The polymerisation of cyclic ethers at high pressures

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Higher Order Couplings in the Clustering of Biased Tracers of Large-Scale Structure

The Large-Scale Structure (LSS) of the Universe, i.e. the distribution of matter and luminous tracers (such as galaxies), contains a wealth of information about the origin, composition, and evolution of the Universe. In order to extract this information, the non-linearities present in late-time observables provided by LSS surveys must be understood well. In general, there are three main sources of non-linearities: (1) non-linear matter clustering due to gravity; (2) non-linear biasing, i.e. the relation between the distribution of tracers and dark matter; and (3) primordial non-Gaussianity, which induces non-linearities in the initial conditions. The Effective Field Theory of Large-Scale Structure (EFTofLSS) provides a powerful framework to model the non-linear clustering due to gravity. In this thesis, we focus on understanding the non-linearities due to galaxy biasing using the EFTofLSS and numerical N-body simulations. This thesis is comprised of the following three projects: In the first part, we present a novel method to constrain quadratic and cubic galaxy bias parameters in dark matter simulations. The natural statistics to constrain quadratic and cubic bias parameters are tree-level bispectrum and trispectrum, respectively. Since these statistics are computationally quite expensive, we use efficient squared and cubic field estimators that contain integrated bispectrum and trispectrum information. We use the constraints to model the one-loop halo-matter power spectrum and show that the results agree with simulations up to kmax = 0.1h Mpc 1 once an additional derivative bias is implemented (Published in: Abidi & Baldauf, JCAP07(2018)029). In the second part, we develop a formalism to reconstruct the linear density field based on quadratic couplings in galaxy clustering. We employ a quadratic estimator inspired by Cosmic Microwave Background (CMB) lensing reconstruction. We incorporate non-linearities due to gravity, galaxy biasing and primordial non-Gaussianity, and verify our predictions with N-body simulations. We perform a Fisher matrix analysis on how the reconstructed field in combination with the biased tracer field can improve constraints on local type primordial non-Gaussianity. We find significant improvement on constraints due to cosmic variance cancellation resulting from the additional correlated modes of the reconstructed field, similar to multi-tracer analyses. In the third part, we develop a method to constrain non-linear galaxy bias parameters using the two- and three-point functions of projected galaxy clustering in correlation with CMB lensing convergence. The project thus aims to bring the methodology developed in project 1 above closer to data. We develop the quadratic field method for projected fields to avoid complications from non-linear redshift space distortions. We perform a Fisher forecast to show that this method can indeed be used to put constraints on bias parameters and the amplitude of matter fluctuations. Finally, using N-body simulations we ascertain that the projected statistics do indeed reduce the impact of finger-of-god corrections.My PhD was generously funded by the Cambridge Commonwealth, European and International Trust and the Higher Education Commission Pakistan. I have additionally received invaluable financial assistance from St. Edmunds College, Cambridge, the Cambridge Philosophical Society, the Centre for Theoretical Cosmology, Dr Blake Sherwin's EPRC grant, the Postgraduate Lundgren Award, and the Santander Award

Sales Forecasting in Industrial Services: Integrating Market Analysis and Historical Data for Sustainable Business Growth – Case from a Norwegian B2B Service Provider

The current era is all about challenges due to competitiveness in the business-to business (B2B) domain, as there are numerous complexities in terms of market dynamics, longer sales cycle, and multiple macroeconomic factors which might change the entire sales of a business. Hence, forecasting the potential sales has become a major challenge in today’s world. The sectors where volatility in business is a mandatory aspect to be considered, like oil and gas companies, it is essential to provide accurate sales forecasts so that appropriate strategic planning and decision making, according to the internal and external factors which might affect the sales, can be made. The main objective of this study is to perform a comparative analysis between the traditional and the novel sales forecasting methods, and analyze the accuracy of both methods. In order to achieve the target, two main forecasting models have been deployed in this study which are Long-Short-Term-Memory networks and Random Forest models. The data which has been acquired and used for this study is from the Norway’s oil and gas industry, between the years 2015 to 2023 from a B2B service provider. In addition to this, some macroeconomic variables have also been deployed and considered in this study which are Interest Rates, Employment Rates, GDP, and Oil Prices, with the help of which accuracy of our model applied can be examined. With respect to the methodology, a multistep method has been employed in this study, which involves data collection, preprocessing, normalization of data, implementation of LSTM and RG models, while considering both with and without macroeconomic variables. Further evaluation has been performed using the performance metrics like R-square, Mean Squared square, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Comparison of the models has been performed on the basis of their predictive accuracy. The implementation of the models, presented the findings which showed that LSTM can be considered as the model which provided highest predictive accuracy without macroeconomic variables. It also demonstrated the ability of this model to predict nonlinear patterns and long-term dependencies in the sales data. On the contrary, it has also been analyzed that by including macroeconomic variables, the accuracy and performance of both the models, including LSTM and RF were reduced, while also increasing the complexity of these models. A detailed literary analysis has also been performed on the topic, so that the research gap can be fulfilled, as the thesis contributes to the existing literature, by providing a detailed comparative analysis between both the traditional and conventional sales forecasting models, while focusing on the oil and gas industries and also highlighted the possible challenges that might arise during sales forecasting. Future research could explore utilising a hybrid model that combines two or more models and then includes macroeconomic variables more sophisticatedly

Modeling and Analysis of Content Caching in Wireless Small Cell Networks

Network densification with small cell base stations is a promising solution to satisfy future data traffic demands. However, increasing small cell base station density alone does not ensure better users quality-of-experience and incurs high operational expenditures. Therefore, content caching on different network elements has been proposed as a mean of offloading he backhaul by caching strategic contents at the network edge, thereby reducing latency. In this paper, we investigate cache-enabled small cells in which we model and characterize the outage probability, defined as the probability of not satisfying users requests over a given coverage area. We analytically derive a closed form expression of the outage probability as a function of signal-to-interference ratio, cache size, small cell base station density and threshold distance. By assuming the distribution of base stations as a Poisson point process, we derive the probability of finding a specific content within a threshold distance and the optimal small cell base station density that achieves a given target cache hit probability. Furthermore, simulation results are performed to validate the analytical model.Comment: accepted for publication, IEEE ISWCS 201

Isolated Parotid Metastasis from Small Cell Lung Cancer

Worldwide, lung cancer accounts for the most cancer mortality in both men and women with 1.6 million deaths in 2012. Small cell lung cancer usually present as a disseminated disease in over 70% of the patients. Common site of metastasis include liver, adrenal, bone and brain. However, metastasis to parotid gland is uncommon described only in case reports. A 75-year old male presented with a mass on right parotid gland, biopsy confirmed metastatic small cell carcinoma. CT chest showed 5.5cm right hilar mass and mediastinal adenopathy T2bN2M1b, stage IV disease. He underwent chemotherapy Carboplatin and Etoposide. Small cell lung cancer diagnosed from isolated metastasis to parotid gland is rare. Physicians should be aware of pulmonary source when presented with a parotid tumor. Overall, the finding has a poor prognosis but main modality of treatment is palliative with systemic chemotherapy and possible irradiation for symptomatic control.

Use of Rice Husk Ash in Concrete

Key words: Rice Husk Ash, Cement, Concrete, Compressive\ud \ud strength.This paper summarizes the research work on the\ud \ud properties of Rice Husk Ash (RHA) when used as partial\ud \ud replacement for Ordinary Portland Cement (OPC) in concrete. OPC\ud \ud was replaced with RHA by weight at 5%, 10% and 15%. 0%\ud \ud replacement served as the control. Compressive Strength test was\ud \ud carried out on hardened 150mm concrete cubes after at1, 3, 7, 28, 45\ud \ud & 56 days curing in water. The results revealed that the Compacting\ud \ud factor decreased as the percentage replacement of OPC with RHA\ud \ud increased. The compressive strength of the hardened concrete also\ud \ud decreased with increasing OPC replacement with RHA. It is\ud \ud recommended that further studies be carried out to gather more facts\ud \ud about the suitability of partial replacement of OPC with RHA in\ud \ud concrete

Predicted Response to S1 Selection for Agronomic and Disease Resistance Traits in Two Sunflower Populations

Variances, heritabilities, genetic and phenotypic correlations, and predicted gains were computed for quantitative disease resistance and agronomic traits from pooled S1 family data of Gene Pool II and ND 761 Helianthus annuus L. populations. Broad-sense heritability estimates for all traits were significant in both populations. Observed genotypic correlation coefficients were larger than their corresponding estimates of phenotypic correlation coefficients. Significant positive genetic correlations between resistance to Alternaria blight and Septoria leaf spot; and non-significant genetic correlations between Sclerotinia wilt disease reaction and agronomic traits were observed in both populations. Resistance to Phoma black stem was not significantly correlated with resistance to other diseases or yield. Genetic correlations of yield/ ha with reaction to Alternaria blight and Septoria leaf spot diseases in Gene Pool II were negative and significant. There were significant positive genotypic correlations between yield/ha and other agronomic traits except days to flower. The genetic correlation between Septoria leaf spot and Sclerotinia wilt disease reactions was positive and significant in ND 761. However, resistance to four diseases in ND 761 was inherited independently of yield/ha. Yield/ha was positively significantly genetically correlated with head diameter, head weight, seeds per head and oil yield in ND 761. The Smith-Hazel index in both populations was efficient in improving predicted gains of resistance to all four diseases (Alterna ria leaf blight, Septoria leaf spot, Phoma and Sclerotinia wiIt) when selection was focused on Alternaria blight and Sclerotinia wiIt resistance simultaneously. This selection index was also effective for both populations in improving gain for agronomic traits (head weight, 200-seed weight, oil content and yield/ha) when selection was for oil percent and yield/ha simultaneously. Smith-Hazel and desired gain indices with simultaneous selection of Alternaria blight and Sclerotinia wilt resistance, oil percent and yield/ha are suggested for the improvement of multiple disease resistance and agronomic traits in Gene Pool 11 and ND 761, respectively. The restricted selection index and desired gain index were most efficient in controlling gains for restricted traits, plant height and days to flower