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The 7d unconfined compressive strength tests of alkali-activated tungsten tailings and the microscopic characteristics tests of scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were conducted to investigate the variation law and mechanism of unconfined compressive strength of alkali-activated tungsten tailings under the conditions of seven alkali-solid ratios with the same moisture content. The test results indicate that the unconfined compressive strength of alkali-activated tungsten tailings is increased with the alkali-solid ratio. However, the strength decreases slightly when the alkali-solid ratio is 12%. Alkali activation does not change the crystal type on the micro-scale but forms gels including C-A-S-H and N-A-S-H. The microstructures of the gel are affected by the alkali-solid ratio: The microstructure is honeycomb in low alkali-solid ratio (7%, 8% and 10%), with N-A-S-H as its primary form, and flocculation in high alkali-solid ratio (14% and 15%), mainly in the form of C-A-S-H. When the solid alkali ratio is at the medium level (12%), the microstructure is a small round bead, and the N-A-S-H is equivalent to the C-A-S-H. The generation of C-A-S-H and N-A-S-H gels is the key to increase the strength of alkali activated tungsten tailings. The more C-A-S-H content, the greater the strength. This study can provide a scientific basis and technical reference for the resource utilization of tungsten tailings.

The 7d unconfined compressive strength tests of alkali-activated tungsten tailings and the microscopic characteristics tests of scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were conducted to investigate the effect of alkali-solid ratio on the properties of alkali-activated tungsten tailings. The test results indicate that the unconfined compressive strength of alkali-activated tungsten tailings increased with the alkali-solid ratio. However, the strength decreases slightly when the alkali-solid ratio is 12%. The microstructures of the gels generated in the alkali-activated tungsten tailings are affected by the alkali-solid ratio. The details are as follows: the microstructure is honeycomb in low alkali-solid ratio (7%, 8% and 10%), with N-A-S–H as its primary form, and flocculation in high alkali-solid ratio (14% and 15%), mainly in the form of C-A-S–H. When the alkali-solid ratio is at the medium level (12%), the microstructure is a small round bead, and the N-A-S–H is equivalent to the C-A-S–H. The more C-A-S–H content, the greater the strength. This study can provide a scientific basis and technical reference for the resource utilization of tungsten tailings.

Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.

Understanding the dynamics of air traffic flow is important to achieving advanced air traffic management. This work explores the dynamic evolution and fluctuation characteristics of multistate air traffic time series from a complex network perspective, which is essential for understanding the nature of an air traffic system. With the application of the fundamental diagram (FD), we discover that the relative velocity, flight distance and trajectory similarity are the three key variables for interpreting the arrival traffic flow states of the Xiamen Gaoqi International Airport. According to these three variables, time series are classified into four traffic states based on the K-means algorithm: free flow (FF), transitional flow (TF), slightly congested flow (SCF) and heavily congested flow (HCF). The extracted time series in different states are converted into complex networks using the visibility graph method. We analyze and compare the statistical features of the networks in the four states in terms of indexes, such as the degree distribution and network structure. The results indicate that the complex network characteristics can be used to distinguish air traffic states from the original traffic flow. Our work may be helpful for scholars and engineers to better understand the intrinsic nature of air traffic and for the development of intelligent assistant decision-making systems for air traffic management.

This paper optimizes the meteorological drought assessment method in Fujian Province, China, making it more suitable for long-term serial drought statistics. Using the precipitation data of Pingtan National Basic Weather Station from 1971 to 2016, according to the optimized meteorological drought assessment method, statistics Pingtan meteorological drought process, and its characteristics analysis; through the multi-model integrated forecasting super-aggregate method, the 2011-2014 Fujian precipitation reality and the European Center, T639, WRF, Japan and other numerical forecast products were used for comparative analysis. The use of multiple regression method was used to forecast precipitation, by the localized precipitation correction scheme. The early warning method of single station meteorological drought process based on precipitation reality and precipitation forecast is obtained, and the platformization idea is proposed.

To achieve flight risk identification and assessment in the new field of UAVs, two index systems of risk prediction and safety evaluation are constructed in this work by categorizing the set of UAV flight risks, and the identification subset is set according to the UAV characteristics and use. Risk identification elements are set for the identification subset, and the qualitative attributes of risk identification elements are quantified by set-valued statistics. A general quantitative model for the flight risk identification of UAVs is established. According to the different flight risk factors, UAV flight risks are categorized, the fuzzy comprehensive evaluation method is adopted, and case data are used to quantitatively evaluate different types of risks. Finally, the application of the techno-economic evaluation method to the feasibility evaluation of the UAV flight risk prevention scheme is proposed to provide support for UAV flight risk management.

