New possibilities are developing in the infrastructure of electrical systems to meet the new demand of more volatile power generation. This study focuses on German household reactions to price changes and their economic and renewable utilization effects.   In order to model the effects of flexible prices in the Freiamt region, the basic research – including interviews and data collecting – is carried out in the fields of economics and renewable energy. An elasticity model based on the Spees and Lave study in used to simulate consumer behaviour to changing prices.   Two pricing structures with daily and hourly changing prices are found to lower the average electrical prices in both cases. These benefits are larger overall with the hourly price changes when all other variables are kept constant. This study finds that the changes to load patterns also seem to correlate with the local renewable energy production. Results suggest that this specific form of energy generation benefits from consumer reactions to changing prices during 2007 and 2008.   In order to validate these results the model should be expanded to include a more differentiated load from different sectors and to include a wider range of the electrical prices advertised to the consumer. However, under given circumstances, this study concludes that using more renewable power generation is possible both generally with daily price changes and also more specifically with hourly changing prices at a more competitive market price.

The high demand on accuracy in radiotherapy is to a large extent ensured through measurements of dose to water. The liquid ionisation chamber (LIC) is a type of detector that has several desirable properties for such measurements, e.g. a small active volume and minimal directional dependent response. There are, however, still gaps in knowledge concerning fundamental characteristics of this kind of detector. One of these characteristics is the detector’s response variation in relation to water with varying beam quality. This work aims to increase the knowledge of the LIC’s behaviour and attempts to come up with a method to construct correction factors for the response variation. The response model proposed by Eklund and Ahnesjö [2009] has been evaluated for two LICs, one filled with isooctane and the other with tetramethylsilane (TMS). The evaluation was done for two photon beams, 6 and 15 MV. It was found that the energy dependent response calculations from this method could not explain the difference between the LIC and reference air-filled ionisation chamber measurements in the larger fields. The response model leads to corrections for the TMS filled LIC in the direction away from the reference measurements. For the LIC filled with isooctane the corrections points towards the reference but were too small to completely explain the difference. / De höga kraven på noggrannhet i radioterapi kontrolleras genom mätningar av dos till vatten. Vätskejonisationskammaren (LIC) är en detektor med flera önskvärda egenskaper för sådana mätningar, den har exempelvis en liten aktiv volym och en respons med litet vinkelberoende. Fortfarande finns luckor i kunskapen om denna detektors grundläggande egenskaper. En av dessa är hur detektorns respons skiljer sig från vatten beroende på strålkvalitet. Syftet med detta arbete har varit att öka kunskapen om LICens beteende samt att försöka komma fram till en metod för att konstruera korrektionsfaktorer för den strålkvalitetsberoende responsen. Responsmodellen, presenterad av Eklund och Ahnesjö [2009], utvärderades för två LICar, en fylld med isooktan och en med tetrametylsilan (TMS), i två fotonstrålar, 6 och 15 MV. Den energiberoende responsen som beräknades från responsmodellen kunde inte förklara skillnaden mellan LIC- och referenskammarmätningarna i de stora fälten. Som referenskammare användes en luftfylld jonkammare. Modellen ledde till korrektioner i riktning bort från referenskammarvärdet för den TMS-fyllda kammaren. För kammaren med isooktan skedde korrektionen i riktning mot referenskammaren men var för liten för att helt förklara skillnaden.

There are numerous 100% continuous casting process steel making plants planning to build in the coming years, and therefore competitions in-between are to be more severe. To be competitive in the future , the on-the-time-being steel making furnace plant must be agile and responsive to changing market, yet also able to operate efficiently. Customer Relationship Management (CRM) is necessary for the present enterprise to retain valuable customers and to acquire valuable potential customers rather than to merely achieve market share. In the data based marketing area, customer response model with characters of targeting and predicting customers is gradually important than the past. Further more, supply chain management (SCM) is another important competitive trend in industry, in which information is one of the active performance factors. The company of this case study has abundant transactions data and relies heavy on information communicating with customers. This study following the rule of CRM 10C model, gathering customers relationship data in the first place, distinguishing customers with customer¡¦s value and customer¡¦s response model, can hopefully extract customerized needs of implicit information sharing knowledge on the purpose of CRM by improving SCM efficiency. There are four distinct clusters from analyzing 414,651 customer¡¦s transaction data of two years which are ¡§The right Stuff¡¨ , ¡§ Fatal Attractions¡¨ ,¡¨Odd Couples¡¨ and ¡§Ghosts¡¨ , and clustering were verified by the ¡§K-means¡¨ method commonly used in Clustering analysis. By analyzing the questionaries collected from customers, subjected to the aspect of needs of information sharing levels, types , qualities and extents came out different cluster¡¦s needs vary in information levels, qualities and extents. As more study on information sharing and customer¡¦s loyalty, results of customer loyalty affected by information sharing were found and would be more dominant if trust is add in between information sharing and customer¡¦s purchasing will .

No description available.

abstract: The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for Type I error rate and statistical power even when applied to a sparse table. In this dissertation, goodness-of-fit statistics using orthogonal components based on second- third- and fourth-order marginals were examined. If lack-of-fit is present in higher-order marginals, then a test that incorporates the higher-order marginals may have a higher power than a test that incorporates only first- and/or second-order marginals. To this end, two new statistics based on the orthogonal components of Pearson's chi-square that incorporate third- and fourth-order marginals were developed, and the Type I error, empirical power, and asymptotic power under different sparseness conditions were investigated. Individual orthogonal components as test statistics to identify lack-of-fit were also studied. The performance of individual orthogonal components to other popular lack-of-fit statistics were also compared. When the number of manifest variables becomes larger than 20, most of the statistics based on marginal distributions have limitations in terms of computer resources and CPU time. Under this problem, when the number manifest variables is larger than or equal to 20, the performance of a bootstrap based method to obtain p-values for Pearson-Fisher statistic, fit to confirmatory dichotomous variable factor analysis model, and the performance of Tollenaar and Mooijaart (2003) statistic were investigated. / Dissertation/Thesis / Doctoral Dissertation Statistics 2018

No description available.

The Daya Bay reactor neutrino experiment is a high sensitivity experiment designed to determine the last unknown neutrino mixing angle $theta_{13}$ by measuring disappearance of reactor antineutrinos emitted from six 2.9 $GW_{th}$ reactors at the Daya Bay Nuclear Power Station. There are eight identical Gd-loaded liquid scintillator detectors deployed in two near (flux-weighted baseline 512 $m$ and 561 $m$) and one far (1579 $m$) underground experimental halls to detect the inverse beta decay interaction. This dissertation describes the Daya Bay Experiment and individual contributions to this experiment. Chapter 1 reviews the history of the neutrino and the neutrino oscillation phenomena. The reactor based neutrino experiments in different times are described in this chapter in detail. It presents the motivation of the Daya Bay Experiment. In Chapter 2, the neutrino detection method and the $theta_{13}$ relative measurement method are introduced. This chapter focuses on the design of the Daya Bay Experiment, including antineutrino detector, calibration system, muon veto system and muon tagging system. Chapter 3 shows the design, development, construction, and assembly of Muon Pool PMT calibration system, and presents an algorithm of calculating the muon pool PMT timing offset values. Chapter 4 focuses on the manufacture, installation and commissioning of RPC HV system. Chapter 5 presents the analyses of the radioactive isotopes induced by comic muons. The Daya Bay detector energy response model is also described in detail. The relative rate analysis results exclude a zero value from $sin^22theta_{13}$ with a significance of 7.7 standard deviation using 139 days of data, 28909 (205308) antineutrino candidates which were recorded at the far hall (near halls) and shows $sin^22theta_{13} = 0.089pm0.011$ in a three-neutrino framework. A combined analysis of the $overline nu_e$ rates and energy spectra based on the detector energy response model improved measurement of the mixing angle $sin^22theta_{13} = 0.090^{+0.008}_{-0.009}$ by using 217 days of data, 41589 (203809 and 92912) antineutrino candidates were detected in the far hall (near halls). Also the first direct measurement of the $overline nu_e$ mass-squared difference $|Delta m^2_{ee}|= (2.59^{+0.19}_{-0.20})times10^{-3}$ $eV^2$. It is consistent with $|Delta m^2_{mumu}|$ measured by muon neutrino disappearance, supporting the three-flavor oscillation model. / Ph. D.

This thesis introduces a novel spectral response (SR) measurement technique using polychromatic filters (filters with very broad spectral transmittances) to determine SR of large area PV devices. Conventionally, SR of a photovoltaic (PV) device is determined by illuminating the device under test (DUT) with a series of monochromatic beams at different wavelengths as described in the international standard IEC 60904-8, or beams of limited spectral content using narrow band pass filters or monochromator. One significant problem associated with the application of the narrow band pass filters for a large-area SR measurement is that low light intensity produced on the measurement plane particularly in certain wavelength ranges: the ultraviolet and infrared. This can produce weak signal responses from a tested PV device. In addition, the imperfection of the filter s mounting position can shift the peak wavelength of the filter s transmittance at angle of incidence greater than 10°. This can cause stray light on the measurement plane. The proposed SR measurement method is called the 'polychromatic SR fitting method' or, in short, it is known as the 'polychromatic method'. The advantage of this method is that higher beam intensity can be produced on the measurement plane as a result of large spectral transmittance of the polychromatic filters. This can improve the signal strength of a tested PV device. This new SR measurement method works by comparing the variations in the currents which are measured at different spectra to the currents which are calculated at the same spectral conditions using the SR model. Validations of this method for a large- and small-area SR determinations show that it is potentially feasible as a new technique for determining SR of a PV device with deviations within ±2% across the wavelength bands.

Online retailing has grown rapidly and nowadays consumers’ needs become more and more demanding. Mass customization satisfies the need of customers for individuality. However, in order for retailers to offer a satisfying shopping experience, they need to take into account their retail environment. In online retailing, atmospherics play a significant role in consumers’ attitudes towards the store. The present study aims to analyze the role of high task-relevant atmospheric cues, thus those atmospherics that are strongly connected with the shopping goal, such as verbal content, display methods and navigation aids. Furthermore, the author will analyze the effect of the above atmospherics on consumers’ response towards the online mass customization environment. In order for the purpose to be fulfilled, semi-structured interviews were conducted in a purposive sample of ten participants. The results were analyzed based on the Stimulus-Organism-Response model. Finally, high task-relevant atmospheric cues are strongly related to the consumers’ final response by provoking either positive or negative emotions. More specifically, verbal content and display methods have a crucial effect on consumers’ end response. On the other hand, navigation aids contribute to a pleasant online experience but they do not affect final consumers; response significantly. Conclusively, the present study aims to be considered as a useful tool for marketing purposes as it can provide knowledge on how consumers behave in a mass customization online environment in the fashion industry. Such knowledge may appear crucial for professionals who want to meet their customers’ needs directly and effectively.

Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.