Automorphism Groups of Generalized (Binary) Icosahedral, Tetrahedral and Octahedral Groups

Let G be any of the (binary) icosahedral, generalized octahedral (tetrahedral) groups or their quotients by the center. We calculate the automorphism group Aut(G).

Thermal analysis and compatibility studies of prednicarbate with excipients used in semi solid pharmaceutical form

Differential Scanning Calorimetry (DSC), thermogravimetry/derivative thermogravimetry (TG/DTG) and infrared spectroscopy (IR) techniques were used to investigate the compatibility between prednicarbate and several excipients commonly used in semi solid pharmaceutical form. The thermoanalytical studies of 1:1 (m/m) drug/excipient physical mixtures showed that the beginning of the first thermal decomposition stage of the prednicarbate (T (onset) value) was decreased in the presence of stearyl alcohol and glyceryl stearate compared to the drug alone. For the binary mixture of drug/sodium pirrolidone carboxilate the first thermal decomposition stage was not changed, however the DTG peak temperature (T (peak DTG)) decreased. The comparison of the IR spectra of the drug, the physical mixtures and of the thermally treated samples confirmed the thermal decomposition of prednicarbate. By the comparison of the thermal profiles of 1:1 prednicarbate:excipients mixtures (methylparaben, propylparaben, carbomer 940, acrylate crosspolymer, lactic acid, light liquid paraffin, isopropyl palmitate, myristyl lactate and cetyl alcohol) no interaction was observed.

Agar-based films for application as polymer electrolytes

New types of polymer electrolytes based on agar have been prepared and characterized by impedance spectroscopy, X-ray diffraction measurements, UV-vis spectroscopy and scanning electronic microscopy (SEMI). The best ionic conductivity has been obtained for the samples containing a concentration of 50 wt.% of acetic acid. As a function of the temperature the ionic conductivity exhibits an Arrhenius behavior increasing from 1.1 x 10(-4) S/cm at room temperature to 9.6 x 10(-4) S/cm at 80 degrees C. All the samples showed more than 70% of transparency in the visible region of the electromagnetic spectrum, a very homogeneous surface and a predominantly amorphous structure. All these characteristics imply that these polymer electrolytes can be applied in electrochromic devices. (C) 2009 Elsevier Ltd. All rights reserved.

Diagnostics for the Developing World: Microfluidic Paper-Based Analytical Devices

Microfluidic paper-based analytical devices (mu PADs) are a new class of point-of-care diagnostic devices that are inexpensive, easy to use, and designed specifically for use in developing countries. (To listen to a podcast about this feature, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html.)

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

A Contour-based Separation of VerticallyAttached Traffic Signs

This report presents an algorithm for locating the cut points for and separatingvertically attached traffic signs in Sweden. This algorithm provides severaladvanced digital image processing features: binary image which representsvisual object and its complex rectangle background with number one and zerorespectively, improved cross correlation which shows the similarity of 2Dobjects and filters traffic sign candidates, simplified shape decompositionwhich smoothes contour of visual object iteratively in order to reduce whitenoises, flipping point detection which locates black noises candidates, chasmfilling algorithm which eliminates black noises, determines the final cut pointsand separates originally attached traffic signs into individual ones. At each step,the mediate results as well as the efficiency in practice would be presented toshow the advantages and disadvantages of the developed algorithm. Thisreport concentrates on contour-based recognition of Swedish traffic signs. Thegeneral shapes cover upward triangle, downward triangle, circle, rectangle andoctagon. At last, a demonstration program would be presented to show howthe algorithm works in real-time environment.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Shared decision-making in mental health care : a user perspective on decisional needs in community-based services

BACKGROUND: Shared decision-making (SDM) is an emergent research topic in the field of mental health care and is considered to be a central component of a recovery-oriented system. Despite the evidence suggesting the benefits of this change in the power relationship between users and practitioners, the method has not been widely implemented in clinical practice. OBJECTIVE: The objective of this study was to investigate decisional and information needs among users with mental illness as a prerequisite for the development of a decision support tool aimed at supporting SDM in community-based mental health services in Sweden. METHODS: Three semi-structured focus group interviews were conducted with 22 adult users with mental illness. The transcribed interviews were analyzed using a directed content analysis. This method was used to develop an in-depth understanding of the decisional process as well as to validate and conceptually extend Elwyn et al.'s model of SDM. RESULTS: The model Elwyn et al. have created for SDM in somatic care fits well for mental health services, both in terms of process and content. However, the results also suggest an extension of the model because decisions related to mental illness are often complex and involve a number of life domains. Issues related to social context and individual recovery point to the need for a preparation phase focused on establishing cooperation and mutual understanding as well as a clear follow-up phase that allows for feedback and adjustments to the decision-making process. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: The current study contributes to a deeper understanding of decisional and information needs among users of community-based mental health services that may reduce barriers to participation in decision-making. The results also shed light on attitudinal, relationship-based, and cognitive factors that are important to consider in adapting SDM in the mental health system.

Data based Calibration System for Radar used by Vehicle Activated Signs

The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.

A Common-feature approach for testing present-value restrictions with financial data

It is well known that cointegration between the level of two variables (labeled Yt and yt in this paper) is a necessary condition to assess the empirical validity of a present-value model (PV and PVM, respectively, hereafter) linking them. The work on cointegration has been so prevalent that it is often overlooked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. The basis of this result is the use of rational expectations in forecasting future values of variables in the PVM. If this condition fails, the present-value equation will not be valid, since it will contain an additional term capturing the (non-zero) conditional expected value of future error terms. Our article has a few novel contributions, but two stand out. First, in testing for PVMs, we advise to split the restrictions implied by PV relationships into orthogonality conditions (or reduced rank restrictions) before additional tests on the value of parameters. We show that PV relationships entail a weak-form common feature relationship as in Hecq, Palm, and Urbain (2006) and in Athanasopoulos, Guillén, Issler and Vahid (2011) and also a polynomial serial-correlation common feature relationship as in Cubadda and Hecq (2001), which represent restrictions on dynamic models which allow several tests for the existence of PV relationships to be used. Because these relationships occur mostly with nancial data, we propose tests based on generalized method of moment (GMM) estimates, where it is straightforward to propose robust tests in the presence of heteroskedasticity. We also propose a robust Wald test developed to investigate the presence of reduced rank models. Their performance is evaluated in a Monte-Carlo exercise. Second, in the context of asset pricing, we propose applying a permanent-transitory (PT) decomposition based on Beveridge and Nelson (1981), which focus on extracting the long-run component of asset prices, a key concept in modern nancial theory as discussed in Alvarez and Jermann (2005), Hansen and Scheinkman (2009), and Nieuwerburgh, Lustig, Verdelhan (2010). Here again we can exploit the results developed in the common cycle literature to easily extract permament and transitory components under both long and also short-run restrictions. The techniques discussed herein are applied to long span annual data on long- and short-term interest rates and on price and dividend for the U.S. economy. In both applications we do not reject the existence of a common cyclical feature vector linking these two series. Extracting the long-run component shows the usefulness of our approach and highlights the presence of asset-pricing bubbles.

The experience of semi-formal infrastructure finance in developing countries: proposals for Brazil and Latin America

This paper has the purpose of analyzing the role of civil society in funding and providing nfrastructure projects in developing countries. Considering that local associations around the world have been directly engaged on some infrastructure projects – some scholars define it as “semi-formal finance” –, the intention is to demonstrate that the experiences on such arrangements in developing countries have been responsible for fostering infrastructure investments in the poorer regions where the government is more absent. Based upon legal, economic and social aspects, this paper aims to contribute to a broader debate for the development of infrastructure in emerging countries. The conclusion is that, under a more social approach, the legal and economic mechanisms in developing countries are able to consider such arrangements in the benefit of their development.

A JML-Based strategy for incorporating formal specifications into the software development process

This thesis presents a JML-based strategy that incorporates formal specifications into the software development process of object-oriented programs. The strategy evolves functional requirements into a “semi-formal” requirements form, and then expressing them as JML formal specifications. The strategy is implemented as a formal-specification pseudo-phase that runs in parallel with the other phase of software development. What makes our strategy different from other software development strategies used in literature is the particular use of JML specifications we make all along the way from requirements to validation-and-verification.

New method for determination of (E)resveratrol in wine based on microextraction using packed sorbent and ultra-performance liquid chromatography

An ultra-fast and improved analytical methodology based on microextraction by packed sorbent (MEPS) combined with ultra-performance LC (UPLC) was developed and validated for determination of (E)-resveratrol in wines. Important factors affecting the performance of MEPS such as the type of sorbent material (C2, C8, C18, SIL, and M1), number of extraction cycles, and sample volume were studied. The optimal conditions of MEPS extraction were obtained using C8 sorbent and small sample volumes (50–250mL) in one extraction cycle (extract–discard) and in a short time period (about 3 min for the entire sample preparation step). (E)-Resveratrol was eluted by 1 250mL of the mixture containing 95% methanol and 5% water, and the separation was carried out on a highstrength silica HSS T3 analytical column (100 mm 2.1 mm, 1.8mm particle size) using a binary mobile phase composed of aqueous 0.1% formic acid (eluent A) and methanol (eluent B) in the gradient elution mode (10 min of total analysis). The method was fully validated in terms of linearity, detection (LOD) and quantification (LOQ) limits, extraction yield, accuracy, and inter/intra-day precision, using a Madeira wine sample (ET) spiked with (E)-resveratrol at concentration levels ranging from 5 to 60mg/mL. Validation experiments revealed very good recovery rate of 9575.8% RSD, good linearity with r2 values 40.999 within the established concentration range, excellent repeatability (0.52%), and reproducibility (1.67%) values (expressed as RSD), thus demonstrating the robustness and accuracy of the MEPSC8/UPLC-photodiode array (PDA) method. The LOD of the method was 0.21mg/mL, whereas the LOQ was 0.68mg/mL. The validated methodology was applied to 30 commercial wines (24 red wines and six white wines) from different grape varieties, vintages, and regions. On the basis of the analytical validation, the MEPSC8/UPLC-PDA methodology shows to be an improved, sensitive, and ultra-fast approach for determination of (E)-resveratrol in wines with high resolving power within 6 min.