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Developing an intermediate-temperature solid oxide fuel cell (IT-SOFC) is one of the most promising ways of achieving carbon neutrality, but its air-electrode is restricted by the conflict between the sluggish catalytic activity and durability. Herein, an A-site high-entropy perovskite composite La0.2Ba0.2Sr0.2Ca0.2Ce0.2−xCoO3−δ–xCeO2 (LBSCCC–CeO2) air-electrode material is fabricated via a one-step self-constructing strategy, which shows excellent oxygen reduction activity and stability due to the high-entropy structure and the synergy effect between LBSCCC and interfacial CeO2. This work provides a new way of fabricating high-performance air-electrodes in IT-SOFCs. [ABSTRACT FROM AUTHOR]

The assessment of coal burst risk is a complex and systematic process; the variations among the indicator systems and the stability of the evaluation models used can influence the results. In this study, an index system for the analytic hierarchy process was constructed based on 21 geomechanically influential factors on rock bursts. The multi-weight combination optimization model was used to synthesize the subjective weights derived by the four experts using AHP and the objective weights derived through the inter-criteria correlation method to obtain the unique optimization weights. After normalizing the original evaluation data, the Gram–Schmidt orthogonalization method was employed to eliminate correlations among factors. The optimized factor weights and data were subsequently input into a linearly weighted comprehensive evaluation model to determine the coal burst risk. The proposed method was applied to assess the coal burst risk of a coal seam in the Liang Jia Coal Mine. These results align with those of the actual coal mine scenario. Indeed, the proposed linear weighted comprehensive evaluation model provided enhanced accuracy and reliability with improved practicality compared to previously proposed methods. [ABSTRACT FROM AUTHOR]

The Dunhuang ancient manuscripts are an excellent and precious cultural heritage of humanity. However, due to their age, the vast majority of these treasures are damaged and fragmented. Faced with a wide range of sources and numerous fragments, the process of restoration generally involves two core elements: sibling fragments identification and fragment assembly. Currently, fragment restoration still heavily relies on manual labor. During the long practice, a consensus has been reached on the importance of edge features for not only assembly but also for identification. However, accurate extraction of edge features and their use for efficient identification requires extensive knowledge and strong memory. This is a challenge for the human brain. So that in previous studies, fragment edge features have been used for assembly validation but rarely for identification. Therefore, an edge matcher is proposed, working like a bloodhound, capable of "sniffing out" specific "flavors" in edge features and performing efficient sibling fragment identification accordingly, providing guidance when experts perform entity assembly subsequently. Firstly, the fragmented images are standardized. Secondly, traditional methods are used to compress the representation of fragment edges and obtain paired local edge images. Finally, these images are fed into the edge matcher for classification discrimination, which is a CNN-based pairwise similarity metric model proposed in this paper, introducing residual blocks and depthwise separable convolutions, and adding multi-scale convolutional layers. With the edge matcher, a complex matching problem is successfully transformed into a simple classification problem. In the absence of a standard public dataset, a Dunhuang manuscript fragment edge dataset is constructed. Experiments are conducted on that dataset, and the accuracy, precision, recall, and F1 scores of the edge matcher all exceeded 97%. The effectiveness of the edge matcher is demonstrated by comparative experiments, and the rationality of the method design is verified by ablation experiments. The method combines traditional methods and deep learning methods to creatively use the edge geometric features of fragments for sibling fragment identification in a natural rather than coded way, making full use of the computer's computational and memory capabilities. The edge matcher can significantly reduce the time and scope of searching, matching, and inferring fragments, and assist in the reconstruction of Dunhuang ancient manuscript fragments. [ABSTRACT FROM AUTHOR]

Studies of the damage process of fiber-reinforced concrete through acoustic emission are very significant for concrete structural health monitoring. In this study, three specifications of fiber concrete and one group of plain concrete were prepared to carry out the uniaxial compression test and acoustic emission monitoring test; then, b value, entropy H, and variance D, were calculated and compared their characterization effect. The main results showed that fibers increased the degree of internal inhomogeneity of the specimens, making the acoustic emission response more active. For every 2% increase in fiber content, the total acoustic emission count and energy increased by about 20%, the acoustic emission precursor parameters changed more significantly, the b-value decreased by 2%–10%, the entropy and variance increased by 3%–5% and 2%–22%, respectively. The variation of b value, entropy, and variance can be divided into three phases: initial rising/falling, unstable transition, and fluctuating slow-rising/falling, which had good consistency with the stress curve. According to the linear fitting results, the b value that dropped below the envelope in the post-peak phase can be taken as the damage precursor point, and its accuracy and generalizability were better. The entropy at the failure moment was around 0.6, but the value close to or above 0.6 occurred several times during the damage process, and taking the entropy value beyond the envelope range as the failure precursor point may lead to the error early warning. The variance was slightly worse to distinguish small-scale fracture, but was not susceptible to high-energy events. Therefore, variances close to 5 or beyond the envelope interval can be regarded as the precursor of final failure. As for studying concrete damage processes with acoustic emission, it is suggested to combine multiple parameters for comprehensive discrimination. [ABSTRACT FROM AUTHOR]

Nowadays, the development and utilization of more and more engineering construction are closely related to granite. However, many granite rock masses in Qingdao contain natural hole defects, which have a great impact on the mechanical properties of granite. It may even cause instability of surrounding rock and safety accidents. Therefore, in this paper, we discuss the influence of the hole diameter on the mechanical properties and stability of granite rock surrounding tunnels. Uniaxial compression experiments were conducted on granite with different hole diameters, and monitoring was carried out using the acoustic emission system and the XTDIC (Xintuo 3D Digital Image Correlation) three-dimensional–full-field strain-measurement systems. The relationship between the strength, deformation, and hole size of granite was investigated. In addition, using the Yangkou tunnel as the prototype and the PFC2D (Particle Flow Code of 2D) particle-flow–numerical-simulation program, a working tunnel model with different hole sizes was established to simulate the influence of natural hole defect sizes on the stability of rock. The results show that: (1) with an increase in hole diameter, the uniaxial compressive strength and elastic modulus of the granite sample gradually decreased. The brittleness of the granite samples gradually decreased, and the ductility gradually increased. (2) Under the action of axial stress and with an increase in the hole diameter, the sample was more likely to produce a stress concentration around the hole defect, which increased the deformation localization band, development, and expansion, as well as the intersection degree. As a result, granite samples are more likely to develop new cracks. These cracks increase in number and size, reducing the compressive strength of the granite sample. (3) The size of the hole defects significantly affected the damage and mechanical properties of the model surrounding rock. When increasing the hole diameter, the defect area increased and the tensile stress concentration near the hole in the localized rock became more evident. In addition, the stability of the rock surrounding the tunnel was significantly reduced, and its bearing capacity was weakened, leading to easier crack initiation and rock damage. [ABSTRACT FROM AUTHOR]

