Search results for: annual and daily flow duration curve

Authors: Zahir Bakiri, Saci Nacefa

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In this paper a comparison in made between two models, with and without dispersion term, and focused on the characterization of the movement of the sludge blanket in the secondary sedimentation tank using the solid flux theory and the velocity settling. This allowed us develop a one-dimensional models, with and without dispersion based on a thorough experimental study carried out in situ and the application of online data which are the mass load flow, transfer concentration, and influent characteristic. On the other hand, in the proposed model, the new settling velocity law (double-exponential function) used is based on the Vesilind function.

Keywords: wastewater, activated sludge, sedimentation, settling velocity, settling models

Procedia PDF Downloads 357

Authors: Mahdi Hamzehei, Seyyed Amin Hakim, Nahid Taherian

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Fins play an important role in increasing the efficiency of compact shell and tube heat exchangers by increasing heat transfer. The objective of this paper is to determine the optimum fin wave angle, as one of the geometric parameters affecting the efficiency of the heat exchangers. To this end, finite volume method is used to model and simulate the flow in heat exchanger. In this study, computational fluid dynamics simulations of wave channel are done. The results show that the wave angle affects the temperature output of the heat exchanger.

Keywords: fin wave angle, tube, intercooler, optimum, performance

Procedia PDF Downloads 348

Authors: Hsien-Te Lin

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The purpose of this paper is to introduce the BCFES (building carbon footprint evaluation system), which is a LCA (life cycle assessment) tool developed by the Low Carbon Building Alliance (LCBA) in Taiwan. A qualified BCFES for the building industry should fulfill the function of evaluating carbon footprint throughout all stages in the life cycle of building projects, including the production, transportation and manufacturing of materials, construction, daily energy usage, renovation and demolition. However, many existing BCFESs are too complicated and not very designer-friendly, creating obstacles in the implementation of carbon reduction policies. One of the greatest obstacle is the misapplication of the carbon footprint inventory standards of PAS2050 or ISO14067, which are designed for mass-produced goods rather than building projects. When these product-oriented rules are applied to building projects, one must compute a tremendous amount of data for raw materials and the transportation of construction equipment throughout the construction period based on purchasing lists and construction logs. This verification method is very cumbersome by nature and unhelpful to the promotion of low carbon design. With a view to provide an engineer-oriented BCFE with pre-diagnosis functions, a component input/output (I/O) database system and a scenario simulation method for building energy are proposed herein. Most existing BCFESs base their calculations on a product-oriented carbon database for raw materials like cement, steel, glass, and wood. However, data on raw materials is meaningless for the purpose of encouraging carbon reduction design without a feedback mechanism, because an engineering project is not designed based on raw materials but rather on building components, such as flooring, walls, roofs, ceilings, roads or cabinets. The LCBA Database has been composited from existing carbon footprint databases for raw materials and architectural graphic standards. Project designers can now use the LCBA Database to conduct low carbon design in a much more simple and efficient way. Daily energy usage throughout a building's life cycle, including air conditioning, lighting, and electric equipment, is very difficult for the building designer to predict. A good BCFES should provide a simplified and designer-friendly method to overcome this obstacle in predicting energy consumption. In this paper, the author has developed a simplified tool, the dynamic Energy Use Intensity (EUI) method, to accurately predict energy usage with simple multiplications and additions using EUI data and the designed efficiency levels for the building envelope, AC, lighting and electrical equipment. Remarkably simple to use, it can help designers pre-diagnose hotspots in building carbon footprint and further enhance low carbon designs. The BCFES-LCBA offers the advantages of an engineer-friendly component I/O database, simplified energy prediction methods, pre-diagnosis of carbon hotspots and sensitivity to good low carbon designs, making it an increasingly popular carbon management tool in Taiwan. To date, about thirty projects have been awarded BCFES-LCBA certification and the assessment has become mandatory in some cities.

Keywords: building carbon footprint, life cycle assessment, energy use intensity, building energy

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Authors: Sélima Baccar, Ephraim Clark

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This paper can be considered as a new perspective to analyse credit spreads. A comprehensive empirical analysis of conditional variance of credit spreads indices is performed using various GARCH models. Based on a comparison between traditional and asymmetric GARCH models with alternative functional forms of the conditional density, we intend to identify what macroeconomic and financial factors have driven daily changes in the US Dollar credit spreads in the period from January 2011 through January 2013. The results provide a strong interdependence between credit spreads and the explanatory factors related to the conditions of interest rates, the state of the stock market, the bond market liquidity and the exchange risk. The empirical findings support the use of asymmetric GARCH models. The AGARCH and GJR models outperform the traditional GARCH in credit spreads modelling. We show, also, that the leptokurtic Student-t assumption is better than the Gaussian distribution and improves the quality of the estimates, whatever the rating or maturity.

Keywords: corporate bonds, default risk, credit spreads, asymmetric garch models, student-t distribution

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Authors: P. Fucik, J. Vymazal, M. Seres

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Pollution from agricultural drainage is a severe issue for water quality, and it is a major reason for the failure in accomplishment of 'good chemical status' according to Water Framework Directive, especially due to high nitrogen and pesticide burden of receiving waters. Constructed wetlands were proposed as a suitable measure for removal of nitrogen from agricultural drainage in the early 1990s. Until now, the vast majority of constructed wetlands designed to treat tile drainage were free-surface constructed wetlands. In 2018, three small experimental constructed wetlands with horizontal subsurface flow were built in Czech Highlands to treat tile drainage from 15.73 ha watershed. The wetlands have a surface area of 79, 90 and 98 m² and were planted with Phalaris arundinacea and Glyceria maxima in parallel bands. The substrate in the first two wetlands is gravel (4-8 mm) mixed with birch woodchips (10:1 volume ratio). In one of those wetlands, the water level is kept 10 cm above the surface; in the second one, the water is kept below the surface. The third wetland has 20 cm layer of birch woodchips on top of gravel. The drainage outlet, as well as wetland outlets, are equipped with automatic discharge-gauging devices, temperature probes, as well as automatic water samplers (Teledyne ISCO). During the monitored period (2018-2019), the flows were unexpectedly low due to a drop of the shallow ground water level, being the main source of water for the monitored drainage system, as experienced at many areas of the Czech Republic. The mean water residence time was analyzed in the wetlands (KBr), which was 16, 9 and 27 days, respectively. The mean total nitrogen concentration eliminations during one-year period were 61.2%, 62.6%, and 70.9% for wetlands 1, 2, and 3, respectively. The average load removals amounted to 0.516, 0.323, and 0.399 g N m-2 d-1 or 1885, 1180 and 1457 kg ha-1 yr-1 in wetlands 1, 2 and 3, respectively. The plant uptake and nitrogen sequestration in aboveground biomass contributed only marginally to the overall nitrogen removal. Among the three variants, the one with shallow water on the surface was revealed to be the most effective for removal of nitrogen from drainage water. In August 2019, herbicide Metazachlor was experimentally poured in time of 2 hours at drainage outlet in a concentration of 250 ug/l to find out the removal rates of the aforementioned wetlands. Water samples were taken the first day every six hours, and for the next nine days, every day one water sample was taken. The removal rates were as follows 94, 69 and 99%; when the most effective wetland was the one with the longest water residence time and the birch woodchip-layer on top of gravel.

Keywords: constructed wetlands, metazachlor, nitrogen, tile drainage

Procedia PDF Downloads 116

Authors: Dwi Puspita Sari, Gulnur Baltabayeva, Nadia Salman, Maxut Toleuov, Vijay Kanabar

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Technology era drives people to use mobile phone to support their daily life activities. Technology development has a rapid phase which pushes the IT company to adjust any technology changes in order to fulfill customer’s satisfaction. As a result of that, many companies in the USA emerged from systematics software development approach to agile software development approach in developing systems and applications to develop many mobile phone applications in a short phase to fulfill user’s needs. As a systematic approach is considered as time consuming, costly, and too risky, agile software development has become a more popular approach to use for developing software including mobile applications. This paper reflects a short-term project to develop a diet tracker mobile application using agile software development that focused on applying scrum framework in the development process.

Keywords: agile software development, scrum, diet tracker, mobile application

Procedia PDF Downloads 222

Authors: Mbadugha Esther Ifeoma

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Soybeans, like other legumes are rich in nutrients. However, the nutrient profile of soybeans differs in some important ways from most other legumes. Among other nutrients, soy is high in protein, carbohydrates, and fibers, is rich in vitamins, minerals and unsaturated fatty acids and is low in saturated fatty acids. Because of its high nutritional value, it has been rated to be equivalent to meats, eggs and milk. Soy has many health benefits including prevention of coronary heart disease, prevention of cancer growth, improvement of cognitive function, promotion of bone health, prevention of obesity, prevention of type II diabetes and promotion of growth of normal floras in the colon. Soybean consumption is also associated with some side effects which include allergy, flatulence and abdominal discomfort. Nurses/health care providers should therefore, educate clients on the precautionary measures to be taken in preparing soy food products in order to reduce to the barest minimum the side effects, while encouraging them to include soy as part of their daily meals for optimal health and vitality.

Keywords: health benefit, health education, nutritional benefit, soybeans

Procedia PDF Downloads 454

Authors: Qianqian He, Naian Liu, Xiaodong Xie, Linhe Zhang, Yang Zhang, Weidong Yan

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In the wild, the vegetation layer with exceptionally complex fuel composition and heterogeneous spatial distribution strongly affects the rate of fire spread (ROS) and fire intensity. Clarifying the influence of fuel bed structure on fire spread behavior is of great significance to wildland fire management and prediction. The packing ratio is one of the key physical parameters describing the property of the fuel bed. There is a threshold value of the packing ratio for ROS, but little is known about the controlling mechanism. In this study, to address this deficiency, a series of fire spread experiments were performed across a discrete fuel bed composed of some regularly arranged laser-cut cardboards, with constant wind speed and different packing ratios (0.0125-0.0375). The experiment aims to explore the relative importance of the internal and surface heat transfer with packing ratio. The dependence of the measured ROS on the packing ratio was almost consistent with the previous researches. The data of the radiative and total heat fluxes show that the internal heat transfer and surface heat transfer are both enhanced with increasing packing ratio (referred to as ‘Stage 1’). The trend agrees well with the variation of the flame length. The results extracted from the video show that the flame length markedly increases with increasing packing ratio in Stage 1. Combustion intensity is suggested to be increased, which, in turn, enhances the heat radiation. The heat flux data shows that the surface heat transfer appears to be more important than the internal heat transfer (fuel preheating inside the fuel bed) in Stage 1. On the contrary, the internal heat transfer dominates the fuel preheating mechanism when the packing ratio further increases (referred to as ‘Stage 2’) because the surface heat flux keeps almost stable with the packing ratio in Stage 2. As for the heat convection, the flow velocity was measured using Pitot tubes both inside and on the upper surface of the fuel bed during the fire spread. Based on the gas velocity distribution ahead of the flame front, it is found that the airflow inside the fuel bed is restricted in Stage 2, which can reduce the internal heat convection in theory. However, the analysis indicates not the influence of inside flow on convection and combustion, but the decreased internal radiation of per unit fuel is responsible for the decrease of ROS.

Keywords: discrete fuel bed, fire spread, packing ratio, wildfire

Procedia PDF Downloads 104

Authors: Luciana C. Ramos, Leandro J. Sousa, Antônio Ferreira da Silva, Valéria Gomes Oliveira Falcão, Suzana T. Cunha Lima

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The commercial production of biodiesel using microalgae demands a high-energy input for harvesting biomass, making production economically unfeasible. Methods currently used involve mechanical, chemical, and biological procedures. In this work, a flocculation system is presented as a cost and energy effective process to increase biomass production of Phaeodactylum tricornutum. This diatom is the only species of the genus that present fast growth and lipid accumulation ability that are of great interest for biofuel production. The algae, selected from the Bank of Microalgae, Institute of Biology, Federal University of Bahia (Brazil), have been bred in tubular reactor with photoperiod of 12 h (clear/dark), providing luminance of about 35 μmol photons m^-2s^-1, and temperature of 22 °C. The medium used for growing cells was the Conway medium, with addition of silica. The seaweed growth curve was accompanied by cell count in Neubauer camera and by optical density in spectrophotometer, at 680 nm. The precipitation occurred at the end of the stationary phase of growth, 21 days after inoculation, using two methods: centrifugation at 5000 rpm for 5 min, and electro-flocculation at 19 EPD and 95 W. After precipitation, cells were frozen at -20 °C and, subsequently, lyophilized. Biomass obtained by electro-flocculation was approximately four times greater than the one achieved by centrifugation. The benefits of this method are that no addition of chemical flocculants is necessary and similar cultivation conditions can be used for the biodiesel production and pharmacological purposes. The results may contribute to improve biodiesel production costs using marine microalgae.

Keywords: biomass, diatom, flocculation, microalgae

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Authors: N. Thawornwisit, P. Pradoo, S. Nuypree, L. Jarukasetrporn, S. Jitpukdeebodintra

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Objective: The aim of this study was to evaluate the duration of the Enterococcus faecalis (E. faecalis) penetration into the gap between root canal wall and filling material at a 3 to 6 mm distance from the cementoenamel junction (CEJ) in the dislodged temporary filling, in vitro. Material and methods: Thirty-four single root canal mandibular premolars were divided into two experimental groups (N = 15) and one negative control (N = 4). Root canals were prepared and obturated with gutta-percha using lateral condensation technique, X-ray checked, and sterilized. Leakages were set up using the modified bacterial leakage model, and E. faecalis was used as a microbial marker. Leakages were evaluated at 3 and 7 days by culturing gutta-percha and dentine drilled from a 3-6 mm distance from CEJ. Broth turbidity was recorded and compared. Result: All four negative control and the 3-day experimental group showed no broth turbidity. For the 7-day experimental group, there was 33.3% leakage. Conclusion: Penetration of E. faecalis into the gap between root canal wall and filling material at a 3 to 6 mm distance from CEJ in the dislodged temporary filling were not found at three days. However, at seven days of exposure, bacteria could penetrate into the interface of the root canal and filling materials.

Keywords: coronal leakage, bacterial leakage model, enterococcus faecalis

Procedia PDF Downloads 69

Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Jarek Krajewski, David Daxberger

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We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

Procedia PDF Downloads 100

Authors: Masoumeh Moghbel

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Nowadays, carbon dioxide which is produced by human activities is considered as the main effective factor in the global warming occurrence. Regarding to the role of CO2 and its ability in trapping the heat, the main objective of this research is study the effect of atmospheric CO2 (which is recorded in Manaloa) on variations of temperature parameters (daily mean temperature, minimum temperature and maximum temperature) in 5 meteorological stations in Iran which were selected according to the latitude and altitude in 40 years statistical period. Firstly, the trend of temperature parameters was studied by Regression and none-graphical Man-Kendal methods. Then, relation between temperature variations and CO2 were studied by Correlation technique. Also, the impact of CO2 amount on temperature in different atmospheric levels (850 and 500 hpa) was analyzed. The results illustrated that correlation coefficient between temperature variations and CO2 in low latitudes and high altitudes is more significant rather than other regions. it is important to note that altitude as the one of the main geographic factor has limitation in affecting the temperature variations, so that correlation coefficient between these two parameters in 850 hpa (r=0.86) is more significant than 500 hpa (r = 0.62).

Keywords: altitude, atmospheric carbon dioxide, latitude, temperature variations

Procedia PDF Downloads 377

Authors: Alexandra-Veronica Luca, Letitia Petrescu

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Steel production is a major contributor to global warming potential. An average value of 1.83 tons of CO₂ is emitted for every ton of steel produced, resulting in over 3.3 Mt of CO₂ emissions each year. The present paper is focused on the investigation and comparison of two O₂ separation methods and two CO₂ capture technologies applicable to iron and steel industry. The O₂ used in steel production comes from an Air Separation Unit (ASU) using distillation or from air separation using membranes. The CO₂ capture technologies are represented by a two-stage membrane separation process and the gas-liquid absorption using methyl di-ethanol amine (MDEA). Process modelling and simulation tools, as well as environmental tools, are used in the present study. The production capacity of the steel mill is 4,000,000 tones/year. In order to compare the two CO₂ capture technologies in terms of efficiency, performance, and sustainability, the following cases have been investigated: Case 1: steel production using O₂ from ASU and no CO₂ capture; Case 2: steel production using O₂ from ASU and gas-liquid absorption for CO₂ capture; Case 3: steel production using O₂ from ASU and membranes for CO₂ capture; Case 4: steel production using O₂ from membrane separation method and gas-liquid absorption for CO₂ capture and Case-5: steel production using membranes for air separation and CO₂ capture. The O₂ separation rate obtained in the distillation technology was about 96%, and about 33% in the membrane technology. Similarly, the O₂ purity resulting in the conventional process (i.e. distillation) is higher compared to the O₂ purity obtained in the membrane unit (e.g., 99.50% vs. 73.66%). The air flow-rate required for membrane separation is about three times higher compared to the air flow-rate for cryogenic distillation (e.g., 549,096.93 kg/h vs. 189,743.82 kg/h). A CO₂ capture rate of 93.97% was obtained in the membrane case, while the CO₂ capture rate for the gas-liquid absorption was 89.97%. A quantity of 6,626.49 kg/h CO₂ with a purity of 95.45% is separated from the total 23,352.83 kg/h flue-gas in the membrane process, while with absorption of 6,173.94 kg/h CO₂ with a purity of 98.79% is obtained from 21,902.04 kg/h flue-gas and 156,041.80 kg/h MDEA is recycled. The simulation results, performed using ChemCAD process simulator software, lead to the conclusion that membrane-based technology can be a suitable alternative for CO₂ removal for steel production. An environmental evaluation using Life Cycle Assessment (LCA) methodology was also performed. Considering the electricity consumption, the performance, and environmental indicators, Case 3 can be considered the most effective. The environmental evaluation, performed using GaBi software, shows that membrane technology can lead to lower environmental emissions if membrane production is based on benzene derived from toluene hydrodealkilation and chlorine and sodium hydroxide are produced using mixed technologies.

Keywords: CO₂ capture, gas-liquid absorption, Life Cycle Assessment, membrane separation, steel production

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Authors: Sujata Sethi, Pawan Kumar, Sameer Aggarwal

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Depression and anxiety are of significant concern in youth with type 1 diabetes mellitus (T1DM) and these are correlated with glycemic control in multiple ways. The extent of depression and anxiety in children with T1DM remains poorly studied in India. The index study aimed to find the prevalence of depression and anxiety and their correlation with HbA1c (glycated hemoglobin) levels in children with T1DM. Material and methods: This study was a cross-sectional study carried out on a purposive sample of 45 children with T1DM. Depressive symptoms were assessed using Children’s Depression Rating Scale-Revised (CDRS-R) and anxiety symptoms were assessed using Spence Children’s Anxiety Scale (SCAS). Glycated hemoglobin (HbA1c) levels of all the participants were recorded. Results: 43 out of 45 children were analyzed as HbA1c status for two was not known. 48.8% were females. Mean age was 12.95+2.04. The average duration of diabetes was 3.63+1.82. Mean CDRS-R score was 41.6+12.25 and mean SCAS score was 33.07+12.29. Mean recording of HbA1c level was 7.90+1.51. 27 (62.8%) out of 43 participants had abnormal scores on CDRS-R and 24 (55.8%) out of 43 had abnormal scores on SCAS. The correlation coefficient between HbA1c levels and the CDRS-R score came out to be 0.57 and between HbA1c and SCAS, it was 0.53. Both correlations were significant with the p-value of < 0.02. Conclusion: Children with T1DM have high co-morbidity of depression and anxiety which is significantly correlated with the HbA1c levels. Thus, it becomes important to screen the patients for depression and anxiety for better outcomes.

Keywords: anxiety, depression, HbA1c, T1DM

Procedia PDF Downloads 205

Authors: Mudassar Hameed, Khushi Muhammad, Aamir Ghafoor, Masood Rabbani, Momena Habib, Jawad Nazir

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Comparative efficacy of three formulations (non-adjuvant, gel, and oil adjuvant) of monovalent and combined PPR and FMD virus vaccines was evaluated in goats. All kinds of monovalent PPRV vaccines elicited protective antibody titers at one-month post vaccination (PV) that remained so till six months PV. Monovalent non-adjuvant (NA) FMDV vaccine provoked non-protective antibody titers that declined to undetectable levels after three months. In case of combined vaccines, all of the formulations elicited protective antibody titers against PPRV in vaccinated animals which remained above that limit for six months. However, an exceptional immune response against FMDV was observed in combined NA vaccine group where antibody titers were extremely high and remained above protective level till 4 months PV in animals who received a single vaccine shot and till six months PV in booster group. Although, adjuvant or NA combined vaccines can induce protective antibody titers against both of the viruses within one month PV, but a booster vaccine shot is needed to retain protective antibody level for 6 months duration. Immune response elicited by combined vaccines is comparable or superior to the monovalent vaccines. Hence combined vaccine can be effectively used for the control and prevention of both of the diseases.

Keywords: antibody titer, protective, combined vaccine, non adjuvant

Procedia PDF Downloads 179

Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

Procedia PDF Downloads 239

Authors: Oualid Walid Ben Ali

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Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

Procedia PDF Downloads 457

Authors: Andre M. Carvalho, Pedro Sebastiao

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This article is based on a dissertation that intends to analyze and make a model, intelligently, algorithms based on stochastic processes of a gamification application applied to marketing. Gamification is used in our daily lives to engage us to perform certain actions in order to achieve goals and gain rewards. This strategy is an increasingly adopted way to encourage and retain customers through game elements. The application of gamification aims to encourage children between 6 and 10 years of age to have healthy habits and the purpose of serving as a model for use in marketing. This application was developed in unity; we implemented intelligent algorithms based on stochastic processes, web services to respond to all requests of the application, a back-office website to manage the application and the database. The behavioral analysis of the use of game elements and stochastic processes in children’s motivation was done. The application of algorithms based on stochastic processes in-game elements is very important to promote cooperation and to ensure fair and friendly competition between users which consequently stimulates the user’s interest and their involvement in the application and organization.

Keywords: engage, games, gamification, randomness, stochastic processes

Procedia PDF Downloads 302

Authors: Ranjitkumar Patil

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Age estimation has its importance in forensic dentistry. Dental age estimation has emerged as an alternative to skeletal age determination. The methods based on stages of tooth formation, as appreciated on radiographs, seems to be more appropriate in the assessment of age than those based on skeletal development. The study was done to evaluate dental age in north Indian population using Cameriere’s method. Aims/Objectives: The study was conducted to assess the dental age of North Indian children using Cameriere’smethodand to compare the chronological age and dental age for validation of the Cameriere’smethod in the north Indian population. A comparative study of 02 year duration on the OPG (using PLANMECA Promax 3D) data of 497 individuals with age ranging from 5 to 15 years was done based on simple random technique ethical approval obtained from the institutional ethical committee. The data was obtained based on inclusion and exclusion criteria was analyzed by a software for dental age estimation. Statistical analysis: Student’s t test was used to compare the morphological variables of males with those of females and to compare observed age with estimated age. Regression formula was also calculated. Results: Present study was a comparative study of 497 subjects with a distribution between male and female, with their dental age assessed by using Panoramic radiograph, following the method described by Cameriere, which is widely accepted. Statistical analysis in our study indicated that gender does not have a significant influence on age estimation. (R2= 0.787). Conclusion: This infers that cameriere’s method can be effectively applied in north Indianpopulation.

Keywords: Forensic, Chronological Age, Dental Age, Skeletal Age

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Authors: Shekhar Chatterjee

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Architectural marvels of the Patan stepwell in Gujarat state in India were studied, to look into the cultural and design attributes in them. Direct observation, photography and interviewing the local people (especially senior citizens) were the methodology adopted. The aim was to look for clues into how culture and design affected architectural marvels of a building and convey that to the tourists. These interpretations from this building can offer many ideas to the contemporary design world in the form of design of modern day garments for various occasions, ornaments or accessory products for daily usage like bags, shoes and similar products. These monuments currently lack proper information system for guiding a tourist. Absence of any qualified tourist guides at the site compounds the problem further. This project investigates the feasibility of making the space more interactive for the tourist through proper digital information design and installations at places. Along with this, illumination and sound are also being used to narrate the history of these ancient monuments so that tourists get a flavor of the medieval past. Most importantly, all these digital interventions are low cost and done with easily available throw-away materials and can be replicated for other monuments as well.

