A New Viscoelastic Mechanics Model for the Creep Behaviour of Fibre Reinforced Asphalt Concrete

Based on the Burgers model, by adding a damper unit, this paper proposes a new viscoelastic model with five units and eight parameters to characterize the viscoelastic deformation of fibre reinforced asphalt concrete (FRAC). According to the creep tests of FRAC beams, this paper studies both the parameters in the model and the viscoelastic behaviour of FRAC with different fibre volume fraction and aspect ratio. In this model, this paper establishes the viscoelastic constitutive equation of asphalt concrete, which takes into account the impacts of fibre content characteristic parameter. Both the experimental study and theoretical analysis show that the new model has a high correlation with the results of creep experiment and plays a key role in describing the whole creep process of FRAC. The fibre content characteristic parameter can comprehensively reflect the effects of the fibre volume fraction and aspect ratio on the viscoelastic behaviour of FRAC. Within the range of this test, the optimum fibre volume fraction, fibre aspect ratio and fibre content characteristic parameter are 0.35%, 324 and 1.13

Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them prone to overfitting when relying solely on training data. Recent developments in pre-trained vision-language models inspire us to leverage the rich ordinal priors in human language by converting the original task into a visionlanguage alignment task. Consequently, we propose L2RCLIP, which fully utilizes the language priors from two perspectives. First, we introduce a complementary prompt tuning technique called RankFormer, designed to enhance the ordering relation of original rank prompts. It employs token-level attention with residual-style prompt blending in the word embedding space. Second, to further incorporate language priors, we revisit the approximate bound optimization of vanilla cross-entropy loss and restructure it within the cross-modal embedding space. Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment. Extensive experiments on three ordinal classification tasks, including facial age estimation, historical color image (HCI) classification, and aesthetic assessment demonstrate its promising performance. The code is available at https://github.com/raywang335/L2RCLIP.Comment: Accepted by NeurIPS 202

FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition

We present FastPoseGait, an open-source toolbox for pose-based gait recognition based on PyTorch. Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks. Unlike other pose-based projects that focus on a single algorithm, FastPoseGait integrates several state-of-the-art (SOTA) algorithms under a unified framework, incorporating both the latest advancements and best practices to ease the comparison of effectiveness and efficiency. In addition, to promote future research on pose-based gait recognition, we provide numerous pre-trained models and detailed benchmark results, which offer valuable insights and serve as a reference for further investigations. By leveraging the highly modular structure and diverse methods offered by FastPoseGait, researchers can quickly delve into pose-based gait recognition and promote development in the field. In this paper, we outline various features of this toolbox, aiming that our toolbox and benchmarks can further foster collaboration, facilitate reproducibility, and encourage the development of innovative algorithms for pose-based gait recognition. FastPoseGait is available at https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will continue updating this report as we add new features.Comment: 10 pages, 4 figure

Experimental investigation on the flexural behavior of concrete reinforced by various types of steel fibers

The benefit of steel fiber on the mechanical behaviors of concrete has been well accepted. The flexural behavior of steel fiber reinforced concrete (SFRC) is complicated which depends on many factors, such as matrix properties, fiber material properties, fiber geometries, fiber volume contents, and interface properties. Thus, the investigations on the flexural behavior of SFRC are needed to be expanded. In this study, the effects of fiber type with varying shapes and aspect ratios on the flexural performance of SFRC were investigated. Five steel fibers were adopted in this study: milled fiber (M), corrugated fiber (C) and three hooked fibers with aspect radios of 45 (HA), 55 (HB), and 65 (HC). Two volume fractions (0.4% and 1.0%) of steel fiber and two compressive strengths (normal and high strengths) of matrix were considered. The load-deflection curves, energy absorption capacity and equivalent flexural strength were discussed. The results show that the flexural behavior of SFRC beams reinforced by 1.0% fibers is significantly higher than that of the beams reinforced by 0.4% fibers. Hooked fiber reinforced beams performed the best flexural load-deflection response compared to the beams reinforced by milled fiber and corrugated fiber reinforced, and exhibited an increasing trend of flexural performance as the fiber aspect ratio increased. The differences between specimens with different fibers for high strength matrix are more obvious compared to the normal strength matrix

QAGait: Revisit Gait Recognition from a Quality Perspective

Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.Comment: Accepted by AAAI 202

