Search results for: total capacity algorithm

Authors: Lucero Gonzalez, Salvador Alfaro

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Carbon dioxide (CO2) and methane (CH4) are considered as the compounds with higher abundance among the greenhouse gases (CO2, NOx, SOx, CxHx, etc.), due to its higher concentration, this two gases have a greater impact in the environment pollution and provokes global warming. So, recovery, disposal and subsequent reuse, are of great interest, especially from the ecological and health perspective. By one hand, porous inorganic materials are good candidates to capture gases, because these type of materials are higher stability from the point view of thermal, chemical and mechanical under adsorption gas processes. By another hand, during the design and the synthetic preparation of the porous materials is possible add other intrinsic properties (physicochemical and structural) by adding chemical compounds as dopants or using structured directed agents or surfactants to improve the porous structure, the above features allow to have alternative materials for separation, capture and storage of greenhouse gases. In this work, ordered mesoporous materials base silica were prepared using Surfynol as surfactant. The surfactant micelles are commonly used as self-assembly templates for the development of new structure porous silica’s, adding a variety of textures and structures. By another hand, the Surfynol is a commercial surfactant, is non-ionic, for that is necessary determine its critical micelles concentration (cmc) by the pyrene I1/I3 ratio method, before to prepare silica particles. One time known the CMC, a precursor gel was prepared via sol-gel process at room temperature using TEOS as silica precursor, NH4OH as catalyst, Surfynol as template and H2O as solvent. Then, the gel precursor was treatment hydrothermally in a Teflon-lined stainless steel autoclave with a volume of 100 mL and kept at 100 ºC for 24 h under static conditions in a convection oven. After that, the porous silica particles obtained were impregnated with lithium to improve the CO2 adsorption capacity. Then the silica particles were characterized physicochemical, morphology and structurally, by XRD, FTIR, BET and SEM techniques. The thermal stability and the CO2 adsorption capacity was evaluated by thermogravimetric analysis (TGA). According the results, we found that the Surfynol is a good candidate to prepare silica particles with an ordered structure. Also the TGA analysis shown that the particles has a good thermal stability in the range of 250 °C and 800 °C. The best materials had, the capacity to adsorbing 70 and 90 mg per gram of silica particles and its CO2 adsorption capacity depends on the way to thermal pretreatment of the porous silica before of the adsorption experiments and of the concentration of surfactant used during the synthesis of silica particles. Acknowledgments: This work was supported by SIP-IPN through project SIP-20161862.

Keywords: CO2 adsorption, lithium as dopant, porous silica, surfynol as surfactant, thermogravimetric analysis

Procedia PDF Downloads 268

Authors: Sihem Benmimoune, Abdelbaki Lemgharbi, Ahmed Ait Yahia, Abdelkrim Kameli

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Our study is based on chemical and phytochemical characterization of Artemisia absinthium L and in vitro tests to demonstrate the biological activities of essential oil and natural extract. A qualitative and quantitative comparison of the essential oil extracted by two extraction procedures was performed by analysis of CG/SM and the yield calculation. The method of hydrodistillation has a chemical composition and provides oil content than the best training water vapor. These oils are composed mainly of thujone followed chamazulene and ρ-cymene. The antimicrobial activity of wormwood oil was tested in vitro by two methods (agar diffusion and microdilution) on four plant pathogenic fungi (Aspergillus sp, Botrytis cinerea, Fusarium culmorum and Helminthosporium sp). The study of the antifungal effect showed that this oil has an inhibitory effect counterpart the microorganisms tested in particular the strain Botrytis cinerea. Otherwise, this activity depends on the nature of the oil and the germ itself. The antioxidant activity in vitro was studied with the DPPH method. The activity test shows that the oil and extract of Artemisia absinthium have a very low antioxidant capacity compared to the antioxidants used as a reference. The extract has a potentially high antiradical power not from its oil. The quantitative determinations of phenolic compounds by the Folin-Ciocalteu revealed that absinthe is low in total polyphenols and tannins.

Keywords: artemisia absinthium, biological activities, essential oil, extraction processes

Procedia PDF Downloads 341

Authors: A. Bere, H. G. Sithuba, K. Kyei, C. Sigauke

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We employ a discrete logit survival model to investigate the risk factors for early alcohol intake among students at two tertiary institutions in Thohoyandou, South Africa. Data were collected from a sample of 744 students using a self-administered questionnaire. Significant covariates were arrived at through a regularization algorithm implemented using the glmmLasso package. The tuning parameter was determined using a five-fold cross-validation algorithm. The baseline hazard was modelled as a smooth function of time through the use of spline functions. The results show that the hazard of initial alcohol intake peaks at the age of about 16 years and that at any given time, being of a male gender, prior use of other drugs, having drinking peers, having experienced negative life events and physical abuse are associated with a higher risk of alcohol intake debut.

Keywords: cross-validation, discrete hazard model, LASSO, smooth baseline hazard

Procedia PDF Downloads 192

Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

Procedia PDF Downloads 293

Authors: Emad Askar, Eldesoky Elsoaly, Mohamed Kamel, Hisham Kamel

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The objective of this article is to improve the passive vibration damping of solar array (SA) used in space structures, by the effective application of numerical optimization. A case study of a SA is used for demonstration. A finite element (FE) model was created and verified by experimental testing. Optimization was then conducted by implementing the FE model with the genetic algorithm, to find the optimal placement of aluminum circular patches, to suppress the first two bending mode shapes. The results were verified using experimental testing. Finally, a parametric study was conducted using the FE model where patch locations, material type, and shape were varied one at a time, and the results were compared with the optimal ones. The results clearly show that through the proper application of FE modeling and numerical optimization, passive vibration damping of space structures has been successfully achieved.

Keywords: damping optimization, genetic algorithm optimization, passive vibration damping, solar array vibration damping

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Ismail Shahin

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A blind source separation method is proposed; in this method we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full bandwidth, and can promote better rates of convergence.

Keywords: blind source separation, computational complexity, subband, convergence speed, mixture

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Authors: Bello Yusuf Idi

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The Gongola arm of the Upper Banue Trough, Northeastern Nigeria is predominantly covered by the outcrops of Limestone-bearing rocks in form of Sandstone with intercalation of carbonate clay, shale, basaltic, felsphatic and migmatide rocks at subpixel dimension. In this work, subpixel classification algorithm was used to classify the data acquired from landsat 7 Enhance Thematic Mapper (ETM+) satellite system with the aim of producing fractional distribution image for three most economically important solid minerals of the area: Limestone, Basalt and Migmatide. Linear Spectral Unmixing (LSU) algorithm was used to produce fractional distribution image of abundance of the three mineral resources within a 100Km2 portion of the area. The results show that the minerals occur at different proportion all over the area. The fractional map could therefore serve as a guide to the ongoing reconnaissance for the economic potentiality of the formation.

Keywords: linear spectral un-mixing, upper benue trough, gongola arm, geological engineering

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Authors: Marco Tulio C. Faria

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Fixed-geometry hydrodynamic journal bearings are one of the best supporting systems for several applications of rotating machinery. Cylindrical journal bearings present excellent load-carrying capacity and low manufacturing costs, but they are subjected to the oil-film instability at high speeds. An attempt of overcoming this instability problem has been the development of non-circular journal bearings. This work deals with an analysis of oil-lubricated elliptical journal bearings using the finite element method. Steady-state and dynamic performance characteristics of elliptical bearings are rendered by zeroth- and first-order lubrication equations obtained through a linearized perturbation method applied on the classical Reynolds equation. Four-node isoparametric rectangular finite elements are employed to model the bearing thin film flow. Curves of elliptical bearing load capacity and dynamic force coefficients are rendered at several operating conditions. The results presented in this work demonstrate the influence of the bearing ellipticity on its performance at different loading conditions.

