Automated Assessment Through Integration of Heterogeneous Systems with a Workflow Engine

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Facile synthesis of polymer-peptide conjugates via direct amino acid coupling chemistry

Polymer-peptide conjugates (also known as biohy-brids) are attracting considerable attention as injectable materials owing to the self-assembling behavior of the peptide and the ability to control the material properties using the polymer component. To this end, a simple method for preparing poly(ethylene oxide)-oligophenylalanine polymer-peptide conjugates (mPEOm-F n-OEt) using isobutylchloroformate as the activating reagent has been identified and developed. The synthetic approach reported employs an industrially viable route to produce conjugates with high yield and purity. Moreover, the approach allows judicious selection of the precursor building blocks to produce libraries of polymer-peptide conjugates with complete control over the molecular composition. Control over the molecular make-up of the conjugates allows fine control of the physicochemical properties, which will be exploited in future studies into the prominent self-assembling behavior of such materials. © 2013 Wiley Periodicals, Inc.

When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits

Objective: Images on food and dietary supplement packaging might lead people to infer (appropriately or inappropriately) certain health benefits of those products. Research on this issue largely involves direct questions, which could (a) elicit inferences that would not be made unprompted, and (b) fail to capture inferences made implicitly. Using a novel memory-based method, in the present research, we explored whether packaging imagery elicits health inferences without prompting, and the extent to which these inferences are made implicitly. Method: In 3 experiments, participants saw fictional product packages accompanied by written claims. Some packages contained an image that implied a health-related function (e.g., a brain), and some contained no image. Participants studied these packages and claims, and subsequently their memory for seen and unseen claims were tested. Results: When a health image was featured on a package, participants often subsequently recognized health claims that—despite being implied by the image—were not truly presented. In Experiment 2, these recognition errors persisted despite an explicit warning against treating the images as informative. In Experiment 3, these findings were replicated in a large consumer sample from 5 European countries, and with a cued-recall test. Conclusion: These findings confirm that images can act as health claims, by leading people to infer health benefits without prompting. These inferences appear often to be implicit, and could therefore be highly pervasive. The data underscore the importance of regulating imagery on product packaging; memory-based methods represent innovative ways to measure how leading (or misleading) specific images can be. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Degree of vertical integration and its buffering effects on political uncertainty

The focus of this study is on the governance decisions in a concurrent channels context, in the case of uncertainty. The study examines how a firm chooses to deploy its sales force in times of uncertainty, and the subsequent performance outcome of those deployment choices. The theoretical framework is based on multiple theories of governance, including transaction cost analysis (TCA), agency theory, and institutional economics. Three uncertainty variables are investigated in this study. The first two are demand and competitive uncertainty which are considered to be industry-level market uncertainty forms. The third uncertainty, political uncertainty, is chosen as it is an important dimension of institutional environments, capturing non-economic circumstances such as regulations and political systemic issues. The study employs longitudinal secondary data from a Thai hotel chain, comprising monthly observations from January 2007 – December 2012. This hotel chain has its operations in 4 countries, Thailand, the Philippines, United Arab Emirates – Dubai, and Egypt, all of which experienced substantial demand, competitive, and political uncertainty during the study period. This makes them ideal contexts for this study. Two econometric models, both deploying Newey-West estimations, are employed to test 13 hypotheses. The first model considers the relationship between uncertainty and governance. The second model is a version of Newey-West, using an Instrumental Variables (IV) estimator and a Two-Stage Least Squares model (2SLS), to test the direct effect of uncertainty on performance and the moderating effect of governance on the relationship between uncertainty and performance. The observed relationship between uncertainty and governance observed follows a core prediction of TCA; that vertical integration is the preferred choice of governance when uncertainty rises. As for the subsequent performance outcomes, the results corroborate that uncertainty has a negative effect on performance. Importantly, the findings show that becoming more vertically integrated cannot help moderate the effect of demand and competitive uncertainty, but can significantly moderate the effect of political uncertainty. These findings have significant theoretical and practical implications, and extend our knowledge of the impact on uncertainty significantly, as well as bringing an institutional perspective to TCA. Further, they offer managers novel insight into the nature of different types of uncertainty, their impact on performance, and how channel decisions can mitigate these impacts.

Surface plasmon resonance-induced photocatalysis by Au nanoparticles decorated mesoporous g-C3N4 nanosheets under direct sunlight irradiation

In recent years, surface plasmon-induced photocatalytic materials with tunable mesoporous framework have attracted considerable attention in energy conversion and environmental remediation. Herein we report a novel Au nanoparticles decorated mesoporous graphitic carbon nitride (Au/mp-g-C3N4) nanosheets via a template-free and green in situ photo-reduction method. The synthesized Au/mp-g-C3N4 nanosheets exhibit a strong absorption edge in visible and near-IR region owing to the surface plasmon resonance effect of Au nanoparticles. More attractively, Au/mp-g-C3N4 exhibited much higher photocatalytic activity than that of pure mesoporous and bulk g-C3N4 for the degradation of rhodamine B under sunlight irradiation. Furthermore, the photocurrent and photoluminescence studies demonstrated that the deposition of Au nanoparticles on the surface of mesoporous g-C3N4 could effectively inhibit the recombination of photogenerated charge carriers leading to the enhanced photocatalytic activity. More importantly, the synthesized Au/mp-g-C3N4 nanosheets possess high reusability. Hence, Au/mp-g-C3N4 could be promising photoactive material for energy and environmental applications.

Direct activation of microcrystalline zeolites

In this work a direct activation route of zeolites is assessed. It consists of NH4-exchanging the as-synthesized solids before removing the organic template. Calcination afterwards serves to combust the organic template and creates the Brønsted sites directly; thus applying merely a single thermal step. This method simplifies their activation and the material suffers less thermal stress. The approach was particularly effective for microcrystalline beta and ferrierite zeolites. Thorough investigation of the template content and materials' texture points out to three relevant effects that can explain the effective exchange process: partial removal of the template during exchange creates substantial microporosity (ferrierite), the remaining template is reorganized within the pores (ferrierite) and finally, void space can exist due to the non-perfect matching between the network and template (beta). This shorter method appears suited for microcrystalline zeolites; it was ineffective for crystalline MFI types. © 2013 Elsevier B.V. All rights reserved.

Direct sequence spread spectrum based PWM strategy for harmonic reduction and communication

Switched mode power supplies (SMPSs) are essential components in many applications, and electromagnetic interference is an important consideration in the SMPS design. Spread spectrum based PWM strategies have been used in SMPS designs to reduce the switching harmonics. This paper proposes a novel method to integrate a communication function into spread spectrum based PWM strategy without extra hardware costs. Direct sequence spread spectrum (DSSS) and phase shift keying (PSK) data modulation are employed to the PWM of the SMPS, so that it has reduced switching harmonics and the input and output power line voltage ripples contain data. A data demodulation algorithm has been developed for receivers, and code division multiple access (CDMA) concept is employed as communication method for a system with multiple SMPSs. The proposed method has been implemented in both Buck and Boost converters. The experimental results validated the proposed DSSS based PWM strategy for both harmonic reduction and communication.

The integration of ProxiMAX randomisation with CIS display for the production of novel peptides

Saturation mutagenesis is a powerful tool in modern protein engineering, which permits key residues within a protein to be targeted in order to potentially enhance specific functionalities. However, the creation of large libraries using conventional saturation mutagenesis with degenerate codons (NNN or NNK/S) has inherent redundancy and consequent disparities in codon representation. Therefore, both chemical (trinucleotide phosphoramidites) and biological methods (sequential, enzymatic single codon additions) of non-degenerate saturation mutagenesis have been developed in order to combat these issues and so improve library quality. Large libraries with multiple saturated positions can be limited by the method used to screen them. Although the traditional screening method of choice, cell-dependent methods, such as phage display, are limited by the need for transformation. A number of cell-free screening methods, such as CIS display, which link the screened phenotype with the encoded genotype, have the capability of screening libraries with up to 1014 members. This thesis describes the further development of ProxiMAX technology to reduce library codon bias and its integration with CIS display to screen the resulting library. Synthetic MAX oligonucleotides are ligated to an acceptor base sequence, amplified, and digested, subsequently adding a randomised codon to the acceptor, which forms an iterative cycle using the digested product of the previous cycle as the base sequence for the next. Initial use of ProxiMAX highlighted areas of the process where changes could be implemented in order to improve the codon representation in the final library. The refined process was used to construct a monomeric anti-NGF peptide library, based on two proprietary dimeric peptides (Isogenica) that bind NGF. The resulting library showed greatly improved codon representation that equated to a theoretical diversity of ~69%. The library was subsequently screened using CIS display and the discovered peptides assessed for NGF-TrkA inhibition by ELISA. Despite binding to TrkA, these peptides showed lower levels of inhibition of the NGF-TrkA interaction than the parental dimeric peptides, highlighting the importance of dimerization for inhibition of NGF-TrkA binding.

Spillovers from Foreign Direct Investment in Central and Eastern Europe. An index for measuring a country’s potential to benefit from technology spillovers

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implication both the investment strategies of multinationals and government FDI policies.

Measuring Benefits of Technology Spillovers from Foreign Direct Investment in Central and Eastern Europe

In this paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implications both for the investment strategies of multinationals and government FDI policies.

The integration by trade and FDI of emerging economies: The Asian example

The paper investigates the role of regionalization and regional identity in the endeavours of emerging economies to connect successfully to the global world economy. It addresses the question of whether the Association of Southeast Asian Nations (ASEAN), with its loose institutional integration framework, has contributed to the global integration of its very heterogenous members in the first decade of the 21st century – and, if so, what are the drivers behind this. The paper summarizes connecting theories, using a multidisciplinary approach, and uses descriptive statistical analysis to identify the achievements of the ASEAN-6 countries within global trade and foreign direct invesment (FDI) flows in the given time period. We suggest that ASEAN countries, with their efforts to initiate interconnecting regional organizations in Asia, most specifically the ASEAN+3 (APT) construction, did contribute to greater integratedness of member countries; and they have created a regional image with a common market and production base. Such achievements, however, can be in great part attributed to the micro-level activities of international and regional firms wishing to establish cross-border production networks in these countries.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Lattice Boltzmann method for flow and heat transfer in microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Exploring the integration of data mining and data visualization

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be effective, it is important to include the visualization techniques in the mining process and to generate the discovered patterns for a more comprehensive visual view. In this dissertation, four related problems: dimensionality reduction for visualizing high dimensional datasets, visualization-based clustering evaluation, interactive document mining, and multiple clusterings exploration are studied to explore the integration of data mining and data visualization. In particular, we 1) propose an efficient feature selection method (reliefF + mRMR) for preprocessing high dimensional datasets; 2) present DClusterE to integrate cluster validation with user interaction and provide rich visualization tools for users to examine document clustering results from multiple perspectives; 3) design two interactive document summarization systems to involve users efforts and generate customized summaries from 2D sentence layouts; and 4) propose a new framework which organizes the different input clusterings into a hierarchical tree structure and allows for interactive exploration of multiple clustering solutions.