No description available.

No description available.

Raman spectroscopy is among the most widely employed methods to investigate the properties of materials in several fields of study. Evolution in materials science allowed us to fabricate suitable substrates, at the nanoscale, capable to enhance the electromagnetic field of the signals coming from the samples which at this range turn out to be in most cases singles or a few molecules. This particular variation of the classical technique is called SERS (Surface Enanched Raman Spectroscopy). In this work, the enhancement of the electromagnetic field is obtained by manipulation of the optical properties of metals with respect to their size. By using electroless deposition (bottom up technique), gold and silver nanoparticles were deposited in nanostructured patterns obtained on silicon wafers by means of electron beam lithography (top down technique). Rhodamine 6G in aqueous solution at extremely low concentration (10-8 M) was absorbed on the resultant dimers and the collection of the Raman spectra demonstrated the high efficiency of the substrates.

Indiana University-Purdue University Indianapolis (IUPUI) / As the amount of information grows, the desire to efficiently filter out unnecessary information and retain relevant or interested information for people is increasing. To extract the information that will be of interest to people efficiently, we can utilize recommender systems. Recommender systems are information filtering systems that predict the preference of a user to an item. Based on historical data of users, recommender systems are able to make relevant recommendations to users. Due to its usefulness, Recommender systems have been widely used in many applications, including e-commerce and healthcare information systems. However, existing recommender systems suffer from several issues, including data sparsity and user/item heterogeneity. In this thesis, a hybrid dynamic and multi-collaborative filtering based recommendation technique has been developed to recommend search terms for physicians when physicians review a large number of patients’ information. Besides, a local sparse linear method ensemble has been developed to tackle the issues of data sparsity and user/item heterogeneity. In health information technology systems, most physicians suffer from information overload when they review patient information. A novel hybrid dynamic and multi-collaborative filtering method has been developed to improve information retrieval from electronic health records. We tackle the problem of recommending the next search term to a physician while the physician is searching for information about a patient. In this method, I have combined first-order Markov Chain and multi-collaborative filtering methods. For multi-collaborative filtering methods, I have developed the physician-patient collaborative filtering and transition-involved collaborative filtering methods. The developed method is tested using electronic health record data from the Indiana Network for Patient Care. The experimental results demonstrate that for 46.7% of test cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in. The local sparse linear model ensemble has been developed to tackle both the data sparsity and the user/item heterogeneity issues for the top-n recommendation. Multiple local sparse linear models are learned for all the users and items in the system. I have developed similarity-based and popularity-based methods to determine the local training data for each local model. Each local model is trained on Sparse Linear Method (SLIM) which is a powerful recommendation technique for top-n recommendation. These learned models are then combined in various ways to produce top-N recommendations. I have developed model results combination and model combination methods to combine all learned local models. The developed methods are tested on a benchmark dataset and its sparsified datasets. The experiments demonstrate 18.4% improvement from such ensemble models, particularly on sparse datasets.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers. / 2019-06-21

No description available.

No description available.

Yes / The fluidized bed coating system is a conventional process of particles coating in various industries. In this work, an experimental investigation was conducted using Response Surface Methodology (RSM) to optimize the coating mass of particles in a top-spray fluidized bed coating. The design of experiments (DOEs) is a useful tool for controlling and optimization of products in industry. Thus, DOE was conducted using MINITAB software, version 16. This process used a sodium silicate solution for coating the sodium percarbonate particles. The effect of the fluidization air flow rate, atomization air flow rate and liquid flow rate on the coating mass in the top-spray fluidized bed coating was investigated. The experimental results indicated that the coating mass of particles is directly proportional to the liquid flow rate of the coating solution and inversely proportional to the air flow rate. It was demonstrated that the flow rate of the coating solution had the greatest influence on the coating efficiency. / Metallic Material Processing Research Group, ACECR, Branch of Tehran University, Tehran, Iran.

No description available.

No description available.