National Research Council Canada

Steel Thin-walled Beams with restrained flanges are commonly used in structural systems where partial or full lateral and rotational restraints may occur, such as in steel–concrete composite beams used in slabs and in highway bridge decks. While these restraints enhance global stability, they can also trigger complex buckling behaviours. One such mode is lateral-distortional buckling (LDB), which arises when the tension flange is restrained against both lateral translation and rotation, leading to web distortion and twisting of the compression flange during buckling. Numerous studies have shown that existing empirical methods for predicting the elastic critical moment of LDB lack sufficient accuracy. This paper presents a numerical approach for estimating the elastic lateral and distortional buckling capacity of thin-walled I-beams under uniformly distributed loading. The developed method, referred to as Distortional Beam Formulations (DBF13), offers an efficient and practical modeling framework that incorporates second-order shell kinematics to accurately capture LDB behaviour. To validate the proposed formulation, Shell-element models were used in case studies and compared against existing experimental and analytical data. A total of 3540 analyses were conducted on doubly-symmetric and mono-symmetric I-section models under various boundary conditions to evaluate DBF13 across different buckling scenarios. Additionally, the effects of the cross-section classification, beam span, flange and web slenderness ratios were examined. The case study results underscore the reliability of DBF13 in predicting the Lateral Torsional Buckling as well as the Lateral Distortional Buckling behaviour. In the eigenvalue predictions, DBF13 has shown an average difference of less than 11% compared to shell model results.

A deeper understanding of the transport processes and pathways of pollutants and plastic debris in aquatic environments, such as water bodies, riverine and coastal zones, is required to effectively understand, prevent, and mitigate marine pollution. In this study, we present an updated version of the high-performance, three-dimensional Canadian Microplastics Simulation (CaMPSim-3D) particle tracking model (PTM), focused on simulating microplastics transport in marine and riverine environments. CaMPSim-3D is an efficient tool that utilizes the principles of ray tracing to enhance the accuracy and performance of particle simulations. New features of the PTM include the implementation of free-slip boundary conditions, particle settling and resuspension mechanisms, and turbulent diffusion, which improve the solution of the diffusion equations. A novel method for computing gradients over triangular prism elements and a double ray casting approach to solve the diffusion equations are also proposed. Additionally, accurate particle settling velocity for circular and elliptical microplastic fibres has been incorporated into the model. Several test cases are presented to demonstrate the new features of the PTM. A sensitivity analysis is performed to investigate the effects of different boundary conditions, particle settling velocity, and turbulent diffusion on the transport and fate of microplastics at two sites: the Saguenay Fjord and the Ottawa River. A test case for model validation against field sampling is also presented for the Ottawa River. The results show that the PTM is efficient, and the use of turbulent diffusion and settling/resuspension mechanisms is necessary, particularly when simulating sinking particles with higher density than water. The findings of this study provide valuable insights into the transport processes of microplastics in aquatic environments and can be used to improve the accuracy of microplastics simulations as well as to identify microplastics accumulation areas.

ABSTRACT Polyphenylene sulfide (PPS) was compounded with recycled carbon fibers (rCF) and thermal black (TB) particles, followed by injection molding (IM) and extrusion of the compounded composites into filaments for fused filament fabrication (FFF). The effect of rCF and TB contents on the microstructure, crystallization kinetics, and mechanical performance of the PPS composites was evaluated and analyzed. At the incorporation of 20 wt.% rCF, the tensile strength of IM specimens increased from 73 MPa up to 151 MPa, and the tensile modulus increased from ~3.5 GPa up to ~20 GPa, respectively. FFF printed specimens with 20 wt.% of rCF showed a tensile strength of 86 MPa and a tensile modulus of 6 GPa, higher when compared to IM neat PPS. Crystallization kinetics of extruded pellets, IM, and FFF printed PPS composites were investigated. The results revealed that the incorporation of rCF and TB significantly hindered the crystallization of PPS. The microstructural characterization provided quantitative results for porosity content and the length distribution and deviation angle of rCF. These characterizations helped to analyze the mechanical performance of IM and FFF printed parts, providing quantitative results to explain the differences in mechanical properties of specimens fabricated through the two methods.

