134 research outputs found

    Heuristics with Performance Guarantees for the Minimum Number of Matches Problem in Heat Recovery Network Design

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    Heat exchanger network synthesis exploits excess heat by integrating process hot and cold streams and improves energy efficiency by reducing utility usage. Determining provably good solutions to the minimum number of matches is a bottleneck of designing a heat recovery network using the sequential method. This subproblem is an NP-hard mixed-integer linear program exhibiting combinatorial explosion in the possible hot and cold stream configurations. We explore this challenging optimization problem from a graph theoretic perspective and correlate it with other special optimization problems such as cost flow network and packing problems. In the case of a single temperature interval, we develop a new optimization formulation without problematic big-M parameters. We develop heuristic methods with performance guarantees using three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy packing. Numerical results from a collection of 51 instances substantiate the strength of the methods

    Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

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    Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process catalyst. We study a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pre-trained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models, or they may wish to optimize a discrete model that particularly well-represents a data set. We develop several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance

    Nurses are Key Members of the Abortion Care Team: Why aren’t Schools of Nursing Teaching Abortion Care?

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    Abortion is a common and safe procedure in Canada, with the Canadian Institute for Health Information reporting approximately 100,000 procedures per year. Yet access remains problematic. As abortion is unrestricted by criminal law in Canada, access is limited by geographic barriers and by a shortage of providers. We present a feminist critical lens to describe how the marginalization of nursing and nurses in abortion care contributes to social stigma and public misunderstanding about abortion access. The roles of registered nurses and nurse practitioners in abortion advocacy, service navigation, counselling, education, support, physiological care and follow up are underutilized and under-researched. In 2015, decades after its availability elsewhere in the world, Health Canada approved mifepristone (a pill for medical abortion). In 2017, provincial regulators began to authorize nurse practitioners to independently provide medical abortion care, as appropriate given the inclusion in nurse practitioner scope of practice to order diagnostic tests, make diagnoses, and treat health conditions. Ensuring nurse practitioners are able to practice medical abortion has the potential to significantly increase abortion access for rural, remote and other marginalized populations. There is also an opportunity to optimize the registered nurse role in abortion care. However, achieving these improvements is challenging as abortion is not routinely taught in Canadian Schools of Nursing. We argue that to destigmatize abortion and improve access, undergraduate nursing and nurse practitioner programs across the country must begin to include abortion and family planning competencies

    Designing Energy-Efficient Heat Recovery Networks using Mixed-Integer Nonlinear Optimisation

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    Many industrial processes involve heating and cooling liquids: a quarter of the EU 2012 energy consumption came from industry and industry uses 73% of this energy on heating and cooling. We discuss mixed-integer nonlinear optimisation and its applications to energy efficiency. Our particular emphasis is on algorithms and solution techniques enabling optimisation for large-scale industrial networks. As a first application, optimising heat exchangers networks may increase efficiency in industrial plants. We develop deterministic global optimisation algorithms for a mixed-integer nonlinear optimisation model that simultaneously incorporates utility cost, equipment area, and hot/cold stream matches. We automatically recognise and exploit special mathematical structures common in heat recovery. We also computationally demonstrate the impact on the global optimisation solver ANTIGONE and benchmark large-scale test cases against heuristic approaches. As a second application, we discuss special structure in nonconvex quadratically-constrained optimisation problems, particularly through the lens of stream mixing and intermediate blending on process systems engineering networks. We take a parametric approach to uncovering topological structure and sparsity of the standard pooling problem in its p-formulation. We show that the sparse patterns of active topological structure are associated with a piecewise objective function. Finally, the presentation explains the conditions under which sparsity vanishes and where the combinatorial complexity emerges to cross over the P/NP boundary. We formally present the results obtained and their derivations for various specialised instances

    Key environmental stress biomarker candidates for the optimisation of chemotherapy treatment of leukaemia

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    The impact of fluctuations of environmental parameters such as oxygen and starvation on the evolution of leukaemia is analysed in the current review. These fluctuations may occur within a specific patient (in different organs) or across patients (individual cases of hypoglycaemia and hyperglycaemia). They can be experienced as stress stimuli by the cancerous population, leading to an alteration of cellular growth kinetics, metabolism and further resistance to chemotherapy. Therefore, it is of high importance to elucidate key mechanisms that affect the evolution of leukaemia under stress. Potential stress response mechanisms are discussed in this review. Moreover, appropriate cell biomarker candidates related to the environmental stress response and/or further resistance to chemotherapy are proposed. Quantification of these biomarkers can enable the combination of macroscopic kinetics with microscopic information, which is specific to individual patients and leads to the construction of detailed mathematical models for the optimisation of chemotherapy. Due to their nature, these models will be more accurate and precise (in comparison to available macroscopic/black box models) in the prediction of responses of individual patients to treatment, as they will incorporate microscopic genetic and/or metabolic information which is patient-specific.peer-reviewe
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