The enzymatic hydrolysis of cellulose to glucose by cellulases is one of the major steps in the conversion of lignocellulosic biomass to biofuel. This hydrolysis by cellulases, a heterogeneous reaction, currently suffers from some major limitations, most importantly a dramatic rate slowdown at high degrees of conversion in the case of crystalline cellulose. Various rate-limiting factors were investigated employing experimental as well as computational studies. Cellulose accessibility and the hydrolysable fraction of accessible substrate (a previously undefined and unreported quantity) were shown to decrease steadily with conversion, while cellulose reactivity, defined in terms of hydrolytic activity per amount of actively adsorbed cellulase, remained constant. Faster restart rates were observed on partially converted cellulose as compared to uninterrupted hydrolysis rates, supporting the presence of an enzyme clogging phenomenon. Cellulose crystallinity is a major substrate property affecting the rates, but its quantification has suffered from lack of consistency and accuracy. Using multivariate statistical analysis of X-ray data from cellulose, a new method to determine the degree of crystallinity was developed. Cel7A CBD is a promising target for protein engineering as cellulose pretreated with Cel7A CBDs exhibits enhanced hydrolysis rates resulting from a reduction in crystallinity. However, for Cel7A CBD, a high throughput assay is unlikely to be developed. In the absence of a high throughput assay (required for directed evolution) and extensive knowledge of the role of specific protein residues (required for rational protein design), the mutations need to be picked wisely, to avoid the generation of inactive variants. To tackle this issue, a method utilizing the underlying patterns in the sequences of a protein family has been developed.

<p>Arbetet som presenteras i den här rapporten handlar om ansiktsigenkänning från webbkameror med hjälp av principal component analysis samt artificiella neurala nätverk av typen feedforward. Arbetet förbättrar tekniken med hjälp av filterbaserade metoder som bland annat används inom ansiktsdetektering. Dessa filter bygger på att skicka med redundant data av delregioner av ansiktet.</p>

Brain decoding involves the determination of a subject’s cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of classification from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. The approaches are illustrated in an application where the task is to infer from brain activity measured with magnetoencephalography (MEG) the type of video stimulus shown to a subject. / Graduate

The research project, Optimization of an array of Peptidic Indicator Displacement Assays for the Discrimination of Cabernet Sauvignon Wines, describes the multiple step lab trials conducted to optimize an array of ensembles composed of synthesized peptides and PCV:Cu+2 complexes for the differentiation of seven Cabernet Sauvignon wines with different tannin levels. This report also includes the methods and analysis used. The analysis interpreted by principal component analysis. / text

Στην παρούσα διπλωματική εργασία μελετώνται μέθοδοι διαχωρισμού σημάτων σε ανεξάρτητες συνιστώσες. Αφού δοθεί ο ορισμός του προβλήματος και μια αναφορά στις κυριότερες μεθόδους για την αντιμετώπισή του, γίνεται σαφές ότι δεν μπορούν να σχεδιαστούν γενικές μέθοδοι διαχωρισμού σημάτων. Παρά την πληθώρα των πρακτικών προβλημάτων στα οποία βρίσκει εφαρμογή το μαθηματικό πρότυπο, δεν είναι δυνατός ο σχεδιασμός μιας ενιαίας μεθόδου που να αντιμετωπίζει αποτελεσματικά όλες τις περιπτώσεις διαχωρισμού σημάτων. Ο αναγνώστης πληροφορείται για τις περιοχές έρευνας και ανάπτυξης των διαφόρων μεθόδων καθώς και για τις εφαρμογές τους σε διάφορους τομείς της σύγχρονης επιστήμης. Στη συνέχεια, υλοποιούνται κάποιες από αυτές τις μεθόδους και παρουσιάζονται τα αποτελέσματα προσομοίωσης πραγματικών πειραματικών δεδομένων που λήφθηκαν για την εκπόνηση της συγκεκριμένης διπλωματικής εργασίας. Τα αποτελέσματα εξάγονται με την χρήση και υλοποίηση αλγόριθμου επεξεργασίας των δεδομένων στο πρόγραμμα Matlab και μελετώνται εκτενέστερα με το πρόγραμμα Adobe Audition 1.5. Τέλος, παρουσιάζονται τα συμπεράσματα από την εφαρμογή του αλγόριθμου στα πραγματικά δεδομένα και δίνεται μια μαθηματική- θεωρητική βάση για την βελτιστοποίηση των μεθόδων διαχωρισμού σημάτων. / -

The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on developing a model to forecast the demand of low cost housing in urban areas. The study is focused on eight states in Peninsular Malaysia, as most of these states are among the areas predicted to have achieved the highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan, Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of nine indicators including: population growth; birth rate; child mortality rate; unemployment rate; household income rate; inflation rate; GDP; poverty rate and housing stocks have been used to forecast the demand on low cost housing using Artificial Neural Network (ANN) approach. The data is collected from the Department of Malaysian Statistics, the Ministry of Housing and the Housing Department of the State Secretary. The Principal Component Analysis (PCA) method has been adopted to analyze the data using SPSS 18.0 package. The performance of the Neural Network is evaluated using R squared (R2) and the accuracy of the model is measured using the Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed using Visual Basic. From the results, it was found that the best Neural Network to forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan 2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-24-1. In conclusion, the evaluation performance of the model through the MAPE value shows that the NN model can forecast the low-cost housing demand very good in Pulau Pinang, Johor, Pahang and Kelantan, where else good in Kedah and Terengganu while in Perlis and Perak it is not accurate due to the lack of data. The study has successfully developed a user friendly interface to retrieve and view all the data easily.

The subject of this paper is microfinance and the question: Do self-sustainable MFI:s alleviate poverty?. A MFI is a micro financial institution, a regular bank or a NGO that has transformed into a licensed financial institutions, focused on microenterprises. To answer the question data has been gathered in Ecuador, South America. South America have a large amount of self sustainable MFI:s. Ecuador was selected as the country to be studied as it has an intermediate level of market penetration in the micro financial sector. To determine relative poverty before and after the access to microcredit, interviews were used. The data retrieved in the interviews was used to determine the impact of micro credit on different aspects of relative poverty using the Difference in Difference method. Significant differences are found between old and new clients as well as for the change over time. But no significant results are found for the difference in change over time for clients compared to the non-clients. The author argues that the insignificant result can either be a result of a too small sample size, disturbances in the sample selection or that this specific kind of institution have little or no affect on the current clients economical development.

No description available.

Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

Obesity is a highly cited risk factor for knee osteoarthritis (OA) associated with increased risk of development of OA and accelerated disease progression. Rates of obesity are increasing internationally, and while obesity is well established as a risk factor, the precise role of obesity in knee OA pathogenesis and progression is not as clearly understood. Mechanical loading has been implicated as an important factor in knee OA initiation and progression. The purpose of this thesis was to further examine the roles of moderate knee OA disease presence and obesity on knee joint mechanics during gait, and to characterize their mechanical interaction. Two methods have been applied. First, principal component analysis has been applied to resultant waveforms from gait analysis and second, a sagittal plane joint contact force model has been applied. Using both methods, statistical differences in biomechanical loading has been associated with obesity, moderate knee OA, and their interaction.