Search results for: improved rate

Authors: Sidhartha Panda, N. P. Padhy

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Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.

Keywords: Thyristor Controlled Series Compensator, geneticalgorithm; particle swarm optimization; Phillips-Heffron model;power system stability.

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Authors: Azam Tabatabaee, Fereshteh Dastgoshadeh, Akram Tabatabaee

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This paper describes the use of by-products as adsorbents for removing heavy metals from aqueous effluent solutions. Products of almond skin, walnut shell, saw dust, rice bran and egg shell were evaluated as metal ion adsorbents in aqueous solutions. A comparative study was done with commercial adsorbents like ion exchange resins and activated carbon too. Batch experiments were investigated to determine the affinity of all of biomasses for, Cd(ΙΙ), Cr(ΙΙΙ), Ni(ΙΙ), and Pb(ΙΙ) metal ions at pH 5. The rate of metal ion removal in the synthetic wastewater by the biomass was evaluated by measuring final concentration of synthetic wastewater. At a concentration of metal ion (50 mg/L), egg shell adsorbed high levels (98.6 – 99.7%) of Pb(ΙΙ) and Cr(ΙΙΙ) and walnut shell adsorbed high levels (35.3 – 65.4%) of Ni(ΙΙ) and Cd(ΙΙ). In this study, it has been shown that by-products were excellent adsorbents for removal of toxic ions from wastewater with efficiency comparable to commercially available adsorbents, but at a reduced cost. Also statistical studies using Independent Sample t Test and ANOVA Oneway for statistical comparison between various elements adsorption showed that there isn’t a significant difference in some elements adsorption percentage by by-products and commercial adsorbents.

Keywords: Adsorbents, heavy metals, commercial adsorbents, wastewater, by-products.

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Authors: Christos Bouras, Eirini Stergiou, Charitini Karakostaki, Vasileios Tzanos, Vasileios Kokkinos

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Wind power represents a major pathway to curtailing greenhouse gas emissions and thus reducing the rate of climate change. A wind turbine runs practically emission-free for 20 years, representing one of the most environmentally sustainable sources of energy. Nevertheless, environmental and biodiversity concerns can often slow down or halt the deployment of wind farms due to local public opposition. This opposition is often fuelled by poor relationships between wind energy stakeholders and civil society, which in many cases led to conflictual protests and property damage. In this context, addressing these concerns is essential in order to facilitate the proliferation of wind farms in Europe and the phase-out of fossil fuels from the energy mix. The aim of this study is to identify a number of good practices and cases to avoid increasing biodiversity protection at all stages of wind farms’ lifecycle in three participating countries, namely Greece, Latvia, and Poland. The results indicate that although available technological solutions are already being exploited worldwide, in these countries, there is still room for improvement. To address this gap, a set of policy recommendations is proposed to accomplish the wind energy targets in the near future while simultaneously mitigating the pertinent biodiversity risks.

Keywords: Biodiversity protection, environmental impact, social acceptance, wind energy.

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Authors: P. Nafisi Poor, P. Javid

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Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: Adaptive buildings, energy efficiency, intelligent buildings, user comfortability.

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Authors: Ismail Karaca, Ridvan Saracoglu, Omer Soner

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Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.

Keywords: Calculation of risk factor, fuzzy logic, fuzzy programming for ship, safe navigation of ships.

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Authors: H. C. Chinwenyi, H. D. Ibrahim, J. O. Adekunle

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The incubation period is defined as the time from infection with a microorganism to development of symptoms. In this research, two disease models: one with incubation period and another without incubation period were studied. The study involves the use of a  mathematical model with a single incubation period. The test for the existence and stability of the disease free and the endemic equilibrium states for both models were carried out. The fourth order Runge-Kutta method was used to solve both models numerically. Finally, a computer program in MATLAB was developed to run the numerical experiments. From the results, we are able to show that the endemic equilibrium state of the model with incubation period is locally asymptotically stable whereas the endemic equilibrium state of the model without incubation period is unstable under certain conditions on the given model parameters. It was also established that the disease free equilibrium states of the model with and without incubation period are locally asymptotically stable. Furthermore, results from numerical experiments using empirical data obtained from Nigeria Centre for Disease Control (NCDC) showed that the overall population of the infected people for the model with incubation period is higher than that without incubation period. We also established from the results obtained that as the transmission rate from susceptible to infected population increases, the peak values of the infected population for the model with incubation period decrease and are always less than those for the model without incubation period.

