94 resultados para Pulp, Helmut Krausser, Cannibali, Berlino


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This chapter critiques the imagined geography of creative cities and the creative industries, which presumes that inner cities are densely clustered hubs of urban culture and creativity while suburbs are dull, homogeneous dormitories from which creative people must escape in order to realize their potential. Drawing upon a study on creative industries workers in Melbourne and Brisbane, the authors argue that these workers are as likely to be located in the suburbs as in the inner city, and that they clearly identify advantages to being in outer suburban locations. Their findings provide a corrective to dominant urban cultural policy narratives that stress cultural amenity in the inner cities.

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Cross-nationally, the introduction of New Public Management coincides with a significant growth phase of the nonprofit or third sector. This growth has disproportionately been an expansion of the economic dimensions (employment, turnover) and basically involved the greater use of third sector organisations as service providers. Such provision uses complex contract regimes, and typically takes place in some form of public-private partnership with either public or private funding agencies. Other parts of the third sector such as membership, volunteering and giving have generally grown less. The paper suggests that the sector is becoming qualitatively different, although the nature and strength of this change depends on the nonprofit regime type in a given country. Generally, however, third sector growth has led to differentiation processes that involve new organisational forms, and changes in activities and overall composition. The paper explores the measurement aspects of the quantitative-qualitative jump in third sector development by trying to "map" changes in core facts or dimensions over time. In closing, the paper suggests to examine recombination and refunctionality processes in the third sector.

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Carotenoids occur in all photosynthetic organisms where they protect photosystems from auto-oxidation, participate in photosynthetic energy-transfer and are secondary metabolites. Of the more than 600 known plant carotenoids, few can be converted into vitamin A by humans and so these pro-vitamin A carotenoids (pVAC) are important in human nutrition. Phytoene synthase (PSY) is a key enzyme in the biosynthetic pathway of pVACs and plays a central role in regulating pVAC accumulation in the edible portion of crop plants. Bananas are a major commercial crop and serve as a staple crop for more than 30 million people. There is natural variation in fruit pVAC content across different banana cultivars, but this is not well understood. Therefore, we isolated PSY genes from banana cultivars with relatively high (cv. Asupina) and low (cv. Cavendish) pVAC content. We provide evidence that PSY in banana is encoded by two paralogs (PSY1 and PSY2), each with a similar gene structure to homologous genes in other monocots. Further, we demonstrate that PSY2 is more highly expressed in fruit pulp compared to leaf. Functional analysis of PSY1 and PSY2 in rice callus and E. coli demonstrate that both genes encode functional enzymes, and that Asupina PSYs have approximately twice the enzymatic activity of the corresponding Cavendish PSYs. These results suggest that differences in PSY enzyme activity contribute significantly to the differences in Asupina and Cavendish fruit pVAC content. Importantly, Asupina PSY genes could potentially be used to generate new cisgenic or intragenic banana cultivars with enhanced pVAC content.

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The already considerable debate about what constitutes a 'creative' city becomes ever more critical as the world urbanizes at a rapid pace. In this chapter the author argues that the key tensions in discussions over what makes cities more conducive to and supportive of creativity revolve around perspectives that are either production-centric or consumption-centric. Scholars are increasing prepared to claim priority for the city-region over the nation-state as an economic and cultural agent in the contemporary world, but are they ready to deal with major changes in the nature of cultural production and consumption themselves? A number of examples of new challenges for the creative cities 'discourse' rounds out the chapter.

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The sugar industry is pursuing diversification options using bagasse as a feedstock. Depithing, the removal of the smaller bagasse particles, is an integral part of the manufacturing processes for bagasse by-products such as pulp and paper. There are possible environmental and economic benefits associated with incorporating depithing operations into a sugar factory. However there have only been limited investigations into the effects of depithing operations on a sugar factory boiler station. This paper describes a modelling investigation, using the lumped parameter boiler design tool BOILER and the CFD code FURNACE, to predict the effects of pith, depithed bagasse and mixed bagasse/pith firing on the efficiency, fuel consumption and combustion performance of a typical sugar factory boiler.

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The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

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For the evaluation, design, and planning of traffic facilities and measures, traffic simulation packages are the de facto tools for consultants, policy makers, and researchers. However, the available commercial simulation packages do not always offer the desired work flow and flexibility for academic research. In many cases, researchers resort to designing and building their own dedicated models, without an intrinsic incentive (or the practical means) to make the results available in the public domain. To make matters worse, a substantial part of these efforts pertains to rebuilding basic functionality and, in many respects, reinventing the wheel. This problem not only affects the research community but adversely affects the entire traffic simulation community and frustrates the development of traffic simulation in general. For this problem to be addressed, this paper describes an open source approach, OpenTraffic, which is being developed as a collaborative effort between the Queensland University of Technology, Australia; the National Institute of Informatics, Tokyo; and the Technical University of Delft, the Netherlands. The OpenTraffic simulation framework enables academies from geographic areas and disciplines within the traffic domain to work together and contribute to a specific topic of interest, ranging from travel choice behavior to car following, and from response to intelligent transportation systems to activity planning. The modular approach enables users of the software to focus on their area of interest, whereas other functional modules can be regarded as black boxes. Specific attention is paid to a standardization of data inputs and outputs for traffic simulations. Such standardization will allow the sharing of data with many existing commercial simulation packages.

