Search results for: continuous variable transmission.

Authors: Satyasen Panda, Urmila Bhanja

Abstract:

This paper presents a SAC-OCDMA code with zero cross correlation property to minimize the Multiple Access Interface (MAI) as New Zero Cross Correlation code (NZCC), which is found to be more scalable compared to the other existing SAC-OCDMA codes. This NZCC code is constructed using address segment and data segment. In this work, the proposed NZCC code is implemented in an optical system using the Opti-System software for the spectral amplitude coded optical code-division multiple-access (SAC-OCDMA) scheme. The main contribution of the proposed NZCC code is the zero cross correlation, which reduces both the MAI and PIIN noises. The proposed NZCC code reveals properties of minimum cross-correlation, flexibility in selecting the code parameters and supports a large number of users, combined with high data rate and longer fiber length. Simulation results reveal that the optical code division multiple access system based on the proposed NZCC code accommodates maximum number of simultaneous users with higher data rate transmission, lower Bit Error Rates (BER) and longer travelling distance without any signal quality degradation, as compared to the former existing SAC-OCDMA codes.

Keywords: Cross Correlation, Optical Code Division Multiple Access, Spectral Amplitude Coding Optical Code Division Multiple Access, Multiple Access Interference, Phase Induced Intensity Noise, New Zero Cross Correlation code.

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: Artificial Neural Network, Data Mining, Electroencephalogram, Epilepsy, Feature Extraction, Seizure Detection, Signal Processing.

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Authors: Panatcha Bovornseripatai, Siriporn Jongpatiwut, Somchai Osuwan, Suchada Butnark

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Hydrogenated biodiesel is one of the most promising renewable fuels. It has many advantages over conventional biodiesel, including higher cetane number, higher heating value, lower viscosity, and lower corrosiveness due to its absence of oxygen. From previous work, Pd/TiO2 gave high conversion and selectivity in hydrogenated biodiesel. In this work, the effect of biomass feedstocks (i.e. beef fat, chicken fat, pork fat, and jatropha oil) on the production of hydrogenated biodiesel over Pd/TiO2 has been studied. Biomass feedstocks were analyzed by ICP-OES (inductively coupled plasma optical emission spectrometry) to identify the content of impurities (i.e. P, K, Ca, Na, and Mg). The deoxygenation catalyst, Pd/TiO2, was prepared by incipient wetness impregnation (IWI) and tested in a continuous flow packed-bed reactor at 500 psig, 325°C, H2/feed molar ratio of 30, and LHSV of 4 h-1 for its catalytic activity and selectivity in hydrodeoxygenation. All feedstocks gave high selectivity in diesel specification range hydrocarbons and the main hydrocarbons were n-pentadecane (n-C15) and n-heptadecane (n- C17), resulting from the decarbonylation/decarboxylation reaction. Intermediates such as oleic acid, stearic acid, palmitic acid, and esters were also detected in minor amount. The conversion of triglycerides in jatropha oil is higher than those of chicken fat, pork fat, and beef fat, respectively. The higher concentration of metal impurities in feedstock, the lower conversion of feedstock.

Keywords: Hydrogenated biodiesel, hydrodeoxygenation, Pd/TiO2, biomass feedstock

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Authors: A. Zahiri, A. A. Dehghani

Abstract:

Although many researchers have studied the flow hydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different methods have been presented for these channels but extending them for all types of compound channels with different geometrical and hydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating curves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed slope, main channel side slopes, flood plains side slopes and berm inclination and one output variable (flow discharge), have been used in ANNs. Comparison of ANNs model and traditional method (divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and relative roughness have 19.3 and 12.2 percent of importance, respectively. On the other hand, shape parameter, main channel and flood plains side slopes with 2.1, 3.8 and 3.8 percent of contribution, have the least importance.