Keywords: elliptical journal bearings, non-circular journal bearings, hydrodynamic bearings, finite element method

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Authors: G. Torabian, A. Fathi, P. Valtchev, F. Dehghani

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The superior polyphenol content of Sambucus nigra berries has high health potentials for the production of nutraceutical products. Numerous factors influence the polyphenol content of the final products including the berries’ source and the subsequent processing production steps. The aim of this study is to compare the polyphenol content of berries from two different sources and also to optimise the polyphenol extraction process from elderberries. Berries from source B obtained more acceptable physical properties than source A; a single berry from source B was double in size and weight (both wet and dry weight) compared with a source A berry. Despite the appropriate physical characteristics of source B berries, their polyphenolic profile was inferior; as source A berries had 2.3 fold higher total anthocyanin content, and nearly two times greater total phenolic content and total flavonoid content compared to source B. Moreover, the result of this study showed that almost 50 percent of the phenolic content of berries are entrapped within their skin and pulp that potentially cannot be extracted by press juicing. To address this challenge and to increase the total polyphenol yield of the extract, we used cold-shock blade grinding method to break the cell walls. The result of this study showed that using cultivars with higher phenolic content as well as using the whole fruit including juice, skin and pulp can increase polyphenol yield significantly; and thus, may boost the potential of using elderberries as therapeutic products.

Keywords: different sources, elderberry, grinding, juicing, polyphenols

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 131

Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

Procedia PDF Downloads 194

Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

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

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

Procedia PDF Downloads 151

Authors: Seung-Won Lee, JongSoo Lee, Won-Jik Yang, Hyung-Joon Kim

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A capacity spectrum method (CSM), one of methodologies to evaluate seismic fragilities of building structures, has been long recognized as the most convenient method, even if it contains several limitations to predict the seismic response of structures of interest. This paper proposes the procedure to estimate seismic fragility curves using an incremental dynamic analysis (IDA) rather than the method adopting a CSM. To achieve the research purpose, this study compares the seismic fragility curves of a 5-story reinforced concrete (RC) moment frame obtained from both methods, an IDA method and a CSM. Both seismic fragility curves are similar in slight and moderate damage states whereas the fragility curve obtained from the IDA method presents less variation (or uncertainties) in extensive and complete damage states. This is due to the fact that the IDA method can properly capture the structural response beyond yielding rather than the CSM and can directly calculate higher mode effects. From these observations, the CSM could overestimate seismic vulnerabilities of the studied structure in extensive or complete damage states.

Keywords: seismic fragility curve, incremental dynamic analysis, capacity spectrum method, reinforced concrete moment frame

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Authors: Azin Nasrollah Zadeh

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A factorial experiment was applied to evaluate the effect of chemical and biological fertilizer on yield, total nitrogen uptake and NHI of rice. Four biological treatments including:(M1:no fertilizer),( M2:10 ton/ha cow dung ),(M3:20 ton/ha cow dung) and (M4:5 ton/ha azolla compost) and four chemical fertilizer treatments including: (S1: no fertilizer),(S2:40 kg N /ha),(S3:60 kg N /ha) and ( S4:80 kg N /ha ) were compared. Results showed that highest rate of yield (3387 kg/ha) and total nitrogen uptake (81.4 kg/ha) were reached the highest value at M4. Among the chemical fertilizers the highest grain yield (3373 kg/ha) and total nitrogen uptake (87.7) belonged to highest nitrogen level (S4).Also biological and chemical fertilizers were no significant on Harvest index (NHI). Interaction effect of chemical × biological fertilizers didn't show significant difference between all parameters except of yield, as the most grain yield were obtained in M4S4. So it can be concluded that using of bioilogical fertilizers at appropriate rate and type, considering plant requirement, may improve grain yield, nitrogen uptake and use efficiency in rice.

Keywords: azolla, fertilizer, nitrogen uptake, rice, yield

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Ahmed Bellakhdhar

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This paper aims to test the impact of various variables-namely, investment in physical capital, investment in human capital, openness to trade and foreign direct investments, and distance from the technology frontier-on economic growth in the Tunisian context during the period 1976-2010. Empirical results identify that the impact of human capital is significantly positive. This finding confirms the hypothesis that human capital is a main driver of economic performance through its role of improving the internal productive capacity and the absorption of foreign technology especially via foreign direct investments. The effect of FDI is significantly positive in all alternative regressions and the coefficient associated to physical capital variable is positive, but not significant overall. Concerning the import of technologically advanced equipments, our estimates show the absence of a significant direct impact on economic growth in Tunisia. Our empirical results also support the assumption of a non linear relationship between tax and growth and demonstrate the existence of an inverted-U curve between the two variables, in the spirit of the “Laffer curve”.

Keywords: Endogenous growth, Human capital, Technology transfer, Absorptive capacity

Procedia PDF Downloads 133

Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

Procedia PDF Downloads 151

Authors: Fatima Benchikh-Amiraa, Hocine Laouerb, Smain Amiraa, Guido Flaminic

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The essential oils of the aerial parts of Lavandula multifida and L. stoechas were extracted at the full bloom stage by hydrodistillation and theirs chemical compositions were estimated by means of gas chromatography–mass spectrometry (GC–MS). A total of 46 and 67 constituents were identified representing 95.5% and 98.2% of the total oils, respectively. The main components of L. multifida oil were carvacrol (63.8%), beta-bisabolene (8.7%), spathulenol (6.2%), caryophyllene oxide (3.6%) and linalool (2.9%). The oil of L. stoechas was dominated by fenchone (63.9%), camphor (7.8%), 1,8-cineole (5.3%) and myrtenyl acetate (4.2).

Keywords: essential oils, Lavandula multifida, Lavandula stoechas, chemical and molecular engineering

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Authors: Reta

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Coffee is widely consumed not only because of its typical taste, but coffee has antioxidant properties because of its polyphenols, and it stimulates brain's performance. The main problem with the consumption of coffee is its content of caffeine. Caffeine, when consumed in excess, can increase muscle tension, stimulate the heart, and increase the secretion of gastric acid. In this research, we applied ohmic-based fermentation technology, which is specially designed to mimic the stomach. We used Arabica coffee, which although cheaper than Luwak coffee, has high acidity, which needs to be reduced. Hence, we applied the ohmic technology, varied the time and temperature of the process and measured the total acidity of the coffee to determine optimum fermentation conditions. Results revealed total acidity of the coffee varied with fermentation conditions; 0.32% at 400C and 12 hr, and 0.52% at 400C and 6 hr. The longer the fermentation, the lower was the acidity. The acidity of the mongoose-fermented (natural fermentation) beans was 2.34%, which is substantially higher than the acidity of the ohmic samples. Ohmic-based fermentation technology, therefore, offers improvements in coffee quality, and this is discussed to highlight the potential of ohmic technology in coffee processing.

Keywords: ohmic technology, fermentation, coffee quality, Arabica coffee

Procedia PDF Downloads 342

Authors: Burcu Akyildiz, Cigdem Kadaifci, Y. Ilker Topcu, Burc Ulengin

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In today’s business environment, companies should make strategic decisions to gain sustainable competitive advantage. Order selection is a crucial issue among these decisions especially for steel production industry. When the companies allocate a high proportion of their design and production capacities to their ongoing projects, determining which customer order should be chosen among the potential orders without exceeding the remaining capacity is the major critical problem. In this study, it is aimed to identify and prioritize the evaluation factors for the customer order selection problem. Conjoint analysis is used to examine the importance level of each factor which is determined as the potential profit rate per unit of time, the compatibility of potential order with available capacity, the level of potential future order with higher profit, customer credit of future business opportunity, and the negotiability level of production schedule for the order.

Keywords: conjoint analysis, order prioritization, profit management, structural steel firm

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Authors: Vivek Anand, Rainer Naegele

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Industry 4.0 envisages 'smart' manufacturing and services, taking the automation of the 3rd Industrial Revolution to the autonomy of the 4th Industrial Revolution. Powered by innovations in technology and business models, this disruptive transformation is revitalising industry by integrating silos across and beyond value chains. Motivated by the challenges faced by SMEs and Startups in understanding and adopting Industry 4.0, this paper aims to analyse the concept of Growth Poles and evaluate the possibility of its application to Innovation Clusters that strive to propel Industry 4.0 adoption and capacity building. The proposed paper applies qualitative research methodologies including focus groups and survey questionnaires to identify the various factors that affect formation and development of Innovation Clusters. Employing content analysis, the interaction between SMEs and other ecosystem players in such clusters is studied. A strong collaborative culture is a key driver of digital transformation and technology adoption across sectors, value chains and supply chains; and will position these cluster-based growth poles at the forefront of industrial renaissance. Motivated by this argument, and based on the results of the qualitative research, a roadmap will be proposed to position Innovation Clusters as Growth Poles and effective ecosystems to support Industry 4.0 adoption in a region in the medium to long term. This paper will contribute to the current understanding of the role of Innovation Clusters in capacity building. Relevant management and policy implications stem from the analysis. Furthermore, the findings will be helpful for academicians and policymakers alike, who can leverage an ‘innovation cluster policy’ to enable Industry 4.0 Growth Poles in their regions.

Keywords: digital transformation, fourth industrial revolution, growth poles, industry 4.0, innovation clusters, innovation policy, SMEs and startups

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Authors: Ali Heidari

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This paper presents the principles for improving flood mitigation operation in multipurpose dams and maximizing reservoir performance during flood occurrence with a focus on the real-time operation of gated spillways. The criteria of operation include the safety of dams during flood management, minimizing the downstream flood risk by decreasing the flood hazard and fulfilling water supply and other purposes of the dam operation in mid and long terms horizons. The parameters deemed to be important include flood inflow, outlet capacity restrictions, downstream flood inundation damages, economic revenue of dam operation, and environmental and sedimentation restrictions. A simulation model was used to determine the real-time release of the Dez dam located in the Dez rivers in southwest Iran, considering the gate regulation curves for the gated spillway. The results of the simulation model show that there is a possibility to improve the current procedures used in the real-time operation of the dams, particularly using gate regulation curves and early flood forecasting system results. The Dez dam operation data shows that in one of the best flood control records, % 17 of the total active volume and flood control pool of the reservoir have not been used in decreasing the downstream flood hazard despite the availability of a flood forecasting system.

Keywords: dam operation, flood control criteria, Dez dam, Iran

Procedia PDF Downloads 225

Authors: Rung-Shiang Cheng, Wei-Jun Hong, Jheng-Syun Wang, Kawuu W. Lin

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Users rely increasingly on Location-Based Services (LBS) and automated navigation/guidance systems nowadays. However, while such services are easily implemented in outdoor environments using Global Positioning System (GPS) technology, a requirement still exists for accurate localization and guidance schemes in indoor settings. Accordingly, the present study presents a methodology based on GPS, Bluetooth Low Energy (BLE) beacons, and Near Field Communication (NFC) technology. Through establishing graphic information and the design of algorithm, this study develops a guidance system for indoor and outdoor on smartphones, with aim to provide users a smart life through this system. The presented system is implemented on a smartphone and evaluated on a student campus environment. The experimental results confirm the ability of the presented app to switch automatically from an outdoor mode to an indoor mode and to guide the user to the requested target destination via the shortest possible route.

Keywords: beacon, indoor, BLE, Dijkstra algorithm

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Authors: Yue-Tzu Yang, Shu-Ching Liao

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This study presents the numerical simulation of three-dimensional incompressible steady and laminar fluid flow and conjugate heat transfer of a trapezoidal microchannel heat sink using water as a cooling fluid in a silicon substrate. Navier-Stokes equations with conjugate energy equation are discretized by finite-volume method. We perform numerical computations for a range of 50 ≦ Re ≦ 600, 0.05W ≦ P ≦ 0.8W, 20W/cm2 ≦ ≦ 40W/cm2. The present study demonstrates the numerical optimization of a trapezoidal microchannel heat sink design using the response surface methodology (RSM) and the genetic algorithm method (GA). The results show that the average Nusselt number increases with an increase in the Reynolds number or pumping power, and the thermal resistance decreases as the pumping power increases. The thermal resistance of a trapezoidal microchannel is minimized for a constant heat flux and constant pumping power.

Keywords: microchannel heat sinks, conjugate heat transfer, optimization, genetic algorithm method

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Authors: Hamza A. Al-Sewadi, Akram N. A. Aldakari

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Copyright protection and ownership proof of digital multimedia are achieved nowadays by digital watermarking techniques. A text watermarking algorithm for protecting the property rights and ownership judgment of color images is proposed in this paper. Embedding is achieved by inserting texts elements randomly into the color image as noise. The YIQ image processing model is found to be faster than other image processing methods, and hence, it is adopted for the embedding process. An optional choice of encrypting the text watermark before embedding is also suggested (in case required by some applications), where, the text can is encrypted using any enciphering technique adding more difficulty to hackers. Experiments resulted in embedding speed improvement of more than double the speed of other considered systems (such as least significant bit method, and separate color code methods), and a fairly acceptable level of peak signal to noise ratio (PSNR) with low mean square error values for watermarking purposes.

Keywords: steganography, watermarking, time complexity measurements, private keys

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Authors: M. Mana, M. S. Ouali, L. C. de Menorval

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A spent bleaching earth from an edible oil refinery has been treated by impregnation with a normal sodium hydroxide solution followed by mild thermal treatment (100°C). The obtained material (TSBE) was washed, dried and characterized by X-ray diffraction, FTIR, SEM, BET, and thermal analysis. The clay structure was not apparently affected by the treatment and the impregnated organic matter was quantitatively removed. We have investigated the comparative sorption of safranine and methylene blue on this material, the spent bleaching earth (SBE) and the virgin bleaching earth (VBE). The kinetic results fit the pseudo second order kinetic model and the Weber & Morris, intra-particle diffusion model. The pH had no effect on the sorption efficiency. The sorption isotherms followed the Langmuir model for various sorbent concentrations with good values of determination coefficient. A linear relationship was found between the calculated maximum removal capacity and the solid/solution ratio. A comparison between the results obtained with this material and those of the literature highlighted the low cost and the good removal capacity of the treated spent bleaching earth.

Keywords: basic dyes, isotherms, sorption, spent bleaching earth

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Authors: Muhammad Ali Siddiquee, Md. Alauddin, Md. Omar Faruque, Zakir Hossain Howlader, Mohammad Asaduzzaman

Abstract:

Rice bran (RB) and rice bran oil (RBO) are explored as prominent food components worldwide. In this study, fermented rice bran (FRB) was produced by employing edible gram-positive bacteria (Lactobacillus acidophilus, Lactobacillus bulgaricus, and Bifidobacterium bifidum) at 125 x 10⁵ spore g⁻¹ of rice bran, and investigated to evaluate nutritional quality. The crude rice bran oil (CRBO) was extracted from RB, and its quality was also investigated compared to market-available rice bran oil (MRBO) in Bangladesh. We found that fermentation of rice bran with lactic acid bacteria increased total proteins (29.52%), fat (5.38%), ash (48.47%), crude fiber (38.96%), and moisture (61.04%) and reduced the carbohydrate content (36.61%). We also found that essential amino acids (methionine, tryptophan, threonine, valine, leucine, lysine, histidine, and phenylalanine) and non-essential amino acids (alanine, aspartate, glycine, glutamine, proline, serine, and tyrosine) were increased in FRB except methionine and proline. Moreover, total phenolic content, tannin content, flavonoid content, and antioxidant activity were increased in FRB. The RBO analysis showed that γ-oryzanol content (10.00mg/g) was found in CRBO compared to MRBO (ranging from 7.40 to 12.70 mg/g) and Vitamin-E content 0.20% was found higher in CRBO compared to MRBO (ranging 0.097 to 0.12%). The total saturated (25.16%) and total unsaturated fatty acids (74.44%) were found in CRBO, whereas MRBO contained total saturated (22.08 to 24.13%) and total unsaturated fatty acids (71.91 to 83.29%), respectively. The physiochemical parameters were found satisfactory in all samples except acid value and peroxide value higher in CRBO. Finally, animal experiments showed that FRB and CRBO reduce the body weight, glucose, and lipid profile in high-fat diet-induced animal models. Thus, FRB and RBO could be value-added food supplements for human health.

Keywords: fermented rice bran, crude rice bran oil, amino acids, proximate composition, gamma-oryzanol, fatty acids, heavy metals, physiochemical parameters

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. A. El-Rahman, Hassan T. Dorra

Abstract:

In this paper, the optimum design for renewable energy system powered an aquaculture pond was determined. Hybrid Optimization Model for Electric Renewable (HOMER) software program, which is developed by U.S National Renewable Energy Laboratory (NREL), is used for analyzing the feasibility of the stand-alone and hybrid system in this study. HOMER program determines whether renewable energy resources satisfy hourly electric demand or not. The program calculates energy balance for every 8760 hours in a year to simulate operation of the system. This optimization compares the demand for the electrical energy for each hour of the year with the energy supplied by the system for that hour and calculates the relevant energy flow for each component in the model. The essential principle is to minimize the total system cost while HOMER ensures control of the system. Moreover the feasibility analysis of the energy system is also studied. Wind speed, solar irradiance, interest rate and capacity shortage are the parameters which are taken into consideration. The simulation results indicate that the hybrid system is the best choice in this study, yielding lower net present cost. Thus, it provides higher system performance than PV or wind stand-alone systems.

