Effect modification of time spent outdoors on the association between early childhood overweight and myopia: a one-year follow-up study.

BACKGROUND: This study examined the moderating role of outdoor time on the relationship between overweight and myopia. METHODS: The data for this study was obtained from a prospective study in Shanghai, where non-myopic children wore wristwear and were followed up for 1 year. Eye examinations were performed at each visit. The modification effect was assessed on the additive scale using multivariable logistic regression, and relative excess risk due to interaction was used to calculate the modification effect. RESULTS: A total of 4683 non-myopic children were included with 32.20% being overweight at baseline. Following a 1-year period, 17.42% of children had myopia. When compared to those who spent <90 minutes outdoors, children who spent >120 had a relative risk of myopia onset that was reduced to 0.61. As time spent outdoors decreased, more risks of myopia onset were identified among overweight children than among normal children, the modification effect on the additive scale was -0.007, with ~70% of this effect attributed to the modifying influence of outdoor time. CONCLUSIONS: Increasing outdoor time can reduce myopia more among overweight children than normal. Future interventions should focus on outdoor activities among overweight children to reduce myopia risks.

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach.

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.

A Multiobjective Framework for Many-Objective Optimization.

It is known that many-objective optimization problems (MaOPs) often face the difficulty of maintaining good diversity and convergence in the search process due to the high-dimensional objective space. To address this issue, this article proposes a novel multiobjective framework for many-objective optimization (Mo4Ma), which transforms the many-objective space into multiobjective space. First, the many objectives are transformed into two indicative objectives of convergence and diversity. Second, a clustering-based sequential selection strategy is put forward in the transformed multiobjective space to guide the evolutionary search process. Specifically, the selection is circularly performed on the clustered subpopulations to maintain population diversity. In each round of selection, solutions with good performance in the transformed multiobjective space will be chosen to improve the overall convergence. The Mo4Ma is a generic framework that any type of evolutionary computation algorithm can incorporate compatibly. In this article, the differential evolution (DE) is adopted as the optimizer in the Mo4Ma framework, thus resulting in an Mo4Ma-DE algorithm. Experimental results show that the Mo4Ma-DE algorithm can obtain well-converged and widely distributed Pareto solutions along with the many-objective Pareto sets of the original MaOPs. Compared with seven state-of-the-art MaOP algorithms, the proposed Mo4Ma-DE algorithm shows strong competitiveness and general better performance.

Health Inequalities of STEMI Care Before Implementation of a New Regional Network: A Prefecture-Level Analysis of Social Determinants of Healthcare in Yunnan, China.

BACKGROUND: As one of the most serious types of coronary heart disease, ST-elevation myocardial infarction (STEMI) faces huge challenges in the equal management and care of patients due to its life-threatening and time-critical condition. Health inequalities such as sex and age differences in STEMI care have been reported from developed countries. However, limited outcomes have been investigated and the major drivers of inequality are still unclear, especially in under-developed areas. This study aimed to explore the major drivers of health inequalities in STEMI care before implementation of a new regional network in the south-west of China. METHODS: Prefecture-level data of STEMI patients before the implementation of a regional network were analysed retrospectively. Drivers of inequality were identified from six social determinants of health, namely area of residence, ethnicity, sex, age, education and occupation. Outcomes of STEMI care included timely presentation, reperfusion therapy, timely reperfusion therapy, heart failure, inpatient mortality, length of hospital stay, hospital costs, and various intervals of ischaemic time. RESULTS: A total of 376 STEMI patients in the research area before implementation of the STEMI network were included. Compared with urban residents, rural patients were significantly less likely to have timely presentation (odds ratio [OR]=0.47, 95% CI: 0.28-0.80, P=.004) and timely reperfusion therapy (OR=0.32, 95% CI: 0.14-0.70, P=.005). Rural residents were less likely to present to hospital promptly than urban residents (HR=0.65, 95% CI=0.52-0.82, P<.001). In the first 3 hours of percutaneous coronary intervention (PCI) reperfusion delay and first 6 hours of total ischaemic time, rural patients had a significantly lower probability to receive prompt PCI (hazard ratio [HR]=0.40, 95% CI: 0.29-0.54, P<.001) and reperfusion therapy (HR=0.37, 95% CI: 0.25-0.56, P<.001) compared to urban patients. CONCLUSION: Rural residents were a major vulnerable group before implementation of the regional STEMI network. No obvious inequalities in ethnicity, sex, age, education or occupation existed in STEMI care in Chuxiong Prefecture of China.