Comparative evaluation of PROMETHEE and ELECTRE with application to sustainability assessment

The selection of robust method for sustainability assessment of companies is a challenging decision, particularly for manufacturers with high safety requirements and large number of consumers such as aerospace, automotive components and, oil & gas companies. These overriding industries consider environmental, social and governance (ESG) criteria as well as non-financial factors that have direct effect on infrastructure investments to reach monetary value for its stakeholders and development of a sustainable long term strategy for their portfolio company. These factors however may be often associated with internal and external uncertainties making it difficult to obtain precise sustainability measurement. Actually, the problem comes from addressing 'how' and 'which' questions to select a solid ranking method for sustainability assessment. In this thesis, we investigate the application of outranking based Multi-Criteria Decision Making (MCDM) methods called ELECTRE III and PROMETHEE I & II for sustainability assessment of industrial organizations. ELECTRE III is a preference based method that considers pseudo-criteria which can be applied for uncertain, imprecise and ill-determined data. PROMETHEE I is a positive and negative flow based multi-criteria method that generates partial rankings. PROMETHEE II is net flow based method and generates complete ranking for alternatives. PROMETHEE methods are more compatible with human judgments. To compare the performance of ELECTRE III and PROMETHEE I & II, we conducted a sustainability assessment case study and performed model verification and robustness analysis, model validation and sensitivity analysis. The data for the study was obtained from Sustainalytics, a firm specializing in sustainability. The results of our study show that ELECTRE III method outperforms PROMETHEE I & II and is therefore recommended for sustainability assessment of industrial organizations

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders

Hereditary spastic paraplegias (HSPs) are neurodegenerative motor neuron diseases characterized by progressive age-dependent loss of corticospinal motor tract function. Although the genetic basis is partly understood, only a fraction of cases can receive a genetic diagnosis, and a global view of HSP is lacking. By using whole-exome sequencing in combination with network analysis, we identified 18 previously unknown putative HSP genes and validated nearly all of these genes functionally or genetically. The pathways highlighted by these mutations link HSP to cellular transport, nucleotide metabolism, and synapse and axon development. Network analysis revealed a host of further candidate genes, of which three were mutated in our cohort. Our analysis links HSP to other neurodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease