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Highlights Ordered intermetallic pallium-zinc (PdZn) electrocatalyst comprising a high density of PdZn pairs is synthesized for achieving co-adsorption and co-activation carbon dioxide and nitrate. Both operando measurements and theoretical calculations reveal that the PdZn pairs provide a dual-site geometric structure conducive to the key C–N coupling with a low kinetical barrier. The intermetallic PdZn displays excellent performance for the co-reduction of nitrate (NO3 –) and carbon dioxide (CO2) toward urea, with a maximum FEurea of 62.78% at a small potential of – 0.4 V versus RHE.

Abstract The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better.

Abstract Running rails are the return conductors of the traction current in DC subway systems, which should be insulated from the earth. Due to the large length of the line, as well as the humidity and metal dust in the tunnel, grounding fault of the running rails usually occurs, which will increase stray current (SC) leakage and endanger power supply safety. In this paper, a method of grounding fault diagnosis of running rails based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) is proposed. Firstly, a platform for the dynamic distribution of SC and rail potential (RP) with grounding faults existing in the running rails is established, which generates the dynamic RP data with various grounding faults. Secondly, a grounding fault diagnostic method of running rails based on MS-1DCNN is proposed, so as to realize the effective identification of ground fault types in subway systems. Thirdly, with the proposed diagnostic method, the datasets under two operating conditions of a single train and two trains are tested; a comparison test between MS-1DCNN and the 1D convolutional neural network (1DCNN) is carried out, and the effectiveness of the proposed method is verified. Results demonstrate that the proposed model can significantly improve the ground fault diagnostic accuracy of running rails. The dynamic RP simulation platform for trains established in this paper lays a theoretical foundation for the grounding fault research of running rail. Moreover, the deep learning method is proposed for the first time to diagnose the grounding fault of running rail, and the high diagnostic accuracy is obtained, which is of great significance for the safe and stable operation of the subway line.

Abstract Large birefringence is a crucial but hard‐to‐achieve optical parameter that is a necessity for birefringent crystals in practical applications involving modulation of the polarization of light in modern opto‐electronic areas. Herein, an oxyanion polymerization strategy that involves the combination of two different types of second‐order Jahn–Teller distorted units is employed to realize giant anisotropy in a covalent molybdenum tellurite. Mo(H2O)Te2O7 (MTO) exhibits a record birefringence value for an inorganic UV‐transparent oxide crystalline material of 0.528 @ 546 nm, which is also significantly larger than those of all commercial birefringent crystals. MTO has a UV absorption edge of 366 nm and displays a strong powder second‐harmonic generation response of 5.4 times that of KH2PO4. The dominant roles of the condensed polytellurite oxyanions [Te8O20]8− in combination with the [MoO6]6− polyhedra in achieving the giant birefringence in MTO are clarified by structural analysis and first‐principles calculations. The results suggest that polymerization of polarizability‐anisotropic oxyanions may unlock the promise of birefringent crystals with exceptional birefringence.

Abstract Currently, in multitrain DC subway system, abnormal increase of rail potential (RP) and stray current (SC) has seriously threatened the safe operation of the system. Over voltage protection device (OVPD) is generally chosen to control the RP, but its action process may increase the amplitude of SC seriously. Here, the grounding resistance of OVPD is optimized by a proposed multi‐objective decomposition algorithm based on differential evolution (MOEA/D‐DE) to suppress the rise of RP and SC synergistically. Firstly, the simulation model of the DC subway system with multitrain is built, the power flow calculation is conducted, and the dynamic RP and leakage current (LC) at the location of traction substation are obtained. Secondly, the double objective optimization model of maximum RP and LC is established and solved by MOEA/D‐DE. Finally, the effectiveness of the proposed method is verified based on the data of Guangzhou Metro Line 2. Results show that the SC of the system can be controlled effectively with OVPD grounding mode after optimization, and integrated control of RP and SC can be accomplished.

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.

Due to the advantage of high spatial coverage, using satellite-retrieved aerosol optical depth (AOD) data to estimate PM2.5 and PM10 mass concentrations is a current research priority. Statistical models are the common method of PM estimation currently, which do not require the knowledge of complex chemical and physical interactions. However, the statistical models rely on station data, which results in less accurate PM estimation concentrations in areas where station data are missing. Hence, a new hybrid model, with low dependency on on-site data, was proposed for PM2.5 and PM10 mass concentration estimation. The Gaofen-1 satellite and MODIS data were employed to estimate PM2.5 and PM10 concentrations with 100 m spatial resolution in Beijing, China. Then, the estimated PM2.5/10 mass concentration data in 2020 were employed to conduct a spatio-temporal analysis for the investigation of the particulate matter characteristic in Beijing. The estimation result of PM2.5 was validated by the ground stations with R2 ranging from 0.91 to 0.98 and the root mean square error (RMSE) ranging from 4.51 μg/m3 to 17.04 μg/m3, and that for PM10 was validated by the ground stations with R2 ranging from 0.85 to 0.98 and the RMSE ranging from 6.98 µg/m3 to 29.00 µg/m3. The results showed that the hybrid model has a good performance in PM2.5/10 estimation and can improve the coverage of the results without sacrificing the effectiveness of the model, providing more detailed spatial information for urban-scale studies.

Images captured by deep space probes exhibit large-scale variations, irregular overlap, and remarkable differences in field of view. These issues present considerable challenges for the registration of multi-view asteroid sensor images. To obtain accurate, dense, and reliable matching results of homonymous points in asteroid images, this paper proposes a new scale-invariant feature matching and displacement scalar field-guided optical-flow-tracking method. The method initially uses scale-invariant feature matching to obtain the geometric correspondence between two images. Subsequently, scalar fields of coordinate differences in the x and y directions are constructed based on this correspondence. Next, interim images are generated using the scalar field grid. Finally, optical-flow tracking is performed based on these interim images. Additionally, to ensure the reliability of the matching results, this paper introduces three methods for eliminating mismatched points: bidirectional optical-flow tracking, vector field consensus, and epipolar geometry constraints. Experimental results demonstrate that the proposed method achieves a 98% matching correctness rate and a root mean square error of 0.25 pixels. By combining the advantages of feature matching and optical-flow field methods, this approach achieves image homonymous point matching results with precision and density. The matching method exhibits robustness and strong applicability for asteroid images with cross-scale, large displacement, and large rotation angles.

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a “Time-Longitude-Latitude” three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities.

Abstract Variational mode decomposition (VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold (Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment (LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method. Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD (RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor (Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.

Aerosol Optical Depth (AOD), quantifying the amount of aerosol in the atmosphere, is widely regarded as a crucial indicator for research on atmospheric physics and regional air quality. At present, the inversion of AOD from observation of satellite remote sensing sensors has become the main technology for large-scale monitoring of aerosol load. The Huaihai Economic Zone is the connecting belt of two key areas of atmospheric governance (the Yangtze River Delta and the Beijing-Tianjin-Hebei region, China), and it has been suffering from air pollution for many years and few studies of AOD focus on this region. Therefore, the spatial and temporal characteristics of the AOD are explored using MODIS AOD data and AVHRR AOD data in this region during the period from 1982 to 2021 in this study. The correlation coefficients between the AOD of satellite observation and actual air pollution were analyzed by combining PM2.5 pollutant concentration and air quality index (AQI) data. The results showed that the AOD is higher in the northwest than in the southeast, and it is different from season to season. The annual variation of AOD in the Huaihai Economic Zone is a W-shaped trend from 1982 to 2011, while the trend of annual AOD is decreasing after 2011. In terms of seasons, the whole differences in AOD are evident, exhibiting AOD values in summer > those in spring > those in autumn > and those in winter. Furthermore, it indicated that the quarterly and monthly variation of the AOD tends to be flat in recent years. Since 2015, the concentration of PM2.5 has continued to decline, the same as that of AQI. Meanwhile, the quarterly and monthly differences in PM2.5 are still obvious, with higher PM2.5 in winter and lower PM2.5 in summer. However, it also represented that PM2.5 is significantly higher in spring than in autumn from 2015 to 2018, which is the opposite for 2019 to 2021. Lastly, the correlation between AOD and PM2.5/AQI is also given; i.e., the correlation coefficients of AOD with PM2.5/AQI are 0.84/0.82, with the highest correlation coefficient in autumn (R = 0.86/0.91) and the lowest in winter (R = 0.46/0.48).

Bimetallic materials are one of the most promising catalysts for the electrochemical reduction of CO2, but there are still many challenges to be overcome on the route to industrialization. Herein, a series of carbon nanofiber-supported bimetallic cobalt–copper catalysts (CoxCuy/CFs) are designed and constructed through the electrospinning technique and a subsequent pyrolysis procedure. Small-sized Co–Cu nanoparticles are homogenously distributed on the porous carbon nanofibers, which can significantly improve the utilization rate of metal sites and greatly reduce the loading amount of metals. Moreover, different product distributions and catalytic performance can be obtained in CO2 reduction via adjusting the metal proportion of CoxCuy/CFs. Especially, Co3Cu/CFs can bring forth a 97% total faradaic efficiency (FE) of CO (68%) and HCOOH (29%) at –0.8 VRHE cathode potential in 0.5 M KHCO3 electrolyte. Furthermore, the hierarchical pores can firmly confine the small Co–Cu nanoparticles and keep them from easy agglomeration during electrolysis, eventually leading to 60 h of stability for Co3Cu/CFs in CO2 electroreduction. This study might provide a facile and economic method to fabricate efficient bimetallic catalysts for CO2 electroreduction and other electrocatalysis applications.

Anomalous mechanical behaviors provide an opportunity to regulate the functions of materials. Here the authors show that materials with coexisting negative, zero and positive compressibilities can “squeeze” volume compressibility into one direction, and thus stabilize transmission processes under pressure.

Rotating machinery has been developed with high complexity and precision, and bearings and gears are crucial components in the machinery system. Deep learning has attracted considerable attention from researchers in this area. The convolutional neural network (CNN) is a typical deep learning model that has a strong capability for automatically extracting features from raw data. This capability minimizes dependence on expert knowledge during feature extraction and selection. In CNN, hyperparameters, such as activation functions, can directly influence the performance of the model. In this study, the improved rectified linear units (ReLU)-CNNs are proposed for machinery fault diagnosis. The model's input are raw vibration signals without feature extraction and selection. It is experimentally validated for fault diagnosis using bearing and gearbox datasets. Results show that the proposed method can obtain satisfactory accuracy with enhanced convergence speed. For both datasets, the proposed method gives better diagnosis accrues than the other compared models. The proposed model can take advantages of standard ReLU-CNN, and these advantages can overcome traditional activation functions' vanishing gradient problems. Meanwhile, the improved ReLU-CNN has a new property that makes it perform better than the standard ReLU-CNN.

In this paper, we have experimentally demonstrated a secure 100 Gb/s intensity-modulation and direct-detection transmission over 100 km standard single-mode fiber (SSMF) based on quantum noise stream cipher (QNSC) for the first time. The original 4-level pulse-amplitude modulation (PAM4) data is mapped to M-level data sequence randomly, and quantum noise such as the generated amplified spontaneous emission noise and the shot noise in the channel can mask these adjacent signal levels. The legitimate receiver needs to distinguish not M-level encrypted data but 4-level data with the shared key. In our scheme, a calculated detection failure probability of 98.72% is achieved. Apparently, more quantum noise contributes to higher security but the worse signal to noise ratio. To balance algorithm complexity, transmission performance and security performance, a sparse ℓ1 regularization based on recursive least square (RLS) algorithm is proposed and firstly used in Volterra equalizer. To eliminate the power fading induced by fiber dispersion, a dual-drive Mach-Zehnder modulator is used to achieve single-sideband modulation, and no dispersion compensation is required. By these means, 100 Gb/s PAM4-QNSC signal transmission over 100 km SSMF with the bit error rate (BER) below the 7% overhead hard-decision forward error correction threshold of 3.8 x 10-3 is achieved, and >59% complexity reduction of Volterra equalizer is realized. Moreover, the measured BER of 150 Gb/s PAM8-QNSC signal transmission over 50 km SSMF could go below the 20% overhead soft-decision forward error correction threshold of 2.0x10-2. The results validate that the proposed scheme is effective to realize low-cost, high-speed (>100 Gb/s), and secure optical fiber transmission in the data center.

Sparse representation is the important principle of unsupervised learning method. In order to accurately identify the fault condition of machines, the desired feature distribution should show population sparsity and lifetime sparsity. In this paper, to improve the accuracy and robustness of the classification, a novel fault diagnosis method named Cross-sparse Filtering (Cr-SF) is proposed based on the cross l1/2-norms of the feature matrix, which mean the population sparsity and lifetime sparsity terms. After the weights training process, a novel nonlinear activation function is used for feature extraction in the test process. Cr-SF can learn discriminative features from the raw data and accurately identify the fault condition. Rolling bearing fault and gear-box fault datasets are employed to validate the performance of the proposed method. The verification results confirm that Cr-SF is an effective tool for handling big data. The robustness and accuracy of the classification results using Cr-SF are comparable to convolutional networks with a much faster training process.

Tacholess order tracking techniques based on time-frequency (TF) ridge detection have been extensively used in bearing fault diagnosis under varying speed conditions for decades. However, the signal components of a fault bearing related to shaft rotational frequency (SRF) is difficult to be accurately extracted by these methods because of TF resolution limitation and strong noise interference. A new TF decomposing algorithm, that is, variational nonlinear chirp mode decomposition (VNCMD) is effective to extract the time-varying feature under limited TF resolution. However, its performance is influenced by prior knowledge of initial parameters. Besides, ridge information hidden in noise is difficult to be mined effectively, which increases the difficulty of ridge extraction. In this study, a feature isolation technology is proposed to enhance fault-related features and reduce the interference of noise and irrelevant components. Then inspired by the decomposing properties research on the convergence characteristics of VNCMD, an optimization tendency guiding mode decomposition (OTGMD) method is proposed to track the instantaneous frequency (IF) of fault-related mode, which can alleviate the personnel experience requirement and is not affected by the set of TF resolution. The proposed method mainly consists of three steps. First, SRF-related information is highlighted through low-pass filtering, and the dominant IF is achieved through ridge detection method. Subsequently, for the convenience of mode extraction, the fault characteristic is augmented through iterative envelope analysis. Then, the OTGMD optimization strategy is developed to gradually decompose the target mode on the basis of the above process. Finally, a stopping criterion based on characteristic frequency ratios (CFRs) is constructed to adaptively terminate the iteration process. Simulation and experiments demonstrate that the proposed method is effective and suitable for bearing fault diagnosis under varying speed conditions.

Transfer learning based models have been employed for intelligent fault diagnosis under different working conditions. However, an actual and important problem is neglected in existing intelligent fault diagnosis methods, which is that target domain mechanical fault datasets are always highly imbalanced with abundant normal condition mechanical samples but a paucity of samples from rare fault conditions. To solve this actual problem, this paper proposed a novel adaptive cross-domain feature extraction (ACFE) method which can automatically extract similar features between different feature spaces. ACFE wants to obtain more information for determining the category of each training target sample, so it avoids the distribution adaptation and is suitable for the imbalanced problem. Specifically, high dimensional distance through kernel method is employed for ensuring the strong identify ability first. Then, for automatically extracting cross-domain feature, we calculate the posterior probability of category of each target domain training sample based on high dimensional distance, and employ entropy loss to capture the cross-domain information. Besides, we propose the guide loss to avoid the features of a category overall falling into false category caused by imbalanced dataset. Based on ACFE, the intelligent fault diagnosis method for dealing with the imbalanced target dataset is described. To verify the effectiveness, we carry out two specially designed experiments, and the results shows that, comparing with related method, the proposed method achieve a superior performance.

The problem of insufficient datasets has long been a hot topic in the field of prognosis and health management of rotary machines. Generative adversarial network (GAN) and other data augmentation algorithms can solve the problem of insufficient samples. However, the premise of the above method is the signal collected at a constant speed rather than at large speed fluctuation. To deal with data augmentation under large speed fluctuation, this article proposes an effective deep learning method, namely, domain adaptive efficient sub-pixel network (DAESPN). The core idea of DAESPN is to enhance the resolution of the original sample for data augmentation. The DAESPN framework is implemented as follows: after the data passes through the fully connected neural network, the multi-feature maps of the four channels are outputted. A group of high resolution (HR) features is obtained through the sub-pixel fully connected layer. In addition, maximum mean discrepancy (MMD) and mean square error (MSE) are used to construct the loss function of the model. Experimental results of gearbox and bearing datasets show that the DAESPN model has strong feasibility to carry out data augmentation for fault diagnosis of rotating machines under speed fluctuation condition. In addition, the feature learning process of DAESPN is visually displayed and analyzed.

Existing generative adversarial networks (GAN) have potential in data augmentation and in the intelligent fault diagnosis of bearings. However, most relevant studies only focus on the fault diagnosis of rotating machines with sufficient fault-type samples, and some rare fault-type samples may be missing in training in practical engineering. To address those deficiencies, this paper presents an intelligent fault diagnosis method based on the dynamic simulation model and Wasserstein generative adversarial network with gradient normalization (WGAN-GN). The dynamic simulation model of bearing faults is constructed to obtaining simulation signals to replace and complement the missing fault samples, which are combined with the measured signals as training data and then input into the proposed WGAN-GN model for expanding and enhancing the data. To test the effectiveness of the simulated samples, a fault classification model constructed by stacked autoencoders (SAE) is used to classify the enhanced dataset. According to the results, the proposed model performs well when used to diagnose faults under missing samples and is preferable to other methods.

Described herein is the first application of perfluorinated solvent in the stereoselective formation of O-/S-glycosidic linkages that occurs via a Ferrier rearrangement of acetylated glycals. In this system, the weak interactions between perfluoro-n-hexane and substrates could augment the reactivity and stereocontrol. The initiation of transformation requires only an extremely low loading of resin-H+ and the mild conditions enable the accommodation of a broad spectrum of glycal donors and acceptors. The ‘green’ feature of this chemistry is demonstrated by low toxicity and easy recovery of the medium, as well as operational simplicity in product isolation.

The Advanced Geostationary Radiation Imager (AGRI) is one of the primary payloads aboard the FY-4A geostationary meteorological satellite, which can provide high-frequency, wide coverage, and multiple spectral channel observations for China and surrounding areas. There are currently few studies on aerosol optical depth (AOD) inversion from FY-4A AGRI data. Based on AGRI data, a new land AOD retrieval algorithm called the band ratio library (BRL) algorithm was proposed in this study. The monthly average surface reflectance band ratio library was established after obtaining the relationship of band surface reflectance ratio from the MODIS combined AOD dataset. In order to calculate the hourly AOD, look-up tables (LUT) for the various aerosol models were constructed using the 6SV model. We quantitatively compared AOD produced from AGRI data with AERONET ground observations to validate the BRL algorithm. AGRI-retrieved AOD is in good agreement with AOD measured by AERONET, which has a correlation coefficient of R is 0.84, the linear regression function is AODAGRI = 0.80 ∗ AODAERONET − 0.004, the root-mean-square error (RMSE) is 0.16, and approximately 60% of the AGRI AOD results fall within the uncertain range of AOD = ±(0.2 × AODAERONET + 0.05). A cross-comparison was made with the MODIS AOD product provided by NASA. The comparison and verification show the proposed algorithm has a good accuracy of land AOD estimation from AGRI data.

Variational mode decomposition (VMD) has been widely applied to the field of machinery fault diagnosis due to its good time-frequency decomposition capability. However, the performance of the VMD for extraction of fault transient components is dependent on proper parameters in the method and is not satisfactory under noisy conditions. This paper proposes a new method, termed multi-bandwidth mode manifold (Triple M, TM), to extract the real fault transient components for fault diagnosis of rolling bearings. The new TM method unites multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm. The modes with large bandwidths contain more fault-related information while those with small bandwidths contain less fault-unrelated components and noise. The advantages of different fault-related modes are fully used by the proposed method, leading to clear fault transients that facilitates the bearing fault detection. First, an efficiency-improved VMD method named recycling VMD (RVMD) is performed on the bearing signal repeatedly with different parameter values to obtain the fault-related modes with different bandwidths, i.e., the multi-bandwidth modes. Then, the manifold learning algorithm is carried out to extract the intrinsic manifold structure of bearing fault transient components from the multi-bandwidth modes. Finally, the bearing fault type is identified from the extracted feature with clear fault transients. The enhanced performance of the proposed method over the traditional methods is validated by a simulation analysis and two experimental applications of bearing defect identifications.