Keywords: Asymptotic stability, incubation period, Routh-Hurwitz criterion, Runge Kutta method.

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Authors: K. T. Sun, Y. H. Tai, H. W. Yang, H. T. Lin

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This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli of attended button on the BCI, the evoked potentials of brain waves of the target button, N2P3, had the greatest amplitude among all control buttons. An experimental scene had been constructed that the robot required walking to a specific position and move the view of camera to see the instruction of the mission, and then completed the task. Twelve volunteers participated in this experiment, and experimental results showed that the correct rate of BCI control achieved 80% and the average of execution time was 353 seconds for completing the mission. Four main contributions included in this research: (1) find an efficient component of ERPs, N2P3, for BCI control, (2) embed robot's viewpoint image into user interface for robot control, (3) design an experimental scene and conduct the experiment, and (4) evaluate the performance of the proposed system for assessing the practicability.

Keywords: Brain-computer interface (BCI), event-related potentials (ERPs), robot control, severe physical disabilities.

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Authors: Ayman El Sabagh, Sobhy Sorour, Abd Elhamid Omar, Adel Ragab, Mohammad Sohidul Islam, Celaleddin Barutçular, Akihiro Ueda, Hirofumi Saneoka

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Salinity is one of the major factors limiting crop production in an arid environment. Despite its global importance soybean production suffer the problems of salinity stress causing damages at plant development. So it is implacable to either search for salinity enhancement of soybean plants. Therefore, in the current study we try to clarify the mechanism that might be involved in the ameliorating effects of osmo-protectants such as proline and glycine betaine as well as, compost application on soybean plants grown under salinity stress. The experiment was conducted under greenhouse conditions at the Graduate School of Biosphere Science Laboratory of Hiroshima University, Japan in 2011. The experiment was designed as a spilt-split plot based on randomized complete block design with four replications. The treatments could be summarized as follows; (i) salinity concentrations (0 and 15 mM), (ii) compost treatments (0 and 24 t ha-1) and (iii) the exogenous, proline and glycine betaine concentrations (0 mM and 25 mM) for each. Results indicated that salinity stress induced reduction in growth and physiological aspects (dry weight per plant, chlorophyll content, N and K+ content) of soybean plant compared with those of the unstressed plants. On the other hand, salinity stress led to increases in the electrolyte leakage ratio, Na and proline contents. Special attention was paid to, the tolerance against salt stress was observed, the improvement of salt tolerance resulted from proline, glycine betaine and compost were accompanied with improved K+, and proline accumulation. While, significantly decreased electrolyte leakage ratio and Na+ content. These results clearly demonstrate that harmful effect of salinity could reduce on growth aspects of soybean. Consequently, exogenous osmoprotectants combine with compost will effectively solve seasonal salinity stress problem and are a good strategy to increase salinity resistance of soybean in the drylands.

Keywords: Compost, glycine betaine, growth, proline, salinity tolerance, soybean.

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Authors: Ahmed Mostafa, Mohamed Mahmoud, Nesreen H. Hafez, Mohamed Fahim

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This retrospective study includes 60 patients with pre-invasive breast cancer. Aim of the study: Evaluation of premalignant lesions of the breast (DCIS), different treatment methods and outcome. Patients and methods: 60 patients with DCIS were studied from the period between 2005 to 2012, for 38 patients the primary surgical method was wide local resection (WLE) (63.3%) and the other cases (22 patients, 36.7%) had mastectomy, fourteen cases from those who underwent local excision received radiotherapy, while no adjuvant radiotherapy was given for those who underwent mastectomy. In case of hormonal receptor positive DCIS lesions hormonal treatment (Tamoxifen) was given after local control. Results: No difference in overall survival between mastectomy & breast conserving therapy (wide local excision and adjuvant radiotherapy), however local recurrence rate is higher in case of breast conserving therapy, also no role of Axillary evacuation in case of DCIS. The use of hormonal therapy decreases the incidence of local recurrence by about 98%. Conclusion: The main management of DCIS is local treatment (wide local excision and radiotherapy) with hormonal treatment in case of hormone receptor positive lesions.