A New Viscoelastic Mechanics Model for the Creep Behaviour of Fibre Reinforced Asphalt Concrete

Based on the Burgers model, by adding a damper unit, this paper proposes a new viscoelastic model with five units and eight parameters to characterize the viscoelastic deformation of fibre reinforced asphalt concrete (FRAC). According to the creep tests of FRAC beams, this paper studies both the parameters in the model and the viscoelastic behaviour of FRAC with different fibre volume fraction and aspect ratio. In this model, this paper establishes the viscoelastic constitutive equation of asphalt concrete, which takes into account the impacts of fibre content characteristic parameter. Both the experimental study and theoretical analysis show that the new model has a high correlation with the results of creep experiment and plays a key role in describing the whole creep process of FRAC. The fibre content characteristic parameter can comprehensively reflect the effects of the fibre volume fraction and aspect ratio on the viscoelastic behaviour of FRAC. Within the range of this test, the optimum fibre volume fraction, fibre aspect ratio and fibre content characteristic parameter are 0.35%, 324 and 1.13, respectively

Effects of NH3 and alkaline metals on the formation of particulate sulfate and nitrate in wintertime Beijing

Sulfate and nitrate from secondary reactions remain as the most abundant inorganic species in atmospheric particle matter (PM). Their formation is initiated by oxidation (either in gas phase or particle phase), followed by neutralization reaction primarily by NH3, or by other alkaline species such as alkaline metal ions if available. The different roles of NH3 and metal ions in neutralizing H2SO4 or HNO3, however, are seldom investigated. Here we conducted semi-continuous measurements of SO4 2−, NO3 −, NH4 +, and their gaseous precursors, as well as alkaline metal ions (Na+, K+, Ca2+, and Mg2+) in wintertime Beijing. Analysis of aerosol acidity (estimated from a thermodynamic model) indicated that preferable sulfate formation was related to low pH conditions, while high pH conditions promote nitrate formation. Data in different mass fraction ranges of alkaline metal ions showed that in some ranges the role of NH3 was replaced by alkaline metal ions in the neutralization reaction of H2SO4 and HNO3 to form particulate SO4 2− and NO3 −. The relationships between mass fractions of SO4 2− and NO3 − in those ranges of different alkaline metal ion content also suggested that alkaline metal ions participate in the competing neutralization reaction of sulfate and nitrate. The implication of the current study is that in some regions the chemistry to incorporate sulfur and nitrogen into particle phase might be largely affected by desert/fugitive dust and sea salt, besides NH3. This implication is particularly relevant in coastal China and those areas with strong influence of dust storm in the North China Plain (NCP), both of which host a number of megacities with deteriorating air quality

Brown Carbon Aerosol in Urban Xi’an, Northwest China: TheComposition and Light Absorption Properties

Light-absorbing organic carbon (i.e., brown carbon or BrC) in the atmospheric aerosol has significant contribution to light absorption and radiative forcing. However, the link between BrC optical properties and chemical composition remains poorly constrained. In this study, we combine spectrophotometric measurements and chemical analyses of BrC samples collected from July 2008 to June 2009 in urban Xi'an, Northwest China. Elevated BrC was observed in winter (5 times higher than in summer), largely due to increased emissions from wintertime domestic biomass burning. The light absorption coefficient of methanol-soluble BrC at 365 nm (on average approximately twice that of water-soluble BrC) was found to correlate strongly with both parent polycyclic aromatic hydrocarbons (parent-PAHs, 27 species) and their carbonyl oxygenated derivatives (carbonyl-OPAHs, 15 species) in all seasons (r(2) > 0.61). These measured parent-PAHs and carbonyl-OPAHs account for on average similar to 1.7% of the overall absorption of methanol-soluble BrC, about 5 times higher than their mass fraction in total organic carbon (OC, similar to 0.35%). The fractional solar absorption by BrC relative to element carbon (EC) in the ultraviolet range (300-400 nm) is significant during winter (42 +/- 18% for water-soluble BrC and 76 +/- 29% for methanol-soluble BrC), which may greatly affect the radiative balance and tropospheric photochemistry and therefore the climate and air quality

A Computational Method Based on the Integration of Heterogeneous Networks for Predicting Disease-Gene Associations

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation