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Green hydrogen produced by water splitting using renewable energy is the most promising energy carrier of the low-carbon economy. However, the geographic mismatch between renewables distribution and freshwater availability poses a significant challenge to its production. Here, we demonstrate a method of direct hydrogen production from the air, namely, in situ capture of freshwater from the atmosphere using hygroscopic electrolyte and electrolysis powered by solar or wind with a current density up to 574 mA cm −2 . A prototype of such has been established and operated for 12 consecutive days with a stable performance at a Faradaic efficiency around 95%. This so-called direct air electrolysis (DAE) module can work under a bone-dry environment with a relative humidity of 4%, overcoming water supply issues and producing green hydrogen sustainably with minimal impact to the environment. The DAE modules can be easily scaled to provide hydrogen to remote, (semi-) arid, and scattered areas. While obtaining H 2 from water splitting offers a promising strategy for renewable fuel production, current technologies rely on liquid freshwater. Here, authors use a hygroscopic electrolyte to achieve electrocatalytic water vapor splitting driven by renewable resources without liquid water.

This paper presents an innovative approach to detecting cracks in asphalt pavement using an FCN-attention model, which integrates attention mechanisms into a fully convolutional network (FCN) for enhanced pixel-level segmentation. The model employs a ResNet-50-based encoder and incorporates channel-wise and spatial attention modules to refine feature extraction and focus on the most relevant image regions. The results demonstrate that the FCN-attention model outperforms traditional models such as VGG-16, AlexNet, MobileNet, and GoogleNet across multiple evaluation metrics. Specifically, the FCN-attention model achieves a global accuracy rate of 90.79%, with a precision of 92.3%, recall of 89.5%, and an F1-score of 90.9%. Additionally, the model achieves an average intersection-over-union (IoU) ratio of 69.7% and a test duration of 109.1 ms per image. The proposed method also excels in crack length and width calculation, providing real-world dimensions for the detected cracks. The model’s effectiveness is further validated through an ablation study, which highlights the significant impact of the attention mechanism on model performance.

In this letter, an enhanced coprime array with compressed inter‐element spacing is designed for direction of arrival estimation. First, we analyse and conclude that the 0th sensor of coprime array with compressed inter‐element spacing is redundant for the consecutive lags, thus it is rearranged to the negative part to obtain enhanced coprime array with compressed inter‐element spacing. Then, on the basis of discussing the effect of the shift sensor on the difference co‐array, the number of unique lags and the range of consecutive lags are derived. Theoretical properties show that the closed‐form expressions of coprime array with compressed inter‐element spacing and enhanced coprime array with compressed inter‐element spacing are similar, while more difference lags can be generated from the latter. Moreover, larger inter‐element spacing can slightly alleviate the mutual coupling. Numerical examples verify the advantages of enhanced coprime array with compressed inter‐element spacing in direction of arrival estimation over coprime array with compressed inter‐element spacing configuration and other coprime arrays.

This letter proposes an efficient tensor model for Direction of Departure (DOD) and Direction of Arrival (DOA) estimation based on bistatic nested Multiple‐Input Multiple‐Output (MIMO) radar. Tensor offers a robust solution in characterizing multi‐dimensional data structures, ensuring data integrity, and has demonstrated remarkable effectiveness in the realm of multi‐dimensional parameter estimation. However, the complexity of traditional methods for constructing tensors is a major obstacle to practical applications. In response, this research introduces a novel approach that utilizes an iterative two‐dimensional Toeplitz matrix reconstruction method to rapidly derive expanded virtual covariance matrices prior to tensor construction. This streamlines the algorithm and improves efficiency. Numerical simulations have verified the superiority of this algorithm. 1. The proposed approach utilizes Toeplitz matrix‐based quadratic reconstruction to address rank deficiency issues and obtain an equivalent virtual covariance matrix with minimal complexity. 2. Results demonstrate that this approach achieves reduced computational complexity while maintaining high estimation performance.

This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

Patients with early-stage COPD were assigned to usual care plus tiotropium or placebo. Tiotropium resulted in better FEV 1 values. The annual decline in the prebronchodilator FEV 1 was similar in the two groups, but a benefit from tiotropium was seen in postbronchodilator FEV 1 .

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a uniform linear array to generate the covariance matrix. Then, we reconstructed the Toeplitz matrix and established a matrix optimization recovery model according to the kernel norm minimization theory. Finally, the reduced dimension multiple signal classification algorithm was applied to estimate the angle of the coherent targets, with which the automatic pairing of DOD and DOA could be realized. With the same number of physical sensors, the proposed method expanded the array aperture effectively, so that the degree of freedom and angular resolution could be improved significantly for coherent signals. However, the effectiveness of the method was largely limited by the signal-to-noise ratio. The superiority and effectiveness of the method were proved using simulation experiments.

For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.

To address the problems of low accuracy and low robustness of the conventional algorithm in estimating the direction of arrival (DOA) of coherent signals against a composite background of strong interference and non-uniform noise, in this paper, a coherent signal DOA estimation algorithm based on fixed projection blocking is proposed in conjunction with a multi-input multi-output (MIMO) radar. The covariance matrix of the received signal is first decomposed by eigenvalues, and a fixed projection matrix orthogonal to the interference guidance vector is constructed as the interference blocking matrix. Then, the received array signal is pre-processed to re-form the covariance matrix, and this matrix is rendered decoherent through a Toeplitz reconstruction. Finally, the reconstructed covariance matrix is estimated by DOA using the propagation operator algorithm to reduce the complexity. The simulation verifies that the proposed algorithm has a better robustness and higher accuracy than conventional algorithms for the DOA estimation of coherent signals in composite backgrounds.

To assess the function of the erector spinae muscle’s cross-sectional area (ESM CSA )as a biomarker for the outcome of AECOPD hospitalized patients. Based on chest CT imaging, ESM CSA were caculated following admission. Cox regression analyses, including univariate and multivariate approaches, were utilized to determine risk factors associated with 1-year mortality and initial hospitalization in patients with AECOPD. Additionally, Poisson regression was implemented to assess the rate of rehospitalization. There were 236 AECOPD patient included in the present study, including 59 and 177 patients in the ESM CSA lower group and normal groups respectively. Seventeen patients died within 1 year after discharged from the hospital, and the 1-year mortality rates were 15.3% and 4.5% for the ESM CSA lower group and normal group. A total of 112 patients suffered from 273 rehospitalizations for AECOPD within 1 year after discharged from hospital. Cox regression analysis showed that ESM CSA were associated with the 1-year first hospitalization for AECOPD. Poisson regression analysis showed that ESM CSA were associated with the rate of rehospitalization for AECOPD (IRR = 0.57, 95% CI 0.45–0.73 for univariate analysis, and IRR = 0.56, 95% CI 0.43-0.72for multivariate analysis). Both univariate (HR = 0.29 95% CI: 0.11–0.75) and multivariate Cox regression analyses (HR = 0.35, 95% CI: 0.12–0.99) showed that ESM CSA was associated with 1-year mortality. Lower ESM CSA was a risk factor of 1-year mortality and 1-year rehospitalization for AECOPD.