1,962 research outputs found
Comparative genomics and signatures of selection in North Atlantic eels
No full textComparative genomic approaches can identify putative private and shared signatures of selection. We performed a comparative genomic study of North Atlantic eels, European eel (Anguilla Anguilla) and American eel (A. rostrata). The two sister species are nearly undistinguishable at the phenotypic level and despite a wide non-overlapping continental distribution, they spawn in partial sympatry in the Sargasso Sea. Taking advantage of the newly assembled and annotated genome, we used genome wide RAD sequencing data of 359 individuals retrieved from Sequence Nucleotide Archive and state-of-the-art statistic tests to identify putative genomic signatures of selection in North Atlantic eels. First, using the FST and XP-EHH methods, we detected apparent islands of divergence on a total of 7 chromosomes, particularly on chromosomes 6 and 10. Gene ontology analyses suggested candidate genes mainly related to energy production, development and regulation, which could reflect strong selection on traits related to eel migration and larval duration time. Gene effect prediction using SNPeff showed a high number of SNPs in noncoding regions, pointing to a possible regulatory role. Second, using the iHS method we detected shared regions under selection on a total of 11 chromosomes. Several hypotheses might account for the detection of shared islands of selection in North Atlantic eels, including parallel evolution due to adaptation to similar environments and introgression. Future comparative genomic studies will be needed to further clarify the causes and consequences of introgression, including the directionality of these introgression events
Footprints of Natural Selection in North Atlantic Eels: A Review
No full textThe study of natural selection and local adaptation is a thriving field of research. Local adaptation is driven by environment components and results in locally adapted phenotypes with higher fitness relative to other phenotypes from other locations in the species range. Tests of local adaptations have traditionally been done using transplant experiments, but the advent of next-generation sequencing methods have allowed the study of local adaptation to move from a phenotypic to a genomic approach. By using genome scans and state-of-the-art statistical tests, researchers can identify genes putatively under selection and study the genomic architecture of local adaptation, which often includes the observation of clustering of adaptive genes concentrated in fewer genomic regions known as “genomic islands of divergence”. The two species of North Atlantic eels, the European and the American eel, are excellent species for studying selection since they are panmictic and present large population sizes, show a wide distribution range across extremely heterogenous environments, and are subject to high mortalities. We reviewed studies of natural selection and local adaptation in American eel, European eel, between life cycle stages, between European and American eel. Finally, we discussed genome architecture in relation to local adaptation in eels and the role of both genetic (i.e., local adaptation) and non-genetic (i.e., phenotypic plasticity) in the survival of eels across their distribution range
Identifying active constraintregions for optimal operationof process plants: With application to LNG and distillation processes
No full textOptimal operation and control of chemical processes depends on external conditions or disturbances. In order to achieve optimal or near-optimal control,one wants to control the active constraints, and the active constraints will frequently change with disturbances.
Any remaining degrees of freedom can be used to control variables whose optimal values are relatively insensitive to disturbances, these are called self-optimizing variables. However, when disturbances cause the active constraintsto change, the best choice of self-optimizing variables will change as well. Thus it is important to have knowledge of how the set of active constraints changes with disturbances. This is particularly important when designing the control structure for a process.
The first chapter of the thesis deals with identifying active constraint regions, and describes a simple method for doing this in the case of two disturbances. This method is then later in the thesis applied on distillation case studies, and on a natural gas liquefaction process.
The second half of the thesis focuses on optimization and optimal operationof natural gas liquefaction plants. Liquefied natural gas (LNG) hasbeen an important product in the gas industry since the 1960s, but optimal operation of liquefaction plants has not gained much attention in the open literature until the last decade. The thesis aims to give an overview overearlier work in this area. It is found that most attempts at optimization ofsuch processes involves use of gradient-free optimization methods. A chapter of the thesis is dedicated to studying challenges in optimization, and serves to partly explain why this is the case. In particular, this chapter discusses the effect of model formulation on optimization performance.
Finally, the findings of the previous chapters are used to identify active constraint regions for the PRICO liquefaction process, which is much used in earlier academic case studies because of its simplicity. In this chapter, acontrol structure for the PRICO process is also suggested.PhD i kjemisk prosessteknologiPhD in Chemical Process Engineerin
Memorandum : betr. die Sicherung und Erschliessung der Quellen zur juedischen Kulturgeschichte und Familienkunde.
No full textDocument about the proposed establishment of a center for German Jewish culture and genealogy in Berlin or HamburgdigitizedThe manuscript has been removed from the ‘Lehranstalt fuer die Wissenschaft des Judentums Collection’, AR 11844Born in Hamburg on February 26, 1896, Erna Magnus was a social worker who was engaged in an historical study of the Jewish community of Hamburg during the 1930s. She emigrated to the United States in 1939, where she held various social work and teaching position
Portrait of Paul Heyse.
No full textPhotograph of an oil painting by Eduard Magnus depicting the author, translator and Nobel laureate for literature (1910), Paul Johann Ludwig von Heyse.Digital ImageArtwork
Das rhetorische Ich: Hans Magnus Enzensbergers Selbstinszenierungen
No full textThe article discusses the rhetorical strategies underlying Hans Magnus Enzensberger's presentation of his work as an author, editor and poet
Das rhetorische Ich: Hans Magnus Enzensbergers Selbstinszenierungen
No full textThe article discusses the rhetorical strategies underlying Hans Magnus Enzensberger's presentation of his work as an author, editor and poet
Comparing consortial repositories: a model-driven analysis
No full textThis study aims to provide a comparative assessment of different repository consortia as a reference to inform future work in the area. A review of the literature was used to identify repository consortia, and their features were compared. Three models of consortial repositories were derived from this comparison, based on their structure and aims. The consortial models were based around either: creating a shared repository for the members, developing a repository software platform or creating a metadata harvesting service to aggregate content. Using case studies of each type of repository consortium, each model was assessed in terms of its particular strengths and weaknesses. These strengths were then compared across the models to enable those considering a consortial repository project to assess which model, or combination of models, would best address their needs and to aid in project planning
ARCHITECTURE AND URBAN DESIGN FOR THE AGEING SOCIETY, Review
No full textAuthor: Deane Simpson Young-Old: Urban Utopias of an Ageing Society, Zurich: Lars Müller Publishers, 2015 Reviewer: Magnus Rön
Heuristically Solving the Time Slot Management Problem Using Machine Learning-Based Delivery Cost Approximations
No full textI denne masteroppgaven studerer vi «time slot management»-problemet (TSMP) i samarbeid med Oda, en norsk matbutikk på nett. I Odas virksomhet blir kunder tilbudt et utvalg av tidsvinduer, hvorav de velger når de mottar matvarer. Dersom en kunde bestiller i et tidsvindu, må Oda levere varene innen det valgte tidsvinduet, modellert som et "vehicle routing"-problem med "time windows" (VRPTW). Denne prosessen kan formuleres som et to-stegs, stokastisk problem med endogen usikkerhet, der målet er å maksimere profitt gitt kundenes preferanser og kostnaden av å levere varene. Førstestegsproblemet er å definere utvalget av tidsvinduer for en lengre tidshorisont bestående av flere skift, der tilbudet påvirker kundenes beslutninger. Andrestegsproblemet er å planlegge rutene for hvert skift, slik at hver kunde blir betjent innen sitt valgte tidsvindu. I denne masteroppgaven anvender vi løsningsmetoder som involverer søkeheuristikker for førstestegsproblemet, modellerer kundeoppførsel basert på utvalget av tidsvinduer, og approksimerer objektivverdien av løsningen til et påfølgende "multi-shift" VRPTW (MSVRPTW). Vi tester løsningen vår på en instans med rundt 1,000 mulige tidsvinduer under ulike antagelser om kundeoppførsel.
For å søke gjennom førstestegsbeslutninger har vi implementert fire metaheuristikker; et "iterative local search" (ILS), et "very large neighborhood search" (VLNS), "particle swarm optimization" (PSO) og en "genetic algorithm" (GA). Av disse presterer VLNS best, som finner de beste løsningene etter rundt 6 timer. Dette utgjør en forbedring på 10 -20% fra en naiv løsning, avhengig av antagelsene rundt kundeoppførsel. Den stokastiske kundeetterspørselen er modellert med en "generalized attraction model" (GAM) som tar høyde for kundenes misnøye når tidsvinduer ikke tilbys. Vi bruker GAM til å trekke ordresett som scenarier i vår modell. Gjennom stabilitetstesting finner vi at 16 scenarier holder for å nå en stabilitet på rundt 2\% med vårt løsningsrammeverk. For å estimere leveringskostnadene implementerer vi en maskinlæringsmodell (ML) og en kontinuerlig approksimasjonfunksjon (CA), der ML presterer raskere og mer presist for en rekke instansstørrelser. Sammenlignet med en toppmoderne VRPTW-løser treffer ML modellen vår innen 3% av total leveringskostnaden i gjennomsnitt.
Vi bruker VLNS i et case-studie der vi analyserer implikasjonene av gode og dårlige løsninger, og viser at vårt løsningsrammeverk kan tilpasse seg til ulike antagelser om kundeoppførsel. Vi simulerer et år med hjemmelevering av matvarer for Oda og finner at vår løsning kan øke profitt med nesten 5% sammenlignet med å tilby alle tidsvinduer.In this Master's thesis, we study the Time Slot Management Problem (TSMP) in collaboration with Oda, a Norwegian online grocery retailer. In Oda's operations, customers are given a time slot offering, a selection of time slots to choose amongst when ordering groceries. The time slot chosen is the window of time in which delivery of orders to the customer's address must occur. Thus, the selected time slots of customers set the constraints under which Oda must plan the delivery process, modelled as a Vehicle Routing Problem with Time Windows (VRPTW). This gives rise to a two-stage stochastic problem with endogenous uncertainty. The goal is to maximize profits given customers' preference for the offered time slots and the costs of order delivery. The first-stage problem is to decide the time slot offering for a planning horizon with multiple shifts, which affects customers' decisions for placing orders. The second-stage problem is to decide the routing plan for each shift, so that orders are delivered within the customers' selected time slots. We propose solution methods involving search heuristics for the first-stage problem, modelling realized orders based on the first-stage solution, and an approximation of the objective value to the Multi-Shift VRPTW (MSVRPTW) in the second-stage problem. We test the solution methods on an instance with nearly 1,000 possible time slots under various assumptions regarding customer demand.
For searching through the first-stage solutions, we implement four metaheuristics; Iterative Local Search (ILS), a Very Large Neighborhood Search (VLNS), Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). Of these, VLNS is the highest performer, finding the best known solution within 6 hours and improving upon a naive solution by 10-20% depending on the demand assumptions. Stochastic customer demand is modelled using a Generalized Attraction Model (GAM), which factors in customers' attraction to and dissatisfaction with the offered time slots. We sample customer sets as scenarios and use the GAM to generate realized order sets, and find that 16 scenarios is enough to reach stability of close to 2% with our solution evaluation framework. To estimate delivery costs, we implement and compare two approaches; a Machine Learning model (ML) and Continuous Approximation (CA). We find that ML is faster and more precise than CA for all instance sizes, on average predicting costs within 3% for costs in the second-stage problem compared to solutions found by a state-of-the-art Hybrid Genetic Search solver for VRPTWs.
We use VLNS in a case-study to analyze the real-world implications of good and poor solutions, and show that the solution methods can adapt to different demand assumptions. We simulate a year of operations, and find that the best known solution can improve profitability by nearly 5% compared to offering all time slots
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