Aiming at the problem of strong mining pressure in the near shallow buried and super-large mining height face, and considering the first 108 working faces in Jinjitan Coal Mine as the engineering background, the movement pattern and pressure distribution characteristics of the overlying rock layer on the 8.2 m fully mechanized mining face were analyzed from the perspective of theoretical analysis, field monitoring data, and numerical simulation. The results of the spatial structure mechanics model and FLAC3D numerical model of the mining face with a super-large mining height established in this study, which indicated that the mining operation of the mining face with a super-large mining height experienced rock dynamic load pressure and large-small periodic pressure phenomena. The fracture of the lower keystone beam leads to a small pressure cycle, and a large pressure cycle occurs when both the upper and lower keystone beams are fractured. Generally, the step distance during the size cycle is about twice the normal cycle. The site monitoring data shows that the initial incoming pressure step is 102 m and the periodic incoming pressure step is about 28.7 m, which is consistent with the theoretical value. When the working surface advances slowly, the dynamic load factor is smaller, and the incoming pressure step and duration are shorter, and vice versa. The research results are of important reference significance for mine pressure law and disaster prevention in similar conditions. [ABSTRACT FROM AUTHOR]

Purpose: Long-term hospitalized patients with schizophrenia (SCZ) are vulnerable to physical illness, leading to impaired life expectancy and treatment outcomes. There are few studies on the influence of non-alcoholic fatty liver disease (NAFLD) in long-term hospitalized patients. This study aimed to investigate the prevalence of and influence factors for NAFLD in hospitalized patients with SCZ. Patients and Methods: This cross-sectional retrospective study included 310 patients who had experienced long-term hospitalization for SCZ. NAFLD was diagnosed based on the results of abdominal ultrasonography. The T-test, Mann–Whitney U-test, correlation analysis, and logistic regression analysis were used to determine the influence factors for NAFLD. Results: Among the 310 patients who had experienced long-term hospitalization for SCZ, the prevalence of NAFLD was 54.84%. Antipsychotic polypharmacy (APP), body mass index (BMI), hypertension, diabetes, total cholesterol (TC), apolipoprotein B (ApoB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglycerides (TG), uric acid, blood glucose, gamma-glutamyl transpeptidase (GGT), high-density lipoprotein, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio significantly differed between the NAFLD and non-NAFLD groups (all P< 0.05). Hypertension, diabetes, APP, BMI, TG, TC, AST, ApoB, ALT, and GGT were positively correlated with NAFLD (all P< 0.05). The results of the logistic regression analysis indicated that APP, diabetes, BMI, ALT, and ApoB were the influence factors for NAFLD in patients with SCZ. Conclusion: Our results suggest a high prevalence of NAFLD among patients hospitalized long-term due to severe SCZ symptoms. Moreover, a history of diabetes, APP, overweight/obese status, and increased levels of ALT and ApoB were identified as negative factors for NAFLD in these patients. These findings may provide a theoretical basis for the prevention and treatment of NAFLD in patients with SCZ and contribute to the development of novel targeted treatments. [ABSTRACT FROM AUTHOR]

The relationship between cancer and iron metabolism has received more attention, but there is less research on nutritional support to improve the nutritional and inflammatory status of cancer patients by regulating iron metabolism. A survey was conducted involving 90 patients with lung cancer. The control group was given routine hospitalization diet and the oral nutrition supplement (ONS) group was given oral nutrient solution on the basis of routine hospitalization diet. It lasted for 2 weeks. The changes of iron metabolism, nutritional status, and inflammatory indicators of the two groups were compared. After 2 weeks of intervention, the levels of ALB and PA in the ONS group were increased than that in the control group (P <.05), the inflammation was significantly decreased in the ONS group (P <.05), and the levels of Hb, SI, and TIBC in the ONS group were higher than that in the control group, while the SF was significantly reduced (P <.05). ONS could improve iron metabolism, nutritional status, and inflammatory levels. We speculate that the decrease in inflammation may be due to the changes of iron metabolism, thereby improving nutritional status. It may become a new target for tumor treatment. [ABSTRACT FROM AUTHOR]

Graph-based clustering plays an important role in the clustering area. Recent studies about graph neural networks (GNN) have achieved impressive success on graph-type data. However, in general clustering tasks, the graph structure of data does not exist such that GNN can not be applied to clustering directly and the strategy to construct a graph is crucial for performance. Therefore, how to extend GNN into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, AdaGAE, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-euclidean structure sufficiently. Importantly, we find that the simple update of the graph will result in severe degeneration, which can be concluded as better reconstruction means worse update. We provide rigorous analysis theoretically and empirically. Then we further design a novel mechanism to avoid the collapse. Via extending the generative graph models to general type data, a graph auto-encoder with a novel decoder is devised and the weighted graphs can be also applied to GNN. AdaGAE performs well and stably in different scale and type datasets. Besides, it is insensitive to the initialization of parameters and requires no pretraining. [ABSTRACT FROM AUTHOR]

Increasing knowledge about calcification together with improved imaging techniques provided evidence that intracranial arterial calcification (IAC) can be divided into two distinct entities: intimal and medial calcification. The purpose of this study was to investigate the association between kidney function and the two patterns of IAC, which could clarify the underlying mechanisms of intimal or medial calcification and its clinical consequence. A total of 516 participants were enrolled in this study. Kidney function was assessed using the estimated glomerular filtration rate (eGFR) based on modified glomerular filtration rate estimating equation. The degree of IAC measured by IAC scores was evaluated on non-contrast head computed tomography (CT) images and IAC was classified as intimal or medial calcification. Associations of kidney function with IAC scores and patterns were assessed sing multivariate logistic regression analysis. In 440 patients (85.27%) with IAC, 189 (42.95%) had predominant intimal calcifications and 251 (57.05%) had predominant medial calcifications. Multivariate analysis revealed that lower eGFR level (eGFR <60 ml/min/1.73 m2) was associated with higher IAC scores (odds ratio [OR] 2.01; 95% confidence interval [CI], 1.50–2.71; p < 0.001). Medial calcification was more frequent in the lower eGFR group (eGFR <60 ml/min/1.73 m2) compared to the other two groups with eGFR 60 to 89 and eGFR >90 ml/min/1.73 m2 (78.72% vs. 53.65%, p < 0.001; 78.72% vs. 47.78%, p < 0.001). In multivariable analysis, impaired kidney function was associated with an increased odds of medial calcification presence in patients with eGFR <60 ml/min/1.73 m2 (OR, 1.47; 95% CI, 1.05 to 2.06). Our findings demonstrated that impaired renal function was independently associated with a higher degree of calcification in intracranial arteries, especially medial calcification, which reflects a distinction between two types of arterial calcification and raise the possibility for specific prevention of lesion formation. [Display omitted] • Impaired kidney function was independently associated with higher degrees of calcification in the intracranial arteries, especially medial calcification. • We found a high prevalence of medial calcification among asymptomatic patients with eGFR <60 ml/min/1.73 m2. • The findings of this study may help optimize cerebrovascular disease prevention and therapeutics. [ABSTRACT FROM AUTHOR]

Graph-based clustering plays an important role in the clustering area. Recent studies about graph neural networks (GNN) have achieved impressive success on graph-type data. However, in general clustering tasks, the graph structure of data does not exist such that GNN can not be applied to clustering directly and the strategy to construct a graph is crucial for performance. Therefore, how to extend GNN into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, AdaGAE, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-euclidean structure sufficiently. Importantly, we find that the simple update of the graph will result in severe degeneration, which can be concluded as better reconstruction means worse update. We provide rigorous analysis theoretically and empirically. Then we further design a novel mechanism to avoid the collapse. Via extending the generative graph models to general type data, a graph auto-encoder with a novel decoder is devised and the weighted graphs can be also applied to GNN. AdaGAE performs well and stably in different scale and type datasets. Besides, it is insensitive to the initialization of parameters and requires no pretraining. [ABSTRACT FROM AUTHOR]

A large population and rapid urbanization dramatically promote the heating supply demand, the combined heating and power (CHP) system for energy cascade utilization came into being. However, the research on the recovery and utilization of condensing heat, the exploration of the coupling law between power generation and heating supply, and the influence of heat source parameters on thermo-economic performance are still insufficient. To this end, two combined heating and power (CHP) systems coupled with an organic Rankine cycle (ORC) and vapor compression cycle (VCC) are proposed, and their thermodynamic and economic performances are optimized and analyzed by the laws of thermodynamics. Results show that the increase of the volume flow will increase the power generation and heating supply quantity of the system, and there is an optimal evaporation temperature range of 130–140 °C to optimize the performance of the system. The increase of heat source temperature will improve the economic performance of the system, but it will reduce the exergetic efficiency. Therefore, two factors should be comprehensively considered in practical engineering. There is mutual exclusivity between the net power output of the system and the heating supply quantity, it should be reasonably allocated according to the actual needs of users in engineering applications. In addition, the exergetic efficiency of the two systems can reach more than 60%, and the energy utilization rate is high, which indicates that the cascade utilization mode is reasonable. [ABSTRACT FROM AUTHOR]

Surrounding rock control and support stability in the process of coal seam mining in ultra-large height mining face are the key to normal mine operation. In this study, the roof movement and deformation of an ultra-large height mining face are analyzed, and the working resistance of the ultra-large height mining face is obtained by introducing the equivalent immediate roof. By analyzing the coal wall spalling, the multiple positions of the spalling and the required support force of the support are obtained. At the same time, ultra-large height supports are more prone to instability problems. In this study, the stability of the ultra-large height supports was analyzed by establishing a mechanical model. The results show that: 1. The overturning limit angle of support has a hyperbolic relationship with the center of gravity. 2. Under the condition of ultra-large height, the increase in the base width of the bracket significantly improves the stability of the supports. 3. The sliding limit angle of support is positively correlated with the support load and the friction coefficient between the support and the floor. The above conclusions can provide guidance on the selection of supports and the adoption of measures to enhance the stability of the supports during use under ultra-large height conditions. The working resistance of the ultra-large height supports in the 108 mining face of the Jinjitan Coal Mine was monitored. The monitoring results show that: The average resistance of the supports is 22.6 MPa. The selected supports can meet the stability requirements of the working face support. The frequency of mining resistance in 0~5 MPa accounts for 28.38%, which indicates that some supports are insufficient for the initial support force during the moving process. Furthermore, the stability of the supports can be enhanced by adjusting the moving process. This study provides a reference for the selection of supports in ultra-large height mining faces and proposes measures to enhance the stability of the supports, which provides guidance for the safe mining of coal in ultra-large height mining faces. [ABSTRACT FROM AUTHOR]

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies a kNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the $5^{th}\sim 7^{th}$ 5 t h ∼ 7 t h bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images. [ABSTRACT FROM AUTHOR]

Gait recognition has become a mainstream technology for identification due to its ability to capture gait features over long distances without subject cooperation and resistance to camouflage. However, current gait recognition methods face challenges as they use a single network to extract both temporal and spatial features from gait sequences. This approach imposes a heavy burden on the network, resulting in reduced extraction efficiency. To solve this problem, we propose a two-branch network to extract the spatio-temporal features of gait sequences. One branch primarily focuses on spatial feature extraction, while the other concentrates on temporal feature extraction. This design can make one branch focus on a specific task, leading to significant performance improvements. For temporal feature extraction, we propose the Global Temporal Information Extraction Network (GTIEN). GTIEN extracts temporal features of gait sequences by sequentially exploring the relationship between adjacent gait silhouettes from pixel and block levels. For spatial feature extraction, we introduce the Selective Horizontal Pyramid Convolution Network (SHPCN). SHPCN explores the multi-granularity features of gait silhouettes from global and local perspectives and assigns them appropriate weights according to their importance. By reasonably combining the temporal features extracted from GTIEN and spatial features extracted from SHPCN, we can effectively learn the spatial–temporal information of the gait sequences. Extensive experiments on CASIA-B and OUMVLP demonstrate that our method has better performance than some state-of-the-art methods. • We sequentially explores the relationship between adjacent gait silhouettes from the pixel level and block level. • We explore global and local features of gait silhouettes from different ranges and selectively assign higher weights to important features. • We combine SHPCN and GTIEN and propose a two-branch network for spatio-temporal gait feature extraction. [ABSTRACT FROM AUTHOR]

In order to study the gas occurrence regularity and the main controlling factors of Panxi Mine, gas parameters of the mine were determined through the combination of field survey and experiment, and the distribution regularity of gas content in coal seam strike and dip was analyzed. Besides, the fault structure was quantitatively researched based on the fractal theory. The experimental results show that the fractal dimension of the mine mostly ranges from 0.7 to 1.6. The fault structure becomes more complex with the increase of fractal dimension, and the gas content in the region with a larger fractal dimension is higher. The fractal dimension of fault can reflect complexity of geological structure of the mine. The research is targeted to the prevention and control of gas accidents in Panxi Mine, and it has important theoretical and practical significance for promoting safety production and gas development and utilization in coal mines. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an essential research topic for spectral data analysis due to the existence of mixed pixels. Recently, many methods based on sparse nonnegative matrix factorization (NMF) have been widely used for unmixing by incorporating similarity preserving. However, most of them conduct the similarity learning and unmixing by using two separate steps, which may lead to the case that the learned similarity matrix is not the optimal one for unmixing. Thus, the performance of unmixing would be influenced and become undesirable. To alleviate this problem, we propose an adaptive relationship preserving-based sparse NMF (ARP-NMF) for hyperspectral unmixing. Typically, we regard similarity learning and unmixing as an alternative optimization process. During this process, the learned similarity weights and unmixing results can be mutually improved. Thus, our proposed method can learn the optimal similarity weights for unmixing and obtain better generalization ability for different hyperspectral images than traditional methods. Moreover, by using the spectral and spatial local structure, our ARP-NMF method effectively preserves structure consistency between pixels and abundances. Experimental results both on the synthetic data and the real data reveal that our proposed method outperforms several representative sparse NMF-based methods. [ABSTRACT FROM AUTHOR]

Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure. [ABSTRACT FROM AUTHOR]

The objective was to investigate the safety and efficacy of the nutrition protocol in enhanced recovery after surgery (ERAS) of hypopharyngeal cancer. Protocol focus was patient consumption of nutritional supplements perioperatively. In this retrospective study, a total of 78 patients with hypopharyngeal cancer were divided into the ERAS group (n = 39) and the control group (n = 39). The data were collected from two groups of three time points: 1 day before surgery, 1 day after surgery, and 7 days after surgery. The difference between two groups of the nutritional and immune status, postoperative exhaust time, hospitalization expense and hospitalization time were compared. The nutritional and immune status in the ERAS group were better than that in the control group at 7 days after surgery (P <.05); The hospitalization expense and hospitalization time in the ERAS group were lower comparing with the control group (P <.05). Our nutrition protocol is effective and safe in ERAS of patients with hypopharyngeal cancer. It's significant to implement ERAS of hypopharyngeal cancer patients with nutritional protocol during peroperative period, which will improve immune system, maintain health metabolic functions, and reduce the hospitalization time as well as the hospitalization expense. [ABSTRACT FROM AUTHOR]

The Enhanced Geothermal System (EGS) is a recognized geothermal exploitation system for hot dry rock (HDR), which is a rich resource in China. In this study, a numerical simulation method is used to study the effects of geothermal fluid dryness and non‐condensable gas content on the specific enthalpy of geothermal fluid. Combined with the organic Rankine cycle (ORC), a numerical model is established to ascertain the difference in power generation caused by geothermal fluid dryness and non‐condensable gas content. The results show that the specific enthalpy of geothermal fluid increases with the increase of geothermal fluid temperature and geothermal fluid dryness. If the dryness of geothermal fluid is ignored, the estimation error will be large for geothermal fluid enthalpy. Ignoring non condensable gas will increase the estimation of geothermal fluid enthalpy, so the existence of the non‐condensable gas tends to reduce the installed capacity of a geothermal power plant. Additionally, both mass flow of the working medium and net power output of the ORC power generation system are increased with increasing dryness of geothermal fluid, however there is some impact of geothermal fluid dryness on thermal efficiency. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is regarded as an indispensable preprocessing procedure for many field of spectral data analysis because of the existence of mixed pixels. However, the unmixing algorithms are implemented under the presupposition of special mixing models. In other words, any unmixing algorithm only works on a special mixing model of the spectra. This leads to low generalization performance of most unmixing algorithms. To mitigate this problem, a robust unmixing method is proposed, which exploits dual views with adaptive weights for HU (AwDvHU). The proposed method utilizes multi-kernel learning to construct a high-dimensional space that can reflect the nonlinear interaction between spectra optimally. Then, through fusing the unmixing object of original data and the mapped high-dimensional features, the AwDvHU method takes full advantage of the complementary characteristics of features in dual views. Moreover, the AwDvHU method automatically learns the weights for dual views according to the importance of different feature spaces. Its effectiveness in unmixing is verified by experimental results both on the synthetic and real data. [ABSTRACT FROM AUTHOR]

Excessive alcohol and iron intake can reportedly cause liver damage. In the present study, we investigated the effect of Lactobacillus casei on liver injury in rats co‐exposed to alcohol and iron and evaluated its possible mechanism. Sixty male Wistar rats were randomly divided into three groups for 12 weeks: the Control group (administered normal saline by gavage and provided a normal diet); alcohol +iron group (Model group, treated with alcohol [3.5–5.3 g/kg/day] by gavage and dietary iron [1,500 mg/kg]); Model group supplemented with L. casei (8 × 108 CFU kg−1 day−1) (L. casei group). Using hematoxylin and eosin (HE) staining and transmission electron microscopy, we observed that L. casei supplementation could alleviate disorders associated with lipid metabolism, inflammation, and intestinal mucosal barrier injury. Moreover, levels of serum alanine aminotransferase, gamma‐glutamyl transferase, triglyceride (TG), and hepatic TG were significantly increased in the model group; however, these levels were significantly decreased following the 12‐week L. casei supplementation. In addition, we observed notable improvements in intestinal mucosal barrier function and alterations in T lymphocyte subsets and natural killer cells in L. casei‐treated rats when compared with the model group. Furthermore, L. casei intervention alleviated serum levels of tumor necrosis factor‐α and interleukin‐1β, accompanied by decreased serum endotoxin levels and downregulated expression of toll‐like receptor 4 and its related molecules MyD88, nuclear factor kappa‐B p65, and TNF‐α. Accordingly, supplementation with L. casei could effectively improve liver injury induced by the synergistic interaction between alcohol and iron. The underlying mechanism for this improvement may be related to immune regulation and inhibition of enterogenic endotoxin‐mediated inflammation. [ABSTRACT FROM AUTHOR]

