Structural and surface property characterization of titanium dioxide nanotubes for orthopedic implants

This research focused on the to modification of the surface structure of titanium implants with nanostructured morphology of TiO2 nanotubes and studied the interaction of nanotubes with osteoblast cells to understand the parameters that affect the cell growth. The electrical, mechanical, and structural properties of TiO2 nanotubes were characterized to establish a better understanding on the properties of such nanoscale morphological structures. To achieve the objectives of this research work I transformed the titanium and its alloys, either in bulk sheet form, bulk machined form, or thin film deposited on another substrate into a surface of titania nanotubes using a low cost and environmentally friendly process. The process requires only a simple electrolyte, low cost electrode, and a DC power supply. With this simple approach of scalable nanofabrication, a typical result is nanotubes that are each approximately 100nm in diameter and have a wall thickness of about 20nm. By changing the fabrication parameters, independent nanotubes can be fabricated with open volume between them. Titanium in this form is termed onedimensional since electron transport is narrowly confined along the length of the nanotube. My Ph.D. accomplishments have successfully shown that osteoblast cells, the cells that are the precursors to bone, have a strong tendency to attach to the inside and outside of the titanium nanotubes onto which they are grown using their filopodia – cell’s foot used for locomotion – anchored to titanium nanotubes. In fact it was shown that the cell prefers to find many anchoring sites. These sites are critical for cell locomotion during the first several weeks of maturity and upon calcification as a strongly anchored bone cell. In addition I have shown that such a surface has a greater cell density than a smooth titanium surface. My work also developed a process that uses a focused and controllably rastered ion beam as a nano-scalpel to cut away sections of the osteoblast cells to probe the attachment beneath the main cell body. Ultimately the more rapid growth of osteoblasts, coupled with a stronger cell-surface interface, could provide cost reduction, shorter rehabilitation, and fewer follow-on surgeries due to implant loosening

Application of variations of non-linear CCA for feature selection in drug sensitivity prediction

Cancer arises due to the genetic alteration in patient DNA. Many studies indicate the fact that these alterations vary among patients and can affect the therapeutic effect of cancer treatment dramatically. Therefore, extensive studies focus on understanding these alterations and their effects. Pre-clinical models play an important role in cancer drug discovery and cancer cell lines are one of the main ingredients of these pre-clinical studies which can capture many different aspects of multi-omics properties of cancer cells. However, the assessment of cancer cell line responses to different drugs is faulty and laborious. Therefore, in-silico models, which perform accurate prediction of drug sensitivity values, enhance cancer drug discovery. In the past decade, many computational methods achieved high performances by studying similarity between cancer cell lines and drug compounds and used them to obtain an accurate predictive model for unknown instances. In this thesis, we study the effect of non-linear feature selection through two variations of canonical correlation analysis, KCCA, and HSIC-SCCA, on the prediction of drug sensitivity. To estimate the performance of these features we use pairwise kernel ridge regression to predict the drug sensitivity, measured as IC50 values. The data set under study is a subset of Genomics of Drug Sensitivity in Cancer comprise of 124 cell lines and 124 drug compounds. The high diversity between cell lines and drug compound samples and the high dimension of data matrices reduce the accuracy of the model obtained by pairwise kernel ridge regression. This accuracy reduced by employing HSIC-SCCA method as a dimension reduction step since the HSIC-SCCA method increased the differences among the samples by employing different projection vectors for samples in different folds of cross-validation. Therefore, the obtained variables are rotated to provide more homogeneous samples. This step slightly improved the accuracy of the model

Modeling ferroresonance phenomena on voltage transformer (VT)

Ferroresonance in electromagnetic voltage transformers, fed through circuit breaker grading capacitance, is studied using nonlinear dynamics methods. The magnetising charact'eristic of a typical l00VA voltage transformer is represented by a single-valued two-term polynomial of the order seven.The system exhibits three types of ferroresonance: fundamental frequency ferroresonance, subharmonic ferroresonance and chaotic fcrroresonance, similar to high capacity power transformers fed through capacitive coupling from neighbouring lines or phases. Results also show that while fundamental frequency and subharmonic ferroresonancc can occur under commonplace operating conditions, chaotic states are unlikely in practice

Compositions, methods and devices for generating nanotubes on a surface

A method for modifying a surface by generating nanotubes at one or more selected sites on the surface, the surface including a first metal. The method includes the steps of positioning at least one cathode and at least one anode relative to the surface in an electrolyte solution including a fluoride salt of a second metal, and applying a voltage between the at least one anode and the at least one cathode sufficient to generate nanotubes at one or more selected sites on the surface and to inhibit nanotube formation at one or more of the other selected sites, wherein the nanotubes include the first metal and the second metal.https://digitalcommons.mtu.edu/patents/1130/thumbnail.jp

