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In the actual production of coal working face, due to the roof conditions, mining influence, hydraulic support attitude and their mutual influence, the column pressure after the initial support of the hydraulic support may change, thus affecting the load bearing effect after the initial support of the support. The pressure failure of hydraulic supports after initial support may lead to problems such as coal wall lining, inter frame leakage, support leaning forward, and overturning. At present, most of the control strategies for the initial support force of hydraulic supports in intelligent mining working faces directly determine whether the column pressure reaches the rated initial support force when lifting the column. It lacks consideration for the load bearing effect caused by the change in column pressure after initial support. In order to solve the above problems, a real-time prediction method for the load bearing effect of hydraulic support after initial support based on column pressure data-driven approach is proposed. The method divides the historical data of column pressure within 3 minutes after initial support of hydraulic supports into 6 typical working conditions. The method classifies the 6 typical working conditions into effective load bearing or failure load bearing according to the different load bearing effects after initial support. Through correlation analysis, five feature factors affecting the load bearing effect of the support after initial support are identified. The method manually annotates the effective or failed load bearing samples of the column, and extracts features. The method inputs the feature values into four different algorithms: decision tree, random forest, support vector machine, and K-nearest neighbor (KNN) to establish classification models. After comparative analysis, the random forest model has the highest precision, reaching 95.60%, which basically meets the accuracy requirements of the model application. A real-time prediction model for the load bearing effect of hydraulic supports after initial support based on random forest is established. On this basis, a real-time prediction system for the load bearing effect of hydraulic supports after initial support is developed and deployed to coal mine sites. After continuous operation for 25 days, the system collects the column pressure within 3 minutes after the initial support of the hydraulic support and could output the load bearing effect of the hydraulic support within 5 seconds. The accuracy of the prediction results compared with the actual operation records is 82.48%, indicating that the system has high accuracy and stability in predicting the load bearing effect.

The energy and environmental crisis pose a great challenge to human development in the 21st century. The design and development of clean and renewable energy and the solution for environmental pollution have become a hotspot in the current research. Based on the preparation of transition metal phosphates, transition metals were used as raw materials, Prussian blue-like NiFe(CN)6 as a precursor, which was in situ grown on nickel foam (NF) substrate. After low temperature phosphating treatment, a bimetallic phosphide electrocatalyst (NiFe)2P/NF was prepared on NF substrate. Using 1 mol·L−1 KOH solution as a basic electrolyte, based on the electrochemical workstation of a three-electrode system, the electrochemical catalytic oxygen evolution performance of the material was tested and evaluated. Experiments show that (NiFe)2P/NF catalyst has excellent oxygen evolution performance. In an alkaline medium, the overpotential required to obtain the catalytic current density of 10 mA·cm−2 is only 220 mV, and the Tafel slope is 67 mV·dec−1. This is largely due to: (1) (NiFe)2p/NF nanocatalysts were well dispersed on NF substrates, which increased the number of active sites exposed; (2) the hollow heterostructure of bimetallic phosphates promotes the electron interaction between (NiFe)2P and NF, increased the rate of charge transfer, and the electrical conductivity of the material is improved; and (3) theoretical calculations show that (NiFe)2P/NF hollow heterostructure can effectively reduce the dissociation barrier of water, promote the dissociation of water; furthermore, the kinetic reaction rate of electrocatalytic oxygen evolution is accelerated. Meanwhile, the catalyst still has high activity and high stability in 30 wt% concentrated alkali solution. Therefore, the construction of (NiFe)2P/NF electrocatalysts enriches the application of non-noble metal nanomaterials in the field of oxygen production from electrolytic water.

The intrinsic magnetization compensation behaviors of rare-earth iron garnets (REIGs) make the material promising for applications in ultrafast spin storage devices. REIG/heavy metal heterostructures such as TbIG/Pt often display two sign crossovers of anomalous Hall effect resistance with varying temperatures. One of these crossovers is attributed to the magnetization compensation of REIG, and the other to the competition between the magnetic proximity effect and the spin Hall effect. Here, we design trilayer REIG heterostructures based on two rare-earth species (Tb and Eu). We modulate the layer stacking of the TbIG/EuIG/TbIG sandwich with a fixed total thickness and explore the contributions of REIG bulk and REIG/Pt interfaces on these two crossover points. As TbIG gradually moves away from Pt, the compensation temperature shows some fluctuations. However, when TbIG is entirely out of contact with Pt, the second crossover point undergoes a change that shows REIG/Pt interface dependency. The results highlight the dominance of REIG bulk on the compensation behavior and the interface sensitivity of the second crossover point. This study provides a reference for designing controllable spintronics devices, such as magnon valve applications.

Artemisia argyi leaf polysaccharide (AALPs) were prepared through ultrasound-assisted extraction (UAE), and their antifatigue activities were evaluated. Extraction was optimized using response surface methodology (RSM), which yielded the following optimal UAE conditions: ultrasonication power of 300 W, extraction temperature of 51 °C, liquid:solid ratio of 20 mL/g, and ultrasonication time of 47 mins. The above optimal conditions resulted in the maximum extraction rate of 10.49 %. Compared with hot water extraction (HWE), UAE supported higher yields and total sugar, uronic acid, and sulfate contents of AALPs. Meanwhile, AALP prepared through UAE (AALP-U) exhibited higher stability due to its smaller particle size and higher absolute value of zeta potential than AALP prepared through HWE (AALP-H). In addition, AALP-U demonstrated stronger antioxidant activity than AALP-H. In forced swimming tests on mice, AALP-U could significantly prolong swimming time with a dose-dependent effect, increase liver and muscle glycogen levels, and improve other biochemical indices, thus showing great potential for application in functional food.

A precise and fast digital twin mapping method for hydraulic support attitude in fully mechanized working face is proposed to address the problems of low precision, large time delay, and difficulty in balancing precision and time delay in implementing attitude mapping using digital twins. The study designs a digital twin system architecture for hydraulic support attitude, which includes physical perception layer, data layer, business logic layer, and presentation layer. At the business logic layer, by retaining the external shape of the high-precision model of the hydraulic support and merging key parts into lightweight structural components, a digital twin model of the hydraulic support attitude is established. It reduces rendering delay. By establishing a conversion relationship between the angle measured by the inclination sensor and the rotation angle of the digital twin model, consistent of virtual-real mapping of the digital twin model of the hydraulic support attitude and real entities of the hydraulic support is achieved, ensuring the precision of the hydraulic support attitude mapping. At the data layer, transmission intervals are divided from each data transmission link and parameter constraints are applied. Parameter programming is applied to optimize the data update interval of adjustable links, reducing resource waste and latency in the data transmission process of digital twin systems. A precise and fast digital twin mapping platform for hydraulic support attitude is built, and the time delay and precision of virtual real mapping for hydraulic support attitude are tested. The results show that this method has a lower time delay while ensuring the precision of hydraulic support attitude mapping.

The development of digital technology and artificial intelligence technology hastens the progress in the field of digital security. As the latest technology in the field of natural language processing, ChatGPT has become the focus of attention around the world since its birth, and also has a significant impact on the field of digital security. This paper reviews the basic situation of ChatGPT, summarizes and combs the basic concepts of digital security, and then examines and analyzes ChatGPT from the perspective of digital security. It analyzes and sorts out the impact of ChatGPT from six aspects: ChatGPT’s own security, intellectual property security, network security, information security, social security and cognitive security to clarify twenty specific digital security risks. On this basis, the impact of ChatGPT on the fields of equipment intelligence analysis, equipment production and development, and equipment logistics support is analyzed. Finally, this paper provides countermeasures and suggestions from the perspectives of ChatGPT industry development, regulatory construction, false information disposal, and equipment intelligence development.

Objective: Postoperative pancreatic fistula (POPF) is a common and challenging complication following pancreaticoduodenectomy (PD), occurring in 2% to 46% of cases. Despite various pancreaticojejunostomy techniques, an effective method to prevent POPF has not been established. This study aimed to develop and evaluate a novel 3D-printed biodegradable pancreatic duct stent to simplify the surgical process of pancreaticojejunostomy, reduce anastomotic complexity, and minimize postoperative complications. Methods: Data from 32 patients undergoing total laparoscopic pancreaticoduodenectomy were utilized. Preoperative CT scans were transformed into 3D reconstructions to guide the design and printing of customized stents using polylactic acid (PLA). The stents were assessed for mechanical integrity, surface texture, and thermal stability. Animal experiments were conducted on 16 mini pigs, with the experimental group receiving the novel stent and the control group receiving traditional silicone stents. Results: The 3D-printed stents demonstrated accurate dimensional replication and mechanical reliability. In the animal experiments, the experimental group showed no significant difference in postoperative complications compared to the control group. At 4 weeks post-surgery, CT scans revealed well-healed anastomoses in both groups, with no significant inflammation or other complications. Histological examination and 3D reconstruction models confirmed good healing and device positioning in the experimental group. Conclusion: The 3D-printed biodegradable pancreatic duct stent offers a promising solution for pancreaticojejunostomy, with comparable safety and efficacy to traditional methods. Further research is needed to validate its clinical application.

Intelligent decision-making support for multi-behavior collaborative control in coal mine excavation is one of the core functions of the coal mine excavation working face. The optimal time series planning of multi-behavior collaborative control in excavation is the key to intelligent decision-making. In order to solve the problems of single control mode, solidification and poor collaborative operation capability of multi-behavior in coal mine excavation, an intelligent decision-making model of multi-behavior collaborative control in coal mine excavation is designed. It realizes the collaborative operation of multi-behavior in the optimal time series. Firstly, an intelligent decision-making method for excavation multi-behavior collaborative control is proposed. The feasible time series planning set and multi-objective optimal time series planning strategy for excavation multi-behavior are determined. Secondly, based on the regulations and process requirements of the excavation site, a set of excavation action events is determined. By analyzing the time relationship between two action events in the event set, a constraint matrix for the time relationship of excavation multi-behaviors is obtained. Thirdly, based on the transformation method of the time relationship constraint matrix, the multi-behavior time relationship constraint matrix of excavation is transformed into a time relationship constraint matrix. The feasible time series planning set of excavation multi-behavior is obtained. Finally, the solving functions for different excavation objectives are defined and the optimal time series for different excavation objectives is obtained. The experimental results show that the excavation robot can work collaboratively without interference according to the optimal time series planning results of the excavation action determined by the model under different excavation objects. The execution time of one working cycle of the excavation operation is basically consistent with the time calculated by the decision-making model.

The degradation process of shearer bearings is not a simple linear or exponential relationship. It should be analyzed in different stages. However, the current prediction method for the remaining useful life (RUL) of shearer bearings does not fully consider this factor. In order to solve this problem, a method for predicting the remaining useful life of shearer bearings based on an improved similarity model is proposed. The model uses a universal similarity model to describe the process of equipment degradation. Based on this, through root mean square clustering analysis, the bearing degradation process is divided into the stable operation stage, initial degradation stage, and accelerated degradation stage. With the help of traditional similarity model ideas, the health condition of shearer bearings is calculated by segment. And it is fitted to obtain a degradation curve sample library, Through data preprocessing and similarity analysis on offline sample library data and real-time data of online shearers, the bearing RUL of the shearer is ultimately obtained. The experimental results show that the mean absolute error values of the RUL prediction method for shearer bearings based on improved similarity model are reduced by 30.49%, 7.54%, 16.98%, 24.74%, 17.96% and 9.49% respectively, compared to the convolutional gated recurrent unit (ConvGRU), convolutional long short-term memory neural network (ConvLSTM), convolutional neural networks (CNN), self-organizing map (SOM), recurrent neural networks (RNN), and traditional similarity models. The proposed model can effectively predict bearing RUL. The on-site test results show that after continuous monitoring of the bearing of the shearer for 87 days, the health condition of the bearing is gradually reduced from 0.997 to 0.972. The result is basically consistent with the actual use of the bearing of the shearer on site. It verifies the effectiveness of this method.

