Search

Water stable Zr-BTDB ultrathin nanosheets incorporated with benzothiadiazole moieties enable efficient aerobic oxidation of a wide range of sulfides to sulfoxides under visible light irradiation in water at room temperature. [Display omitted] • Ultrathin, water stable and photoactive Zr-MOF nanosheets were synthesized. • Zr-MOF nanosheets exhibit superior photocatalytic activity for aerobic oxidation of sulfides. • Singlet oxygen and superoxide radical are reactive oxygen species for the oxidation of sulfides. Ultrathin metal-organic frameworks (MOFs) nanosheets incorporating photochromic units are highly efficient heterogeneous photocatalysts, thanks to their highly exposed catalytic sites, short diffusion pathways, and efficient separation of photo-generated carriers. Herein, we report on the solvothermal synthesis of photoactive benzothiadiazole (BTz) functionalized Zr-MOF nanosheets (Zr-BTDB-NS), which are constructed from Zr 12 nodes linked by nine BTDB2− (BTDB = 4,4′-(benzo[c][1,2,5]thiadiazole-4,7-diyl)dibenzoate) ligands. The Zr-BTDB-NS exhibited superior photocatalytic activity towards the aerobic oxidation of a broad range of sulfides to their corresponding sulfoxides under visible light irradiation in water at room temperature, even in a gram-scale. The Zr-BTDB-NS can be efficiently recycled and reused for seven cycles without a decrease in conversion or selectivity. Detailed mechanism studies revealed that the Zr-BTDB-NS generates singlet oxygen (1O 2) and superoxide radical (O 2 –) reactive oxygen species under visible light irradiation, which are responsible for the oxidation of sulfides into their corresponding sulfoxides. The simplicity of the synthetic approach as well as the diversity of photoactive organic units hold great promise for designing ultrathin, water-stable, and photoactive Zr-MOF nanosheets. This work will also be of added value for selective photooxidation of sulfides under green and mild conditions. [ABSTRACT FROM AUTHOR]

• Individual response to drugs is crucial for personalized medicine. • We developed DREMO for predicting drug response in individuals. • 3 cell line similarity networks and 2 drug similarity networks were constructed. • The similarity information networks were fused with low-dimensional feature vectors. • DREMO addresses the "small n, large p" issue to a certain extent. Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (D rug R esponse pr E diction based on M ulti O mics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly. [ABSTRACT FROM AUTHOR]

Fan noise has become an important noise component of civil aviation engines with the increasing bypass ratio. The mode identification method is the key to in-duct fan noise testing. The number of modes that can propagate through the duct increases dramatically for high-frequency in-duct fan noise, which requires a large number of microphones to measure the sound field in the duct. Since the number of modes is usually larger than the number of microphones, it is difficult to realize mode identification. A Sparse Bayesian Learning algorithm for mode identification is proposed to solve this problem in this paper. The in-duct sound field is described by a statistical model, and the inverse problem of mode identification is expressed in a Bayesian framework. The non-convex optimization problem of the cost function is considered, iteratively achieving the sparsest solution via a block coordinate descent algorithm in a majorization-minimization framework. The mode identification method based on Sparse Bayesian Learning has high sparsity and can achieve parameter adaptation. The effectiveness of the Sparse Bayesian Learning method for duct mode identification is verified by numerical simulations and experimental tests. The results show that the mode identification method of Sparse Bayesian Learning can accurately identify the target mode using fewer microphones. • Applying the Sparse Bayesian Learning (SBL) method for mode identification, the strong sparse induction performance of the SBL method is better at suppressing the non-target modes and identifying accurate sparse target modes. • The sparsest mode coefficient amplitudes are solved indirectly by estimating the hyperparameters. The optimal parameters can be determined adaptively by iterations without the need for manual selection of parameters. • It is verified through numerical simulations and experimental tests that the mode identification method of SBL can accurately identify target modes with a small number of microphones, and its results have strong sparsity and robustness. [ABSTRACT FROM AUTHOR]

The measurements of aero-engine fan noise are crucial to engine-noise reduction and silent-turbofan design. The duct mode identification is adpoted to decompose inner duct noises into spinning modes from acoustic pressure measured by a microphone array. However, modes identification of the aero-engine fan are difficult cause the number of modes is usually much larger than the number of microphones and leading to an ill-posed problem during the inverse process. A mode identification method based on Iterative Bayesian Focusing is used to solve this problem. The in-duct sound field is described by a statistical model, and the inverse problem of mode identification is expressed in a Bayesian framework. The sparsity of the target acoustic modes is applied as a priori condition, and a Mode Aperture Function (MAF) is developed. The aperture width of the MAFis automatically updated and contracted through iterations, after which mode coefficients are contracted to a sparsest solution. The used mode identification method is firstly validated through numerical simulations, and the effects of the number of microphones and SNR on mode identification results are discussed. Then the Iterative Bayesian Focusing-based mode identification method is experimentally verified through a well designed Spinning Mode Synthesizer. Both numerical and experimentalresults indicate that target modes can be accurately identified when the number of microphones are much less than the number of all propagating modes. [ABSTRACT FROM AUTHOR]

Helicopter detection is an important part of the safety and security during flight. Most of the aerodynamic noise of modern helicopters comes from the main rotor, the main rotor aerodynamic noise thus can be used for helicopter detection. However, the Signal-to-Noise Ratio (SNR) of the measurement noise has always been quite small while the helicopter is flying over long distance away from the microphone. The current passive detection methods are not robust enough in the presence of substantial interference. A robust passive sound detection method is proposed to detect the far-field helicopter based on the cyclostationarity of the main rotor noise. The Sparsity-Enhanced Spectral Coherence (SESC) is derived from the spectral coherence decomposition to improve the detection robustness at far distances and low SNR. Furthermore, a global detector is constructed to detect helicopters adaptively by fusing the detection of multiple orders of BPF, which can be performed on the calculated SESC. More accurate and robust detection of the far-field helicopter can be obtained by the proposed SESC detector. The effectiveness of the proposed detection approach is demonstrated and contrasted by using simulation and far-field flight test measurements of the ROBINSON R22 helicopter. [ABSTRACT FROM AUTHOR]