Severe acute respiratory syndrome coronaviruses use the ACE2 receptor for viral entry while downregulating its activity, potentially leading to hypertension and major organ injuries. Dual-action technologies based on soluble ACE2 aimed to neutralize the virus while restoring ACE2's normal enzymatic activity. Here, we describe a novel general molecular design, VHHSpike-ACE2ECD-VHHAlbumin, in the toolbox of ACE2-centric therapeutic modalities. The optimized nanobody module VHHSpike afforded strong pan-specific binding against the entire sarbecoviral clade. This correlated with potent in vitro neutralization of pseudotyped virus variants of concern, with IC50 values in the picomolar range. Exogenous enzymatic activity was provided by the ACE2ECD module, which also contributed binding avidity via intrinsic homodimerization. Persistence of enzymatic activity in circulation was increased in vivo via the nanobody module VHHAlbumin optimized for serum albumin binding. Single-dose therapeutic administration of lead compound 72opt-ACE2-R28 demonstrated virus neutralization in lungs of hamsters at day 5 post-infection with SARS-CoV-2. In hypertensive mice maintained under continuous injection of angiotensin II, a single dose of 72opt-ACE2-R28 normalized systolic blood pressure, maintaining a 30 mmHg reduction after 24 h. Overall, encouraging coronavirus neutralization and hypertension reduction showed magnitudes and timeframes appropriate for treatment of typical acute infections. We discuss molecular bases of interactions with coronavirus spike protein molecules and future scale-up manufacturability toward clinical development of this modular design scaffold with high potential against emerging SARS-CoV-2 variants.

• High-precision testing and empirical characterization of three WRBs • Spatial modeling of WRBs with quantified moisture transport properties • Wall configuration and climate effects on the severity of WRB performance outcomes • System-level mould index comparison for three wall types and four climates • Seasonal mould-index peaks analyzed across the wall types and climates . Water-resistive barriers (WRBs) are essential in building envelope systems, especially in wood-frame structures where moisture-related decay is a major concern. This study delivers a detailed experimental characterization of three WRB membranes, spun-bonded polyolefin (SBPO), a self-adhesive film, and building paper, quantifying key transport properties such as vapor permeability, liquid diffusivity, moisture storage, and saturation content using high-precision gravimetric methods. The moisture-dependent functions were integrated into DELPHIN hygrothermal simulations, and mould growth risk was assessed using the VTT model across varying wall configurations and climates. The results show that although material properties like vapor permeability and liquid diffusivity strongly influence wall moisture behavior, they are not sufficient on their own to ensure effective moisture control. Wall design and climate conditions also play a critical role in determining overall performance. Results show that in cold-wet conditions, wall types I and III incorporating the high vapor-resistance SBPO membrane exceeded the ASHRAE 160 mould index threshold of 3.0 at the outer OSB surface, indicating visible mould potential. In the warm-humid climate, Wall Type II showed a marked increase in mould index at the inner OSB surface, as the drying direction reverses relative to cold climates. Under these conditions, it behaved oppositely to Types I and III, with greater inward moisture accumulation. These results first show that mould severity is higher on the outer surface of OSB; second, show that WRB vapor resistance influences the magnitude of mould index in a climate-dependent manner, higher resistance increased mould severity in cold climates by limiting outward drying, while in warm-humid climates higher liquid water transport governed mould accumulation due to inward drying. Moreover, the timing of mould peaks was controlled by climate and wall configuration rather than membrane selection. These results, derived from precise laboratory measurements and system-level simulations, form the core contribution of this research and support a data-driven framework linking WRB properties to wall durability while guiding membrane selection across different climates and wall designs. .

Developing certified reference materials (CRMs) for natural toxins is challenging due to the scarcity of raw materials, their high toxicity, and structural complexity. Typically, purified toxins in microgram to low milligram amounts are quantitated as stock solutions using quantitative nuclear magnetic resonance spectroscopy (qNMR) and then accurately diluted to their final concentrations. With the limited sensitivity of qNMR, additional techniques are required for homogeneity assessment, quantitation, and stability monitoring of less concentrated solutions. Liquid chromatography with charged aerosol detection (LC-CAD) enables nonspecific detection of compounds down to nanogram-on-column levels. A reversed-phase LC-CAD method was developed that also incorporates MS and DAD. Large volume injection programs were implemented to improve detection for samples at low μg mL-1 concentrations. A retention index standard was used to monitor the overall method performance, while selectivity, repeatability, reproducibility, and response uniformity were evaluated using calibration solution CRMs from several toxin classes. Estimated repeatability was 3.9%, while the intermediate precision of normalized relative response factors was 1.6-8.5%. A relative response factor RSD of 13% was observed for 19 toxins from six different classes, but an average relative response factor of 4.8% within the class demonstrated that LC-CAD is highly suitable for within-class quantitation. The utility of LC-CAD is demonstrated at multiple stages of toxin calibration solution CRM development including purity assessment of stock materials, postcertification stability monitoring, and characterization measurements. The work highlights the important and broadly applicable role that LC-CAD can play in the development of vital reference materials for sample-limited natural products.

