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Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.

Sea surface salinity (SSS) in coastal zones with high temporal and spatial resolutions is also needed for scientific research and marine economy. However, one of the limitations of airborne L-band real-aperture microwave radiometers and synthesis aperture interferometric radiometers is high mass and volume or the high complexity of the system. To overcome the limitations, an L-band phased array radiometer is proposed as an alternative solution to monitor SSS in coastal zones from a small unmanned aerial vehicle (UAV). The L-band phased array radiometer is introduced, which only comprises a phased array and a single receiver. In addition, the calibration methods of the L-band phased array, radiometer that the external calibration method and the internal calibration method, are also introduced. A series of experiments was performed to assess the performance of the L-band phased array radiometer. The results indicate that the L-band phased array radiometer show a good performance in low pointing angles and low scanning angles. As a conclusion, the L-band phased array radiometer can be an alternative solution to monitor SSS in coastal zones with high temporal and spatial resolutions from a small UAV.

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.

The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions.

Aimed at the metrology support requirements of weapons found north orientation equipment, a gyro north-seeker calibration system is designed. The system uses one plane mirror and two 1 m collimators to set up three north benchmarks at the same time, and they are assigned to the north. The accuracy of the introduced three northward azimuthal angles is as follows: ±0.20″, ±0.18″, ±0.17″. Between the three north benchmarks, the mutual check function can be realised. The system can realise the calibration of the gyro theodolite and gyro north-seeker with auto-collimation function and no auto-collimation function. The composition and working principle of the system are described in detail. After examination, the north-seeking precision accuracy of the system is 3″ (k = 2). It satisfies the metrological calibration of the gyro theodolite and gyro north-seeker whose north-seeking precision is below 12″. Through practice, the system is accurate, stable and reliable.