Keywords: wind stand-alone system, photovoltaic stand-alone system, hybrid system, optimum system sizing, feasibility, cost analysis

Procedia PDF Downloads 340

Authors: Ahmed Elaksher

Abstract:

Over the last few years, significant progresses have been made and new approaches have been proposed for efficient collection of 3D spatial data from Unmanned aerial vehicles (UAVs) with reduced costs compared to imagery from satellite or manned aircraft. In these systems, a low-cost GPS unit provides the position, velocity of the vehicle, a low-quality inertial measurement unit (IMU) determines its orientation, and off-the-shelf cameras capture the images. Structure from Motion (SfM) and photogrammetry are the main tools for 3D surface reconstruction from images collected by these systems. Unlike traditional techniques, SfM allows the computation of calibration parameters using point correspondences across images without performing a rigorous laboratory or field calibration process and it is more flexible in that it does not require consistent image overlap or same rotation angles between successive photos. These benefits make SfM ideal for UAVs aerial mapping. In this paper, a direct comparison between SfM Digital Elevation Models (DEM) and those generated through traditional photogrammetric techniques was performed. Data was collected by a 3DR IRIS+ Quadcopter with a Canon PowerShot S100 digital camera. Twenty ground control points were randomly distributed on the ground and surveyed with a total station in a local coordinate system. Images were collected from an altitude of 30 meters with a ground resolution of nine mm/pixel. Data was processed with PhotoScan, VisualSFM, Imagine Photogrammetry, and a photogrammetric algorithm developed by the author. The algorithm starts with performing a laboratory camera calibration then the acquired imagery undergoes an orientation procedure to determine the cameras’ positions and orientations. After the orientation is attained, correlation based image matching is conducted to automatically generate three-dimensional surface models followed by a refining step using sub-pixel image information for high matching accuracy. Tests with different number and configurations of the control points were conducted. Camera calibration parameters estimated from commercial software and those obtained with laboratory procedures were comparable. Exposure station positions were within less than few centimeters and insignificant differences, within less than three seconds, among orientation angles were found. DEM differencing was performed between generated DEMs and few centimeters vertical shifts were found.

Keywords: UAV, photogrammetry, SfM, DEM

Procedia PDF Downloads 295

Authors: Adeline Fontvieille, Hugo Parent-Roberge, Marie-France Langlois, Tamas Fulop, Michel Pavic, Eleonor Riesco

Abstract:

Introduction: Total lean body mass is reduced during cancer treatment. This loss is called cancer cachexia and is accompanied by a progressive loss of fat mass. In older adults, these body composition changes can have a larger impact on metabolic health, physical autonomy, and cancer survival. Although currently untreatable, exercise training could reduce these effects. Hence, the objective of this pilot study is to investigate if 12 weeks of exercise training during cancer treatment can mitigate the loss of muscle mass and fat mass in older adults. Methods: A total of 40 older adults (65-80 years) with an ongoing treatment for a curable cancer are currently recruited and randomised in two groups: 1) Combined training (EX, n=20) and 2) Control group (CON, n=20). All variables are measured before and after 12 weeks of intervention: Anthropometry (weight, height, body mass index), body composition (total fat mass, visceral adipose tissue, total and appendicular muscle mass; DXA), metabolic profile (HDL-C and LDL-C, triglycerides, glucose and insulin levels). Results: Preliminary analyses revealed no impact of exercise training on appendicular muscle mass (p=0,31) and fat mass (p=0,31). Furthermore, total body weight, waist circumference, HDL-cholesterol, LDL-cholesterol, glucose and insulin levels remained unchanged (all p ≥ 0.79) after 12 weeks of training. However, statistical analyses revealed that triglyceride levels slightly increased (p=0.03), irrespective of the group. Conclusion: Preliminary analyses did not reveal any impact of aerobic and resistance exercise training on body composition in oncogeriatric patients. Furthermore, exercise training seems not efficient to prevent the cancer treatment-related triglyceride levels increase.

Keywords: muscle mass, fat mass, metabolic profile, combined training, aging, cancer

Procedia PDF Downloads 364