Bergenin suppresses the growth of colorectal cancer cells by inhibiting PI3K/AKT/mTOR signaling pathway

Purpose: To investigate anticancer effects of bergenin on human colorectal cancer cell lines.Methods: Human colorectal adenocarcinoma cell line HCT116 was treated with various concentrations of bergenin for 24 and 48 h. Cell viability, apoptosis, cell cycle arrest and reactive oxygen species (ROS) level were analyzed by MTT, flow cytometry and fluorescent dye assays, respectively. DNA damage-associated protein expressions were analyzed by Western blotting.Results: Bergenin significantly suppressed the viability of HCT116 cells. Moreover, bergenin induced cells to accumulate in G1 phase and resulted in DNA breaks in HCT116 cells. It also led to marked accumulation of intracellular reactive oxygen species (ROS), a breaker of DNA strand in HCT116 cells. Interestingly, bergenin inhibited PI3K/AKT/mTOR pathway.Conclusion: Bergenin effectively suppresses the growth of colorectal  adenocarcinoma by inducing generation of intracellular ROS, DNA damage and consequent G1 phase arrest via inhibition of PI3K/AKT/mTOR pathway.Keywords: Bergenin, Colorectal cancer, DNA damage, Cell cycle arrest,  PI3K/AKT/mTO

Research on middle beam pin shaft node of hydraulic support test bench based on AHP-Fuzzy analysis model

In order to improve the situation of discontinuity in the range of stresses caused by pin deformation, the mathematical theoretical model of peak contact stresses after pin node deformation in the test rig was established based on the Hertzian contact theory, and the pin node model was simulated and analysed using ANSYS software and compared with the theoretical model. The results show that the simulation results are in good agreement with the theoretical data. Combined with the actual engineering field application, five main indicators for reducing the peak stress in the contact area of the pin node hole wall were determined, namely: shaft hole gap, shaft casing thickness, node plate thickness, chamfer length, chamfer angle, and the use of Minitab software to establish the main effect cloud diagram for the three evaluation indicators: stress, tangential stress and strain; establish the affiliation function of the three evaluation indicators according to the fuzzy mathematical theory, and use the hierarchical Analysis of Hierarchy (AHP) was used to determine the weight vector of the evaluation indexes, and the optimal combination of parameters for the five main indexes was obtained by combining fuzzy comprehensive evaluation and the principle of maximum subordination, and the combination of parameters was simulated. The results show that the optimized pin node column peak stress reduction ratio is 58.77%, and the peak stress reduction ratio of the trunnion node is 12%. The optimal combination of solution parameters was applied in the field in the design and development of the 50 MN hydraulic support test stand, which verified the reasonableness of the optimised parameters and effectively improved the service life of the centre beam pin during the loading test of the hydraulic support test stand. It was further demonstrated that by adding slope chamfers to the pin nodes, setting reasonable initial clearances and plate thicknesses, the peak pin node loads could be reduced to a large extent and the length of the stress range improved, providing a reference for the practical engineering application of the design of plug-in and plug-out positioning pin assemblies for heavy equipment

Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Water quality data cleaning is important for the management of water environments. A framework for water quality time series cleaning is proposed in this paper. Considering the nonlinear relationships among water quality indicators, support vector regression (SVR) is used to forecast water quality indicators when some indicators are missing or when they show abnormal values at a certain point in time. Considering the time series of water quality information, long short-term memory (LSTM) networks are used to forecast water quality indicators when all indicators are missing at a certain point in time. A parallel model based on particle swarm optimization (PSO) and LSTM is realized based on a microservices architecture to improve the efficiency of model execution and the predictive accuracy of the LSTM networks. The performance of the model is evaluated in terms of the mean absolute error (MAE) and root-mean-square error (RMSE). Inlet water quality data from a wastewater treatment plant in Gaobeidian, Beijing, China is considered as a case study to examine the effectiveness of this approach. The experimental results reveal that this model has better predictive accuracy than other data-driven models because of smaller MAE and RMSE and has an advantage in terms of time consumption compared with standalone serial algorithms

Analysis on the influence law of traction speed on the cutting performance of coal containing hard concretion

Due to the great differences in mechanical properties between hard concretion and coal, the shearer drum cutting coal and rock containing hard concretion will inevitably lead to a series of problems, such as severe load impact, increased cutting specific energy consumption and low coal loading efficiency. Taking the cutting part of a thin coal seam shearer in service in the target mining area as the prototype, the mechanical models of shearer drum and rocker arm are established by using UG, and the discrete element model of coal wall with hard concretions is built by importing it into EDEM. The influence law of traction speed on coal loading rate, drum load and cutting specific energy consumption is analyzed, and the corresponding fitting relationship function is obtained. Taking the traction speed as the design variable, and the drum load, cutting specific energy consumption and coal loading rate as the comprehensive indicators, a multi-objective optimization function is established. The optimal solution is obtained by using NSGA II optimization algorithm, and the accuracy of the simulation is verified by experiments. This method has certain engineering significance for the reasonable selection of shearer traction speed

Research on the "shape-performance-control" integrated digital twin system for boom-type roadheaders

Abstract The boom-type roadheader plays a crucial role in coal mining. However, conducting the real-time monitoring of the mechanical performance and comprehensive adaptive cutting in the dynamic cutting process are challenging. To address these issues, a digital twin system that integrates the elements of “shape, performance, and control” for roadheaders is presented in this paper. The system comprises three components: physical space, service space, and twin space. The service space forms the core of the entire system. Within this space, twin models and control models are created using numerical simulation, artificial intelligence and multi-source data fusion technology. These models serve the purpose of predicting the roadheader’s mechanical performance and controlling the swing speed of the cutting arm. The physical space is built using technologies such as robot kinematics, electrical systems, hydraulic transmission, and other relevant techniques. This approach facilitates the transmission of multi-sensor data to twin models. The control model then manages the roadheader’s function based on the output signals from the control model. The twin space is constructed utilizing physical rendering engines, databases, and 3D modelling tools. This space visualizes and stores the movement, performance, and control parameters of the roadheader. The results demonstrate that the average absolute error between the measured data from the test’s three position strain gauges and the predicted data from the twin system is 10.38 MPa. Furthermore, the twin system achieves an average update interval of 0.34 s, allowing real-time stress monitoring of the structural components of the roadheader and preventing damage caused by overload. The proposed control model enables adaptive adjustment of the swing speed of the cutting arm in approximately 0.3 s. This improvement significantly enhances the adaptive cutting capabilities of roadheaders when dealing with complex coal and rock formations

Forming characteristics analysis of variable-process alternated overlapping strategy based on contour description for laser melting deposition

To predict the geometric characteristics and improve the forming quality and macro-micro defects of multi-track overlapping cladding layer effectively, the experimental research of laser melting deposition single-track Co-based alloy on 42CrMo substrate was carried out. The influence trends of laser process parameters on the single-track cladding height and cladding width were analyzed, and the regression prediction models were established. A variable-process alternated overlapping strategy was proposed using the geometric functions to fit the single-track profile. The surface forming quality and macro-micro defect of different overlapping cladding layers were compared and evaluated. The results showed that the cladding height and cladding width regression models established by an improved optimization algorithm had an excellent prediction effect. The variable-process alternated overlapping cladding layer prepared by the parabolic function had a flat surface, and the fluctuation distance was 81.56 μm, which was significantly improved compared with other overlapping strategies. The surface flatness of the continuous overlapping cladding layer was the worst and the spheroidization defect was the most unsatisfied. The internal defects of the constant-process alternated overlapping cladding layer were the most terrible, with more non-fusion and cracks caused by oxides. The variable-process alternated overlapping strategy proposed in this study can effectively prepare a multi-track cladding layer with a smooth surface and dense interior, which provides essential theoretical guidance for the near-net forming of component surface deposition and repair

A review on the fatigue performance of micro-arc oxidation coated Al alloys with micro-defects and residual stress

Unavoidable issues of micro-arc oxidation (MAO) coated Al alloys are the micro-defects and residual stress generation. Some scientists have reported the effects of micro-defects and residual stress on the fatigue performance of MAO coated samples. However, the influence of the residual stress sign in the MAO coating and substrate beneath the interface on the fatigue damage behavior of the substrate was controversial. Moreover, the coupling mechanism between micro-defects and residual stress on the fatigue performance of the MAO coated samples was nebulous. These present a challenge for the collaborative design of micro-defects and residual stress to optimize fatigue performance. Aiming at the issues, this paper summarized the influencing factors of MAO coatings on the fatigue performance of the substrate. From two aspects of microstructure characterization and stress analysis, the coupling mechanism between micro-defects and residual stress in fatigue failure was elucidated. And the mechanism of the micro-defects and residual stress on the fatigue damage behavior was developed. Finally, strategies to optimize the fatigue performance of MAO coated Al alloys were proposed. The results of this study have great guiding significance in regulating micropores, cracks, interface defects, and residual stress in MAO coatings in order to extend fatigue life