A New Strategy of Guidance Command Generation for Re-entry Vehicle

Guidance command for re-entry vehicle can be in lots of formats, but the Euler angles can be provided directly by gyros, so designers used to develop autopilot with commands of Euler angles. After the generation of commands of attack angle and sideslip angle, it’s important to settle how to convert commands of attack angle and sideslip angle to commands of Euler angles. Traditional conversion strategy relies on bank angle, solution to bank angle comprises complicated calculation and can’t be precise. This paper introduces a new conversion strategy of guidance command. This strategy relies on the relative position and velocity measured by seeker, an auxiliary coordinateis established as a transition, the transformation matrix from launch coordinate to body coordinate is solved in a new way, then the commands of Euler angles are obtained. The calculation of bank angle is avoided. The autopilot designed with the converted Euler-angle commands, can track commands of attack angle and sideslip angle steadily.The vehicle reaches the target point precisely. Simulation results show that the new conversion strategy based on seeker information from commands of attack angle and sideslip angle to Euler-angle commands is right.Defence Science Journal, 2013, 63(1), pp.93-100, DOI:http://dx.doi.org/10.14429/dsj.63.236

High-Temperature Performance of Polymer-Modified Asphalt Mixes: Preliminary Evaluation of the Usefulness of Standard Technical Index in Polymer-Modified Asphalt.

The objectives of this study are to evaluate the high-temperature performance of polymer-modified asphalt and asphalt mixtures, and to investigate if the standard technical indexes are useful in the performance evaluation of the polymer-modified asphalt. There are four typically used polymer-modified asphalt types employed in the study. The standard high-temperature rheological test, such as the temperature sweep test, was used to express the high-temperature performance of the polymer-modified asphalt. Also, considering the non-Newtonian fluid properties of the polymer-modified asphalt, the multiple stress creep recovery (MSCR) and zero-shear viscosity (ZSV) tests were employed for the characterizations. Besides, based on the mixture design of SMA-13, the high temperature of the polymer-modified asphalt mixture was evaluated via Marshall stability and rutting tests. The test results concluded that the ranking of the four kinds of polymer-modified asphalt was different in various laboratory tests. The TB-APAO has the best technical indexes in MSCR and ZSV tests, while the WTR-APAO performed best in the temperature sweep test. In addition, the correlation between the polymer-modified asphalt and the asphalt mixture was very poor. Thus, the present standard technical indexes for the profoundly polymer-modified asphalt mixtures are no longer suitable

Epidemiology and grade 2 disability of leprosy among migrant and resident patients in Guangdong: an unignorable continued transmission of leprosy

IntroductionLeprosy remains a major public health concern worldwide and one of the leading causes of disability. New cases of leprosy with grade 2 disability (G2D) often reflect delayed detection due to the limited capacity of the health system to recognize leprosy early. This study aimed to describe the epidemiology and G2D of leprosy among migrant and resident patients with leprosy in Guangdong province, China.MethodsData on newly diagnosed cases of leprosy were collected from the leprosy management information system in China. Descriptive statistical analysis was used to describe the status of G2D. Joinpoint regression model and logistic regression were performed to analyze the temporal trends and influencing factors for G2D.ResultsThe G2D rate among migrant, resident, and total patients with leprosy was 17.5%, 18.7%, and 18.4%, respectively. The total G2D rate increased significantly from 18.0% in 2001 to 25.7% in 2021 (average annual per cent change: 2.5%). Multivariate analysis revealed that factors that negatively influence G2D between migrant and resident patients included delayed discovery time (migrants: OR = 2.57; residents: OR = 4.99) and nerve damage when diagnosed (migrants: OR = 9.40; residents: OR = 21.28).DiscussionOur findings indicate that the targeted intervention measures implemented by our health system are urgently needed to improve the current situation, such as programs to promote early detection, strengthen awareness and skills of healthcare workers, and rehabilitation for disabled patients to improve their quality of life

On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving

The pursuit of autonomous driving technology hinges on the sophisticated integration of perception, decision-making, and control systems. Traditional approaches, both data-driven and rule-based, have been hindered by their inability to grasp the nuance of complex driving environments and the intentions of other road users. This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving. The advent of Visual Language Models (VLM) represents a novel frontier in realizing fully autonomous vehicle driving. This report provides an exhaustive evaluation of the latest state-of-the-art VLM, GPT-4V(ision), and its application in autonomous driving scenarios. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our comprehensive tests span from basic scene recognition to complex causal reasoning and real-time decision-making under varying conditions. Our findings reveal that GPT-4V demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems. It showcases the potential to handle out-of-distribution scenarios, recognize intentions, and make informed decisions in real driving contexts. However, challenges remain, particularly in direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks. These limitations underscore the need for further research and development. Project is now available on GitHub for interested parties to access and utilize: \url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration

Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning

Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions

Predicting protein-ligand binding residues with deep convolutional neural networks

Abstract Background Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study. Results In this study, we propose a new sequence-based approach called DeepCSeqSite for ab initio protein-ligand binding residue prediction. DeepCSeqSite includes a standard edition and an enhanced edition. The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features. The size of the effective context scope is expanded as the number of convolutional layers increases. The long-distance dependencies between residues can be captured by the large effective context scope, and stacking several layers enables the maximum length of dependencies to be precisely controlled. The extracted features are ultimately combined through one-by-one convolution kernels and softmax to predict whether the residues are binding residues. The state-of-the-art ligand-binding method COACH and some of its submethods are selected as baselines. The methods are tested on a set of 151 nonredundant proteins and three extended test sets. Experiments show that the improvement of the Matthews correlation coefficient (MCC) is no less than 0.05. In addition, a training data augmentation method that slightly improves the performance is discussed in this study. Conclusions Without using any templates that include 3D-structure data, DeepCSeqSite significantlyoutperforms existing sequence-based and 3D-structure-based methods, including COACH. Augmentation of the training sets slightly improves the performance. The model, code and datasets are available at https://github.com/yfCuiFaith/DeepCSeqSite

Use of amorphous-poly-alpha-olefin as an additive to improve terminal blend rubberized asphalt

Terminal blend rubberized asphalt (TB), a rubber-modified asphalt, exhibits excellent storage stability and compatibility compared with traditional rubber modified asphalt. However, the investigation regarding its performance at high temperatures is not sufficient. In this study, the amorphous-poly-alpha-olefin (APAO) additive was employed as a modifier to improve the performance of the TB asphalt at both high- and low-temperature. Firstly, the general performance and rotational viscosity were tested via conventional tests in the laboratory. Besides, the rheological properties at both high and low temperatures were evaluated by a dynamic shear rheometer (DSR) test and a bending beam rheometer (BBR) test, respectively. Finally, the micromorphology of specimens was detected through an environmental scanning electron microscope (ESEM) test. The experiment results showed that with the increase in APAO content in the TB asphalt, the increasing trend of rotational viscosity and the softening point was noticeable. In addition, the rutting factor (G*/sinδ), recovery percentage (R), and non-recoverable compliance (Jnr) suggested that the APAO modified TB asphalt presented a remarkable rutting resistance at high-temperature. When the content of APAO reached 6% (wt.), the low-temperature crack resistance of asphalt was significantly improved through the results of low-temperature toughness. Meanwhile, the ESEM test results presented that the APAO modifications were evenly distributed in asphalt and formed a dense grid structure, which helped improve the binding force between the components in the polymer modified asphalt

Combined Fourier-wavelet transforms for studying dynamic response of anisotropic multi-layered flexible pavement with linear-gradual interlayers

The objective of this study is to develop an analytical solution for studying dynamic response of anisotropic multi-layered flexible pavement with linear-gradual interlayers under a harmonic moving load. In this study, the flexible pavement structure is simplified as one multi-layered flexible system, which is assumed to be a semi-infinite medium. A new approach combining the Fourier transform with the wavelet transform for solving the dynamic analytical solution. The wavelet transforms for solving inverse Fourier transform, in solving the solution in the physical domain, is superior to the conventional inverse Fourier transform. The linear-gradual interlayer between the adjacent layers is defined using the shear spring model, and the anisotropic property is simplified as transverse isotropy. Also, in the dynamic analytical solving processes, the motion in the transversely isotropic medium is decoupled into in-plane motion and out-of-plane motion because of the propagation of the waves in a transversely isotropic medium with coupling phenomena. The corresponding analytical solution is entered into a MATLAB-based computer program, which can compute the dynamic responses of an anisotropic multi-layered medium at different interlayer conditions. The accuracy of this program is confirmed through comparison with the results from the examples from the references. The influence analyses of linear-gradual interlayers and anisotropic properties of structural layers are illustrated. It is concluded that the proposed analytical solution-based computer program could be used in the multi-layered flexible pavement structural design and risk management in civil engineering