Assessing the quality of transmission of lightpaths in multiband C+L networks through Gaussian noise models

In an optical network scenario, wavelength division-multiplexing (WDM) channels are constantly being added and dropped, leading to dynamic traffic variations in the lightpaths. In this work, the impact of the network traffic load and spectral occupancy on the quality of transmission, namely on the normalized nonlinear interference (NLI) power, power transfer due to stimulated Raman scattering (SRS) and optical signal-to-noise ratio (OSNR) of the lightpaths in a C+L multiband optical network is assessed using the recently proposed closed-form interchannel SRS Gaussian noise model (ISRS GN-model). We show that, due to the dynamic traffic behavior, the normalized NLI power can oscillate up to 2 dB in the highest frequency channels due to NLI variations when the tested channels have unequal spacing along the spectrum. For the optimum channel launch power and by increasing the network traffic load, the power transfer between the outer channels can increase up to 5.1 dB due to the SRS effect. With 201 WDM channels, high traffic load and for the optimum channel power, we obtained a maximum OSNR variation along the channel frequencies of only about 0.7 dB. A comparison between the OSNR predictions of the closed-form ISRS GN-model and a closed-form Gaussian noise (GN) model that does not take into account the SRS effect is also performed. In all results obtained, the maximum difference between the OSNR predictions of GN (without SRS) and ISRS GN models is below 0.7 dB at optimum OSNR and maximum C+L band occupancy. For channel launch powers higher than the optimum, the OSNR differences increase up to 3 dB.info:eu-repo/semantics/publishedVersio

Impact of traffic load and spectral occupancy on Gaussian noise models performance for multiband networks

In a network scenario, wavelength division multiplexing channels are added and dropped leading to fluctuations on the network traffic loads along the optical path. In this work, a comparison between the optical signal-to-noise ratio (OSNR) predictions of the recently proposed closed-form generalized Gaussian noise (GGN) model and a closed-form Gaussian noise (GN) model that does not take into account the stimulated Raman scattering (SRS) is performed, for different network traffic loads and spectral occupancy over the entire C+L band. In all results obtained, the maximum difference between the OSNR predictions of GN (without SRS) and GGN models closed forms is below 0.7 dB at optimum OSNR and maximum C+L band occupancy, indicating that the GN-model can also be used in C+L band transmission. For channel launch powers higher than the optimum, the OSNR differences increase up to 3 dB, being the GN-model (without SRS) unsuitable to assess the network performance in such situations.info:eu-repo/semantics/acceptedVersio

Effect of the curing time on the numerical modelling of the behaviour of a chemically stabilised soft soil

The ability of the Modified Cam Clay (MCC) model combined with the Von Mises (VM) model, considering the effect of curing time on the enhancement of the mechanical properties of a chemically stabilised soft soil is examined. The evolution of the strength and stiffness over time is based on the results of undrained compressive strength (UCS) tests carried out for different curing times (from 28 days to 360 days). Initially, the MCC/VM models associated with the effect of curing time are validated by CIU triaxial tests, for curing times of 28 and 90 days. Finally, the behaviour of an embankment built on a soft soil reinforced with deep mixing columns is predicted based on the previously validated models. The results show that the increase of curing time of the DMCs slightly decreases the settlement obtained with a curing time of 28 days

Negotiating the urban terrain : representations of the city of Glasgow in the visual arts

