Search results for: 7A6 series manipulator

Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

Procedia PDF Downloads 179

Authors: Changhong Guo, Shaomei Fang, Yong He

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In this paper, fractional Black-Scholes models for the European option pricing were established based on the fractional G-Brownian motion (fGBm), which generalizes the concepts of the classical Brownian motion, fractional Brownian motion and the G-Brownian motion, and that can be used to be a tool for considering the long range dependence and uncertain volatility for the financial markets simultaneously. A generalized fractional Black-Scholes equation (FBSE) was derived by using the Taylor’s series of fractional order and the theory of absence of arbitrage. Finally, some explicit option pricing formulas for the European call option and put option under the FBSE were also solved, which extended the classical option pricing formulas given by F. Black and M. Scholes.

Keywords: European option pricing, fractional Black-Scholes equations, fractional g-Brownian motion, Taylor's series of fractional order, uncertain volatility

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of carbon dioxide (CO2) emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, and electricity), CO2 emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey-Fuller (ADF) test for stationarity, Johansen’s maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests no effects of the CO2 emissions and energy use on the GDP in Turkey. There exists a short-run bidirectional relationship between the electricity and natural gas consumption, and also there is a negative unidirectional causality running from the GDP to electricity use. Overall, the results partly support arguments that there are relationships between energy use and economic output; however, the effects may differ due to the source of energy such as in the case of Turkey for the period of 1980-2010. However, there is no significant relationship between the CO2 emissions and the GDP and between the CO2 emissions and the energy use both in the short term and long term.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: A. Fettahoglu

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Theory of viscoelasticity is used by many researchers to represent the behavior of many materials such as pavements on roads or bridges. Several researches used analytical methods and rheology to predict the material behaviors of simple models. Today, more complex engineering structures are analyzed using Finite Element Method, in which material behavior is embedded by means of three dimensional viscoelastic material laws. As a result, structures of unordinary geometry and domain can be analyzed by means of Finite Element Method and three dimensional viscoelastic equations. In the scope of this study, rheological models embedded in ANSYS, namely, generalized Maxwell model and Prony series, which are two methods used by ANSYS to represent viscoelastic material behavior, are presented explicitly. Afterwards, a guide is illustrated to ease using of viscoelasticity tool in ANSYS.

Keywords: ANSYS, generalized Maxwell model, finite element method, Prony series, viscoelasticity, viscoelastic material curve fitting

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Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

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Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

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Authors: Manisha Mukherjee, Diptarka Saha

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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.

Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function

Procedia PDF Downloads 127

Authors: Selim M. Khan, Dustin D. Pearson, Tryggve Rönnqvist, Markus E. Nielsen, Joshua M. Taron, Aaron A. Goodarzi

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Accumulation of radioactive radon gas in indoor air poses a serious risk to human health by increasing the lifetime risk of lung cancer and is classified by IARC as a category one carcinogen. Radon exposure risks are a function of geologic, geographic, design, and human behavioural variables and can change over time. Using time series and deep machine learning modelling, we analyzed long-term radon test outcomes as a function of building metrics from 25,489 Canadian and 38,596 Swedish residential properties constructed between 1945 to 2020. While Canadian and Swedish properties built between 1970 and 1980 are comparable (96–103 Bq/m³), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that 21st Century Canadian houses show 467% greater average radon (131 Bq/m³) relative to Swedish equivalents (28 Bq/m³). These trends are consistent across housing types and regions within each country. The introduction of energy efficiency measures within Canadian and Swedish building codes coincided with opposing radon level trajectories in each nation. Deep machine learning modelling predicts that, without intervention, average Canadian residential radon levels will increase to 176 Bq/m³ by 2050, emphasizing the importance and urgency of future building code intervention to achieve systemic radon reduction in Canada.

