Search results for: data reduction

Authors: P. Acosta-Santamaría, A. Ibatá-Soto, A. López-Vásquez

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Organic compounds in wastewaters coming from textile and pharmaceutical industry generated multiple harmful effects on the environment and the human health. One of them is the methyl orange (MeO), an azoic dye considered to be a recalcitrant compound. The heterogeneous photocatalysis emerges as an alternative for treating this type of hazardous compounds, through the generation of OH radicals using radiation and a semiconductor oxide. According to the author’s knowledge, catalysts such as TiO₂ doped with metals show high efficiency in degrading MeO; however, this presents economic limitations on industrial scale. Black sand can be considered as a naturally doped catalyst because in its structure is common to find compounds such as titanium, iron and aluminum oxides, also elements such as zircon, cadmium, manganese, etc. This study reports the photocatalytic activity of the mineral black sand used as semiconductor in the discoloration of MeO by oxidation and reduction photocatalytic techniques. For this, magnetic composites from the mineral were prepared (RM, M1, M2 and NM) and their activity were tested through MeO discoloration while TiO₂ was used as reference. For the fractions, chemical, morphological and structural characterizations were performed using Scanning Electron Microscopy with Energy Dispersive X-Ray (SEM-EDX), X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF) analysis. M2 fraction showed higher MeO discoloration (93%) in oxidation conditions at pH 2 and it could be due to the presence of ferric oxides. However, the best result to reduction process was using M1 fraction (20%) at pH 2, which contains a higher titanium percentage. In the first process, hydrogen peroxide (H₂O₂) was used as electron donor agent. According to the results, black sand mineral can be used as natural semiconductor in photocatalytic process. It could be considered as a photocatalyst precursor in such processes, due to its low cost and easy access.

Keywords: black sand mineral, methyl orange, oxidation, photocatalysis, reduction

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Authors: Gabi N. Nehme

Abstract:

The reduction of phosphorus and sulfur in engine oil are the main topics of this paper. Very reproducible boundary lubrication tests were conducted as part of Design of Experiment software (DOE) to study the behavior of fluorinated catalyst iron fluoride (FeF3), and polutetrafluoroethylene or Teflon (PTFE) in developing environmentally friendly (reduced P and S) anti-wear additives for future engine oil formulations. Multi-component Chevron fully formulated oil (GF3) and Chevron plain oil were used with the addition of PTFE and catalyst to characterize and analyze their performance. Lower phosphorus blends were the goal of the model solution. Experiments indicated that new sub-micron FeF3 catalyst played an important role in preventing breakdown of the tribofilm.

Keywords: wear, SEM, EDS, friction, lubricants

Procedia PDF Downloads 287

Authors: Fahad Hanash Alzahrani

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This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

Procedia PDF Downloads 404

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Alireza Taghdiri, Sara Ghanbarzade Ghomi

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With the gravity load reduction in the structural and non-structural components, the lightweight construction will be achieved as well as the improvement of efficiency and functional specifications. The advantages of lightweight construction can be examined in two levels. The first is the mass reduction of load bearing structure which results in increasing internal useful space and the other one is the mass reduction of building which decreases the effects of seismic load as a result. In order to achieve this goal, the essential building materials specifications and also optimum load bearing geometry of structural systems and elements have to be considered, so lightweight materials selection particularly with lightweight aggregate for building components will be the first step of lightweight construction. In the next step, in addition to selecting the prominent samples of Iran's traditional architecture, the process of these works improvement is analyzed through the viewpoints of structural efficiency and lightweighting and also the practical methods of lightweight construction have been extracted. The optimum design of load bearing geometry of structural system has to be considered not only in the structural system elements, but also in their composition and the selection of dimensions, proportions, forms and optimum orientations, can lead to get a maximum materials efficiency for loads and stresses bearing.

Keywords: gravity load, light-weighting structural system, load bearing geometry, seismic behavior

Procedia PDF Downloads 547

Authors: Mehmet Akif Aslan, Mehmet Simsek, Eyup Sensoy

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Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics.

Keywords: big data, logistics, operational efficiency, risk management

Procedia PDF Downloads 642

Authors: N. Chahmana, I. Zerroual

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The aim of our work is the contribution to the improvement of the performances of the positive plate of the lead acid battery. For that, we synthesized two varieties of PbO2 used in industry, alpha and beta PbO2 by electrochemical way starting from the not formed industrial plates. We studied the kinetics of reduction of the alpha varieties and PbO2 beta on electrode with microcavity in sulphuric medium. The electrochemical study of the powders of α and β-PbO2 was made by cyclic voltamperometry with sweeping of potential by using a traditional assembly with three electrodes. Values of the coefficient of diffusion of the proton in α and β-PbO2 are respectively equal to 0.498*10-8cm2 /s and 0.793*10-8 cm2 /s. During the cycling of the two varieties of PbO2, we obtain a clear increase in the capacity.

Keywords: lead accumulator, α and β - PbO2, synthesis, kinetics, cyclic voltametry, coefficient of diffusion

Procedia PDF Downloads 577

Authors: Adepoju A. A., Raufu M. O., Ganiyu M. O., Olawuyi S. O., Olalere J. O., Ogunkunle A. A.

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This study investigated the effects of social safety nets on food security among farming households in Southwest Nigeria. The study used a multistage sampling technique, purposively selecting two states from southwest Nigeria, Oyo and Ogun as the study area with eight Agricultural Development Programme (ADP) agricultural zones. The Local Government Areas (LGAs) were stratified into urban and rural LGAs. Sixteen villages from Oyo and 12 villages from Ogun were randomly selected from the rural LGAs using a proportionate to-size sampling, resulting in 472 respondents, with 271 and 201 from Oyo and Ogun states, respectively. The data was analyzed using descriptive statistics like mean, standard deviation, frequency and percentages, while logistic regression analysis examines the association between independent variables and dependent variables. The study found that poverty reduction, social empowerment, food security palliative, microcredit, and agricultural empowerment are the most prevalent social safety nets among farming households. School feed programs are the most prevalent form of poverty reduction, while training for empowerment improves wellbeing. Food item distribution is the most beneficial for food security and wellbeing. Self-empowerment-based micro-credit support is the most effective, while Anchor Borrower's project is the most beneficial for agricultural empowerment. The study found that 62.68% of the variance in food security status is explained by independent variables. females farmers have a 56% higher likelihood of being food secure than their male counterparts. An additional increase in age decreases the likelihood of being food secure by 6%. Married individuals have a 58% lower likelihood of being food secure compared to singles, possibly due to increased financial responsibilities. A larger household size increases the likelihood of being food secure by 3.41%. Larger households may benefit from economies of scale or shared resources and social safety net programs. Engagement in farming as a primary occupation increases the likelihood of being food secure by 62%. The study further reveals that participation in poverty reduction and microcredit programs significantly increases the likelihood of food security by 30,069% and 135.48%, respectively. The study therefore recommends expanding school feed programs, improving empowerment training, strengthening food distribution, promoting micro-credit, supporting agricultural empowerment, and addressing gender disparities in social safety net programs.

Keywords: poverty reduction, food distribution, micro-credit, household well-being

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Authors: Salvatore Luongo, Carlo Luongo

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This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

Procedia PDF Downloads 286

Authors: Shuwen Wu, Zhi LI

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Atomically dispersed Fe‒N₄ catalysts are proven as promising alternatives to commercial Pt/C for the oxygen reduction reaction. Most reported Fe‒N₄ catalysts suffer from inferior O‒O bond-breaking capability due to superoxo-like O₂ adsorption, though the isolated dual-atomic metal sites strategy is extensively adopted. Atomic Fe clusters hold greater promise for promoting O‒O bond cleavage by forming peroxo-like O₂ adsorption. However, the excessively strong binding strength between Fe clusters and oxygenated intermediates sacrifices the activity. Here, we first report a Fex/Cu‒N@CF catalyst with atomic Fe clusters functionalized by adjacent single Cu‒N₄ sites anchoring on a porous carbon nanofiber membrane. The theoretical calculation indicates that the single Cu‒N₄ sites can modulate the electronic configuration of Fe clusters to reduce O₂* protonation reaction free energy, which ultimately enhances the electrocatalytic performance. Particularly, the Cu‒N₄ sites can increase the overlaps between the d orbitals of Fe and p orbitals of O to accelerate O‒O cleavage in OOH*. As a result, this unique atomic catalyst exhibits a half potential (E1/2) of 0.944 V in an alkaline medium exceeding that of commercial Pt/C, whereas acidic performance E1/2 = 0.815 V is comparable to Pt/C. This work shows the great potential of single atoms for improvements in atomic cluster catalysts.

Keywords: Hierarchical porous fibers, atomic Fe clusters, Cu single atoms, oxygen reduction reaction; O-O bond cleavage

Procedia PDF Downloads 117

Authors: Iman Shabanzadeh

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Earthquake is known as one of the most frequent natural hazards in Iran. Therefore, policy making to improve the strengthening of structures is one of the requirements of the approach to prevent and reduce the risk of the destructive effects of earthquakes. In order to choose the optimal policy in the face of earthquakes, this article tries to examine the cost of financial damages caused by earthquakes in the building sector and compare it with the costs of retrofitting. In this study, the results of adopting the scenario of "action after the earthquake" and the policy scenario of "strengthening structures before the earthquake" have been collected, calculated and finally analyzed by putting them together. Methodologically, data received from governorates and building retrofitting engineering companies have been used. The scope of the study is earthquakes occurred in the geographical area of Iran, and among them, eight earthquakes have been specifically studied: Miane, Ahar and Haris, Qator, Momor, Khorasan, Damghan and Shahroud, Gohran, Hormozgan and Ezgole. The main basis of the calculations is the data obtained from retrofitting companies regarding the cost per square meter of building retrofitting and the data of the governorate regarding the power of earthquake destruction, the realized costs for the reconstruction and construction of residential units. The estimated costs have been converted to the value of 2021 using the time value of money method to enable comparison and aggregation. The cost-benefit comparison of the two policies of action after the earthquake and retrofitting before the earthquake in the eight earthquakes investigated shows that the country has suffered five thousand billion Tomans of losses due to the lack of retrofitting of buildings against earthquakes. Based on the data of the Budget Law's of Iran, this figure was approximately twice the budget of the Ministry of Roads and Urban Development and five times the budget of the Islamic Revolution Housing Foundation in 2021. The results show that the policy of retrofitting structures before an earthquake is significantly more optimal than the competing scenario. The comparison of the two policy scenarios examined in this study shows that the policy of retrofitting buildings before an earthquake, on the one hand, prevents huge losses, and on the other hand, by increasing the number of earthquake-resistant houses, it reduces the amount of earthquake destruction. In addition to other positive effects of retrofitting, such as the reduction of mortality due to earthquake resistance of buildings and the reduction of other economic and social effects caused by earthquakes. These are things that can prove the cost-effectiveness of the policy scenario of "strengthening structures before earthquakes" in Iran.

Keywords: disaster economy, earthquake economy, cost-benefit analysis, resilience

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Authors: Muhammad Dawood Idrees, Maria Hafeez, Arsalan Ansari

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Toll tax collection is the earliest method of tax collection and revenue generation. This revenue is utilized for the development of roads networks, maintenance, and connecting to roads and highways across the country. Pakistan is one of the biggest countries, covers a wide area of land, roads networks, and motorways are important source of connecting cities. Every day millions of people use motorways, and they have to stop at toll plazas to pay toll tax as majority of toll plazas are manually collecting toll tax. The purpose of this study is to calculate the waiting time of vehicles at Karachi Hyderabad (M-9) motorway. As Karachi is the biggest city of Pakistan and hundreds of thousands of people use this route to approach other cities. Currently, toll tax collection is manual system which is a major cause for long time waiting at toll plaza. This study calculates the waiting time of vehicles, fuel consumed in waiting time, manpower employed at toll plaza as all process is manual, and it also leads to mental and physical fatigue of driver. All wastages of sources are also calculated, and a most feasible automatic toll tax collection system is proposed which is not only beneficial to reduce waiting time but also beneficial in reduction of fuel, reduction of manpower employed, and reduction in physical and mental fatigue. A cost comparison in terms of wastages is also shown between manual and automatic toll tax collection system (E-Z Pass). Results of this study reveal that, if automatic tool collection system is implemented at Karachi to Hyderabad motorway (M-9), there will be a significance reduction in waiting time of vehicles, which leads to reduction of fuel consumption, environmental pollution, mental and physical fatigue of driver. All these reductions are also calculated in terms of money (Pakistani rupees) and it is obtained that millions of rupees can be saved by using automatic tool collection system which will lead to improve the economy of country.

Keywords: toll tax collection, waiting time, wastages, driver fatigue

Procedia PDF Downloads 153

Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

Procedia PDF Downloads 227

Authors: Sudawan Somjai

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The purpose of this paper was to find an appropriate outsourcing system model for the Thai electrical appliances industry. The objective was to increase competitive capability of the industry with an outsourcing system. The population for this study was the staff in the selected 10 companies in Thai electrical appliances industry located in Bangkok and the eastern part of Thailand. Data collecting techniques included in-depth interviews, focus group and storytelling techniques. The data was collected from 5 key informants from each company, making a total of 50 informants. The findings revealed that an outsourcing model would consist of important factors including outsourcing system, labor flexibility, capability of business process, manpower management efficiency, cost reduction, business risk elimination, core competency and competitiveness. Different suggestions were made as well in this research paper.

Keywords: outsourcing system, model, Thailand, electrical appliances industry

Procedia PDF Downloads 591

Authors: Li Mengmeng, Kang Jian

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With the rapid development of the city, the noise pollution becomes more and more serious. Noise has seriously affected people's normal life, study and work. In addition, noise has seriously affected the city's ecological environment and the migration of birds. Therefore, it is urgent to control the noise. As one of natural noise-reducing materials, plants have been paid more and more attention. In urban landscape design, it is very important to choose plant species with good noise reduction effect to the sustainable development of urban ecology. The aim of this paper is to find out the characteristics of the plant with good noise reduction effect and apply it in urban landscape design. This study investigated the vibration of leaves of six plant species in a sound field using a Keyence (IG-1000/CCD) Laser Micrometer. The results of the experiments showed that the vibration speed of plant leaves increased obviously after being stimulated by sound source, about 5-10 times. In addition, when driven by the same sound, the speed of all leaves varied with the difference of leaf thickness, leaf size and leaf mass. The speed of all leaves would increase with the increase of leaf size and leaf mass, while those would decrease with the increase of leaf thickness.

Keywords: landscape design, leaf vibration , noise attenuation, plants configuration

Procedia PDF Downloads 230

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

Procedia PDF Downloads 162

Authors: John M. Ikome, Martha E. Ikome, Therese Van Wyk

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Productivity optimization of a company can significantly increase the company’s output and productivity which can be in the form of corrective actions of ineffective activities, process simplification, and reduction of variations, responsiveness, and reduction of set-up-time which are all under the classification of waste within the manufacturing environment. Deriving a means to eliminate a number of these issues has a key importance for manufacturing organization. This paper focused on a number of industrial engineering techniques which include a cause and effect diagram, to identify and optimize the method or systems being used. Based on our results, it shows that there are a number of variations within the production processes that can significantly disrupt the expected output.

Keywords: optimization, fishbone, diagram, productivity

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Authors: Ekele T. Ochedi, Ahmad H. Taki, Birgit Painter

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The effect of the sun and its path on thermal comfort and energy consumption in residential buildings in tropical climates constitute a serious concern for designers, building owners, and users. Passive design approaches based on the sun and its path have been identified as a means of reducing energy consumption as well as enhancing thermal comfort in buildings worldwide. Hence, a thorough understanding regarding the sun path is key to achieving this. This is necessary due to energy need, poor energy supply, and distribution, energy poverty, and over-dependence on electric generators for power supply in Nigeria. These challenges call for a change in the approach to energy-related issues, especially in terms of buildings. The aim of this study is to explore the influence of building orientation, glazing and the use of shading devices on residential buildings in Nigeria. This is intended to provide data that will guide designers in the design of energy-efficient residential buildings. The paper used EnergyPlus to analyze a typical semi-detached residential building in Lokoja, Nigeria using hourly weather data for a period of 10 years. Building performance was studied as well as possible improvement regarding different orientations, glazing types and shading devices. The simulation results show some reductions in energy consumption in response to changes in building orientation, types of glazing and the use of shading devices. The results indicate 29.45% reduction in solar gains and 1.90% in annual operative temperature using natural ventilation only. This shows a huge potential to reduce energy consumption and improve people’s well-being through the use of proper building orientation, glazing and appropriate shading devices on building envelope. The study concludes that for a significant reduction in total energy consumption by residential buildings, the design should focus on multiple design options rather than concentrating on one or few building elements. Moreover, the investigation confirms that energy performance modeling can be used by building designers to take advantage of the sun and to evaluate various design options.

Keywords: energy consumption, energy-efficient buildings, glazing, thermal comfort, shading devices, solar gains

Procedia PDF Downloads 212

Authors: Alireza Taghdiri, Sara Ghanbarzade Ghomi

Abstract:

With gravity load reduction in the structural and non-structural components, the lightweight construction will be achieved as well as the improvement of efficiency and functional specifications. The advantages of lightweight construction can be examined in two levels. The first is the mass reduction of load bearing structure which results in increasing internal useful space and the other one is the mass reduction of building which decreases the effects of seismic load as a result. In order to achieve this goal, the essential building materials specifications and also optimum load bearing geometry of structural systems and elements have to be considered, so lightweight materials selection particularly with lightweight aggregate for building components will be the first step of lightweight construction. In the next step, in addition to selecting the prominent samples of Iran's traditional architecture, the process of these works improvement is analyzed through the viewpoints of structural efficiency and lightweighting and also the practical methods of lightweight construction have been extracted. The optimum design of load bearing geometry of structural system has to be considered not only in the structural system elements, but also in their composition and the selection of dimensions, proportions, forms and optimum orientations, can lead to get a maximum materials efficiency for loads and stresses bearing.

Keywords: gravity load, lightweighting structural system, load bearing geometry, seismic behavior

Procedia PDF Downloads 524

Authors: Buti Suryabrahmam, V. A. Raghunathan

Abstract:

We study the effect of short chain alcohols on mechanical properties of saturated lipid bilayers in the fluid phase. The Bending rigidity of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) membrane was measured at 28 °C by employing Vesicle Fluctuation Analysis technique. The concentration and chain length (n) of alcohol in the buffer solution were varied from 0 to 1.5 M and from 2 to 8 respectively. We observed a non-linear reduction in the bending rigidity from ~17×10⁻²⁰ J to ~10×10⁻²⁰ J, for all chain lengths of alcohols used in our experiment. We observed approximately three orders of the concentration difference between ethanol and octanol, to show the similar reduction in the bending values. We attribute this phenomenon to thinning of the bilayer due to the adsorption of alcohols at the bilayer-water interface.

Keywords: alcohols, bending rigidity, DMPC, lipid bilayers

Procedia PDF Downloads 148

Authors: Bahia Braktia, S. Anna Marcela Montenegro, Imene Abdessemed

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Adult education is still considered a crucial area of education. In a developing framework, it is regarded as a practical approach for social inclusion and poverty reduction. They are also perceived as a way to serve adults who did not have the chance to education in their early ages by providing them knowledge, skills and values. Non-formal adult education and trainings are critical means in a society to break poverty and unemployment, and to decrease the social inequality. This paper investigates the perception of women trainees about a series of workshops in natural beauty products, held in Algeria and Costa Rica and organized by a non-profit educational organization, to improve their socio-economic status. This research seeks to explore ways of empowering women by assessing their needs and providing them with skills to start their own business. A questionnaire is administered before the workshops and focus groups are held at the end. A qualitative research method is employed to analyze the data. Preliminary results show that the trainees aspire to create their businesses with the objectives of poverty reduction and social inclusion. The findings also reveal the need for small business funding programs and entrepreneurial training programs.