An NLP-Deep Learning approach for Product Rating Prediction Based on Online Reviews and Product Features

This study focuses on predicting the popularity of a product based on its overall rating score. Unlike previous studies that focus on predicting the review rating based on sentiment analysis and polarity of the reviews, in this thesis, the effect of product features in determining its popularity is directly measured and analyzed in order to predict its overall rating score. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to a Deep Learning (DL) model. Phase 2 identifies other features that customers care about the most by applying the Named Entity Recognition (NER) algorithm to the customer online reviews; and lastly, Phase 3 feeds the combination of the general and custom features to the DL model to predict the overall rating score of the product. The experimental results on a dataset of laptop products, collected from Amazon, indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features to the inputs of the DL algorithm when compared with the existing method. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today's competitive markets

A model for the molecular organisation of the IS911 transpososome

Tight regulation of transposition activity is essential to limit damage transposons may cause by generating potentially lethal DNA rearrangements. Assembly of a bona fide protein-DNA complex, the transpososome, within which transposition is catalysed, is a crucial checkpoint in this regulation. In the case of IS911, a member of the large IS3 bacterial insertion sequence family, the transpososome (synaptic complex A; SCA) is composed of the right and left inverted repeated DNA sequences (IRR and IRL) bridged by the transposase, OrfAB (the IS911-encoded enzyme that catalyses transposition). To characterise further this important protein-DNA complex in vitro, we used different tagged and/or truncated transposase forms and analysed their interaction with IS911 ends using gel electrophoresis. Our results allow us to propose a model in which SCA is assembled with a dimeric form of the transposase. Furthermore, we present atomic force microscopy results showing that the terminal inverted repeat sequences are probably assembled in a parallel configuration within the SCA. These results represent the first step in the structural description of the IS911 transpososome, and are discussed in comparison with the very few other transpososome examples described in the literature

Evaluation of FIT impacts on market clearing price in the restructured power market

The development of intermittent wind firms in the restructured power market has required the entry of several creative approaches in evaluating the market clearing price in the restructured power market. This study proposes a model to investigate the seasonal market clearing price by considering the hybrid wind-heat firm. In this article, fixed and variable Feed in Tariff (FIT) is considered and compared as two regulatory policies for wind generation units. Wind resources modeled by using the scenario based technique. Furthermore, strategic behavior of other investors is considered based on the game theory concepts. The Cournot game concept has been applied to determine the Nash equilibrium for each state of stochastic programming. This model has been implemented on a test system. The effects of the fixed and variable FIT and the variations of electricity price cap have been investigated on the profit of each firm in the restructured power market. Moreover, the market clearing price offered for each season and load level

Sex differences in conventional and some behavioral cardiovascular risk factors, Analysis of the prevention clinic database

Background: An increase in Cardiovascular Disease (CVD) frequency was observed over the past three decades in low- and middle income countries, especially in Iran. The purpose of the present study was to review and compare the frequencies of conventional and some non-conventional CVD risk factors between men and women in a tertiary level referral cardiovascular teaching hospital in a six month period in the North of Iran.  Methods: A descriptive cross-sectional study was conducted using medical databases including conventional risk factors: opium consumption, physical inactivity, high salt diet, and serum vitamin D level. The chi-square and independent t tests were used to assess the differences between groups.  Results: A total of 740 (55% women) who had available full medical history data were recruited in the study. Approximately 62% of the participants were older than 45 years with the mean age of 54 (14.2) years old. Percentages of hypertension, diabetes, dyslipidemia, and obesity in women were significantly higher than those of men (P<0.05). A total of 50% of all the participants were physically inactive. Men had higher frequency of opium and saltshaker use than women (P<0.05).  Conclusion: The current study indicated that despite the importance of conventional CVD risk factors like diabetes, hypertension, dyslipidemia, and obesity, educational programs should be considered to improve physical activity and reducing salt consumption and awareness about opium use complications

A numerical method for calculating the loss of life of power transformers

Temperature, especially the hot spot temperature (HST) and top oil temperature (TOT), has played the important rule and became the most effective factor in determination of the insulation life of the transformer. The prediction of HST and TOT is very important for estimating the loss of life (LOL) of the transformer in power system. Therefore, an accurate technique is needed for solving the thermal models. This paper presents a numerical method which can accurately calculate the HST and thus provides an effective evaluation of LOL of the transformer. An alternative solution for solving the thermal model is proposed in this work and results are compared with the actual temperature, measured by fiber optic sensors and placed on the 30MV A power transformer