Neutrons produced by cosmic ray muons are an important background for underground experiments studying neutrino oscillations, neutrinoless double beta decay, dark matter, and other rare-event signals. A measurement of the neutron yield in the three different experimental halls of the Daya Bay Reactor Neutrino Experiment at varying depth is reported. The neutron yield in Daya Bay’s liquid scintillator is measured to be $Y_n=(10.26\pm 0.86)\times {10}^{-5}$, $(10.22\pm 0.87)\times {10}^{-5}$, and $(17.03\pm 1.22)\times {10}^{-5}{\mu }^{-1}{\mathrm{g}}^{-1}{\mathrm{cm}}^2$ at depths of 250, 265, and 860 meters-water-equivalent. These results are compared to other measurements and the simulated neutron yield in Fluka and Geant4. A global fit including the Daya Bay measurements yields a power law coefficient of $0.77\pm 0.03$ for the dependence of the neutron yield on muon energy.

This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-based models. By taking advantage of the inherent structure of GAN, the PICGAN eliminates the need for an explicit weighting parameter typically used in the combination of traditional physics-based and data-driven models. The effectiveness of the proposed model is substantiated through case studies using the NGSIM I-80 dataset. These studies demonstrate the model’s superior trajectory reproduction, suggesting its potential as a strong contender to replace conventional models in trajectory prediction tasks. Furthermore, the deployment of PICGAN significantly enhances the stability and efficiency in mixed traffic flow environments. Given its reliable and stable results, the PICGAN framework contributes substantially to the development of efficient longitudinal control strategies for connected autonomous vehicles (CAVs) in real-world mixed traffic conditions.

In order to resolve the imbalance of demand-capacity and airspace congestion, improve the performance of the en route air traffic management, promote the development of air traffic control automation system in the future, this paper proposes an En route air traffic control process model from the perspective of operation requirements. Taking the minimization of operation time, instantaneous density, maximum lateral displacement and air traffic controllers' workload as the optimization objectives, the commonly used air traffic control instructions such as climb and descent and speed restriction are set as constraints, the algorithm is designed based on the air traffic control scheme, and a complete air traffic control process are modeled which outputs instructions for each aircraft. Finally, the model is applied to a case study in the northwest region of China. The simulation results show that compared with the actual operation process, the total operation time is reduced by 18.6%, the variance of the lateral displacement and the vertical separation are efficiently reduced, and the en route air traffic capacity is improved. The proposed model envisages the following two innovations: (ⅰ) the whole process of air traffic controllers' command is considered in the model, especially the control scheme and different types of instructions, and (ⅱ) the en route historical trajectory data of aircraft is used to as the key parameters of the input data to efficiently yield the acceptable results of the model. By quantifying the operation requirements of air traffic control, this model can also balance the distribution of traffic flow density, reduce the utilization rate of horizontal airspace, alleviate flight conflicts on air routes, and lessen the workload of controllers.

The rapid expansion of China’s civil aviation sector, particularly within the Beijing-Tianjin-Hebei airport cluster, has led to significant airspace congestion and operational inefficiencies. This study develops a dynamic flight slot allocation model that integrates both airport and airspace capacity constraints, accounting for real-time fluctuations in resource availability. The model aims to optimize slot distribution, minimize delays, and enhance operational efficiency by adapting to variations in airport and waypoint capacities, offering a more flexible solution compared with traditional static approaches. A case study based on real-world data from the Beijing-Tianjin-Hebei region demonstrates the model’s effectiveness. Computational experiments show that incorporating capacity fluctuations significantly reduces the need for slot adjustments, particularly at secondary airports with volatile demand. The results indicate a marked improvement in operational efficiency, including reduced adjustment times and fewer affected flights. This research underscores the value of adaptive data-driven strategies in managing complex air traffic systems and provides valuable insights for policymakers and aviation authorities. Future research could extend this work by incorporating additional dynamic factors, such as weather conditions and emerging technologies, to further enhance the sustainability and efficiency of air traffic management.

To efficiently handle the projected growth in air traffic, operational concepts such as "corridors-in-the-sky" have been developed in future Air Traffic Management (ATM) plans. Corridors-in-the-sky are defined as reserved airspace where aircraft move in a common direction along parallel lanes on high flight levels. The on and off ramps play an important role in connecting this airspace structure to the jet route. This paper addresses the scheduling problem for flight sequences entering the corridors-in-the-sky via the on-ramp. To satisfy the requirements of various stakeholders, this paper proposes time-based and cost-based aircraft scheduling optimization models for the on-ramp of the corridors-in-the-sky. An improved Particle Swarm Optimization (PSO) algorithm is also developed to solve a case study of the air route B215 in the Beijing control area. Computational results indicate that the optimized models have advantages in reducing the delay time and cost for both single aircraft and flight sequences, compared with the First-come, First-served (FCFS) approach.

For the sake of refined assessment of airspace operation status, improvement of the en route air traffic management performance, and alleviation of the imbalance of demand-capacity and airspace congestion, an en route accessible capacity evaluation model (based on aircraft trajectory data) is proposed in this paper. Firstly, from the perspective of flux, the en route capacity is defined and expanded from a two-dimensional concept to a three-dimensional concept. Secondly, based on the indicators of spatial flow and instantaneous density, an evaluation model of en route capacity is given. Finally, a case study is performed to validate the applicability and feasibility of the model. Results show that the en route accessible capacity, instantaneous density, and spatial flow can describe the temporal and spatial distribution of air traffic flow more precisely, as compared to the conventional indicators, such as route capacity, density, and flow. The proposed model envisages three innovations: (ⅰ) the definition of airspace accessible capacity with reference to capacity of road traffic, (ⅱ) the computation model for flux-based airspace accessible capacity and en route accessible capacity, and (ⅲ) two indicators of en route characteristics named instantaneous density and spatial flow are introduced for evaluating the micro-state of the en route. Furthermore, because of the capacity depiction of the spatial and temporal distribution of air traffic congestion within an airspace unit, this model can also help air traffic controllers balance the distribution of traffic flow density, reduce the utilization rate of horizontal airspace, and resolve flight conflicts on air routes in advance.