Green and chemical-free pretreatment of corn straw using cold isostatic pressure for methane production.

In this study, the effect of cold isostatic pressure (CIP) pretreatment on the physicochemical properties and subsequent anaerobic digestion (AD) performance of corn straw (CS) was explored. The CS was subjected to CIP pretreatment by pressures of 200, 400 and 600 MPa, respectively, while AD was carried out at medium temperature (35 ± 2 °C). The results showed that CIP pretreatment disrupted the dense structure of the CS and altered the crystallinity index and surface hydrophobicity of the CS, thereby affecting the AD process. The presence of CIP pretreatment increased the initial reducing sugar concentration by 0.11-0.27 g/L and increased the maximum volatile fatty acids content by 112.82-436.64 mg/L, which facilitated the process of acidification and hydrolysis of the AD. It was also observed that the CIP pretreatment maintained the pH in the range of 6.37-7.30, maintaining the stability of the overall system. Moreover, the cumulative methane production in the CIP pretreatment group increased by 27.17 %-64.90 % compared to the control group. Analysis of the microbial results showed that CIP pretreatment increased the abundance of cellulose degrading bacteria Ruminofilibacter from 21.50 % to 27.53 % and acetoclastic methanogen Methanosaeta from 45.48 % to 56.92 %, thus facilitating the hydrolysis and methanogenic stages. The energy conversion analysis showed that CIP is a green and non-polluting pretreatment strategy for the efficient AD of CS to methane.

The effect of heat pre-treatment on the anaerobic digestion of high-solid pig manure under high organic loading level.

Since more and more large-scale farms appear in China and changes in fecal sewage source disposal, the production of high-concentration solid manure waste is also increasing, and its conversion and utilization are gaining attention. This study investigated the effect of heat pre-treatment (HPT) on the thermophilic anaerobic digestion (AD) of high-solid manure (HSM). Pig manure (PM) feed with a total solids of 13% was used for the HPT and subsequent anaerobic digestion (AD) test. The HPT was carried out at 60°C, 80°C, and 100°C, respectively, for 15 min after the heating reached the set temperature. The results show that HPT led to PM feed COD solubilization, observing a maximum increase of 24.57% after pretreated at 100°C, and the treated PM feed under this condition received the maximum methane production potential of 264.64 mL·g-1 VS in batch AD test, which was 28.76% higher than that of the untreated group. Another semi-continuous AD test explored the maximum volume biogas production rate (VBPR). It involves two organic loading rates (OLR) of 13.4 and 17.8 g VSadded·L-1·d-1. The continuous test exhibited that all the HPT groups could produce biogas normally when the OLR increased to the high level, while the digester fed with untreated PM showed failure. The maximum VBPR of 4.71 L L-1·d-1 was observed from PM feed after pre-treated at 100°C and running at the high OLR. This reveals that thermal treatment can weaken the impact of a larger volume of feed on the AD system. Energy balance analysis demonstrates that it is necessary to use a heat exchanger to reuse energy in the HPT process to reduce the amount of energy input. In this case, the energy input to energy output (E i /E o ) ranged from 0.34 to 0.55, which was much less than one, suggesting that biogas increment due to heat treatment can reasonably cover the energy consumption of the pre-treatment itself. Thus combining HPT and high-load anaerobic digestion of PM was suitable.

Highly efficient adsorptive extraction of uranium from wastewater by novel kaolin aerogel.

An environment-friendly, low-cost and efficient kaolin aerogel adsorbent (named as KLA) was synthesized via a freeze-drying-calcination method to solve the defect of low uranium removal rate for kaolin (KL). The removal rate of uranium on KLA reached 90.6 %, which was much higher than that of KL (69.2 %) (C0 = 10 mg L-1, t = 24 h, pH = 5.0, T = 298 K and m/V = 1.0 g L-1). The uranium removal behavior on KLA was satisfied with Pseudo-second-order and Langmuir model, which meant that the uranium ions were immobilized on the surface of KLA via chemical reaction. Meanwhile, high temperature was in favor of the removal of uranium on KLA, indicating that the removal process was a spontaneous endothermic reaction. Compared with KL, KLA also presented better cycle ability and its removal rate of uranium was up to 80.5 % after three cycles, which was still higher than that of KL at the first cycle (74.5 %). On basis of the results of SEM, XRD, FT-IR and XPS, it could be concluded that uranium ions were adsorbed by KLA via complexation. Hence, KLA could be regarded as a feasible candidate for the removal of uranium from aqueous solution.

Explorations on efficient extraction of uranium with porous coal fly ash aerogels.

In order to explore a suitable uranium adsorbent with the advantages of low-cost, recyclability and high efficiency, porous coal fly ash aerogels with different size of coal fly ash were synthesized. Among them, PCFAA-1250 (prepared with 1250 mesh coal fly ash (CFA)) showed better adsorption performance and the maximum adsorption efficiency even approached 96.5% (C0 = 10 mg L-1, m/V = 1.0 g L-1, T = 298 K, t = 24 h and pH = 3.0), which was higher than most of previous adsorbents. Langmuir and pseudo-second-order models were more likely to be used to determine the removal behavior of uranium on PCFAA, illustrating that the adsorption reaction was uniform chemisorption. Meanwhile, the adsorption process on PCFAA was spontaneous. Notably, the desorption efficiencies of all of PCFAA were more than 80% after five cycles, which suggested that PCFAA possessed good recyclability, especially PCFAA-1250. Besides, the adsorption mechanism was further revealed via XPS and the uranium ions were immobilized on the surface of adsorbents through complexation. Based on above conclusions, it could be concluded that PCFAA-1250 had the potential to be a candidate for the extraction of uranium from wastewater.

Highly enhanced adsorption performance to uranium(VI) by facile synthesized hydroxyapatite aerogel.

In order to protect environment and save uranium resources, it was necessary to find a highly efficient adsorbent for uranium recovery from wastewater. In this work, we used a freeze-drying-calcination method to synthesize HAP aerogel to effectively remove uranium. Compared with commercially available nano-hydroxyapatite, HAP aerogel presented better adsorption performance. This was because the as-prepared HAP aerogel presented continuous porous structure, which could provide more active sites for the adsorption to uranium. The uranium removal efficiency of HAP aerogel arrived 99.4% within 10 min and the maximum adsorption capacity was up to 2087.6 mg g-1 at pH = 4.0 and 298 K. In addition, the immobilization of uranium on HAP aerogel was chemisorption, which was probably due to adsorption, dissolution-precipitation and ions exchange. These results indicated that the as-prepared HAP aerogel could be widely used as a high efficiency and potential adsorbent for the treatment of uranium-containing wastewater in the future.

Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables.

Objective.To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database.Approach.In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the 'Emory Twin Study Follow-up' (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. The relative performance of PPG, and actigraphy (Act), plus combinations of these two signals, with and without transfer learning was assessed.Main results.The performance of our model with transfer learning showed higher accuracy (1-9 percentage points) and Cohen's Kappa (0.01-0.13) than those without transfer learning for every classification category. Statistically significant, though relatively small, incremental differences in accuracy occurred for every classification category as tested with the McNemar test. The out-of-sample classification performance using features from PPG and actigraphy for four-class classification was Accuracy (Acc) = 68.62% and Kappa = 0.44. For two-class classification, the performance was Acc = 81.49% and Kappa = 0.58.Significance.We proposed a combined PPG and actigraphy-based sleep stage classification approach using transfer learning from a large ECG sleep database. Results demonstrate that the transfer learning approach improves estimates of sleep state. The use of automated beat detectors and quality metrics means human over-reading is not required, and the approach can be scaled for large cross-sectional or longitudinal studies using wrist-worn devices for sleep staging.

Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network.

OBJECTIVE: Ventricular contractions in healthy individuals normally follow the contractions of atria to facilitate more efficient pump action and cardiac output. With a ventricular ectopic beat (VEB), volume within the ventricles are pumped to the body's vessels before receiving blood from atria, thus causing inefficient blood circulation. VEBs tend to cause perturbations in the instantaneous heart rate time series, making the analysis of heart rate variability inappropriate around such events, or requiring special treatment (such as signal averaging). Moreover, VEB frequency can be indicative of life-threatening problems. However, VEBs can often mimic artifacts both in morphology and timing. Identification of VEBs is therefore an important unsolved problem. The aim of this study is to introduce a method of wavelet transform in combination with deep learning network for the classification of VEBs. APPROACH: We proposed a method to automatically discriminate VEB beats from other beats and artifacts with the use of wavelet transform of the electrocardiogram (ECG) and a convolutional neural network (CNN). Three types of wavelets (Morlet wavelet, Paul wavelet and Gaussian derivative) were used to transform segments of single-channel (1D) ECG waveforms to two-dimensional (2D) time-frequency 'images'. The 2D time-frequency images were then passed into a CNN to optimize the convolutional filters and classification. Ten-fold cross validation was used to evaluate the approach on the MIT-BIH arrhythmia database (MIT-BIH). The American Heart Association (AHA) database was then used as an independent dataset to evaluate the trained network. MAIN RESULTS: Ten-fold cross validation results on MIT-BIH showed that the proposed algorithm with Paul wavelet achieved an overall F1 score of 84.94% and accuracy of 97.96% on out of sample validation. Independent test on AHA resulted in an F1 score of 84.96% and accuracy of 97.36%. SIGNIFICANCE: The trained network possessed exceptional transferability across databases and generalization to unseen data.

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.

OBJECTIVE: This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN). APPROACH: An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in 5 min windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm. MAIN RESULTS: Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of [Formula: see text] = 0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc = 81.6% and [Formula: see text] = 0.63 for the classification of Wake, REM sleep and NREM sleep and Acc = 85.1% and [Formula: see text] = 0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB. SIGNIFICANCE: The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over ten times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis.