Scenarios, sustainability, and critical infrastructure risk mitigation in water planning

This paper examines the state of water supply planning facing unprecedented challenges for ensuring reliable, resilient, safe, and affordable water supplies in Texas and throughout the US. Analysis of water planning methods and practices reveals a robustly sophisticated quantitative modeling capability. Its focus is on both near-term and long-term capital investment requirements and managing operating costs. Water planning focuses on drought mitigation and flood risk management as predominant concerns. But climate change is impacting whole watersheds as well as water systems subject to sea level rise incursions that disrupt wastewater systems. Significant cross-impacts between energy and water add new risks to both energy and water infrastructure, with uncertainties still difficult to robustly quantify. Energy-water nexus issues reflect deeper planning challenges concerning critical infrastructures. Critical infrastructure planning tends to be sectoral-specific even though interdependencies and cross impacts can create broadly impactful cascade effects. Future-state water planning should be done in the context of critical infrastructure planning. Both will benefit from integrating qualitative scenario planning into established quantitative planning models. Doing so expands the complexity that can be captured in planning while providing narratives and using decision-making and public communications tools

Water planning in an age of change

This review paper examines a variety of methodologies that underpin current water planning in the United States – spanning the city, state, and Federal scales – and identifies ways in which changing realities and greater interdependencies between various different critical infrastructures are driving the need for new water planning approaches and processes. Specifically, new sources of uncertainty and their implications are examined, and challenges relating to water supply, allocation, decision making, safety and security, and the information and processes of planning are delineated. In this context, the usefulness of adding scenario planning to current water planning processes is assessed, and ways in which it can be implemented effectively are described. Opportunities for One Water planning to be augmented by critical infrastructure planning and enhanced risk mitigation are also discussed. Recommendations are articulated that are relevant to states, cities, and utility agencies, in order to ensure that they are more resiliently prepared for a substantially more uncertain planning environment in the future, with particular attention to critical infrastructure for water and for other services and the interrelationships between them

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Big data analytics tools for improving the decision-making process in agrifood supply chain

Introduzione: Nell'interesse di garantire una sicurezza alimentare a lungo termine di fronte a circostanze mutevoli, è necessario comprendere e considerare gli aspetti ambientali, sociali ed economici del processo di produzione. Inoltre, a causa della globalizzazione, sono stati sollevati i problemi delle lunghe filiere agroalimentari, l'asimmetria informativa, la contraffazione, la difficoltà di tracciare e rintracciare l'origine dei prodotti e le numerose questioni correlate quali il benessere dei consumatori e i costi sanitari. Le tecnologie emergenti guidano verso il raggiungimento di nuovi approcci socioeconomici in quanto consentono al governo e ai singoli produttori agricoli di raccogliere ed analizzare una quantità sempre crescente di dati ambientali, agronomici, logistici e danno la possibilità ai consumatori ed alle autorità di controllo della qualità di accedere a tutte le informazioni necessarie in breve tempo e facilmente. Obiettivo: L'oggetto della ricerca riguarda lo studio delle modalità di miglioramento del processo produttivo attraverso la riduzione dell'asimmetria informativa, rendendola disponibile alle parti interessate in un tempo ragionevole, analizzando i dati sui processi produttivi, considerando l'impatto ambientale della produzione in termini di ecologia, economia, sicurezza alimentare e qualità di cibo, costruendo delle opportunità per le parti interessate nel prendere decisioni informate, oltre che semplificare il controllo della qualità, della contraffazione e delle frodi. Pertanto, l'obiettivo di questo lavoro è quello di studiare le attuali catene di approvvigionamento, identificare le loro debolezze e necessità, analizzare le tecnologie emergenti, le loro caratteristiche e gli impatti sulle catene di approvvigionamento e fornire utili raccomandazioni all'industria, ai governi e ai policy maker.Introduction: In the interest of ensuring long-term food security and safety in the face of changing circumstances, it is interesting and necessary to understand and to take into consideration the environmental, social and economic aspects of food and beverage production in relation to the consumers’ demand. Besides, due to the globalization, the problems of long supply chains, information asymmetry, counterfeiting, difficulty for tracing and tracking back the origin of the products and numerous related issues have been raised such as consumers’ well-being and healthcare costs. Emerging technologies drive to achieve new socio-economic approaches as they enable government and individual agricultural producers to collect and analyze an ever-increasing amount of environmental, agronomic, logistic data, and they give the possibility to the consumers and quality control authorities to get access to all necessary information in a short notice and easily. Aim: The object of the research essentially concerns the study of the ways for improving the production process through reducing the information asymmetry, making it available for interested parties in a reasonable time, analyzing the data about production processes considering the environmental impact of production in terms of ecology, economy, food safety and food quality and build the opportunity for stakeholders to make informed decisions, as well as simplifying the control of the quality, counterfeiting and fraud. Therefore, the aim of this work is to study current supply chains, to identify their weaknesses and necessities, to investigate the emerging technologies, their characteristics and the impacts on supply chains, and to provide with the useful recommendations the industry, governments and policymakers

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

Wright State University\u27s Celebration of Research, Scholarship, and Creative Activities Book of Abstracts from Friday, April 11, 2014

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s second annual Celebration of Research, Scholarship and Creative Activities on April 11, 2014.https://corescholar.libraries.wright.edu/urop_celebration/1007/thumbnail.jp