Backscattering of linearly polarized light from turbid tissue-like scattering medium with rough surface

In the framework of further development of a unified computational tool for the needs of biomedical optics, we introduce an electric field Monte Carlo (MC) model for simulation of backscattering of coherent linearly polarized light from a turbid tissue-like scattering medium with a rough surface. We consider the laser speckle patterns formation and the role of surface roughness in the depolarization of linearly polarized light backscattered from the medium. The mutual phase shifts due to the photons' pathlength difference within the medium and due to reflection/refraction on the rough surface of the medium are taken into account. The validation of the model includes the creation of the phantoms of various roughness and optical properties, measurements of co-and cross-polarized components of the backscattered/reflected light, its analysis and extensive computer modeling accelerated by parallel computing on the NVIDIA graphics processing units using compute unified device architecture (CUDA). The analysis of the spatial intensity distribution is based on second-order statistics that shows a strong correlation with the surface roughness, both with the results of modeling and experiment. The results of modeling show a good agreement with the results of experimental measurements on phantoms mimicking human skin. The developed MC approach can be used for the direct simulation of light scattered by the turbid scattering medium with various roughness of the surface

Enhanced diagnostic of skin conditions by polarized laser speckles:Phantom studies and computer modeling

The incidence of the skin melanoma, the most commonly fatal form of skin cancer, is increasing faster than any other potentially preventable cancer. Clinical practice is currently hampered by the lack of the ability to rapidly screen the functional and morphological properties of tissues. In our previous study we show that the quantification of scattered laser light polarization provides a useful metrics for diagnostics of the malignant melanoma. In this study we exploit whether the image speckle could improve skin cancer diagnostic in comparison with the previously used free-space speckle. The study includes skin phantom measurements and computer modeling. To characterize the depolarization of light we measure the spatial distribution of speckle patterns and analyse their depolarization ratio taken into account radial symmetry. We examine the dependences of depolarization ratio vs. roughness for phantoms which optical properties are of the order of skin lesions. We demonstrate that the variation in bulk optical properties initiates the assessable changes in the depolarization ratio. We show that image speckle differentiates phantoms significantly better than free-space speckle. The results of experimental measurements are compared with the results of Monte Carlo simulation

Skin Roughness Assessment

Non

Editorial: Risk-based, Pro-poor Urban Design and Planning for Tomorrow's Cities

The Sendai Framework for DRR 2015–2030 identifies an urgent need for a global effort by researchers, practitioners, and governments to integrate science with action to support risk-sensitive decision making . Tomorrow's Cities aims to co-produce methodologies and guidelines for this action-oriented, pro-poor, multi-hazard risk-based decision-making agenda. Understanding and acting on risk is complex. Risk assessments are necessarily based on significant simplifications of the underlying physical and social processes, they are difficult to validate, and the reporting process often obscures caveats implicit in underlying assumptions. Technical outputs may have an inappropriate impact due to inaccurate expectations and limited comprehension). Experience also shows that state-of-the-art risk modelling on its own is not sufficient to build risk reduction into development planning and to support a movement to pro-poor, resilient actions. Institutional inertia, exclusive decision-making structures, and competing interests can mean even the best new knowledge is used only to enhance existing policy and practice. This means that risk science has to be built on the best current methods and must also understand the development context within which risk and resilience are positioned by competing actors in a city. It must then be used to convene policy and practical spaces for new coalitions of interest to cohere and bring pro-poor resilience into policy and action. This editorial outlines Tomorrow's Cities new approach to risk

INNOVATIONS in earthquake risk reduction for resilience: RECENT advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

Post-Earthquake Real Estate Decision-Making : Repair or Replace?

Many buildings with relatively low damage from the 2010-2011 Canterbury were deemed uneconomic to repair and were replaced [1,2]. Factors that affected commercial building owners’ decisions to replace rather than repair, included capital availability, uncertainty with regards to regional recovery, local market conditions and ability to generate cash flow, and repair delays due to limited property access (cordon). This poster provides a framework for modeling decision-making in a case where repair is feasible but replacement might offer greater economic value – a situation not currently modeled in engineering risk analysis

Methods for evaluation and treatment of epistemic uncertainty in portfolio losses due to earthquakes

Assessment of seismic losses in a portfolio of buildings can be a challenging task, since there can be large epistemic uncertainties associated with the different steps of the probabilistic seismic risk analysis: hazard estimation, exposure modeling, fragility functions, and damage-to-loss estimation. Refining models and gathering more data to reduce the epistemic uncertainties can require substantial time investment and incur significant costs; therefore, to make this process more efficient, variables that drive the epistemic uncertainty must be identified. This paper explores the use of two sensitivity analysis methods to evaluate the effect of uncertain variables on the epistemic uncertainty of portfolio losses from earthquakes: 1) a well established variance-based sensitivity analysis technique and 2) a novel method that leverages regression tree ensemble methods with functional outputs. The two methods are examined using a fictional portfolio of 20 buildings in the San Francisco Bay Area. The results from the methods are compared, and advantages and disadvantages of the regression tree ensemble method are highlighted. Also discussed are recommendations for treatment of uncertain input variables based on insights about epistemic uncertainty in the losses

Local measures of disruption for quantifying seismic risk and reliability of complex networks

This paper presents a study of seismic risk to a complex transportation system while quantifying disruption at the local level. The San Francisco Bay Area transportation system is considered as a case study. The network consists of 32,858 road segments, 3152 bridges subject to damage, and 43 transit modes. A refined model of this network’s performance under damage, incorporating features such as transportation mode choice and dynamic demand, is used to predict disruption. Disruption is caused by earthquake shaking, where a full suite of earthquake scenarios in the region (with associated occurrence rates) are considered in order to obtain a fully probabilistic description of risk. Several strategies to manage the computational cost of this analysis are discussed. A number of network performance metrics are presented to provide insight into the disruption risks faced by residents of the region. Mode-destination accessibility, a metric based on network users’ utility functions, is used here as a limit state to evaluate the potential disruption to individual users of the transportation system as it is a performance metric of interest to urban planners. Additionally, local measures of disruption, such as changes in the number of trips in and out of individual locales, are used to identify regions where users may be at particularly high risk of disruption. Using this complex network model, computationally efficient analysis strategies, and refined measures of disruption, we obtain new insights about users’ risk, and obtain results in formats that are usable by urban planners responsible for long-term management of the transportation system’s risk.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.Facult

Integrating PBEE and Network Analysis to Measure Resilience Performance Objectives

Objectives and Scope • The objective of this research is to provide an analytical framework to support on-going community resilience planning initiatives, incorporating the analysis of built environment vulnerabilities and key urban interdependencies as outlined in [3]. • This poster describes the proposed framework that uses a probabilistic approach to measure “current” resilience performance and assesses the likelihood of reaching community scale Resilience Performance Objectives (RPO) (e.g., performance targets in SPUR) by utilizing and drawing inspiration from the modular analysis of Performance Based Earthquake Engineering (PBEE) and explicitly incorporating network analysis of interdependent urban systems. • This framework does not attempt to refine or advance specific risk or network analysis techniques, but to provide a way to unify current resilience, network and risk research and channel it towards helping decision makers measure resilience goals