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This paper determines the optimal surfactant concentration for enhancing coal's wettability and explores the wetting mechanism at surfactant concentrations above the critical micelle concentration (CMC) during coal seam water injection. In this study, laboratory experiments and field tests were used to investigate the influence of monomeric surfactants and compound surfactants at various concentrations on coal's wettability. The results showed that when the surfactant solution concentration was greater than the CMC, the coal's wettability was significantly enhanced as the surfactant concentration increased. However, the coal's wettability did not monotonically increase with the concentration, and the maximum value was reached in the range of 0.5-3 wt.%. Increasing the surfactant adsorption density and changing the adsorption state on the coal surface were the essential reasons surfactants continued improving the coal's wettability at concentrations above the CMC. The Marangoni flow effect and changes in the viscosity of the surfactant solution with concentration were also important factors that affected the coal's wettability.

Experimental studies in behavioral finance historically have confirmed that subjects are highly influenced by reference points when making economic decisions. A recent study by Baucells, Weber and Welfens (2011) analyzed the dynamics of reference price formation using experimental methods with subjects forming reference prices for stocks based on observed price sequences. Their study helps to clarify behavioral effects of past prices on reference price formation but it does not consider contextual information. We extend the BWW study by adding market price information as background to the experiment, and we investigate how this additional information affects reference point formation and updating. Our overarching hypothesis is that if the market background information has no impact then it should not alter the results of BWW; otherwise, the additional market information should be an explanatory variable for the reference point. Our results confirm the hypothesis that market information matters. We also investigate the impact of pessimism and optimism on reference prices by the combining the BWW framework with a model of disappointment aversion and anticipatory feelings developed by Gollier and Muerman (2010). Our study fills a void in the literature by providing new evidence on the impact of contextual market information on reference point formation in an investment setting.

Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems.

An in situ analysis for DNA content detection without DNA separation in samples, which circumvents the influence of the chromophore, fluorophore and other interferents in vivo, based on long-persistent luminescence of organic doped-crystals.

Wind turbine blades are critical components in wind energy generation, and blade health management is a challenging issue for the operation and maintenance of wind turbines. In this paper, an adaptive method is developed to identify blade damages based on the microphone array and compressive beamforming, and global and remote health assessment can be accomplished. In this method, the generalized minimax-concave penalty function is employed to enhance sparse recovery capacities, and step-sizes in computation processes are adjusted adaptively to adapt to variational conditions. Besides, potential damage locations are extracted in coarse acoustic maps to improve convergence rates. Numerical simulations show that high spatial resolutions can be achieved by the proposed method, and the computation time for solving acoustic inverse problems is less than using existing algorithms, especially with low-frequency sources. Moreover, experiments are conducted with a small-scale wind turbine. Results demonstrate that several damages in operating blades can be precisely recognized with high efficiencies, and the deterioration of acoustic maps induced by improper step-sizes can be avoided. The proposed method provides a promising way for in-situ health monitoring of wind turbine blades.

Background Lack of clinically specific biomarkers has impeded the precise diagnosis of schizophrenia, meanwhile, limited comprehending of pathogenesis for schizophrenia has restricted the effective treatment. Method An integrated multi-omic approach, combining metabolomic platform (LC-MS and 1H-NMR) and transcriptomic platform, was established to differentiate healthy subjects from schizophrenia patients. Based on filtered metabolites and genes, characteristic spectrums were further built. Then, representative metabolites and genes were screened out through Boruta algorithm. Moreover, characteristic diagnostic formulas were established via LASSO regression analysis. Result As a result, 86 differential metabolites (in line with amino acid metabolism, etc.) and 189 differential expression genes (involving in amino acid metabolic process, etc.) were obtained as potential biomarkers for schizophrenia. The latent interaction between metabolites with genes, such as HMGCLL1 with energy metabolism, etc., was further studied through the analysis of pathway-based integration. Moreover, fine predictive ability was attributed to characteristic metabolomic/transcriptomic diagnostic spectrums/formulas. Conclusion The functional relationships of filtered metabolites and genes were studied, which could elaborate the pathological process of schizophrenia more systemically, supplying more precise information on mechanism description and diagnostic evidence of schizophrenia.

Condition monitoring of assets is significant to the efficiency and reliability of industrial automation systems. However, the accuracy of condition monitoring results is easily impaired by variational environments and volatile operations, especially for complex automation systems. In this article, an environmentally adaptive and contrastive representation learning method is proposed to address the problem. To suppress the unexpected effects of environmental variations on operating data, a regression model between the operational and environmental variables is developed. The variable regression adjustment is achieved by solving a penalized optimization problem based on spline functions, and the solution is explicitly derived. Then, negative samples and pseudo labels are generated based on the designed pattern of data augmentation, and valid data representations for asset condition monitoring can be obtained by contrastive learning. Moreover, the reference statue of healthy assets is established by kernel density estimation, and control charts are employed for online monitoring with alarm thresholds. Taking wind turbine blades as examples, the remarkable performance of the developed method is demonstrated with real-world measurements from wind farms. Furthermore, comparative analysis with benchmark approaches and ablation study are conducted to reveal the superiority and effectiveness of the proposed method.

Green credit is a vital instrument for promoting low-carbon transition. However, designing a reasonable development pattern and efficiently allocating limited resources has become a challenge for developing countries. The Yellow River Basin, a critical component of the low-carbon transition in China, is still in the early stages of green credit development. Most cities in this region lack green credit development plans that suit their economic conditions. This study examined the impact of green credit on carbon emission intensity and utilized a k-means clustering algorithm to categorize the green credit development patterns of 98 prefecture-level cities in the Yellow River Basin based on four static indicators and four dynamic indicators. Regression results based on city-level panel data from 2006 to 2020 demonstrated that the development of green credit in the Yellow River Basin can effectively reduce local carbon emission intensity and promote low-carbon transition. We classified the development patterns of green credit in the Yellow River Basin into five types: mechanism construction, product innovation, consumer business expansion, rapid growth, and stable growth. Moreover, we have put forward specific policy suggestions for cities with different development patterns. The design process of this green credit development patterns is characterized by its ability to achieve meaningful outcomes while relying on fewer numbers of indicators. Furthermore, this approach boasts a significant degree of explanatory power, which may assist policy makers in comprehending the underlying mechanisms of regional low-carbon governance. Our findings provide a new perspective for the study of sustainable finance.

Tuning the HOMO and LUMO energy strategically of triphenylamine derivatives plays an important role towards understanding and design of high performance organic photovoltaics. To improve on the charge separation and electron transfer properties, twenty one D-pi-A organic dyes with nitrostyryl triphenylamine-based structures (NTPAs) were designed and computationally studied, in which triphenylamine, carbazole, or indoline structures were used as donor and NO2 group as acceptor. The LUMOs of these dyes are more localized at the nitrostyryl moiety with almost the same energy and the HOMOs, which are orthogonal to the LUMOs, can be conveniently tuned by changing the donor structure and using different functional groups substituted on the donor. Compared with triphenylamine molecules, carbazole molecules decrease the HOMO energy of NTPA, whereas cis N-phenylindoline structure increase the HOMO energy. In addition, the exciton sizes generated by these dyes are also tuned in a range of 7.0-12.7 A. These results show that introduction of dimethylamino group on the triphenylamine moiety greatly increases the HOMO energy and prolongs the maximum absorption wavelength as well as increases the exciton size, indicating that NTPA dyes containing strong electron donating dimethylamino group on the donor part are promising candidates as sensitizers in the dye-sensitized solar cell applications. The results of the structural and spectral properties show good correlation with the experimental results, which confirms that the DFT method employed in this work is an effective way to design organic small molecule based sensitizers.

In order to prevent the slow degradation of pristine water ecosystems caused by increasing SO42− in natural water bodies (lakes and rivers), innovative and cost-effective remediation techniques are urgently required.

In this paper, a novel time–frequency (TF) analysis method, called the short-time Fourier transform using odd symmetric window function (OSTFT), is proposed using odd symmetric window function to replace the conventional even window function of STFT. Different from conventional STFT acquiring the amplitude maximum at time and frequency centers, OSTFT acquires the minimum amplitude of 0. Hence, OSTFT can obtain a TF representation (TFR) with high TF resolution by utilizing the leaked energy rather than restraining it. It is worth to mention that the proposed OSTFT can vitiate the effect of window size we choose on the TFR obtained. Furthermore, it also has a good performance on signals with complex instantaneous frequencies (IFs), even crossing IFs. Because we just replace the conventional window function of STFT, the time-consuming of the proposed OSTFT is at the same level as the conventional STFT. The effectiveness of proposed OSTFT has been validated on two complex multi-component simulated numerical signals and a signal collected from the brown bat.

The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

In this study, a novel time–frequency (TF) analysis method, referred to as the scaling-basis chirplet transform (SBCT), is developed by extending the conventional chirplet transform. This method includes a replacement kernel function that can vary the chirp rate with frequency and time by scaling the TF basis at and around the corresponding time center. This enables the corresponding chirplets to accurately match the targeted slopes for every trajectory of a multicomponent signal and within any window length. Therefore, the TF representation obtained via the SBCT can achieve significantly higher energy concentrations even for multicomponent signals with close-spaced frequencies and high levels of background noise. The effectiveness of the proposed SBCT approach was demonstrated by analyzing a numerical multicomponent signal and a vibration signal obtained from a gearbox test rig. Both numerical and experimental results showed that the SBCT can satisfactorily handle multicomponent signals with nonlinear frequency trajectories, close-spaced frequencies, and noisy backgrounds, demonstrating its superiority.

Fault diagnosis techniques are vital to the condition-based maintenance strategy of wind turbines, which enables the reliable and economical operation and maintenance for wind farms. Due to the complex kinematic mechanism and modulation characteristic, planet bearing is the most challenging component for fault diagnosis in wind turbine drivetrains. To address this challenge for planet bearing fault diagnosis, we propose an enhanced sparse representation-based intelligent recognition (ESRIR) method, which involves two stages of structured dictionary designs and intelligent fault recognition. In the first stage, the structured dictionary designs are achieved with the overlapping segmentation strategy, which exploits the strong periodic self-similarity and shift-invariance property in planet-bearing vibration signals to enhance the representation and discrimination power of ESRIR. In the second stage, the intelligent fault recognition of planet bearings is implemented with the sparsity-based diagnosis strategy utilizing the minimum sparse reconstruction error-based discrimination criterion. Finally, the applicability of ESRIR for planet bearing fault diagnosis has been validated with the wind turbine planetary drivetrain test rig, demonstrating that ESRIR yields the superior recognition accuracy of 100% and 99.9% for diagnosing three and four planet-bearing health states, respectively. Comparative studies show that ESRIR outperforms the deep convolution neural network and four classical sparse representation-based classification methods on the recognition performances and computation costs.