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SSRN 2025年

作者： Zhai, Guocong Xie, Kun Yang, Di Yang, Hong School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University NorfolkVA23529 United States Department of Transportation & Urban Infrastructure Studies Morgan State University BaltimoreMD21251 United States Department of Electrical & Computer Engineering Old Dominion University NorfolkVA23529 United States Singapore

Electric vehicles (EVs) possess distinct physical characteristics compared to conventional vehicles, including greater mass, larger torque, higher acceleration rates, and a lower center of gravity. However, the safety implications of these differences remain underexplored in existing literature. This study examines the relationship between vehicle type and crash injury severity by employing a rigorous causal inference framework. We analyzed seven years of police-reported crash data (2017-2023) from Virginia, USA, treating EV crashes as the treatment group and conventional vehicle crashes as the control group. To address potential confounding issues in this high-dimensional covariate space, we implement a doubly robust estimator that integrates Bayesian Additive Regression Trees for matching-based estimation (inverse probability of treatment weighting) with logistic regression for outcome modeling. This approach provides reliable causal estimates even if one component is incorrectly specified. Our findings reveal that EV crashes are associated with a 14% reduction in the odds of severe injury compared to conventional vehicle crashes, with consistent estimates across methodological approaches. This study advances knowledge on EV safety and contributes methodologically by developing a doubly robust framework for binary safety outcomes through log-transformed causal odds ratios. From a practical perspective, our findings can guide transportation agencies in developing EV-related regulations, inform insurance providers in designing premium structures for EVs, and help consumers make informed choices on EV adoptions. © 2025, The Authors. All rights reserved.

关键词： Vehicle safety

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Life cycle assessment for rare earth production from ion-adsorption deposits: A comparative study of magnesium sulfate and ammonium sulfate leaching techniques

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Journal of environmental management 2025年 385卷 125627页

作者： Ying Wang Feng Gao Boxue Sun Wenjuan Chen Zuoren Nie National Engineering Laboratory for Industrial Big-data Application Technology Faculty of Materials and Manufacturing Beijing University of Technology Beijing 100124 China. National Engineering Laboratory for Industrial Big-data Application Technology Faculty of Materials and Manufacturing Beijing University of Technology Beijing 100124 China. Electronic address: gaofeng@***.

The production of rare earth elements, key raw materials for high-end technological innovation, is associated with considerable energy consumption and environmental pollution. This study focused on the mining of ion-adsorption deposits in southern China and examined two principal production processes: magnesium salt leaching and ammonium salt leaching. A life cycle inventory for the production of 1 kg of individual rare earth oxides and rare earth elements was compiled via field research and resource consumption allocation modeling of the hydrometallurgical system. The environmental impacts of these processes were assessed using the ReCiPe 2016 method. The scope of the assessment in this study included the rare earth oxide production chain of in situ leaching, solvent extraction, and oxidation roasting and the three major metal reduction processes: molten salt electrolysis, calcium thermal reduction, and cerium thermal reduction. The findings indicated that among the 15 rare earth oxides and elements, yttrium oxide and yttrium metal had the most pronounced environmental impacts. The in situ leaching stage exerted the most considerable environmental influence throughout the production process. A comparison of the magnesium salt leaching and ammonium salt leaching processes revealed that the former exhibited a significantly lower environmental impact than the latter, particularly regarding Terrestrial Eco-toxicity Potential (TETP), which was reduced by 87.55 %. This study elucidates the environmental challenges inherent to the production of rare earths and provides a scientific foundation for the optimization of the production process and reduction of environmental pollution.

关键词： Environmental impacts Life cycle assessment Life cycle inventory Rare earth elements Rare earth oxides

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Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Adaptation Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wu, Ruiqi Jiao, Bingliang Liu, Meng Wang, Shining Wang, Wenxuan Wang, Peng Northwestern Polytechnical University School of Computer Science Xi'an710072 China Ningbo Institute Northwestern Polytechnical University Ningbo315000 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Shenzhen Research Institute of Northwestern Polytechnical University Shenzhen518057 China

The Visible-Infrared Person Re-identification (VI ReID) aims to achieve cross-modality re-identification by matching pedestrian images from visible and infrared illumination. A crucial challenge in this task is mitigating the impact of modality divergence to enable the VI ReID model to learn cross-modality correspondence. Regarding this challenge, existing methods primarily focus on eliminating the information gap between different modalities by extracting modality-invariant information or supplementing inputs with specific information from another modality. However, these methods may overly focus on bridging the information gap, a challenging issue that could potentially overshadow the inherent complexities of cross-modality ReID itself. Based on this insight, we propose a straightforward yet effective strategy to empower the VI ReID model with sufficient flexibility to adapt diverse modality inputs to achieve cross-modality ReID effectively. Specifically, we introduce a Modality-aware and Instance-aware Visual Prompts (MIP) network, leveraging transformer architecture with customized visual prompts. In our MIP, a set of modality-aware prompts is designed to enable our model to dynamically adapt diverse modality inputs and effectively extract information for identification, thereby alleviating the interference of modality divergence. Besides, we also propose the instance-aware prompts, which are responsible for guiding the model to adapt individual pedestrians and capture discriminative clues for accurate identification. Through extensive experiments on four mainstream VI ReID datasets, the effectiveness of our designed modules is evaluated. Furthermore, our proposed MIP network outperforms most current state-of-the-art methods. © 1991-2012 IEEE.

关键词： Infrared devices

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Investigation into the Methodology and Implementation of Life Cycle engineering under China’s Carbon Reduction Target in the Process Industry

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engineering 2024年第9期40卷 87-99页

作者： Mingyang Li Feng Gao Zuoren Nie Boxue Sun Yu Liu Xianzheng Gong National Engineering Laboratory for Industrial Big-data Application Technology College of Material Science and EngineeringBeijing University of TechnologyBeijing 100124China

The industrial sector is the primary source of carbon emissions in *** pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing *** effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering *** strategy aims to attain an optimal solution for material performance,resource consumption,and environmental *** this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and *** meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing *** coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an *** results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

关键词： Carbon neutrality Life cycle engineering Process Industry Carbon reduction technology Eco-design

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Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications

arXiv

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arXiv 2025年

作者： Yang, Songjie Wan, Zihang Xiu, Yue Ning, Boyu Li, Yong Liu, Yuenwei Yuen, Chau National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Wireless Optical Communication Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Hefei230026 China Henan Agricultural Environment Monitoring and Big Data Analysis Engineering Research Center Henan Agricultural University Zhengzhou450046 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Movable antennas offer new potential for wireless communication by introducing degrees of freedom in antenna positioning, which has recently been explored for improving sum rates. In this paper, we aim to fully leverage the capabilities of movable antennas (MAs) by assuming that both the transmitter and receiver can optimize their antenna positions in multi-user multiple-input multiple-output (MU-MIMO) communications. Recognizing that WMMSE beamforming is a highly effective method for maximizing the MU-MIMO sum rate, we modify it to integrate antenna position optimization for MA systems, which we refer to as flexible WMMSE (F-WMMSE) beamforming. Importantly, we reformulate the subproblems within WMMSE to develop regularized sparse optimization frameworks to achieve joint beamforming (antenna coefficient optimization) and element movement (antenna position optimization). We then propose a regularized least squares-based simultaneous orthogonal matching pursuit (RLS-SOMP) algorithm to address the resulting sparse optimization problem. To enhance practical applications, the low-complexity implementation of the proposed framework is developed based on the pre-calculations and matrix inverse lemma. The overall F-WMMSE algorithm converges similarly to WMMSE, and our findings indicate that F-WMMSE achieves a significant sum rate improvement compared to traditional WMMSE, exceeding 20% under appropriate simulation conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Least squares approximations

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Exploring the Inter-Relationship Among Hazardous Actions, Causal Factors, and Motorcyclist Injury Severity Using Path analysis

SSRN

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SSRN 2025年

作者： Yu, Qiong Niu, Jiageng Ou, Jushang Bai, Wei Wang, Nengfeng Jiang, Xinguo College of Civil Aviation Safety Engineering Civil Aviation Flight University of China Guanghan China School of Transportation and Logistics Southwest Jiaotong University Chengdu China Department of Road Traffic Management Sichuan Police College Sichuan Luzhou China POWERCHINA Chengdu Engineering Corporation Limited Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology West Park High-Tech District Chengdu China School of Transportation Fujian University of Technology Fuzhou China

Hazardous actions are the critical contributor to motorcyclist injury severity, which are typically treated in parallel with other causal factors in crash severity models. However, hazardous actions may be the direct outcome of road users’ interaction with the roadway and environment. Consequently, in addition to direct effects, there may also be indirect effects of causal factors on motorcyclist injury severity as a result of hazardous actions. The research attempts to investigate both the direct, indirect, and total effects of causal factors on motorcyclist injury severity. A path analysis approach is employed to model the inter-relationship among hazardous actions, other causal factors, and motorcyclist injury severity. Specifically, with motorcycle crash data in Michigan from 2015 to 2018, two models are developed, namely, a random parameters logit model to examine the relationship between contributing factors and hazardous actions, and a correlated random parameters logit model with heterogeneity in means to assess the correlation between contributing factors and motorcyclist injury severity. Results indicate that factors such as stop/yield signs, intersections, one-way traffic, two-lane roads, midnight/early morning, weekends, and dawn/dusk, while not having a significantly direct impact on motorcyclist injury severity, indirectly increase the risk of injury severity through hazardous actions. Furthermore, under the influence of indirect effects, alcohol or drug use, speed limits of 55 mph or higher, and motorcycle age of six years or older are associated with a greater likelihood of increasing injury severity. Additionally, divided median strips with or without traffic barriers, rain/snow weather, and darkness with streetlights directly reduce motorcyclist injury severity but indirectly increase it through hazardous actions. The study reveals the complex underlying relationship among hazardous actions, other causal factors, and motorcyclist injury severity, w

关键词： Motorcycles

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UAE: Universal Anatomical Embedding on multi-modality medical images

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Medical Image analysis 2025年 103卷 103562页

作者： Bai, Xiaoyu Bai, Fan Huo, Xiaofei Ge, Jia Lu, Jingjing Ye, Xianghua Shu, Minglei Yan, Ke Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China Beijing United Family Hospital China The First Affiliated Hospital Zhejiang University Hangzhou China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Peking Union Medical College Hospital Beijing China Jinan Shandong250014 China DAMO Academy Alibaba Group China Hupan Lab Hangzhou310023 China

Identifying anatomical structures (e.g., lesions or landmarks) is crucial for medical image analysis. Exemplar-based landmark detection methods are gaining attention as they allow the detection of arbitrary points during inference without needing annotated landmarks during training. These methods use self-supervised learning to create a discriminative voxel embedding and match corresponding landmarks via nearest-neighbor searches, showing promising results. However, current methods still face challenges in (1) differentiating voxels with similar appearance but different semantic meanings (e.g., two adjacent structures without clear borders);(2) matching voxels with similar semantics but markedly different appearance (e.g., the same vessel before and after contrast injection);and (3) cross-modality matching (e.g., CT-MRI landmark-based registration). To overcome these challenges, we propose a Unified framework for learning Anatomical Embeddings (UAE). UAE is designed to learn appearance, semantic, and cross-modality anatomical embeddings. Specifically, UAE incorporates three key innovations: (1) semantic embedding learning with prototypical contrastive loss;(2) a fixed-point-based matching strategy;and (3) an iterative approach for cross-modality embedding learning. We thoroughly evaluated UAE across intra- and inter-modality tasks, including one-shot landmark detection, lesion tracking on longitudinal CT scans, and CT-MRI affine/rigid registration with varying fields of view. Our results suggest that UAE outperforms state-of-the-art methods, offering a robust and versatile approach for landmark-based medical image analysis tasks. Code and trained models are available at: https://***/alibaba-damo-academy/self-supervised-anatomical-embedding-v2. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Monthly streamflow forecasting with temporal-periodic transformer

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Journal of Hydrology 2025年 660卷

作者： Yin, Hanlin Zheng, Qirui Wei, Chenxu Liang, Congcong Fan, Minhao Zhang, Xiuwei Zhang, Yanning Shenzhen Research Institute of Northwestern Polytechnical University Sanhang Science & Technology Building No. 45th Gaoxin South 9th Road Nanshan District Shenzhen City518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Hydrology Bureau of the Yellow River Conservancy Commission Zhengzhou450004 China

Monthly streamflow forecasting is important for water resources planning and management in hydrology. In recent years, deep learning based data-driven approaches have received significant attention, especially the Long Short-Term Memory (LSTM) and the Transformer. Among the above two sorts of models for such a task, hardly any model considers the periodic information from the same month of different years directly. This periodic information is important for monthly streamflow forecasting and we propose a periodic attention mechanism to explore it in this paper. Specifically, we propose a novel Temporal-Periodic Transformer (TPT) model, which has temporal-periodic attention modules exploring the temporal information and the periodic information. As a comparison, the original Transformer-based streamflow forecasting model does not consider such periodic information explicitly. To show the performance of our TPT model, two datasets including the Catchment Attributes and Meteorology for Large-sample Studies in Australia (CAMELS-AUS) and a dataset from the Tangnaihai Hydrological Station located in Qinghai Province of China are employed in this paper. Our TPT model outperforms the benchmark Transformer model significantly, e.g., for Nash–Sutcliffe efficiency, the TPT model improves over the original Transformer-based model in 45.9% and furthermore its NSE achieves 0.9108 in Tangnaihai by pretraining in 20 selected basins in CAMELS-AUS. For monthly streamflow forecasting, the TPT model is a good choice. © 2025 Elsevier B.V.

关键词： Water management

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MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

arXiv

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arXiv 2025年

作者： Wang, Tianyi Fan, Jianan Zhang, Dingxin Liu, Dongnan Xia, Yong Huang, Heng Cai, Weidong School of Computer Science The University of Sydney SydneyNSW2006 Australia National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Department of Computer Science University of Maryland College ParkMD20742 United States

Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and be

关键词： Self-supervised learning

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Attaining the stabilizing solution of model unavailable modified algebraic Riccati equation using Q-learning algorithm

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Information Sciences 2025年 717卷

作者： Jie Gao Tao Feng Fei Yan School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan PR China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu PR China

This paper attempts first to solve the discrete-time modified algebraic Riccati equation (MARE) when the system model is completely unavailable. To achieve this, a new iterative algorithm is proposed to solve the MARE based on its equivalent discrete-time algebraic Riccati equation (DARE) which has an identical solution. The algorithm iterates between two steps: 1) the solution improvement of the equivalent DARE; 2) the input weighting improvement. It is proved theoretically that the algorithm strictly converges to the stabilizing solution of the MARE. As an outstanding advantage over the existing methods, the initiation of the algorithm becomes fairly simple which can be initialized by an arbitrary positive input weighting for the single-input scenario or a pre-given input weighting matrix of a sufficiently large magnitude for the multi-input scenario. Furthermore, the proposed algorithm provides an iterative structure which is particularly suitable for developing the Q-learning (QL) algorithm where only the input/output data of the related linear quadratic regulator (LQR) problem is required, therefore the MARE can be solved successfully without using the system model. Finally, numerical simulation examples are given to verify the effectiveness of the theoretical results and the developed algorithm.

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