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Introduction Endometriosis is one of the common, gynaecological disorders associated with chronic pelvic pain and subfertility affecting ~10% of reproductive age women. The clinical presentation, etiopathogenesis of endometriosis subtypes and associated risk factors are largely unknown. Genome-Wide Association (GWA) Studies (GWAS) provide strong evidence for the role of genetic risk factors contributing to endometriosis. However, no studies have investigated the association of the GWAS-identified single-nucleotide polymorphism (SNPs) with endometriosis risk in the Indian population; therefore, one-sixth of the world’s population is not represented in the global genome consortiums on endometriosis. The Endometriosis Clinical and Genetic Research in India (ECGRI) study aims to broaden our understanding of the clinical phenotypes and genetic risks associated with endometriosis. Methods and analysis ECGRI is a large-scale, multisite, case–control study of 2000 endometriosis cases and 2000 hospital controls to be recruited over 4 years at 15 collaborating study sites across India covering representative Indian population from east,north-east, north, central, west and southern geographical zones of India. We will use the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonisation Project (WERF-EPHect) data collection instruments for capturing information on clinical, epidemiological, lifestyle, environmental and surgical factors. WERF-EPHect standard operating procedures will be followed for the collection, processing and storage of biological samples. The principal analyses will be for main outcome measures of the incidence of endometriosis, disease subtypes and disease severity determined from the clinical data. This will be followed by GWAS within and across ethnic groups. Ethics and dissemination The study is approved by the Institutional Ethics Committee of Indian Council of Medical Research-National Institute for Research in Reproductive Health and all participating study sites. The study is also approved by the Health Ministry Screening Committee of the Government of India. The results from this study will be actively disseminated through discussions with endometriosis patient groups, conference presentations and published manuscripts.

In this paper, we present a hybrid modelling approach and formulation using simulation-based optimisation (SbO) for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem is NP-Hard. Traditionally, the problem is modelled as a Mixed-Integer Programming (MIP) model and solved using exact algorithms (branch-and-bound, branch-and-cut, etc) or using meta-heuristics (Genetic Algorithm, Particle Swarm Optimisation, etc). In our hybrid SbO approach, we propose a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities, are introduced. The performances of the MIP-based approach and the proposed hybrid approach are compared through the number of decision variables, run time and the objective values for select deterministic benchmark problem instances. The results clearly indicate that the hybrid approach outperforms the traditional MIP for all large-scale problems, resulting in solutions closer to optimum in a much lesser computational time. Interestingly, it is also observed that the introduction of an 'error' term in the objective of the deterministic problem improves performance. Finally, the performance of the proposed SbO approach is analysed for stochastic job shops.

Social networks are used by cities primarily for announcing local-area events, but also for increasing engagement of citizens in votes and elections. Given the current plethora of heterogeneous social networks, city administrators can benefit from social networks to promote initiatives, which are important to a current smart city as well use them to discover future needs in order to manage resources more efficiently. Our focus in this paper is how we can adapt commercial and viral marketing techniques to smart city systems to influence the behavior, opinion and choices of citizens in order to improve their well being and that of the whole society as well as predicting future trends and events.

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