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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]

Recently, remote sensing image captioning (RSIC) has drawn an increasing attention. In this field, the encoder–decoder-based methods have become the mainstream due to their excellent performance. In the encoder–decoder framework, the convolutional neural network (CNN) is used to encode a remote sensing image into a semantic feature vector, and a sequence model such as long short-term memory (LSTM) is subsequently adopted to generate a content-related caption based on the feature vector. During the traditional training stage, the probability of the target word at each time step is forcibly optimized to 1 by the cross entropy (CE) loss. However, because of the variability and ambiguity of possible image captions, the target word could be replaced by other words like its synonyms, and therefore, such an optimization strategy would result in the overfitting of the network. In this article, we explore the overfitting phenomenon in the RSIC caused by CE loss and correspondingly propose a new truncation cross entropy (TCE) loss, aiming to alleviate the overfitting problem. In order to verify the effectiveness of the proposed approach, extensive comparison experiments are performed on three public RSIC data sets, including UCM-captions, Sydney-captions, and RSICD. The state-of-the-art result of Sydney-captions and RSICD and the competitive results of UCM-captions achieved by TCE loss demonstrate that the proposed method is beneficial to RSIC. [ABSTRACT FROM AUTHOR]

During the “12th Five-Year Plan” period (2011–2015), the safety production situation of coal mines in Guizhou province was generally good. The main types of accidents are gas, flood and roof accidents. The average fatalities for each type of accident were 6.2, 6.8 and 1.2, respectively. During the “12th Five-Year Plan” period, 45 gas accidents occurred and 277 people died, accounting for 18.03% and 48.94% of the total accidents, respectively; 10 flood accidents caused 68 deaths, accounting for 4.1% and 12.01% of the total accidents, respectively; 106 roof accidents caused 129 deaths, accounting for 43.44% and 22.79% of the total accidents, respectively. The number of accidents and casualties is decreasing year by year. Compared with 2015 and 2010, the number of accidents and fatalities decreased by 213 and 324, decreased by 96.82% and 95.27%, respectively. The accident rate of state-owned coal mines was relatively high, and the rate of decline was higher than that of private coal mines. After 2012, the accident rate of production mines was lower than that of construction mines, and preventive measures were explained in terms of safety law enforcement, supervision and technology. The research results have certain reference significance for the government supervision departments to strengthen coal mine safety production, and help to take safety precautions to avoid coal mine accidents. [ABSTRACT FROM AUTHOR]

The variation of the in situ stress state is closely related to various factors. In situ stress state is also an important indicator to guide mining production. The study of in situ stress measurement and its distribution characteristics has always been a basic and very important work in mine production. In this study, the deep mines of Linyi Mining Area were considered as the research object. In this regard, the stress distribution law of each mine was studied. We found that the relationship between principal stresses was σH > σ v > σh, which belongs to the strike-slip stress regime. In this stress regime, the lateral Earth pressure coefficient was greater than one, and the magnitude of the three principal stresses all showed an increasing trend with the increase of depth. The maximum horizontal stress direction of the Gucheng Coal Mine, Guotun Coal Mine, and Pengzhuang Coal Mine was NW-SE under the influence of regional geological structure, while the maximum horizontal stress direction of Wanglou Coal Mine was NE-SW under the influence of local geological structure. Besides, the relationship between mine in situ stress and mine geological structure, the impact of original rock stress on stope stability, and the effect of original rock stress on floor water inrushing were also investigated. We believe that the research results are beneficial to mine disaster prevention and safety production. [ABSTRACT FROM AUTHOR]