To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of the air traffic network are constructed based on collected historical data, considering the current status and the predicted future evolution trends. Considering the randomness and fuzziness of factors affecting reliability, a comprehensive evaluation model of air traffic networks based on the uncertainty transformation model is established. Finally, the reliability of the US air traffic network is analyzed based on data published by the Transportation Statistics Bureau of the US Department of Transportation. The results show that the connectivity reliability is 0.4073, the capacity reliability is 0.8300, the travel-time reliability is 0.9180, and the overall reliability evaluated is &ldquo, relatively reliable&rdquo, This indicates that although the US structural reliability is relatively low, the US air traffic management is very efficient, and the overall reliability is strong. The reliability in nonpeak hours is much higher than that in peak hours. The method can identify air traffic network reliability efficiently. The main factors affecting reliability can be found in the calculation process, and are beneficial for air traffic planning and management. The empirical analysis also reflects that the evaluation model based on the uncertainty transformation model can transform the quantitative data of network structure and traffic function into the qualitative language of reliability.

To better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport delay. Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. An integrated airport-based Susceptible-Infected-Recovered-Susceptible (ASIRS) epidemic model for air traffic delay propagation is developed from the network-level perspective, to create a simulator for reproducing the delay propagation in airport networks. The evolution of airport delay propagation is obtained by analyzing the phase trajectory of the model. The simulator is run using the empirical data of China. The simulation results show that the model can reproduce the evolution of the delay propagation in the long term and its accuracy for predicting the number of delayed airports in the short term is much higher than the probabilistic prediction method. The model can thus help managers as a tool to effectively predict the temporal and spatial evolution of air traffic delay.

Flight operation data provides a record of air traffic behaviour in detail, and has become an important resource for studying air traffic patterns. In this paper, the degree characteristic of air traffic networks is studied using flight operation data in order to explain air traffic behaviour. The degree indexes were founded from the perspective of network structure and network function based on complex network theory and traffic flow theory. The connectivity degree, traffic degree and delay degree were established, and the characteristics of these three degrees were studied based on American flight data from different days in 2015 over different time periods of each day. Research shows that at all scales the number degrees, volume degrees and duration degrees are all of power-law distribution, and the power exponents are different depending on the scale, date and index. The power exponents fluctuate from 0.5 to 1.5. In general, the connectivity degree exponent is greater than that of the traffic degree and the delay degree. The duration degree exponent is greater than the traffic degree. The exponents are greater on holidays than they are on non-holidays, as well as during non-working hours than during working hours of the day. Compared with non-holidays, the average connectivity degree and delay degree are larger on holidays, and the average traffic degree is smaller. This reveals that most airports only provide flights to one airport, and the airports’ numbers and flight volumes both decrease on holidays. The power indexes of the delay degree and traffic degree are basically the same. The main mode of traffic (arrivals or departures) during each time period is also studied.

A novel Pt–IrO 2 electrocatalyst was prepared using the dip-coating/calcinations method on titanium substrates. Titanium electrodes coated with oxides were investigated for oxygen evolution. Experimental results showed that Ti/Pt–IrO 2 electrode containing 30 mol% Pt in the coating exhibited significantly higher electrocatalytic activity for oxygen evolution compared to Ti/IrO 2 prepared by the same method, which is also supported by the electrochemical impedance data. Stability tests demonstrated Pt–IrO 2 electrocatalyst had a service cycle of 10,000 times in 0.1 M H 2 SO 4 solution. And the anode surface had hardly discovered cracks and had compact structures, which contributed to stable nature of the electrode together with good conductivity and specific interaction between Pt and IrO 2 formed during the calcination. Furthermore, the enhanced catalytic activity for O 2 evolution at Ti/Pt–IrO 2 electrode is preliminarily discussed using the Mott–Schottky analysis.

The cooperation of UTCS (Urban Traffic Control System) and UTFGS (Urban Traffic Flow Guidance System) performs an important function at alleviating traffic jam, improving urban traffic environment and advancing the street capacity. Because it’ s a complex system, we have to study it with intelligent methods. Firstly, the system framework based on Multi-Agent is discussed after the relationship of them is analyzed in this paper. And then we present the distributed hierarchal architecture of the cooperative system. The cooperative model is established and the Genetic Algorithm (GA) is applied to solve this model. At last, the validity and rationality of this model is tested by simulation.

Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency are established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China’s airport networks show that the evaluation method proposed in this paper is the most accurate. The vulnerability of US and China’s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.