Keywords: Ductal carcinoma in situ, surgical treatment, radiotherapy, breast conserving therapy, hormonal treatment.

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Authors: Rafic Ayoubi, Jean-Pierre Dubois, Rania Minkara

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In this paper, we study FPGA implementation of a novel supra-optimal receiver diversity combining technique, generalized maximal ratio combining (GMRC), for wireless transmission over fading channels in SIMO systems. Prior published results using ML-detected GMRC diversity signal driven by BPSK showed superior bit error rate performance to the widely used MRC combining scheme in an imperfect channel estimation (ICE) environment. Under perfect channel estimation conditions, the performance of GMRC and MRC were identical. The main drawback of the GMRC study was that it was theoretical, thus successful FPGA implementation of it using pipeline techniques is needed as a wireless communication test-bed for practical real-life situations. Simulation results showed that the hardware implementation was efficient both in terms of speed and area. Since diversity combining is especially effective in small femto- and picocells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to the hardware of IP-based 4th generation networks.

Keywords: Femto-internet cells, field-programmable gate array, generalized maximal-ratio combining, Lyapunov fractal dimension, pipelining technique, wireless SIMO channels.

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Authors: C. Nutphuang, S. Chirarattananon, V.D. Hien

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In research on natural ventilation, and passive cooling with forced convection, is essential to know how heat flows in a solid object and the pattern of temperature distribution on their surfaces, and eventually how air flows through and convects heat from the surfaces of steel under roof. This paper presents some results from running the computational fluid dynamic program (CFD) by comparison between natural ventilation and forced convection within roof attic that is received directly from solar radiation. The CFD program for modeling air flow inside roof attic has been modified to allow as two cases. First case, the analysis under natural ventilation, is closed area in roof attic and second case, the analysis under forced convection, is opened area in roof attic. These extend of all cases to available predictions of variations such as temperature, pressure, and mass flow rate distributions in each case within roof attic. The comparison shows that this CFD program is an effective model for predicting air flow of temperature and heat transfer coefficient distribution within roof attic. The result shows that forced convection can help to reduce heat transfer through roof attic and an around area of steel core has temperature inner zone lower than natural ventilation type. The different temperature on the steel core of roof attic of two cases was 10-15 oK.

Keywords: CFD program, natural ventilation, forcedconvection, heat transfer, air flow.

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Authors: H. Salonen, E. Castagnoli, C. Vornanen-Winqvist, R. Mikkola, C. Duchaine, L. Morawska, J. Kurnitski

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A wide range of health effects among occupants are associated with the exposure to bioaerosols from fungal sources. Although the accurate role of these aerosols in causing the symptoms and diseases is poorly understood, the important effect of bioaerosol exposure on human health is well recognized. Thus, there is a need to determine all of the contributing factors related to the concentration of fungi in indoor air. In this study, we reviewed and summarized the different factors affecting the concentrations of viable fungi in school buildings. The literature research was conducted using Pubmed and Google Scholar. In addition, we searched the lists of references of selected articles. According to the literature, the main factors influencing the concentration of viable fungi in the school buildings are moisture damage in building structures, the season (temperature and humidity conditions), the type and rate of ventilation, the number and activities of occupants and diurnal variations. This study offers valuable information that can be used in the interpretation of the fungal analysis and to decrease microbial exposure by reducing known sources and/or contributing factors. However, more studies of different local factors contributing to the human microbial exposure in school buildings—as well as other type of buildings and different indoor environments—are needed.

Keywords: Fungi, concentration, indoor, school, contributing factor.

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Authors: Josef Fládr, Jiří Němeček, Veronika Koudelková, Petr Bílý

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Mechanical parameters of cementitious composites differ quite significantly based on the composition of cement matrix. They are also influenced by mixing times and procedure. The research presented in this paper was aimed at identification of differences in microstructure of normal strength (NSC) and differently mixed high strength (HSC) cementitious composites. Scanning electron microscopy (SEM) investigation together with energy dispersive X-ray spectroscopy (EDX) phase analysis of NSC and HSC samples was conducted. Evaluation of interfacial transition zone (ITZ) between the aggregate and cement matrix was performed. Volume share, thickness, porosity and composition of ITZ were studied. In case of HSC, samples obtained by several different mixing procedures were compared in order to find the most suitable procedure. In case of NSC, ITZ was identified around 40-50% of aggregate grains and its thickness typically ranged between 10 and 40 µm. Higher porosity and lower share of clinker was observed in this area as a result of increased water-to-cement ratio (w/c) and the lack of fine particles improving the grading curve of the aggregate. Typical ITZ with lower content of Ca was observed only in one HSC sample, where it was developed around less than 15% of aggregate grains. The typical thickness of ITZ in this sample was similar to ITZ in NSC (between 5 and 40 µm). In the remaining four HSC samples, no ITZ was observed. In general, the share of ITZ in HSC samples was found to be significantly smaller than in NSC samples. As ITZ is the weakest part of the material, this result explains to large extent the improved mechanical properties of HSC compared to NSC. Based on the comparison of characteristics of ITZ in HSC samples prepared by different mixing procedures, the most suitable mixing procedure from the point of view of properties of ITZ was identified.

Keywords: Energy dispersive X-ray spectroscopy, high strength concrete, interfacial transition zone, mixing procedure, normal strength concrete, scanning electron microscopy.

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Authors: Dipti Patra, Mridula J

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In this paper, we propose a new image segmentation approach for colour textured images. The proposed method for image segmentation consists of two stages. In the first stage, textural features using gray level co-occurrence matrix(GLCM) are computed for regions of interest (ROI) considered for each class. ROI acts as ground truth for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Statistical mean feature at certain inter pixel distance (IPD) of I2 component was considered to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and modeled as Markov Random Field (MRF) model to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of the existing schemes, JSEG and another scheme which uses GLCM and MRF in RGB colour space. The proposed method is found to be outperforming the existing ones in terms of segmentation accuracy with acceptable rate of convergence. The results are validated with synthetic and real textured images.

Keywords: Texture Image Segmentation, Gray Level Cooccurrence Matrix, Markov Random Field Model, Ohta colour space, ICM algorithm.

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Authors: Mardiati Zain, N. Jamarun, A. S. Tjakradidjaja

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The effect of phosphorus supplementation of ammoniated rice straw was studied. The in vitro experiment was carried out following the first stage of Tilley and Terry method. The treatments consisting of four diets were A = 50% ammoniated rice straw + 50% concentrate (control), B = A + 0.2% Phosphor (P) supplement, C = A + 0.4% Phosphor (P) supplement, and D = A + 0.6% Phosphor (P) supplement of dry matter. Completely randomized design was used as the experimental design with differences among treatment means were examined using Duncan multiple range test. Variables measured were total bacterial and cellulolytic bacterial population, cellulolytic enzyme activity, ammonia (NH3) and volatile fatty acid (VFA) concentrations, as fermentability indicators and synthesized microbial protein, as well as degradability indicators including dry matter (DM), organic matter (OM), neutral detergent fibre (NDF), acid detergent fibre (ADF) and cellulose. The results indicated that fermentability and degradability of diets consisting ammoniated rice straw with P supplementation were significantly higher than the control diet (P< 0.05). It is concluded that P supplementation is important to improve fermentability and degradability of rations containing ammoniated RS and concentrate. In terms of the most effective level of P supplementation occurred at a supplementation rate of 0.4% of dry matter.

Keywords: Ammoniated rice straw, phosphorus, fermentability, degradability and synthesized microbial protein.

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, Fuzzy c means, Liver segmentation.

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Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

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This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

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Authors: S. R. M. Kutty, S. N. I. Ngatenah, M. H. Isa, A. Malakahmad

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Water hyacinth has been used in aquatic systems for wastewater purification in many years worldwide. The role of water hyacinth (Eichhornia crassipes) species in polishing nitrate and phosphorus concentration from municipal wastewater treatment plant effluent by phytoremediation method was evaluated. The objective of this project is to determine the removal efficiency of water hyacinth in polishing nitrate and phosphorus, as well as chemical oxygen demand (COD) and ammonia. Water hyacinth is considered as the most efficient aquatic plant used in removing vast range of pollutants such as organic matters, nutrients and heavy metals. Water hyacinth, also referred as macrophytes, were cultivated in the treatment house in a reactor tank of approximately 90(L) x 40(W) x 25(H) in dimension and built with three compartments. Three water hyacinths were placed in each compartments and water sample in each compartment were collected in every two days. The plant observation was conducted by weight measurement, plant uptake and new young shoot development. Water hyacinth effectively removed approximately 49% of COD, 81% of ammonia, 67% of phosphorus and 92% of nitrate. It also showed significant growth rate at starting from day 6 with 0.33 shoot/day and they kept developing up to 0.38 shoot/day at the end of day 24. From the studies conducted, it was proved that water hyacinth is capable of polishing the effluent of municipal wastewater which contains undesirable amount of nitrate and phosphorus concentration.

Keywords: water hyacinth, phytoremediation, nutrient removal, Eichhornia crassipes

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Authors: M. A Anjum, M. Y. Javed, A. Basit

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In this paper a new approach to face recognition is presented that achieves double dimension reduction making the system computationally efficient with better recognition results. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results improve with increase in face image resolution and levels off when arriving at a certain resolution level. In the proposed model of face recognition, first image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to better computational speed and feature extraction potential of Discrete Cosine Transform (DCT) it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A trade of between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL database, Yale database and a color database. The proposed technique has performed much better compared to other techniques. The significance of the model is two fold: (1) dimension reduction up to an effective and suitable face image resolution (2) appropriate DCT coefficients are retained to achieve best recognition results with varying image poses, intensity and illumination level.

Keywords: Biometrics, DCT, Face Recognition, Feature extraction.

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Authors: Rangoli Goyal, Rama Bhargava

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The triple diffusive boundary layer flow of nanofluid under the action of constant magnetic field over a non-linear stretching sheet has been investigated numerically. The model includes the effect of Brownian motion, thermophoresis, and cross-diffusion; slip mechanisms which are primarily responsible for the enhancement of the convective features of nanofluid. The governing partial differential equations are transformed into a system of ordinary differential equations (by using group theory transformations) and solved numerically by using variational finite element method. The effects of various controlling parameters, such as the magnetic influence number, thermophoresis parameter, Brownian motion parameter, modified Dufour parameter, and Dufour solutal Lewis number, on the fluid flow as well as on heat and mass transfer coefficients (both of solute and nanofluid) are presented graphically and discussed quantitatively. The present study has industrial applications in aerodynamic extrusion of plastic sheets, coating and suspensions, melt spinning, hot rolling, wire drawing, glass-fibre production, and manufacture of polymer and rubber sheets, where the quality of the desired product depends on the stretching rate as well as external field including magnetic effects.

Keywords: FEM, Thermophoresis, Diffusiophoresis, Brownian motion.

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Authors: A. El-S. Makled, M. K. Al-Tamimi

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A mathematical model to predict the performance parameters (thrusts, chamber pressures, fuel mass flow rates, mixture ratios, and regression rates during firing time) of hybrid rocket motor (HRM) is evaluated. The internal ballistic (IB) hybrid combustion model assumes that the solid fuel surface regression rate is controlled only by heat transfer (convective and radiative) from flame zone to solid fuel burning surface. A laboratory HRM is designed, manufactured, and tested for low thrust profile space missions (10-15 N) and for validating the mathematical model (computer program). The polymer material and gaseous oxidizer which are selected for this experimental work are polymethyle-methacrylate (PMMA) and polyethylene (PE) as solid fuel grain and gaseous oxygen (GO₂) as oxidizer. The variation of various operational parameters with time is determined systematically and experimentally in firing of up to 20 seconds, and an average combustion efficiency of 95% of theory is achieved, which was the goal of these experiments. The comparison between recording fire data and predicting analytical parameters shows good agreement with the error that does not exceed 4.5% during all firing time. The current mathematical (computer) code can be used as a powerful tool for HRM analytical design parameters.

Keywords: Hybrid combustion, internal ballistics, hybrid rocket motor, performance parameters.

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.

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Authors: Y. Al-Mahroqi, I.A. Metwally, A. Al-Hinai, A. Al-Badi

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Losses reduction initiatives in distribution systems have been activated due to the increasing cost of supplying electricity, the shortage in fuel with ever-increasing cost to produce more power, and the global warming concerns. These initiatives have been introduced to the utilities in shape of incentives and penalties. Recently, the electricity distribution companies in Oman have been incentivized to reduce the distribution technical and non-technical losses with an equal annual reduction rate for 6 years. In this paper, different techniques for losses reduction in Mazoon Electricity Company (MZEC) are addressed. In this company, high numbers of substation and feeders were found to be non-compliant with the Distribution System Security Standard (DSSS). Therefore, 33 projects have been suggested to bring non-complying 29 substations and 28 feeders to meet the planed criteria and to comply with the DSSS. The largest part of MZEC-s network (South Batinah region) was modeled by ETAP software package. The model has been extended to implement the proposed projects and to examine their effects on losses reduction. Simulation results have shown that the implementation of these projects leads to a significant improvement in voltage profile, and reduction in the active and the reactive power losses. Finally, the economical analysis has revealed that the implementation of the proposed projects in MZEC leads to an annual saving of about US$ 5 million.

Keywords: Losses Reduction, Technical Losses, Non-Technical Losses, Cost Analysis

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Authors: Mohammad Mirwais Yusufi

Abstract:

Skillful management is one of the most important success factors for today’s farms. When a farm is well managed, it can generate funds for its sustainability. Grape is one of the most diffused fruits in the world and one of the most important cash crops with high potential of production in Afghanistan as well. While there are several organizations intervening for improvement of this cash crop, the quality and quantity are still not satisfactory for producers and external markets. The situation has not changed over the years. Therefore, a survey was conducted in 2017 with 60 grape growers, supported by questionnaires in Mirbachakot, Kalakan and Shakardara districts of Kabul province. The purpose was to get an understanding of the current socio-demographic characteristics of farmers, management methods, constraints, farm size, yield and contribution of grape farming to household income. Findings indicate that grape farming was predominant 83.3% male, 16.6% female and small-scale farmers were the main grape producers, 60% < 1 ha of land under grape production. Likewise, 50% had more than > 10 years and 33.3% between 1-5 years’ experience in grape farming. The high level of illiteracy and diseases had significant digit effect on growth, yield and quality of grapes. The results showed that vineyard management operations to protect grapes from mechanical damage are very poor or completely absent. Comparing developed countries, table grape is one of the fruits with the highest input of technology, while in developing countries the cost of labor is low but the purchase of the equipment is very high due to financial situation. Hence the low quality and quantity of grape are influenced by poor management methods, such as non-availability of experts and lack of technical guidance in the study site. Thereby, the study suggested that improved agricultural extension services and managerial skills could contribute to addressing the problems.

Keywords: Efficient resources use, management skills, constraints factors, Kabul.

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Authors: Nader Nabhani, Amir Tofighi

Abstract:

Nanotechnology is the science of creating, using and manipulating objects which have at least one dimension in range of 0.1 to 100 nanometers. In other words, nanotechnology is reconstructing a substance using its individual atoms and arranging them in a way that is desirable for our purpose. The main reason that nanotechnology has been attracting attentions is the unique properties that objects show when they are formed at nano-scale. These differing characteristics that nano-scale materials show compared to their nature-existing form is both useful in creating high quality products and dangerous when being in contact with body or spread in environment. In order to control and lower the risk of such nano-scale particles, the main following three topics should be considered: 1) First of all, these materials would cause long term diseases that may show their effects on body years after being penetrated in human organs and since this science has become recently developed in industrial scale not enough information is available about their hazards on body. 2) The second is that these particles can easily spread out in environment and remain in air, soil or water for very long time, besides their high ability to penetrate body skin and causing new kinds of diseases. 3) The third one is that to protect body and environment against the danger of these particles, the protective barriers must be finer than these small objects and such defenses are hard to accomplish. This paper will review, discuss and assess the risks that human and environment face as this new science develops at a high rate.