Using the self-developed three-axis servo fluid-solid coupling system with gas-solid coupling of gas-bearing coal, the variation law of the permeability of gas coal under the stress cycle loading and unloading path was studied. The qualitative and quantitative relationships between permeability, axial force, and radial stress of gas-bearing coals were established, and the variation law of permeability of gas-bearing coals was discussed. The results show that (1) different cyclic loading and unloading stress paths correspond to the permeability characteristics of different gas-bearing coals. (2) Permeability of gas-bearing coal decreases with the increase of axial stress and radial stress, and it has a logarithmic function with axial stress and radial stress. This shows that axial stress and radial stress are important factors affecting the permeability characteristics of gas-bearing coal. (3) Under the same stress loading and unloading conditions, the axial stress is less than radial stress on the permeability of gas-bearing coal. In the cyclic loading and unloading axial stress process, the permeability of the gas-bearing coal varies by a smaller extent than the cyclically unloaded confining force. (4) The cumulative damage rate of gas-bearing coal under axial stress gradually increases with the increase of the number of cycles of loading and unloading, and the rate of the cumulative damage rate of permeability is less than the corresponding rate of radial stress. [ABSTRACT FROM AUTHOR]

In this study, we designed an experimental system of surface potential during coal adsorption of gas to examine the characteristics of surface potential in this process. The results suggested that surface potential was generated in this process and that it was enhanced with the passage of time. In adsorption cycles, the higher the pressure, the greater the surface potential signals, and they increase exponentially. Thereby, research on surface potential during gas adsorption on coal holds great theoretical significance for understanding the physical properties of coal. [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 fusion of hyperspectral (HS) and multispectral (MS) images designed to obtain high-resolution HS (HRHS) images is a very challenging work. A series of solutions has been proposed in recent years. However, the similarity in the structure of the HS image has not been fully used. In this article, we present a novel HS and MS image-fusion method based on nonlocal low-rank tensor approximation and sparse representation. Specifically, the HS image and the MS image are considered the spatially and spectrally degraded versions of the HRHS image, respectively. Then, the nonlocal low-rank constraint term is adopted in order to form the nonlocal similarity and the spatial–spectral correlation. Meanwhile, we add the sparse constraint term to describe the sparsity of abundance. Thus, the proposed fusion model is established and its optimization is solved by alternative direction method of multipliers (ADMM). The experimental results on three synthetic data sets and one real data set show the advantages of the proposed method over several state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

The rapid construction of a shaft is very important for shortening the shaft construction period and improving the mine economic benefits. In addition to the effects of hydrogeological conditions, the shaft construction speed also affects the selection of operation mode and the rationality of mechanical equipment. Considering the specific geological conditions of thick alluvium and large aquifers on the surface of auxiliary shaft in Fucheng mine, the main factors which affect the construction speed of the large-section vertical shaft in the thick alluvium are analyzed in this study. Based on the field investigations and hydrogeological conditions of the auxiliary shaft, the freezing method is determined as a rapid construction scheme for the auxiliary shaft as well as designing the arrangement of freezing holes and optimization of freezing parameters. The dynamic adjustment of freezing parameters is identified by studying the information provided by freezing construction technology. After shaft excavation, the deformation of the shaft lining in the alluvium section of topsoil is analyzed. It is concluded that the deformation of the shaft lining increases with the depth of the topsoil layer, however, the maximum deformation is within the allowable range of design. The short section excavation and masonry mixed operation mode is selected, and “five major and one deep” rapid mechanized operation lines are matched. The scientific operation cycle chart is compiled. And operation results fulfilled the actual needs. These results will provide guidance and reference significance for mine construction under similar geological conditions. [ABSTRACT FROM AUTHOR]

Collective behavior, from murmurations to synchronized beating of heart cells, governs some of the most beautiful and important aspects of nature. Likewise, cooperation—the act of sacrificing personal benefits for the common good—is one of the pillars of social evolution, and it is the basis for the emergence of collective organized actions from single-cell organisms to modern human societies. Here we merge these two phenomena into a single model, considering an ensemble of networked oscillators, where each oscillator can be either a cooperator or a defector, and with only cooperators contributing to synchrony. At the same time, the value of the order parameter in the neighborhood of each oscillator is considered as an effective local temperature which determines the strategy updating procedure in the evolutionary game. The emergence of cooperation is thus intertwined with that of synchronization, producing a novel and fascinating dynamics which includes a double explosive transition. [ABSTRACT FROM AUTHOR]

Liquid nitrogen (LN2) is an important means to improve the permeability of coal reservoirs. The LN2 soaking method has a wide range of application prospects. However, not enough studies exist to highlight the effect of increasing the permeability of different coal samples using LN2. Hence, this investigation was carried out to examine the effect of cold soaking in LN2 using different coal ranks such as fat coal, lean coal and anthracite coal, on their pore structures and fractal characteristics. Frenkel-Halsey-Hill (FHH) fractal theory was used to understand the fractal dimension characteristics of pores in these different coals before and after cold soaking in LN2. Furthermore, the relationship between the fractal dimensions and pore parameters was also studied. The results show that low-temperature nitrogen adsorption curves of fat coal, lean coal and anthracite before and after cold soaking in LN2 are “S” type. An increase in the total pore volume and pore surface area of coal after LN2 cold soaking is noted. LN2 cold soaking is found to transform the internal pore structure of different coals effectively. Moreover, the heterogeneity of the porous structure of coal is enhanced after LN2 cold soaking along with an increase in the fractal dimension. The growth also decreases with an increase in the metamorphism. The uneven shrinkage of coal occurred during LN2 cold soaking, and the thermal stress generated is greater than the tensile strength of coal, which promotes the development of the porous structure of coal. These results are beneficial in revealing the macroscopic and microscopic pore fissure space expansion and connectivity law of coal seam during the process of LN2 cracking. [ABSTRACT FROM AUTHOR]

Binary representations have demonstrated remarkable performance in face recognition for its robustness to local changes and computation efficiency. However, the performance of face recognition based on most binary descriptors are not satisfactory when dealing with blurred face images. To solve this problem, we propose a novel blur invariant binary descriptor for face recognition. Particularly, we maximize the correlation between the binary codes of sharp face images and blurred face images of positive image pairs for learning the projection matrix. After that, we use the learned projection matrix to obtain blur-robust binary codes by quantizing projected pixel difference vectors (PDVs) in the testing stage. Experiment results on FERET and CMU-PIE show that our method achieves better recognition performance than representative binary descriptors LBP and CBFD. [ABSTRACT FROM AUTHOR]

The consistent coefficient f of coal is a comprehensive index of the ability to resist the damage of external forces determined by various properties of coal. In the Chinese coal industry, it is listed as one important basic index of the four single indexes to judge the risk of coal and gas outburst. At present, the consistent coefficient f can only be determined by the dropping hammer method in the laboratory after the coal sample is collected on the working surface, and cannot be directly determined under coal mines. But the dropping hammer method is relatively cumbersome to operate, and the determination process is easily affected by the operation of the experimenters, so it cannot achieve the purpose of rapid prediction of coal and gas outburst danger. Therefore, a new method for rapidly determining the consistent coefficient f of coal based on the pulverization method is proposed, the principle of this method and the specific determination steps are given. And the optimal combination scheme of the key parameters of coal sample pulverization is established by the orthogonal test, which are given as follows: the initial particle size of the coal sample is 6 ~ 7 mm, the pulverization speed is 8000 rpm, the coal sample mass is 150 g, and the pulverization time is 10 s. Based on the analysis of pulverization data of the original coal samples collected from more than sixty coal mines in eleven major coal-producing areas with different geographical distribution in China, the experimental results show that the logarithmic fitting relationship is shown between the ƒ value and the mass ratios of the coal particles under different particle size after coal samples pulverized. With the coal particle size increasing, the fitting correlation degree gradually decreases. Among them, the fitting correlation degree between the mass ratio of coal particles below 0.5 mm particle size and the f value is the highest, and the correlation coefficient R2 is 91.7%. Based on the findings, the model for rapidly determining the consistent coefficient f of coal based on the pulverization method is established. And the consistent coefficient ƒ determined by this new method is very close to the ƒ value determined by the dropping hammer method. The absolute value of the error is within the range of 0 ~ 0.06, and the absolute value of the relative error is controlled within 10%, about 7.9%. This new method realizes rapid determination of f value of coal, overcomes the factors affecting the accuracy of determination by the dropping hammer method widely used at present, has the characteristics of simple and convenient operation, fast and accurate determination, and can meet the actual determination requirements under various conditions. The research results lay a strong research foundation for directly and rapidly determining the consistent coefficient ƒ of coal under coal mines. [ABSTRACT FROM AUTHOR]

Existing video summarization approaches mainly concentrate on the sequential or structural characteristic of video data. However, they do not pay enough attention to the video summarization task itself. In this article, we propose a meta learning method for performing task-driven video summarization, denoted by MetaL-TDVS, to explicitly explore the video summarization mechanism among summarizing processes on different videos. Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote the generalization ability of the trained model. MetaL-TDVS regards summarizing each video as a single task to make better use of the experience and knowledge learned from processes of summarizing other videos to summarize new ones. Furthermore, MetaL-TDVS updates models via a twofold backpropagation, which forces the model optimized on one video to obtain high accuracy on another video in every training step. Extensive experiments on benchmark datasets demonstrate the superiority and better generalization ability of MetaL-TDVS against several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) has shown their advantages in salient object detection. CNN can generate great saliency maps because it can obtain high-level semantic information. And the semantic information is usually achieved by stacking multiple convolutional layers and pooling layers. However, multiple pooling operations will reduce the size of the feature map and easily blur the boundary of the salient object. Therefore, such operations are not beneficial to generate great saliency results. To alleviate this issue, we propose a novel edge information-guided hierarchical feature fusion network (HFFNet). Our network fuses features hierarchically and retains accurate semantic information and clear edge information effectively. Specifically, we extract image features from different levels of VGG. Then, we fuse the features hierarchically to generate high-level semantic information and low-level edge information. In order to retain better information at different levels, we adopt a one-to-one hierarchical supervision strategy to supervise the generation of low-level information and high-level information respectively. Finally, we use low-level edge information to guide the saliency map generation, and the edge guidance fusion is able to identify saliency regions effectively. The proposed HFFNet has been extensively evaluated on five traditional benchmark datasets. The experimental results demonstrate that the proposed model is fairly effective in salient object detection compared with 10 state-of-the-art models under different evaluation indicators, and it is superior to most of the comparison models. [ABSTRACT FROM AUTHOR]

Data reconstruction, which aims at preserving statistical properties of the data during the reconstruction has become a new criterion for feature selection. Although feature selection could benefit from the perspective of data reconstruction, it is unable to exploit other crucial information, namely, graph structure and pairwise constraints. To address previously mentioned deficiency, we propose a novel feature selection approach in this paper, known as unsupervised feature selection via data reconstruction and side information. More specifically, the proposed method takes advantage of the prior knowledge regarding pairwise constraints (side information), the minimization of data reconstruction error, and the graph embedding simultaneously, such that pivotal features are selected with preserving data manifold structure. To obtain the robust solution, a robust loss function is applied to the feature selection problem, which interpolates between $\ell _{1}$ -norm and $\ell _{2}$ -norm. Eventually, extensive experiments are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

People in crowd scenes always exhibit consistent behaviors and form collective motions. The analysis of collective motion has motivated a surge of interest in computer vision. Nevertheless, the effort is hampered by the complex nature of collective motions. Considering the fact that collective motions are formed by individuals, this paper proposes a new framework for both quantifying and detecting collective motion by investigating the spatio-temporal behavior of individuals. The main contributions of this work are threefold: 1) an intention-aware model is built to fully capture the intrinsic dynamics of individuals; 2) a structure-based collectiveness measurement is developed to accurately quantify the collective properties of crowds; 3) a multi-stage clustering strategy is formulated to detect both the local and global behavior consistency in crowd scenes. Experiments on real world data sets show that our method is able to handle crowds with various structures and time-varying dynamics. Especially, the proposed method shows nearly 10% improvement over the competitors in terms of NMI, Purity and RI. Its applicability is illustrated in the context of anomaly detection and semantic scene segmentation. [ABSTRACT FROM AUTHOR]

The existing unsupervised feature extraction methods frequently explore low-redundant features by an uncorrelated constraint. However, the constrained models might incur trivial solutions, due to the singularity of scatter matrix triggered by high-dimensional data. In this paper, we propose a regularized regression model with a generalized uncorrelated constraint for feature selection, which leads to three merits: 1) exploring the low-redundant and discriminative features; 2) avoiding the trivial solutions and 3) simplifying the optimization. Besides that, the local cluster structure is achieved via a novel constrained spectral analysis for the unsupervised learning, where Must-Links and Cannot-Links are transformed into a intrinsic graph and a penalty graph respectively, rather than incorporated into a mixed affinity graph. Accordingly, a discriminative and uncorrelated feature selection with constrained spectral analysis (DUCFS) is proposed with adopting $\sigma $ -norm regularization for interpolating between F-norm and $\ell _{2,1}$ -norm. Due to the flexible gradient and global differentiability, our model converges fast. Extensive experiments on benchmark datasets among several state-of-the-art approaches verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Graph clustering plays an important role in data mining. Based on an input data graph, data points are partitioned into clusters. However, most existing methods keep the data graph fixed during the clustering procedure, so they are limited to exploit the implied data manifold and highly dependent on the initial graph construction. Inspired by the recent development on manifold learning, this paper proposes an Adaptive Consistency Propagation (ACP) method for graph clustering. In order to utilize the features captured from different perspectives, we further put forward the Multi-view version of the ACP model (MACP). The main contributions are threefold: (1) the manifold structure of input data is sufficiently exploited by propagating the topological connectivities between data points from near to far; (2) the optimal graph for clustering is learned by taking graph learning as a part of the optimization procedure; and (3) the negotiation among the heterogeneous features is captured by the multi-view clustering model. Extensive experiments on real-world datasets validate the effectiveness of the proposed methods on both single- and multi-view clustering, and show their superior performance over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Non-negative Matrix Factorization (NMF) has been one of the most popular clustering techniques in machine leaning, and involves various real-world applications. Most existing works perform matrix factorization on high-dimensional data directly. However, the intrinsic data structure is always hidden within the low-dimensional subspace. And, the redundant features within the input space may affect the final result adversely. In this paper, a new unsupervised matrix factorization method, Discrimination-aware Projected Matrix Factorization (DPMF), is proposed for data clustering. The main contributions are threefold: (1) The linear discriminant analysis is jointly incorporated into the unsupervised matrix factorization framework, so the clustering can be accomplished in the discriminant subspace. (2) The manifold regularization is introduced to perceive the geometric information, and the ${\ell _{2,1}}$ ℓ 2 , 1 -norm is utilized to improve the robustness. (3) An efficient optimization algorithm is designed to solve the proposed problem with proved convergence. Experimental results on one toy dataset and eight real-world benchmarks show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Mine fires are becoming a serious issue as the intensity of mining increases, especially in deep mines. Loose coal gob has a hidden ignition location and a high possibility of spontaneous combustion, which makes fire prevention difficult. Therefore, based on the theory of gas seepage and the characteristics of loose coal, a model of air leakage and spontaneous combustion in gob is established in this paper. Using working face #10414 in the Yangliu coal mine as an example, the relationship between the three spontaneous coal combustion (CSC) zones and the three stress zones is analyzed and verified by combining a FLAC3D simulation with field monitoring. In addition, the influence of advancing speed on the CSC is discussed, and suggestions for fire prevention are presented. The results show that the variation in the calorific value of the CSC with increasing degree of looseness of the residual coal in the gob forms an arch‐shape. There is a one‐to‐one relationship between the distribution of the three stress zones and the three CSC zones. In addition, as the advancing speed increases, the contact time between the loose coal body and the air decreases and the possibility of CSC decreases. This study provides a scientific basis for fire prevention and control in mines. [ABSTRACT FROM AUTHOR]

• 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]