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Group recommender systems aim to provide recommendations to a group of users as a whole rather than to individual users. Nonetheless, prevailing methodologies predominantly aggregate user preferences without adequately accounting for the unique individual intents influencing item selection. This oversight becomes particularly problematic in the context of ephemeral groups formed by users with limited shared historical interactions, which exacerbates the data sparsity challenge. In this paper, we introduce a novel Disentangled Self-Attention Group Recommendation framework with auto-regressive contrastive learning method, termed DAGA. This framework not only employs disentangled neural architectures to reconstruct the multi-head self-attention network but also incorporates modules for mutual information optimization via auto-regressive contrastive learning to better leverage the context information of user–item and group–item historical interactions, obtaining group representations and further executing recommendations. Specifically, we develop a disentangled model comprising multiple components to individually assess and interpret the diverse intents of users and their impacts on collective group preferences towards items. Building upon this model, we apply the principle of contrastive mutual information maximization to train our framework, aligning the group representations with the corresponding user representations derived from each factor of the disentangled model, thereby enriching the contextual understanding required to effectively address the challenges posed by ephemeral groups. Empirical evaluations conducted on three real-world benchmark datasets substantiate the superior performance of our proposed framework over existing state-of-the-art group recommendation approaches.

Performance-based seismic design methods for bridges are advancing, yet limited research has explored the damage mechanisms of bridges subjected to extreme seismic effects, such as those near or across faults. To investigate the damage mechanisms under bedrock dislocation and bridge rupture resistance, providing essential insights for the standardized design and construction of bridges in close proximity to seismic rupture sites, we developed a large-scale device to model bridges in the immediate vicinity of tilted-slip strong seismic rupture sites. This included a synchronous bedrock dislocation loading system. Four sets of typical sandy soil modeling tests were concurrently conducted. The results indicate: (1) The overall shear deformation zone of the foundation and surface uneven deformation primarily concentrate the overburdened soil body along the fault dip. The damaged area under the low-dip reverse fault is lighter on the surface and inside the soil body compared to the high-dip-positive fault. (2) The presence of bridges reduces the width of the main rupture zone and avoidance distance to some extent. However, this reduction is not as significant as anticipated. The damage to the bridge pile foundation along the fault dislocation tendency notably leads to the bending damage of the bridge deck. (3) Input parameters for fracture-resistant bridge design (surface rupture zone location, extent, maximum deformation, etc.) can be deduced from the free site. Within the rupture zone, a “fuse” design can be implemented using simply supported girders. Additionally, combining the “fuse” design with simple supported girders on both sides and utilizing simple support beams for “fuse” design within the rupture zone, along with structural “disconnection”, allows for reinforcing measures on the bridge structure’s foundation platform and pile in the soil body.

With the rapid development of autonomous driving technology, future road traffic must be composed of autonomous vehicles and artificial vehicles. Although autonomous vehicles have greatly improved road capacity, few studies have involved capacity at signal-controlled intersections, and most of the studies are based on experimental simulation. As such, there is a need to more scientifically analyze the impact of autonomous vehicles on road and intersection capacity. Based on three theories of flow-density relationships, traffic flow equilibrium analysis, and the following model, this paper firstly deduces the flow-density relationship of different vehicle types in a single environment. Secondly, flow-density relationships under different proportions of self-driving vehicles are derived. Through the derivation of these two models, the basic road saturation flow rates under different permeabilities of self-driving vehicles, can be obtained. Based on these results, a revised calculation model for the capacity of signalized intersections with different proportions of autonomous vehicles is proposed, which is essentially to revise the basic saturation flow rate under different permeabilities of autonomous vehicles. By using SUMO 1.15.0 traffic simulation software, the theoretical models are individually tested. The results show that the error rate between the theoretical calculation results and the SUMO simulation results, is less than 16%. This study can provide a basis for the calculation of basic capacity of roads and intersections in a future man-machine hybrid driving environment, and provide theoretical guidance for traffic management and control.

With the acceleration of the process of agricultural modernization, many pesticides (insecticides, fungicides, and herbicides) are applied to the field and finally brought into the soils, causing serious damage to the environment. The problem of pesticide pollution has become increasingly prominent. This has highlighted the urgent need for effective and efficient remediation treatment technology for pesticide-contaminated soils. Biochar has a high specific surface area, high porosity, and strong adsorption capacity, making it a soil amendment agent and carbon fixation agent that can improve soil health and enhance adsorption capacity for pesticides to remediate contaminated soils. Recently, efforts have been made to enhance the physicochemical and adsorption properties of biochar by preparing modified biochar, and it has been developed to expand the application of biochar. Specifically, the following aspects were reviewed and discussed: (i) source and modification methods of biochar for pesticide remediation; (ii) the effect of biochar on the environmental fate of remediating pesticides; (iii) the effect of biochar on pesticide-contaminated soils; and (iv) potential problems for the large-scale promotion and application of biochar remediation of pesticides. In conclusion, this review may serve as a reference and guide for pesticide remediation, hence reducing the environmental concerns associated with pesticides in soil.

This paper presents an efficient real-coefficient fitting both Green’s function and its gradient with Fast Fourier Transform (RFGG-FG-FFT) with cube polynomial inter/extrapolation method (CPIE), which is established for the analysis of scattering from antenna over a wide frequency. To improve the computation efficiency, the CPIE is utilized. In order to reduce memory requirements and accelerate matrix vector multiplication, the RFGG-FG-FFT is adopted. The accuracy, correctness and efficiency of the new method are researched on some examples. Compared with the direct method, the examples show that the new method is superior in broadband without loss of accuracy.