Taking Qidong Coal Mine as an example, this paper studies the law of groundwater migration and the characteristics of structural water control in the region, in order to provide reference for advanced precise control and regional prevention of coal mine water disasters. Based on the theory of structural water control and combined with the geological and structural background of Qidong Coal Mine and its mining area and coalfield, the step-by-step control effect of structural water control and the mode of structural water control are studied. The results are as follows: The bedrock formation morphology of Qidong Coal Mine is controlled by Sunan syncline as a whole, partly by Weimiao fault and other structures, and the Cenozoic formation morphology is also indirectly controlled by structures. Under the control of Sunan syncline, Permian coal-bearing sandstone fractured aquifer, Taihui, Ordovician limestone and aquifer IV angle unconformity contact after weathering inside or outside the mine; under the control of Weimiao fault, the aquifer of partial Permian coal-bearing sandstone fractured aquifer in the southern mining area of the mine outcrops again, and contacts with the aquifer IV angle unconformity, which makes the hydraulic connection between the aquifers. Based on the analysis results, it is considered that ① the geological structure controls the underground aquifer water by controlling the formation morphology ; ② Geological structure controls the hydraulic connection between aquifers by controlling the contact between aquifers. In order to further verify the hydraulic connection between aquifers, using the comparative analysis of water level changes and Pearson correlation coefficient, the borehole water level observation results of aquifer IV, Taihui and aquifer IV, Taihui and Ordovician limestone in the southern mining area during the water discharge test and normal water level observation period were analyzed. The results show that: ① During the water discharge test in the southern mining area, the water level changes of aquifer IV (SQ10-14) and Taihui (ST4) are basically synchronous and highly correlated; ② During the normal water level observation, there is a strong correlation between the water level changes of aquifer IV (SQ10), Taihui (ST4) and Ordovician limestone (SO2), and the correlation between different observation holes in the same aquifer is quite different. It is confirmed that, controlled by the structure, there is hydraulic connection in the angular unconformity contact area between the fourth aquifer and the bedrock aquifer in the local area of the mine. The groundwater model system (GMS) and parameter estimation (PEST) are used to study the distribution and runoff law of four water levels. The results show that the four water levels are about−7−−57m, the water level in the south is higher than that in the north, and the hydraulic gradient is larger at the Weimiao fault. The four water-bearing runoff is concentrated in the southwest and central part of the mine. The southwest runoff is northward as a whole, and the central part is mainly east-west runoff. The four water-bearing runoff bypasses the Weimiao fault.

The deep integration of the coal industry and artificial intelligence (AI) is an important path for modern mines to achieve intelligent personnel reduction, cost reduction, and efficiency improvement. AI empowerment in the entire process and business application scenarios of the coal industry is a specific technical measure to achieve coal mine intelligence. In the context of the current development of intelligent coal mines, a basic paradigm for the evolution of primary intelligent coal mines to new generation intelligent coal mines has been proposed. The composition, functions, and technical connotations of primary intelligent coal mines and new generation intelligent coal mines have been compared and analyzed. It is pointed out the importance of AI empowerment technology for new generation intelligent coal mine and its two key applications and implementation: the coal mine industry mechanism AI model and the coal mine Industry internet platform. The paper summarizes the current research status of industrial mechanism AI models for complex operations such as coal mine geology, mining, excavation, and safety monitoring. The paper clarifies the rapid development trend of industrial mechanism AI analysis in intelligent coal mine construction. A new generation of intelligent coal mine multi-level cloud edge collaborative industrial Internet platform architecture is designed. Using industrial information software and hardware facilities such as group data center, mine data center, production system centralized control center, and combining the features of massive data cloud computing and small amount of data edge computing, a multi-level cloud edge collaborative mechanism of group cloud, mine cloud and link edge, scene edge is proposed. It is pointed out that further research directions in the future should continue to strengthen the development and software research of AI models for coal mining industry mechanisms. Gradually a knowledge software system empowered by AI throughout the entire process of coal mining will be formed. It is suggested to fully utilize the digital resources and information facilities of the coal mining industry Internet platform to gradually realize the AI technology support of the coal mining industry Internet platform.

Quantum computation, as part of the broader field of quantum information, represents an assembly of concepts and techniques that concern the nature and processing of information based on quantum mechanics. Quantum computation utilizes unique resources such as quantum superposition and quantum entanglement to encode and process information and has been proved to be dominantly advantageous over classical computation on certain important scientific and engineering problems. Potential applications of quantum computation are expected to influence future information technology and many other related fields deeply and significantly. In this article, we briefly review the history of quantum computation, including how its fundamental ideas and concepts came into being and the development of its significant theories and algorithms. We also discuss the status and outlook of several representative technical routes in this field, including superconducting quantum computation, distributed superconducting quantum computation, photonic quantum computation, trapped-ion quantum computation, silicon-based quantum computation, as well as other systems. Furthermore, by analyzing certain common issues faced by all routes, we propose some thoughts and suggestions for future development of quantum computation in China. We particularly emphasize the following: reinforcement of strategic planning at a national level, establishment of a research team of high caliber, and boost of relevant fundamental research and development of core techniques and critical instruments.

Abstract Background Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements. Results Here, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory. Conclusions We have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.

Under the background of the current development of the intelligent high-quality coal mine industry, based on the technical mechanism of 'human-cyber-physical system'(HCPS) of new generation intelligent manufacturing , the theoretical system and technical path of the new generation intelligent coal mine with human as the center are proposed. By introducing the evolution of modern coal mine mechanization, informatization and intelligent production mode and the technical development of human, cyber system and physical system, as well as the key scene technologies faced by the development process of the new generation intelligent coal mine, the HCPS technical system of the new generation intelligent coal mine is constructed. The interaction mechanism of human, coal mine cyber system and coal machine equipment physical system is expounded. The HCPS technology elements are given from the four dimensions of human, coal mine cyber system, coal machine equipment physical system and their integration. According to the human-centered development concept, the human-machine cooperation technology path under HCPS theoretical system of the new generation intelligent coal mine is proposed. The core technologies such as human-machine autonomous cooperative interaction mode under the goal of coal mine safety production, situation awareness of coal mine safety production based on man in the loop , coal mine system control sharing under human-machine trust and interaction mode, visualization application development of human-machine information interaction of coal mine task scene are emphatically expounded. It is pointed out that training and practice of the multi-disciplinary talent of mining, machinery, cyber, computer and management, and the innovation of coal mine management, safety system, production mode and personnel type of work are the two key points of the development of the new generation intelligent coal mine.

The intelligent working face has abnormal working conditions such as lost support, uneven straightness, and support skew after the automatic following of hydraulic support. Therefore, manual regulation and control are needed. At present, the research lacks the knowledge discovery of manual regulation and control working conditions after the hydraulic support automation in the production process of the intelligent working face. This is not conducive for workers to quickly judge the number of hydraulic support requiring manual regulation and control. In order to solve the above problems, based on the identification of the number of hydraulic support that is not up to the standard after the hydraulic support automation, a manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face is put forward. Firstly, the historical data of the working face is analyzed. It is concluded that after the automatic following of the hydraulic support is finished, three characteristic values can be used as important characteristics for judging whether the hydraulic support carries out manual regulation and control after the automatic following of the hydraulic support. The characteristic values include the distance of the automatic following of the hydraulic support, the stroke variation of the pushing oil cylinder before and after the automatic following of the hydraulic support, and the absolute difference between the number of the hydraulic support at the position of the shearer and the number of the judged hydraulic support. According to the above conclusion, the structure of the manual control decision model after the hydraulic support cluster automation is proposed. The data acquisition module is used for providing the original data. The data preprocessing module prepares the original data by outlier processing, filtering, sorting and correlation analysis. The characteristic engineering module calculates and standardizes the above three characteristic values to provide a sample set for the classification model. After the classification model divides the sample set, the ID3 decision tree is used for classification. Finally, the number of hydraulic supports needing normal working conditions and the number of hydraulic supports nedeing manual control are output. The results of the model evaluation show that, compared with the traditional K-nearest neighbor (KNN), support vector machine (SVM), logical regression (LR) classification algorithms, the training set accuracy of the ID3 decision tree based classification model for the working conditions of hydraulic supports in the middle of the intelligent working face is 92.27%. The test set accuracy is 93.75%. The model can better distinguish the manual control hydraulic support number after automation.

Autonomous and accurate positioning of roadheader is the key to realize intelligent and unmanned heading face. The combination of strapdown inertial navigation and odometer is an ideal positioning scheme for roadheader. The positioning error of strapdown inertial navigation accumulates with time and the position and posture perception capability of single odometer is limited. In order to solve the above problems, a roadheader combined positioning method based on strapdown inertial navigation and differential odometer is proposed by introducing differential odometer as auxiliary positioning on the basis of strapdown inertial navigation. The realization of the method consists of three parts: position and posture perception based on strapdown inertial navigation, dead reckoning based on differential odometer, and data fusion based on Kalman filtering. The reference displacement and attitude angle of roadheader in navigation coordinate system are obtained by strapdown inertial navigation. The differential odometer is composed of two odometers installed on the left and right tracks of the roadheader, and the dead reckoning of the roadheader is calculated by the differential odometer. Kalman filter is designed according to the error equation of strapdown inertial navigation and differential odometer. The difference between the position and posture data of roadheader obtained by strapdown inertial navigation and differential odometer is used as the input of Kalman filter. The output value of Kalman filter is used to correct and compensate the strapdown inertial navigation data. The validity judgment of odometer data is integrated into the combined positioning method, so that the influence of track slipping on the positioning precision is avoided. The positioning experiment of roadheader is carried out in the simulated roadway. The results show that the heading angle error measured by the combined positioning method can be controlled within 0.6° and the position error can be controlled within 0.19 m. The method has high positioning precision.

The current real-time and integrity of massive data acquisition in fully mechanized working faces are poor. The abnormal data cleaning takes a long time. The data mining delays are large. This leads to low utilization rate of fully mechanized working data and incapability to assist management in issuing decision-making instructions in real-time. In order to solve the above problems, a massive data mining and analysis platform for fully mechanized working faces is designed. The platform consists of a data source layer, a data acquisition and storage layer, a data mining layer, and a front-end application layer. The data source layer is provided with raw data by various hardware devices on the working surface. The data acquisition and storage layer uses the OPC UA gateway to collect real-time monitoring information from underground sensors, and then stores the data in the InfluxDB storage engine through the MQTT protocol and RESTful interface. The data mining layer uses the Hive data engine and Yarn resource manager to filter out abnormal data caused by workplace interference during the data acquisition process. It solves the problem of local data acquisition order disorder caused by network latency. The Spark distributed mining engine is used to explore the potential value of massive working condition data in the working face device group, improving the running speed of the data mining model. The front-end application layer utilizes visual components to associate with the back-end database. It interacts with the back-end data in real-time through AJAX technology to achieve visual display of model mining results and various monitoring data. The test results show that the platform can fully ensure the real-time and integrity of data acquisition. The cleaning efficiency is 5 times better than a standalone MySQL query engine and the mining efficiency is 4 times better than a standalone Python mining engine.

