The Public Health Exposome: A Population-Based, Exposure Science Approach to Health Disparities Research

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training

Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

Ensemble Feature Learning-Based Event Classification for Cyber-Physical Security of the Smart Grid

The power grids are transforming into the cyber-physical smart grid with increasing two-way communications and abundant data flows. Despite the efficiency and reliability promised by this transformation, the growing threats and incidences of cyber attacks targeting the physical power systems have exposed severe vulnerabilities. To tackle such vulnerabilities, intrusion detection systems (IDS) are proposed to monitor threats for the cyber-physical security of electrical power and energy systems in the smart grid with increasing machine-to-machine communication. However, the multi-sourced, correlated, and often noise-contained data, which record various concurring cyber and physical events, are posing significant challenges to the accurate distinction by IDS among events of inadvertent and malignant natures. Hence, in this research, an ensemble learning-based feature learning and classification for cyber-physical smart grid are designed and implemented. The contribution of this research are (i) the design, implementation and evaluation of an ensemble learning-based attack classifier using extreme gradient boosting (XGBoost) to effectively detect and identify attack threats from the heterogeneous cyber-physical information in the smart grid; (ii) the design, implementation and evaluation of stacked denoising autoencoder (SDAE) to extract highlyrepresentative feature space that allow reconstruction of a noise-free input from noise-corrupted perturbations; (iii) the design, implementation and evaluation of a novel ensemble learning-based feature extractors that combine multiple autoencoder (AE) feature extractors and random forest base classifiers, so as to enable accurate reconstruction of each feature and reliable classification against malicious events. The simulation results validate the usefulness of ensemble learning approach in detecting malicious events in the cyber-physical smart grid

Global Risks 2015, 10th Edition.

The 2015 edition of the Global Risks report completes a decade of highlighting the most significant long-term risks worldwide, drawing on the perspectives of experts and global decision-makers. Over that time, analysis has moved from risk identification to thinking through risk interconnections and the potentially cascading effects that result. Taking this effort one step further, this year's report underscores potential causes as well as solutions to global risks. Not only do we set out a view on 28 global risks in the report's traditional categories (economic, environmental, societal, geopolitical and technological) but also we consider the drivers of those risks in the form of 13 trends. In addition, we have selected initiatives for addressing significant challenges, which we hope will inspire collaboration among business, government and civil society communitie

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention