Risk-Based Decision Model for Microbial Risk Mitigation in Drinking Water Systems

Microbial risks in drinking water systems can cause sporadic pathogenic infections and waterborne outbreaks resulting in large costs for society. In 2010 for example, around 27,000 persons were infected with Cryptosporidium in 6stersund, Sweden. It is so far the largest waterborne outbreak in Europe, and societal costs were estimated at SEK 220 million (approx. 20 million €). To achieve a safe drinking water supply, assessment of microbial risks and, when needed, implementation of risk mitigation measures is necessary. However, drinking water systems are complex, and risk mitigation measures are expensive. A thorough evaluation of possible mitigation measures is thus essential for identification of the most suitable alternative and efficient use of societal resources. In this thesis, a risk-based decision model for evaluating and comparing microbial risk mitigation measures in drinking water systems is presented and illustrated using two Swedish case studies. The decision model combines quantitative microbial risk assessment and cost-benefit analysis in order to evaluate decision alternatives from the perspective of social profitability. The quantitative microbial risk assessment is complemented with water quality modelling and consideration of unexpected risk events, such as extreme weather events and combined sewer overflows, in order to reflex the complexity of drinking water systems. To facilitate transparent cost-benefit analyses, the effect of different health valuation methods on the output from the decision model is presented. In the decision model, health benefits and other benefits are monetised for each mitigation measure and compared to the costs for implementing the measure. It is possible to combine decision criteria such as tolerable risk levels and maximising the net present value when applying the decision model. The decision model integrates several scientific disciplines, thus constituting a novel approach to evaluate microbial risk mitigation measures in drinking water systems and provides a structured analysis that includes often neglected aspects. The model provides transparent and holistic decision support and facilitates well-founded decisions balancing risks, costs and societal benefits

Drivers\u27 Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it "frees up time" for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers\u27 attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to "free up time" and enable drivers to engage in non-driving related activities

Quantum Deletion Codes Derived From Quantum Reed-Solomon Codes

This manuscript presents a construction method for quantum codes capable of correcting multiple deletion errors. By introducing two new alogorithms, the alternating sandwich mapping and the block error locator, the proposed method reduces deletion error correction to erasure error correction. Unlike previous quantum deletion error-correcting codes, our approach enables flexible code rates and eliminates the requirement of knowing the number of deletions

New algorithms for Steiner tree reoptimization

Reoptimization is a setting in which we are given an (near) optimal solution of a problem instance and a local modification that slightly changes the instance. The main goal is that of finding an (near) optimal solution of the modified instance. We investigate one of the most studied scenarios in reoptimization known as Steiner tree reoptimization. Steiner tree reoptimization is a collection of strongly NP-hard optimization problems that are defined on top of the classical Steiner tree problem and for which several constant-factor approximation algorithms have been designed in the last decade. In this paper we improve upon all these results by developing a novel technique that allows us to design polynomial-time approximation schemes. Remarkably, prior to this paper, no approximation algorithm better than recomputing a solution from scratch was known for the elusive scenario in which the cost of a single edge decreases. Our results are best possible since none of the problems addressed in this paper admits a fully polynomial-time approximation scheme, unless P=NP

Modular Subset Sum, Dynamic Strings, and Zero-Sum Sets

The modular subset sum problem consists of deciding, given a modulus $m$, a multiset $S$ of $n$ integers in $0..m-1$, and a target integer $t$, whether there exists a subset of $S$ with elements summing to $t \mod m$, and to report such a set if it exists. We give a simple $O(m \log m)$-time with high probability (w.h.p.) algorithm for the modular subset sum problem. This builds on and improves on a previous $O(m \log^7 m)$ w.h.p. algorithm from Axiotis, Backurs, Jin, Tzamos, and Wu (SODA 19). Our method utilizes the ADT of the dynamic strings structure of Gawrychowski et al. (SODA~18). However, as this structure is rather complicated we present a much simpler alternative which we call the Data Dependent Tree. As an application, we consider the computational version of a fundamental theorem in zero-sum Ramsey theory. The Erd\H{o}s-Ginzburg-Ziv Theorem states that a multiset of $2n - 1$ integers always contains a subset of cardinality exactly $n$ whose values sum to a multiple of $n$. We give an algorithm for finding such a subset in time $O(n \log n)$ w.h.p. which improves on an $O(n^2)$ algorithm due to Del Lungo, Marini, and Mori (Disc. Math. 09).Comment: To appear at the SIAM Symposium on Simplicity in Algorithms (SOSA21

On the Impact of Emotions on Author Profiling

This is the author’s version of a work that was accepted for publication in Information Processing and Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Processing and Management 52 (2016) 73–92. DOI 10.1016/j.ipm.2015.06.003.[EN] In this paper, we investigate the impact of emotions on author profiling, concretely identifying age and gender. Firstly, we propose the EmoGraph method for modelling the way people use the language to express themselves on the basis of an emotion-labelled graph. We apply this representation model for identifying gender and age in the Spanish partition of the PAN-AP-13 corpus, obtaining comparable results to the best performing systems of the PAN Lab of CLEF. © 2015 Elsevier B.V. All rights reserved.The work of the first author was partially funded by Autoritas Consulting SA and by Spanish Ministry of Economics under grant ECOPORTUNITY IPT-2012-1220-430000. The work of the second author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS: Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. A special mention to Maria Dolores Rangel Pardo for her linguistic contribution to this investigation.Rangel-Pardo, FM.; Rosso, P. (2016). On the Impact of Emotions on Author Profiling. Information Processing and Management. 52(1):73-92. https://doi.org/10.1016/j.ipm.2015.06.003S739252