The existing coal preparation plant information management system uses nonstandard interface, which leads to repeated data collection, and each system is independent of each other, and the capability to process multi-source heterogeneous data is weak. In order to solve above problems, based on big data technology of Hadoop ecosystem, a coal preparation data center platform design scheme based on Hadoop ecosystem is proposed. The system integration is realized by defining data standards through master data management system and enterprise service bus. Normalization, correlation coefficient matrix and noise abnormal point detection programs are designed to realize data processing. DS (Dempster-Shafer) evidence theory, Hadoop and Hive data warehouse are combined to design multi-source heterogeneous data fusion subsystem to realize data fusion. Highcharts data visualization components are used to achieve interactive visualization of data. The practical application results show that the data center platform realizes the standardization of master data definition standard and system integration interface, improves the processing capability of coal preparation data, realizes the fusion and sharing of multi-source heterogeneous coal preparation data, and realizes the real-time interactive visualization of data.

Abstract Background Preoperative serum gamma-glutamyl transferase (γ-GT) levels is significantly related to the prognosis of hepatocellular carcinoma (HCC), but its clinical value in the management of postoperative adjuvant transarterial chemoembolization (PA-TACE) has rarely been explored. This study aimed to investigate whether γ-GT levels could be taken as a biomarker to guide the management of PA-TACE in resectable HCC. Methods HCC patients receiving radical resection were identified through the primary liver cancer big data (PLCBD) from December 2012 to December 2015. Prognostic factors of overall survival (OS) and disease-free survival (DFS) were identified by univariate and multivariate cox analyses, and subgroup analysis was conducted between PA-TACE group and non-TACE stratified by γ-GT levels before and after 1:1 propensity score matching (PSM). Results γ-GT level was found to be an independent risk factor of OS and DFS in 1847 HCC patients receiving radical resection (both P 54.0 U/L) have a shortened median OS and DFS, compared with those with normal γ-GT (both P 0.05), and PA-TACE was not a significant prognostic factor of both OS and DFS before and after PSM (all P > 0.05). In the subgroup of patients with elevated γ-GT, significant differences were found between groups of PA-TACE and non-TACE in terms of median OS and DFS before and after PSM (all P

The concept of “Mosaic Warfare”proposed by the Defense Advanced Research Projects Agency (DARPA) describes the main features and operational styles of the future fully intelligent warfare, which has been widely concerned by countries all over the world. So, for the current level of artificial intelligence technology, is there any technological fog and trap in the fully intelligent warfare represented by “Mosaic Warfare”? Where are the bottlenecks? To address these problems, this paper analyzes more than 50 typical military intelligence projects of the US army in the field of air operations in recent years, and extracts 10 typical capability characteristics of intelligent air operations. Then, this paper studies and combs 20 basic intelligence technology groups in the field of artificial intelligence, clarifies the overall architecture of artificial intelligence technology supporting the future fully intelligent operations, and points out the bottlenecks of fully intelligent warfare. On this basis, the focuses and directions in future military intelligence research are given.

The support process of hydraulic support is a dynamic change process in time and space regions. At present, the evaluation of support quality of hydraulic support mostly focuses on the static characteristics of support. There is little research on the dynamic change of support column pressure. In order to solve the above problems, a dynamic evaluation model of support quality of hydraulic support in the space-time region based on improved LeNet-5 network is established by using the deep learning method. Firstly, the column pressure data of hydraulic support in working face is preprocessed (missing value filling, abnormal value processing, screening, sorting, etc.) to obtain more complete pressure data hydraulic support. Secondly, the preprocessed column pressure data of the hydraulic support is arranged according to time and space. The important characteristics are extracted such as initial setting force, circulating end-resistance, time-weighted resistance, and spatial distribution condition of resistance, which reflect the support condition of the hydraulic support in intelligent mining working face. The pressure time-sequence and the pressure space-sequence are combined into the total space-time pressure matrix in 2D space-time region. Thirdly, according to the support requirements of the working face, the support quality of the space-time region into seven types, namely, support quality preliminary deterioration, support quality continuous deterioration, support quality deep deterioration, support quality general maintenance, support quality preliminary optimization, support quality continuous optimization and support quality good maintenance. On the total space-time pressure matrix, the sliding window is used to intercept the sub-matrix with the given size at a certain interval. The sub-matrix is one-to-one corresponding to the seven support quality types in space-time regions to form samples and labels. Finally, the samples and the labels are input into the improved LeNet-5 network for training. The dynamic evaluation model of support quality of hydraulic support in space-time region is constructed, which evalues the support condition of hydraulic support in the region in real-time. The experimental results show that the model based on improved LeNet-5 network can be used to identify the dynamic effect of support quality in the working face, and provide the basis for the field operators to adjust the support state of the hydraulic support pertinently. The classification accuracy is 85.25%, which is 12% higher than that of the model based on LeNet-5 network. At the same time, the improved LeNet-5 network can converge to the optimal solution quickly in the training process, which accelerates the training speed of the network. The result verifies the advantages of the improved LeNet-5 network in evaluation of the support quality of hydraulic support in space-time region of intelligent working face.

The local static characteristics of coal slime flotation foam image are similar. The dynamic characteristics of flotation foam image are needed for judging some complex working conditions. The existing extraction method for the dynamic features of the froth velocity of coal slime flotation has the problems of insufficient accuracy, real-time performance and stability. In order to solve the above problems, a feature extraction method of froth velocity in coal slime flotation based on image feature matching is proposed. Firstly, the contrast limited adaptive histogram equalization (CLAHE) and block-matching and 3D filtering(BM3D) are used to preprocess the flotation froth image to improve the quality of the image and highlight the edge details of the image. Secondly, the accelerated-KAZE (AKAZE) algorithm of accelerated features in nonlinear scale space is used to detect the feature points of flotation froth features. Thirdly, on the basis of rough matching of froth image features by brute-force matching (BF), a grid-based motion statistics (GMS) algorithm is used to quickly and reliably distinguish correct and wrong feature matching. Finally, the method calculates the slime foam velocity according to the feature matching results. The foam velocity is taken as the measured value. The Kalman motion estimation method is used to iteratively modify the measured values to obtain more stable foam velocity characteristics of coal slime flotation. The experimental results show the following points. ① The AKAZE-GMS algorithm can solve the problem of feature point clustering well and keep more feature points as much as possible. This is because the preprocessed image is less affected by noise, has better contrast, and has more prominent edge features. ② Compared with SIFT (scale-invariant feature transform), SURF (speeded up robust features) and AKAZE, the AKAZE-GMS algorithm has a more uniform distribution of matching pairs, retains more correct matching pairs. The method achieves a matching accuracy of 99.99%. The running time is only 3.73 s. ③ The measured value of froth velocity directly calculated from the feature matching results fluctuates greatly. The velocity estimated value of the measured value corrected by Kalman motion estimation is more stable, which is more consistent with the real working condition.

As a vegetable oil, consisting principally of triacylglycerols, is the major storage form of photosynthetically-fixed carbon in oilseeds which are of significant agricultural and industrial value. Photosynthesis in chlorophyll-containing green seeds, along with photosynthesis in leaves and other green organs, generates ATP and reductant (NADPH and NADH) needed for seed fatty acid production. However, contribution of seed photosynthesis to fatty acid accumulation in seeds have not been well-defined. Here, we report the contribution of seed-photosynthesis to fatty acid production by probing segregating green (photosynthetically-competent) and non-green or yellow (photosynthetically-non-competent) seeds in siliques of an Arabidopsis chlorophyll synthase mutant. Using this mutant, we found that yellow seeds lacking photosynthetic capacity reached 80% of amounts of oil in green seeds at maturity. Combining this with studies using shaded siliques, we determined that seed-photosynthesis accounts for 20% and silique and leaf/stem photosynthesis each account for ~40% of the ATP and reductant for seed oil production. Transmission electron microscopy (TEM) and pyridine nucleotides and ATP analyses revealed that seed photosynthesis provides ATP and reductant for oil production mostly during early development, as evidenced by delayed oil accumulation in non-green seeds. Transcriptomic analyses suggests that the oxidative pentose phosphate pathway could be the source of carbon, energy and reductants required for fatty acid synthesis beyond the early stages of seed development.

Abstract Developmental patterning in Caenorhabditis elegans is known to proceed in a highly stereotypical manner, which raises the question of how developmental robustness is achieved despite the inevitable stochastic noise. We focus here on a population of epidermal cells, the seam cells, which show stem cell-like behaviour and divide symmetrically and asymmetrically over post-embryonic development to generate epidermal and neuronal tissues. We have conducted a mutagenesis screen to identify mutants that introduce phenotypic variability in the normally invariant seam cell population. We report here that a null mutation in the fusogen eff-1 increases seam cell number variability. Using time-lapse microscopy and single molecule fluorescence hybridisation, we find that seam cell division and differentiation patterns are mostly unperturbed in eff-1 mutants, indicating that cell fusion is uncoupled from the cell differentiation programme. Nevertheless, seam cell losses due to the inappropriate differentiation of both daughter cells following division, as well as seam cell gains through symmetric divisions towards the seam cell fate were observed at low frequency. We show that these stochastic errors likely arise through accumulation of defects interrupting the continuity of the seam and changing seam cell shape, highlighting the role of tissue homeostasis in suppressing phenotypic variability during development.

The concept of mosaic warfare has become the focus of attention of various military powers in the world. Firstly, the basic concepts and main characteristics of mosaic warfare are analyzed, and seven key operational application technologies are summarized that affect mosaic warfare. Secondly, according to the current situation of artificial intelligence field, 20 general artificial intelligence technology domains are summarized. Thirdly, the relationship between mosaic war and artificial intelligence is analyzed, the research on the support of general artificial intelligence technology to the key operational application technology of mosaic warfare is carried out, and the architecture diagram of artificial intelligence supporting mosaic warfare is given. Finally, the artificial intelligence technology domains that need to be studied in the future are pointed out.

Shore-side container cranes (SCC) are widely used in ship loading and unloading. However, the SCC is often damaged by the seismic wave shock. In seismic response analysis, the setting of damping coefficients has a large impact on the accuracy of the calculation results. In order to better design the SCC mechanical performance, the improved damping coefficient calculation method is proposed. The results of the simulation and the experiment are compared under the different seismic conditions. The results show that the influence of the damping coefficient calculation method on the SCC seismic response increases with strengthened seismic intensity. When the peak acceleration is small, the simulation results under different damping coefficients are almost the same and are close to the experiment values. When the peak acceleration is large, only the error between the simulation results obtained by the improved method and the experimental values is less than 5%. It is thus concluded that the improved damping coefficient calculation method is accurate and reliable.

Polygonati Rhizoma, a typical homology of medicine and food, possesses remarkable anti-fatigue, anti-aging, metabolic regulatory, immunomodulatory, anti-inflammatory, neuroprotective, anti-diabetes, and anti-cancer effects. Among bioactive phytochemicals in Polygonati Rhizoma, polysaccharides play important roles in the health-promoting activities through the mechanisms mentioned above and potential synergistic effects with other bioactives. In this review, we briefly introduce the updated biosynthesis of polysaccharides, the purification method, the structure characterization, and food applications, and discuss in detail the biological activities of Polygonati Rhizoma polysaccharides and associated mechanisms, aiming at broadening the usage of Polygonati Rhizoma as functional food and medicine.

Chrysanthemum morifolium cv. Fubaiju (CMF) has been used as a dietary herb in East Asia for centuries. The chemical composition and antifungal activity of CMF essential oil (CMF-EO) were investigated in this study. We have found that CMF-EO was a light-yellow, transparent liquid (0.61% yield, w/w) and majorly contain 31 volatile components with a total of 98.41% determined by GS and GC/MS. Camphor was the major compound (16.87%), followed by 1,8-cineole (10.62%), β-curcumene (9.51%), bornyl acetate (7.43%), α-zingiberene (6.97%), β-elemene (6.47%), α-bisabolol (4.74%), borneol (4.24%) and eugenol (4.23%). In addition, contents with more than 2% of the total CMF-EO were 1-caryophyllene (3.92%), chrysanthenyl acetate (3.29%), caryophyllene oxide (2.28%), and chamazulene (2.23%). Six fungal strains were used to study the antifungal activities and mechanisms of CMF-EO using fluorescence probe–membrane integrity assay, analysis of cytoplasmic membrane permeability, electron microscopy, monitoring of the effect of the mitochondrial membrane potential and DNA binding assay. CMF-EO effectively inhibited the growth of C. albicans, C. glabrata, C. tropicalis, S. cerevisiae, D. hansenii, and Z. parabailii with minimum inhibition concentrations of 31.25, 31.25, 62.5, 31.25, 15.625, and 15.625 μg/mL, respectively, and minimum fungicidal concentrations of 62.5, 125.0, 125.0, 62.5, 31.25, and 62.5 μg/mL, respectively. The elucidated antifungal mechanisms of CMF-EO included the destruction of cytoplasmic membrane and the damage of mitochondria and DNA. The multiple antifungal mechanisms made it difficult for fungi to develop drug resistance, and this finding might shed new light on the development of new fungal agents.

According to connotation of intelligent coal preparation plant, framework of intelligent coal preparation plant was introduced from aspects of Internet of things layer, control layer, management layer and decision-making layer. Key technologies adopted in construction of intelligent coal preparation plant were expounded emphatically, including intellectualization of dense medium separation process, flotation process, slime water health guarantee system and coal preparation manufacturing execution system, which laid technical foundation for development of coal preparation plant from automation and informatization to intelligence.

In view of problems existing in current coal preparation equipments fault prediction, such as equipments monitoring data conversion method was not unified, single fault classification model cannot meet requirements of multiple fault types prediction, and the fault classification model cannot be updated and modified online. Based on remote operation and maintenance mode, a remote fault prediction system for coal preparation equipment was designed. By preprocessing sensor data of coal preparation equipment, full information is generated and input to various fault classification models. Overall fault index is weighted and fault prediction result is obtained through comprehensive decision. It can provide technical support for the whole life cycle management of intelligent coal preparation plant equipments. The application results verify effectiveness of the system.

In order to meet demand of adjusting dense medium suspension density in wide range due to change of raw coal quality, an intelligent control system for dense medium suspension density with wide domain was designed by using reverse split technology in dense medium separation process. BP neural network is used to establish liquid level prediction model of qualified medium barrel. Deviation between actual value and set value of suspension density, actual liquid level of qualified medium barrel and opening degree of shunt valve and water replenished valve are taken as input variables of the model, and predicted liquid level of qualified medium barrel is calculated through the model. According to liquid level deviation of qualified medium barrel and the density deviation, control mode switching of adding medium, steady state, density step up and density step down is realized through one-to-one multi-classification algorithm based on support vector machine. Opening degree of shunt valve, water replenished valve and water adding valve and opening time of thick medium pump and reverse shunt pump are automatically adjusted according to control mode, so as to realize wide range adjustment of density. Density fluctuation range is stable within ±0.005 g/cm3 and density adjustment time is short after application of the system.

In order to solve problems of subjectivity, hysteresis and extensiveness existed in reagent amount addition of coal slime flotation in coal preparation plant depended on manual intervention, a coal slime flotation foam image classification method based on semi-supervised clustering was proposed. Firstly, coal slime flotation foam images under known reagent-addition ratio and unknown reagent-addition ratio are collected, and the foam images are preprocessed to extract morphological characteristics such as bubble number, bubble area and bubble perimeter. Then, foam image morphological characteristic samples under known reagent-addition ratio are marked, while foam image morphological characteristic samples under unknown reagent-addition ratio are not marked, and the marked foam image morphological characteristic samples and the unmarked foam image morphological characteristic samples are mixed. Finally, semi-supervised clustering method based on Gaussian mixture model is used to cluster the mixed samples, so as to get various clusters, and information of the marked foam image morphological characteristic samples is mapped to the unmarked foam image morphological characteristic samples in various clusters. The application results show that the method can provide guidance for adjustment of reagent-addition amount in coal slime flotation production process, reduce consumption of reagent, and improve flotation automation level and economic benefit of coal preparation plant.

It is difficult to meet requirement of field production by theoretically calculating fixed ratio of raw coal in blended coal preparation process of coal preparation plant, and problems such as large randomness, low quality of clean coal and high labor intensity are existed in raw coal ratio adjustment according to manual experience. According to above problems, an intelligent ratio control system for raw coal in coal preparation plant was designed. Least squares support vector machine is used to establish a prediction model of intelligent raw coal ratio, and particle swarm optimization algorithm is adopted to optimize model parameters. The system takes measured ash content value of raw coal, measured sulfur content value of raw coal, average feed amount of raw coal per hour, separation density, measured ash content value of clean coal, ash content target value of clean coal, measured sulfur content value of clean coal and sulfur content target value of clean coal as input variables of the model, so as to obtain predicted value of raw coal ratio. Coal feed amount of coal feeder is measured by belt scale to calculate measured value of raw coal ratio, and deviation of raw coal ratio is obtained by comparing the measured value with the predicted value. PID controller controls frequency converter of the coal feeder according to the deviation amount to achieve accurate adjustment of raw coal ratio. The actual application results show that fluctuation range of ash and sulfur content of clean coal is significantly reduced and quality stability of clean coal is good. Difference between ash content of clean coal and ash content target value is controlled within ±0.2%, and difference between sulfur content of clean coal and sulfur content target value is controlled within ±0.15%, which verify quality improvement of clean coal.

In view of the problems of low measurement dimension, large error and easy to be affected by dust in current straightness measurement method of hydraulic support, a straightness measurement method of hydraulic support based on inter supports walking robot was proposed. This measurement method is mainly used to chock-shield hydraulic support, sensors are arranged on the inter supports walking robot, which is used for directly measuring multi-dimensional position offset information such as transverse offset, longitudinal offset, transverse bevel angle and longitudinal dip angle of hydraulic support base, then the straightness information of the hydraulic support is indicated by the support position offset. The experimental results show that the deviation error of the two adjacent supports is within 0.2 cm and the angle error of the two adjacent supports is within 10′. When the experimental supports model increased to 100 units, the accumulative error of position offset is expected to 10 cm, angle cumulative error is 8°20′ according to center distance of 1.5 m for conventional hydraulic support on the working face length of 150 m. For length of fully mechanized working face is less than 150 m, the accumulated error is in the acceptable range, can meet measurement demand of hydraulic support straightness. Compared with the traditional one-dimensional measurement information, the method can provide more detailed reference for the control of hydraulic support group through the four-dimensional position offset information.

In order to improve ability of hydraulic support following shearer in working face, coupling relationship among support action velocity, hydraulic system pressure and flow rate of hydraulic fluid supply was revealed by theory analysis of support hydraulic fluid supply process. Simulation model of hydraulic system on working face was established and correctness of the model was verified. Effect of hydraulic system pressure and flow rate on support action velocity was analyzed, and a stabilized pressure hydraulic fluid supply technology adapted to support action was proposed. The technology takes support action type and hydraulic system pressure limitation as criterion, and realizes slow and steady changing process of hydraulic system pressure with slight rise during support action by adjusting flow rate, so as to improve support action velocity. The technology was tested by use of multi-pump and multi-frequency hydraulic fluid supply system, and validity of the technology was verified.

AbstractThe transcription factor RUNX1 is a master regulator of hematopoiesis and is frequently mutated in myeloid malignancies. Mutations in its runt homology domain (RHD) frequently disrupt DNA binding and result in loss of RUNX1 function. However, it is not clearly understood how other RUNX1 mutations contribute to disease development. Here, we characterized RUNX1 mutations outside of the RHD. Our analysis of the patient data sets revealed that mutations within the C-terminus frequently occur in hematopoietic disorders. Remarkably, most of these mutations were nonsense or frameshift mutations and were predicted to be exempt from nonsense-mediated messenger RNA decay. Therefore, this class of mutation is projected to produce DNA-binding proteins that contribute to the pathogenesis in a distinct manner. To model this, we introduced the RUNX1R320∗ mutation into the endogenous gene locus and demonstrated the production of RUNX1R320∗ protein. Expression of RUNX1R320∗ resulted in the disruption of RUNX1 regulated processes such as megakaryocytic differentiation, through a transcriptional signature different from RUNX1 depletion. To understand the underlying mechanisms, we used Global RNA Interactions with DNA by deep sequencing (GRID-seq) to examine enhancer-promoter connections. We identified widespread alterations in the enhancer-promoter networks within RUNX1 mutant cells. Additionally, we uncovered enrichment of RUNX1R320∗ and FOXK2 binding at the MYC super enhancer locus, significantly upregulating MYC transcription and signaling pathways. Together, our study demonstrated that most RUNX1 mutations outside the DNA-binding domain are not subject to nonsense-mediated decay, producing protein products that act in concert with additional cofactors to dysregulate hematopoiesis through mechanisms distinct from those induced by RUNX1 depletion.

Essential oils (EOs) have been used in cosmetics and food due to their antimicrobial and antiviral effects. However, the applications of EOs are compromised because of their poor aqueous solubility and high volatility. Qiai (Artemisia argyi Levl. et Van. var. argyi cv. Qiai) is a traditional Chinese herb and possesses strong antibacterial activity. Herein, we report an innovative formulation of EO as nanohydrogels, which were prepared through co-assembly of Qiai EO (QEO) and Pluronic F108 (PEG-b-PPG-b-PEG, or PF108) in aqueous solution. QEO was efficiently loaded in the PF108 micelles and formed nanohydrogels by heating the QEO/PF108 mixture solution to 37 °C, by the innate thermo-responsive property of PF108. The encapsulation efficiency and loading capacity of QEO reached 80.2% and 6.8%, respectively. QEO nanohydrogels were more stable than the free QEO with respect to volatilization. Sustained QEO release was achieved at body temperature using the QEO nanohydrogels, with the cumulative release rate reaching 95% in 35 h. In vitro antibacterial test indicated that the QEO nanohydrogels showed stronger antimicrobial activity against S. aureus and E. coli than the free QEO due to the enhanced stability and sustained-release characteristics. It has been attested that thermo-responsive QEO nanohydrogels have good potential as antibacterial cosmetics.

In view of problems of poor accuracy and high medium consumption of manual control of shunt in dense-medium separation process , an automatic shunt control system in dense-medium separation process was designed, and opening prediction model of the shunt valve based on the least squares support vector machines was established. The parameters including suspension density, coal slime content, qualified medium tank level and opening degree of water supply valve are collected by sensors, which are used as input variables of opening prediction model of the shunt valve. Output value of opening degree of the shunt valve is calculated by the model and then is sent to PLC, so as to control operation of the shunt valve and realize automatic shunt control. The application results show that the density fluctuation of suspension liquid can be controlled in the range of ±0.005 g/cm3, and the ton coal dense-medium consumption is reduced by 0.216 kg.

In view of problem that current lubricating oil temperature monitoring method of emulsion pump can only identify oil temperature overrunning but cannot monitor abnormal temperature rise, an oil temperature monitoring method based on statistical process control was proposed according to statistical properties of oil temperature process data, and an implementation scheme of exponentially weighted moving average control chart applied to oil temperature monitoring was given. On the basis of the scheme, an oil temperature monitoring system of emulsion pump was designed. The practical application results show that the system can monitor oil temperature of emulsion pump in real time and send out fault alarm accurately.

In view of problem of high impulse current when scheme of multiple pumps linkage with one frequency converter applied in coal mine emulsion pump station, asynchronous switching and synchronous switching control scheme between variable frequency and power frequency of emulsion pump were proposed. The field test results show that the synchronous switching control can avoid impulse current more effectively and truly realize free switching between variable frequency and power frequency.