Keywords: ANN model, compound channels, divided channel method (DCM), flow rating curve

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Authors: Md Abdullah Al Faruque, Ram Balachandar

Abstract:

Sudden change of bed condition is frequent in open channel flow. Change of bed condition affects the turbulence characteristics in both streamwise and wall-normal direction. Understanding the turbulence intensity in open channel flow is of vital importance to the modeling of sediment transport and resuspension, bed formation, entrainment, and the exchange of energy and momentum. A comprehensive study was carried out to understand the extent of the effect of Reynolds number and bed roughness on different turbulence characteristics in an open channel flow. Four different bed conditions (impervious smooth bed, impervious continuous rough bed, pervious rough sand bed, and impervious distributed roughness) and two different Reynolds numbers were adopted for this cause. The effect of bed roughness on different turbulence characteristics is seen to be prevalent for most of the flow depth. Effect of Reynolds number on different turbulence characteristics is also evident for flow over different bed, but the extent varies on bed condition. Although the same sand grain is used to create the different rough bed conditions, the difference in turbulence characteristics is an indication that specific geometry of the roughness has an influence on turbulence characteristics. Roughness increases the contribution of the extreme turbulent events which produces very large instantaneous Reynolds shear stress and can potentially influence the sediment transport, resuspension of pollutant from bed and alter the nutrient composition, which eventually affect the sustainability of benthic organisms.

Keywords: Open channel flow, Reynolds Number, roughness, turbulence.

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Authors: O. Ologe, A. I. Augie

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A geophysical investigation involving geoelectric depths sounding has been conducted as pre-foundation study in part of Ilorin, Nigeria. The area is underlain by the Precambrian basement complex rocks. 15 sounding stations were established along five traverses. The Vertical Electrical Sounding (VES) (three-five) conducted along each of the traverses was subjected to computer iteration using IP2Win software. Three -five subsurface geologic layers were delineated in the study area. These include the topsoil with resistivity and thickness values ranging from 103 Ωm-210 Ωm and 0 m-1 m; lateritic (117 Ωm-590 Ωm and 1 m-4.7 m); sandy clay (137 – 859 Ωm and 2.9 m – 4.3 m); weathered (60.5 Ωm to 2539 Ωm and 3,2 m-10 m) and fresh basement (2253-∞ and 7.1 m-∞) respectively. The resistivity pseudosection shows continuous high resistivity zone on the surface. Resistivity of this layer from depth 0-5 m varies from 300-800 Ωm along traverse 1 and 2. Hence, this layer is rated competent as it has the ability to support engineering structure. However, along traverse 1, very low resistive layer occurs between VES 5 and 15 with resistivity values ranging from 30 Ωm-70 Ωm. This layer was rated incompetent based on the competence rating. This study revealed the importance of geophysical survey as a pre-construction engineering survey at any civil engineering site since it can reliably evaluate the competence of the subsurface geomaterials.

Keywords: Competence rating, geoelectric, pseudosection, soil, vertical electrical sounding.

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Authors: Abed Sami Qawasme, Sameer Khader

Abstract:

This paper describes the effects of photovoltaic voltage changes on Multi-level inverter (MLI) due to solar irradiation variations, and methods to overcome these changes. The irradiation variation affects the generated voltage, which in turn varies the switching angles required to turn-on the inverter power switches in order to obtain minimum harmonic content in the output voltage profile. Genetic Algorithm (GA) is used to solve harmonics elimination equations of eleven level inverters with equal and non-equal dc sources. After that artificial neural network (ANN) algorithm is proposed to generate appropriate set of switching angles for MLI at any level of input dc sources voltage causing minimization of the total harmonic distortion (THD) to an acceptable limit. MATLAB/Simulink platform is used as a simulation tool and Fast Fourier Transform (FFT) analyses are carried out for output voltage profile to verify the reliability and accuracy of the applied technique for controlling the MLI harmonic distortion. According to the simulation results, the obtained THD for equal dc source is 9.38%, while for variable or unequal dc sources it varies between 10.26% and 12.93% as the input dc voltage varies between 4.47V nd 11.43V respectively. The proposed ANN algorithm provides satisfied simulation results that match with results obtained by alternative algorithms.

Keywords: Multi level inverter, genetic algorithm, artificial neural network, total harmonic distortion.

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Authors: Martin Leroux, Sylvain Brisebois

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Eating a meal is among the Activities of Daily Living, but it takes a lot of time and effort for people with physical or functional limitations. Dedicated technologies are cumbersome and not portable, while general-purpose assistive robots such as wheelchair-based manipulators are too hard to control for elaborate continuous motion like eating. Eating with such devices has not previously been automated, since there existed no description of a feeding motion for uncontrolled environments. In this paper, we introduce a feeding mode for assistive manipulators, including a mathematical description of trajectories for motions that are difficult to perform manually such as gathering and scooping food at a defined/desired pace. We implement these trajectories in a sequence of movements for a semi-automated feeding mode which can be controlled with a very simple 3-button interface, allowing the user to have control over the feeding pace. Finally, we demonstrate the feeding mode with a JACO robotic arm and compare the eating speed, measured in bites per minute of three eating methods: a healthy person eating unaided, a person with upper limb limitations or disability using JACO with manual control, and a person with limitations using JACO with the feeding mode. We found that the feeding mode allows eating about 5 bites per minute, which should be sufficient to eat a meal under 30min.

Keywords: Assistive robotics, Automated feeding, Elderly care, Trajectory design, Human-Robot Interaction.

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Authors: Mohammad Hossein Hojjati , Fardin Boustani

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Groundwater is one of the most important water resources in Fars province. Based on this study, 95 percent of the total annual water consumption in Fars is used for agriculture, whereas the percentages for domestic and industrial uses are 4 and 1 percent, respectively. Population growth, urban and industrial growth, and agricultural development in Fars have created a condition of water stress. In this province, farmers and other users are pumping groundwater faster than its natural replenishment rate, causing a continuous drop in groundwater tables and depletion of this resource. In this research variation of groundwater level , their effects and ways to help control groundwater levels in some plains of Fars were evaluated .Excessive exploitation of groundwater in Darab, Jahrom, Estahban, Arsanjan, Khir and Niriz plains of Fars caused the groundwater levels fall too fast or to unacceptable levels. The average drawdown of the water table in Arsanjan, Khir. Estahban and Niriz plain plains were 12,8, 9 and 6 meters during 16,11,11 and 13 years ago respectively. This not only reduces available water resources and well yields but also can saline water intrusion, reductions in river flow and in wetland areas , drying springs, and ground subsidence, considerable increase in pumping costs and a significant decline in crop yields as a result of the increasing salinity. Finally based on situation and condition of the aquifer some suggestions are recommended.

Keywords: Fars province , ground water overdraft , water table

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Authors: Lakshmi Pujitha Dachuri, Nalini Uppala

Abstract:

In many applications retransmissions of lost packets are not permitted. OFDM is a multi-carrier modulation scheme having excellent performance which allows overlapping in frequency domain. With OFDM there is a simple way of dealing with multipath relatively simple DSP algorithms.

 In this paper, an image frame is compressed using DWT, and the compressed data is arranged in data vectors, each with equal number of coefficients. These vectors are quantized and binary coded to get the bit steams, which are then packetized and intelligently mapped to the OFDM system. Based on one-bit channel state information at the transmitter, the descriptions in order of descending priority are assigned to the currently good channels such that poorer sub-channels can only affect the lesser important data vectors. We consider only one-bit channel state information available at the transmitter, informing only about the sub-channels to be good or bad. For a good sub-channel, instantaneous received power should be greater than a threshold P_th. Otherwise, the sub-channel is in fading state and considered bad for that batch of coefficients. In order to reduce the system power consumption, the mapped descriptions onto the bad sub channels are dropped at the transmitter. The binary channel state information gives an opportunity to map the bit streams intelligently and to save a reasonable amount of power. By using MAT LAB simulation we can analysis the performance of our proposed scheme, in terms of system energy saving without compromising the received quality in terms of peak signal-noise ratio.

Keywords: Binary channel state, Channel state feedback, DWT-OFDM system, Energy saving, Fading broadcast channel.

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Authors: Amado, M. P., Amado, A., Poggi, F., Correia de Freitas, J.

Abstract:

Today is widely understood that global energy consumption patterns are directly related to the urban expansion and development process. This expansion is based on the natural growth of human activities and has left most urban areas totally dependent on fossil fuel derived external energy inputs. This status-quo of production, transportation, storage and consumption of energy has become inefficient and is set to become even more so when the continuous increases in energy demand are factored in. The territorial management of land use and related activities is a central component in the search for more efficient models of energy use, models that can meet current and future regional, national and European goals.

In this paper a methodology is developed and discussed with the aim of improving energy efficiency at the municipal level. The development of this methodology is based on the monitoring of energy consumption and its use patterns resulting from the natural dynamism of human activities in the territory and can be utilized to assess sustainability at the local scale. A set of parameters and indicators are defined with the objective of constructing a systemic model based on the optimization, adaptation and innovation of the current energy framework and the associated energy consumption patterns. The use of the model will enable local governments to strike the necessary balance between human activities and economic development and the local and global environment while safeguarding fairness in the energy sector.

Keywords: Solar urban planning, solar smart city, urban development, energy efficiency.

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.

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Authors: Robert O. Abolade, Olumide O. Ajayi, Zacheaus K. Adeyemo, Solomon A. Adeniran

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Link adaptation is an important strategy for achieving robust wireless multimedia communications based on quality of service (QoS) demand. Scheme switching in multiple-input multiple-output (MIMO) systems is an aspect of link adaptation, and it involves selecting among different MIMO transmission schemes or modes so as to adapt to the varying radio channel conditions for the purpose of achieving QoS delivery. However, finding the most appropriate switching method in MIMO links is still a challenge as existing methods are either computationally complex or not always accurate. This paper presents an intelligent switching method for the MIMO system consisting of two schemes - transmit diversity (TD) and spatial multiplexing (SM) - using fuzzy logic technique. In this method, two channel quality indicators (CQI) namely average received signal-to-noise ratio (RSNR) and received signal strength indicator (RSSI) are measured and are passed as inputs to the fuzzy logic system which then gives a decision – an inference. The switching decision of the fuzzy logic system is fed back to the transmitter to switch between the TD and SM schemes. Simulation results show that the proposed fuzzy logic – based switching technique outperforms conventional static switching technique in terms of bit error rate and spectral efficiency.

Keywords: Channel quality indicator, fuzzy logic, link adaptation, MIMO, spatial multiplexing, transmit diversity.

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Authors: K. Atashgar

Abstract:

When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.

Keywords: Artificial neural network, Multivariate process, Statistical process control, Change point.

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Authors: Isaac Kwaku Adu, Jacob Doku Tetteh, John Joseph Puthenkalam, Kwabena Effah Antwi

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The continuous increase in population implies increase in food demand. There is, therefore, the need to increase agricultural production and other forest products to ensure food security and economic development. This paper employs the three-level intensity analysis to assess the total change of land-use in two-time intervals (1986-2002 and 2002-2017), the net change and swap as well as gross gains and losses in the two intervals. The results revealed that the overall change in the 31-year period was greater in the second period (2002-2017). Agriculture and forest categories lost in the first period while the other land class gained. However, in the second period agriculture and built-up increased greatly while forest, water bodies and thick bushes/shrubland experienced loss. An assessment revealed a reduction of forest in both periods but was greater in the second period and expansion of agricultural land was recorded as population increases. The pixels gaining built-up targeted agricultural land in both intervals, it also targeted thick bushes/shrubland and waterbody in the second period only. Built-up avoided forest in both intervals as well as waterbody and thick bushes/shrubland. To help in developing the best land-use strategies/policies, a further validation of the social factors is necessary.

Keywords: Agricultural land-use, forest, intensity analysis, land-cover change, sustainable land-use.

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence.

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Authors: Arash Sharghi, Irina Dotu

Abstract:

The developments in the Middle East region have activated the involvement of a numerous diverse state and non-state actors in the regional affairs. The goals, positions, ideologies, different, and even contrast policy behaviors had procured the spreading and continuity of crisis. Non-state actors varying from Islamic organizations to takfiri-terrorist movements on one hand and regional and trans- regional actors, from another side, seek to reach their interests in the power struggle. Here, a research worthy question comes on the agenda: taking into consideration actors’ contradictory interests and constraints what are the regional peace and stability perspectives? Therein, different actors’ aims definition, their actions and behaviors, which affect instability, can be regarded as independent variables; whereas, on the contrary, Middle East peace and stability perspective analysis is a dependent variable. Though, this regional peace and war theory based research admits the significant influence of trans-regional actors, it asserts the roots of violence to derive from region itself. Consequently, hot war and conflict prevention and hot peace assurance in the Middle East region cannot be attained only by demands and approaches of trans-regional actors. Moreover, capacity of trans-regional actors is sufficient only for a cold war or cold peace to be reached in the region. Furthermore, within the framework of current conflict (struggle) between regional actors it seems to be difficult and even impossible to turn the cold war into a cold peace in the region.

Keywords: Cold peace, cold war, hot war, Middle East, non-state actors, regional and Great powers, war theory.

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Authors: Kiurski S. Jelena, Kecić S. Vesna, Aksentijević M. Snežana

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Samples of tap and wastewater were collected in three offset printing facilities in Novi Sad, Serbia. Ten physicochemical parameters were analyzed within all collected samples: pH, conductivity, m - alkalinity, p - alkalinity, acidity, carbonate concentration, hydrogen carbonate concentration, active oxygen content, chloride concentration and total alkali content. All measurements were conducted using the standard analytical and instrumental methods. Comparing the obtained results for tap water and wastewater, a clear quality difference was noticeable, since all physicochemical parameters were significantly higher within wastewater samples. The study also involves the application of simple linear regression analysis on the obtained dataset. By using software package ORIGIN 5 the pH value was mutually correlated with other physicochemical parameters. Based on the obtained values of Pearson coefficient of determination a strong positive correlation between chloride concentration and pH (r = -0.943), as well as between acidity and pH (r = -0.855) was determined. In addition, statistically significant difference was obtained only between acidity and chloride concentration with pH values, since the values of parameter F (247.634 and 182.536) were higher than F_critical (5.59). In this way, results of statistical analysis highlighted the most influential parameter of water contamination in offset printing, in the form of acidity and chloride concentration. The results showed that variable dependence could be represented by the general regression model: y = a₀ + a₁x+ k, which further resulted with matching graphic regressions.

Keywords: Pollution, printing industry, simple linear regression analysis, wastewater.

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Authors: J. Leige Lopes, L. Aparecida Bastos, R. Monteiro da Silva

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The objective of this paper is to study the work of children and adolescents and the vicious circle of poverty from the perspective of Guinar Myrdal’s Theory of Circular Cumulative Causation. The objective is to show that if a person starts working in the juvenile phase of life they will be classified as poor or extremely poor when they are adult, which can to be observed in the case of Brazil, more specifically in the north and northeast. To do this, the methodology used was statistical and econometric analysis by applying a probit model. The main results show that: if people reside in the northeastern region of Brazil, and if they have a low educational level and if they start their professional life before the age 18, they will increase the likelihood that they will be poor or extremely poor. There is a consensus in the literature that one of the causes of the intergenerational transmission of poverty is related to child labor, this because when one starts their professional life while still in the toddler or adolescence stages of life, they end up sacrificing their studies. Because of their low level of education, children or adolescents are forced to perform low-paid functions and abandon school, becoming in the future, people who will be classified as poor or extremely poor. As a result of poverty, parents may be forced to send their children out to work when they are young, so that in the future they will also become poor adults, a process that is characterized as the "vicious circle of poverty."

Keywords: Children, adolescents, Gunnar Myrdal, poverty, vicious circle.

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Authors: Collins C. Chiemeke, A. Onugba, P. Sule

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High resolution seismic reflection has recently been carried out on Zaria batholith, with the aim of characterizing the granitic Zaria batholiths in terms of its lithology. The geology of the area has revealed that the older granite outcrops in the vicinity of Zaria are exposures of a syntectonics to late-tectonic granite batholiths which intruded a crystalline gneissic basement during the Pan-African Orogeny. During the data acquisition the geophone were placed at interval of 1 m, variable offset of 1 and 10 m was used. The common midpoint (CMP) method with 12 fold coverage was employed for the survey. Analysis of the generated 3D surface of the p wave velocities from different profiles for densities and bulk modulus revealed that the rock material is more consolidated in South East part of the batholith and less consolidated in the North Western part. This was in conformity with earlier identified geology of the area, with the South Eastern part majorly of granitic outcrop, while the North Western part is characterized with the exposure of gneisses and thick overburden cover. The difference in lithology was also confirmed by the difference in seismic sections and Arial satellite photograph. Hence two major lithologies were identified, the granitic and gneisses complex which are characterized by gradational boundaries.

Keywords: Basement Complex, Batholith, High Resolution, Lithologies, Seismic Reflection.

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Authors: E. Neamatollahi, M. Bannayan, A. Souhani Darban, A. Ghanbari

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In production of medicinal plants, seed germination is very important problem. The treated seeds (control, hydro priming and ZnSO4) of Cumin (Cuminum cyminum L.) were evaluated at germination and seedling growth for tolerance to salt (NaCl and Na2SO4) conditions at the same water potentials of 0.0, -0.3, -0.6, - 0.9 and -1.2MPa. Electrical conductivity (EC) values of the NaCl solutions were 0.0, 6.5, 12.7, 18.4 and 23.5 dSm-1, respectively. The objective of the study was to determine factors responsible for germination and early seedling growth due to salt toxicity or osmotic effect and to optimize the best priming treatment for these stress conditions. Results revealed that germination delayed in both solutions, having variable germination with different priming treatments. Germination, shoot and weight, root and shoot length were higher but mean germination time and abnormal germination percentage were lower in NaCl than Na2SO4 at the same water potential. The root / shoot weight and R/S length increased with increase in osmotic potential in both NaCl and Na2SO4 solutions. NaCl had less inhibitor effect on seedling growth than the germination. It was concluded that inhibition of germination at the same water potential of NaCl and Na2SO4 resulted from salt toxicity rather than osmotic effect. Hydro priming increased germination and seedling growth under salt stress. This protocol has practical importance and could be recommended to farmers to achieve higher germination and uniform emergence under field conditions.

