Exploring the potentials and tools of systems engineering and MBSE in machine design

Abstract. This thesis explores Systems Engineering (SE) and Model-Based Systems Engineering (MBSE) in the context of modern machine design. The primary objective is to understand how SE’s interdisciplinary and holistic methodologies, once rooted in the telephone industry, can be seamlessly adapted into the intricate realm of machine design. One of the key findings suggests that, despite the growing intrigue around MBSE as a novel approach to systems engineering, there is still a lack of concrete evidence to substantiate its effectiveness. However, certain studies have highlighted the strengths of MBSE, especially its tools’ capability for parametric and numerical analyses. These tools integrate smoothly with the initial phases of the design process, enabling continuous exploration of a system’s dynamic behavior. While MBSE is still emerging, it offers several apparent advantages, such as improved communication, increased consistency, and efficient use of both time and financial resources. With the knowledge that mechanical engineering these days means working with many different specialists from various fields, we can safely say that engineering machines like cars and planes fall into the realm of systems engineering. The primary methodology employed for data acquisition in this thesis was a literature review.Systeemitekniikan ja MBSE:n mahdollisuudet ja työkalut koneensuunnittelussa. Tiivistelmä. Tämä opinnäytetyö tutkii Systeemitekniikan (SE) ja Mallipohjaisen Systeemitekniikan (MBSE) käsitteitä modernin koneensuunnittelun kontekstissa. Pääasiallinen tavoite on ymmärtää, miten SE:n monitieteelliset ja kokonaisvaltaiset menetelmät, jotka alun perin juontavat juurensa puhelinalaan, voivat saumattomasti soveltua monimutkaisen koneensuunnittelun maailmaan. Yksi keskeisistä havainnoista viittaa siihen, että vaikka MBSE herättää kasvavaa kiinnostusta uutena lähestymistapana systeemitekniikkaan, sen tehokkuutta tukevasta konkreettisesta näytöstä on edelleen niukasti saatavilla. Kuitenkin tietyt tutkimukset ovat korostaneet MBSE:n vahvuuksia, erityisesti sen työkalujen kykyä parametriseen ja numeeriseen analyysiin. Nämä työkalut integroituvat saumattomasti suunnitteluprosessin alkuvaiheisiin, mahdollistaen järjestelmän dynaamisen käyttäytymisen jatkuvan tutkimisen. Vaikka MBSE on edelleen kehittyvä alue, se tarjoaa useita selkeitä etuja, kuten parannetun kommunikaation, lisääntyneen johdonmukaisuuden sekä ajan ja taloudellisten resurssien tehokkaamman hyödyntämisen. Kun otetaan huomioon, että nykyaikainen koneensuunnittelu edellyttää usein yhteistyötä eri alojen erikoisasiantuntijoiden kanssa, voidaan perustellusti väittää, että monimutkaisten koneiden, kuten autojen ja lentokoneiden, suunnittelu kuuluu systeemitekniikan piiriin. Tämän opinnäytetyön tärkein tutkimusmenetelmä oli kirjallisuuskatsaus

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

In the HTML version of this article initially published, the author groups ‘CHD Exome+ Consortium’, ‘EPIC-CVD Consortium’, ‘ExomeBP Consortium’, ‘Global Lipids Genetic Consortium’, ‘GoT2D Genes Consortium’, ‘EPIC InterAct Consortium’, ‘INTERVAL Study’, ‘ReproGen Consortium’, ‘T2D-Genes Consortium’, ‘The MAGIC Investigators’ and ‘Understanding Society Scientific Group’ appeared at the end of the author list but should have appeared earlier in the list, after author Krina T. Zondervan. The errors have been corrected in the HTML version of the article

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure underpinning obesity

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, non-coding variants from which pinpointing causal genes remains challenging. Here, we combined data from 718,734 individuals to discover rare and low-frequency (MAF<5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which eight in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2, ZNF169) newly implicated in human obesity, two (MC4R, KSR2) previously observed in extreme obesity, and two variants in GIPR. Effect sizes of rare variants are ~10 times larger than of common variants, with the largest effect observed in carriers of an MC4R stop-codon (p.Tyr35Ter, MAF=0.01%), weighing ~7kg more than non-carriers. Pathway analyses confirmed enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically-supported therapeutic targets to treat obesity

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity