It is difficult to decide which power generation system is the most sustainable when environmental, economic and social sustainability aspects are taken into account. Problems with conventional environmental sustainability assessment methods are that no consensus exists about the applied models and weighting factors and that exergy losses are not considered. Economic sustainability assessment methods do not lead to results that are independent of time because they are influenced by market developments, while social sustainability assessment methods suffer from the availability and qualitative or semi-quantitative nature of data. Existing exergy analysis methods do not take into account all exergy losses and/or are extended with factors or equations that are not commonly accepted. The new Total Cumulative Exergy Loss (TCExL) method is based on fundamental thermodynamic equations and takes into account all exergy losses caused by a technological system during its life cycle, i.e. internal exergy losses, exergy losses caused by emission abatement and exergy losses related to land use. The development of the TCExL method is presented as well as the application of this method and environmental, economic and social sustainability assessment methods to two case studies: power generation in combination with LNG evaporation and Fossil versus renewable energy sources for power generation. According to the results of the assessments, large differences exist between the environmental sustainability assessment and TCExL methods in the sense that different parts of the systems contribute most to their overall scores. It is concluded from the case studies that involving the TCExL method in choices between power generation systems with the same energy sources has no consequences, i.e. it does not result in a different ranking of the systems, but can lead to the choice of a system that has a lower economic sustainability if the assessed systems use different energy sources. However, it must be noted that the economic sustainability changes over time, while the results of the TCExL method do not.

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The selection of power generation systems is important when striving for a more sustainable society. However, the results of environmental, economic and social sustainability assessments are subject to new insights into the calculation methods and to changing needs, economic conditions and societal preferences. Researchers active in the field of exergy and sustainability claim that exergy losses and sustainability are related. The Total Cumulative Exergy Loss method and the exergy replacement costs of minerals are used to assess and compare power generation systems that make use of fossil and renewable energy carriers. These power generation systems are the following: an ultrasupercritical coal power plant, a power plant that co-fires coal and biomass, a wind farm, and a combined cycle power plant that uses bioethanol originating from the fermentation of verge grass. Furthermore, environmental, economic and social sustainability assessment methods are applied to assess the four power generation systems as well. On the basis of the results of the assessments, it is concluded that the wind farm system is preferred from the environmental, social and exergetic sustainability points of view, but not from the economic sustainability viewpoint. The advantage of the exergetic sustainability assessment method is that its results are not influenced by choices like whether verge grass should be considered a waste product or not. The influence of the exergy replacement costs on the results of the exergetic assessment is small, because less than 5 per cent of the exergy input of the systems during construction, operation and commission is of mineral origin. When looking at the infrastructural part of the systems only, the influence of the exergy replacement costs is larger because about 25 to 40 per cent of the exergy input is of mineral origin. @en

The human genome is diploid, that is each of its chromosomes comes in two copies. This requires to phase the single nucleotide polymorphisms (SNPs), that is, to assign them to the two copies, beyond just detecting them. The resulting haplotypes, lists of SNPs belonging to each copy, are crucial for downstream analyses in population genetics. Currently, statistical approaches, which avoid making use of direct read information, constitute the state-of-the-art. Haplotype assembly, which addresses phasing directly from sequencing reads, suffers from the fact that sequencing reads of the current generation are too short to serve the purposes of genome-wide phasing.
Future sequencing technologies, however, bear the promise to generate reads of lengths and error rates that allow to bridge all SNP positions in the genome at sufficient amounts of SNPs per read. Existing haplotype assembly approaches, however, profit precisely, in terms of computational complexity, from the limited length of current-generation reads, because their runtime is usually exponential in the number of SNPs per sequencing read. This implies that such approaches will not be able to exploit the benefits of long enough, future-generation reads.
Here, we suggest WhatsHap, a novel dynamic programming approach to haplotype assembly. It is the first approach that yields provably optimal solutions to the weighted minimum error correction (wMEC) problem in runtime linear in the number of SNPs per sequencing read, making it suitable for future-generation reads. WhatsHap is a fixed parameter tractable (FPT) approach with coverage as the parameter. We demonstrate that WhatsHap can handle datasets of coverage up to 20x, processing chromosomes on standard workstations in only 1-2 hours. Our simulation study shows that the quality of haplotypes assembled by WhatsHap significantly improves with increasing read length, both in terms of genome coverage as well as in terms of switch errors. The switch error rates we achieve in our simulations are superior to those obtained by state-of-the-art statistical phasers.
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