New oil modified acrylic polymer for pH sensitive drug release:experimental results and statistical analysis

We report results of an experimental study, complemented by detailed statistical analysis of the experimental data, on the development of a more effective control method of drug delivery using a pH sensitive acrylic polymer. New copolymers based on acrylic acid and fatty acid are constructed from dodecyl castor oil and a tercopolymer based on methyl methacrylate, acrylic acid and acryl amide were prepared using this new approach. Water swelling characteristics of fatty acid, acrylic acid copolymer and tercopolymer respectively in acid and alkali solutions have been studied by a step-change method. The antibiotic drug cephalosporin and paracetamol have also been incorporated into the polymer blend through dissolution with the release of the antibiotic drug being evaluated in bacterial stain media and buffer solution. Our results show that the rate of release of paracetamol getss affected by the pH factor and also by the nature of polymer blend. Our experimental data have later been statistically analyzed to quantify the precise nature of polymer decay rates on the pH density of the relevant polymer solvents. The time evolution of the polymer decay rates indicate a marked transition from a linear to a strictly non-linear regime depending on the whether the chosen sample is a general copolymer (linear) or a tercopolymer (non-linear). Non-linear data extrapolation techniques have been used to make probabilistic predictions about the variation in weight percentages of retained polymers at all future times, thereby quantifying the degree of efficacy of the new method of drug delivery.

Solving a Direct Marketing Problem by Three Types of ARTMAP Neural Networks

An important task for a direct mailing company is to detect potential customers in order to avoid unnecessary and unwanted mailing. This paper describes a non-linear method to predict profiles of potential customers using dARTMAP, ARTMAP-IC, and Fuzzy ARTMAP neural networks. The paper discusses advantages of the proposed approaches over similar techniques based on MLP neural networks.

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 implications both the investment strategies of multinationals and government FDI policies.

Direct design of high channel-count fiber Bragg grating filters with low index modulation

A novel method for designing high channel-count fiber Bragg gratings (FBGs) is proposed. For the first time, tailored group delay is introduced into the target reflection spectra to obtain a more even distribution of the refractive index modulation. This approach results in the reduction of the maximum refractive index modulation to physically realizable levels. The maximum index modulation reduction factors are all greater than 5.5. This is a significant improvement compared with previously reported results. Numerical results show that the thus designed high channel-count FBG filters exhibit superior characteristics including 30 dB channel isolation, a flat-top and near 100% reflectivity in each channel. © 2012 Optical Society of America.

Polarization multiplexed 16QAM transmission employing modified digital back-propagation

We experimentally demonstrate performance enhancements enabled by weighted digital back propagation method for 28 Gbaud PM-16QAM transmission systems, over a 250 km ultra-large area fibre, using only one back-propagation step for the entire link, enabling up to 3 dB improvement in power tolerance with respect to linear compensation only. We observe that this is roughly the same improvement that can be obtained with the conventional, computationally heavy, non-weighted digital back propagation compensation with one step per span. As a further benchmark, we analyze performance improvement as a function of number of steps, and show that the performance improvement saturates at approximately 20 steps per span, at which a 5 dB improvement in power tolerance is obtained with respect to linear compensation only. Furthermore, we show that coarse-step self-phase modulation compensation is inefficient in wavelength division multiplexed transmission.

Estimating direct and indirect effects of foreign direct investment on firm productivity in the presence of interactions between firms

We implement a method to estimate the direct effects of foreign-ownership on foreign firms' productivity and the indirect effects (or spillovers) from the presence of foreign-owned firms on other foreign and domestic firms' productivity in a unifying framework, taking interactions between firms into account. To do so, we relax a fundamental assumption made in empirical studies examining a direct causal effect of foreign ownership on firm productivity, namely that of no interactions between firms. Based on our approach, we are able to combine direct and indirect effects of foreign ownership and calculate the total effect of foreign firms on local productivity. Our results show that all these effects vary with the level of foreign presence within a cluster, an important finding for the academic literature and policy debate on the benefits of attracting foreign owned firms.

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)

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.

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.

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. ^

Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.