In software engineering, optimal feature selection for software product lines (SPLs) is an important and complicated task, involving simultaneous optimization of multiple competing objectives in large but highly constrained search spaces. A feature model is the standard representation of features of all possible products as well as the relationships among them for an SPL. Recently, various multi-objective evolutionary algorithms have been used to search for valid product configurations. However, the issue of the balance between correctness and diversity of solutions obtained in a reasonable time has been found very challenging for these algorithms. To tackle this problem, this paper proposes a novel aggregation-based dominance (ADO) for Pareto-based evolutionary algorithms to direct the search for high-quality solutions. Our method was tested on two widely used Pareto-based evolutionary algorithms: NSGA-II and SPEA2+SDE and validated on nine different SPLs with up to 10, 000 features and two real-world SPLs with up to 7 objectives. Our experiments have shown the effectiveness and efficiency of both ADO-based NSGA-II and SPEA2+SDE: (1) Both algorithms could generate 100% valid solutions for all feature models. (2) The performance of both algorithms was improved as measured by the hypervolume metric in 7/9 and 8/9 feature models. (3) Even for the largest tested feature model with 10,000 features, it required under 40 seconds on a standard desktop to find 100% valid solutions in a single run of both algorithms.
Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decisionmakers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service.
The Percheron artery (artery of Percheron, AOP) is a rare variant vessel. Its acute occlusion can cause a bilateral symmetrical thalamic stroke; typical symptoms of bilateral paramedian thalamic infarcts due to occlusion of AOP are vertical gaze palsy, memory impairment, confusion, drowsiness, hypersomnolence, or coma. We present the MR imaging findings in two cases with cerebral infarction caused by Percheron artery occlusion. Due to the difficulty in the diagnosis of acute Percheron arterial infarction, early conservative treatment is used. The prognosis of the disease is poor, with few patients completely rehabilitating. Therefore, clinicians must understand the characteristics of the disease, provide early diagnosis and administer timely and effective treatment to reduce the patient’s disability rate and fatality rate and therefore improve the quality of life of patients. The patient’s prognosis has extraordinary significance.
We present our agent-based CoronAvirus Lifelong Modelling and Simulation (CALMS) model that aspires to predict the lifelong impacts of Covid-19 on the health and economy of a population. CALMS considers individual characteristics as well as comorbidities in calculating the risk of infection and severe disease. We conduct two sets of experiments aiming at demonstrating the validity and capabilities of CALMS. We run simulations retrospectively and validate the model outputs against hospitalisations, ICU admissions and fatalities in a UK population for the period between March and September 2020. We then run simulations for the lifetime of the cohort applying a variety of targeted intervention strategies and compare their effectiveness against the baseline scenario where no intervention is applied. Four scenarios are simulated with targeted vaccination programmes and periodic lockdowns. Vaccinations are targeted first at individuals based on their age and second at vulnerable individuals based on their health status. Periodic lockdowns, triggered by hospitalisations, are tested with and without vaccination programme in place. Our results demonstrate that periodic lockdowns achieve reductions in hospitalisations, ICU admissions and fatalities of 6-8% compared to the baseline scenario, with an associated intervention cost of £173 million per 1,000 people and targeted vaccination programmes achieve reductions in hospitalisations, ICU admissions and fatalities of 89-90%, compared to the baseline scenario, with an associated intervention cost of £51,924 per 1,000 people. We conclude that periodic lockdowns alone are ineffective at reducing health-related outputs over the long-term and that vaccination programmes which target only the clinically vulnerable are sufficient in providing healthcare protection for the population as a whole.
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