Experimental and Theoretical Investigation of External Electric-Field-Induced Crystallization of TKX-50 from Solution by Finite-Temperature String with Order Parameters as Collective Variables for Ionic Crystals.

External electric fields are an effective tool to induce phase transformations. The crystallization of ionic crystals from solution is a common phase transformation. However, understanding of mechanisms is poor at the molecular level. In this work, we carried out an experimental and theoretical investigation of the external electric-field-induced crystallization of TKX-50 from saturated formic acid solution by finite-temperature string (FTS) with order parameters (OPs) as collective variables for ionic crystals. The minimum-free-energy path was sketched by the string method in collective variables. The results show that the K-means clustering algorithm based on Euclidean distance and density weights can be used for enhanced sampling of the OPs in external electric-field-induced crystallization of ionic crystal from solution, which improves the conventional FTS. The crystallization from solution is a process of surface-mediated nucleation. The external electric field can accelerate the evolution of the string and decrease the difference in the potential of mean forces between the crystal and the transition state. Due to the significant change in OPs induced by the external electric field in nucleation, the crystalline quality was enhanced, which explains the experimental results that the external electric field enhanced the density, detonation velocity, and detonation pressure of TKX-50. This work provides an effective way to explore the crystallization of ionic crystals from solution at the molecular level, and it is useful for improving the properties of ionic crystal explosives by using external electric fields.

A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles.

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.

Experimental and theoretical investigation into the response to shock wave for booster explosives JO9C, JH14, JH6, and insensitive RDX.

In order to reduce the vulnerability, the responses to shock waves for booster explosives JO9C, JH14, JH6, and insensitive RDX were evaluated using shock wave partition loading test. To explain the experimental results, molecular dynamics simulation, intermolecular interaction and bond dissociation energy (BDE), and shock initiation pressures were evaluated using the B3LYP, MP2 (full), and M06-2X methods with the 6-311 + + G(2df,2p) basis set. The order of the responsivity is JO9C > JH14 > JH6 > insensitive RDX. The binding energies follow the order of JH14* ≈ JO9C* < insensitive RDX* < JH6*. The interaction energies and BDEs are in RDX∙∙∙(CH3COOCa)+ > RDX∙∙∙CH3COOH > RDX∙∙∙CH2FCH2F. Thus, it can be inferred that for the RDX-based explosives, the stronger the binding energy, intermolecular interaction, and BDE are, the more insensitive the booster is, and thus, the larger energy has to be consumed to overcome the above three kinds of energies during the initiation process, leading to the smaller energy output and weaker response. However, it should be noted that it is mainly the density and the type of explosive that influence the depth of the dent produced on the steel witness block. The essence of the responses to shock waves is revealed by the reduced density gradient, atoms in molecules, and surface electrostatic potentials. HIGHLIGHTS: • Response of booster to shock wave was evaluated by shock wave partition loading test. • Responsivity to shock wave is explained by binding energy, intermolecular interaction, and BDE. • Shock initiation pressures were evaluated. • Essence of responses to shock wave is revealed by RDG, AIM and ESP.

Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction.

Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy.

WER-Net: A New Lightweight Wide-Spectrum Encoding and Reconstruction Neural Network Applied to Computational Spectrum.

The computational spectrometer has significant potential for portable in situ applications. Encoding and reconstruction are the most critical technical procedures. In encoding, the random mass production and selection method lacks quantitative designs which leads to low encoding efficiency. In reconstruction, traditional spectrum reconstruction algorithms such as matching tracking and gradient descent demonstrate disadvantages like limited accuracy and efficiency. In this paper, we propose a new lightweight convolutional neural network called the wide-spectrum encoding and reconstruction neural network (WER-Net), which includes optical filters, quantitative spectral transmittance encoding, and fast spectral reconstruction of the encoded spectral information. The spectral transmittance curve obtained by WER-net can be fabricated through the inverse design network. The spectrometer developed based on WER-net experimentally demonstrates that it can achieve a 2-nm high resolution. In addition, the spectral transmittance encoding curve trained by WER-Net has also achieved good performance in other spectral reconstruction algorithms.

A Review on Injury Mechanism of Intracerebral Hemorrhage in Vehicle Accidents.

BACKGROUND: Intracerebral hemorrhage is one of the most common injuries in vehicle accidents. The aim of this paper is to survey the injury mechanism of intracerebral hemorrhage in vehicle accidents, including contusion, subarachnoid hemorrhage (SAH), subdural hematoma (SDH) and diffuse axonal injury (DAI). METHODS: A condensed overview is given based on the published studies in biomechanical studies on intracerebral hemorrhage. Animal tests, cadaver tests, accident investigations and numerical simulation are the main method used for the mechanism studies. RESULTS: Angular velocity and acceleration can be used to predict these injuries and they are the main causation of DAI. Intracranial pressure is the main causation of coup/contrecoup contusion. Shear stress and strain contribute to the rupture of bridging veins that result in SDH, SAH. CONCLUSION: Injury mechanism of intracerebral hemorrhage in vehicle accidents is complicated that with multiple causations. In-depth works need to be carried out in mechanism studies especially for child head injuries.