Currently, most of the vibration signal analysis methods for bearing fault diagnosis under varying speed conditions are based on the resampling technology with shaft rotational frequency (SRF). However, the SRF obtained by a fixed tachometer or time-frequency (TF) ridge detection reduces measuring flexibility or introduces errors inevitably. In this paper, a multi-band feature extraction method using coarse TF ridge-guided variational nonlinear chirp mode decomposition (VNCMD) is proposed for bearing fault diagnosis under varying speed conditions. Specifically, the proposed method is conducted as follows. First, the low-frequency component (LFC) and resonance component are extracted by the low-pass filtering and the fast kurtogram method, respectively, to alleviate the noise interference. Second, the coarse TF ridges are identified by a tractable ridge estimation method that is based on the TF representation for preliminary selection of the initial instantaneous frequency. Third, the coarse TF ridge-guided VNCMD is constructed to track the SRF and instantaneous fault characteristic frequency (IFCF) from the envelope signals of the LFC and the resonance component, respectively. Finally, the characteristic frequency ratio is computed on the basis of the values of SRF and IFCF to determine the fault type of ball bearing without resampling. The simulation studies and experimental verifications confirm that the proposed method can accurately locate bearing defect types and outperforms some existing methods.

Mechanical fault diagnosis is essential in ensuring the safety of production and economic development. In the field of fault diagnosis, deep learning has been extensively used due to its excellent feature learning ability. However, it still suffers from several issues; for example, 1) simultaneous requirements of features from multiple aspects, including sparsity and robustness, are hardly met due to the limited feature learning ability of a single model, and 2) most methods deal with preprocessed signals instead of original time domain signals because of the noise interference and deficiency of a single model. To solve these problems, this study proposes a new deep fusion network for fault feature learning, which combines two types of deep learning models, namely, sparse autoencoder and contractive autoencoder, which are respectively applied to enhance features' sparsity and robustness and thereby guarantee the representativeness of extracted features and gain strong anti-interference ability. Consequently, fault diagnosis with original time domain signals can be realized. Bearing and gearbox fault diagnosis experiments are conducted to verify the performance of the presented network. Results show that the diagnosis accuracies for two cases are higher than those of networks based on single contractive autoencoder and sparse autoencoder. These results demonstrate that the proposed fusion network has superior feature learning ability relative to single model networks and can deal with original time domain signals by simultaneously enhancing features' sparsity and robustness.

To perform bearing fault diagnosis under variable speeds, the optimal resonant frequency (ORF) band selection and diagnosis strategy are pivotal. Indexes, such as kurtosis, crest factor (CF) and smoothness index (SI), are extensively used for guiding ORF selection. Due to that each index has unique advantages, the hybrid of such indexes has been developed. However, applications of the current index hybrid method are impeded by problems of: 1) ineffectiveness for signal corrupted by impulsive noises and 2) equal segmentation of frequency band with human intervention. This paper, therefore, firstly proposes a dual-guidance based scheme with an embedded tunable Q-factor wavelet transform (TQWT) to address the problems. The so-called dual-guidance scheme contains two guidance procedures: 1) the SI guided pre-process for obtaining weight vectors and 2) the index hybrid output guided scheme for ORF selection. The embedded TQWT is used for frequency band segmentation and sub-band signal acquisition without subjective interventions. With the proposed scheme, the ORF band can be determined for bearing fault feature extraction. Then, an algorithm for multiple instantaneous frequency (IF) ridge identification is exploited based on the peak search algorithm for diagnosis. To tackle the difficulty that, at each time instance, the amplitudes of IF ridges of interest do not always dominate the time frequency representation (TFR), a starting point search tactic with a synchronization step is explored. A diagnosis vector can subsequently be obtained by calculating the average ratios of the identified ridges and bearing fault diagnosis can then be done by matching the elements of the diagnosis vector with fault characteristic coefficient (FCC). Comparisons are performed to illustrate the superiority of the proposed method. The experimental analyses are also conducted to validate the proposed method for bearing fault diagnosis under variable speeds.

Optical quality cm-sized LiInSe2 crystals were grown using the Bridgman–Stockbarger method, starting from pure element reagents, under the conditions of a low temperature gradient of 5–6 degrees/cm and a slight melt overheating. The phase purity of the grown crystal was verified by the powder XRD analysis. The thermophysical characteristics of LiInSe2 were determined by the XRD measurements in the temperature range of 303–703 K and strong anisotropy of the thermal expansion coefficients was established. The following values of thermal expansion coefficients were determined in LiInSe2: αa = 8.1 (1), αb = 16.1 (2) and αc = 5.64 (6) MK−1. The electronic structure of LiInSe2 was measured by X-ray photoelectron spectroscopy. The band structure of LiInSe2 was calculated by ab initio methods.

Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing.

Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years. However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy. But MMD can not guarantee the different domain features to be similar enough. Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN). The DAAN comprises a condition recognition module and domain adversarial learning module. The condition recognition module is constructed with a generator to extract features and classify the health condition of machinery automatically. The domain adversarial learning module is achieved with a discriminator based on Wasserstein distance to learn domain-invariant features. Then spectral normalization (SN) is employed to accelerate convergence. The effectiveness of DAAN is demonstrated through three transfer fault diagnosis experiments, and the results show that the DAAN can converge to zero after approximately 15 training epochs, and all the average testing accuracies in each case can achieve over 92%. It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.

The layered oxyselenide BiCuSeO was recently discovered to be a promising high thermoelectric material due to its intrinsically low thermal conductivity. However, the rather high electrical resistivity of undoped BiCuSeO may preclude its potential thermoelectric applications. Herein, we achieved enhanced thermoelectric performance in rare-earth Sm-doped BiCuSeO by the band structure engineering. The replacement of Bi3+ by Sm3+ leads to the improvement of electrical conductivity, from 34 S cm−1 for pristine BiCuSeO to 79 S cm−1 for that of Bi0.94Sm0.06CuSeO at 873 K. Coupled with the large Seebeck coefficient (251 μVK−1) and relatively low thermal conductivity (0.58 Wm−1K−1), the figure of merit ZT is significantly increased from 0.49 for pristine BiCuSeO to 0.74 for Bi0.94Sm0.06CuSeO at 873 K. According to the first-principles calculation, the enhanced thermoelectric properties was ascribed to the decreased band gap by the substitution of Sm. Upon 10%Sm substitution, the band gap of BiCuSeO decreases from 0.50 eV to 0.34 eV, which promotes the density of states near the Fermi level. The present study suggests that rare-earth element can be effective in modifying the band structure of BiCuSeO and thus improving its electronic transport properties. Keywords: Sm-doping, Thermoelectric properties, Band structure, First-principles calculation

The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole.

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement.

The positive thermal expansion exhibited by most materials at increased temperatures is a severe issue for many high precision applications. Here, Xing and co-workers show that redox intercalation of Li ions into a ScF3framework offers effective control of the thermal expansion for this simple material.

We present an optically coupled chaotic system involving three-phase modulated electro-optic nonlinear loops and an optical coupler. The dynamical properties and the time delay signature (TDS) suppressing performance of the system is analyzed in detail. Numerical results show that the TDS can be suppressed not only under statistical analysis of a single output, but also under mutual statistical analysis of the multiple outputs. Compared with the intensity modulated electrically coupled scheme, the presented system has less interior noise due to the simpler construction.

As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly reflected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.

Thermal expansion is a crucial factor for the performance of laser devices, since the induced thermal stress by laser irradiation would strongly affect the optical beam quality. For BaAlBO3F2 (BABF), a good non-linear optical (NLO) crystal, due to the highly anisotropic thermal expansion its practical applications are strongly affected by the “tearing” stress with the presence of local overheating area around the laser spot. Recently, the strategy to place the optical crystals in low-temperature environment to alleviate the influence of the thermal effect has been proposed. In order to understand the prospect of BABF for this application, in this work, we investigated its thermal expansion behavior below room temperature. The variable-temperature XRD showed that the ratio of thermal expansion coefficient between along c- and along a(b)- axis is high as 4.5:1 in BABF. The Raman spectrum combined with first-principles phonon analysis revealed that this high thermal expansion anisotropy mainly ascribe to progressive stimulation of the respective vibration phonon modes related with the thermal expansion along a(b)- and c-axis. The good NLO performance in BABF can be kept below room temperature. The work presented in this paper provides an in-depth sight into the thermal expansion behavior in BABF, which, we believe, would has significant implication to the manipulation in atomic scale on the thermal expansion of the materials adopted in strong-field optical facility.

Deep eutectic solvents (DESs), a new group of ecofriendly solvent combined with the ultrasonic-assisted extraction (UAE) technique, were first successfully used for extraction of resveratrol from peanut roots. Resveratrol in the extract was analyzed and quantified using a HPLC-UV method. A series of DESs consisting of choline chloride (ChCl) and 1,4-butanediol, citric acid, and ethylene glycol were formulated, finding ChCl/1,4-butanediol was a most proper extraction system. The optimal extraction parameters were obtained using a Box–Behnken design (BBD) test combined with response surface methodology as follows: 40% of water in ChCl/1,4-butanediol (1/3, g/g) at 55°C for 40 min and solid/liquid ratio of 1:30 g/mL. The total extraction content and extraction yield of resveratrol from peanut roots could reach 38.91 mg/kg and 88.19%, respectively, under such optimal conditions. The present study will provide a typical example for using DESs to extract natural bioactive compounds from plants.

