Search results for: optimal reaction network

Authors: Natan C. G. Silva, Carlos A. C. Girao Neto, Marcele M. S. Vasconcelos, Luciana R. B. Goncalves, Maria Valderez P. Rocha

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Galactooligosaccharides (GOS) are sugars with prebiotic function that can be synthesized chemically or enzymatically, and this last one can be promoted by the action of β-galactosidases. In addition to favoring the transgalactosylation reaction to form GOS, these enzymes can also catalyze the hydrolysis of lactose. A highly studied type of GOS is lactulose because it presents therapeutic properties and is a health promoter. Among the different raw materials that can be used to produce lactulose, whey stands out as the main by-product of cheese manufacturing, and its discarded is harmful to the environment due to the residual lactose present. Therefore, its use is a promising alternative to solve this environmental problem. Thus, lactose from whey is hydrolyzed into glucose and galactose by β-galactosidases. However, in order to favor the transgalactosylation reaction, the medium must contain fructose, due this sugar reacts with galactose to produce lactulose. Then, the glucose-isomerase enzyme can be used for this purpose, since it promotes the isomerization of glucose into fructose. In this scenario, the aim of the present work was first to develop β-galactosidase biocatalysts of Kluyveromyces lactis and to apply it in the integrated reactions of hydrolysis, isomerization (with the glucose-isomerase from Streptomyces murinus) and transgalactosylation reaction, using whey as a substrate. The immobilization of β-galactosidase in chitosan previously functionalized with 0.8% glutaraldehyde was evaluated using different enzymatic loads (2, 5, 7, 10, and 12 mg/g). Subsequently, the hydrolysis and transgalactosylation reactions were studied and conducted at 50°C, 120 RPM for 20 minutes. In parallel, the isomerization of glucose into fructose was evaluated under conditions of 70°C, 750 RPM for 90 min. After, the integration of the three processes for the production of lactulose was investigated. Among the evaluated loads, 7 mg/g was chosen because the best activity of the derivative (44.3 U/g) was obtained, being this parameter determinant for the reaction stages. The other parameters of immobilization yield (87.58%) and recovered activity (46.47%) were also satisfactory compared to the other conditions. Regarding the integrated process, 94.96% of lactose was converted, achieving 37.56 g/L and 37.97 g/L of glucose and galactose, respectively. In the isomerization step, conversion of 38.40% of glucose was observed, obtaining a concentration of 12.47 g/L fructose. In the transgalactosylation reaction was produced 13.15 g/L lactulose after 5 min. However, in the integrated process, there was no formation of lactulose, but it was produced other GOS at the same time. The high galactose concentration in the medium probably favored the reaction of synthesis of these other GOS. Therefore, the integrated process proved feasible for possible production of prebiotics. In addition, this process can be economically viable due to the use of an industrial residue as a substrate, but it is necessary a more detailed investigation of the transgalactosilation reaction.

Keywords: beta-galactosidase, glucose-isomerase, galactooligosaccharides, lactulose, whey

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Authors: Shangerganesh Lingeshwaran

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In this presentation, we have performed numerical simulations for a reaction-diffusion equation with various nonlinear density-dependent diffusion operators and proliferation functions. The mathematical model represented by parabolic partial differential equation is considered to study the invasion of gliomas (the most common type of brain tumors) and to describe the growth of cancer cells and response to their treatment. The unknown quantity of the given reaction-diffusion equation is the density of cancer cells and the mathematical model based on the proliferation and migration of glioma cells. A standard Galerkin finite element method is used to perform the numerical simulations of the given model. Finally, important observations on the each of nonlinear diffusion functions and proliferation functions are presented with the help of computational results.

