DevOps is a collection of practices used to automate theprocesses between the development and the operations teams. Thereexist various barriers between these two teams as they are traditionallyseparate. As a result, software organizations implement DevOpsto mitigate these challenges. At the same time, communication challengesarise while implementing DevOps as it is not a simple task. The aim of the research is to investigate the communicationchallenges faced by developers while implementing DevOps andderiving strategies to overcome these challenges. The overall researchgoal is to find out how communication takes place in a software organization/multinational company, the communication challenges faced during the implementation of DevOps and finally the strategies followedto overcome these challenges. An empirical study has been conducted using SystematicMapping method and Interview method for data collection. 18 outof 538 papers have been selected, to identify various factors affectingcommunication and perform systematic mapping. For the interviewprocess, six participants working at various companies in the roles relatedto DevOps have been selected and semi-structured interviewshave been conducted. The data analysis has been performed usingimmersion approach and data triangulation method. A total of 12 factors affecting communication in DevOpshave been identified from the Systematic mapping. Though no newchallenge has been reported by the participants, the factors identifiedfrom the literature have been confirmed from the responses of theinterview participants and the mitigation strategies to the challengesfaced by each of these participants have been listed out by them. These findings can be used by companies struggling toimplement DevOps, to curb various communication challenges beforethey arise as well as to mitigate already facing challenges by followingthe strategies implemented by other companies. It has to be notedthat this list is not complete as new challenges may arise dependingon the software development practices of the company.

This dissertation examines issues related to avian influenza (AI) disease. This is done via three essays that individually examine: (1) the impacts of climate change on the probability and expected numbers of AI outbreaks and associated economic loss; (2) the effects that media coverage of AI outbreaks has on meat demand in the United States, and (3) the potential effectiveness of AI mitigation strategies on poultry production and welfare under a simulated AI outbreak in United States. The climate change and spread of AI outbreaks study finds that the probability and expected number of AI outbreaks increases as climate change proceeds. Particularly, past climate change has contributed to the current spread of AI disease by 11% and the future climate change will increase this spread by another 12%. Moreover, the underreporting probability of AI outbreaks is also examined and results show that the underreporting probability is much higher in countries with lower gross domestic production level, larger export of poultry products and more numbers of AI confirmed human deaths. Therefore, disease prevention and control plans should focus on these economically poor and climatically changed regions. AI outbreak information has significant effects on meat demand in the United States. In particular, impacts of overseas AI human deaths on meat demand equal 0.02% for beef, -0.005% for pork, and -0.01% for chicken for sample when there was no AI occurred in the United States, while it has smaller impacts on meat expenditure when using the whole sample. In addition, human deaths due to AI disease will increase beef demand and decrease that for pork and chicken. However, AI media coverage in short-run has insignificant effect on meat demand, which suggests that consumers are more cautious when cases occur within the United States as opposed to international cases. In the study on the effects and welfare implications of AI mitigation strategies, results find that vaccination strategy is welfare decreasing under most cases of demand shocks but is desirable in some regions when both domestic and excess demand decrease. Under the assumption of one AI outbreak in the United States, the associated mitigation costs because of past climate change are relatively small.

This thesis proposes a classification system of risk mitigation strategies based on literature search and industry interviews. Following that, a list of generic properties was generated to describe individual strategies. In parallel, populating the properties of a large number of identified strategies was attempted. The practical implications are discussed mainly focusing on knowledge management for risk mitigation strategies.

This thesis proposes a classification system of risk mitigation strategies based on literature search and industry interviews. Following that, a list of generic properties was generated to describe individual strategies. In parallel, populating the properties of a large number of identified strategies was attempted. The practical implications are discussed mainly focusing on knowledge management for risk mitigation strategies.

This thesis proposes a classification system of risk mitigation strategies based on literature search and industry interviews. Following that, a list of generic properties was generated to describe individual strategies. In parallel, populating the properties of a large number of identified strategies was attempted. The practical implications are discussed mainly focusing on knowledge management for risk mitigation strategies. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

acase@tulane.edu / 1 / Shelly L Harris

Abstract Background: Consumer interest and demand for healthy and ecologically produced local food has led to a high market demand that local production cannot meet. Product attributes of perishability and short life cycles ensure that even local supply chains are challenging to manage. This increases potential for risks occurrence in an Organic Food Supply Chain (OFSC) especially with unreliable supply of products. Small and established food retailers import organic food products from across the world. Custom delays, high transport charges, commodity costs and regulatory requirements are associated with food imports. This renders OFSCs complex and vulnerable to disruptions or breakdowns that require appropriate strategies to identify and mitigate risks. Purpose: To gain an overall insight of risks mitigation in OFSCs. The purpose of this thesis is to assess risks affecting suppliers and retailers of organic food and propose risk mitigation strategies to prevent or minimise supply chain breakdowns. Method: This qualitative study utilizes a case study strategy involving seven case firms and seven research respondents. Data is collected through semi-structured interviews and documentary secondary data. The analysis of the empirical findings is conducted by cross analysing empirical findings of respective case firms and then emerging patterns are formulated into a general framework. Conclusions: Low conversion rates of farms for organic production, high costs of investments and regulatory requirements have contributed to prevailing production risks that partly cause low organic output. Other risks identified that could disrupt the food chain under study include sourcing, warehousing, demand, price, financial and institutional risks. Mitigation strategies proposed include production procedures, CAP, supply chain flexibility, supply chain visibility, certification, diversification of retail channels, brand image building, horizontal & vertical cooperation and buyer-supplier relations.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / The science of complex networks has significantly advanced in the last decade and has provided valuable insights into the properties of real world systems by evaluating their structure and construction. Several phenomena occurring in real technological and social systems can be studied, evaluated, quantified, and remedied with the help of network science. The electric power grid is one such real technological system that can be studied through the science of complex networks. The electric grid consists of three basic sub-systems: Generation, Transmission, and Distribution. The transmission sub-system is of particular interest in this work because its mesh-like structure offers challenging problems to complex networks researchers. Cascading dynamics of power grids is one of the problems that can be studied through complex networks. The North American Electric Reliability Corporation (NERC) defines a cascading failure as the uncontrolled successive loss of system elements triggered by an incident at any location. In this dissertation, we primarily discuss the dynamics of cascading failures in the power transmission grid, from a complex networks perspective, and propose possible solutions for mitigating their effects. We evaluate the grid dynamics for two specific scenarios, load growth and random fluctuations in the grid, to study the behavior of the grid under critical conditions. Further, we propose three mitigation strategies for reducing the damage caused by cascading failures. The first strategy is intentional islanding in the power transmission grid. The aim of this method is to intentionally split the grid into two or more separate self- sustaining components such that the initial failure is isolated and the separated components can function independently, with minimum load shedding. The second mitigation strategy involves controlled placement of distributed generation (DG) in the transmission system in order to enhance robustness of the grid. The third strategy requires the addition of a link in the transmission grid by reduction of the average spectral distance, utilizing the Ybus matrix of the grid and a novel algorithm. Through this dissertation, we aim to successfully cover the gap present in the complex networks domain, with respect to the vulnerability analysis of power grid networks.

Recent World Animal Health Organization (OIE) reports on Avian Influenza (AI) outbreaks in Asia, Europe and Canada suggest that there is a nonzero probability that an outbreak may occur anywhere in the world, including the US. To help evaluate possible policy in the face of such an event, this dissertation does an economic evaluation of the implications of using two mitigation strategies: one corresponding to the currently response strategy; and the other an OIE recommended one utilizing vaccination. To do this, the dissertation develops and uses an integrated economic-epidemic model. In this effort, I first estimate the cost of an AI outbreak under a deterministic disease spread assumption where a new vaccination strategy and the current strategy are compared. Subsequently, I introduce risk in the model and construct 95 percent confidence intervals for the outbreak costs, and I rank the outcomes of the alternative strategies using stochastic dominance criteria. In addition, during both phases, I develop and estimate the breakeven probability for an event where ex-ante fixed costs of vaccine stockpiling are justified by the reduction in disease event damages. Results under deterministic disease spread assumption suggest that the vaccination strategy lowers the cost of outbreaks as opposed to the current strategy. This happens because vaccination reduces the number of culled and quarantined flocks. The study is conducted in three locations, yielding the finding that the costs of an outbreak vary depending on the densities of poultry flocks. I also find that when consumer demand shifts due to the outbreak, the costs are much larger. Finally, I find that ex-ante vaccine stockpiling is justified for all the sub-regions if the probability of outbreak exceeds 0.07. The stochastic disease spread assumption results also show that the vaccination strategy dominates in first degree stochastic dominance sense. Consistent with stochastic dominance results, the 95 percent confidence intervals have narrower ranges under the vaccination strategy than without it. Finally, the distribution of the breakeven probability for vaccine stocking has a mode of 0.07 and that the probability is accurate with 82 percent likelihood. However, the threshold varies with the disease transmission parameters and could reach up to 0.32.

Context and rationale – This work originates from policy priorities established within Defra to manage exotic animal diseases (EAD); specifically to understand the causes of low probability events, and to establish contingencies to manage outbreak incidents. Outbreaks of exotic animal diseases, e.g. FMD, CSF and HPAI, can cause economic and social impacts of catastrophic proportions. The UK’s government develops and implements policies and controls to prevent EAD and thus minimise these impacts. Control policies to achieve this are designed to address the vulnerabilities within the control systems. However, data are limited for both the introduction of an EAD as well as its resurgence following the disposal of infected carcasses, i.e. the pre-outbreak and post-outbreak phases of an EAD event. These lack of data compromises the development of policy interventions to improve protection. To overcome these data limitations, predictive models are used to predict system vulnerabilities. Cont/d.