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The shield type temporary support robot is an important component of the intelligent excavation robot system for large section roadways that adapts to the coexistence of dirt and debris. Its main function is to provide a safe and reliable workspace for achieving 'parallel excavation and support' operations. In order to enhance the safe and stable support of the shield type temporary support robot for surrounding rock during its pushing and driving process, based on the structure, working environment, and operational requirements of the shield type temporary support robot, a mathematical model of its pushing amount and support force during pressurized driving, as well as a dynamic model of pressurized driving, are established. A hydraulic control system for the shield type temporary support robot driving under pressure is designed. The system mainly consists of a support hydraulic system and a driving hydraulic system. During static support, the support hydraulic system needs to constantly output a support force greater than the weight of the upper shield body itself, and the driving hydraulic system is in standby mode. When driving under pressure, the support hydraulic system and the driving hydraulic system work simultaneously, ensuring that the temporary support robot 'reducing stress without leaving the roof' while steadily moving forward with the roof under pressure at all times. A precise control method for shield type temporary support robot driving under pressure based on fuzzy PID is proposed. The pressure and displacement signals of the temporary support robot are collected in real time by displacement sensors integrated on the displacement cylinder and pressure sensors in the hydraulic circuit. The signals are used to reflect the changes in support force and driving displacement during the temporary support robot's driving under pressure. Based on the error and error rate of the support force and displacement, the fuzzy PID algorithm is used to modify the control parameters of the support force and displacement, achieving reliable control of driving under pressure based on the fuzzy PID algorithm. Both simulation and experimental results show that the effect of fuzzy PID control is superior to traditional PID control. Under fuzzy PID control, the relative error of support force during the pushing and driving process of the shield type temporary support robot is less than 1%, and the driving displacement error is less than 2 mm. Moreover, the response speed of support force and pushing amount control is fast, ensuring the safe and stable support of the surrounding rock during the pushing and driving process.

Macroscopic and microscopic mechanical responses, damage behavior and microstructure evolution of NiCoCrFe high entropy alloys (HEAs) during finite deformation under quasi-static loading were investigated by experiments and crystal plasticity finite element method. The microstructure of NiCoCrFe before and after tensile deformation was characterized by electron backscattering diffraction technique(EBSD). The internal state variables of dislocation density and continuum damage factors were introduced into the CPFEM model by modifying the strengthening model and the flow criterion, and the NiCoCrFe related model parameters were determined by combining the stress-strain curves of the tensile test. The results show that the CPFEM model considering the dislocation density and damage can effectively describe the macroscopic and microscopic mechanical responses of NiCoCrFe. CPFEM model can reasonably predict the deformation shape and size of NiCoCrFe necking region, among which, the length of the necking region obtained in the experiment is 7% smaller than the predicted result, and the width of the necking region predicted by CPFEM is 23% larger than the experimental result. The texture evolution predicted by CPFEM model after NiCoCrFe tensile deformation is in good agreement with the results that characterized by EBSD, showing weak (100)//RD and strong (111)//RD fiber texture. In the analysis of the 3D micro- structure damage, the damage predicted by the current CPFEM model appears as an inter-granular damage mechanism at the grain boundary where stress and dislocation density are concentrated, and the damage gradually expands to the grain interior with the increase of deformation.

Energy resource-consuming industries such as coal, coking, and steel, are crucial for socio-economic development; however, they are also related to high energy consumption and environmental pollution. As carbon peak and carbon neutral goals were porposed, it becomes increasingly urgent to accelerate the revolution of energy production and consumption in China. To this end, we investigate the current status of coal-based hydrogen production technologies and propose a coal–coking–hydrogen–steel industrial chain in this study. The industrial chain is proposed considering the resource endowment, environmental capacity, and industry foundation in China,and it is expected to be green, low-carbon, secure, and highly efficient. Subsequently, we compare and evlatuate five technological paths for this industry chain from the aspects of economy, energy consumption, and carbon emissions, and analyze the potentials and path choices for coupling hydrogen production with direct reduced iron. Moreover, we elaborate the strategic goals and whole layout of the coal–coking–hydrogen–steel industry chain using Shanxi Province as an example. Furthermore, we suggest that a clean and lowcarbon development concept should be established to promote energy transformation in China, an overall development plan should be formulated for the industry chain, and policies, science, technologies, personnels, and market should be further integrated.

In the field of computational molecule biology, there is still a challenging question of how to detect non-coding RNA gene in lots of unlabeled sequences. Generally, the methods of machine learning and classification are employed to answer this question. However, only a limited number of positive training samples and unlabeled samples are available. The negative samples are difficult to define appropriately, yet they are necessary for usual learning-then-classification method. The common way for most of the existing non-coding RNA gene finding methods is to produce a number of random sequences as negative samples, which may hold some characteristic of positive sample sequences. Consequently, the contrived uncertain factor was introduced and the performance of methods was not good enough. In this paper, Support Vector Data Description (SVDD) is in use for to learning and classification as well as for detecting non-coding RNA gene in lots of unlabeled sequences, and the k-means clustering algorithm is employed before SVDD training to deal with the high flase positive fault in the result of SVDD. The training samples (target samples) are non-coding RNA genes validated by experiment. Moreover, appropriate features were constructed by Principal Component Analysis (PCA). The effectiveness and performance of the method are demonstrated by testing the cases in NONCODE databases and E. coli genome.

The comparative sequence analysis is the most reliable method for RNA secondary structure prediction, and many algorithms based on it have been developed in last several decades. This paper considers RNA structure prediction as a 2-classes classification problem: given a sequence alignment, to decide whether or not two columns of alignment form a base pair. We employed Support Vector Machine (SVM) to predict potential paired sites, and selected co-variation information, thermodynamic information and the fraction of complementary bases as feature vectors. Considering the effect of sequence similarity upon co-variation score, we introduced a similarity weight factor, which could adjust the contribution of co-variation and thermodynamic information toward prediction according to sequence similarity. The test on 49 Rfam-seed alignments showed the effectiveness of our method, and the accuracy was better than many similar algorithms. Furthermore, this method could predict simple pseudoknot.