Gamma-proteobacteria is a class of gram-negative opportunistic pathogens existing in the intestinal flora, often leading to diarrhea and intestinal infectious diseases, and plays an important role in maintaining intestinal homeostasis. Type III secretion system (T3SS), an important virulence system, is closely related to the adhesion and invasion and pathogenicity to host cells. Therefore, anti-virulence agents targeting T3SS are important strategies for controlling pathogenic infections. In this study, the anti-Salmonella T3SS active compounds neochebulagic acid (1) , ellagic acid (2) and urolithin M5 (3) were isolated from seed extract of Terminalia citrina by activity-guided isolation method. Based on the fact that urolithins are the main and stable intestinal microbiota metabolites of hydrolysable tannins, we found that the metabolite urolithin B repressed translation and secretion of SipC through the Hha–H-NS–HilD–HilC–RtsA–HilA regulatory pathway. The results provide evidence for Terminalia seeds and ellagitannin-rich berries and nuts in regulating intestinal homeostasis and treating bacterial infection. [Display omitted] • Neochebulagic acid (1), ellagic acid (2) and urolithin M5 (3) are anti-T3SS compounds from Terminalia citrina. • ET-derived intestinal microbiota metabolite urolithin B was identified as a T3SS inhibitor of Salmonella. • Urolithin B represses the T3SS through the Hha–H-NS–HilD–HilC–RtsA–HilA regulatory pathway. [ABSTRACT FROM AUTHOR]

Proton exchange membrane (PEM) is one of the core materials which were widely used in the field of fuel cells (FCs). In recent years, the metal organic frameworks (MOFs) and polymer composite membranes as tunable proton-conducting materials have showed great potential in this field. Herein, a Cu(II)-based metal organic framework (Cu-MOF) with acid/base stability was introduced into poly(vinylpyrrolidone)/poly(vinylidene fluoride) (PVP/PVDF, PP) to fabricate hybrid membranes(Cu-MOF@PP-X, where X is the % Cu-MOF content). The effects of Cu-MOF loading amounts on the conductive behavior of Cu-MOF@PP-X composites have been documented. It is worth noting that Cu-MOF@PP-50 exhibited the proton conductivity up to 4.36 ​× ​10−4 ​S ​cm−1 at 353 ​K and 98% relative humidity (RH). In addition, the value of proton conduction is about two orders of magnitude higher than that of the parent Cu-MOF (1.91 ​× ​10−6 ​S ​cm−1). This work contributes to driving further advances of MOF/polymer composites in proton-conducting apparatus. [Display omitted] • A Cu-MOF with acid/base stability was introduced into polymers (PVP/PVDF, PP) to fabricate hybrid membranes. • The effects of Cu-MOF loading amounts on the conductive behavior of Cu-MOF@PP-X composites have been documented. • Cu-MOF@PP-50 exhibited a super protonic conductivity up to 4.36 ​× ​10−4 ​S ​cm−1 at 353 ​K and 98% relative humidity (RH). • The value of proton conduction of Cu-MOF@PP-50 is about two orders of magnitude higher than that of the parent Cu-MOF. [ABSTRACT FROM AUTHOR]

Helicopter rotor aerodynamic noise research has become essential as helicopters' reliability and comfort requirements have increased. Significant progress has been made in modeling the aerodynamically generated sound of helicopter rotors. The cyclostationary signal processing methods have been widely used for machine fault diagnosis, machinery system identification and mechanical source separation. However, the lack of a generally accepted formal definition of cyclostationarity based on the basic aerodynamic theory and wave propagation model limits cyclostationary methods' application to the helicopter rotor aerodynamic noise. A formal cyclostationary modeling process to the helicopter rotor aerodynamic noise based on aerodynamic and wave propagation theory is proposed in this paper for subsequent cyclostationary signal processing. The rotor blade noise is mathematically formulated as a cyclostationary process by imposing the periodic Green's function based on the Wold–Cramer decomposition form of the Ffowcs Williams–Hawkings (FW–H) equation, and the formal definition of the rotor blade noise cyclostationary process is derived. Then, the cyclostationarity is defined for both the tonal noise and the broadband noise of rotor aerodynamic noise, respectively. Furthermore, a periodic amplitude modulation (PAM) signal model is proposed to approximate the measured rotor aerodynamic noise signal. The proposed signal model is validated by the measurement in the wind tunnel test. • Cyclostationarity of the aerodynamically generated sound of helicopter rotors is proved. • A cyclostationary signal model is proposed for helicopter rotor aerodynamic noise. • The theory and signal model is validated by the measurement in the wind tunnel test. [ABSTRACT FROM AUTHOR]

• Space-snapshot representation for acoustic imaging in a complex, noisy environment is investigated. • Low rank and Mixture of Gaussian modeling are proposed to achieve significant denoising performance. • Comparisons with other methods are made in simulations and experiments to verify the effect of the state-of-the-art methods. The microphone array can simultaneously obtain the multi-dimensional information (space-time-frequency) of sound sources, which is recognized as a fundamental and powerful tool in acoustic imaging. Acoustic Beamforming is one of the widely used methods in acoustic imaging. However, most of the applications of beamforming are based on the Gaussian noise assumption, which is not always accurate in on-site measurements. For example, shock noise with a skewed probability density function (PDF) may appear on the signal record when the turbulent eddies are not controlled. Thus, in this paper, the conventional Gaussian noise model is extended to a Gaussian mixture noise model, which can approximate any probability distribution of the noise in theory. The space-snapshot representation of microphone array measurements is further modeled as a combination of the low-rank matrix part (measurements from the sound sources) and a Gaussian mixture matrix part (measurement noise). The signal from the sources of interest is finally recovered by the Expectation–maximization algorithm, which iterates between the low-rank approximation of the sound sources and the estimation of the parameter of the Gaussian mixture model. The proposed method is further investigated with simulations and compared with robust principal component analysis (RPCA) and Gaussian-based probabilistic factor analysis (PFA). It is concluded that the proposed method outperforms the state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

The tunable luminescent materials based on photochromic reactions have received considerable interest due to its potential applications in switches and optical memory devices. However, constructing such materials with ideal properties remains a challenge. In this context, two rare viologen-based host guest materials, 2[MV]·[Zn 6 (PMA) 4 (H 2 O) 4 ] (1), 2[PBM]·[Zn 2 (PMA) 2 (H 2 O) 6 ] (2), (MV·2NO 3 ​= ​1,1'-dimethyl-(4,4'-bipyridine)-1,1'-diium dinitrate; PBM·2NO 3 ​= ​4,4'-(1,4-phenylene)bis(1-methylpyridin-1-ium) dinitrate; H 2 PMA ​= ​pyromellitic acid), have been prepared by encapsulating viologen derivatives into the anionic metal-organic frameworks produced from pyromellitic acid ligands and zinc cations. Due to the presence of photoactive guest MV2+/PBM2+ and the short O⋯N+ distances between the oxygen atom of carboxylate groups and pyridine ring of guest, both complexes display fast-responsive reversible naked photochromism from colorless to blue or yellow upon UV–Vis irradiation At the same time, these host-guest materials exhibit good fluorescence emission behavior and their fluorescence can be reversible modulation under light irradiation. These are rare examples of fluorescence switching materials based on host-guest system that involve the electron-transfer photochromic process. Two novel MOF-based host-guest compounds were prepared by incorporating the photoactive viologen derivatives into the anionic framework, which exhibit photochromism upon UV–Vis light irradiation and photocontrolled luminescence. [Display omitted] ● Two host-guest complexes are prepared by incorporating the viologen derivatives into the anionic MOFs. ● Complexes 1 and 2 exhibit fast-responsive photochromism upon UV–Vis light irradiation. ● Complexes 1 and 2 show good luminescent emission and reversible luminescence switching. [ABSTRACT FROM AUTHOR]

The Non-Synchronous Measurements is particularly effective at expanding the operating frequency range of a single microphone array. The completed cross-spectral matrix of the synthetic array, when combined with high-resolution imaging algorithms, can yield accurate sound source identification results. However, when matrix completion algorithms are applied to large synthetic arrays, their computational complexity increases significantly. Furthermore, existing integrations with high-resolution imaging algorithms often encounter difficulties in the presence of coherent sound sources. To address this problem, this paper introduces the accelerated gradient descent algorithm for the covariance matrix fitting by orthogonal least squares. The matrix completion model for non-synchronous measurements is initially simplified through matrix decomposition, thereby enabling the rapid completion of the cross-spectral matrix using the accelerated gradient descent algorithm. This is then followed by the application of the covariance matrix fitting by orthogonal least squares to achieve quick and precise identification of coherent sound sources employing the completed cross-spectral matrix. The performance of the algorithm is evaluated using numerical simulations and validated through loudspeaker experiments in an anechoic chamber. The results from these simulations and experiments reveal that the proposed algorithm not only improves matrix completion performance on large synthetic arrays but also accurately identifies the locations and correlation coefficients of coherent sound sources. [ABSTRACT FROM AUTHOR]

• A novel method was proposed to identify RNA m2G sites using hybrid features. • The over-sample method SMOTE was adopted to deal with the problem of data imbalance. • After using MRMD to select features, the performance of the model is improved. • The RFhy-m2G is superior to other methods, which can effective identify m2G sites. N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors. [ABSTRACT FROM AUTHOR]

Noise source localization is considered a key technology for the low-noise design of machinery and equipment. The spatial resolution of conventional beamforming localization methods is effectively improved by the fast iterative shrinkage thresholding algorithm(FISTA). The application of FISTA for sound source localization in reverberant environments is limited by the selection of reflection parameters. In this paper, FISTA extends to FISTA-Net and is proposed to solve the problem of adaptive fitting of reflection parameters to improve the performance of sound source localization in a reverberant environment. In FISTA-Net, the network model can be established by expanding and truncating the FISTA algorithm based on a fixed number of iterations. The parameter iteration steps, shrinkage thresholds and update operators are updated in the FISTA-Net model by training. A series of simulations are conducted for various frequencies, reverberation times, and signal-to-noise ratios and compared to several de-reverberation algorithms to evaluate the performance of the proposed algorithm. The loudspeaker source localization experiment was conducted in a closed room to verify the effectiveness of FISTA-Net in real-world scenarios. [ABSTRACT FROM AUTHOR]

Transfer learning techniques have been extensively developed for the intelligent diagnosis of rotating machinery as a critical and valuable tool dedicated to minimizing the distributional discrepancies between different working conditions of the machine. However, conditional probability information about fault classes and the geometric features of data distribution is rarely considered in traditional distance metrics, invalidating cross-domain diagnostic models when faced with significant distributional discrepancies. To address these issues, a new cross-domain diagnostic algorithm is proposed via joint conditional Wasserstein distance matching. First, the conditional Bures–Wasserstein distance is constructed based on the second-order statistic cross-covariance operator, approximating the distributions in the source and target domains while constraining the geometry. Then, to avoid losing first-order fault data information, the conditional probability 1-Wasserstein distance is embedded to construct a joint distance adaptation. The entropy loss is introduced into the training process to build reliable pseudo labels for the target domain samples. In the proposed method, the samples and label features of different domains are mapped to the reproducing kernel Hilbert space (RKHS), and the Feature Extractor and Classifier modules of the model are jointly optimized to obtain a more robust diagnostic model. The proposed cross-domain diagnostic model is experimentally validated on bearing and gearbox datasets under variable loads and speeds with significant diagnostic performance compared to existing transfer models. [ABSTRACT FROM AUTHOR]

