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In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system's ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas. [ABSTRACT FROM AUTHOR]

The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving. [ABSTRACT FROM AUTHOR]

This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ 2 , and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity. • Mixed logit model with heterogeneity in means and variance for crash analysis. • Scrambled and random Halton sequences optimize estimation accuracy. • Study achieves robustness and efficiency in crash severity modeling. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to share the road for the foreseeable future, understanding how to improve the stability of mixed-autonomy platoons is crucial. This paper introduces a novel adaptive control strategy tailored for a specific platoon configuration termed as “1+n+1\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$1+n+1$$\end{document}”, consisting of a leading AV, n intervening vehicles, and a trailing AV. Utilizing vehicle-to-vehicle communication, the trailing AV adapts to real-time traffic states, thereby promoting overall platoon stability. Our analysis, grounded in Lyapunov theory, demonstrates that stabilizing only the trailing vehicle is sufficient to ensure the entire system reaches a stable state. To mitigate the negative effects of sensor and actuator uncertainties, we also introduce a corrective signal framework capable of nullifying adverse inputs. Numerical experiments validate the effectiveness of the proposed strategy in platoon control, which can also be adapted to other platoon configurations. Additionally, the performance of this strategy on macroscopic traffic flow is explored, suggesting substantial potential throughput increases compared with the benchmark strategy of Cooperative Adaptive Cruise Control (CACC). [ABSTRACT FROM AUTHOR]

• Distinct Bi 2 Te 3 family materials show substantial variations in module modeling. • Current models, vendor dataset, and test results reveal gaps in TE properties. • TE module parameter changes must be considered for temperature lifts above 15 K. • TE module parameter shifts cause up to 40 % cooling capacity errors. Thermoelectric (TE) cooling has experienced rapid advancements with the foundational understanding of TE materials. TE modules, compact and lightweight devices, have become the prevalent approach for implementing TE technologies. Accurately quantifying TE physical parameters (Seebeck coefficient α , thermal conductivity κ , and thermal resistance ρ) is challenging due to the dynamic temperature changes in operation. Furthermore, extracting lumped property parameters is crucial for designing energy systems using TE modules. Existing research has several limitations, such as lack of comparative analysis between prevalent formulae, reliance on potentially inaccurate vendor-supplied data, disregard for fundamental assumptions, and absence of empirical measurements. This study addresses these gaps by conducting TE material characterization, comparing three existing formulae using vendor datasheets, designing a laboratory test facility for model validation and refinement, and outlining a structured data extraction procedure. The study's novelty lies in multiple key contributions: (1) a detailed comparative analysis of existing formulae for extracting TE property parameter; (2) executing experimental work in a laboratory setting to validate the model and elucidate its limitations; (3) highlighting potential risks; (4) clarifying possible assumptions from both material and engineering perspectives; and (5) considering temperature differential impacts. This comprehensive approach addresses the current research gaps and provides valuable insights into the design and application of TE modules in various energy systems. [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations. Plain Language Summary: Numerical weather prediction models depend on certain empirical formulations known as parameterizations to estimate precipitation. However, these methods often fall short due to the intricate dynamics of rainfall, which involves numerous small‐scale interactions that these models are unable to fully capture. To counteract these limitations, our study deploys a form of machine learning known as deep learning (DL) to predict precipitation. This DL model utilizes fundamental weather variables derived from NWP models to make its estimations, serving as a remedy for the inherent weaknesses of traditional models caused by the uncertainties in their parameterization schemes. The implementation of our DL model resulted in a significant enhancement in rainfall prediction accuracy, particularly in the case of extreme precipitation events. This suggests that the application of machine learning strategies could be a promising approach to improve the reliability of rainfall forecasts, a crucial element for effective weather prediction and water resource management. Key Points: We employ a transformer based deep learning model to improve the accuracy of precipitation estimation in numerical weather prediction modelsVarious training strategies were implemented to manage the highly skewed precipitation data leading to improvements in heavy rainfall eventsEvaluation conducted with multiple metrics including skill score, quantile and spatial distribution as well as two case studies [ABSTRACT FROM AUTHOR]

Background: Studies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC. Methods: In this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization. Results: In the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells. Conclusions: CCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed. [ABSTRACT FROM AUTHOR]