Keywords: Priming, Germination, Nacl, ZnSo4, Na2So4.

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Authors: S. M. Giripunje, Shikha Jindal

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Zinc sulphide (ZnS) quantum dots (QDs) were synthesized successfully via simple sonochemical method. X-ray diffraction (XRD), scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM) analysis revealed the average size of QDs of the order of 3.7 nm. The band gap of the QDs was tuned to 5.2 eV by optimizing the synthesis parameters. UV-Vis absorption spectra of ZnS QD confirm the quantum confinement effect. Fourier transform infrared (FTIR) analysis confirmed the formation of single phase ZnS QDs. To fabricate the diode, blend of ZnS QDs and P3HT was prepared and the heterojunction of PEDOT:PSS and the blend was formed by spin coating on indium tin oxide (ITO) coated glass substrate. The diode behaviour of the heterojunction was analysed, wherein the ideality factor was found to be 2.53 with turn on voltage 0.75 V and the barrier height was found to be 1.429 eV. ZnS-Graphene QDs nanocomposite was characterised for the surface morphological study. It was found that the synthesized ZnS QDs appear as quasi spherical particles on the graphene sheets. The average particle size of ZnS-graphene nanocomposite QDs was found to be 8.4 nm. From voltage-current characteristics of ZnS-graphene nanocomposites, it is observed that the conductivity of the composite increases by 10⁴ times the conductivity of ZnS QDs. Thus the addition of graphene QDs in ZnS QDs enhances the mobility of the charge carriers in the composite material. Thus, the graphene QDs, with high specific area for a large interface, high mobility and tunable band gap, show a great potential as an electron-acceptors in photovoltaic devices.

Keywords: Graphene, mobility, nanocomposites, photovoltaics, quantum dots, zinc sulphide.

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Authors: Shin-Hyung Cho, Dong-Kyu Kim, Seung-Young Kho

Abstract:

Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.

Keywords: Crash severity, structural equation modeling, truck-involved crashes, multiple group analysis, crash on freeway.

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Authors: Mahmoud Shaker, Ala'a Imran

Abstract:

Greenhouse is a building, which provides controlled climate conditions to the plants to keep them from external hard conditions. Greenhouse technology gives freedom to the farmer to select any crop type in any time during year. The quality and productivity of plants inside greenhouse is highly dependent on the management quality and a good management scheme is defined by the quality of the information collected from the greenhouse environment. Therefore, Continuous monitoring of environmental variables such as temperature, humidity, and soil moisture gives information to the grower to better understand, how each factor affects growth and how to manage maximal crop productiveness. In this piper, we designed and implemented climate monitoring with irrigation control system based on Wireless Sensor Network (WSN) technology. The designed system is characterized with friendly to use, easy to install by any greenhouse user, multi-sensing nodes, multi-PAN ID, low cast, water irrigation control and low operation complexity. The system consists of two node types (sensing and control) with star topology on one PAN ID. Moreover, greenhouse manager can modifying system parameters such as (sensing node addresses, irrigation upper and lower control limits) by updating corresponding data in SDRAM memory. In addition, the designed system uses 2*16 characters. LCD to display the micro climate parameters values of each plants row inside the greenhouse.

Keywords: ZigBee, WSN, Arduino platform, Greenhouse automation, micro climate monitoring, smart Irrigation control.

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Authors: Sharareh Mohseni, Reihaneh Ahmadzadeh Ghavidel

Abstract:

Tomato powder has good potential as substitute of tomato paste and other tomato products. In order to protect physicochemical properties and nutritional quality of tomato during dehydration process, investigation was carried out using different drying methods and pretreatments. Solar drier and continuous conveyor (tunnel) drier were used for dehydration where as calcium chloride (CaCl2), potassium metabisulphite (KMS), calcium chloride and potassium metabisulphite (CaCl2 +KMS), and sodium chloride (NaCl) selected for treatment.. lycopene content, dehydration ratio, rehydration ratio and non-enzymatic browning in addition to moisture, sugar and titrable acidity were studied. Results show that pre-treatment with CaCl2 and NaCl increased water removal and moisture mobility in tomato slices during drying of tomatoes. Where CaCl2 used along with KMS the NEB was recorded the least compared to other treatments and the best results were obtained while using the two chemicals in combination form. Storage studies in LDPE polymeric and metalized polyesters films showed less changes in the products packed in metallized polyester pouches and even after 6 months lycopene content did not decrease more than 20% as compared to the control sample and provide extension of shelf life in acceptable condition for 6 months. In most of the quality characteristics tunnel drier samples presented better values in comparison to solar drier.

Keywords: Dehydration, Tomato powder, Lycopene, Browning

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Authors: Jahar Sarkar

Abstract:

In the present study, a steady-state simulation model has been developed to evaluate the system performance of a transcritical carbon dioxide heat pump system for simultaneous water cooling and heating. Both the evaporator (including both two-phase and superheated zone) and gas cooler models consider the highly variable heat transfer characteristics of CO2 and pressure drop. The numerical simulation model of transcritical CO2 heat pump has been validated by test data obtained from experiments on the heat pump prototype. Comparison between the test results and the model prediction for system COP variation with compressor discharge pressure shows a modest agreement with a maximum deviation of 15% and the trends are fairly similar. Comparison for other operating parameters also shows fairly similar deviation between the test results and the model prediction. Finally, the simulation results are presented to study the effects of operating parameters such as, temperature of heat exchanger fluid at the inlet, discharge pressure, compressor speed on system performance of CO2 heat pump, suitable in a dairy plant where simultaneous cooling at 4oC and heating at 73oC are required. Results show that good heat transfer properties of CO2 for both two-phase and supercritical region and efficient compression process contribute a lot for high system COPs.

Keywords: CO2 heat pump, dairy system, experiment, simulation model, validation.

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

Abstract:

Fuel cells have become one of the major areas of research in the academia and the industry. The goal of most fish farmers is to maximize production and profits while holding labor and management efforts to the minimum. Risk of fish kills, disease outbreaks, poor water quality in most pond culture operations, aeration offers the most immediate and practical solution to water quality problems encountered at higher stocking and feeding rates. Many units of aeration system are electrical units so using a continuous, high reliability, affordable, and environmentally friendly power sources is necessary. Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered a diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by control input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC.

Keywords: PEM fuel cell, Diffused aeration system, Artificialintelligence (AI) techniques, neural network control, fuzzy logiccontrol

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Authors: Branislav Timotijevic, Yves Petremand, Markus Luetzelschwab, Dara Bayat, Laurent Aebi

Abstract:

Optical miniaturized sensors with remote readout are required devices for the monitoring in harsh electromagnetic environments. As an example, in turbo and hydro generators, excessively high vibrations of the end-windings can lead to dramatic damages, imposing very high, additional service costs. A significant change of the generator temperature can also be an indicator of the system failure. Continuous monitoring of vibrations, temperature, humidity, and gases is therefore mandatory. The high electromagnetic fields in the generators impose the use of non-conductive devices in order to prevent electromagnetic interferences and to electrically isolate the sensing element to the electronic readout. Metal-free sensors are good candidates for such systems since they are immune to very strong electromagnetic fields and given the fact that they are non-conductive. We have realized miniature optical accelerometer and temperature sensors for a remote sensing of the harsh environments using the common, inexpensive silicon Micro Electro-Mechanical System (MEMS) platform. Both devices show highly linear response. The accelerometer has a deviation within 1% from the linear fit when tested in a range 0 – 40 g. The temperature sensor can provide the measurement accuracy better than 1 °C in a range 20 – 150 °C. The design of other type of sensors for the environments with high electromagnetic interferences has also been discussed.

Keywords: Accelerometer, harsh environment, optical MEMS, pressure sensor, remote sensing, temperature sensor.

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Authors: Shang-En Yu

Abstract:

Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.

Keywords: Heterogeneity, residential mortgage loans, foreclosure.

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Authors: Satish Kumar Peddapelli

Abstract:

This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have became popular and considerable interest by researcher are given on them. A fast space-vector pulse width modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analyzed.

Keywords: Five-level inverter, Space vector pulse wide modulation, diode clamped inverter.

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