Operating in the polar regions is growing increasingly important due to expanding research needs, emerging economic opportunities and efforts to protect national sovereignty.Ships operating in the Arctic and Antarctic face heightened risks and more severe consequences in the event of an accident due to sea ice, icebergs, harsh sea states, low visibility and extreme temperatures. These hazards are compounded by sparse infrastructures and remoteness, with immediate assistances not readily available during emergencies.While climate change is quickly reducing the amount of sea ice, this does not necessarily translate to a reduction in risk in the short-term. On the contrary, uncertainly due to changing sea ice conditions, and an increase in icebergs due to melting glaciers, can increase risk.The operational sea ice community provides information to lessen risk to life, property and the environment and to improve operational efficiency. That information is used by ships to avoid or navigate through sea ice, by ship operators in planning polar voyages, and by policy makers to assess the impact of climate change on future decisions regarding polar operations. Beyond safety, these services also increase operational efficiency by enabling optimized routing, reduced fuel consumption, and shorter transit times.The information provided by the operational sea ice community comes from in-situ measurements, satellite earth observation data, and sophisticated models of the atmosphere, oceans, sea ice, and icebergs. These data streams have historically been assembled and interpreted by highly trained human analysts. Given the rapid increase in the amount of data that needs to be analyzed and the constraints on workforces due to government fiscal reductions, their work is increasingly being assisted by artificial intelligence. Furthermore, because sea ice is highly dynamic and can change within a matter of hours, automation and AI are indispensable for providing real-time and forecast information.The operational sea ice workflows have many of the attributes of an Earth System Digital Twin:Utilization of detailed digital models of relevant earth systems (atmosphere, ocean, sea ice),Continuous incorporation of near-real-time data from in-situ sensors and earth observation satellites,Use of artificial intelligence and machine learning,Generation of predictive models and forecasts, andProvision of advance analytics and decision support tools to allow end-users to optimize their choices.In fact, much of the information both used and generated by the operational sea ice community is available in Destination Earth, the initiative of the European Commission to develop a digital twin of the Earth.This presentation will examine the provision of operational sea ice information as an example of the application of digital twins to provide actionable insights on climate adaptation and disaster risk reduction. It will present current capabilities, AI-enabled workflows, and lessons learned from operational implementation, with a focus on supporting safe and sustainable polar shipping.

Traditionally streamflow forecasting is accomplished using process-based hydrological models. These models could range from simple lumped type to more detailed distributed models. Lumped type models are easy to setup while distributed models require considerable skill and experience for setup. Due to the growing availability of large amounts of spatial and temporal data from various sources, such as remote sensing and re-analyses, and recent advances in the computational power, machine learning models are gaining momentum for solving applied engineering problems, triggering conceptual shifts perhaps led by rapid progress in data science and availability of ready-to-be-deployed software tools. These models have the ability to extract complex dynamical nonlinearities without explicitly defining the involved physical processes and their governing mathematical formulations, as is followed in the case of hydrological models. It is believed that new trends and conceptual shifts are essential for generating new knowledge, challenging or validating prevailing assumptions, and enhancing operational applications, which may include several water management-related functions, hydropower generation operations, and flood risk management across a range of temporal and spatial scales.In this study, two deep learning variants of machine learning models, i.e., (1) the attention-based encoder-decoder bidirectional long short-term memory (AB-ED-BiLSTM) network and (2) the attention-based encoder-decoder bidirectional gated recurrent units (AB-ED-BiGRU) network, were tested on multiple watersheds selected from the Ottawa River Basin, Canada. After developing and successfully evaluating watershed-specific models, regional versions of both models were developed and tested based on the leave-one-watershed-out strategy to emulate an ungauged scenario. Both models were driven mainly by soil moisture states of watersheds and meteorological data in order to evaluate their usefulness for streamflow forecasting at ungauged locations. Although not as ideal as one would desire, these models demonstrated reasonable skill in forecasting streamflow with one to seven days lead time when assessed in terms of coefficient of determination, Nash-Sutcliff Efficiency, and Kling-Gupta Efficiency performance metrics. However, considerable discrepancies were noticed in simulating peak flow values for certain watersheds. Overall results of the study suggest that soil moisture driven machine learning models can potentially be used to develop streamflow forecasting tools for ungauged locations, with AB-ED-BiGRU being computationally an inexpensive option compared to the AB-ED-BiLSTM model. Additional investigations will be required to improve their performance further, e.g., by employing multiple soil moisture products, available through remote sensing and re-analyses sources, and ensemble modelling techniques. Based on continuous scientific progress, emerging machine learning frameworks and architectures, and better understanding of the origins and limitations of existing models, improved hydrological forecasting at ungagged locations can be made possible. In essence, this study contributes towards enhancing our understanding of the role of soil moisture in developing machine learning based streamflow modelling and forecasting tools to support operational applications at ungauged locations, which are often neglected when developing real-time streamflow forecasting systems.