Rolling element bearings and gears are the most common machine elements. As they are extensively used in rotating machinery, their health conditions are crucial to the safe operation. The signals measured from rotating machines are usually affected by the working conditions and background noises. Thus, identifying faults from the mixed signals is a challenging and important task. Deep learning is initially developed for image recognition. Recently, it has attracted increasing attention in machinery fault diagnosis research. However, the generalization ability of the default classifier of it is not very satisfying. Thus, combining the feature learning ability of deep learning and the existing classifiers with satisfactory generalization ability is necessary. In this article, a hybrid technique based on convolutional neural network and support vector regression is proposed. The former part is used to promote feature extraction capability, and the latter part is used for multi-class classification. The efficiency of the proposed scheme is validated using the real acoustic signals measured from locomotive bearings and vibration signals measured from the automobile transmission gearbox. Results confirm that the method proposed is able to capture fault characteristics from the raw data, and both bearing faults and gear faults can be detected successfully.

Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation−maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency.

This work presents the first example of utilization of amphiphilic block copolymer PCL-PEG-PCL as a stationary phase for capillary gas chromatographic (GC) separations. The PCL-PEG-PCL capillary column fabricated by static coating provides a high column efficiency of 3951 plates/m for n-dodecane at 120 °C. McReynolds constants and Abraham system constants were also determined in order to evaluate the polarity and possible molecular interactions of the PCL-PEG-PCL stationary phase. Its selectivity and resolving capability were investigated by using a complex mixture covering analytes of diverse types and positional, structural, and cis-/trans-isomers. Impressively, it exhibits high resolution performance for aliphatic and aromatic isomers with diverse polarity, including those critical isomers such as butanol, dichlorobenzene, dimethylnaphthalene, xylenol, dichlorobenzaldehyde, and toluidine. Moreover, it was applied for the determination of isomer impurities in real samples, suggesting its potential for practical use. The superior separation performance demonstrates the potential of PCL-PEG-PCL and related block copolymers as stationary phases in GC and other separation technologies.

Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

This paper proposes the time-delayed cubic velocity feedback control strategy to improve the isolation performance of High-Static-Low-Dynamic-Stiffness (HSLDS) vibration isolator. Firstly, the primary resonance of the controlled HSLDS vibration isolator is obtained by using multiple scales method. The equivalent damping ratio and equivalent resonance frequency are defined to study the effects of feedback gain and time delay on the primary resonance. The jump phenomenon analysis of the controlled system without and with time delay is investigated by using Sylvester resultant method and optimization method, respectively. The stability analysis of the controlled system is also considered. Then, the 1/3 subharmonic resonance of the controlled system is studied by using multiple scales method. The effects of feedback gain and time delay on the 1/3 subharmonic resonance are also presented. Finally, force transmissibility is proposed to evaluate the performance of the controlled system and compared with an equivalent linear passive vibration isolator. The results show that the vibration amplitude of the controlled system around the resonance frequency region decreases and the isolation frequency band is larger compared to the equivalent one. A better isolation performance in the high frequency band can be achieved compared to the passive HSLDS vibration isolator.

The elastic properties of a zeolitic metal-azolate framework, Zn(mtz)2 (MAF-7, mtz− = 3-methyl-1,2,4-triazolate), have been examined from the view point of the first principles calculations and experiments. Our results demonstrate that the three independent elastic constants of MAF-7 are about 5.0–73.3% higher than those of ZIF-8, though they are isomorphic. The electron-donating effect of the nitrogen atom at the 2-position in mtz- ring dominantly accounts for such a prominent difference. The detailed analysis of the full elastic tensors reveals that the volume moduli, shear moduli, and Poisson’s ratios of MAF-7 are about 3.4% to 20.1%, 3.2% to 20.6%, and −30.3% to 12.3% higher than those of ZIF-8. The underlying structural reasons were discussed to explain the anisotropic difference of those properties. Moreover, the conclusion deduced from first-principle calculations was also been verified by nanoindentation and high-pressure synchrotron X-ray diffraction measurements.