Keywords: radon health risk, time-series, deep machine learning, lung cancer, Canada, Sweden

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Authors: Luthfia A. Eka, Bertri M. Masita, G. Indah Lestari, Rizka. Ryanindya, Anindita D. Nur, Asih. Setiarini

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The preferred habit nowadays is to watch television or listen to the radio rather than reading a newspaper or magazine. The low interest in reading is the reason to the Indonesian government passed a regulation to foster interest in reading early in schoolchildren through literacy programs. Literacy programs are held for the first 10 - 15 minutes before classes begin and children are asked to read books other than textbooks such as storybooks or magazines. In addition, elementary school children have a tendency to buy less healthy snacks around the school and do not know the nutrition fact from the food purchased. Whereas snacks contribute greatly in the fulfillment of energy and nutrients of children every day. The purpose of this study was to increase reading interest as well as knowledge of nutrition and health for elementary school students. This study used quantitative method with experimental study design for four months with twice intervention per week and deepened by qualitative method in the form of interview. The participants were 130 students consisting of 3rd and 4th graders in selected elementary school in Depok City. The Interventions given using KUCERIA (Child Storybook) which were storybooks with pictures consisting of 12 series about nutrition and health given at school literacy hours. There were five questions given by using the crossword method to find out the students' understanding of the story content in each series. To maximize the understanding and absorption of information, two students were asked to retell the story in front of the class and one student to fill the crossword on the board for each series. In addition, interviews were conducted by asking questions about students' interest in reading books. Intervention involved not only students but also teachers and parents in order to optimize students' reading habits. Analysis showed > 80% of student could answer 3 of 5 questions correctly in each series, which showed they had an interest in what they read. Research data on nutrition and health knowledge were analyzed using Wilcoxon and Chi-Square Test to see the relationship. However, only 46% of students completed 12 series and the rest lost to follow up due to school schedule incompatibility with the program. The results showed that there was a significant increase of knowledge (p = 0.000) between before intervention with 66,53 score and after intervention with 81,47 score. Retention of knowledge was conducted one month after the last intervention was administered and the analysis result showed no significant decrease of knowledge (p = 0,000) from 79,17 score to 75,48 score. There is also no relationship between sex and class with knowledge. Hence, an increased interest in reading of elementary school students and nutritional knowledge interventions using KUCERIA was proved successful. These interventions may be replicated in other schools or learning communities.

Keywords: literation, reading interest, nutrition knowledge, school children

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Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: H. Khaled, F. Chaabani, F. Boulvain

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This paper focuses on the studied of Aptian series that crops out at the Jebel Serdj in the north central Tunisia. The study series is about 590 meters thick and it is consisting of limestones, marly limestones associated with some levels of siltstones and marls. Two sections are studied in detail regarding lithology, microfacies, magnetic susceptibility and mineralogical composition to provide new insights into the paleoenvironmental evolution and paleoclimatological implications during this period. The following facies associations representing different ramp palaeoenvironments have been identified: mudstone–wackestone outer ramp facies; skeletal grainstone- packstone mid-ramp facies, packstone-grainstone inner-ramp facies which include a variety of organisms such as rudists and ooids and mudstone–wackestone coastal facies rich with miliolidea and orbitolines. The magnetic susceptibility (Xᵢₙ) of all samples was compared with the lithological and microfacies variation. We show that high values of magnetic susceptibility are correlated with the distal facies.

Keywords: Aptian, Serdj Formation, geochemical, mineralogy

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Authors: Samar Aoujia, Théo Guérineaub, Rayan Zaitera, Evelyne Fargina, Younès Messaddeqb, Thierry Cardinala

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Two barium gallo-germanate glass series were elaborated to investigate the effect of the yttrium introduction on the glass physicochemical properties and crystallization behavior. One to twenty mol% of YO3/2 were either added into the glass matrix or substituted for gallium oxide. The glass structure was studied by Raman spectroscopy, and the thermal, optical, thermo-mechanical and physical properties are examined. The introduction of yttrium ions in both glass series increases the glass transition temperature, crystallization temperature, softening temperature, coefficient of linear thermal expansion and density. Through differential scanning calorimetry and X-ray diffraction analyses, it was found that competition occurs between the gallo-germanate zeolite-type phase and the yttrium-containing phase. From 13 mol% of YO3/2, the yttrium introduction impedes the formation of surface crystallization in these glasses.

Keywords: photonic, heavy-metal oxide, glass, crystallization

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Authors: Eugene Kogan

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We analytically study the localized running waves in the discrete Josephson transmission lines (JTL), constructed from Josephson junctions (JJ) and capacitors. The quasi-continuum approximation reduces the calculation of the running wave properties to the problem of equilibrium of an elastic rod in the potential field. Making additional approximations, we reduce the problem to the motion of the fictitious Newtonian particle in the potential well. We show that there exist running waves in the form of supersonic kinks and solitons and calculate their velocities and profiles. We show that the nonstationary smooth waves, which are small perturbations on the homogeneous non-zero background, are described by Korteweg-de Vries equation, and those on zero background -by the modified Korteweg-de Vries equation. We also study the effect of dissipation on the running waves in JTL and find that in the presence of the resistors, shunting the JJ and/or in series with the ground capacitors, the only possible stationary running waves are the shock waves, whose profiles are also found.

Keywords: Josephson transmission line, shocks, solitary waves, nonlinear waves

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Authors: Qinghua Zhang, Yanhe Zhu, Xiang Zhao, Yeqin Yang, Hongwei Jing, Guoan Zhang, Jie Zhao

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This paper presents a wearable reconfigurable supernumerary robotic limb with differential actuated joints, which is lightweight, compact and comfortable for the wearers. Compared to the existing supernumerary robotic limbs which mostly adopted series structure with large movement space but poor carrying capacity, a prototype with the series-parallel configuration to better adapt to different task requirements has been developed in this design. To achieve a compact structure, two kinds of cable-driven mechanical structures based on guide pulleys and differential actuated joints were designed. Moreover, two different tension devices were also designed to ensure the reliability and accuracy of the cable-driven transmission. The proposed device also employed self-designed bearings which greatly simplified the structure and reduced the cost.

Keywords: cable-driven, differential actuated joints, reconfigurable, supernumerary robotic limb

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Authors: Naimi Abdelhamid, Othmane Abdelkhalek

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With the current rise in the demand of electrical energy, present-day power systems which are large and complex, will continue to grow in both size and complexity. Flexible AC Transmission System (FACTS) controllers provide new facilities, both in steady state power flow control and dynamic stability control. Thyristor Controlled Series Capacitor (TCSC) is one of FACTS equipment, which is used for power flow control of active power in electric power system and for increase of capacities of transmission lines. In this paper, a Backstepping Power Flow Controller (BPFC) for TCSC in multimachine power system is developed and tested. The simulation results show that the TCSC proposed controller is capable of controlling the transmitted active power and improving the transient stability when compared with conventional PI Power Flow Controller (PIPFC).

Keywords: FACTS, thyristor controlled series capacitor (TCSC), backstepping, BPFC, PIPFC

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Authors: Malaw Ndiaye, Karim Konate

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Smart contracts are computer protocols that facilitate, verify, and execute the negotiation or execution of a contract, or that render a contractual term unnecessary. Blockchain and smart contracts can be used to facilitate almost any financial transaction. Thanks to these smart contracts, the settlement of dividends and coupons could be automated. Smart contracts have become lucrative and profitable targets for attackers because they can hold a great amount of money. Smart contracts, although widely used in blockchain technology, are far from perfect due to security concerns. Since there are recent studies on smart contract security, none of them systematically study the strengths and weaknesses of smart contract security. Some have focused on an analysis of program-related vulnerabilities by providing a taxonomy of vulnerabilities. Other studies are responsible for listing the series of attacks linked to smart contracts. Although a series of attacks are listed, there is a lack of discussions and proposals on improving security. This survey takes stock of smart contract security from a more comprehensive perspective by correlating the level of vulnerability and systematic review of security levels in smart contracts.

Keywords: blockchain, Bitcoin, smart contract, criminal smart contract, security

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Authors: Thanaa Abdel Moghny, Mohamed Keshawy, Mahmoud Fathy, Abdul-Raheim M. Abdul-Raheim, Khalid I. Kabel, Ahmed F. El-Kafrawy, Mahmoud Ahmed Mousa, Ahmed E. Awadallah

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Ecological pollution is of great concern for human health and the environment. Numerous organic and inorganic pollutants usually discharged into the water caused carcinogenic or toxic effect for human and different life form. In this respect, this work aims to treat water contaminated by organic and inorganic waste using sorbent based on polystyrene. Therefore, two different series of adsorbent material were prepared; the first one included the preparation of polymeric sorbent from the reaction of styrene acrylate ester and alkyl acrylate. The second series involved syntheses of composite ion exchange resins of waste polystyrene and   amorphous carbon thin film (WPS/ACTF) by solvent evaporation using micro emulsion polymerization. The produced ACTF/WPS nanocomposite was sulfonated to produce cation exchange resins ACTF/WPSS nanocomposite. The sorbents of the first series were characterized using FTIR, ¹H NMR, and gel permeation chromatography. The thermal properties of the cross-linked sorbents were investigated using thermogravimetric analysis, and the morphology was characterized by scanning electron microscope (SEM). The removal of organic pollutant was determined through absorption tests in a various organic solvent. The chemical and crystalline structure of nanocomposite of second series has been proven by studies of FTIR spectrum, X-rays, thermal analysis, SEM and TEM analysis to study morphology of resins and ACTF that assembled with polystyrene chain. It is found that the composite resins ACTF/WPSS are thermally stable and show higher chemical stability than ion exchange WPSS resins. The composite resin was evaluated for calcium hardness removal. The result is evident that the ACTF/WPSS composite has more prominent inorganic pollutant removal than WPSS resin. So, we recommend the using of nanocomposite resin as new potential applications for water treatment process.

Keywords: nanocomposite, sorbent materials, waste water, waste polystyrene

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Authors: Nora A. Hamad, Ayman M. Atta, Adel A. H. Abdel-Rahman

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Two malice anhydride esters were prepared using long chain aliphatic alcohols (C8H17OH and C12H25OH, 1:1 mole ratio). Three series of crosslinked homo and copolymers of maleate esters with octadecyl acrylate and acrylic acid were prepared respectively through suspension copolymerization. The monomers were mixed with 0.02 Wt% of BP initiator, PVA 1% (170 ml for each 100g of monomers) and different weight ratios of DVB crosslinked (1% and 4%) in cyclohexane. The prepared crosslinked homo and copolymers were characterized by SEM, TGA and FTIR spectroscopic analyses. The prepared polymers were coated onto poly (ethylene terephethalate) nonwoven fiber (NWPET). The effect of copolymerization feed composition, crosslinker wt% and reaction media or solvent on swelling properties of crosslinked polymers were studied through the oil absorption tests in toluene and 10% of diluted crude oil with toluene.

Keywords: acrylic acid, crosslinked copolymers, maleate ester, poly(ethylene terephethalate) nonwoven fiber (NWPET), oil absorbency, octadecyl acrylat

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Authors: José Carlos Rodríguez, Mario Gómez

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This paper analyses innovation trends in Latin America countries by means of the number of patent applications filed by residents and non-residents during the period 1965 to 2012. Making use of patent data released by the World Intellectual Property Organization (WIPO), we search for the presence of multiple structural changes in patent application series in Argentina, Brazil Chile, and Mexico. These changes may suggest that firms’ innovative activity has been modified as a result of implementing a particular science, technology and innovation (STI) policy. Accordingly, the new regulations implemented in these countries during 1980s and 1990s have influenced their intellectual property regimes. The question conducting this research is thus how STI policies in these countries have affected their innovation activity? The results achieved in this research confirm the existence of multiple structural changes in the series of patent applications resulting from STI policies implemented in these countries.