Keywords: glioma invasion, nonlinear diffusion, reaction-diffusion, finite eleament method

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Authors: Teresa Azevedo PerdicoúLis, Paulo Lopes Dos Santos

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The conceptualisation of the problem of network optimisation as a noncooperative game sets up a holistic interactive approach that brings together different network features (e.g., com-pressor stations, sources, and pipelines, in the gas context) where the optimisation objectives are different, and a single optimisation procedure becomes possible without having to feed results from diverse software packages into each other. A mathematical model of this type, where independent entities take action, offers the ideal modularity and subsequent problem decomposition in view to design a decentralised algorithm to optimise the operation and management of the network. In a game framework, compressor stations and sources are under-stood as players which communicate through network connectivity constraints–the pipeline model. That is, in a scheme similar to tatonnementˆ, the players appoint their best settings and then interact to check for network feasibility. The devolved degree of network unfeasibility informs the players about the ’quality’ of their settings, and this two-phase iterative scheme is repeated until a global optimum is obtained. Due to network transients, its optimisation needs to be assessed at different points of the control interval. For this reason, the proposed approach to optimisation has two stages: (i) the first stage computes along the period of optimisation in order to fulfil the requirement just mentioned; (ii) the second stage is initialised with the solution found by the problem computed at the first stage, and computes in the end of the period of optimisation to rectify the solution found at the first stage. The liability of the proposed scheme is proven correct on an abstract prototype and three example networks.

Keywords: connectivity matrix, gas network optimisation, large-scale, noncooperative game, system decomposition

Procedia PDF Downloads 152

Authors: Allah Bakhsh Javid, Ali Mashayekh-Salehi, Fatemeh Davardoost

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This study presents the preparation, characterization and catalytic activity of a novel natural mineral-based catalyst for destructive adsorption of tetracycline (TTC) as water emerging compounds. Degradation potential of raw and calcined magnetite catalyst was evaluated at different experiments situations such as pH, catalyst dose, reaction time and pollutant concentration. Calcined magnetite attained greater catalytic potential than the raw ore in the degradation of tetracycline, around 69% versus 3% at reaction time of 30 min and TTC aqueous solution of 50 mg/L, respectively. Complete removal of TTC could be obtained using 2 g/L calcined nanoparticles at reaction time of 60 min. The removal of TTC increased with the increase in solution temperature. Accordingly, considering its abundance in nature together with its very high catalytic potential, calcined pyrite is a promising and reliable catalytic material for destructive decomposition for catalytic decomposition and mineralization of such pharmaceutical compounds as TTC in water and wastewater.

Keywords: catalytic degradation, tetracycline, pyrite, emerging pollutants

Procedia PDF Downloads 192

Authors: Ameur Soukaina, Zeroual Abdellah, Mazoir Noureddine

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Molecular Electron Density Theory (MEDT) elucidates the regioselectivity of the [4+2] cycloaddition reaction between 3-aroylpyrrolo[1,2-α]quinoxaline-1,2,4(5H)-trione and butyl vinyl ether Regioselectivity and stereoselectivity. The regioselectivity mechanisms of these reactions were investigated by evaluating potential energy surfaces calculated for cycloaddition processes and DFT density-based reactivity indices. These methods have been successfully applied to predict preferred regioisomers for different method alternatives. Reactions were monitored by performing transition state optimizations, calculations of intrinsic reaction coordinates, and activation energies. The observed regioselectivity was rationalized using DFT-based reactivity descriptors such as the Parr function. Solvent effects were also investigated in 1,4-dioxane solvent using a field model for self-consistent reactions. The results were compared with experimental data to find good agreement.

Keywords: cycloaddition, DFT, ELF, MEDT, parr, stereoselectivité

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Authors: Ismail Seckin Cardakli, Mustafa Engin Kocadagistan, Ersin Arslan

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In this study, mechanochemical behaviour of aluminium - boron oxide - melamine ternary system was investigated by high energy ball milling. According to the reaction Al + B₂O₃ = Al₂O₃ + B, stochiometric amount of aluminium and boron oxide with melamine up to ten percent of total weight was used in the experiments. The powder characterized by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscopy (SEM) after leaching of product by 1M HCl acid. Results show that mechanically induced self-sustaining reaction (MSR) between aluminium and boron oxide takes place after four hours high energy ball milling. Al₂O₃/h-BN composite powder is obtained as the product of aluminium - boron oxide - melamine ternary system.

Keywords: high energy ball milling, hexagonal boron nitride, mechanically induced self-sustaining reaction, melamine

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: Yin-Yann Chen, Tzu-Ling Chen

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This study presents the case of an actual Taiwanese semiconductor assembly and testing manufacturer. Three major bottleneck manufacturing processes, namely, die bond, wire bond, and molding, are analyzed to determine how to use finite resources to achieve the optimal capacity allocation. A medium-term capacity allocation planning model is developed by considering the optimal total profit to satisfy the promised volume demanded by customers and to obtain the best migration decision among production lines for machines and tools. Finally, sensitivity analysis based on the actual case is provided to explore the effect of various parameter levels.

Keywords: capacity planning, capacity allocation, machine migration, resource configuration

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Sharad Shrivastava, Arun Jalan

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In this study tensile testing was performed on randomly oriented short glass fiber/epoxy resin composite specimens which were prepared using hand lay-up method. Samples were tested over a wide range of strain rate/loading rate from 2mm/min to 40mm/min to see the effect on ultimate tensile strength of the composite. A multi layered 'back propagation artificial neural network of supervised learning type' was used to analyze and predict the tensile properties with strain rate and temperature as given input and output as UTS to predict. Various network structures were designed and investigated with varying parameters and network sizes, and an optimized network structure was proposed to predict the UTS of short glass fiber/epoxy resin composite specimens with reasonably good accuracy.

Keywords: glass fiber composite, mechanical properties, strain rate, artificial neural network

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Authors: Francys Souza, Alberto Ohashi, Dorival Leao

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We present a general solution for finding the ε-optimal controls for non-Markovian stochastic systems as stochastic differential equations driven by Brownian motion, which is a problem recognized as a difficult solution. The contribution appears in the development of mathematical tools to deal with modeling and control of non-Markovian systems, whose applicability in different areas is well known. The methodology used consists to discretize the problem through a random discretization. In this way, we transform an infinite dimensional problem in a finite dimensional, thereafter we use measurable selection arguments, to find a control on an explicit form for the discretized problem. Then, we prove the control found for the discretized problem is a ε-optimal control for the original problem. Our theory provides a concrete description of a rather general class, among the principals, we can highlight financial problems such as portfolio control, hedging, super-hedging, pairs-trading and others. Therefore, our main contribution is the development of a tool to explicitly the ε-optimal control for non-Markovian stochastic systems. The pathwise analysis was made through a random discretization jointly with measurable selection arguments, has provided us with a structure to transform an infinite dimensional problem into a finite dimensional. The theory is applied to stochastic control problems based on path-dependent stochastic differential equations, where both drift and diffusion components are controlled. We are able to explicitly show optimal control with our method.

Keywords: dynamic programming equation, optimal control, stochastic control, stochastic differential equation

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Authors: H. Zormati, J. Chebil, J. Bel Hadj Taher

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The goal of this paper is to present an addressing scheme that allows for assigning a unique IPv6 address to each node in the Internet of Things (IoT) network. This scheme guarantees uniqueness by extracting the clock skew of each communication device and converting it into an IPv6 address. Simulation analysis confirms that the presented scheme provides reductions in terms of energy consumption, communication overhead and response time as compared to four studied addressing schemes Strong DAD, LEADS, SIPA and CLOSA.

Keywords: addressing, IoT, IPv6, network, nodes

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Authors: Virender Ranga, Mayank Dave, Anil Kumar Verma

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Recently wireless sensor networks (WSNs) are used in many real life applications like environmental monitoring, habitat monitoring, health monitoring etc. Due to power constraint cheaper devices used in these applications, the energy consumption of each device should be kept as low as possible such that network operates for longer period of time. One of the techniques to prolong the network lifetime is an intelligent grouping of sensor nodes such that they can perform their operation in cooperative and energy efficient manner. With this motivation, we propose a novel approach by organize the sensor nodes in cooperative multihop mini-groups so that the total global energy consumption of the network can be reduced and network lifetime can be improved. Our proposed approach also reduces the number of transmitted messages inside the WSNs, which further minimizes the energy consumption of the whole network. The experimental simulations show that our proposed approach outperforms over the state-of-the-art approach in terms of stability period and aggregated data.

Keywords: clustering, cluster-head, mini-group, stability period

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Fatima Ammari, Meriem Chenouf

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Synthesis of gold nanoparticles (AuNPs) has attracted much attention since the pioneering discovery of the high catalytic activity of supported gold nanoparticles in the reaction of CO oxidation at low temperature. In this research field, we used montmorillonite pre-acidified under gentle conditions for AuNPs stabilization; using different loading percentage 1, 2 and 5%. The gold nanoparticles were obtained using chemical reduction method using NaBH4 as reductant agent. The obtained gold nanoparticles stabilized in acid-activated montmorillonite were used as catalysts for reduction of 4-nitrophenol to aminophenol with sodium borohydride at room temperature The UV-Vis results confirm directly the gold nanaoparticles formation. The XRD N2 adsorption and MET results showed the formation of gold nanoparticles in the pores of preacidified montmorillonite with an average size of 5.7nm. The reduction reaction of 4-nitrophenol into 4-aminophenol with NaBH4 catalyzed by Au°-montmorillonite catalyst exhibits remarkably a high activity; the reaction was completed within 4.5min.

Keywords: gold, acid-activated montmorillonite, nanoparticles, 4-nitrophenol

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Authors: Manju Verma, Parag A. Deshpande

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Electronic structure calculations of hydrogen terminated metal-porphyrin graphene were carried out to explore the catalytic activity for CO2 hydration reaction. A ruthenium atom was substituted in place of carbon atom of graphene and ruthenium chelated carbon atoms were replaced by four nitrogen atoms in metal-porphyrin graphene system. Ruthenium atom created the active site for CO2 hydration reaction. Ruthenium-porphyrin graphene followed the mechanism of carbonic anhydrase enzyme for CO2 conversion to HCO3- ion. CO2 hydration reaction over ruthenium-porphyrin graphene proceeded via the elementary steps: OH- formation from H2O dissociation, CO2 bending in presence of nucleophilic attack of OH- ion, HCO3- ion formation from proton migration, HCO3- ion desorption by H2O addition. Proton transfer to yield HCO3- ion was observed as a rate limiting step from free energy landscape.

Keywords: ruthenium-porphyrin graphene, CO2 hydration, carbonic anhydrase, heterogeneous catalyst, density functional theory

Procedia PDF Downloads 259

Authors: L. Janská, J. Kubrický

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The article describes a research focused on the influence of the information and communication technology (ICT) on the pupils' learning. The investigation deals with the influences that distinguish between the group of pupils influenced by ICT and the group of pupils not influenced by ICT. The group influenced by ICT should evince a different approach in number of areas (in managing of two and more activities at once, in a quick orientation and searching for information on the Internet, in an ability to quickly and effectively assess the data sources, in the assessment of attitudes and opinions of the other users of the network, in critical thinking, in the preference to work in teams, in the sharing of information and personal data via the virtual social networking, in insisting on the immediate reaction on their every action etc.).

Keywords: ICT influence, digital natives, pupil´s learning

Procedia PDF Downloads 291

Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decision-making problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security) by modeling the multi-job collaborative cryptographic service scheduling problem as a multi-objective optimized job flow scheduling problem and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real-time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: cloud computing, cryptography on-demand service, reinforcement learning, workflow scheduling

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: S. Totong, P. Daorattanachai, N. Laosiripojana

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Lignin depolymerization into phenolic-based chemicals is an interesting process for utilizing and upgrading a benefit and value of lignin. In this study, the depolymerization reaction was performed to convert alkaline lignin into smaller molecule compounds. Fumed SiO₂ was used as a catalyst to improve catalytic activity in lignin decomposition. The important parameters in depolymerization process (i.e., reaction temperature, reaction time, etc.) were also investigated. In addition, gas chromatography with mass spectrometry (GC-MS), flame-ironized detector (GC-FID), and Fourier transform infrared spectroscopy (FT-IR) were used to analyze and characterize the lignin products. It was found that fumed SiO₂ catalyst led the good catalytic activity in lignin depolymerization. The main products from catalytic depolymerization were guaiacol, syringol, vanillin, and phenols. Additionally, metal supported on fumed SiO₂ such as Cu/SiO₂ and Ni/SiO₂ increased the catalyst activity in terms of phenolic products yield.

Keywords: alkaline lignin, catalytic, depolymerization, fumed SiO₂, phenolic-based chemicals

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Authors: Rodrigo S. Moreira, Nelson F. F. Ebecken

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This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs.

Keywords: ram memory, WiSARD weightless neural network, object tracking, quantization

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Lin Dong, Fei Shi

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Based on the railway network and the principles of space syntax, the study attempts to reconstruct the spatial relationship of the passenger network connections from space and time perspective. According to the travel time data of main stations in the Yangtze River Delta urban agglomeration obtained by the Internet, the topological drawing of railway network under different time sections is constructed. With the comprehensive index composed of connection and integration, the accessibility and network operation efficiency of the railway network in different time periods is calculated, while the fairness of the network is analyzed by the fairness indicators constructed with the integration and location entropy from the perspective of horizontal and vertical fairness respectively. From the analysis of the efficiency and fairness of the railway passenger transport network, the study finds: (1) There is a strong regularity in regional system accessibility change; (2) The problems of efficiency and fairness are different in different time periods; (3) The improvement of efficiency will lead to the decline of horizontal fairness to a certain extent, while from the perspective of vertical fairness, the supply-demand situation has changed smoothly with time; (4) The network connection efficiency of Shanghai, Jiangsu and Zhejiang regions is higher than that of the western regions such as Anqing and Chizhou; (5) The marginalization of Nantong, Yancheng, Yangzhou, Taizhou is obvious. The study explores the application of spatial syntactic theory in regional traffic analysis, in order to provide a reference for the development of urban agglomeration transportation network.

Keywords: spatial syntax, the Yangtze River Delta, railway passenger time, efficiency and fairness

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Authors: K. Gomathi

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Mobile Ad hoc Networks (MANET) is defined as collection of routable wireless mobile nodes with no centralized administration and communicate each other using radio signals. Especially MANETs deployed in hostile environments where hackers will try to disturb the secure data transfer and drain the valuable network resources. Since MANET is battery operated network, preserving the network resource is essential one. For resource constrained computation, efficient routing and to increase the network stability, the network is divided into smaller groups called clusters. The clustering architecture consists of Cluster Head(CH), ordinary node and gateway. The CH is responsible for inter and intra cluster routing. CH election is a prominent research area and many more algorithms are developed using many different metrics. The CH with longer life sustains network lifetime, for this purpose Secondary Cluster Head(SCH) also elected and it is more economical. To nominate efficient CH, a Enhanced Distributed Weighted Clustering Algorithm (EDWCA) has been proposed. This approach considers metrics like battery power, degree difference and speed of the node for CH election. The proficiency of proposed one is evaluated and compared with existing algorithm using Network Simulator(NS-2).

Keywords: MANET, EDWCA, clustering, cluster head

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Authors: Selim Kermasha

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A process for the synthesis of structured lipids (SLs) by the lipase-catalyzed interesterification of selected endogenous edible oils such as flaxseed oil (FO) and medium-chain triacylglyceols such as tricaprylin (TC) in non-conventional media (NCM), including organic solvent media (OSM) and solvent-free medium (SFM), was developed. The bioconversion yield of the medium-long-medium-type SLs (MLM-SLs were monitored as the responses with use of selected commercial lipases. In order to optimize the interesterification reaction and to establish a model system, a wide range of reaction parameters, including TC to FO molar ratio, reaction temperature, enzyme concentration, reaction time, agitation speed and initial water activity, were investigated to establish the a model system. The model system was monitored with the use of multiple response surface methodology (RSM) was used to obtain significant models for the responses and to optimize the interesterification reaction, on the basis of selected levels and variable fractional factorial design (FFD) with centre points. Based on the objective of each response, the appropriate level combination of the process parameters and the solutions that met the defined criteria were also provided by means of desirability function. The synthesized novel molecules were structurally characterized, using silver-ion reversed-phase high-performance liquid chromatography (RP-HPLC) atmospheric pressure chemical ionization-mass spectrophotometry (APCI-MS) analyses. The overall experimental findings confirmed the formation of dicaprylyl-linolenyl glycerol, dicaprylyl-oleyl glycerol and dicaprylyl-linoleyl glycerol resulted from the lipase-catalyzed interesterification of FO and TC.

Keywords: enzymatic interesterification, non-conventinal media, nutraceuticals, structured lipids

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Authors: Chrislaura Carmo, Luca Deiana, Mafalda Laranjo, Abilio Sobral, Armando Cordova

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Photodynamic therapy (PDT) is currently used for the treatment of malignancies and premalignant tumors. It is based on the capture of a photosensitizing molecule (PS) which, when excited by light at a certain wavelength, reacts with oxygen and generates oxidizing species (radicals, singlet oxygen, triplet species) in target tissues, leading to cell death. BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-s-indaceno) derivatives are emerging as important candidates for photosensitizer in photodynamic therapy of cancer cells due to their high triplet quantum yield. Today these dyes are relevant molecules in photovoltaic materials and fluorescent sensors. In this study, it will be demonstrated the possibility that BODIPY can be covalently linked to thioglycolic acid through the click reaction. Thiol−ene click chemistry has become a powerful synthesis method in materials science and surface modification. The design of biobased allyl-terminated precursors with high renewable carbon content for the construction of the thiol-ene polymer networks is essential for sustainable development and green chemistry. The work aims to synthesize the BODIPY (10-(4-(allyloxy) phenyl)-2,8-diethyl-5,5-difluoro-1,3,7,9-tetramethyl-5H-dipyrrolo[1,2-c:2',1'-f] [1,3,2] diazaborinin-4-ium-5-uide) and to click reaction with Thioglycolic acid. BODIPY was synthesized by the condensation reaction between aldehyde and pyrrole in dichloromethane, followed by in situ complexation with BF3·OEt2 in the presence of the base. Then it was functionalized with allyl bromide to achieve the double bond and thus be able to carry out the click reaction. The thiol−ene click was performed using DMPA (2,2-Dimethoxy-2-phenylacetophenone) as a photo-initiator in the presence of UV light (320–500 nm) in DMF at room temperature for 24 hours. Compounds were characterized by standard analytical techniques, including UV-Vis Spectroscopy, 1H, 13C, 19F NMR and mass spectroscopy. The results of this study will be important to link BODIPY to polymers through the thiol group offering a diversity of applications and functionalization. This new molecule can be tested as third-generation photosensitizers, in which the dye is targeted by antibodies or nanocarriers by cells, mainly in cancer cells, PDT and Photodynamic Antimicrobial Chemotherapy (PACT). According to our studies, it was possible to visualize a click reaction between allyl BODIPY and thioglycolic acid. Our team will also test the reaction with other thiol groups for comparison. Further, we will do the click reaction of BODIPY with a natural polymer linked with a thiol group. The results of the above compounds will be tested in PDT assays on various lung cancer cell lines.

Keywords: bodipy, click reaction, thioglycolic acid, allyl, thiol-ene click

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Authors: Durata Haciu, Ozgur Birer

Abstract:

Zinc acetate solution is sonicated at high power in water and in ethanol in the absence and presence of various peroxides. In the absence of peroxides, the products are zinc oxide and layered hydroxy zinc acetate in water and in ethanol, respectively. Layered basic zinc acetate are prepared for the first time using sonochemical methods. The addition of peroxides alters the reaction mechanisms. In water, insoluble peroxides produce zinc oxides while the water soluble peroxide, i.e.hydrogen peroxide, completely destroyed the structure and casted a doubt on the accepted peroxide initiated mechanism of reactions. In ethanol,peroxide addition caused the reaction mechanism to change and some oxide formation is observed. The reaction mechanism is sensitive to water/ethanol amounts as well as the peroxide to zinc ion mole ratio.Thin zinc oxide wafers (ca. 30 nm) with band gaps of 3.24 eV were obtained.

Keywords: ultrasound, zinc oxide, hydroxy zinc acetate, fenton, peroxide initiation

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Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

Abstract:

Due to the rapidly growing number of conscious customers in the recent years, more and more people look for products with positive physiological effects which may contribute to the preservation of their health. In response to these demands Food Science Research Institute of Budapest develops and introduces into the market new functional foods of guaranteed positive effect that contain bioactive agents. New, efficient technologies are also elaborated in order to preserve the maximum biological effect of the produced foods. The main objective of our work was the development of new functional biscuits fortified with physiologically beneficial ingredients. Bakery products constitute the base of the food nutrients’ pyramid, thus they might be regarded as foodstuffs of the largest consumed quantity. In addition to the well-known and certified physiological benefits of lysine, as an essential amino acid, a series of antioxidant type compounds is formed as a consequence of the occurring Maillard-reaction. Progress of the evoked Maillard-reaction was studied by applying diverse sugars (glucose, fructose, saccharose, isosugar) and lysine at several temperatures (120-170°C). Interval of thermal treatment was also varied (10-30 min). The composition and production technologies were tailored in order to reach the maximum of the possible biological benefits, so as to the highest antioxidant capacity in the biscuits. Out of the examined sugar components, theextent of the Maillard-reaction-driven transformation of glucose was the most pronounced at both applied temperatures. For the precise assessment of the antioxidant activity of the products FRAP and DPPH methods were adapted and optimised. To acquire an authentic and extensive mechanism of the occurring transformations, Maillard-reaction products were identified, and relevant reaction pathways were revealed. GC-MS and HPLC-MS techniques were applied for the analysis of the 60 generated MRPs and characterisation of actual transformation processes. 3 plausible major transformation routes might have been suggested based on the analytical result and the deductive sequence of possible occurring conversions between lysine and the sugars.

Keywords: Maillard-reaction, lysine, antioxidant activity, GC-MS and HPLC-MS techniques

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Authors: Giovanni Meloni

Abstract:

Recent results are presented from experiments carried out at the Advanced Light Source (ALS) at the Chemical Dynamics Beamline of Lawrence Berkeley National Laboratory using multiplexed synchrotron photoionization mass spectrometry. The reaction mixture and a buffer gas (He) are introduced through individually calibrated mass flow controllers into a quartz slow flow reactor held at constant pressure and temperature. The gaseous mixture effuses through a 650 μm pinhole into a 1.5 mm skimmer, forming a molecular beam that enters a differentially pumped ionizing chamber. The molecular beam is orthogonally intersected by a tunable synchrotron radiation produced by the ALS in the 8-11 eV energy range. Resultant ions are accelerated, collimated, and focused into an orthogonal time-of-flight mass spectrometer. Reaction species are identified by their mass-to-charge ratios and photoionization (PI) spectra. Comparison of experimental PI spectra with literature and/or simulated curves is routinely done to assure the identity of a given species. With the aid of electronic structure calculations, potential energy surface scans are performed, and Franck-Condon spectral simulations are obtained. Examples of these experiments are discussed, ranging from new intermediates characterization to reaction mechanisms elucidation and biofuels oxidation pathways identification.

Keywords: mass spectrometry, reaction intermediates, synchrotron photoionization, oxidation reactions

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