Research on the Adhesion Properties of Fast-Melting SBS-Modified Asphalt-Aggregate Based on Surface Free Energy Theory

In order to study the adhesion properties of fast-melting SBS-modified asphalt (SBS-T) at the interface with aggregates, the contact angles of three dosages of SBS-T asphalt with three liquids (distilled water, glycerol, and formamide) were determined by the sessile drop method based on surface free energy theory. The evaluation indexes such as cohesion, asphalt-aggregate adhesion, stripping work and energy ratio of the asphalt were analyzed and the adhesion properties of the asphalt-aggregate system were investigated with the help of micromechanical methods. The results indicate that SBS-T can improve the adhesion properties of the asphalt. Furthermore, as the dosage of the modifier increases, the cohesion work, adhesion work, and energy ratio of the SBS-T asphalt exhibit a similar rise. As the spalling work reduces and the adhesion between asphalt and aggregate improves, it is noteworthy that the SBS-T asphalt-aggregate system exhibits superior adhesion performance compared to the SBS-modified asphalt-aggregate system, despite the same dosage.

Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation

Objective.Sleep stage recognition has essential clinical value for evaluating human physical/mental condition and diagnosing sleep-related diseases. To conduct a five-class (wake, N1, N2, N3 and rapid eye movement) sleep staging task, twenty subjects with recorded six-channel electroencephalography (EEG) signals from the ISRUC-SLEEP dataset is used.Approach.Unlike the exist methods ignoring the channel coupling relationship and non-stationarity characteristics, we developed a brain functional connectivity method to provide a new insight for multi-channel analysis. Furthermore, we investigated three frequency-domain features: two functional connectivity estimations, i.e. synchronization likelihood (SL) and wavelet-based correlation (WC) among four frequency bands, and energy ratio (ER) related to six frequency bands, respectively. Then, the Gaussian support vector machine (SVM) method was used to predict the five sleep stages. The performance of the applied features is evaluated in both subject dependence experiment by ten-fold cross validation and subject independence experiment by leave-one-subject-out cross-validation, respectively.Main results.In subject dependence experiment, the results showed that the fused feature (fusion of SL, WC and ER features) contributes significant gain the performance of SVM classifier, where the mean of classification accuracy can achieve 83.97% ± 1.04%. However, in subject-independence experiment, the individual differences EEG patterns across subjects leads to inferior accuracy. Five typical domain adaptation (DA) methods were applied to reduce the discrepancy of feature distributions by selecting the optimal subspace dimension. Results showed that four DA methods can significantly improve the mean accuracy by 1.89%-5.22% compared to the baseline accuracy 57.44% in leave-one-subject-out cross-validation.Significance.Compared with traditional time-frequency and nonlinear features, brain functional connectivity features can capture the correlation between different brain regions. For the individual EEG response differences, domain adaptation methods can transform features to improve the performance of sleep staging algorithms.

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

Solubilisation of fruits and vegetable dregs through surfactant mediated sonic disintegration: impact on biomethane potential and energy ratio

This study investigates the symbiotic effect of cetyltrimethylammonium bromide (CTAB) coupled with sonication of fruits and vegetable dregs (FVD) on disintegration and subsequent energy efficient methane production. The liquefaction of FVD experiments was conducted by varying dosage of surfactant from 0.001to 0.01 g/g SS for 60 min in mechanical shaker. The optimised dosage of surfactant was combined with sonication. Finally, the combined pretreatment and sole pretreatment were assessed using methane potential assay. The results revealed that at optimised conditions (sonication specific energy of 5400 kJ/kg TS, CTAB dosage of 0.006 g/g SS), the maximum liquefiable organics release rate and solids reduction of CTAB mediated sonic disintegration (CSD) were found respectively to be 27% and 17% more than the ultrasonic disintegration (16% and 10%). CSD was noticed to be superior than ultrasonic disintegration (UD) based on highest volatile fatty acid yield (2000 mg/L vs. 1250 mg/L) and biochemical methane potential (203 mL/g COD vs. 144 mL/g COD). CSD achieved energy ratio of 0.9 which is greater than ultrasonic disintegration energy ratio 0.4.