The fan noise is the primary component of aero-engine noise. Duct acoustic modal identification is essential for analyzing the mechanism of fan noise generation and propagation. The acoustic modal in the duct can be identified by the measurement of the microphone array mounted on the duct wall. It is generally assumed in the conventional modal identification methods that the modal coefficients are independent of each other. However, the realistic duct acoustic modal coefficients exhibit significant statistical dependence. A Boltzmann machine(BM) model considering the statistical dependence between modal coefficients is proposed for recovering the modal coefficients in this paper. The modal coefficients are estimated using a block coordinate optimization method in a Bayesian framework. Moreover, the modal Boltzmann parameters can be learned iteratively by the BM. The effectiveness of the proposed method is verified by numerical simulations and experimental tests. It turns out that the modal coefficients can be accurately identified by the proposed method, and a more significant sparsity is shown by the results. In addition, the statistical dependence between the modal coefficients can be captured by the BM. • A Boltzmann machine modeling method that considers the statistical dependence between the modal coefficients is proposed. • The modal coefficients and the modal Boltzmann parameters can be estimated adaptively and iteratively. • The accuracy and robustness of the modal identification results are analyzed. • The proposed method is evaluated in Spinning Mode Synthesizer experiments. [ABSTRACT FROM AUTHOR]

The localization of cyclostationary sound sources is important for rotating machinery noise source identification and fault diagnosis. Cyclostationary sound sources are a special class of nonstationary sound sources, which exhibit periodic changes in statistics. Most of the conventional sound source identification methods are based on the stationary sound source assumption, which do not consider the cyclostationarity of the radiated sound field by rotating machinery. Thus, they are hard to localize the acoustic sources with different cyclic frequencies. In this paper, a novel technique of localizing the cyclostationary acoustic sources with different cyclic frequencies is proposed. First, the cyclostationary conventional beamforming (CSCBF) method is deviated. In CSCBF, the cyclic-cross-spectral matrix (CCSM) is defined considering all the cross cyclic spectral correlation (CSC) between different microphones in the array. The left steering vector and right steering vector are constructed to focus the CCSM on each scanning grid point at a given combination of cyclic frequency and spectral frequency. Then, the high resolution cyclostationary beamforming (HR-CSBF) is further developed to improve the spatial resolution of CSCBF. In HR-CSBF, two linear equations according to the property of CSC is established as the acoustic inverse problems. With the sparse distribution of the cyclostationary sources in the space as the prior knowledge, the iterated Bayesian focusing (IBF) is utilized to obtain a robust and sparse solution in HR-CSBF. Numerical simulations and experiments are conducted to validate the proposed methods. To explore the potential applications of the proposed methods, the localization of high pressure pump (HPP) and rolling bearing with outer-race failure are also used to evaluate the effectiveness of the proposed methods. It turns out that both CSCBF and HR-CSBF can localize the cyclostationary sources with different cyclic frequencies. Compared with CSCBF, HR-CSBF can achieve a higher spatial resolution in the obtained acoustic map. [ABSTRACT FROM AUTHOR]

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent years. Owing to the advantage of data privacy protection, federated learning has recently received increasing attention; this approach can utilize monitoring data from multiple local clients to train an optimal global diagnosis model. However, low-quality data are often present for some clients, including mislabeled data and incomplete data that lack some health states. Furthermore, the data distributions are usually different across different clients owing to variations in machine operating conditions. Therefore, the performance of the federated diagnostic model may be limited by low-quality data and data distribution discrepancies. To address this issue, a federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation is proposed. First, a dynamic filter is designed by filtering out low label probabilities predicted by low-quality source domain models to construct high-confidence pseudo-labels for the target domain data. Then, the batch normalized maximum mean discrepancy (BN-MMD) distance metric is introduced into the training loss function to reduce the data distribution discrepancy between the source clients and the target client without private data leakage. While building the global model in each training round, a dynamic model aggregation algorithm is proposed to mitigate the influence of low-quality clients. This algorithm evaluates the weight of each client according to its contribution to the total consensus diagnostic knowledge and then aggregates the local models with adaptive weights. Consequently, it can overcome the drawback of the classical federated averaging (FedAvg) algorithm, where all local clients are assigned the same weight when constructing the global model. Experiments are conducted on three bearing datasets under various loads and speeds. Compared with some existing diagnostic methods, the proposed federated transfer learning method can reduce the impact of low-quality data and achieve higher diagnostic accuracy while preserving data privacy. • A novel federated transfer learning method is proposed for bearing fault diagnosis. • Low-quality knowledge of some source clients is eliminated by a dynamic filter. • A dynamic model aggregation algorithm is derived to obtain an optimal global model. [ABSTRACT FROM AUTHOR]

The microphone array is widely used in acoustics as a non-contact measurement tool, which can obtain multi-dimensional information about the sound source, such as spatial, time, and frequency. The microphone array is not always used in an ideal anechoic chamber environment, making the sound source signal contaminated with the background interference. The separation of the sound source signal from the complex background interference is very challenging, especially when arrays are used in wind tunnel measurements. A probability model on the time–frequency matrix is constructed in this paper to address this issue. The background interference is constructed by the Gaussian mixture model to fit its complex probability distributions adaptively. The sound source signal is constructed as a low-rank model according to its correlation characteristics on the microphones. The distributions of parameters involved in the low-rank and Gaussian mixture model are estimated through variational Bayesian inference, which can realize the separation of the sound source signal from the complex background interference. The performance of the proposed method is evaluated by the numerical simulation and the DLR closed wind tunnel experimental. The robustness and the effectiveness of extracting the sound source signal from the complex background interference are also verified. • Complex interference suppression in the microphone array measurement is investigated. • A hierarchical parametric framework is constructed based on the low-rank and Gaussian mixture model. • The optimal model parameters are adaptively determined by the Variational Bayesian inference. • The proposed method can effectively remove the interference for acoustic imaging in the DLR wind tunnel test. [ABSTRACT FROM AUTHOR]