Keywords: adult education, non-formal education, socio-economic status, women empowerment

Procedia PDF Downloads 208

Authors: Ali Hojati Najafabadi, Mohamad Hosein Rahmati, Seyed Ali Madanizadeh

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Governments execute energy subsidy reforms by either increasing energy prices or reducing energy price dispersion. These policies make less use of energy per plant (intensive margin), vary the total number of firms (extensive margin), promote technological progress (technology channel), and make additional resources to redistribute (resource channel). We estimate a structural dynamic firm model with endogenous technology adaptation using data from the manufacturing firms in Iran and a country ranked the second-largest energy subsidy plan by the IMF. The findings show significant dynamics and distributional effects due to an energy reform plan. The price elasticity of energy consumption in the industrial sector is about -2.34, while it is -3.98 for large firms. The dispersion elasticity, defined as the amounts of changes in energy consumption by a one-percent reduction in the standard error of energy price distribution, is about 1.43, suggesting significant room for a distributional policy. We show that the intensive margin is the main driver of energy price elasticity, whereas the other channels mostly offset it. In contrast, the labor response is mainly through the extensive margin. Total factor productivity slightly improves in light of the reduction in energy consumption if, at the same time, the redistribution policy boosts the aggregate demands.

Keywords: energy reform, firm dynamics, structural estimation, subsidy policy

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Authors: Ossai Paulinus Edwin

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Cash Management is a broad area having to do with the collection, concentration, and disbursement of cash including measuring the level of liquidity and managing the cash balance and short-Term Investments. Cash Management involves the efficient collection and disbursement of cash and cash equivalents. It also includes management of marketable securities because, in modern Terminology, money comprises marketable securities and actual cash in hand or in a bank. This cash management is concerned with management of cash inflow and cash outflow of a business especially as it concerns a developing nation like Nigeria. The paper throws light on the impact of cashless policy in Nigeria as it was introduced by the Central Bank of Nigeria (CBN) in December 2011 and was kick-started in Lagos in January 2012. Survey research was adopted with the questionnaires as data collection instrument. Responses show that cashless policy if adopted generally shall increase employment opportunities, reduce cash related robbery thereby reducing risk of carrying cash; it shall also reduce cash related corruption and attract more foreign investors to the country. It is expected that the introduction of cashless policy in Nigeria is a step in the right direction as it shall bring about modernization of Nigeria payment system, reduction in the cost of banking services, reduction in high security and safety risk and also curb banking related corruptions.

Keywords: cashless economy, cash management, cashless policy, e-banking, Nigeria

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Authors: Ammar Alali, Mahmoud Abughaban

Abstract:

Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Bakur Gulua

Abstract:

In this work, we consider some boundary value problems for the plate. The plate is the elastic material with voids. The state of plate equilibrium is described by the system of differential equations that is derived from three-dimensional equations of equilibrium of an elastic material with voids (Cowin-Nunziato model) by Vekua's reduction method. Its general solution is represented by means of analytic functions of a complex variable and solutions of Helmholtz equations. The problem is solved analytically by the method of the theory of functions of a complex variable.

Keywords: the elastic material with voids, boundary value problems, Vekua's reduction method, a complex variable

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Authors: Andrew Terhemen Tyowua, Zhibing Zhang, Michael J. Adams

Abstract:

Water vapor sorption data at a range of temperatures (25–70 °C) have been obtained for starch (corn and wheat) and non-starch (carrageenan and xanthan gum) hydrocolloid particles in the form of a thin slab. The results reveal that the data may be more accurately described by an existing sigmoidal rather than a Fickian model. The sigmoidal model accounts for the initial surface sorption before the onset of bulk diffusion. At relatively small water activities (≤ 0.3), the absorption of the moisture caused the particles to be plasticized, but at greater activity values (> 0.3), anti-plasticization was induced. However, it was found that for the whole range of water activities and temperatures studied, the data could be characterized by a single non-dimensional number, which was termed the non-Fickian diffusion number where τ is the characteristic time of surface sorption, D is the bulk diffusion coefficient and L is the thickness of the layer of particles. The activation energy suggested that the anti-plasticization mechanism was the result of a reduction in the molecular free volume or an increase in crystallinity.

