Search results for: behavior against washing machine parameters

Authors: Manjinder Bajwa, Mahipal Singh, Manish Nagpal

Abstract:

This research paper aims to propose a novel approach using ANOVA technique for the strategic investigation of process parameters and their effects on the mechanical properties of Aluminium alloy cast. The two process parameters considered here were permeability of sand and pouring temperature of aluminium alloy. ANOVA has been employed for the first time to determine the effects of these selected parameters on the impact strength of alloy. The experimental results show that this proposed technique has great potential for analyzing sand casting process. Using this approach we have determined the treatment mean square, response mean square and mean square of error as 8.54, 8.255 and 0.435 respectively. The research concluded that at the 5% level of significance, permeability of sand is the more significant parameter influencing the impact strength of cast alloy.

Keywords: aluminium alloy, pouring temperature, permeability of sand, impact strength, ANOVA

Procedia PDF Downloads 448

Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

Abstract:

In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

Procedia PDF Downloads 213

Authors: Ifeanyi Adigwe

Abstract:

The influence of television on adolescents is prevalent and widespread because television is a powerful sex educator for adolescents. This study examined the relationship between viewing sexual content on television and sexual behaviour of adolescents in public senior secondary schools in Lagos, Nigeria. The study employed a survey research design with a structured questionnaire as instrument. The multi-stage sampling technique was adopted. Firstly, purposive sampling was adopted in selecting 3 educational districts namely: Agege, Maryland, and Agboju. These educational districts were chosen for convenience and its wide coverage area of public senior secondary schools in Lagos State. Secondly, the researcher adopted systematic sampling to select the schools. The schools were listed in alphabetical order in each district and every 10th school were selected, yielding 13 schools altogether. A total of 501 copies of questionnaire were administered to the students and a total 491 copies of the questionnaire were retrieved. Only 453 copies of the questionnaire met the inclusion criteria and were used for analysis. Data were analyzed using descriptive statistics, Pearson Correlation, Principal components analysis, and regression analysis. Results of correlation analysis showed a positive and significant relationship between adolescent sexual belief and their preference for sexual content in television (r =0.117, N =453, p=0.13), viewing sexual content on television and adolescent sexual behavior, (r =-0.112, N =453, p<0.05), adolescent television preference and their preference for sexual content in television (r =0.328, N =453, p<0.05), adolescent television preference and adolescent’s sexual behavior (r=0.093, N =453, p<0.05). However, a negative but significant relationship exists between adolescent’s sexual knowledge and their sexual behavior (r=-122, N=453, p=0.0009). Pearson’s correlation between adolescents’ sexual knowledge and sexual behavior shows that there is a positive significant but strong relationship between adolescent’s sexual knowledge and their sexual behavior (r=0.967, N=453, p<0.05). The results also show that adolescent’s preference for sexual content in television informs them about their sexuality, development and sexual health. The descriptive and inferential analysis of data revealed that the interaction among adolescent sexual belief, knowledge and adolescents’ preference of sexual in television and its resultant effect on adolescent sexual behavior is apparent because sexual belief and norms about sex of an adolescent can induce his television preference of sexual content on television. The study concludes that exposure to sexual content in television can impact on adolescent sexual behaviour. There is no doubt that the actual outcome of television viewing and adolescent sexual behavior remains controversial because adolescent sexual behavior is multifaceted and multi-dimensional. Since behavior is learned overtime, the frequency of exposure and nature of sexual content viewed overtime induces and hastens sexual activity.

Keywords: adolescent sexual behavior, Nigeria, sexual belief, sexual content, sexual knowledge, television preference

Procedia PDF Downloads 392

Authors: E. Hovad, J. H. Walther, P. Larsen, J. Thorborg, J. H. Hattel

Abstract:

In sand casting of metal parts for the automotive industry such as brake disks and engine blocks, the molten metal is poured into a sand mold to get its final shape. The DISAMATIC molding process is a way to construct these sand molds for casting of steel parts and in the present work numerical simulations of this process are presented. During the process green sand is blown into a chamber and subsequently squeezed to finally obtain the sand mould. The sand flow is modelled with the Discrete Element method (DEM) and obtaining the correct material parameters for the simulation is the main goal. Different tests will be used to find or calibrate the DEM parameters needed; Poisson ratio, Young modulus, rolling friction coefficient, sliding friction coefficient and coefficient of restitution (COR). The Young modulus and Poisson ratio are found from compression tests of the bulk material and subsequently used in the DEM model according to the Hertz-Mindlin model. The main focus will be on calibrating the rolling resistance and sliding friction in the DEM model with respect to the behavior of “real” sand piles. More specifically, the surface profile of the “real” sand pile will be compared to the sand pile predicted with the DEM for different values of the rolling and sliding friction coefficients. When the DEM parameters are found for the particle-particle (sand-sand) interaction, the particle-wall interaction parameter values are also found. Here the sliding coefficient will be found from experiments and the rolling resistance is investigated by comparing with observations of how the green sand interacts with the chamber wall during experiments and the DEM simulations will be calibrated accordingly. The coefficient of restitution will be tested with different values in the DEM simulations and compared to video footages of the DISAMATIC process. Energy dissipation will be investigated in these simulations for different particle sizes and coefficient of restitution, where scaling laws will be considered to relate the energy dissipation for these parameters. Finally, the found parameter values are used in the overall discrete element model and compared to the video footage of the DISAMATIC process.

Keywords: discrete element method, physical properties of materials, calibration, granular flow

Procedia PDF Downloads 482

Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

Abstract:

Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

Procedia PDF Downloads 154

Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

Abstract:

Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

Procedia PDF Downloads 97

Authors: Othman Mohamed Altheni, Abdurrahman Abusaada

Abstract:

This study presents optimization of multiple performance characteristics [material removal rate (MRR), surface roughness (Ra), and overcut (OC)] of hardened AISI D3 tool steel in electrical discharge machining (EDM) using Taguchi method and Grey relational analysis. Machining process parameters selected were pulsed current Ip, pulse-on time Ton, pulse-off time Toff and gap voltage Vg. Based on ANOVA, pulse current is found to be the most significant factor affecting EDM process. Optimized process parameters are simultaneously leading to a higher MRR, lower Ra, and lower OC are then verified through a confirmation experiment. Validation experiment shows an improved MRR, Ra and OC when Taguchi method and grey relational analysis were used

Keywords: edm parameters, grey relational analysis, Taguchi method, ANOVA

Procedia PDF Downloads 294

Authors: E. Güler, M. Güler

Abstract:

We present some elastic parameters of cubic fluorite type uranium dioxide (UO2) with a recent EAM type interatomic potential through geometry optimization calculations. Typical cubic elastic constants, bulk modulus, shear modulus, young modulus and other related elastic parameters were calculated during research. After calculations, we compared our results not only with the available theoretical data but also with previous experimental results. Our results are consistent with experiments and compare well the former theoretical results of the considered parameters of UO2.

Keywords: UO2, elastic constants, bulk modulus, mechanical properties

Procedia PDF Downloads 412

Authors: Rik van Leeuwen, Ger Koole

Abstract:

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

Procedia PDF Downloads 108

Authors: D. I. George Amalarethinam, A. Emima

Abstract:

Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

Procedia PDF Downloads 117

Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

Abstract:

Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

Procedia PDF Downloads 663

Authors: Anna Martin, Raffael Osen

Abstract:

