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The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics: 1) Fairly weak signal strength; 2) Submerged in powerful background noise; 3) Coupling of different fault data. These features not only increase the diagnostic difficulty, but also make the existing methods hardly to get the desired results. Consequently, the early compound faults diagnosis commonly in industrial systems is still a thorny and urgent problem. Therefore, in order to solve this problem and provide technical support for the practical industrial machinery fault diagnosis, a denoising-integrated sparse autoencoder (DISAE) model for early compound faults diagnosis is proposed in this paper. The innovation points of this study mainly include: 1) A feature-enhanced and denoising solution based on fault sensitivity degree (FSD) is designed, and the reconstructed diagnostic signals are acquired. 2) A disassociation framework is formulated, and the data coupling is solved. 3) A weight constraint term of SAE is constructed to improve the effectiveness and diversity of feature learning. 4) An adaptive loss function and a DISAE model is formed, and the early compound faults diagnosis is achieved. Finally, different trials and comparison results display the effectiveness and superiority of the designed DISAE based scheme.

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.

IntroductionProtein corona (PCN) adsorbed on the surface of nanoparticles has brought new research perspectives for the interaction between nanoparticles and microorganisms. In this study, the responses of saccharomyces cerevisiae’ membrane lipid composition, the average length of the fatty acyl chains and the average number of unsaturation of fatty acids to ultrasound combined with nano-Fe3O4@PCN with time-limited proteolysis (nano-Fe3O4@TLP-PCN) was investigated.MethodsLipidomic data was obtained using Ultra-high performance liquid chromatography coupled with a Q-Exactive plus mass spectrometer. The membrane potential, proton motive force assay and the membrane lipid oxidation were measured using Di-BAC4(3), DISC3(5) and C11-BODIPY581/591 as the probes. Combined with the approach of feasible virtual samples generation, the back propagation artificial neural network (BP-ANN) model was adopted to establish the mapping relationship between lipids and membrane properties.ResultsThe time-limited proteolysis targeting wheat PCN-coated Fe3O4 nanoparticles resulted in regular changes of hydrodynamic diameters, ζ-potentials, and surface hydrophobicity. In addition, with the prolongation of PCN proteolysis time, disturbances of 3 S.cerevisiae membrane characteristics, and membrane lipidomic remodeling in response to ultrasound+ nano-Fe3O4@PCN were observed. The analysis of relative importance which followed revealed that ergosterol, phosphatidylserine, and phosphatidylinositol phosphate had the greatest influence on membrane potential. For membrane lipid oxidation, ceramide, phosphatidylethanolamine, and sitosterol ester contribute 16.2, 14.9, and 13.1%, respectively. The relative contributions of six lysolecithins to the dissipation of proton motive force remained limited.DiscussionAn adaptation mechanism of cell membrane to proteolyzed PCN, wherein lipidome remodeling could preserved functional membrane phenotypes was revealed. Furthermore, it is highlighted that the relative importances of SiE, Cer, PE and PIP in determining membrane potential, PMF dissipation and membrane lipid oxidation by establishing FVSG-BP-ANN model.

The rise of digital platforms intensifies the price competition among agents. Agents often use low price strategies to attract consumers. However, the low-price strategy is often filled with false information and consumers perceive the non-truthfulness of the price information. Then, consumers’ trust in agents gradually decreases, which inhibits the growth of online shopping. Blockchain is seen as a solution to the trust crisis between agents and consumers. Our research is based on two competing agents selling the same type of goods on the same platform. We discuss agents’ blockchain technology application strategies in three scenarios, which are defined by whether agents choose to apply blockchain technology to improve consumer trust. The results show that the application of blockchain technology is beneficial to agents only when consumer trust is low. Furthermore, the YN strategy is regarded as a possible equilibrium strategy, which depends on the blockchain application cost and consumer trust. Some extended cases are discussed for post-blockchain consumer welfare, cost-sharing contracts, dishonesty penalties, and variable blockchain costs, and the results show that the analysis in this manuscript is robust. Our findings have important practical significance for promoting the application of blockchain technology and alleviating the problem of price information asymmetry in platform shopping.

Kucha is a rare variety of tea tree that produces the non-stimulatory theacrine instead of caffeine. Here the authors show that theacrine synthase from Kucha has N9-methyltransferase activity resulting from amino acid substitutions that explain substrate specificity and could potentially guide production of caffeine-free tea.

This paper considers a dual control problem for stochastic linear MIMO (Multiple Input Multiple Output) systems with parameter uncertainty. A novel dual adaptive control law for MIMO systems is proposed. The design is based on the innovation of the dual control cost function, which was originally developed for conventional adaptive control of linear systems. However, the design process is modified and developed to cater to the stochastic MIMO case. This is a more challenging problem because the superposition of parameter uncertainty and the MIMO property makes the problem more intractable. As in all dual adaptive strategies, it leads to a control law that balances out the need for caution, due to parameter uncertainty, with the conflicting requirement of probing that acts to quickly reduce parameter uncertainty, which is the nature of dual control. The proposed control law has two parts. One reflects the goal of regulating the output, and the second reflects the ability to handle uncertain parameters. A learning factor is introduced to balance these two parts to obtain a control law that can be applied to the original system. In the simulation examples, the uncertain parameters can be estimated quickly and accurately for the unknown but constant case and the abrupt parameter case. Furthermore, it is shown that the novel dual control law is superior to other control strategies by comparing the performance of the cost function in a statistical sense.

Protein corona (PC) adsorbed on the surface of nanoparticles brings new research perspectives on the interaction between nanoparticles and fermentative microorganisms. Herein, the proteolysis of wheat PC adsorbed on a nano-Se surface using cell-free protease extract from S. cerevisiae was conducted. The proteolysis caused monotonic changes of ζ-potentials and surface hydrophobicity of PC. Notably, the innermost PC layer was difficult to be proteolyzed. Furthermore, when S. cerevisiae was stimulated by ultrasound + 0.1 mg/mL nano-Se@PC, the proportion of lethal and sublethal injured cells increased as a function of the proteolysis time of PC. The transcriptomics analysis revealed that 34 differentially expressed genes which varied monotonically were related to the plasma membrane, fatty acid metabolism, glycerolipid metabolism, etc. Significant declines in the membrane potential and proton motive force disruption of membrane were found with the prolonged proteolysis time; meanwhile, higher membrane permeability, membrane oxidative stress levels, membrane lipid fluidity, and micro-viscosity were triggered.

In this paper, the quadratic optimal control problem is investigated for the discrete-time linear systems with process and measurement noises which belong to specified ellipsoidal sets. As the noises are non-stochastic, the traditional Kalman filtering and Dynamic Bellman Equation are not applicable for the proposed control problem. To obtain the optimal control, we firstly converted the multi-step quadratic global optimal control problem to multiple one-step quadratic local approximate optimal control problems. For each one-step quadratic optimal control problem, considering that the system states are not fully available, the set-membership filtering is applied to estimate the true state feasible set. Then based on robust optimization, a robust state feedback control strategy can be obtained by solving a certain semidefinite programming (SDP) problem. The method can not only achieve the optimal control, but also estimate the system states more accurately. Finally, the simulation results verify the effectiveness of the proposed algorithm.

This work investigated the effects of fermented ginseng leaf (FGL) consumption on metabolites of muscular interstitial fluid, satellite cell-mediated muscle cell repair and gut microbiota in exercise-induced fatigue rats. First nano Fe3O4@glucose oxidase-assisted fermentation significantly increased the ginsenosides contents of Rh2 and Rg3. Furthermore, the levels of hypoxanthine and isoprostane significantly increased in fatigue rats compared with the control. Meanwhile, remarkable decline of bacterial diversity, abundances of Bacteroidaceae, Allobaculum and Akkermansia were found. However, FGL consumption ameliorated these biomarkers. Exhaustive Exercise significantly elevated the protein expression of MyHC-I and MyHC-II, and decreased fiber cross sectional areas. FGL significantly increased protein expression of Pax7 and MyoD1, and decreased gene expression of MyHC-I and MyHC-IIb. The levels of acetic acid, propionic acid, total short chain fatty acids, TNF-α and IL-10 significantly altered in fatigue rats, whilst FGL significantly ameliorate them. These findings suggested potential benefits of FGL for alleviating exercise-induced fatigue.

The space heating system accounts for 20%~50% of building energy consumption, and may lead to energy waste due to unreasonable controls. In this study, an energy-efficient floor heating system with intelligent control was proposed to improve energy efficiency of the system. In order to validate the concept of the proposed intelligent control, an experimental system was designed and constructed in Chengdu, China. Temperature, control cycle and energy consumption were then studied under different control strategies. The result shows that a larger flow rate of supply water will result in a longer control cycle and a lower control frequency, i.e., the average control cycle at 7 L min−1 is 1.7 h during the test day, while it is 1.5 h at 5 L min−1. Moreover, adopting water with a higher temperature and flow rate could achieve a higher efficiency of the system. The energy consumptions in case 1 (5 L min−1, 50 °C), case 2 (5 L min−1, 55 °C), case 5 (7 L min−1, 55 °C) and case 6 (7 L min−1, 60 °C) are 4746 kJ, 3534 kJ, 3093 kJ and 3028 kJ, respectively. Based on the experimental data, the supply water temperature is suggested to set lower than 60 °C considering human comfort.

Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.

In order to acquire a high resolution multispectral (HRMS) image with the same spectral resolution as multispectral (MS) image and the same spatial resolution as panchromatic (PAN) image, pansharpening, a typical and hot image fusion topic, has been well researched. Various pansharpening methods that are based on convolutional neural networks (CNN) with different architectures have been introduced by prior works. However, different scale information of the source images is not considered by these methods, which may lead to the loss of high-frequency details in the fused image. This paper proposes a pansharpening method of MS images via multi-scale deep residual network (MSDRN). The proposed method constructs a multi-level network to make better use of the scale information of the source images. Moreover, residual learning is introduced into the network to further improve the ability of feature extraction and simplify the learning process. A series of experiments are conducted on the QuickBird and GeoEye-1 datasets. Experimental results demonstrate that the MSDRN achieves a superior or competitive fusion performance to the state-of-the-art methods in both visual evaluation and quantitative evaluation.

As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility.

Abstract Objective To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules. Materials and methods This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (ModelAP, ModelVP and ModelCombination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort. Results A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of ModelAP, ModelVP and ModelCombination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682–0.948), 0.7485 (95% CI 0.602–0.895), and 0.8772 (95% CI 0.780–0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between ModelAP and ModelCombination (P = 0.0396) and between ModelVP and ModelCombination (P = 0.0465). However, the difference in AUCs between ModelAP and ModelVP was not significant (P = 0.5061). These results demonstrate that ModelCombination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model. Conclusions We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.