Keywords: interaction, well, building, context

Procedia PDF Downloads 248

Authors: Ahmed Khalil

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The discoveries in material sciences create an impulse in renewable energy transmission. Application techniques become more accessible by applied sciences. Variety of materials, application methods, and performance analyzing techniques can convert daily life functions to energy sources. These functions not only include natural sources like sun, wind, or water but also comprise the motion of tools used by human beings. In line with this, vehicles' motion, speed and weights come to the scene as energy sources together with piezoelectric nano-generators beneath the roads. Numerous application examples are put forward with repeated average performance, versus the differentiating challenges depending on geography and project conditions. Such holistic approach provides way for feed backs on research and improvement process of nano-generators beneath asphalt roads. This paper introduces the specific application methods of piezoelectric nano-generator beneath asphalt roads of Ahmadi Township in Kuwait.

Keywords: nano-generator pavements, piezoelectric, renewable energy, transducer

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Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Nathen A. Spitz, Dennis Martin Kivlighan III, Arwa Aburizik

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Background and Purpose: Presently, there is a paucity of naturalistic studies that directly compare the effectiveness of psychotherapy versus concurrent psychotherapy and psychiatric care for the treatment of depression and anxiety in cancer patients. Informed by previous clinical trials examining the efficacy of concurrent approaches, this study sought to test the hypothesis that a combined approach would result in the greatest reduction of depression and anxiety symptoms. Methods: Data for this study consisted of 433 adult cancer patients, with 252 receiving only psychotherapy and 181 receiving concurrent psychotherapy and psychiatric care at the University of Iowa Hospitals and Clinics. Longitudinal PHQ9 and GAD7 data were analyzed between both groups using latent growth curve analyses. Results: After controlling for treatment length and provider effects, results indicated that concurrent care was more effective than psychotherapy alone for depressive symptoms (γ₁₂ = -0.12, p = .037). Specifically, the simple slope for concurrent care was -0.25 (p = .022), and the simple slope for psychotherapy alone was -0.13 (p = .006), suggesting that patients receiving concurrent care experienced a greater reduction in depressive symptoms compared to patients receiving psychotherapy alone. In contrast, there were no significant differences between psychotherapy alone and concurrent psychotherapy and psychiatric care in the reduction of anxious symptoms. Conclusions: Overall, as both psychotherapy and psychiatric care may address unique aspects of mental health conditions, in addition to potentially providing synergetic support to each other, a combinatorial approach to mental healthcare for cancer patients may improve outcomes.

Keywords: psychiatry, psychology, psycho-oncology, combined care, psychotherapy, behavioral psychology

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Varun Dongre, Stefan Pirker, Stefan Heinrich

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Over the last decades, the numerical modelling of fluidized bed processes has become feasible even for industrial processes. Commonly, continuous two-fluid models are applied to describe large-scale fluidization. In order to allow for coarse grids novel two-fluid models account for unresolved sub-grid heterogeneities. However, computational efforts remain high – in the order of several hours of compute-time for a few seconds of real-time – thus preventing the representation of long-term phenomena such as heating or particle conversion processes. In order to overcome this limitation, data-based recurrence computational fluid dynamics (rCFD) has been put forward in recent years. rCFD can be regarded as a data-based method that relies on the numerical predictions of a conventional short-term simulation. This data is stored in a database and then used by rCFD to efficiently time-extrapolate the flow behavior in high spatial resolution. This study will compare the numerical predictions of rCFD simulations with those of corresponding full CFD reference simulations for lab-scale and pilot-scale fluidized beds. In assessing the predictive capabilities of rCFD simulations, we focus on solid mixing and secondary gas holdup. We observed that predictions made by rCFD simulations are highly sensitive to numerical parameters such as diffusivity associated with face swaps. We achieved a computational speed-up of four orders of magnitude (10,000 time faster than classical TFM simulation) eventually allowing for real-time simulations of fluidized beds. In the next step, we apply the checkerboarding technique by introducing gas tracers subjected to convection and diffusion. We then analyze the concentration profiles by observing mixing, transport of gas tracers, insights about the convective and diffusive pattern of the gas tracers, and further towards heat and mass transfer methods. Finally, we run rCFD simulations and calibrate them with numerical and physical parameters compared with convectional Two-fluid model (full CFD) simulation. As a result, this study gives a clear indication of the applicability, predictive capabilities, and existing limitations of rCFD in the realm of fluidization modelling.

