A systematic mixed-integer differential evolution approach for water network operational optimization

The operational management of potable water distribution networks presents a great challenge to water utilities, as reflected by the complex interplay of a wide range of multidimensional and nonlinear factors across the water value chain including the network physical structure and characteristics, operational requirements, water consumption profiles and the structure of energy tariffs. Nevertheless, both continuous and discrete actuation variables can be involved in governing the water network, which makes optimizing such networks a mixed-integer and highly constrained decision-making problem. As such, there is a need to situate the problem holistically, factoring in multidimensional considerations, with a goal of minimizing water operational costs. This paper, therefore, proposes a systematic optimization methodology for (near) real-time operation of water networks, where the operational strategy can be dynamically updated using a model-based predictive control scheme with little human intervention. The hydraulic model of the network of interest is thereby integrated and successively simulated with different trial strategies as part of the optimization process. A novel adapted mixed-integer differential evolution (DE) algorithm is particularly designed to deal with the discrete-continuous actuation variables involved in the network. Simulation results on a pilot water network confirm the effectiveness of the proposed methodology and the superiority of the proposed mixed-integer DE in comparison with genetic algorithms. It also suggests that 23.69% cost savings can be achieved compared with the water utility's current operational strategy, if adaptive pricing is adopted for all the pumping stations

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

A simulation framework for the analysis of reusable launch vehicle operations and maintenance

During development of a complex system, feasibility initially overshadows other concerns, in some cases leading to a design which may not be viable long-term. In particular for the case of Reusable Launch Vehicles, Operations&Maintenance comprises the majority of the vehicle's LCC, whose stochastic nature precludes direct analysis. Through the use of simulation, probabilistic methods can however provide estimates on the economic behavior of such a system as it evolves over time. Here the problem of operations optimization is examined through the use of discrete event simulation. The resulting tool built from the lessons learned in the literature review simulates a RLV or fleet of vehicles undergoing maintenance and the maintenance sites it/they visit as the campaign evolves over a period of time. The goal of this work is to develop a method for uncovering an optimal operations scheme by investigating the effect of maintenance technician skillset distributions on important metrics such as the achievable annual flight rate and maintenance man hours spent on each vehicle per flight. Using these metrics, the availability of technicians for each subsystem is optimized to levels which produce the greatest revenue from flights and minimum expenditure from maintenance.MSCommittee Chair: Mavris, Dimitri; Committee Member: Edwards, Stephen; Committee Member: Volovoi, Vital

Dynamic Neuromechanical Sets for Locomotion

Most biological systems employ multiple redundant actuators, which is a complicated problem of controls and analysis. Unless assumptions about how the brain and body work together, and assumptions about how the body prioritizes tasks are applied, it is not possible to find the actuator controls. The purpose of this research is to develop computational tools for the analysis of arbitrary musculoskeletal models that employ redundant actuators. Instead of relying primarily on optimization frameworks and numerical methods or task prioritization schemes used typically in biomechanics to find a singular solution for actuator controls, tools for feasible sets analysis are instead developed to find the bounds of possible actuator controls. Previously in the literature, feasible sets analysis has been used in order analyze models assuming static poses. Here, tools that explore the feasible sets of actuator controls over the course of a dynamic task are developed. The cost-function agnostic methods of analysis developed in this work run parallel and in concert with other methods of analysis such as principle components analysis, muscle synergies theory and task prioritization. Researchers and healthcare professionals can gain greater insights into decision making during behavioral tasks by layering these other tools on top of feasible sets analysis

Water Banks: What Have We Learnt from the International Experience?

In recent decades, the use of economic instruments has been promoted as a way to improve water demand management, required due to the difficulty of further supply increases. Against this backdrop, this paper analyses the potential of water banks as a type of water market that can provide institutional flexibility in the allocation of water resources among different users. Research has involved an extensive review of the literature, which has allowed us to identify different types of water banks that operate around the world, as well as an analysis of the experiences of water banks implemented to date, in order to assess the performance of this economic instrument in improving water management. This has provided evidence that water banks, if properly implemented, can be a useful tool for improving governance of water resources. Finally, the analysis has enabled us to propose a number of guidelines on how to improve the implementation of water banks in different countries around the worl