Nov 2026· Journal of Transportation Engineering Part A Systems· 0 citations· 23 references
TL;DR
A novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction and achieves superior performance compared to other state-of-the-art methods.
Abstract
Recent research typically formulates traffic forecasting tasks as spatiotemporal graph modeling problems. For spatial correlations, researchers typically use predefined graphs to learn short-range spatial dependencies among traffic data, neglecting the learning of long-rang spatial features that are crucial for accurately capturing the dependencies among traffic data. For temporal correlations, studies tend to focus on local continuous correlations while overlooking global temporal correlations. To this end, this paper presents an in-depth study on long-range spatial dependencies and global temporal continuity. Specifically, a novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction. In this approach, an adaptive adjacency matrix–based graph convolution operation is introduced to learn local spatial features. Building on this, global spatial interdependencies between traffic series are represented through graph diffusion convolution, which also relies on the adaptive adjacency matrix. Additionally, a novel module for learning continuous temporal correlations is introduced to effectively capture the sequential patterns in traffic flow data. Additionally, multihead attention is employed to model global temporal dependencies among the traffic series. The experimental results show that the proposed approach achieves superior performance compared to other state-of-the-art methods.
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 1 citation
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
To address uneven air supply among multiple needle tubes during the drying of high-density forage bales, this study investigated the airflow characteristics and structural optimization of the upper and lower air distribution chambers of a needle-type forage dryer. A three-dimensional CFD model was established, and airflow performance was evaluated using the velocity non-uniformity coefficient M and the inlet-to-outlet total pressure drop Δp. Response surface methodology was used to optimize the key structural parameters. For the upper chamber, installation of a T-shaped baffle and optimization of the cavity height Hc, diffuser angle α, and top-plate opening area ratio Ra yielded an optimal combination of Hc = 133.29 mm, α = 12.51°, and Ra = 1.12, reducing M from 11.2264% to 3.3886%. For the lower chamber, a strip-perforated airflow equalizing plate with Hb = 74.82 mm, D = 23.79 mm, and W = 25.03 mm reduced M from 9.8772% to 1.5484%, with Δp of approximately 130 Pa. Mesh-refinement and turbulence-model sensitivity analyses supported the robustness of the numerical predictions. Repeated outlet-velocity measurements yielded mean absolute relative errors of 3.09%–4.58%. Smoke visualization and grayscale analysis further indicated that the optimized structures enhanced airflow diffusion and redistribution. The results provide guidance for air distribution chamber design in needle-type forage dryers.
X. T. Liu, R. Wang, T. C. Ding· Journal of Applied Fluid Mec...· 0 citations
CHAI (Compliant Human-centered Adaptive Interaction), a novel language-driven framework for real-time modulation of a robot’s kinematics and mechanical compliance in real-world environments, introduces on-the-fly language-driven impedance (compliance) modulation along both translational and rotational directions.
Junhui Huang, Xingguang Duan, A. Bucker et al.· IEEE Robotics and Automation...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