The Non-Synchronous Measurements is an efficient technique to improve the acoustic image of conventional beamforming for sound source localization. It is unable to quickly reveal the sound sources of the large machinery which requires a large synthetic array with many microphones to capture enough sound pressure data. In this paper, four Non-Synchronous Measurement algorithms with improved computational efficiency are demonstrated. They are implemented by combining the Alternative Direction Method of Multipliers (ADMM) method with different randomization-based acceleration strategies. The new algorithms are developed firstly by (1) using Randomized Singular Value Decomposition (RSVD) to construct the projection basis, and then by (2) migrating the computation platform from the Central Processing Unit to the Graphics Processing Unit for the Cross-Spectral Matrix completion steps, (3) accelerating the soft threshold shrinkage operator with RSVD, or (4) replacing the soft threshold shrinkage with RSVD-enhanced optimal shrinkage in the Cross-Spectral Matrix completion steps. Numerical simulations are carried out under different conditions, including the number of sequential measurement positions, the step length between two sequential measurement positions, the frequency of the sound sources, and the Signal-to-Noise Ratio of the sound pressure signals measured by the microphones. The sound source localization performance of the improved algorithms is assessed by examining their acoustic maps, and the computational performance of these algorithms is compared through their Cross-Spectral Matrix reconstruction errors and time consumption. • Four accelerated Non-Synchronous Measurement algorithms are proposed. • Proposed algorithms are based on the Randomized Singular Value Decomposition. • The matrix completion accuracy of the original algorithm are optimized. [ABSTRACT FROM AUTHOR]

• A novel computational method identifies enhancers and their strengths at the sequence level. • A light gradient boost machine algorithm is used for feature selection. • In this paper, a new network structure called multiple Laplacian-regularized radial basis function network is proposed. • Compared with the existing methods, this method achieves more excellent performance. An enhancer is a short DNA sequence containing many binding sites of transcription factors that plays a crucial role in the gene expression of major eukaryotes. It is difficult to avoid the time consumption and high cost of experimental methods. Therefore, with the continuous development of genomics, it is an urgent task to identify enhancers and their intensities by computational methods. In this paper, we propose a two-layer model called iEnhancer-MRBF, wherein the first layer is used to identify enhancers, and the identified enhancers are divided into strong enhancers and weak enhancers according to their strength in the second layer. In iEnhancer-MRBF, a new classifier multiple Laplacian-regularized radial basis function network (MLR-RBFN) is proposed, and three feature representation methods, namely, kmer, nucleotide binary profiles (NBP) and ac-cumulated nucleotide frequency (ANF), as well as feature selection, are used to process DNA sequences. The experimental results show that the model is significantly better than the previous prediction models, and the test accuracy rates of the first and second layers of independent datasets are 79.75% and 83.50%, respectively. [ABSTRACT FROM AUTHOR]

Constructed wetlands (CWs) are often used to treat wastewater discharged from wastewater treatment plants (WWTPs), while emerging contaminants (such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS)) have been commonly discovered in WWTPs. However, no research has examined whether PFOA/OS (i.e. PFOA and PFOS) affects the performance of CW. Therefore, this study compared the nutrient removal efficiencies of four CWs with varied configurations under PFOA/OS and no PFOA/OS stress conditions. We found that CW containing plants or/and iron-carbon had higher removal efficiency for nutrients (except NH 4 +–N) than conventional CW in stable operation under wastewater without PFOA/OS. Plants or/and iron increased the nutrient removal efficiency by plant uptake, chemical reaction, and co-precipitation of iron hydroxides. In contrast, the iron-carbon inhibited the nitrification of nitrifying bacteria by consuming dissolved oxygen, converting NO 3 −–N to NH 4 +–N. Although the removal efficiencies of nutrients by CWs differed after introducing PFOA/OS, the removal order was consistent with those before adding PFOA/OS. Plants or/and iron-carbon effectively increased CWs' resistance to PFOA/OS loading and toxicity, and the function of iron-carbon was superior to the plants. In addition, PFOA/OS reduced the abundances of microbes Hydrogenophaga , Pseudomonas , Sphingomonas , Nitrospira , and Candidatus_Accumulibacter that contributed to nutrient removal. [Display omitted] • Additions of iron-carbon increased the nutrient removal excepting NH 4 + − N. • The presence of PFOA/OS inhibited the nutrients removal efficiencies. • Iron-carbon improved the tolerance of CW to PFOA/OS loading and toxicity. [ABSTRACT FROM AUTHOR]

Perfluoroalkyl acid (PFAA) exposure poses a potential hazard to wildlife and humans. Food consumption is one of the main routes of PFAA exposure for the general population, with aquatic organisms being the major contributors. To evaluate the risk of coastal residents' intake of wild aquatic organisms, 14 PFAAs were detected in crucian carp and oriental river prawn from 18 sampling sites from the lower reaches of Yangtze River. The total PFAA (∑PFAA) concentrations ranged from 5.9 to 51.3 ng/g wet weight (ww) in the muscle of crucian carp and river prawn, suggesting the potential risk to human and wildlife. Perfluorooctanesulfonate (PFOS), perfluorooctanoic acid (PFOA) and long-chain PFAAs (C ≥ 10) were the main pollutants in the tissues of crucian carp and river prawn, which are known for their higher bioaccumulation capacity. The ∑PFAA concentration in all the samples showed an increasing trend from upstream to downstream and was higher in the south bank, owing to population density, prevailing winds, background pollution and industrial emission. Principal component analysis-multiple linear regression and Pearson correlation analysis showed that WWTP effluent, industrial pollution and surface runoff ware the main sources of PFAAs in the aquatic organisms and industrial pollution highest contributor, suggesting better regulation is needed to manage them. The assessment of risk to human health and wild life suggested a low risk for most residents of cities along the Yangtze River except for resident of Nantong, where frequent consumption of wild aquatic organisms may cause potential risk to human health, especially for traditional eaters and middle-aged people. • PFAAs were monitored in fish and shrimp along the lower Yangtze River, China. • ∑PFAA ranged from 5.9 to 51.3 ng/g in the muscle of fish and shrimp. • PFAA were higher downstream and near the southern shore, linked with mixed reasons. • Industrial pollution was the main potential source of PFAAs in aquatic products. [ABSTRACT FROM AUTHOR]

• A Kalman filter-based time domain plane wave superposition method is proposed. • The proposed KF-TDPWSM performs a forward process to improve the solving stability. • The superiority of the proposed KF-TDPWSM is proved by comparing with the TDPWSM. A Kalman-filter-based time-domain plane wave superposition method (KF-TDPWSM) is proposed for reconstructing the instantaneous sound field generated by a source. In the KF-TDPWSM, a discrete state formulation is first acquired using the superposition formula of time-varying pressure on a hologram plane, which forms a multiple-input and multiple-output acoustical system. Then, an eigensystem realization algorithm is implemented to obtain the minimum realization of this system. The process and measurement equations of the KF-TDPWSM are established by combining the equation of the first-order smoothness condition with the minimum realization. Finally, the forward iteration calculation of the Kalman filter is used to estimate the state vector, including the pressure time–wavenumber spectra on a virtual source plane. The spectra estimated at all times are employed to calculate the time-varying pressure on a reconstruction plane. The KF-TDPWSM utilizes a forward iteration calculation and does not use the inverse calculation with the time-marching characteristic to improve the reconstruction stability of the instantaneous sound field. A numerical simulation is designed to investigate the reconstruction performance of the KF-TDPWSM. Simulation results confirm that the KF-TDPWSM effectively reconstructs the instantaneous sound field of a source in the time and space domains. The effects of certain parameters of the KF-TDPWSM are examined through simulations, and it is shown that its reconstruction accuracy is superior to that of the time-domain plane wave superposition method. An experiment is conducted to further validate the KF-TDPWSM. [ABSTRACT FROM AUTHOR]

[Display omitted] • Photocatalytic Z-schemes are built for SiSe/SnSe 2 and SiSe/SnSSe heterostructures. • The STH efficiencies of the Z-schemes can reach 19.18%, 14.61%, and 14.24%. • NAMD simulations confirm photocatalytic activity of SiSe/SnSe 2 is well-protected. • Δ G s reveal both HER and OER with SiSe/SnSSe-I can spontaneously proceed. The direct Z-schemes of the photocatalytic overall water splitting with the SiSe/SnSe 2 , SiSe/SnSSe-I, and SiSe/SnSSe-II heterostructures are constructed based on the density functional theory calculations and nonadiabatic molecular dynamics (NAMD) simulations. The maximum solar-to-hydrogen efficiency (ηʹ STH) reaches 19.18% and can be promoted to 28.71% under tensile biaxial strains. NAMD simulations indicate the transfer of the electron for hydrogen evolution reaction (HER) and hole for oxygen evolution reaction (OER) for SiSe/SnSe 2 are apparently slower than those for the other two heterostructures, implicating the reduction and oxidation activities of this heterostructure are well-protected. Moreover, the shortest electron-hole recombination time is attributed to SiSe/SnSSe-I, indicating that it holds the best photocatalytic performance. Remarkably, the Gibbs free energies indicate that HER and OER with SiSe/SnSSe-I can spontaneously proceed, while OERs can but HERs cannot spontaneously proceed with the other two heterostructures. Therefore, the newfound heterostructures, especially SiSe/SnSSe-I, are promising candidates in photocatalytic overall water splitting. [ABSTRACT FROM AUTHOR]

The Modal Composition Beamforming (MCB) method can quickly achieve the localization of fast-rotating sound sources; however, its low resolution and unclear applied conditions severely limit its industrial application. This study aims to investigate the MCB-based high-resolution localization method for multiple rotating sound sources and apply it to the localization and identification of axial-fan blade-noise. In this paper, the Variational Bayesian Approximation (VBA) and the Subspace Variational Bayesian Approximation (SVB) methods are used to solve the MCB-based rotating sound power propagation (RSP) model, denoted as RSP-VBA and RSP-SVB, respectively. The effectiveness of the proposed RSP-VBA and RSP-SVB are experimentally validated for the first time. Compared to the conventional MCB method, the resolution is significantly improved, and the proposed high-resolution methods are even faster than the classical Rotating Source Identifier (ROSI) method in most conditions. More importantly, the applied condition of the MCB, RSP-VBA, and RSP-SVB methods is given and verified by using three evaluation indicators. Then a schematic of the applied condition with examples is provided. With the guide of the applied conditions, the RSP-VBA and RSP-SVB methods are applied to the blade-noise localization of various multiblade high-speed axial fans. [ABSTRACT FROM AUTHOR]

• A nonconvex GMC penalty is introduced into ESM for acoustic source reconstruction. • The GMC penalty-regularized ESM is solved by an efficient ADMM framework. • The proposed method can improve the reconstruction accuracy and spatial resolution. • The feasibility of the sparsity-enhanced ESM in real scenarios is validated. The Equivalent Source Method (ESM) is a powerful technique for acoustic source reconstruction, which has been widely used in noise control and machinery fault detection. Because the inverse acoustic source reconstruction problem is typically ill-conditioned, regularization techniques are often adopted in ESM to achieve meaningful solutions. Based on the sparse distribution assumption of acoustic sources, sparse regularization can be utilized in ESM to promote the spatial resolution of reconstructed results. Existing sparse ESMs often adopt convex l 1 norm or nonconvex l p norm as the regularization terms. However, l 1 norm-regularized ESM suffers from an insufficient sparsity-inducing problem, which decreases the spatial resolution and reconstruction accuracy. Meanwhile, reconstructed results of l p norm-regularized ESM are often unstable due to multiple local minimas. In this paper, a sparsity-enhanced ESM is proposed, which introduces a nonconvex Generalized Minimax-Concave (GMC) penalty into ESM as the regularization term. The GMC penalty can enhance the sparsity of solutions and simultaneously maintain the convexity of the overall objective function in acoustic source reconstruction. Consequently, the GMC penalty-regularized ESM can improve the spatial resolution and reconstruction accuracy of the reconstructed results. To solve the acoustic source reconstruction problem rapidly, a computation framework based on Alternating Direction Method of Multipliers (ADMM) is derived. Simulation studies indicate that the proposed method can improve the acoustic source reconstruction accuracy in a wide frequency band, compared with existing l 1 and l p norm-regularized ESMs. Two experimental cases further verify that the proposed sparsity-enhanced ESM can effectively reconstruct the acoustic sources with high spatial resolution and reconstruction accuracy at higher frequencies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Structural optimizations of acridone derivatives based on E17 and E24. • Structure-activity outcome was enriched. • The most potent compound 6h is a strong topo IIa/b poison. • 6h can cause obvious DNA damage and DSBS breaks. • 6h can induce apoptosis. Previously, an array of N -substituted acridone derivatives have been reported as potent topoisomerase II (topo II) inhibitors, and preliminary structure–activity relationship (SAR) outcomes revealed that the linker between 1-NH and N -methyl piperazine motif of the tricyclic acridone scaffold significantly affected their anti-proliferative potencies. To further explore the SARs of acridone-derived topo II inhibitors, a wider range of novel acridone derivatives were herein synthesized via two rounds of structural optimizations on two validated hits, E17 and E24. Initially, the linker length was optimized, and then influences of N -methyl piperazinyl moiety and disposition of three N atoms on the bioactivity were investigated. As a result, a newly developed topo II inhibitor 6 h was found to be more potent than E17 and E24 , thereby serving as a tool compound for the follow-up mechanistic study. Compound 6 h functioned as a strong topo IIα/β inhibitor, caused obvious DNA damage, and induced apoptosis by triggering the loss of mitochondrial membrane potential (Δψ m). Further molecular docking and MD study illustrated the favorable interactions of 6 h with both topo IIα and topo IIβ subtypes. [ABSTRACT FROM AUTHOR]

This paper deals with the topological entropy for hom Markov shifts T M on d -tree. If M is a reducible adjacency matrix with q irreducible components M 1 , ⋯ , M q , we show that h (T M) = max 1 ≤ i ≤ q ⁡ h (T M i ) fails generally, and present a case study with full characterization in terms of the equality. Though that it is likely the sets { h (T M) : M is binary and irreducible } and { h (T X) : X is a one-sided shift } are not coincident, we show the two sets share the common closure. Despite the fact that such closure is proved to contain the interval [ d log ⁡ 2 , ∞) , numerical experiments suggest its complement contain open intervals. [ABSTRACT FROM AUTHOR]

Accurate prediction of carbon price is of great significance to national energy security and climate environment policies. This paper comes up with a new forecasting model variational mode decomposition, convolutional neural network, bidirectional long short-term memory, and multi-layer perceptron (VMD–CNN–BILSTM-MLP) to predict EUA carbon futures prices in two periods of five years before and after the introduction of emission reduction policies. The parameters of the VMD model are determined by genetic algorithm (GA) firstly, carbon futures prices are broken down into subsequences of different frequencies using the model. The MLP model is then applied to predict the highest frequency sequence. The CNN-BILSTM model is applied to predict other subsequences later. Finally, the predicted values of each subsequence are linearly added to obtain the final result of the entire model. The prediction effect of the model is mainly tested by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2) and the modification of Diebold-Mariano test (MDM). In both periods, the proposed model predicts better than the other models, and the prediction effect of carbon futures price in the first five years is a little better than that in the second five years. In general, the experiment of predicting carbon futures prices in two different periods, the experiment of changing the proportion of data set and the experiment of predicting the whole sample all prove that the mixed model proposed in this paper has good prediction effect. • Carbon futures prices are predicted by a hybrid model VMD–CNN–BILSTM-MLP. • Genetic algorithm is used to optimize variational mode decomposition. • The hierarchical prediction based on subsequence features is better. • The model has great prediction effect before and after emission reduction policies. [ABSTRACT FROM AUTHOR]

A new 3D anionic metal-organic framework material, namely, [Me 2 NH 2 ] 2 [Cd 3 (L)]·2H 2 O (1 , H 4 L ​= ​2,5′-di(3′,5′-dicarboxylphenyl)pyridine) has been synthesized under solvothermal conditions and characterized by single crystal X-ray diffraction, PXRD, thermogravimetric analysis and proton conduction investigation. In 1 , the adjacent Cd(II) ions are joined to form a trinuclear [Cd 3 (COO) 8 ] secondary building unit (SBU) by eight bridging carboxylate groups of L4− ligands. The trinuclear [Cd 3 (COO) 8 ] SBUs are connected by L4− ligands into a 3D (4,8)-connected framework with 1D channels. Most interestingly, due to the guest [Me 2 NH 2 ]+ cations, water molecules and carboxylates sites are lined the pore channels, 1 possesses a remarkable proton conductivity of 1.12 ​× ​10−1 ​S ​cm−1 ​at 60 ​°C and 98% RH, which is comparable to commercially available Nafion. The mechanism of proton conduction was also well discussed. A new 3D anionic metal-organic framework material, namely, [Me 2 NH 2 ] 2 [Cd 3 (L)]·2H 2 O (1 , H 4 L ​= ​2,5′-di(3′,5′-dicarboxylphenyl)pyridine) has been synthesized under solvothermal conditions and characterized by single crystal X-ray diffraction, PXRD, thermogravimetric analysis and proton conduction investigation. Compound 1 exhibits a (4,8)-connected 3D network based on trinuclear [Cd 3 (COO) 8 ] cluster units with excellent water stability. It's worth noting that the value of proton conductivity of 1 reaches 1.12 ​× ​10−1 ​S ​cm−1 ​at 60 ​°C and 98% RH which is comparable to most of the dramatic proton conducting MOFs materials. The activation energy (E a) of 1 is 0.376 ​eV estimated by Arrhenius equation. [Display omitted] • A trinuclear Cd-based 3D (4,8)-connected anionic metal-organic framework were synthesized. • The [Me 2 NH 2 ]+ ​cations, H 2 O and host carboxylates could construct continuous proton-conducting pathways. • The MOF exhibited a remarkable proton conductivity of 1.12×10-1 ​S cm-1 ​at 60°C and 98% RH. [ABSTRACT FROM AUTHOR]

Fault characteristic extraction of rolling bearings is essential for fault diagnosis. Rolling bearings are usually operated at changing speeds, and the nonstationary signals of the bearings are covered by the heavy background noise, making the extraction task of fault features very difficult. To address this issue, a robust fault characteristic extraction approach based on the time–frequency analysis under variable speed conditions is proposed in this paper. Firstly, the sparse property of the time-variant fault characteristics and low-rankness of background noise are explored and utilized in the time–frequency representation (TFR). Then, the sparse and the low-rank components are integrated into a hierarchical Bayesian model, and a random error term is considered to make the Bayesian model more robust. The Gibbs sampler is applied to extract the desired sparsity-enhanced component of the TFR in the Bayesian framework. Eventually, the time–frequency reassignment technique is adopted further to optimize the time–frequency resolution of the sparse component. Two simulated scenarios and a real-data experiment are used to evaluate the suggested approach's performance. It turns out that the proposed approach is robust to noise and can extract the bearing time-varying fault features effectively. • A sparsity enforced time–frequency decomposition model is constructed for bearing fault feature extraction. • The optimal model parameters can be adaptively determined by Gibbs sampler under the Bayesian framework. • The proposed method can effectively denoise and improve the time–frequency resolution of time-varying fault features. • The efficacy and superiority of the proposed method are investigated using simulated and experimental data. [ABSTRACT FROM AUTHOR]

• The proposed MR based pseudo negative stiffness control approach is one of the most practical and efficiency semi-active vibration control for stay cable. • Clarify the gain role of pseudo-negative stiffness force in stay cable vibration control in the theoretical asymptotic solution. • Establish a real-time simulation model of the stay cable coupled with time-varying control system. Stay cables are prone to large amplitude vibrations, due to their low inherent damping and high lateral flexibility. In this study, one magnetorheological (MR) damper based programmable intelligent control device is developed to control the vibration of the stay cable more practically and efficiently. The proposed programmable scheme possesses the advantages of low energy consumption, less real time calculation and dynamic input. Its control oriented dynamic model integrated with programmable pseudo negative stiffness (PNS) strategy is built. And its beneficial role in the vibration control is explained in the complex eigenvalue theory analysis of stay cable with MR damper based programmable PNS force. Finally, the simulation of dynamic response of stay cable shows the superiority of programmable control scheme. Its realization demonstrates the feasibility of the single-input programmable PNS scheme experimentally. The dynamic response simulation of stay cable shows that MR damper based PNS scheme can offer better damping performance than conventional control method and its additional damping ratio is 1.63 times higher than that of optimal MR damper passive-on control. [ABSTRACT FROM AUTHOR]

• The rate of alcohol use in patients with epilepsy (PWE) was 24.2%. • Been males, had a history of alcohol use were associated with alcohol use in PWE. • Heavy, frequent alcohol use were associated with worsening of seizure control in PWE. Alcohol consumption has multiple negative consequences for people with epilepsy, including precipitation of seizure or status epilepticus, worsening of seizure control, increased adverse effects of anti-seizure medications, increased sudden unexpected death in epilepsy, and premature mortality. The aim of this study was to investigate alcohol use and explore the sociodemographic and clinical factors associated with alcohol use among patients with epilepsy in western China. A face-to-face questionnaire on alcohol use was conducted at Sichuan Provincial People's Hospital from December 2020 to June 2021. All adult patients who came to our epilepsy center (inpatient and outpatient) were invited to participate in this study. Logistic regression was used to evaluate the possible risk factors associated with alcohol use within the last 12 months. A total of 425 patients completed this study, 24.2% of patients with epilepsy had used alcohol within the last 12 months, being male and having a history of alcohol use were independently associated factors. Among patients who had used alcohol within the last 12 months, 52.4% complained of worsening of seizure control, heavy alcohol use, and frequent alcohol use were independently associated with worsening of seizure control after alcohol use in patients with epilepsy. This study revealed that the rate of alcohol use among patients with epilepsy was high. Male patients with a history of alcohol use were more prone to alcohol use after a diagnosis of epilepsy. Heavy alcohol use and frequent alcohol use were independently associated with worsening of seizure control after alcohol use in patients with epilepsy. Patient education on the destructive effects of alcohol use is needed for patients with epilepsy. [ABSTRACT FROM AUTHOR]

Larynx carcinoma (LC) is the most prevalent head and neck cancer among adults. LC xenograft mouse model was generated to verify the effect of VEGF on macrophage polarization and tumor growth in vivo. EdU assay was performed to measure the cell proliferation. Transwell assay was applied to assess cell migration. The expression of YAP and STAT3 was also significantly increased in LC tumor tissues. Moreover, both YAP and STAT3 overexpression in LC cells promoted the proliferation, migration, as well as the secretion of PD-L1 in M2-like TAMs. Mechanistically, the interaction between YAP and STAT3 facilitated the transcription of VEGF. Moreover, with a co-culture system, VEGF secretion in LC cells enhanced PD-L1 expression in M2-like TAMs via activating VEGFR1-TGFβ signaling pathway. Furthermore, VEGF secreted from LC cells also promoted the tumor growth of LC in vivo. We revealed that dysregulated YAP/STAT3 activity in LC cells could enhance the secretion of VEGF, which then functioned on M2-like TAMs via activating VEGFR1-TGFββ pathway to promote the expression of PD-L1 and immunosuppressive function of M2-like TAMs. Therefore, VEGF and PD-L1 might have a pivotal crosstalk between M2-like TAMs and LC cells, which provided a novel therapeutic target in regulating the metastasis of LC in future. [ABSTRACT FROM AUTHOR]