Objective: To compare the long-term results of mastoscopic axillary lymph node dissection (MALND) and conventional axillary lymph node dissection (CALND). Patients and Methods: From January 1, 2003, through December 31, 2005, a group of 1027 consecutive patients with operable breast cancer were randomly assigned to 1 of 2 study groups: MALND and CALND. The median follow-up was 63 months. The primary end points of the study were operative outcomes, complication reduction, function conservation, and cosmetics. The secondary end points were disease-free and overall survival. Results: The mean operative blood loss in the MALND group was less than in the CALND group (PϽ.001). The patients who underwent MALND had less axillary pain, numbness or paresthesias, and arm swelling (PϽ.001). The aesthetic appearance of the axilla in the MALND group was much better than that in the CALND group (Pϭ.001 at 6 months and Pϭ.002 at 24 months). A significant difference was found between the 2 groups in distant metastasis (Pϭ.04). The disease-free survival rate was 64.5% in the MALND group and 60.8% in the CALND group (Pϭ.88). The overall survival rate was 81.7% in the MALND group and 78.6% in the CALND group (Pϭ.95).
In order to realize the recognition of coal gangue in the top coal caving process, a scheme of the coal gangue recognition based on the collision vibration signal between coal gangue and the metal plate is proposed in this paper, a systematic and standardized impacting test between coal gangue particles and the metal plate is designed for the first time, the vibration signal standardized processing method by the signal intercepting and the coal gangue impact vibration signal recognition algorithm by stacking integration are innovatively proposed. First, a single particle impact on the metal plate test-bed was designed and constructed. Then 1,000 groups coal and 1,000 groups gangue impact on the metal plate tests were carried out respectively, and the vibration acceleration signals of the metal plate were collected. After that, through the signal intercepting, calculating the time-domain characteristics and HHT processing of the vibration signal, 10 time-frequency characteristics, such as the variance of the intercepted signal and the Hilbert marginal spectrum energy value, are determined to form the feature vector. Finally, based on the two different type of the signal samples, the intercepted signal feature vector, and the original intercepted signal, coal gangue recognition by the seven machine learning algorithms, including the decision tree (DT), random forest (RF), XGBoost, long short-term memory (LSTM), support vector machine (SVM), factorization machine (FM), and stacking integration is carried out respectively, and the basis for selecting recognition schemes is discussed. The results show that the coal gangue recognition rate with the same recognition algorithm by using the intercepted signal samples is higher than that of the feature vector samples, the Staking integration algorithm based on the same sample has the highest recognition rate, and the Staking integration algorithm based on the feature vector has the most significant comprehensive advantage in top coal caving process.
To explore how the cutting parameters affect the conical pick’s cutting force in coal cutting process, some simulation tests were carried with EDEM based on analysis of coal cutting process of conical pick and micro mechanical characteristic of coal particles. In this paper, traction speed, drum angular velocity, and installation angle of conical pick were considered as the main coal cutting parameters. The discrete element simulations were conducted under 7 sets of various cutting parameters, which were determined in the range of empirical parameters. From the results of the simulation tests, it can be concluded that the fragmentation of coal particles appears in a compacted, squeezed, and crumbling state in coal cutting process; the traction speed has the greatest impact on the conical pick’s cutting force and cutting force fluctuation and the influence of drum angular velocity and installation angle reduce in turn; the force and force fluctuation tend to decrease and obviously increase, respectively, with the increase of drum angular velocity and traction speed; and they will reach the minimum when the pick’s installation angle is 45° under certain traction speed and drum angular velocity.
To predict fragment separation during rock cutting, previous studies on rock cutting interactions using simulation approaches, experimental tests, and theoretical methods were considered in detail. This study used the numerical code LS-DYNA (3D) to numerically simulate fragment separation. In the simulations, a damage material model and erosion criteria were used for the base rock, and the conical pick was designated a rigid material. The conical pick moved at varying linear speeds to cut the fixed base rock. For a given linear speed of the conical pick, numerical studies were performed for various cutting depths and mechanical properties of rock. The numerical simulation results demonstrated that the cutting forces and sizes of the separated fragments increased significantly with increasing cutting depth, compressive strength, and elastic modulus of the base rock. A strong linear relationship was observed between the mean peak cutting forces obtained from the numerical, theoretical, and experimental studies with correlation coefficients of 0.698, 0.8111, 0.868, and 0.768. The simulation results also showed an exponential relationship between the specific energy and cutting depth and a linear relationship between the specific energy and compressive strength. Overall, LS-DYNA (3D) is effective and reliable for predicting the cutting performance of a conical pick.
Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.INDEX TERMS Delaunay triangulation, A-star algorithm, mobile robot, map modeling, path planning.
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