不列颠哥伦比亚大学

Surrogate models are essential in structural analysis and optimization. We propose a heterogeneous graph representation of stiffened panels that accounts for geometrical variability, non-uniform boundary conditions, and diverse loading scenarios, using heterogeneous graph neural networks (HGNNs). The structure is partitioned into multiple structural units, such as stiffeners and the plates between them, with each unit represented by three distinct node types: geometry, boundary, and loading nodes. Edge heterogeneity is introduced by incorporating local orientations and spatial relationships of the connecting nodes. Several heterogeneous graph representations, each with varying degrees of heterogeneity, are proposed and analyzed. These representations are implemented into a heterogeneous graph transformer (HGT) to predict von Mises stress and displacement fields across stiffened panels, based on loading and degrees of freedom at their boundaries. To assess the efficacy of our approach, we conducted numerical tests on panels subjected to patch loads and box beams composed of stiffened panels under various loading conditions. The heterogeneous graph representation was compared with a homogeneous counterpart, demonstrating superior performance. Additionally, an ablation analysis was performed to evaluate the impact of graph heterogeneity on HGT performance. The results show strong predictive accuracy for both displacement and von Mises stress, effectively capturing structural behavior patterns and maximum values.

Since the late 2000’s, many countries around the world have experienced rising interest in mass timber construction in commercial and mixed-use building applications. Globally, wood design and research communities have invested heavily in research and development (R&D) to make mass timber viable for large scale, multi-story buildings, targeting commercial markets. Due to the difference in historical design/fabrication practices, local regulatory rules, and cultural differences, the status of mass timber codification and research development are not uniform throughout the world. In this paper, an overview of recent trends in manufacture, codification, and research on mass timber systems is provided. Specifically, this overview was divided into five distinct topics, namely mass timber material standards, mandatory building/design requirements, non-mandatory design guidelines, different approaches to lateral design, and notable mass timber research efforts in the recent decades. Due to the limitation of the authors’ experience, only selected regions around the world are covered in this review.

We study the impact of mobile money transfers to a representative sample of low-income Ghanaians during the COVID-19 pandemic.The announcement of the upcoming transfers affects neither consumption, well-being, nor social distancing.Once disbursed, transfers increase food expenditure by 8%, income by 20%, and a social distancing index by 0.08 standard deviations.Over 40% of the transfers were spent on food.The positive effects on income mostly persist at final measurement, eight months after the last transfer.Together, we learn that cash transfers can support households economically while also promoting adherence to public health protocols during a pandemic.

Ultra-high-performance concrete (UHPC) has emerged as a next-generation construction material for sustainable infrastructure due to its exceptional mechanical and durability properties. Advancements in cement and concrete technology have significantly expanded the design space, necessitating more efficient and intelligent approaches for material development and structural optimisation. Artificial intelligent (AI) and machine learning (ML) have demonstrated remarkable progress across scientific domains, offering powerful tools for data-driven decision-making and performance prediction. The increasing accessibility of open-source libraries, datasets, and computational resources has fuelled a rapid expansion of AI/ML applications in UHPC research. This review synthesises current AI/ML applications in UHPC research, structured through a scale-based framework to examine developments from the micro-scale to the structural level. Emphasis is placed on highlighting challenges associated with data characteristics and model interpretability, identifying research trends, and assessing the effectiveness of existing approaches. While significant advances have been made, other AI/ML paradigms—such as reinforcement learning, physics-informed neural networks, and graph neural networks—remain largely untapped. Moving forward, integrating AI/ML with domain knowledge and physical constraints presents a promising avenue for unravelling new scientific insights and accelerating UHPC innovation.

Bacterial blight (causal agent Pseudomonas syringae complex, Psc) is an endemic and economically important disease of northern highbush blueberry production in Canada and the Pacific Northwest of the USA. To date, there is no comprehensive survey of the disease in the region and detailed characterization of associated pathogens from Pacific western Canada. Therefore, we did comprehensive disease survey and characterization of associated pseudomonads population using pathogen morphology, biochemical tests, and molecular characterization. We isolated 380 strains of pseudomonads from symptomatic plants from 32 research and commercial fields in 10 diverse geographic locations in British Columbia. We used P. syringae specific (Psy) primers and identified 197 Psy-PCR positive isolates out of 380. We further sequenced Psy-PCR positive isolates of pseudomonads using four housekeeping genes and identified four phylogenomic species: P. syringae (40