Keywords: multiphase flow, recurrence CFD, two-fluid model, industrial processes

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Authors: Guermache Ahmed Chafik, Khelfellah Ismail, Ait-Ali Takfarines

Abstract:

The Savonius vertical axis wind turbine is characterized by sufficient starting torque at low wind speeds, simple design and does not require orientation to the wind direction; however, the developed power is lower than other types of wind turbines such as Darrieus. To increase these performances several studies and researches have been developed, such as optimizing blades shape, using passive controls and also minimizing power losses sources like the resisting torque due to friction. This work aims to estimate the performance of a Savonius wind turbine introducing a User Defined Function to the CFD model analyzing resisting torque. This User Defined Function is developed to simulate the action of the wind speed on the rotor; it receives the moment coefficient as an input to compute the rotational velocity that should be imposed on computational domain rotating regions. The rotational velocity depends on the aerodynamic moment applied on the turbine and the resisting torque, which is considered a linear function. Linking the implemented User Defined Function with the CFD solver allows simulating the real functioning of the Savonius turbine exposed to wind. It is noticed that the wind turbine takes a while to reach the stationary regime where the rotational velocity becomes invariable; at that moment, the tip speed ratio, the moment and power coefficients are computed. To validate this approach, the power coefficient versus tip speed ratio curve is compared with the experimental one. The obtained results are in agreement with the available experimental results.

Keywords: resistant torque modeling, Savonius wind turbine, user-defined function, vertical axis wind turbine performances

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Authors: Amine Benmoussa, Mebrouk Rebhi, Rahmani Lakhdar

Abstract:

Mechanically agitated vessels are commonly used for various operations within a wide range process in chemical, pharmaceutical, polymer, biochemical, mineral, petroleum industries. Depending on the purpose of the operation carried out in mixer, the best choice for geometry of the tank and agitator type can vary widely. In this paper, the laminar 2D agitation flow and power consumption of viscoplastic fluids with straight and circular gate impellers in a stirring tank is studied by using computational fluid dynamics (CFD), where the velocity profile, the velocity fields and power consumption was analyzed.

Keywords: CFD, mechanical stirring, power consumption, yield stress fluid

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Authors: Fatima Achouri, Kaddour Chouicha, Abdelwahab Khatir

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It is clear that concrete of quality must be made with selected materials chosen in optimum proportions that remain after implementation, a minimum of voids in the material produced. The different methods of formulations what we use, are based for the most part on a granular curve which describes an ‘optimal granularity’. Many authors have engaged in fundamental research on granular arrangements. A comparison of mathematical models reproducing these granular arrangements with experimental measurements of compactness have to verify that the minimum porosity P according to the following extent granular exactly a power law. So the best compactness in the finite medium are obtained with power laws, such as Furnas, Fuller or Talbot, each preferring a particular setting between 0.20 and 0.50. These considerations converge on the assumption that the optimal granularity Caquot approximates by a power law. By analogy, it can then be analyzed as a granular structure of fractal-type since the properties that characterize the internal similarity fractal objects are reflected also by a power law. Optimized mixtures may be described as a series of installments falling granular stuff to better the tank on a regular hierarchical distribution which would give at different scales, by cascading effects, the same structure to the mix. Likely this model may be appropriate for the entire extent of the size distribution of the components, since the cement particles (and silica fume) correctly deflocculated, micrometric dimensions, to chippings sometimes several tens of millimeters. As part of this research, the aim is to give an illustration of the application of fractal analysis to characterize the granular concrete mixtures optimized for a so-called fractal dimension where different concretes were studying that we proved a fractal structure of their granular mixtures regardless of the method of formulation or the type of concrete.

Keywords: concrete formulation, fractal character, granular packing, method of formulation

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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