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Numerous crops grow in sugar regions that have the potential to increase the amount of biomass available to a small bagasse-based pulp factory. Arundo donax and Sorghum offer unique advantages to farmers compared to other agricultural crops. Sorghum bicolour requires only 1/3 of the water of sugarcane. Arundo donax is a very high yield crop, it can also grow with little water but it has the further advantage in that it is also highly stress tolerant, making it suitable for land which is unsuited to other crops. Pulps produced from these crops were benchmarked against sugarcane bagasse pulp. Arundo, sorghum and bagasse were pulped using KOH and anthraquinone to 20 Kappa number so as to produce a bleachable pulp. The unbleached sorghum pulp has better tensile strength properties than the unbleached Arundo pulp (43.8 Nm/g compared to 21.4 Nm/g) and the bleached sorghum pulp tensile strength was similar to bagasse (28.4 Nm/g). At 20 Kappa number, sorghum pulp had acceptable yield for a non-wood fibre (45% c.f. 55% for bagasse), Arundo donax pulp had low tensile strength, and relatively low yield (38.7%), even for an agricultural fibre and required severe cooking conditions to achieve similar delignification to sugarcane bagasse or sorghum. Sorghum and Arundo donax produced thicker handsheets than bagasse (>160 μm c.f. 122 μm for bagasse). In preliminary experiments sorghum and bagasse responded slightly better to Totally Chlorine Free bleaching (QPP), although none achieved a satisfactory brightness level and more optimisation is needed.

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The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

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Plant growth can be limited by resource acquisition and defence against consumers, leading to contrasting trade-off possibilities. The competition-defence hypothesis posits a trade-off between competitive ability and defence against enemies (e.g. herbivores and pathogens). The growth-defence hypothesis suggests that strong competitors for nutrients are also defended against enemies, at a cost to growth rate. We tested these hypotheses using observations of 706 plant populations of over 500 species before and following identical fertilisation and fencing treatments at 39 grassland sites worldwide. Strong positive covariance in species responses to both treatments provided support for a growth-defence trade-off: populations that increased with the removal of nutrient limitation (poor competitors) also increased following removal of consumers. This result held globally across 4 years within plant life-history groups and within the majority of individual sites. Thus, a growth-defence trade-off appears to be the norm, and mechanisms maintaining grassland biodiversity may operate within this constraint.

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We conducted an association study across the human leukocyte antigen (HLA) complex to identify loci associated with multiple sclerosis (MS). Comparing 1927 SNPs in 1618 MS cases and 3413 controls of European ancestry, we identified seven SNPs that were independently associated with MS conditional on the others (each ). All associations were significant in an independent replication cohort of 2212 cases and 2251 controls () and were highly significant in the combined dataset (). The associated SNPs included proxies for HLA-DRB1*15:01 and HLA-DRB1*03:01, and SNPs in moderate linkage disequilibrium (LD) with HLA-A*02:01, HLA-DRB1*04:01 and HLA-DRB1*13:03. We also found a strong association with rs9277535 in the class II gene HLA-DPB1 (discovery set , replication set , combined ). HLA-DPB1 is located centromeric of the more commonly typed class II genes HLA-DRB1, -DQA1 and -DQB1. It is separated from these genes by a recombination hotspot, and the association is not affected by conditioning on genotypes at DRB1, DQA1 and DQB1. Hence rs9277535 represents an independent MS-susceptibility locus of genome-wide significance. It is correlated with the HLA-DPB1*03:01 allele, which has been implicated previously in MS in smaller studies. Further genotyping in large datasets is required to confirm and resolve this association.

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Recent association studies in multiple sclerosis (MS) have identified and replicated several single nucleotide polymorphism (SNP) susceptibility loci including CLEC16A, IL2RA, IL7R, RPL5, CD58, CD40 and chromosome 12q13–14 in addition to the well established allele HLA-DR15. There is potential that these genetic susceptibility factors could also modulate MS disease severity, as demonstrated previously for the MS risk allele HLA-DR15. We investigated this hypothesis in a cohort of 1006 well characterised MS patients from South-Eastern Australia. We tested the MS-associated SNPs for association with five measures of disease severity incorporating disability, age of onset, cognition and brain atrophy. We observed trends towards association between the RPL5 risk SNP and time between first demyelinating event and relapse, and between the CD40 risk SNP and symbol digit test score. No associations were significant after correction for multiple testing. We found no evidence for the hypothesis that these new MS disease risk-associated SNPs influence disease severity.