The demand for fish from aquaculture is constantly growing. Concurrently, due to a shortage of fishmeal caused by extensive overfishing, fishmeal substitution by plant proteins is getting increasingly important for the production of sustainable aquafeed. Several research studies evaluated the impact of plant protein meals, concentrates or isolates on fish health and fish feed quality. However, these protein raw materials often require elaborate and expensive manufacturing and their availability is limited. Rapeseed press cake (RPC) – a side product of de-oiling processes – exhibits a high potential as a plant-based fishmeal alternative in fish feed for carnivorous species due to its availability, low costs and protein content. In order to produce aquafeed with RPC, it is important to systematically assess i) inclusion levels of RPC with similar pellet qualities compared to fishmeal containing formulations and ii) how extrusion parameters can be adjusted to achieve targeted pellet qualities. However, the effect of RPC on extrusion system parameters and pellet quality has only scarcely been investigated. Therefore, the aim of this study was to evaluate the impact of feed formulation, extruder barrel temperature (90, 100, 110 °C) and screw speed (200, 300, 400 rpm) on extrusion system parameters and the physical properties of fish feed pellets. A co-rotating pilot-scale twin screw extruder was used to produce five iso-nitrogenous feed formulations: a fish meal based reference formulation including 16 g/100g fishmeal and four formulations in which fishmeal was substituted by RPC to 25, 50, 75 or 100 %. Extrusion system parameters, being product temperature, pressure at the die, specific mechanical energy (SME) and torque, were monitored while samples were taken. After drying, pellets were analyzed regarding to optical appearance, sectional and longitudinal expansion, sinking velocity, bulk density, water stability, durability and specific hardness. In our study, the addition of minor amounts of RPC already had high impact on pellet quality parameters, especially on expansion but only marginally affected extrusion system parameters. Increasing amounts of RPC reduced sectional expansion, sinking velocity, bulk density and specific hardness and increased longitudinal expansion compared to a reference formulation without RPC. Water stability and durability were almost not affected by RPC addition. Moreover, pellets with rapeseed components showed a more coarse structure than pellets containing only fishmeal. When the adjustment of barrel temperature and screw speed was investigated, it could be seen that the increase of extruder barrel temperature led to a slight decrease of SME and die pressure and an increased sectional expansion of the reference pellets but did almost not affect rapeseed containing fish feed pellets. Also changes in screw speed had little effects on the physical properties of pellets however with raised screw speed the SME and the product temperature increased. In summary, a one-to-one substitution of fishmeal with RPC without the adjustment of extrusion process parameters does not result in fish feed of a designated quality. Therefore, a deeper knowledge of raw materials and their behavior under thermal and mechanical stresses as applied during extrusion is required.

Keywords: extrusion, fish feed, press cake, rapeseed

Procedia PDF Downloads 148

Authors: Isabela Moreira Queiroz

Abstract:

Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management. 

Keywords: probabilistic methods, risk assessment, risk management, slope stability

Procedia PDF Downloads 392

Authors: Majid Khalili, Hamed Tayebi

Abstract:

This paper considers a stochastic flexible job-shop scheduling (SFJSS) problem in the presence of production disruptions, and reactive scheduling is implemented in order to find the optimal solution under uncertainty. In this problem, there are two main disruptions including machine failure which influences operation time, and modification or cancellation of the order delivery date during production. In order to decrease the negative effects of these difficulties, two derived strategies from reactive scheduling are used; the first one is relevant to being able to allocate multiple machine to each job, and the other one is related to being able to select the best alternative process from other job while some disruptions would be created in the processes of a job. For this purpose, a Mixed Integer Linear Programming model is proposed.

Keywords: flexible job-shop scheduling, reactive scheduling, stochastic environment, mixed integer linear programming

Procedia PDF Downloads 361

Authors: M. J. Davidson, N. Selvaraj, L. Venugopal

Abstract:

The elevated temperature forming studies on the expansion of thin walled tubes have been studied in the present work. The influence of process parameters namely the die angle, the die ratio and the operating temperatures on the expansion of tube ends at elevated temperatures is carried out. The range of operating parameters have been identified by perfoming extensive simulation studies. The hot forming parameters have been evaluated for AA2014 alloy for performing the simulation studies. Experimental matrix has been developed from the feasible range got from the simulation results. The design of experiments is used for the optimization of process parameters. Response Surface Method’s (RSM) and Box-Behenken design (BBD) is used for developing the mathematical model for expansion. Analysis of variance (ANOVA) is used to analyze the influence of process parameters on the expansion of tube ends. The effect of various process combinations of expansion are analyzed through graphical representations. The developed model is found to be appropriate as the coefficient of determination value is very high and is equal to 0.9726. The predicted values are found to coincide well with the experimental results, within acceptable error limits.

Keywords: expansion, optimization, Response Surface Method (RSM), ANOVA, bbd, residuals, regression, tube

Procedia PDF Downloads 509

Authors: Dhiaadin Bahaadin Noory

Abstract:

This research involves the effect of both sizes of reinforced zone and the amount of polypropylene fiber reinforcement on the structural behavior of model-reinforced sand loaded by square footing. The ratio of the side of the square reinforced zone to the footing width (W/B) and the ratio of the square reinforced zone depth to footing width (H/B) has been varied from one to six and from one to three, respectively. The tests were carried out on a small-scale laboratory model in which uniform-graded sand was used as a fill material. It was placed in a highly dense state by hitting a thin wooden board placed on the sand surface with a hammer. The sand was reinforced with randomly oriented discrete fibrillated polypropylene fibers. The test results indicated a significant increase in the bearing capacity and stiffness of the subgrade and a modification of load–the settlement behavior of sand with the size of the reinforced zone and amount of fiber reinforcement. On the basis of the present test results, the optimal side width and depth of the reinforced zone were 4B and 2B, respectively, while the optimal percentage of fibers was 0.4%.

Keywords: square footing, polypropylene fibers, bearing capacity, stiffness, load settlement behavior, relative density

Procedia PDF Downloads 64

Authors: Kishor T. Zingre, Seshadhri Srinivasan

Abstract:

Energy consumption by the building sector is increasing at an alarming rate throughout the world and leading to more building-related CO₂ emissions into the environment. In buildings, the main contributors to energy consumption are heating, ventilation, and air-conditioning (HVAC) systems, lighting, and electrical appliances. It is hypothesised that the energy efficiency in buildings can be achieved by implementing sustainable technologies such as i) enhancing the thermal resistance of fabric materials for reducing heat gain (in hotter climates) and heat loss (in colder climates), ii) enhancing daylight and lighting system, iii) HVAC system and iv) occupant localization. Energy performance of various sustainable technologies is highly dependent on climatic conditions. This paper investigated the use of machine learning techniques for accurate prediction of air-conditioning energy in seasonal climates. The data required to train the machine learning techniques is obtained using the computational simulations performed on a 3-story commercial building using EnergyPlus program plugged-in with OpenStudio and Google SketchUp. The EnergyPlus model was calibrated against experimental measurements of surface temperatures and heat flux prior to employing for the simulations. It has been observed from the simulations that the performance of sustainable fabric materials (for walls, roof, and windows) such as phase change materials, insulation, cool roof, etc. vary with the climate conditions. Various renewable technologies were also used for the building flat roofs in various climates to investigate the potential for electricity generation. It has been observed that the proposed technique overcomes the shortcomings of existing approaches, such as local linearization or over-simplifying assumptions. In addition, the proposed method can be used for real-time estimation of building air-conditioning energy.

Keywords: building energy efficiency, energyplus, machine learning techniques, seasonal climates

Procedia PDF Downloads 114

Authors: Pedro Mauricio Acosta, Carlos Andrés Caro

Abstract:

This research presents the regionalization of IDF curves for the department of Boyacá, Colombia, which comprises 16 towns, including the provincial capital, Tunja. For regionalization adjustment parameters (U and alpha) of the IDF curves stations referred to in the studied area were used. Similar regionalization is used by the interpolation of intensities. In the case of regionalization by parameters found by the construction of the curves intensity, duration and frequency estimation methods using ordinary moments and maximum likelihood. Regionalization and interpolation of data were performed with the assistance of Arcgis software. Within the development of the project the best choice to provide a level of reliability such as to determine which of the options and ways to regionalize is best sought. The resulting isolines maps were made in the case of regionalization intensities, each map is associated with a different return period and duration in order to build IDF curves in the studied area. In the case of the regionalization maps parameters associated with each parameter were performed last.

Keywords: intensity duration, frequency curves, regionalization, hydrology

Procedia PDF Downloads 325

Authors: Ali Kirkbas

Abstract:

R peaks in an electrocardiogram (ECG) are signs of cardiac activity in individuals that reveal valuable information about cardiac abnormalities, which can lead to mortalities in some cases. This paper examines the problem of detecting R-peaks in ECG signals, which is a two-class pattern classification problem in fact. To handle this problem with a reliable high accuracy, we propose to use the common vector approach which is a successful machine learning algorithm. The dataset used in the proposed method is obtained from MIT-BIH, which is publicly available. The results are compared with the other popular methods under the performance metrics. The obtained results show that the proposed method shows good performance than that of the other. methods compared in the meaning of diagnosis accuracy and simplicity which can be operated on wearable devices.

Keywords: ECG, R-peak classification, common vector approach, machine learning

Procedia PDF Downloads 64

Authors: Nasrin Akter, Prem Chhetri, Shams Rahman

Abstract:

The present study tested Ajzen’s Theory of Planned Behavior (TPB) model to explain the formation of business customers’ intention to use 3PL services in Bangladesh. The findings show that the TPB model has a good fit to the data. Based on theoretical support and suggested modification indices, a refined TPB model was developed afterwards which provides a better predictive power for intention. Consistent with the theory, the results of a structural equation analysis revealed that the intention to use 3PL services is predicted by attitude and subjective norms but not by perceived behavioral control. Further investigation indicated that the paths between (attitude and intention) and (subjective norms and intention) did not statistically differ between 3PL user and non-user. Findings of this research provide an evidence base to formulate business strategies to increase the use of 3PL services in Bangladesh to enhance productivity and to gain economic efficiency.

Keywords: Bangladesh, intention, third-party logistics, Theory of Planned Behavior

Procedia PDF Downloads 581

Authors: José M. Liñeira Del Río, Ramón Rial, Khodor Nasser, María J.G. Guimarey

Abstract:

The need to develop new lubricants that offer better anti-friction and anti-wear performance in internal combustion vehicles is one of the great challenges of lubrication in the automotive field. The addition of nanoparticles has emerged as a possible solution and, combined with the lubricating power of ionic liquids, may become one of the alternatives to reduce friction losses and wear of the contact surfaces in the conditions to which tribo-pairs are subjected, especially in the contact of the piston rings and the cylinder liner surface. In this study, the improvement in SAE 10W-40 engine oil tribological performance after the addition of magnesium oxide (MgO) nanoadditives and two different phosphonium-based ionic liquids (ILs) was investigated. The nanoparticle characterization was performed by means of transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM). The tribological properties, friction coefficients and wear parameters of the formulated oil modified with 0.01 wt.% MgO and 1 wt.% ILs compared with the neat 10W-40 oil were performed and analyzed using a ball-on-three-pins tribometer and a 3D optical profilometer, respectively. Further analysis on the worn surface was carried out by Raman spectroscopy and SEM microscopy, illustrating the formation of the protective IL and MgO tribo-films as hybrid additives. In friction tests with sliding steel-steel tribo-pairs, IL3-based hybrid nanolubricant decreased the friction coefficient and wear volume by 7% and 59%, respectively, in comparison with the neat SAE 10W-40, while the one based on IL1 only achieved a reduction of these parameters by 6% and 39%, respectively. Thus, the tribological characterization also revealed that the MgO and IL3 addition has a positive synergy over the commercial lubricant, adequately meeting the requirements for their use in internal combustion engines. In summary, this study has shown that the addition of ionic liquids to MgO nanoparticles can improve the stability and lubrication behavior of MgO nanolubricant and encourages more investigations on using nanoparticle additives with green solvents such as ionic liquids to protect the environment as well as prolong the lifetime of machinery. The improvement in the lubricant properties was attributed to the following wear mechanisms: the formation of a protective tribo-film and the ability of nanoparticles to fill out valleys between asperities, thereby effectively smoothing out the shearing surfaces.

Keywords: lubricant, nanoparticles, phosphonium-based ionic liquids, tribology

Procedia PDF Downloads 82

Authors: Shlomit Hon-Snir, Shosh Shahrabai, Sharon Teitler Regev, Anabel Friedlander-Lifszyc

Abstract:

Today, online travel sites account for a large share of the orders for tourism services, leading to the expectation that many traditional travel agencies will become redundant in the future. Technological changes are offering customers a wider variety and better prices, and the improved competition in the industry has increased customer well-being significantly. Therefore, the question is whether all customers can enjoy this change, specifically whether different groups in the Israeli population enjoy the changes similarly. The purpose of this study is to identify the factors that affect the collection of data and the purchase of tourism products online and in particular to identify the barriers and limitations of technology usage among the population. The results of the current research are of great importance both economically and socially. The theory of Reasoned Action assumes that actual behavior is based on intention. Volitional behavior is predicted by individuals' attitudes to that behavior and by the way they think other people will look at them. Two cognitive variables regarding the use of technology are: perceived usefulness and perceived ease-of-use. Moreover, early adopters of innovations have different characteristics than people that adopt an innovation at a later stage. In the study, we analyze four groups of factors: Customer characteristics, internet usage, technology acceptance and product characteristics. Some of the parameters are gender, age, income level, frequency and type of internet use, proficiency in English, traveler type, number of trips abroad, perceived ease of use, perceived usefulness, perceived risk, perceived trust and product type. We investigate online purchasing and online information search separately. Data will be collected using an online questionnaire distributed among a representative sample of 600 citizens in Israel. Some of the research questions will be based on previous research studies (that underwent reliability and validity testing). Those questions will be translated into Hebrew and adjusted for the tested population.

Keywords: customer characteristics, online travel sites, technology acceptance, tourism

Procedia PDF Downloads 200

Authors: Ram Kishor

Abstract:

The Lyapunov characteristic exponent (LCE) is an important tool to describe behavior of a dynamical system, which measures the average rate of divergence (or convergence) of a trajectory emanating in the vicinity of initial point. To analyze the behavior of nearby trajectory emanating in the neighborhood of an equilibrium point in the restricted three-body problem under the influence of perturbations in the form of radiation pressure and oblateness, we compute LCEs of first order with the help of slandered method which is based on variational equation of the system. It is observed that trajectories are chaotic in nature due positive LCEs. Also, we analyze the effect of radiation pressure and oblateness on the LCEs. Results are applicable to study the behavior of more generalized RTBP in the presence of perturbations such as PR drag, solar wind drag etc.

Keywords: Lyapunov characteristic exponent, RTBP, radiation pressure, oblateness

Procedia PDF Downloads 443

Authors: Shantanu Arya, Sachin Sakhuja, Gunjan Mehta, Sanjay Munjal

Abstract:

Stuttering may be defined as a speech disorder affecting the fluency domain of speech and characterized by covert features like word substitution, omittance and circumlocution and overt features like prolongation of sound, syllables and blocks etc. Many etiologies have been postulated to explain stuttering based on various experiments and research. Moreover, Breathlessness has also been reported by many individuals with stuttering for which breathing exercises are generally advised. However, no studies reporting objective evaluation of the pulmonary capacity and further objective assessment of the efficacy of breathing exercises have been conducted. Pulmonary Function Test which evaluates parameters like Forced Vital Capacity, Peak Expiratory Flow Rate, Forced expiratory flow Rate can be used to study the pulmonary behavior of individuals with stuttering. The study aimed: a) To identify speech motor & physiologic behaviours associated with stuttering by administering PFT. b) To recognize possible reasons for an association between speech motor behaviour & stuttering severity. In this regard, PFT tests were administered on individuals who reported signs and symptoms of stuttering and showed abnormal scores on Stuttering Severity Index. Parameters like Forced Vital Capacity, Forced Expiratory Volume, Peak Expiratory Flow Rate (L/min), Forced Expiratory Flow Rate (L/min) were evaluated and correlated with scores of Stuttering Severity Index. Results showed significant decrease in the parameters (lower than normal scores) in individuals with established stuttering. Strong correlation was also found between degree of stuttering and the degree of decrease in the pulmonary volumes. Thus, it is evident that fluent speech requires strong support of lung pressure and requisite volumes. Further research in demonstrating the efficacy of abdominal breathing exercises in this regard is needed.

Keywords: forced expiratory flow rate, forced expiratory volume, forced vital capacity, peak expiratory flow rate, stuttering

Procedia PDF Downloads 275

Authors: Ashwini V. Chavan, Sukhanand S. Bhosale

Abstract:

Challenges of weak soil subgrade are often resolved either by stabilization or reinforcing it. However, it is also practiced to reinforce the granular fill to improve the load-settlement behavior of over weak soil strata. The inclusion of reinforcement in the engineered granular fill provided a new impetus for the development of enhanced Soil-Structure Interaction (SSI) models, also known as mechanical foundation models or lumped parameter models. Several researchers have been working in this direction to understand the mechanism of granular fill-reinforcement interaction and the response of weak soil under the application of load. These models have been developed by extending available SSI models such as the Winkler Model, Pasternak Model, Hetenyi Model, Kerr Model etc., and are helpful to visualize the load-settlement behavior of a physical system through 1-D and 2-D analysis considering beam and plate resting on the foundation respectively. Based on the literature survey, these models are categorized as ‘Reinforced Pasternak Model,’ ‘Double Beam Model,’ ‘Reinforced Timoshenko Beam Model,’ and ‘Reinforced Kerr Model.’ The present work reviews the past 30+ years of research in the field of SSI models for reinforced foundation systems, presenting the conceptual development of these models systematically and discussing their limitations. Special efforts are taken to tabulate the parameters and their significance in the load-settlement analysis, which may be helpful in future studies for the comparison and enhancement of results and findings of physical models.

