Search results for: interpenetrating polymer network

Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Alvaro Aguinaga, Carlos Avila, Edgar Cando

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This article proposes a n-layer architecture, with a web client as a front-end, for the development of a virtual platform for process simulation on CNC machines. This Open-Source platform includes a CAD-CAM interface drawing primitives, and then used to furnish a CNC program that triggers a touch-screen virtual simulator. The objectives of this project are twofold. First one is an educational component that fosters new alternatives for the CAD-CAM/CNC learning process in undergrad and grade schools and technical and technological institutes emphasizing in the development of critical skills, discussion and collaborative work. The second objective puts together a research and technological component that will take the state of the art in CAD-CAM integration to a new level with the development of optimal algorithms and virtual platforms, on-line availability, that will pave the way for the long-term goal of this project, that is, to have a visible and active graduate school in Ecuador and a world wide Open-Innovation community in the area of CAD-CAM integration and operation of CNC machinery. The virtual platform, developed as a part of this study: (1) delivers improved training process of students, (2) creates a multidisciplinary team and a collaborative work space that will push the new generation of students to face future technological challenges, (3) implements industry standards for CAD/CAM, (4) presents a platform for the development of industrial applications. A protoype of this system was developed and implemented in a network of universities and technological institutes in Ecuador.

Keywords: CAD-CAM integration, virtual platforms, CNC machines, multi-layer based architecture

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Authors: Mariia Zhuldybina, Ahmed Moulay, Mirko Torres, Mike Rozel, Ngoc-Duc Trinh, Chloé Bois

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To reduce the global waste of perishable goods, a solution for monitoring and traceability of their environmental conditions is needed. Temperature is the most controllable environmental parameter determining the kinetics of physical, chemical, and microbial spoilage in food products. To store the time-temperature information, time-temperature indicator (TTI) is a promising solution. Printed electronics (PE) has shown a great potential to produce customized electronic devices using flexible substrates and inks with different functionalities. We propose to fabricate a hybrid printed TTI using environmentally friendly materials. The real-time TTI profile can be stored and transmitted to the smartphone via Near Field Communication (NFC). To ensure environmental performance, Canadian Green Electronics NSERC Network is developing green materials for the ink formulation with different functionalities. In terms of substrate, paper-based electronics has gained the great interest for utilization in a wide area of electronic systems because of their low costs in setup and methodology, as well as their eco-friendly fabrication technologies. The main objective is to deliver a prototype of TTI using small-scale printed techniques under typical printing conditions. All sub-components of the smart labels, including a memristor, a battery, an antenna compatible with NFC protocol, and a circuit compatible with integration performed by an offsite supplier will be fully printed with flexography or flat-bed screen printing.

Keywords: NFC, printed electronics, time-temperature indicator, hybrid electronics

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Authors: Helen Pushkarskaya, David Tolin, Lital Ruderman, Ariel Kirshenbaum, J. MacLaren Kelly, Christopher Pittenger, Ifat Levy

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Obsessive-Compulsive Disorder (OCD) produces profound morbidity. Difficulties with decision making and intolerance of uncertainty are prominent clinical features of OCD. The nature and etiology of these deficits are poorly understood. We used a well-validated choice task, grounded in behavioral economic theory, to investigate differences in valuation and value-based choice during decision making under uncertainty in 20 unmedicated participants with OCD and 20 matched healthy controls. Participants’ choices were used to assess individual decision-making characteristics. Compared to controls, individuals with OCD were less consistent in their choices and less able to identify options that were unambiguously preferable. These differences correlated with symptom severity. OCD participants did not differ from controls in how they valued uncertain options when outcome probabilities were known (risk) but were more likely than controls to avoid uncertain options when these probabilities were imprecisely specified (ambiguity). These results suggest that the underlying neural mechanisms of valuation and value-based choices during decision-making are abnormal in OCD. Individuals with OCD show elevated intolerance of uncertainty, but only when outcome probabilities are themselves uncertain. Future research focused on the neural valuation network, which is implicated in value-based computations, may provide new neurocognitive insights into the pathophysiology of OCD. Deficits in decision-making processes may represent a target for therapeutic intervention.

Keywords: obsessive compulsive disorder, decision-making, uncertainty intolerance, risk aversion, ambiguity aversion, valuation

Procedia PDF Downloads 588

Authors: M. Bortolini, E. Ferrari, F. G. Galizia, C. Mora

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In the last decades, an increasing set of companies adopted lean philosophy to improve their productivity and efficiency promoting the so-called continuous improvement concept, reducing waste of time and cutting off no-value added activities. In parallel, increasing attention rises toward green practice and management through the spread of the green supply chain pattern, to minimise landfilled waste, drained wastewater and pollutant emissions. Starting from a review on contributions deepening lean and green principles applied to supply chain management, the most relevant drivers to measure the performance of industrial processes are pointed out. Specific attention is paid on the role of cost because it is of key importance and it crosses both lean and green principles. This analysis leads to figure out an original reference framework for integrating lean and green principles in designing and managing supply chains. The proposed framework supports the application, to the whole value chain or to parts of it, e.g. distribution network, assembly system, job-shop, storage system etc., of the lean-green integrated perspective. Evidences show that the combination of the lean and green practices lead to great results, higher than the sum of the performances from their separate application. Lean thinking has beneficial effects on green practices and, at the same time, methods allowing environmental savings generate positive effects on time reduction and process quality increase.

Keywords: environmental sustainability, green supply chain, integrated framework, lean thinking, supply chain management

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Ana Neves

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The European Union’s multi-level constitutionalism is grounded in an intricate network of vertical and horizontal legal relationships among different levels and types of public authorities. In a very significant way since the 2008 crisis, evolving institutional arrangements and institutional dynamics in the European Union have been progressively impacting Member States and the terms under which national public authorities are organised, interact and exercise their powers. This impact occurs in both macro and micro dimensions. Several examples are relevant here, such as the involvement of national Parliaments in the activities of the European Union, the enhanced integration of public administrations, the side effects of the Council framework decision on the European Arrest Warrant, the European Union Justice Scoreboard, the protection of whistle-blowers regulation, the enhanced cooperation on the establishment of the European Public Prosecutor’s Office, the regime for the protection of the Union budget and the European Rule of Law Mechanism. A common trend or denominator underlies the deepening of institutional interdependence and the increased interactions between the European Union, Member States, and public authorities at different levels. This seems to be conditionality as a general principle. The European multi-level constitutionalism must be considered in the light of this conditionality principle, which does not “imply a relationship of command and obedience”. Nevertheless, it might be more effective or be a very compelling principle. It is as if the extension of the shared rule is being accompanied by a contrapuntal dialogue. The different public authorities at various levels are being called to rethink and readjust themselves within a broader and more plural framework concerning understanding the limitation of power.

Keywords: european union -, multi-level hierarchy, conditionality, separation of powers

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Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

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Authors: Po-Fang Hsu, Chiching Wei

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In this paper, we present a novel neural graph matching approach applied to document comparison. Document comparison is a common task in the legal and financial industries. In some cases, the most important differences may be the addition or omission of words, sentences, clauses, or paragraphs. However, it is a challenging task without recording or tracing the whole edited process. Under many temporal uncertainties, we explore the potentiality of our approach to proximate the accurate comparison to make sure which element blocks have a relation of edition with others. In the beginning, we apply a document layout analysis that combines traditional and modern technics to segment layouts in blocks of various types appropriately. Then we transform this issue into a problem of layout graph matching with textual awareness. Regarding graph matching, it is a long-studied problem with a broad range of applications. However, different from previous works focusing on visual images or structural layout, we also bring textual features into our model for adapting this domain. Specifically, based on the electronic document, we introduce an encoder to deal with the visual presentation decoding from PDF. Additionally, because the modifications can cause the inconsistency of document layout analysis between modified documents and the blocks can be merged and split, Sinkhorn divergence is adopted in our neural graph approach, which tries to overcome both these issues with many-to-many block matching. We demonstrate this on two categories of layouts, as follows., legal agreement and scientific articles, collected from our real-case datasets.

Keywords: document comparison, graph matching, graph neural network, modification similarity, multi-modal

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Authors: Ahmed Hosny, Haroon Elshaikh, Gaber Hassib, Yassin Ali

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The Moho discontinuity undulations beneath the southern part of Egypt have been defined using the seismic waves generated by tele earthquakes. These earthquakes have been recorded by the Aswan seismic network, which consists of 10 seismic stations established around the lake of Nasser. An additional seismic station was located towards the east of the Lake of Nasser by about ~ 150 km. Receiver functions and H-k stacking methods were used for obtaining the depths of Moho discontinuity and the Vp/Vs ratios beneath each seismic station. Our results revealed that, the depths of Moho discontinuity beneath the stations located around the Lake of Nasser range from 36 to 39 km, with an average value of 37.5 km. These results are consistent with the previous works done on the same area. The obtained Vp/Vs ratios for the crust of this area were ranged from 1.73 to 1.86, with an average value of 1.79. While beneath the station located towards the east, the Moho discontinuity was detected at a shallowest depth of 27 km and the Vp/Vs ratio was 1.82. The difference in the Moho depths beneath the stations located around the Lake of Nasser and the station located to the east revealed the boundary position between the Saharan Metacraton to the west and the Nubian-Arabian Shield to the east. This structural boundary delineates the position of the old collision of the Oceanic crust of the Nubian-Arabian Shield to the east with the Continental crust of the Saharan Metacraton to the west.

Keywords: Moho undulations, south of Egypt, seismic waves, earthquakes

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Authors: Muhammad Bilal, Zhongfeng Qiu

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Aerosols, suspended particles in the atmosphere, play an important role in the earth energy budget, climate change, degradation of atmospheric visibility, urban air quality, and human health. To fully understand aerosol effects, retrieval of aerosol optical properties such as aerosol optical depth (AOD) at high spatiotemporal resolution is required. Therefore, in the present study, hourly AOD observations at 500 m resolution were retrieved from the geostationary ocean color imager (GOCI) using the simplified aerosol retrieval algorithm (SARA) over the urban area of Beijing for the year 2016. The SARA requires top-of-the-atmosphere (TOA) reflectance, solar and sensor geometry information and surface reflectance observations to retrieve an accurate AOD. For validation of the GOCI retrieved AOD, AOD measurements were obtained from the aerosol robotic network (AERONET) version 3 level 2.0 (cloud-screened and quality assured) data. The errors and uncertainties were reported using the root mean square error (RMSE), relative percent mean error (RPME), and the expected error (EE = ± (0.05 + 0.15AOD). Results showed that the high spatiotemporal GOCI AOD observations were well correlated with the AERONET AOD measurements with a correlation coefficient (R) of 0.92, RMSE of 0.07, and RPME of 5%, and 90% of the observations were within the EE. The results suggested that the SARA is robust and has the ability to retrieve high-resolution spatiotemporal AOD observations over the urban area using the geostationary satellite.

Keywords: AEORNET, AOD, SARA, GOCI, Beijing

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Authors: Alireza Ansariyar, Safieh Laaly

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Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates CAVs’ fuel consumption and air pollutants (C.O., PM, and NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models

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Authors: Siti Khadijah Che Osmi, Mohammed Ahmad Syed

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Underground structures are crucial components which required detailed analysis and design. Tunnels, for instance, are massively constructed as transportation infrastructures and utilities network especially in urban environments. Considering their prime importance to the economy and public safety that cannot be compromised, thus any instability to these tunnels will be highly detrimental to their performance. Recent experience suggests that tunnels become vulnerable during earthquakes and blast scenarios. However, a very limited amount of studies has been carried out to study and understanding the dynamic response and performance of underground tunnels under those unpredictable extreme hazards. In view of the importance of enhancing the resilience of these structures, the overall aims of the study are to evaluate probabilistic future performance of shallow tunnels subjected to seismic and blast loads by developing detailed fragility analysis. Critical non-linear time history numerical analyses using sophisticated finite element software Midas GTS NX have been presented about the current methods of analysis, taking into consideration of structural typology, ground motion and explosive characteristics, effect of soil conditions and other associated uncertainties on the tunnel integrity which may ultimately lead to the catastrophic failure of the structures. The proposed fragility curves for both extreme loadings are discussed and compared which provide significant information the performance of the tunnel under extreme hazards which may beneficial for future risk assessment and loss estimation.

Keywords: fragility analysis, seismic loads, shallow tunnels, blast loads

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Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

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Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

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Authors: Jyoti Sahu, Vinay A. Juvekar

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Electrolytes are often used to bring about salting-in and salting-out of organic molecules and polymers (e.g. polyethylene glycols/proteins) from the aqueous solutions. For quantification of these phenomena, a thermodynamic model which can accurately predict activity coefficient of electrolyte as a function of temperature is needed. The thermodynamics models available in the literature contain a large number of empirical parameters. These parameters are estimated using lower/upper critical solution temperature of the solution in the electrolyte/organic molecule at different temperatures. Since the number of parameters is large, inaccuracy can bethe creep in during their estimation, which can affect the reliability of prediction beyond the range in which these parameters are estimated. Cloud point of solution is related to its free energy through temperature and composition derivative. Hence, the Cloud point measurement can be used for accurate estimation of the temperature and composition dependence of parameters in the model for free energy. Hence, if we use a two pronged procedure in which we first use cloud point of solution to estimate some of the parameters of the thermodynamic model and determine the rest using osmotic coefficient data, we gain on two counts. First, since the parameters, estimated in each of the two steps, are fewer, we achieve higher accuracy of estimation. The second and more important gain is that the resulting model parameters are more sensitive to temperature. This is crucial when we wish to use the model outside temperatures window within which the parameter estimation is sought. The focus of the present work is to prove this proposition. We have used electrolyte (NaCl/Na2CO3)-water-organic molecule (Iso-propanol/ethanol) as the model system. The model of Robinson-Stokes-Glukauf is modified by incorporating the temperature dependent Flory-Huggins interaction parameters. The Helmholtz free energy expression contains, in addition to electrostatic and translational entropic contributions, three Flory-Huggins pairwise interaction contributions viz., and (w-water, p-polymer, s-salt). These parameters depend both on temperature and concentrations. The concentration dependence is expressed in the form of a quadratic expression involving the volume fractions of the interacting species. The temperature dependence is expressed in the form .To obtain the temperature-dependent interaction parameters for organic molecule-water and electrolyte-water systems, Critical solution temperature of electrolyte -water-organic molecules is measured using cloud point measuring apparatus The temperature and composition dependent interaction parameters for electrolyte-water-organic molecule are estimated through measurement of cloud point of solution. The model is used to estimate critical solution temperature (CST) of electrolyte water-organic molecules solution. We have experimentally determined the critical solution temperature of different compositions of electrolyte-water-organic molecule solution and compared the results with the estimates based on our model. The two sets of values show good agreement. On the other hand when only osmotic coefficients are used for estimation of the free energy model, CST predicted using the resulting model show poor agreement with the experiments. Thus, the importance of the CST data in the estimation of parameters of the thermodynamic model is confirmed through this work.

Keywords: concentrated electrolytes, Debye-Hückel theory, interaction parameters, Robinson-Stokes-Glueckauf model, Flory-Huggins model, critical solution temperature

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Authors: Jenny Radeva, Anke-Gundula Roth, Christian Goebbert, Robert Niestroj-Pahl, Lars Daehne, Axel Wolfram, Juergen Wiese

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The technological advantages of ceramic filtration elements were combined with polyelectrolyte films in the development process of hybrid membrane for the elimination of pharmaceuticals from Aquarius solutions. Previously extruded alumina ceramic membranes were coated with nanosized polyelectrolyte films using Layer-by-Layer technology. The polyelectrolyte chains form a network with nano-pores on the ceramic surface and promote the retention of small molecules like pharmaceuticals and microplastics, which cannot be eliminated using standard ultrafiltration methods. Additionally, the polyelectrolyte coat contributes with its adjustable (based on application) Zeta Potential for repulsion of contaminant molecules with opposite charges. Properties like permeability, bubble point, pore size distribution and Zeta Potential of ceramic and hybrid membranes were characterized using various laboratory and pilot tests and compared with each other. The most significant role for the membrane characterization played the filtration behavior investigation, during which retention against widely used pharmaceuticals like Diclofenac, Ibuprofen and Sulfamethoxazol was subjected to series of filtration tests. The presented study offers a new perspective on nanosized molecules removal from aqueous solutions and shows the importance of combined techniques application for the elimination of pharmaceutical contaminants from drinking water.

Keywords: water treatment, hybrid membranes, layer-by-layer coating, filtration, polyelectrolytes

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Authors: S. B. Q. Tran

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Drop condensation is the phenomenon that the tiny drops form when the oversaturated vapour present in the environment condenses on a substrate and makes the droplet growth. Recently, this subject has received much attention due to its applications in many fields such as thin film growth, heat transfer, recovery of atmospheric water and polymer templating. In literature, many papers investigated theoretically and experimentally in macro droplet growth with the size of millimeter scale of radius. However few papers about nanodroplet condensation are found in the literature especially theoretical work. In order to understand the droplet growth in nanoscale, we perform the numerical simulation work to study nanodroplet growth. We investigate and discuss the role of the droplet shape and monomer diffusion on drop growth and their effect on growth law. The effect of droplet shape is studied by doing parametric studies of contact angle and disjoining pressure magnitude. Besides, the effect of pinning and de-pinning behaviours is also studied. We investigate the axisymmetric homogeneous growth of 10–100 nm single water nanodroplet on a substrate surface. The main mechanism of droplet growth is attributed to the accumulation of laterally diffusing water monomers, formed by the absorption of water vapour in the environment onto the substrate. Under assumptions of quasi-steady thermodynamic equilibrium, the nanodroplet evolves according to the augmented Young–Laplace equation. Using continuum theory, we model the dynamics of nanodroplet growth including the coupled effects of disjoining pressure, contact angle and monomer diffusion with the assumption of constant flux of water monomers at the far field. The simulation result is validated by comparing with the published experimental result. For the case of nanodroplet growth with constant contact angle, our numerical results show that the initial droplet growth is transient by monomer diffusion. When the flux at the far field is small, at the beginning, the droplet grows by the diffusion of initially available water monomers on the substrate and after that by the flux at the far field. In the steady late growth rate of droplet radius and droplet height follow a power law of 1/3, which is unaffected by the substrate disjoining pressure and contact angle. However, it is found that the droplet grows faster in radial direction than high direction when disjoining pressure and contact angle increase. The simulation also shows the information of computational domain effect in the transient growth period. When the computational domain size is larger, the mass coming in the free substrate domain is higher. So the mass coming in the droplet is also higher. The droplet grows and reaches the steady state faster. For the case of pinning and de-pinning droplet growth, the simulation shows that the disjoining pressure does not affect the droplet radius growth law 1/3 in steady state. However the disjoining pressure modifies the growth rate of the droplet height, which then follows a power law of 1/4. We demonstrate how spatial depletion of monomers could lead to a growth arrest of the nanodroplet, as observed experimentally.

Keywords: augmented young-laplace equation, contact angle, disjoining pressure, nanodroplet growth

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Authors: Yousuf Nasser Al Khamisi

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Purpose: This paper presents a Knowledge Transfer (KT) Framework in healthcare sectors by applying a systematic literature review process to the healthcare organizations domain to identify enablers and barriers of KT in Healthcare. Methods: The paper conducted a systematic literature search of peer-reviewed papers that described key elements of KT using four databases (Medline, Cinahl, Scopus, and Proquest) for a 10-year period (1/1/2008–16/10/2017). The results of the literature review were used to build a conceptual framework of KT in healthcare organizations. The author used a systematic review of the literature, as described by Barbara Kitchenham in Procedures for Performing Systematic Reviews. Findings: The paper highlighted the impacts of using Knowledge Management (KM) concept at a healthcare organization in controlling infectious diseases in hospitals, improving family medicine performance and enhancing quality improvement practices. Moreover, it found that good-coding performance is analytically linked with a knowledge sharing network structure rich in brokerage and hierarchy rather than in density. The unavailability or ignored of the latest evidence on more cost-effective or more efficient delivery approaches leads to increase the healthcare costs and may lead to unintended results. Originality: Search procedure produced 12,093 results, of which 3523 were general articles about KM and KT. The titles and abstracts of these articles had been screened to segregate what is related and what is not. 94 articles identified by the researchers for full-text assessment. The total number of eligible articles after removing un-related articles was 22 articles.

Keywords: healthcare organisation, knowledge management, knowledge transfer, KT framework

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Authors: S. Kudlacik-Kramarczyk, M. Kedzierska, B. Tyliszczak

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Recently, the continuous development of medicine and related sciences has been observed. Particular emphasis is directed on the development of biomaterials, i.e., non-toxic, biocompatible and biodegradable materials that may improve the effectiveness of treatment as well as the comfort of patients. This is particularly important in the case of cancer treatment. Currently, there are many methods of cancer treatment based primarily on chemotherapy and the surgical removal of the tumor, but it is worth noting that these therapies also cause many side effects. Among women, the most common cancer is breast cancer. It may be completely cured, but the consequence of treatment is partial or complete breast mastectomy and radiation therapy, which results in severe skin burns. The skin of the patient after radiation therapy is very burned, and therefore requires intensive care and high frequency of dressing changes. The traditional dressing adheres to the burn wounds and does not absorb adequate amount of exudate from injuries and the patient is forced to change the dressing every 2 hours. Therefore, the main purpose was to develop an innovative combination of dressing material with drug carriers that may be used in anti-cancer therapy. The innovation of this solution is the combination of these two products into one system, i.e., a transdermal system with the possibility of a controlled release of the drug- cytostatic. Besides, the possibility of modifying the hydrogel matrix with aloe vera juice provides this material with new features favorable from the point of view of healing processes of burn wounds resulting from the radiation therapy. In this study, hydrogel materials containing protein spheres with the active substance have been obtained as a result of photopolymerization process. The reaction mixture consisting of the protein (albumin) spheres incorporated with cytostatic, chitosan, adequate crosslinking agent and photoinitiator has been subjected to the UV radiation for 2 minutes. Prepared materials have been subjected to the numerous studies including the analysis of cytotoxicity using murine fibroblasts L929. Analysis was conducted based on the mitochondrial activity test (MTT reduction assay) which involves the determining the number of cells characterized by proper metabolism. Hydrogel materials obtained using different amount of crosslinking agents have been subjected to the cytotoxicity analysis. According to the standards, tested material is defined as cytotoxic when the viability of cells after 24 h incubation with this material is lower than 70%. In the research, hydrogel polymer materials containing protein spheres incorporated with the active substance, i.e. a cytostatic, have been developed. Such a dressing may support the treatment of cancer due to the content of the anti-cancer drug - cytostatic, and may also provide a soothing effect on the healing of the burn wounds resulted from the radiation therapy due to the content of aloe vera juice in the hydrogel matrix. Based on the conducted cytotoxicity studies, it may be concluded that the obtained materials do not adversely affect the tested cell lines, therefore they can be subjected to more advanced analyzes.