Keywords: Nanotechnology, risk assessment, environment.

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Authors: Sanjida Ahmed Sinthia

Abstract:

Bangladesh is one of the poorest countries in the world. There are several reasons for this insufficiency and uncontrolled population growth is one of the prime reasons. Others include low economic progress, imbalanced resource management, unemployment and underemployment, urban migration and natural catastrophes etc. As a result, the rate of urban poor is increasing inevitably in every sphere of urban cities in Bangladesh and Dhaka is the most affected one. Besides there is scarcity of urban land, housing, urban infrastructure and amenities which create pressure on urban cities and mostly encroach the open space, wetlands that causes environmental degradation. Government has no or limited control over these due to poor government policy and management, political pressure and lack of resource management. Unfortunately, over centralization and bureaucracy creates unnecessary delay and interruptions in any government initiations. There is also no coordination between government and private sector developer to solve the problem of urban Poor. To understand the problem of these huge populations this paper analyzes one of the single largest slum areas in Dhaka, Korail Slum. The study focuses on socio demographic analysis, morphological pattern and role of different actors responsible for the improvements of the area and recommended some possible steps for determining the potential outcomes.

Keywords: Demographic analysis, environmental degradation, physical condition, government policy, housing and land management policy.

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Authors: Jahan Zeb, Muhammad Younus Javed, Usman Qayyum

Abstract:

This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.

Keywords: Average filtering, Bicubic Interpolation, Neurons, vectorization.

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Authors: M. R. Daneshgar, S. E. Habibi, E. Daneshgar, A. Daneshgar

Abstract:

In this paper, a two-dimensional method is developed to simulate the fillet welds in a stiffened cylindrical shell, using finite element method. The stiffener material is aluminum 2519. The thermo-elasto-plastic analysis is used to analyze the thermo-mechanical behavior. Due to the high heat flux rate of the welding process, two uncouple thermal and mechanical analysis are carried out instead of performing a single couple thermo-mechanical simulation. In order to investigate the effects of the welding procedures, two different welding techniques are examined. The resulted residual stresses and distortions due to different welding procedures are obtained. Furthermore, this study employed the technique of element birth and death to simulate the weld filler variation with time in fillet welds. The obtained results are in good agreement with the published experimental and three-dimensional numerical simulation results. Therefore, the proposed 2D modeling technique can effectively give the corresponding results of 3D models. Furthermore, by inspection of the obtained residual hoop and transverse stresses and angular distortions, proper welding procedure is suggested.

Keywords: Stiffened cylindrical shell, fillet welds, residual stress, angular distortion, finite element method.

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Authors: Hyun-Koo Kim, Young-Nam Shin, Sa-gong Kuk, Ju H. Park, Ho-Youl Jung

Abstract:

This paper presents an effective traffic lights detection method at the night-time. First, candidate blobs of traffic lights are extracted from RGB color image. Input image is represented on the dominant color domain by using color transform proposed by Ruta, then red and green color dominant regions are selected as candidates. After candidate blob selection, we carry out shape filter for noise reduction using information of blobs such as length, area, area of boundary box, etc. A multi-class classifier based on SVM (Support Vector Machine) applies into the candidates. Three kinds of features are used. We use basic features such as blob width, height, center coordinate, area, area of blob. Bright based stochastic features are also used. In particular, geometric based moment-s values between candidate region and adjacent region are proposed and used to improve the detection performance. The proposed system is implemented on Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the urban and rural road videos. Through the test, we show that the proposed method using PF, BMF, and GMF reaches up to 93 % of detection rate with computation time of in average 15 ms/frame.

Keywords: Night-time traffic light detection, multi-class classification, driving assistance system.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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