STR-922: IMPACT OF SLENDERNESS ON THE SEISMIC RESPONSE OF ROCKING FRAMES IN ONTARIO NUCLEAR POWER PLANTS

Canadian Nuclear Power Plants (NPPs) are located on the eastern side of the North American continent, with the majority of them in Ontario. The Design Basis Earthquake (DBE), based on West Coast records, is prescribed in the Canadian nuclear standards. Seismic Probabilistic Risk Assessment studies of the existing plants consider time histories obtained from the latest research on the East Coast earthquakes as seismic input to the analysis. Although Canadian standards are silent about rocking response of unanchored objects, various industry guidelines and the standard ASCE 43-05 prescribe methodologies in this regard. Applications of a rocking frame in a NPP may vary from squat piers supporting a heavy rigid object to a slender masonry frame consisting of two concrete block walls and a rigid diaphragm on top. The methods of analysis prevalent in the nuclear industry recommend obtaining the response of an individual pier of a rocking frame, rather than an equivalent pier representing the rocking frame. Methods of obtaining an equivalent pier, whose response is the same as that of a rocking frame, have been detailed in the literature where it has been emphasized that rocking frames are more stable than an individual rocking pier. However, it is noticed that the response of rocking frames is influenced by their slenderness and also by the boundary condition at the contact between the piers and the top mass. The support boundary conditions are bounded by two extremes: the full top width of a pier, or a point support at its top center. This paper compares the equivalent block parameters of rocking frames for these two extreme boundary conditions. Also, it presents the seismic response of a slender rocking frame subjected to earthquake records compatible with the DBE spectra of Ontario NPPs, as well as spectra used in risk analysis

STR-850: NUMERICAL MODELLING OF REINFORCED CONCRETE BLOCK STRUCTURAL WALL BUILDINGS UNDER SEISMIC LOADING

With the recent shift of design code developers’ focus from component- to system-level assessment of seismic force resisting systems, there is a need to numerically assess the performance of whole buildings. However, reinforced concrete block structural wall buildings are complex structural systems composed of materials with nonlinear and heterogeneous properties, which makes the numerical investigation challenging, especially when seismic behavior is considered. Most previous numerical models of reinforced concrete block walls have considered individual components, rather than the complete building system, and have used relatively complex micro-models. In this paper, OpenSees (Open System for Earthquake Engineering Simulation) is used to create macro non-linear models to simulate the response of two different buildings under unidirectional cyclic loading that represents earthquake effects. The models are created in such a way as to balance the desire for accuracy with the desire for relatively simple models that can be defined using only the geometry and actual material properties, and that are not excessively demanding computationally. Detailed validation of the models is conducted to compare the hysteretic behaviour of the numerical models with available experimental test results on reinforced concrete block structural wall buildings. This paper demonstrates that simple models can, with proper calibration, capture the cyclic response, including energy dissipation and degradation of strength, very well. In this way, this study significantly enhances the database of validated numerical models for reinforced masonry shear wall buildings

Closed-loop agriculture systems meta-research using text mining

The growing global population and climate change threaten the availability of many critical resources, and have been directly impacting the food and agriculture sector. Therefore, new cultivation technologies must be rapidly developed and implemented to secure the world's future food needs. Closed-loop greenhouse agriculture systems provide an opportunity to decrease resource reliance and increase crop yield. Greenhouses provide versatility in what can be grown and the resources required to function. Greenhouses can become highly efficient and resilient through the application of a closed-loop systems approach that prioritizes repurposing, reusing, and recirculating resources. Here, we employ a text mining approach to research the available research (meta-research) and publications within the area of closed-loop systems in greenhouses. This meta-research provides a clearer definition of the term “closed-loop system” within the context of greenhouses, as the term was previously vaguely defined. Using this meta-research approach, we identify six major existing research topic areas in closed-loop agriculture systems, which include: models and controls; food waste; nutrient systems; growing media; heating; and energy. Furthermore, we identify four areas that require further urgent work, which include the establishment of better connection between academic research to industry applications; clearer criteria surrounding growing media selection; critical operational requirements of a closed-loop system; and the functionality and synergy between the many modules that comprise a closed-loop greenhouse systems

Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

BackgroundThe lack of dark skin images in pathologic skin lesions in dermatology resources hinders the accurate diagnosis of skin lesions in people of color. Artificial intelligence applications have further disadvantaged people of color because those applications are mainly trained with light skin color images. ObjectiveThe aim of this study is to develop a deep learning approach that generates realistic images of darker skin colors to improve dermatology data diversity for various malignant and benign lesions. MethodsWe collected skin clinical images for common malignant and benign skin conditions from DermNet NZ, the International Skin Imaging Collaboration, and Dermatology Atlas. Two deep learning methods, style transfer (ST) and deep blending (DB), were utilized to generate images with darker skin colors using the lighter skin images. The generated images were evaluated quantitively and qualitatively. Furthermore, a convolutional neural network (CNN) was trained using the generated images to assess the latter’s effect on skin lesion classification accuracy. ResultsImage quality assessment showed that the ST method outperformed DB, as the former achieved a lower loss of realism score of 0.23 (95% CI 0.19-0.27) compared to 0.63 (95% CI 0.59-0.67) for the DB method. In addition, ST achieved a higher disease presentation with a similarity score of 0.44 (95% CI 0.40-0.49) compared to 0.17 (95% CI 0.14-0.21) for the DB method. The qualitative assessment completed on masked participants indicated that ST-generated images exhibited high realism, whereby 62.2% (1511/2430) of the votes for the generated images were classified as real. Eight dermatologists correctly diagnosed the lesions in the generated images with an average rate of 0.75 (360 correct diagnoses out of 480) for several malignant and benign lesions. Finally, the classification accuracy and the area under the curve (AUC) of the model when considering the generated images were 0.76 (95% CI 0.72-0.79) and 0.72 (95% CI 0.67-0.77), respectively, compared to the accuracy of 0.56 (95% CI 0.52-0.60) and AUC of 0.63 (95% CI 0.58-0.68) for the model without considering the generated images. ConclusionsDeep learning approaches can generate realistic skin lesion images that improve the skin color diversity of dermatology atlases. The diversified image bank, utilized herein to train a CNN, demonstrates the potential of developing generalizable artificial intelligence skin cancer diagnosis applications. International Registered Report Identifier (IRRID)RR2-10.2196/3489

Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning

Floods have been among the costliest hydrometeorological hazards across the globe for decades, and are expected to become even more frequent and cause larger devastating impacts in cities due to climate change. Digital twin technologies can provide decisionmakers with effective tools to rapidly evaluate city resilience under projected floods. However, the development of city digital twins for flood predictions is challenging due to the time-consuming, uncertain processes of developing, calibrating, and coupling physics-based hydrologic and hydraulic models. In this study, a flood prediction methodology (FPM) that integrates synchronization analysis and deep-learning is developed to directly simulate the complex relationships between rainfall and flood characteristics, bypassing the computationally expensive hydrologic-hydraulic models, with the City of Calgary being used for demonstration. The developed FPM presents the core of data-driven digital twins that, with real-time sensor data, can rapidly provide early warnings before flood realization, as well as information about vulnerable areas—enabling city resilience planning considering different climate change scenarios

Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning

Floods have been among the costliest hydrometeorological hazards across the globe for decades, and are expected to become even more frequent and cause larger devastating impacts in cities due to climate change. Digital twin technologies can provide decisionmakers with effective tools to rapidly evaluate city resilience under projected floods. However, the development of city digital twins for flood predictions is challenging due to the time-consuming, uncertain processes of developing, calibrating, and coupling physics-based hydrologic and hydraulic models. In this study, a flood prediction methodology (FPM) that integrates synchronization analysis and deep-learning is developed to directly simulate the complex relationships between rainfall and flood characteristics, bypassing the computationally expensive hydrologic-hydraulic models, with the City of Calgary being used for demonstration. The developed FPM presents the core of data-driven digital twins that, with real-time sensor data, can rapidly provide early warnings before flood realization, as well as information about vulnerable areas—enabling city resilience planning considering different climate change scenarios

A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges

A substantial body of research has been published on artificial intelligence applications in skin cancer influenced by the latter's rising rates, the scarcity of specialized healthcare professionals, and rapid advancements in automated diagnosis and treatment methods. We present a comprehensive review employing text mining to identify key themes of artificial intelligence in skin cancer diagnosis and treatment research. Our text mining model uncovers nine key topics, including dermatological data, machine and deep learning methods, segmentation, data generation, melanoma, basal cell carcinoma, model validation, and treatment. We extensively review the literature on each topic to offer valuable insights and highlight research gaps. Our findings indicate a need for a comprehensive and diverse dataset that includes lesion images, clinical data, and treatment information. In addition, our topic analysis ranks deep learning-based diagnosis as the top topic, followed by data generation and melanoma diagnosis. These insights demonstrate the bias towards deep learning methods and the shortage of studies on rare and precancerous skin lesions. Despite the gaps defined, artificial intelligence can be utilized for triage, initial screening, providing second opinion in diagnosing complex cases, and educational purposes. Additionally, artificial intelligence models can enhance patient outcomes through early diagnosis, treatment recommendation, and response prediction

Data-Driven Community Flood Resilience Prediction

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies