EXPLORING THE EFFECTS OF CUSTOMER PORTFOLIO MANAGEMENT

Nowadays turbulent business conditions provoke organizations to develop unique competitive advantages through efficient use of the available limited resources. At the same time companies deal with increasingly informed customers, seeking high added value and better service at reasonable prices. In this context, the need for rethinking traditional marketing activities determines the interest in searching and designing tools, which are able to analyze, evaluate, develop and manage the relationships with the customers. Thus the customer portfolio management (CPM) concept gains popularity. The paper offers a systematic view of the multifaceted nature of customer portfolio management and its effects on the economic performance of the company. The effects are classified into four main areas: cost reduction; relationship improvement; risk optimization and revenue increase. A conceptual model, which can be used as a base for the creation of methodological instruments for measurement and assessment of CPM effects, is proposed

Using multi-resolution remote sensing to measure ecosystem sensitivity and monitor land degradation in response to land use and climate variability

Climate change and land degradation, which is defined as the decline in the productive capacity of the land, have profound implications for resource-based livelihoods and food security. In this dissertation, I use remote sensing to improve understanding of how climate variability affects the productivity of global pasturelands and to quantify the spatial and temporal patterns of land degradation in the Southern Cone region (SCR) of South America. In the first chapter, I characterize the sensitivity of global pastureland productivity to climate variability by analyzing the relationship between MODIS enhanced vegetation index and gridded precipitation data. Results show that pasturelands are least capable of withstanding precipitation deficits in Australia, while pasturelands in Latin America recover more slowly after drought compared to other regions. In the second chapter, I use Landsat observations to measure the magnitude, geography, and rate of change in the amount of bare ground, herbaceous and woody vegetation in the SCR since 1999. Paraguay experienced the highest proportional increase in herbaceous cover as a result of agricultural expansion and intensification, while Uruguay experienced the highest proportional increase in woody cover as a result of afforestation. Argentina, the largest and most heterogeneous country in the SCR, experienced widespread land cover changes from deforestation, reforestation, afforestation, and desertification, each of which varied in extent and magnitude by ecoregion. In the third chapter, I assess patterns of land degradation in the SCR using the United Nations Sustainable Development framework. My results show that 67.5% of the SCR experienced changes in land cover properties in the 21st century, with widespread improvement (i.e., increased productive capacity), along with substantial hotspots of degradation caused by expansion of agriculture and systematic decreases in precipitation. Monitoring degradation is necessary to assess ecosystem services, ensure food security, and develop land use policies designed to increase the resilience of land systems to the joint stresses imposed by climate change and a growing global population. The methods, datasets, and results from this dissertation provide an improved basis for creating such policies in some of the world’s most vulnerable and food insecure regions

Metabolomics of chronic obstructive pulmonary disease and obstructive sleep apnea syndrome : response to Maniscalco and Motta

We appreciate Maniscalco and Motta’s comments on our recently published article ‘‘Fusion of the 1H NMR data of serum, urine and exhaled breath condensate in order to discriminate chronic obstructive pulmonary disease and obstructive sleep apnea syndrome’’ (Zabek et al. 2015) and we are grateful for the opportunity to clarify a number of points from our work. We are glad that the authors appreciated our data analysis and interpretation[…

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal’s baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed

Bayesian methods and data science with health informatics data

Cancer is a complex disease, driven by a range of genetic and environmental factors. Every year millions of people are diagnosed with a type of cancer and the survival prognosis for many of them is poor due to the lack of understanding of the causes of some cancers. Modern large-scale studies offer a great opportunity to study the mechanisms underlying different types of cancer but also brings the challenges of selecting informative features, estimating the number of cancer subtypes, and providing interpretative results. In this thesis, we address these challenges by developing efficient clustering algorithms based on Dirichlet process mixture models which can be applied to different data types (continuous, discrete, mixed) and to multiple data sources (in our case, molecular and clinical data) simultaneously. We show how our methodology addresses the drawbacks of widely used clustering methods such as k-means and iClusterPlus. We also introduce a more efficient version of the clustering methods by using simulated annealing in the inference stage. We apply the data integration methods to data from The Cancer Genome Atlas (TCGA), which include clinical and molecular data about glioblastoma, breast cancer, colorectal cancer, and pancreatic cancer. We find subtypes which are prognostic of the overall survival in two aggressive types of cancer: pancreatic cancer and glioblastoma, which were not identified by the comparison models. We analyse a Hospital Episode Statistics (HES) dataset comprising clinical information about all pancreatic cancer patients in the United Kingdom operated during the period 2001 - 2016. We investigate the effect of centralisation on the short- and long-term survival of the patients, and the factors affecting the patient survival. Our analyses show that higher volume surgery centres are associated with lower 90-day mortality rates and that age, index of multiple deprivation and diagnosis type are significant risk factors for the short-term survival. Our findings suggest the analysis of large complex molecular datasets coupled with methodology advances can allow us to gain valuable insights in the cancer genome and the associated molecular mechanisms

Studying the stability of Solvent Red 19 and 23 as excise duty components under the influence of controlled factors

In this study, we examine the chemical stability of two disazo dyes, namely Solvent Red 19 and 23 (SR 19 and SR 23), under simulated conditions. Both dyes are considered to be chemically stable under normal exploitation conditions and therefore, are used extensively as excise duty components that enable a rapid visual verification of the tax levels that were imposed on fuel products as well as identifying fuel usage. However, the results from this study confirmed that the colour of the samples that had been fortified with either SR 19 or SR 23 fades under the influence of external conditions such as UV-A irradiation and temperature over time. The UV-A irradiation was the dominant factor that was responsible for the colour of the samples to fade in two designed experiments that were carried out independently for two model systems. The analysis of the UV/Vis and fluorescence spectra as well as the interpretation of the changes that were observed in the chromatographic profiles provided substantial evidence that the colour fading was caused by the photodegradation of the disazo dyes, which also occurs in non-polar media including fuel products. SR 19 is more stable than SR 23

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints

In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets