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For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings’ RUL prediction suffer from low prediction accuracy because of the difficulty in extracting features. With the aim of improving the accuracy of RUL prediction, an approach based on multi-branch improved convolutional network (MBCNN) with global attention mechanism combined with bi-directional long- and short-term memory (BiLSTM) network is proposed for bearings’ RUL prediction. Firstly, the original vibration signal is fast Fourier transformed to obtain the frequency domain signal and then normalized. Secondly, the original signal and the frequency domain signal are input into the designed MBCNN network as two branches to extract the spatial features, and then input into the BiLSTM network to further extract the timing features, and the RUL of bearings is mapped by the fully connected network to achieve the purpose of prediction. Finally, an example validation was performed on a publicly available bearing degradation dataset. Compared with some existing prediction methods, the mean absolute and root mean square errors of the predictions were reduced by “22.2%” to “50.0%” and “26.1%” to “52.8%”, respectively, which proved the effectiveness and feasibility of the proposed method.

In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value.

Based on the transposon-mutagenized library of Pantoea agglomerans BH18, the mutant strain 2-103 was obtained using mutant screens for high simultaneous production of hydrogen and bioflocculant. The mutant strain 2-103 was able to grow in medium with 50 μg/mL of kanamycin and could be amplified using Tn7-based transposon PCR amplification in cells from several passages, indicating that the mutant strain 2-103 transposon insertion was stable. The mutant strain 2-103 cell culture from batch experiments with varying initial pH values, glucose concentrations and NaCl concentrations was determined. The result indicated that the optimum initial pH for hydrogen production and bioflocculant activity was 7.0 and 8.0, respectively. The optimal glucose concentration for hydrogen and bioflocculant production was 20 g/L. In addition, the mutant strain 2-103 could produce hydrogen and bioflocculant under marine and fresh culture conditions. The mutant strain 2-103 had the highest production of hydrogen and bioflocculant with (1.55 ± 0.07) mol H2/mol glucose and (83.7 ± 5.89)% under marine culture conditions, which increased to (1.78 ± 0.08) mol H2/mol glucose and (93.5 ± 6.24)% under fresh culture conditions. This study found that the mutant strain 2-103 has the ability to produce hydrogen and bioflocculant within a range of initial pH values, glucose and NaCl concentrations, which is beneficial for the improvement of cost-effective bio-treatment processes.

In this paper, a Cauchy problem for the Helmholtz equation is investigated. It is well known that this problem is severely ill-posed in the sense that the solution (if it exists) does not depend continuously on the given Cauchy data. To overcome such difficulties, we propose a modified regularization method to approximate the solution of this problem, and then analyze the stability and convergence of the proposed regularization method based on the conditional stability estimates. Finally, we present two numerical examples to illustrate that the proposed regularization method works well.

This study aimed to investigate the characteristics of hydrogen production by mixed cultures using Laminaria japonica hydrolysates. The hydrolysates of L. japonica were prepared by pretreatment methods, including heat (100°C or 121°C) and acid (HCl or H2SO4) pretreatments. The mixed cultures could produce hydrogen using L. japonica as a substrate, with the highest cumulative hydrogen production of 825 ± 14 mL/L from HCl-pretreated L. japonica. High-throughput sequencing of the 16S rRNA gene revealed that the microbial community in the hydrolysate of HCl-pretreated L. japonica was the most diverse among all the samples, with a Shannon diversity index of 5.253. The mixed culture from HCl-pretreated L. japonica and those from heat-pretreated (100°C and 121°C) L. japonica occupied different regions in a principal component analysis (PCA) plot. The dominant population in the hydrolysate of HCl-pretreated L. japonica was represented by hydrogen-producing bacteria, Clostridium spp. and Bacillus spp. The results suggested that L. japonica was an optimal feedstock for hydrogen production. The acid (HCl) pretreatment method could effectively enhance the hydrogen production from L. japonica.

The internal ring matrix distribution of many special ceramic porous medium tubes on a circular plate constitutes an adsorbent device, which can retain nanoparticles aerosol agglomerate by the way of collision, adsorption and diffusion in the process of mass transfer. In this paper, the behavior of industrial flue dust diffusion in the ceramic porous medium device is researched by computational fluid dynamics (CFD) simulation and experiments. Furthermore, resistance characteristic of aerosol flow, nonlinear time-dependent characteristic of flow field and frequency distribution of escaping dust particles are studied. According to the results of simulation and experiment, the lower inlet velocity can keep the lower pressure drop and obtain higher purification efficiency.