Keywords: anti-plasticization, arrhenius behavior, diffusion coefficient, hygroscopic polymers, moisture migration, non-fickian sigmoidal model

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Authors: Rong Wang, Sandra Hasanefendic, Elizabeth von Hauff, Bart Bossink

Abstract:

Technological learning and learning curve models have been continuously used to estimate the photovoltaics (PV) cost development over time for the climate mitigation targets. They can integrate a number of technological learning sources which influence the learning process. Yet the accuracy and realistic predictions for cost estimations of PV development are still difficult to achieve. This paper develops four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technology experience and the knowledge stock. This paper specifically focuses on the non-linear relationship between the costs and technological learning source and their dynamic interaction and uses the system dynamics approach to predict a more accurate PV cost estimation for future development. As the case study, the data from China is gathered and drawn to illustrate that the learning curve model that incorporates both the global and local experience is more accurate and realistic than the other three models for PV cost estimation. Further, absorbing and integrating the global experience into the local industry has a positive impact on PV cost reduction. Although the learning curve model incorporating knowledge stock is not realistic for current PV cost deployment in China, it still plays an effective positive role in future PV cost reduction.

Keywords: photovoltaic, system dynamics, technological learning, learning curve

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Authors: Maria Kralova, Michal Palecek

Abstract:

We have detected factors leading to recidivism (the Czech Republic data). The employment during imprisonment turned out to be the most significant predictor with a positive effect on reduction of a rate of recidivism. Accordingly, we mainly focus on this predictor and its economic consequences. Smart public policy can cut government costs dramatically as more than a half of prisoners in the Czech Republic are recidivists. The operating cost cut of the Czech prison service could be CZK 127,680,000 (USD 5,889,623) in 2013 if a public policy had been set smarter.

Keywords: cost-cut, effective, optimal, public policy, reducing recidivism

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Authors: Fatemeh Yazdanmehr, Iulian Nistor

Abstract:

The scope of the current study is the evaluation of the electric field effect on electrostatic desalting mathematical modeling with laboratory data. This research study was focused on developing a model for an existing operation desalting unit of one of the Iranian heavy oil field with a 75 MBPD production capacity. The high temperature of inlet oil to dehydration unit reduces the oil recovery, so the mathematical modeling of desalter operation parameters is very significant. The existing production unit operating data has been used for the accuracy of the mathematical desalting plant model. The inlet oil temperature to desalter was decreased from 110 to 80°C, and the desalted electrical field was increased from 0.75 to 2.5 Kv/cm. The model result shows that the desalter parameter changes meet the water-oil specification and also the oil production and consequently annual income is increased. In addition to that, changing desalter operation conditions reduces environmental footprint because of flare gas reduction. Following to specify the accuracy of selected electrostatic desalter electrical field, laboratory data has been used. Experimental data are used to ensure the effect of electrical field change on desalter. Therefore, the lab test is done on a crude oil sample. The results include the dehydration efficiency in the presence of a demulsifier and under electrical field (0.75 Kv) conditions at various temperatures. Comparing lab experimental and electrostatic desalter mathematical model results shows 1-3 percent acceptable error which confirms the validity of desalter specification and operation conditions changes.

Keywords: desalter, electrical field, demulsification, mathematical modeling, water-oil separation

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Authors: Thachamon Sinsoongsud, Noppawan Piaseu

Abstract:

This predictive study was aimed to 1) describe self efficacy for risk reduction and preventive behavior for osteoporosis, and 2) examine factors predicting preventive behavior for osteoporosis in nursing students. Through purposive sampling, the sample included 746 nursing students in a public university in Bangkok, Thailand. Data were collected by a self-reported questionnaire on self efficacy and preventive behavior for osteoporosis. Data were analyzed using descriptive statistics and multiple regression analysis with stepwise method. Results revealed that majority of the students were female (98.3%) with mean age of 19.86 + 1.26 years. The students had self efficacy and preventive behavior for osteoporosis at moderate level. Self efficacy and level of education could together predicted 35.2% variance of preventive behavior for osteoporosis (p< .001). Results suggest approaches for promoting preventive behavior for osteoporosis through enhancing self efficacy among nursing students in a public university in Bangkok, Thailand.

Keywords: osteoporosis, self-efficacy, preventive behavior, nursing students

Procedia PDF Downloads 380