Keywords: geosynthetics, mathematical modeling, reinforced foundation, soil-structure interaction, ground improvement, soft soil

Procedia PDF Downloads 123

Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

Abstract:

The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: educative model, good life, professional social responsibility, values

Procedia PDF Downloads 264

Authors: Anindita Patra, Prasad K. Bhaskaran

Abstract:

The atmosphere-ocean is a dynamically linked system that influences the exchange of energy, mass, and gas at the air-sea interface. The exchange of energy takes place in the form of sensible heat, latent heat, and momentum commonly referred to as fluxes along the atmosphere-ocean boundary. The large scale features such as El Nino and Southern Oscillation (ENSO) is a classic example on the interaction mechanism that occurs along the air-sea interface that deals with the inter-annual variability of the Earth’s Climate System. Most importantly the ocean and atmosphere as a coupled system acts in tandem thereby maintaining the energy balance of the climate system, a manifestation of the coupled air-sea interaction process. The present work is an attempt to understand the long-term variability in atmospheric parameters (from surface to upper levels) and investigate their role in influencing the surface ocean variables. More specifically the influence of atmospheric circulation and its variability influencing the mean Sea Level Pressure (SLP) has been explored. The study reports on a critical examination of both ocean-atmosphere parameters during a monsoon season over the head Bay of Bengal region. A trend analysis has been carried out for several atmospheric parameters such as the air temperature, geo-potential height, and omega (vertical velocity) for different vertical levels in the atmosphere (from surface to the troposphere) covering a period from 1992 to 2012. The Reanalysis 2 dataset from the National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) was used in this study. The study signifies that the variability in air temperature and omega corroborates with the variation noticed in geo-potential height. Further, the study advocates that for the lower atmosphere the geo-potential heights depict a typical east-west contrast exhibiting a zonal dipole behavior over the study domain. In addition, the study clearly brings to light that the variations over different levels in the atmosphere plays a pivotal role in supporting the observed dipole pattern as clearly evidenced from the trends in SLP, associated surface wind speed and significant wave height over the study domain.

Keywords: air temperature, geopotential height, head Bay of Bengal, long-term variability, NCEP reanalysis 2, omega, wind-waves

Procedia PDF Downloads 225

Authors: Y. Madani, H. Achache, B. Boutabout

Abstract:

The purpose of this paper is to analyze numerically by the three-dimensional finite element method, using ABAQUS calculation code, the mechanical behavior of a unidirectional and multidirectional delaminated stratified composite under mechanical loading in Mode II. This study consists of the determination of the energy release rate G in mode II as well as the distribution of equivalent von Mises stresses along the damaged zone by varying several parameters such as the applied load and the delamination length. It allowed us to deduce that the high energy release rate favors delamination at the free edges of a stratified plate subjected to bending.

Keywords: delamination, energy release rate, finite element method, stratified composite

Procedia PDF Downloads 176

Authors: Gultekin Gurcay

Abstract:

The objective of this study was to examine the relationship between transformational leadership and innovative work behavior and to evaluate the mediating role of leader-member exchange relationships (LMX) on the assumed relationship. This study has focused on the suggestion that LMX might emerge through transformational leadership behaviors and thus could mediate the relationship between transformational leadership and innovative behavior. A cross-sectional survey research has been conducted on the relationship these leadership approaches and their impact on organizational HRM-outcomes has been conducted on two organizations operating in the technical sector in Istanbul-Turkey. The results of the research have supported the hypotheses. Transformational leadership was positively related to the innovative behaviors and LMX emerged to mediate that relationship.

Keywords: innovative leadership, leader-member exchange, transformational leadership, Turkey

Procedia PDF Downloads 278

Authors: Christoph Ramsauer, Jaydeep Karandikar, Tony Schmitz, Friedrich Bleicher

Abstract:

Machining stability is an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the Vienna University of Technology, Vienna, Austria. The recorded data from a milling test cut is used to classify the cut as stable or unstable based on the frequency analysis. The test cut result is fed to a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculates the probability of stability as a function of axial depth of cut and spindle speed and recommends the parameters for the next test cut. The iterative process between two transatlantic locations repeats until convergence to a stable optimal process parameter set is achieved.

Keywords: machining stability, machine learning, sensor, optimization

Procedia PDF Downloads 206