energy consumption during a single alarm process is 153 µJ/s. The whole sensor is integrated on a 20 × 26 mm circuit board for compact use.
RESUMEN
Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the test data during training. This is unrealistic and impractical in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments conducted on a simulative dataset, BCI competition IV 2a, 2b, and OpenBMI datasets, demonstrate the superiority and competitive performance of our proposed framework over other state-of-the-art methods. Specifically, EEG-DG achieves average classification accuracies of 81.79% and 87.12% on datasets IV-2a and IV-2b, respectively, and 78.37% and 76.94% for inter-session and inter-subject evaluations on dataset OpenBMI, which even outperforms some domain adaptation methods. Our code is available at https://github.com/zxchit2022/EEG-DG for evaluation.
RESUMEN
Abstract The viability of Lactobacillus bulgaricus in freeze-drying is of significant commercial interest to dairy industries. In the study, L.bulgaricus demonstrated a significantly improved (p < 0.05) survival rate during freeze-drying when subjected to a pre-stressed period under the conditions of 2% (w/v) NaCl for 2 h in the late growth phase. The main energy source for the life activity of lactic acid bacteria is related to the glycolytic pathway. To investigate the phenomenon of this stress-related viability improvement in L. bulgaricus, the activities and corresponding genes of key enzymes in glycolysis during 2% NaCl stress were studied. NaCl stress significantly enhanced (p < 0.05) glucose utilization. The activities of glycolytic enzymes (phosphofructokinase, pyruvate kinase, and lactate dehydrogenase) decreased during freeze-drying, and NaCl stress were found to improve activities of these enzymes before and after freeze-drying. However, a transcriptional analysis of the corresponding genes suggested that the effect of NaCl stress on the expression of the pfk2 gene was not obvious. The increased survival of freeze-dried cells of L. bulgaricus under NaCl stress might be due to changes in only the activity or translation level of these enzymes in different environmental conditions but have no relation to their mRNA transcription level.