Research on Optimization of Mining Substitution in Wangzhuang Mine Based on System Dynamics

The unbalanced mining replacement is a major problem restricting efficient mining. The optimization of mining replacement process can achieve perfect coordination of mining face and tunneling in time and ensure the efficient production of mine to the greatest extent. Based on this, this paper takes Wangzhuang Mine as the research background, applies system dynamics to mining replacement research, builds the Wangzhuang Mine mining-driving system simulation model, and uses this model to dynamically simulate the working face 9102 and its replaced heading face 7106. The research has found that there is an imbalance in the replacement between the two working faces, and the replacement process can be optimized by advancing the driving period or increasing the driving team to work in parallel at the appropriate time point, so as to meet the normal demand of mining replacement and provide guidance for the mining plan arrangement of other similar working faces.

Environmental Effects of Eco-Driving on Courier Delivery

In recent years, eco-driving has proven to be an effective tool for reducing fuel consumption and greenhouse gas (GHG) emissions. Until now, most research carried out has focused on ordinary drivers applying eco-driving techniques on their usual routes. However, there is little research on professional driver couriers. This research is aimed at analyzing the effects that eco-driving has on fuel consumption and GHG emissions on courier deliveries in small cities such as Caceres (Spain). For this purpose, a real-life experiment was performed with professional drivers with Spanish post vans from the public sector company Correos. In the first period, driving was under normal conditions (non-eco), and after a theoretical training eco-driving course, there was a second driving period (eco). Driving parameters (speeds, accelerations, rpm, and consumptions) were recorded on all trips to analyze how effective the eco-driving was. The research concluded that eco-driving training does not correlate with more sustainable driving for professional drivers under pressure with the need to deliver packages on time. However, there is a trend in fuel savings when using higher capacity routes.

Period multiplication cascade at the order-by-disorder transition in uniaxial random field XY magnets

Uniaxial random field disorder induces a spontaneous transverse magnetization in the XY model. Adding a rotating driving field, we find a critical point attached to the number of driving cycles needed to complete a limit cycle, the first discovery of this phenomenon in a magnetic system. Near the critical drive, time crystal behavior emerges, in which the period of the limit cycles becomes an integer n > 1 multiple of the driving period. The period n can be engineered via specific disorder patterns. Because n generically increases with system size, the resulting period multiplication cascade is reminiscent of that occurring in amorphous solids subject to oscillatory shear near the onset of plastic deformation, and of the period bifurcation cascade near the onset of chaos in nonlinear systems, suggesting it is part of a larger class of phenomena in transitions of dynamical systems. Applications include magnets, electron nematics, and quantum gases.

Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals

Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers. We analyze driving behavior in an urban environment prior to turns using data obtained from the CANbus of an instrumented vehicle during a one-hour driving period for 12 different individuals. CANbus data streams such as vehicle speed, gas pedal pressure, brake pedal pressure, steering wheel angle, and acceleration are collected and analyzed for 5, 10, and 15 seconds of driving prior to each turn. We consider all turns for each driver and extract statistical features from the signals and use cluster analysis to categorize drivers into groups reflecting different driving styles. The results show that using this approach we can effectively cluster drivers into two groups. The results show consistency in the membership within a cluster throughout the different timeframes. We conclude that driver behavior classification from such data streams is possible and we hope in the near future to devise driver descriptors that include additional maneuvers.

The On–Line Use of Performance Measures to Predict Driving Fatigue

The experiment reported herein examined the use of driving performance measures to predict whether a driver was suffering from sleep-loss and/or excessive driving time. The experiment was conducted on a closed 7.2 km oval track. Each subject driver drove around the track at about 80 kph for three hours on the first test day. For the next 21 hours, each driver remained awake and, at regular selected intervals, performed a battery of behavioural tests. On day two, 24 hours after beginning the first driving period, each subject began to drive for a second 3-hour period. The data from each driving period in the first 27-hour session were analyzed to determine which set of performance variables best discriminated between the performance from each 3-hour driving period. The resulting variables were then programmed into a micro-computer located on-board the test vehicle. Two weeks later, each subject returned to repeat the original 27-hour schedule. During this session, however, the performance of each subject was monitored on-line by the on-board computer. If their performance deteriorated below the criterion established from the data originally collected, the computer, through a D/A link, attempted to alert each driver. Results suggest that the multivariate criteria are able to discriminate between performance from the second 3-hour drive and that from the first at better than chance levels. The results are discussed in terms of an alertness indicator for highway vehicles.