• We leverage the strengths of neural networks in extracting information to improve image quality for single-pixel imaging. • We design a part-based model to divide image features into different parts to facilitate finer-grained learning. • We continuously incorporate prior information by looping the self-finished reconstructed image feedback back to the network. In this study, we proposed a self-supervised image-loop neural network (ILNet) with a part-based model for single-pixel imaging (SPI). ILNet employs a part-based model that divides image features into different parts to facilitate finer-grained learning, resulting in improved image details when reconstructing a randomly input 2D signal into a 2D object image. Then, the 2D image generated by ILNet can serve as input for the subsequent iteration to continuous incorporation of prior information to ensure high-quality imaging at low sampling rates. 1D signals collected by the single-pixel detector are used as labels for adaptively optimizing and reconstructing the image. Our results show that the ILNet can reconstruct high-quality images with lower sample rates in unknown free-space and underwater experiments, making it a general framework for incorporating physical models into neural networks and expanding the practical application of SPI. [ABSTRACT FROM AUTHOR]

There was an incorrect order of the fifth and sixth affiliations in the list of authors, which was due to a misstep during the submission process. The correct order should be as follows:5School of Mine Safety, North China Institute of Science and Technology, Langfang 101601, China6State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, Jiangsu, People's Republic of ChinaBy Haotian Li; Xuelong Li; Jianhua Fu; Zhenliang Gao; Peng Chen and Zhibo ZhangReported by Author; Author; Author; Author; Author; Author [Extracted from the article]

• The fracture surface fractal and RA/AF value were investigated. • The proportion of tension and shear fracture were less disparate. • The surface of fiber concrete was fragmented. The concrete columns suffer from spalling and flaking in the room and pillar mining method, which affects service performance. The energy absorption and deformation failure laws of fiber concrete structures under compression load need further study. In this study, four types of imitation steel fiber concrete specimens were prepared, and uniaxial compression experiments were carried out. Simultaneous photographic and acoustic emission monitoring was conducted to investigate the compression energy absorption, crack evolution, fracture surface fractal and RA/AF value characteristics of fiber concrete. The results obtained in the present work are as follows. Imitation steel fiber concrete showed the best compressive and energy absorption performance with a 0.4–0.6% fiber/mortar mass ratio. A higher dosage of fibers prolonged the yield time but also led to earlier macroscopic cracking. The bond between imitation steel fiber and matrix can disperse the stress, which significantly improved the toughness of concrete. For every 0.2% increase in fiber content, the pre-peak toughness index CTI and post-peak damage absorption energy FCEC increased by about 10% and 50%, respectively. Both plain concrete and fiber concrete specimens developed macroscopic cracks under local tensile stresses. The proportion of shear fracture in plain concrete reached 80.1% and dominated during the secondary crack development. The proportion of tension and shear fracture in fiber concrete was less disparate and showed a mixed pattern with synergistic action. The proportion of shear fractures gradually increased with the increase in fiber content. The calculated results of the RA/AF method corresponded well with the crack morphology. With the increase in fiber content, the bridging effect of fiber became more obvious, and the ability of specimens to resist cracking improved significantly. The fractal characteristics of the fracture surface of each specimen were investigated. The plain concrete specimens had a relatively complete surface when destroyed, and their fractal dimension was only 1.62, the lowest among all the specimens. The surface of fiber concrete was more fragmented, and their fractal dimension was higher, with the fractal dimension D increasing by 0.2–0.4 for each 0.2% increase in fiber content. This study can provide a reference for the fiber concrete pillar designs. [ABSTRACT FROM AUTHOR]

This paper's goal is to investigate if a gob-side entry retention technique combined with a surrounding rock support system is feasible in three soft coal seams. Field engineering confirmed the results of numerical simulation tests and similar simulation tests, which were conducted in accordance with the actual geological conditions of Zhaojiazhai Mine. The following conclusions are reached after studying the technology and process parameter of the gob-side entry retaining in three soft coal seams in conjunction with theoretical calculations: the coal seam of Zhaojiazhai Coal Mine's 12 209 working face is a part of the soft coal seam, and its loose circle is approximately 1.8 m. The expansion roadway size is 3.5 m, and the potential loose circle range is 1.32 m, according to the same model and numerical simulation test. The support scheme after the expansion of the road working face is determined to be the "anchor rod + anchor cable + hydraulic lifting shed" support method. Furthermore, this article suggests a building method for the reinforcement and enlargement of gob-side entry retaining in three-soft thick coal seam by theoretical analysis and numerical simulation. Roadway shotcrete, advance grouting, building of a large deformation anchor cable and continuous resistance, single column lifting shed, hydraulic lifting shed, and roadway enlargement in advance are all steps in the procedure. Furthermore, an analysis is conducted on the deformation features of the surrounding rock in gob-side entry retention. The study highlights the significance of actively supporting the surrounding rock, fortifying the roof support, guaranteeing the stiffness compatibility between the shoulder filling body and the surrounding rock on the roof, boosting the wall's strength and stability, and enhancing the roadway's stability. [ABSTRACT FROM AUTHOR]

Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches. [ABSTRACT FROM AUTHOR]

Objective: Intracranial arterial calcification (IAC) is a risk factor of ischemic stroke. However, the relationship between IAC patterns and clinical outcome of ischemic stroke remains controversial. We aimed to investigate the correlation between IAC patterns and the effects of reperfusion therapy among acute stroke patients. Methods: Consecutive acute ischemic stroke patients who underwent reperfusion therapy were included. IAC was categorized as intimal or medial. Based on its involvement, IAC was further classified as diffuse or focal. Neurologic dysfunction was assessed by the National Institute of Health stroke scale (NIHSS). Clinical outcome including favorable neurologic outcome (FNO) and early neurologic deterioration (END) were assessed. Results: Of 130 patients, 117 had IAC. Intimal IAC was identified in 74.6% of patients and medial IAC was present in 64.6% of patients. Diffuse IAC was present in 31.5% of patients. All diffuse IACs were medial pattern. Diffuse IAC was associated with higher baseline NIHSS (p = 0.011) and less FNO (p = 0.047). Compared with patients with focal or single diffuse IAC, patients with multiple diffuse IAC had higher baseline NIHSS (p = 0.002) and less FNO (p = 0.024). Multivariable linear regression (p < 0.001) and logistic regression (p = 0.027) suggested that multiple diffuse IAC was associated with higher baseline NIHSS and less FNO. No significant association was found between END and different IAC patterns. Interpretation: Multiple diffuse medial IAC may predict severer neurologic dysfunction and less favorable neurologic outcome after reperfusion therapy in acute stroke patients. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have been widely used in image semantic segmentation. However, continuous downsampling operations in convolutional neural networks (such as pooling or convolution with step size) reduce the initial image resolution and lose the spatial details of the image, resulting in blurred image segmentation results. To alleviate this problem, in this paper we propose a multi-stage context refinement network (MCRNet) for semantic segmentation. Specifically, we first construct a Lowest-resolution Chain Context Aggregation (LCCA) module to encode rich semantic information. For obtaining more spatial detail information, we further build a High-resolution Context Attention Refinement (HCAR) module consisting of context feature extraction and context feature refinement. Finally, MCRNet fuses the context information generated by LCCA and HCAR for pixel prediction. Experimental results on three challenging semantic segmentation datasets, namely PASCAL VOC2012, ADE20K and Cityscapes, reveals that our proposed MCRNet is effective. [ABSTRACT FROM AUTHOR]

The groundwater seepage in the fractured rock mass affects the stability of the surrounding rock of the roadway. To understand the fluid seepage characteristics and the change law of the microscopic pore structure of the fractured rock mass under different loading conditions, the low-field nuclear magnetic resonance technology (LF-NMR) was used to conduct a visual experimental study. The T 2 spectrum distribution curve tested by nuclear magnetic resonance indicated that the pore structure of the fractured sandstone sample had three peaks (namely micropores, mesopores, and macropores) with medium and large pores distributed from 0.63 μm to 100 μm. Quantitative analysis of nuclear magnetic resonance imaging signals during water flooding of fractured sandstones revealed the characteristics of fluid migration and distribution at different times and locations at different flow rates in the core. The height of the fractured core at 23 mm had small water content and low porosity. The water content at the position of 25–40 mm was higher, and the porosity was large. Under the effect of flow velocity, there were obvious differences in the state of fractured core displacement. That is, the greater the flow velocity, the shorter the water flooding time and the more concentrated the fluid sweep area. The dominant channel for seepage was formed quickly. The research results can provide a reference for preventing the erosion of roadway surrounding rock caused by groundwater fluid. • The low-field nuclear magnetic resonance technology (LF-NMR) was used to conduct a visual experimental study. • Quantitative analysis of nuclear magnetic resonance imaging signals during water flooding of fractured sandstones revealed the characteristics of fluid migration and distribution at different times and locations at different flow rates in the core. • The greater the flow velocity, the shorter the water flooding time, and the more concentrated the fluid sweep area. The dominant channel for seepage was formed quickly. [ABSTRACT FROM AUTHOR]

• We proposed a new support plan and conducted field tests. • Broken surrounding rock, low degradation strength, unreasonable support parameters, and uncoupling of support and surrounding rock were the main causes of roadway deformation. • The active–passive coupling support scheme had an amplifying effect. • The deformation of the roadway surrounding rock was effectively controlled and the scheme could ensure the stability of the roadway. The safe mining of Xin'an Coal Mine was challenging because of several problems, including the soft roadway seams, fracture development, fracturing and deformation of the incompact surrounding rock, and the unreasonable support parameters. Through the investigation of these problems and the analysis of the influence of active and passive support on the stability of the roadway combined with the theory, we proposed a new support plan and conducted field tests. The obtained results were as follows: broken surrounding rock, low degradation strength, unreasonable support parameters, and uncoupling of support and surrounding rock were the main causes of roadway deformation. To a certain extent, the passive support could prevent the damage of the surrounding rock, prevent the attenuation of the stress of the rock around the anchor point, and enhance the effect of active support. The active support could pre-support the gravity of the broken surrounding rock, improve the stress environment of the surrounding rock, and increase the self-balancing ability of the surrounding rock structure. It also could control the damage zone of surrounding rock and the expansion of the stress arch to the depth, restrict the range of rock strata that the passive support system needed to control, and play a protective role. The active–passive coupling support scheme had an amplifying effect. In this regard, the active–passive coupling support scheme of "bolt, W steel belt" + "anchor cable, anchor cable beam" + "I-steel shed" was practiced, and the relevant support parameters were determined. The scheme was implemented in the 2301 mining roadway, and the displacement convergence deformation of the two walls and the roof and floor of the roadway was monitored. It was found that the deformation of the roadway surrounding rock was effectively controlled and the scheme could ensure the stability of the roadway. The research results could provide a basis for the stability analysis of large deformation of broken soft coal of mining roadway and also a basis for the optimization of support parameters. [ABSTRACT FROM AUTHOR]

Based on the detection technology, regressing predicted bounding boxes provides an effective approach in multiple object tracking. However, if only the information in the current frame is considered, identity (ID) switch is easy to happen when objects interact. In this paper, we propose an Object-Aware Bounding Box Regression (OABBR) for online multi-object tracking. We first propose an Object-Aware Spatial-Temporal Understanding (OASTU) module to mine the correlated information in corresponding object's trajectory. OASTU updates features of predictions by the correlated information. By using the updated features, we further perform bounding box regression. Besides, to make features extracted by the backbone network contain more ID information, we construct a weak ID constraint in the training phase. The introduced weak ID constraint facilitates OASTU to be ID consistent and further alleviates ID switch. By exploring the spatial-temporal information in corresponding object's trajectory, each prediction is able to know the information of the corresponding object, which makes the purpose of its regression clearer. Experimental results on four public benchmarks demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The deformation control of roadways surrounded by rock in the fully mechanized amplification sections of extra-thick coal seams is problematic. To analyze the failure and failure characteristics of a support frame, as well as the deformation and failure processes of the surrounding rock, through theoretical analysis and industrial tests, the deformation and support conditions of a return airway of a fully mechanized caving face in an extra-thick coal seam in the Yangchangwan Coal Mine, in the Ningdong mining, area were examined. Combined with limit equilibrium theory and roadway section size, the width of the coal pillar of the return air roadway at the 130,205 working face was calculated to be 6 m. The layout scheme and implementation parameters of roof blasting pressure relief, coal pillar grouting modification, and bolt (cable) support were designed. Based on the analysis, a "Coal pillar optimization–roof cutting destressing–routing modification–rock bolting" system for surrounding rock control in synergy with the fully enlarged section mining roadway in the extra-thick coal seam was proposed, and the deformation of the surrounding rock was monitored, along with the stress of the support body and the grouting effect on the site. Field experiments show that after the implementation of the surrounding rock control in synergy with the roadway, the maximum subsidence of the top plate was 55 mm, the maximum bottom heave of the bottom plate was 55 mm, the maximum values of the upper and lower side drums were 30 mm and 70 mm, respectively, and the breaking rate of the bolt (cable) and the deformation of the surrounding rock of the roadway was reduced by more than 90% and 70%, respectively. The effective performance of the coal pillar grouting was observed as well. Field practice of the roadway surrounding rock control in the synergy method indicated that rock deformation was effectively controlled, and the successful application of this technology was able to provide reliable technical and theoretical support for the Ningdong mining area and mines with similar conditions. [ABSTRACT FROM AUTHOR]