Keywords: econometric methods, innovation activity, Latin America countries, patents, science, technology and innovation policy

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Authors: Burçe Boyraz

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This study examines appearances of brand placement as an alternative communication strategy in television series by focusing on Yalan Dünya which is one of the most popular television series in Turkey. Consequently, this study has a descriptive research design and quantitative content analysis method is used in order to analyze frequency and time data of brand placement appearances in first 3 seasons of Yalan Dünya with 16 episodes. Analysis of brand placement practices in Yalan Dünya is dealt in three categories: episode-based analysis, season-based analysis and comparative analysis. At the end, brand placement practices in Yalan Dünya are evaluated in terms of type, form, duration and legal arrangements. As a result of this study, it is seen that brand placement plays a determinant role in Yalan Dünya content. Also, current legal arrangements make brand placement closer to other traditional communication strategies instead of differing brand placement from them distinctly.

Keywords: advertising, alternative communication strategy, brand placement, Yalan Dünya

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Authors: Luis Andrey Fajardo Fajardo

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We are immersed in complex systems. The human brain, the galaxies, the snowflakes are examples of complex systems. An area of interest in Complex systems is the chaos theory. This revolutionary field of science presents different ways of study than determinism and reductionism. Here is where in junction with the Nonlinear DSP, chaos theory offer valuable techniques that establish a link between time series and complex theory in terms of complex networks, so that, the study of signals can be explored from the graph theory. Recently, some people had purposed a method to transform time series in graphs, but no one had developed a suitable implementation in Python with signals extracted from Chaotic Systems or Complex systems. That’s why the implementation in Python of an existing method to transform one dimensional chaotic signals from time domain to graph domain and some measures that may reveal information not extracted in the time domain is proposed.

Keywords: Python, complex systems, graph theory, dynamical systems

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

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In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

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Authors: Ying Xin, Shigeki Kametani

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This paper deals the energy saving performance of GHP (Gas engine heat pump) air conditioning system has improved with time-series variation. There are two types of air conditioning systems, VRF (Variable refrigerant flow) and central cooling and heating system. VRF is classified as EHP (Electric driven heat pump) and GHP. EHP drives the compressor with electric motor. GHP drives the compressor with the gas engine. The electric consumption of GHP is less than one tenth of EHP does. In this study, the energy consumption data of GHP installed the junior high schools was collected. An annual and monthly energy consumption per rated thermal output power of each apparatus was calculated, and then their energy efficiency was analyzed. From these data, we investigated improvement of the energy saving of the GHP air conditioning system by the change in the generation.

Keywords: energy-saving, variable refrigerant flow, gas engine heat pump, electric driven heat pump, air conditioning system

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Authors: L. Q. Yuan, J. Yang, A. Siddiqui

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A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power.

Keywords: CHF experiment, CHF correlation, regression uncertainty, Monte Carlo Method, Taylor Series Method

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Authors: Muhammed Ashiq Villanthenkodath, Shreya Pal

Abstract:

Purpose: This study scrutinizes the impact of economic globalization on ecological footprint while endogenizing economic growth and energy consumption from 1990 to 2018 in India. Design/methodology/approach: The standard unit root test has been employed for time series analysis to unveil the integration order. Then, the cointegration was confirmed using autoregressive distributed lag (ARDL) analysis. Further, the study executed the dynamic ARDL simulation model to estimate long-run and short-run results along with simulation and robotic prediction. Findings: The cointegration analysis confirms the existence of a long-run association among variables. Further, economic globalization reduces the ecological footprint in the long run. Similarly, energy consumption decreases the ecological footprint. In contrast, economic growth spurs the ecological footprint in India. Originality/value: This study contributes to the literature in many ways. First, unlike studies that employ CO2 emissions and globalization nexus, this study employs ecological footprint for measuring environmental quality; since it is the broader measure of environmental quality, it can offer a wide range of climate change mitigation policies for India. Second, the study executes a multivariate framework with updated series from 1990 to 2018 in India to explore the link between EF, economic globalization, energy consumption, and economic growth. Third, the dynamic autoregressive distributed lag (ARDL) model has been used to explore the short and long-run association between the series. Finally, to our limited knowledge, this is the first study that uses economic globalization in the EF function of India amid facing a trade-off between sustainable economic growth and the environment in the era of globalization.

Keywords: economic globalization, ecological footprint, India, dynamic ARDL simulation model

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Authors: S. N. Harun, H. Ahmad

Abstract:

A series of ruthenium(II) complexes, including two novel compounds [Ru(dppz)2(L)]2+ where dppz = dipyrido-[3,2-a:2’,3’-c]phenazine, and L = 2-phenylimidazo[4,5-f][1,10]phenanthroline (PIP) or 2-(4-hydroxyphenyl)imidazo[4,5-f][1,10]phenanthroline (p-HPIP) have been synthesized and characterized. The previously reported complexes [Ru(bpy)2L]2+ and [Ru(phen)2L]2+ were also prepared. All complexes were characterized by elemental analysis, 1H-NMR spectroscopy, ESI-Mass spectroscopy and FT-IR spectroscopy. The photophysical properties were analyzed by UV-Visible spectroscopy and fluorescence spectroscopy. [Ru(dppz)2(PIP)]2+ and [Ru(dppz)2(p-HPIP)]2+ displayed ‘molecular light-switch’ effect as they have high emission in acetonitrile but no emission in water. The cytotoxicity of all complexes against cancer cell lines Hela and MCF-7 were investigated through standard MTT assay. [Ru(dppz)2(PIP)]2+ showed moderate toxicity on both MCF-7 and Hela with IC50 of 37.64 µM and 28.02 µM, respectively. Interestingly, [Ru(dppz)2(p-HPIP)]2+ exhibited remarkable cytotoxicity results with IC50 of 13.52 µM on Hela and 11.63 µM on MCF-7 cell lines which are comparable to the infamous anti-cancer drug, cisplatin. The cytotoxicity of this complex series increased as the ligands size extended in order of [Ru(bpy)2(L)]2+ < [Ru(phen)2(L)]2+ < [Ru(dppz)2(L)]2+.

Keywords: ruthenium, cytotoxicity, molecular light-switch, anticancer

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Authors: Erol Kam, Z. U. Yümün

Abstract:

Natural and artificial radionuclides cause radioactive contamination in environments, just as the other non-biodegradable pollutants (heavy metals, etc.) sink to the sea floor and accumulate in sediments. Especially the habitat of benthic foraminifera living on the surface of sediments or in sediments at the seafloor are affected by radioactive pollution in the marine environment. Thus, it is important for pollution analysis to determine the radionuclides. Radioactive pollution accumulates in the lowest level of the food chain and reaches humans at the highest level. The more the accumulation, the more the environment is endangered. This study used gamma spectrometry to investigate the natural and artificial radionuclide distribution of sediment samples taken from living benthic foraminifera habitats in the Western Marmara Sea. The radionuclides, K-40, Cs-137, Ra-226, Mn 54, Zr-95+ and Th-232, were identified in the sediment samples. For this purpose, 18 core samples were taken from depths of about 25-30 meters in the Marmara Sea in 2016. The locations of the core samples were specifically selected exclusively from discharge points for domestic and industrial areas, port locations, and so forth to represent pollution in the study area. Gamma spectrometric analysis was used to determine the radioactive properties of sediments. The radionuclide concentration activity values in the sediment samples obtained were Cs-137=0.9-9.4 Bq/kg, Th-232=18.9-86 Bq/kg, Ra-226=10-50 Bq/kg, K-40=24.4–670 Bq/kg, Mn 54=0.71–0.9 Bq/kg and Zr-95+=0.18–0.19 Bq/kg. These values were compared with the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) data, and an environmental analysis was carried out. The Ra-226 series, the Th-232 series, and the K-40 radionuclides accumulate naturally and are increasing every day due to anthropogenic pollution. Although the Ra-226 values obtained in the study areas remained within normal limits according to the UNSCEAR values, the K-40, and Th-232 series values were found to be high in almost all the locations.

Keywords: Ra-226, Th-232, K-40, Cs-137, Mn 54, Zr-95+, radionuclides, Western Marmara Sea

Procedia PDF Downloads 387