This thesis analyses representations of the city of Glasgow in visual culture. Given the absence of any coherent study of Glasgow images the primary method is empirical. The thesis explores how the dynamic of change in the urban environment has been rendered in visual media by gathering together paintings, photographs, prints and journal illustrations. In order to contextualise the visual material within the social and historical circumstances that affect its character, this material is considered in relation to pertinent history and theory. Consequently, the disciplines of social and economic history, sociology, philosophy and urban studies are employed. The developing discourse of the city as a visual phenomenon is charted in a broadly chronological manner. Rather than a simplistic historical narrative, this highlights the unfolding connections between the ambitions of Glasgow's 'governors' and the ideological pattern of related images. The thesis opens by revealing the associations between Enlightenment philosophy and the visual interpretation of the increasingly commercial urban environment. It then analyses changes incurred by the projection of a 'municipal' consciousness and shows how the impact of industrialisation was visualised in relation to prevailing artistic styles. Furthermore, it considers the effect of the aesthetic climate on the creation and reception of urban imagery. The thesis then argues that, after the industrial heyday, there was a sense that the essence of Glasgow lay not in its monuments, but in its populace, particularly the working class. Finally, there is a close study of post-industrial Glasgow, accenting patterns of decline and highlighting resistance to commercially inspired and culturally directed 'official' visions. This thesis finds that there was a complex discourse between Glasgow's material reality and its visual representation. It gives full voice to the network of mediating factors and presents a highly specific case study in the aesthetic manifestation of urban life

Increased BDNF levels and NTRK2 gene association suggest a disruption of BDNF/TrkB signaling in autism

The brain-derived neurotrophic factor (BDNF), a neurotrophin fundamental for brain development and function, has previously been implicated in autism. In this study, the levels of BDNF in platelet-rich plasma were compared between autistic and control children, and the role of two genetic factors that might regulate this neurotrophin and contribute to autism etiology, BDNF and NTRK2, was examined. We found that BDNF levels in autistic children (n = 146) were significantly higher (t = 6.82; P < 0.0001) than in control children (n = 50) and were positively correlated with platelet serotonin distribution (r = 0.22; P = 0.004). Heritability of BDNF was estimated at 30% and therefore candidate genes BDNF and NTRK2 were tested for association with BDNF level distribution in this sample, and with autism in 469 trio families. Genetic association analysis provided no evidence for BDNF or NTRK2 as major determinants of the abnormally increased BDNF levels in autistic children. A significant association with autism was uncovered for six single nucleotide polymorphisms (SNPs) [0.004 (Z((1df)) = 2.85) < P < 0.039 (Z((1df)) = 2.06)] and multiple haplotypes [5 × 10(-4) (χ((3df)) = 17.77) < P < 0.042 (χ((9df)) = 17.450)] in the NTRK2 gene. These results do not withstand correction for multiple comparisons, however, reflect a trend toward association that supports a role of NTRK2 as a susceptibility factor for the disorder. Genetic variation in the BDNF gene had no impact on autism risk. By substantiating the previously observed increase in BDNF levels in autistic children in a larger patient set, and suggesting a genetic association between NTRK2 and autism, this study integrates evidence from multiple levels supporting the hypothesis that alterations in BDNF/tyrosine kinase B (TrkB) signaling contribute to an increased vulnerability to autism

Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures

A previsão da resistência à compressão uniaxial (qu) de misturas solo-cimento é de elevada importância durante a fase de projeto. Para a sua quantificação são realizados ensaios laboratoriais, os quais consomem muito tempo e recursos. Neste artigo é apresentada uma nova abordagem para avaliação da qu ao longo do tempo tirando proveito das elevadas capacidades de aprendizagem das técnicas de Inteligência Artificial (IA). Três algoritmos de IA, nomeadamente as Redes Neuronais Artificiais (RNAs), as Máquinas de Vetores de Suporte (MVSs) e Regressões Múltiplas (RMs), foram treinados utilizando uma base de dados composta por 444 registos contemplando solos não coesivos, coesivos e orgânicos, assim como diferentes ligantes, condições de mistura e tempos de cura. Os resultados obtidos evidenciam um desempenho promissor na previsão da qu de misturas laboratoriais de solo-cimento, sendo o melhor desempenho conseguido através da média das previsões obtidas pelas MVSs e RNAs (R2=0.95). Estes modelos reproduzem eficazmente os principais efeitos das variáveis de entrada, nomeadamente da razão água/cimento, teor em cimento, teor em matéria orgânica e tempo de cura, as quais são conhecidas como preponderantes no comportamento de misturas solo-cimento.info:eu-repo/semantics/publishedVersio