Abstract We use InGaAlAs and InGaAsP as materials of 1.55mm multi-quantum-well spot-size converter ridge waveguide lasers. On lateral conversion, we fabricate a taper ridge waveguide. On vertical conversion, we add guard layers on each side of active layer. For InGaAlAs ridge waveguide lasers, simulation results show a far field 16o ¡Ñ 27o¡]lateral ¡Ñ vertical¡^at guard layer width S = 0.1 mm with 300-150-50 mm narrow-tapered waveguide structure. Due to large Zn background contamination in the MOCVD growth chamber, we did not fabricate the InGaAlAs lasers successfully. For the InGaAsP ridge waveguide lasers, we measure a far field 18o ¡Ñ 28o and a threshold current 23 mA for the 200-250-50 mm narrow-tapered waveguide structure; a far field 20o ¡Ñ 26o and a threshold current 22 mA for the 200-250-50 mm wide-tapered waveguide structure.

Weed control is typically achieved by broadcast spraying postemergence herbicides on the entire orchard floor which wastes chemical by spraying bare ground. Growers can account for spatial variation in weed density and only spray weedy areas instead of applying herbicide to entire fields by using the automatic spot-sprayer, WeedSeeker sprayer. We conducted field experiments in flood- and microsprinikler-irrigated lemon orchards, and flood- and sprinkler-irrigated pecan orchards to measure the amount of herbicide applied, weed control, tree yield, and the economic value of adopting the WeedSeeker sprayer compared to conventional boom spray technology. The WeedSeeker sprayer reduced cumulative herbicide use by at least 36% compared to the conventional boom sprayer at all sites. Weed control obtained using the WeedSeeker sprayer was usually similar to the conventional boom sprayer. There were no effects of the treatments on yield at any location. The partial budget analysis, used to determine the economic value of adopting the technology, showed that as the area of the orchard and the cost of the herbicide increased, the time to recover the initial investment in the WeedSeeker sprayer decreased. The investment in the technology can typically be recovered in less than five years in Arizona lemon and pecan orchards.We also conducted greenhouse experiments to determine the effect of various sensitivity settings and leaf area on the operational efficiency of the WeedSeeker sprayer. The WeedSeeker sprayer did not detect and spray all broadleaf and monocot plants unless a sensitive setting was used. The WeedSeeker sprayer was more efficient when detecting broadleaf plants compared to monocot plants. Our results indicate that operating the WeedSeeker sprayer using a sensitive setting (sensitivity level 2) will result in the most efficient detection of weeds.Finally, field studies were conducted in a non-bearing pecan orchard to evaluate weed management systems using various postemergence and preemergence herbicides. Most postemergence herbicides controlled the weed species present in the orchard. Tank mixing the herbicides generally resulted in greater control compared to applying them alone. All preemergence herbicides reduced the weed emergence compared to not using a preemergence herbicide. No pecan tree damage was observed in any treatment.

Being able to predict the future had been an invaluable competitive advantage for any corporation. Forecasting is a vital part of any business, hence a good forecast allows enterprises to invest in a beneficial way. However, there are several ways to prepare forecasts and the forecast methodology can vary. An industry that is dependent on forecasts is the energy industry. By predicting consumers' energy consumption, Swedish energy companies can hedge on the Nordic power market Nord Pool. To know how much volume to hedge, the most accurate forecast possible is needed.A tool used for forecasting in the energy market is normal correction with degree days. The method aims to neutralize the effects of temperature on energy consumption and allows the actors in the electricity trade to see how the energy usage looks like if the weather is considered "normal". The method unfortunately has a couple of pitfalls, and should therefore be evaluated before it is implemented.This study has evaluated whether normal correction method with degree days should be implemented in a case study at Karlstad Energi. The results show that the method does not improve the forecast and the conclusion is that the method should not be implemented. Key words: Business forecasting, Nord Pool, Future, Forward

Typescript (photocopy) / xii, 182 leaves, [60] leaves of plates : ill. ; 30 cm / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.) Dept. of Plant Pathology, University of Adelaide, 1981

x, 99 leaves : ill. ; 25 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.1974) from the Dept. of Plant Pathology, University of Adelaide

[Abstract]: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality.Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliersin high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional streamdata is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the out-lying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams.In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensionaldata streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set oftop sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding theoutlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliersfor high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.

Th.--Signal et images--Paris--ENST, 1993. / Bibliogr. p. 253-270.

Bremen, Univ., Diss., 2004.

Habilitationsschrift--Göttingen. / Bibliography: p. 292-308.

En base a la difusión de un spot televisivo de la empresa Movistar, la autora reflexiona en torno a los procesos de representación de las identidades en un escenario multicultural como el nuestro Lo inicialmente emotivo, termina disfrazando una visión que dista mucho de ser integradora y articuladora de las verdaderas y palpables diferencias que podemos experimentar en nuestro país