Keywords: hydrogel polymers, cytostatics, drug carriers, cytotoxicity

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Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

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Authors: Kimmo J. Kurppa

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This paper describes the development of the buyer-supplier dyad concept over the years and proposes improvements, clarifications and extensions to the prevailing definitions published in 1970’s and 1980’s. This paper suggests a partition of the buyer-supplier dyad to concepts of Commercial Dyad (dyadic interaction in vertical relationships) and Innovative Dyad (dyadic interaction in horizontal relationship) since dyadic interaction takes place in two major types of contexts between industrial firms. Especially the context of joint product development in a dyadic relationship has not been adequately recognized being totally different from the interaction taking place in commercial buyer-supplier interaction. This paper provides therefore a solution to the existing gap in research by clarifying the descriptions and the context where dyadic interaction takes place between industrial firms. This paper also illustrates and explains how the firm’s organization and the interaction taking place inside it, is connected to the dyadic interaction structure between the firm and its partner firm. This theme has been discussed earlier but the phenomenon has not been adequately described and has not been illustrated in earlier research. This conceptual study has been interested in how the dyad concept of Industrial Marketing has been defined in the earlier research and how the definition could be improved. This conceptual paper has been constructed by using the systematic review methodology and proposes avenues for future research. The concept and existence of relationship and interaction between firm’s internal interaction network and external interaction between firm’s dyadic counterparts, need to be verified through empirical research.

Keywords: dyadic interaction, industrial dyad, buyer-supplier relationship, strategic reciprocity, experience, socially adjusted opportunism

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Authors: Houria Rezala, Jose Luis Valverde, Amaya Romero, Alessandra Molinari, Andrea Maldotti

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A pillared montmorillonite containing iron doped titania (Fe/Ti-PILC) has been prepared from a natural clay. This material has been characterized by X-ray diffraction, nitrogen adsorption, temperature programmed desorption of ammonia, inductively coupled plasma atomic emission spectroscopy, atomic absorption, and diffuse reflectance UV-VIS spectroscopy. The layer structure of Fe/Ti-PILC resulted to be ordered with an insertion of pillars, which caused a slight increase in the basal spacing of the clay. Its specific surface area was about three times larger than that of the parent Na-montmorillonite due principally to the creation of a remarkable microporous network. The doped material was a robust photocatalyst able to oxidize liquid alkyl aromatics to the corresponding carbonylic derivatives, using O2 as the oxidizing species, at mild pressure and temperature conditions. Accumulation of valuable carbonylic derivatives was possible since their over-oxidation to carbon dioxide was negligible. Fe/Ti-PILC was able to discriminate between toluene and cyclohexane in favor of the aromatic compound with an efficiency that is about three times higher than that of titanium pillared clays (Ti-PILC). It is likely that the addition of iron favored the formation of new acid sites able to interact with the aromatic substrate. Iron doping caused a significant TiO2 visible light-induced activity (wavelength > 400 nm) with only minor negative effects on its performance under UV-light irradiation (wavelength > 290 nm).

Keywords: alkyl aromatics oxidation, heterogeneous photocatalysis, iron doping, pillared clays

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Authors: Koutsopoulou Ioanna, Gkintoni Evgenia, Halkiopoulos Constantinos, Antonopoulou Hera

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Recent advances in Internet Computer Technology (ICT) and the ever-increasing use of technological equipment amongst adolescents and young adults along with unattended access to the internet and social media and uncontrolled use of smart phones and PCs have caused social problems like sexting to emerge. The main purpose of the present article is first to present an analytic theoretical framework of sexting as a recent social phenomenon based on studies that have been conducted the last decade or so; and second to investigate Greek students’ and also social network users, sexting perceptions and to record how often social media users exchange sexual messages and to retrace demographic variables predictors. Data from 1,000 students were collected and analyzed and all statistical analysis was done by the software package WEKA. The results indicate among others, that the use of data mining methods is an important tool to draw conclusions that could affect decision and policy making especially in the field and related social topics of educational psychology. To sum up, sexting lurks many risks for adolescents and young adults students in Greece and needs to be better addressed in relevance to the stakeholders as well as society in general. Furthermore, policy makers, legislation makers and authorities will have to take action to protect minors. Prevention strategies based on Greek cultural specificities are being proposed. This social problem has raised concerns in recent years and will most likely escalate concerns in global communities in the future.

Keywords: educational ethics, sexting, Greek sexters, sex education, data mining

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Authors: Reuben Newell, Edward Archer, Alistair McIlhagger, Calvin Ralph

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Modern industry has developed a need for innovative 3D composite materials due to their attractive material properties. Composite materials are composed of a fibre reinforcement encased in a polymer matrix. The fibre reinforcement consists of warp, weft and binder yarns or tows woven together into a preform. The mechanical performance of composite material is largely controlled by the properties of the preform. As a result, the bulk of recent textile research has been focused on the design of high-strength preform architectures. Studies looking at optimisation of the weaving process have largely been neglected. It has been reported that yarns experience varying levels of damage during weaving, resulting in filament breakage and ultimately compromised composite mechanical performance. The weaving parameters involved in causing this yarn damage are not fully understood. Recent studies indicate that poor yarn tension control may be an influencing factor. As tension is increased, the yarn-to-yarn and yarn-to-weaving-equipment interactions are heightened, maximising damage. The correlation between yarn tension variation and weaving damage severity has never been adequately researched or quantified. A novel study is needed which accesses the influence of tension variation on the mechanical properties of woven yarns. This study has looked to quantify the variation of yarn tension throughout weaving and sought to link the impact of tension to weaving damage. Multiple yarns were randomly selected, and their tension was measured across the creel and shedding stages of weaving, using a hand-held tension meter. Sections of the same yarn were subsequently cut from the loom machine and tensile tested. A comparison study was made between the tensile strength of pristine and tensioned yarns to determine the induced weaving damage. Yarns from bobbins at the rear of the creel were under the least amount of tension (0.5-2.0N) compared to yarns positioned at the front of the creel (1.5-3.5N). This increase in tension has been linked to the sharp turn in the yarn path between bobbins at the front of the creel and creel I-board. Creel yarns under the lower tension suffered a 3% loss of tensile strength, compared to 7% for the greater tensioned yarns. During shedding, the tension on the yarns was higher than in the creel. The upper shed yarns were exposed to a decreased tension (3.0-4.5N) compared to the lower shed yarns (4.0-5.5N). Shed yarns under the lower tension suffered a 10% loss of tensile strength, compared to 14% for the greater tensioned yarns. Interestingly, the most severely damaged yarn was exposed to both the largest creel and shedding tensions. This study confirms for the first time that yarns under a greater level of tension suffer an increased amount of weaving damage. Significant variation of yarn tension has been identified across the creel and shedding stages of weaving. This leads to a variance of mechanical properties across the woven preform and ultimately the final composite part. The outcome from this study highlights the need for optimised yarn tension control during preform manufacture to minimize yarn-induced weaving damage.

Keywords: optimisation of preform manufacture, tensile testing of damaged tows, variation of yarn weaving tension, weaving damage

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Authors: Maedeh Pourmajidian, Joseph R. McDermid

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Advanced High Strength Steels are revolutionizing both the steel and automotive industries due to their high specific strength and ability to absorb energy during crash events. This allows manufacturers to design vehicles with significantly increased fuel efficiency without compromising passenger safety. To maintain the structural integrity of the fabricated parts, they must be protected from corrosion damage through continuous hot-dip galvanizing process, which is challenging due to selective oxidation of Mn and Si on the surface of this AHSSs. The effects of process atmosphere oxygen partial pressure and small additions of Sn on the selective oxidation of a medium-Mn C-6Mn-2Si advanced high strength steel was investigated. Intercritical annealing heat treatments were carried out at 690˚C in an N2-5%H2 process atmosphere under dew points ranging from –50˚C to +5˚C. Surface oxide chemistries, morphologies, and thicknesses were determined at a variety of length scales by several techniques, including SEM, TEM+EELS, and XPS. TEM observations of the sample cross-sections revealed the transition to internal oxidation at the +5˚C dew point. EELS results suggested that the internal oxides network was composed of a multi-layer oxide structure with varying chemistry from oxide core towards the outer part. The combined effect of employing a known surface active element as a function of process atmosphere on the surface structure development and the possible impact on reactive wetting of the steel substrates by the continuous galvanizing zinc bath will be discussed.

Keywords: 3G AHSS, hot-dip galvanizing, oxygen partial pressure, selective oxidation

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Authors: Lei Zhu, Nan Li

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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

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Authors: Aderonke Oluseun Akinwumi, Olusola A. Komolaf

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This paper gives information about the wide array of challenges facing both the electric utilities and consumers in the distribution system in developing countries, using Obafemi Awolowo University, Ile-Ife Nigeria as a case study. It also proffers cost-effective solution through remote monitoring, diagnostic and operation of distribution networks without compromising the system reliability. As utilities move from manned and unintelligent networks to completely unmanned smart grids, switching activities at substations and feeders will be managed and controlled remotely by dedicated systems hence this design. The Substation Remote Diagnostic and Operation Solution (sRDOs) would remotely monitor the load on Medium Voltage (MV) and Low Voltage (LV) feeders as well as distribution transformers and allow the utility disconnect non-paying customers with absolutely no extra resource deployment and without interrupting supply to paying customers. The aftermath of the implementation of this design improved the lifetime of key distribution infrastructure by automatically isolating feeders during overload conditions and more importantly erring consumers. This increased the ratio of revenue generated on electricity bills to total network load.

Keywords: electric utility, consumers, remote monitoring, diagnostic, system reliability, manned and unintelligent networks, unmanned smart grids, switching activities, medium voltage, low voltage, distribution transformer

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Authors: Muhammad Umair, Yasir Nawab, Khubab Shaker, Muhammad Maqsood, Adeel Zulfiqar, Danish Mahmood Baitab

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Fabric is a flexible woven material consisting of a network of natural or artificial fibers often referred to as thread or yarn. Today fabrics are produced by weaving, braiding, knitting, tufting and non-woven. Weaving is a method of fabric production in which warp and weft yarns are interlaced perpendicular to each other. There is infinite number of ways for the interlacing of warp and weft yarn. Each way produces a different fabric structure. The yarns parallel to the machine direction are called warp yarns and the yarns perpendicular to the machine direction are called weft or filling yarns. Air jet weaving is the modern method of weft insertion and considered as high speed loom. The twist loss in air jet during weft insertion affects the strength. The aim of this study was to investigate the effect of twist change in weft yarn during air-jet weft insertion. A total number of 8 samples were produced using 1/1 plain and 3/1 twill weave design with two fabric widths having same loom settings. Two different types of yarns like cotton and PC blend were used. The effect of material type, weave design and fabric width on twist change of weft yarn was measured and discussed. Twist change in the different types of weft yarn and weave design was measured and compared the twist change in the weft yarn with the yarn before weft yarn insertion and twist loss is measured. Wider fabric leads to higher twist loss in the yarn.

Keywords: air jet loom, twist per inch, twist loss, weft yarn

Procedia PDF Downloads 377

Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Bhupendra G. Prajapati, Alpesh R. Patel

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The aim of this research work is to develop sustained release multi-particulates dosage form of Dexketoprofen trometamol, which is the pharmacologically active isomer of ketoprofen. The objective is to utilization of active enantiomer with minimal dose and administration frequency, extended release multi-particulates dosage form development for better patience compliance was explored. Drug loaded and sustained release coated pellets were prepared by fluidized bed coating principle by wurster coater. Microcrystalline cellulose as core pellets, povidone as binder and talc as anti-tacking agents were selected during drug loading while Kollicoat SR 30D as sustained release polymer, triethyl citrate as plasticizer and micronized talc as an anti-adherent were used in sustained release coating. Binder optimization trial in drug loading showed that there was increase in process efficiency with increase in the binder concentration. 5 and 7.5%w/w concentration of Povidone K30 with respect to drug amount gave more than 90% process efficiency while higher amount of rejects (agglomerates) were observed for drug layering trial batch taken with 7.5% binder. So for drug loading, optimum Povidone concentration was selected as 5% of drug substance quantity since this trial had good process feasibility and good adhesion of the drug onto the MCC pellets. 2% w/w concentration of talc with respect to total drug layering solid mass shows better anti-tacking property to remove unnecessary static charge as well as agglomeration generation during spraying process. Optimized drug loaded pellets were coated for sustained release coating from 16 to 28% w/w coating to get desired drug release profile and results suggested that 22% w/w coating weight gain is necessary to get the required drug release profile. Three critical process parameters of Wurster coating for sustained release were further statistically optimized for desired quality target product profile attributes like agglomerates formation, process efficiency, and drug release profile using central composite design (CCD) by Minitab software. Results show that derived design space consisting 1.0 to 1.2 bar atomization air pressure, 7.8 to 10.0 gm/min spray rate and 29-34°C product bed temperature gave pre-defined drug product quality attributes. Scanning Image microscopy study results were also dictate that optimized batch pellets had very narrow particle size distribution and smooth surface which were ideal properties for reproducible drug release profile. The study also focused on optimized dexketoprofen trometamol pellets formulation retain its quality attributes while administering with common vehicle, a liquid (water) or semisolid food (apple sauce). Conclusion: Sustained release multi-particulates were successfully developed for dexketoprofen trometamol which may be useful to improve acceptability and palatability of a dosage form for better patient compliance.

Keywords: dexketoprofen trometamol, pellets, fluid bed technology, central composite design

Procedia PDF Downloads 112