Crowd localization is a new computer vision task, evolved from crowd counting. Different from the latter, it provides more precise location information for each instance, not just counting numbers for the whole crowd scene, which brings greater challenges, especially in extremely congested crowd scenes. In this paper, we focus on how to achieve precise instance localization in high-density crowd scenes, and to alleviate the problem that the feature extraction ability of the traditional model is reduced due to the target occlusion, the image blur, etc. To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes. Specifically, a window-based vision transformer is introduced into the crowd localization task, which effectively improves the capacity of representation learning. Then, the well-designed dilated convolutional module is inserted into some different stages of the transformer to enhance the large-range contextual information. Extensive experiments evidence the effectiveness of the proposed methods and achieve the state-of-the-art performance on five popular datasets. Especially, the proposed model achieves F1-measure of 77.5% and MAE of 84.2 in terms of localization and counting performance, respectively. [ABSTRACT FROM AUTHOR]

• Based on leakages of underground CBM drainage borehole, a material with dual expansion sources (DE) was proposed. • The expansion ratios (by rubber bag method) and the expansion pressures (under small swelling) of a modified cement (MC) and the DE were tested. • The expansion force of the cement-based material increased first and then tended to become a constant value with time. • The effect of the DE and the bag-grouting sealing method is preferable to that of the MC and the traditional "two sealing and one grouting" method. Coalbed methane (CBM) is not only a hazard to underground coal mines and atmospheric environment, but also an unconventional natural gas. The effect of underground CBM drainage is influenced markedly by borehole sealing quality. This research focused on a novel sealing material and a bag-grouting sealing method for CBM drainage. First, based on leakages of underground CBM drainage borehole, a material with dual expansion sources (DE) was proposed. Then, the expansion ratios (by rubber bag method) and the expansion pressures (under small swelling) of a modified cement (MC) and the DE were tested. Next, a bag-grouting sealing method, characterized by a bag-grouting sealing device, was proposed. Slurry can be grouted into the bag-grouting sealing device continuously, then the borehole can be sealed tightly. Finally, engineering test was performed at No.2 coal mine in Huangling mining area. Laboratory test results show that the DE expands fast at initial hydration stage, then continues to expand slowly. Especially, the expansion ratios of the DE are more than 60%, however, the MC tends to shrink after initial expansion. Besides, the expansion pressures of the DE are above 1.3 MPa, which are much greater than those of the MC. Engineering test results show that the effect of the DE and the bag-grouting sealing method is preferable to that of the MC and the traditional "two sealing and one grouting" method. The average methane concentration of the boreholes, sealed by the bag-grouting sealing method with the DE as sealing material, is 40% within 90 days. Besides, the concentration does not decrease drastically. [ABSTRACT FROM AUTHOR]

In recent years, object counting has been investigated and has made significant progress under a surveillance-view. However, there exists only a few works focusing on the remote sensing object density estimation, and the performance of existing methods is not promising. On the one hand, due to the imbalance distribution of targets in remote sensing images, the model might collapse, leading a severe degradation. On the other hand, the scale of targets in remote sensing images actually varies in real scenarios, which remains a challenge for counting objects accurately. To remedy the above problems, we propose an approach named "SwinCounter" for object counting in remote sensing. Moreover, we introduce a Balanced MSE Loss to pay more attention to the fewer samples, which alleviates the problem of imbalanced object labels. In addition, the attention mechanism in our SwinCounter can precisely capture multi-scale information. Thus, the model is aware of different scales of objects, which capture small and dense targetes more precisely. We build experiments on the RSOC dataset, achieving MAEs of 7.2, 151.5, 14.38, and 52.88 and MSEs of 10.1, 436.0, 22.7, and 74.82 on the Building, Small-Vehicle, Large-Vehicle, and Ship sub-datasets, which demonstrates the competitiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a finite-time convergent continuous action iterated dilemma (CAID) with topological optimization is proposed to overcome the limitations of traditional methods. Asymptotic stability in traditional CAID does not provide information about the rate of convergence or the dynamics of the system in the finite time. There are no effective methods to analyze its convergence time in previous works. We made some efforts to solve these problems. Firstly, CAID is proposed by enriching the players' strategies as continuous, which means the player can choose an intermediate state between cooperation and defection. And discount rate is considered to imitate that players cannot learn accurately based on strategic differences. Then, to analyze the convergence time of CAID, a finite-time convergent analysis based on the Lyapunov function is introduced. Furthermore, the optimal communication topology generation method based on the Deep Q-learning (DQN) is proposed to explore a better game structure. At last, the simulation shows the effectiveness of the proposed method. • The dynamic model of Continuous Action Iterated Dilemma (CAID) with continuous strategy is more realistic. • The convergence time of CAID is analyzed by proposed finite-time convergent analysis method based on the Lyapunov function. • The optimal communication topology generation method based on DQN is proposed to enhance the game structure. [ABSTRACT FROM AUTHOR]

Sparse unmixing (SU) algorithms use the existing spectral library as prior knowledge to analyze the endmembers and estimate abundance maps. The majority of SU algorithms use loss functions based on the $L_{2,1}$ -norm or $F$ -norm to minimize reconstruction error. They have different advantages and shortcomings. In short, $F$ -norm has a differentiable characteristic, and it is easy to minimize as a loss function. However, it is very sensitive to heavy noise and outliers. While the $L_{2,1}$ -norm emphasizes the reconstruction error on each band and is robust to noise with different intensities in different bands. But the $L_{2,1}$ -norm is nondifferentiable at zero-point. This article introduces an adaptive loss function based on the $\sigma $ -norm for SU, which combines the advantages of the $L_{2,1}$ -norm and $F$ -norm. The adaptive loss function is related to a nonnegative parameter $\sigma $. By adjusting the parameter $\sigma $ , the adaptive loss function can approach the $F$ -norm or $L_{2,1}$ -norm. To the best of our knowledge, it is the first time to apply an adaptive loss function to SU. Moreover, the adaptive loss function is globally differentiable, and we propose an optimization algorithm for the adaptive loss function and verify its convergence. Experiments on real-world and synthetic HSIs show that the adaptive loss function effectively enhances the performance of the SU algorithms. [ABSTRACT FROM AUTHOR]

Appearance similarity is of great importance for the association between objects and candidates. Recurrent models and similarity vector are two ways widely used by trackers for calculating similarities between objects and candidates. Recurrent models, like Long Short Term Memory network (LSTM), are capable of modeling the continuous change of object's appearance in trajectory. But it is prone to identity (ID) switch when only employ recurrent models as appearance model. The similarity vector way is able to maintain correct IDs for objects when they reappear. But association fails easily when the object is partially occluded and similarity vector is used as the only appearance model. To obtain more accurate and robust appearance similarity, in this paper, we propose an online association by continuous-discrete appearance similarity measurement, OA-CDASM, for multi-object tracking. For continuous perspective, the concept of "smoothness" is proposed to explicitly model and use the continuous and smooth change of object's appearance in trajectory. For discrete perspective, similarity vector is employed. By taking both continuous smoothness and discrete similarity vector into consideration, we can get the continuous-discrete appearance similarity measurement, CDASM, and further perform online association based on CDASM. Experimental results on three public benchmarks demonstrate the effectiveness of our work. [ABSTRACT FROM AUTHOR]

Sparse unmixing (SU) is a semisupervised learning problem, which performs abundance estimation when a spectral library is given. In this way, the essence of SU is to select the most suitable subset from the spectral library for representing all mixed pixels. Many SU methods adopt joint-sparse and low-rank constraints to guide the abundance estimation. However, the spatial correlation learning in these algorithms is not accurate enough, which seriously affects the unmixing performance. Besides, most pruning-based unmixing methods suffer from complicated pruning strategies and ignore the relationship between the spectral library and mixed pixels. This article proposes a reweighted low-rank and joint-sparse unmixing approach, which combines an effective pruning strategy (RLSU-LP). The RLSU-LP approach consists of rough unmixing stage, library pruning, and fine-tuning unmixing stage. First, the proposed method utilizes image segmentation to obtain different homogeneous regions, i.e., superpixels. A confidence index is introduced to describe the superpixel homogeneity, which is conducive to learning the meticulous spatial correlation. The RLSU-LP method reasonably relaxes or tightens the sparse and low-rank constraints of the abundance matrix by using the confidence index. Furthermore, a supervised library pruning strategy is proposed, which aims to eliminate the inactive endmembers by considering the contribution of representing mixed pixels. Experiments on the synthesized dataset and authentic hyperspectral images verify the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Object detection possesses extremely significant applications in the field of optical remote sensing images. A great many works have achieved remarkable results in this task. However, some common problems, such as scale, illumination, and image quality, are still unresolved. Inspired by the mechanism of cascade attention eagle-eye fovea, we propose a new attention mechanism network named the eagle-eye fovea network (EFNet) which contains two foveae for remote sensing object detection. The EFNet consists of two eagle-eye fovea modules: front central fovea (FCF) and rear central fovea (RCF). The FCF is mainly used to learn the candidate object knowledge based on the channel attention and the spatial attention, while the RCF mainly aims to predict the refined objects with two subnetworks without anchors. Three remote sensing object-detection datasets, namely DIOR, HRRSD, and AIBD, are utilized in the comparative experiments. The best results of the proposed EFNet are obtained on the HRRSD with a 0.622 A P score and a 0.907 A P 50 score. The experimental results demonstrate the effectiveness of the proposed EFNet for both multi-category datasets and single category datasets. [ABSTRACT FROM AUTHOR]

There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. We find that LPP and SR are equivalent if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank $p$ p , where $p$ p is the reduced dimension. Since solving SR is believed faster than solving LPP based on some related literature, the discovery promotes us to seek to construct such specific similarity matrix to speed up LPP solving procedures. We then propose an unsupervised linear method called Unsupervised Large Graph Embedding (ULGE). ULGE starts with a similar idea as LPP but adopts an efficient approach to construct anchor-based similarity matrix and then performs spectral analysis on it. Moreover, since conventional anchor generation strategies suffer kinds of problems, we propose an efficient and effective anchor generation strategy, called Balanced $K$ K -means based Hierarchical $K$ K -means (BHKH). The computational complexity of ULGE can reduce to $O(ndm)$ O (n d m) , which is a significant improvement compared to conventional methods need $O(n^2d)$ O (n 2 d) at least, where $n$ n , $d$ d and $m$ m are the number of samples, dimensions, and anchors, respectively. Extensive experiments on several publicly available datasets demonstrate the efficiency and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection (HAD) as a special target detection can automatically locate anomaly objects whose spectral information are quite different from their surroundings, without any prior information about background and anomaly. In recent years, HAD methods based on the low rank representation (LRR) model have caught much attention, and achieved good results. However, LRR is a global structure model, which inevitably ignores the local geometrical information of hyperspectral image. Furthermore, most of these methods need to construct dictionaries with clustering algorithm in advance, and they are carried out stage by stage. In this paper, we introduce a locality constrained term inspired by manifold learning topreserve the local geometrical structure during the LRR process, and incorporate the dictionary learning into the optimization process of the LRR. Our proposed method is an one-stage algorithm, which can obtain the low rank representation coefficient matrix, the dictionary matrix, and the residual matrix referring to anomaly simultaneously. One simulated and three real hyperspectral images are used as test datasets. Three metrics, including the ROC curve, AUC value, and box plot, are used to evaluate the detection performance. The visualized results demonstrate convincingly that our method can not only detect anomalies accurately, but also suppress the background information and noises effectively. The three evaluation metrics also prove that our method is superior to other typical methods. [ABSTRACT FROM AUTHOR]

Graph clustering has achieved promising performance in various real-world applications, and attracted sufficient attention in machine learning. Generally, it encodes the samples' relationship with an affinity graph, and then conducts graph-theoretic optimization to partition the samples into clusters. The performance of the graph clustering methods may be affected by many factors, i.e., the graph quality, the loss measurement and the ad hoc post-processing. In this paper, a new Robust Doubly Stochastic graph clustering method (RDS) is presented, which has the following advantages: (1) it learns a doubly stochastic graph with the self-expression strategy automatically, and does not need the graph normalization step to improve the graph quality; (2) it utilizes a new loss function to calculate the approximation error, which is robust to the outliers that far from the normal samples; (3) it generates the cluster indicator according to the learned graph directly, such that the uncertainty caused by the post-processing procedure can be avoided. Extensive experiments on eight benchmarks demonstrate the effectiveness of RDS on data clustering, and show its advantages over the previous graph clustering methods are also verified. [ABSTRACT FROM AUTHOR]