Search results for: engine performance analysis

Authors: J. O. Idoko, I. Nwajiaku

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Unripe and ripe plantain were dried and milled into flour and used with wheat flour in biscuit production to determine the best plantain-wheat composite flour for biscuit production. The blends as follows: 100% wheat flour, 100% ripe plantain flour, 100% unripe plantain flour, 50% wheat flour and 50% ripe plantain flour and 50% wheat flour and 50% unripe plantain flour. The Biscuit samples were stored at ambient temperature for 8 weeks after which the equilibrium moisture content and water activity were determined. The sensory evaluation of the biscuit samples was also determined. The results of these analyses showed 100% unripe plantain flour as the most stable of the biscuit samples judging from its equilibrium moisture content level of 0.32% and water activity of 0.62. The sensory evaluation results showed Biscuit made from 150:50 ripe plantain and wheat flour as most generally accepted at 5% level of significance.

Keywords: biscuit, equilibrium moisture content, performance, plantain, water activity

Procedia PDF Downloads 206

Authors: M. A. Hafiz, P. T. Matevenga

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Micro-level machining of metals is a developing field which has shown to be a prospective approach to produce features on the parts in the range of a few to a few hundred microns with acceptable machining quality. It is known that the mechanics (i.e. the material removal mechanism) of micro-machining and conventional machining have significant differences due to the scaling effects associated with tool-geometry, tool material and work piece material characteristics. Shape memory alloys (SMAs) are those metal alloys which display two exceptional properties, pseudoelasticity and the shape memory effect (SME). Nickel-titanium (NiTi) alloys are one of those unique metal alloys. NiTi alloys are known to be difficult-to-cut materials specifically by using conventional machining techniques due to their explicit properties. Their high ductility, high amount of strain hardening, and unusual stress–strain behaviour are the main properties accountable for their poor machinability in terms of tool wear and work piece quality. The motivation of this research work was to address the challenges and issues of micro-machining combining with those of machining of NiTi alloy which can affect the desired performance level of machining outputs. To explore the significance of range of cutting conditions on surface roughness and tool wear, machining tests were conducted on NiTi. Influence of different cutting conditions and cutting tools on surface and sub-surface deformation in work piece was investigated. Design of experiments strategy (L9 Array) was applied to determine the key process variables. The dominant cutting parameters were determined by analysis of variance. These findings showed that feed rate was the dominant factor on surface roughness whereas depth of cut found to be dominant factor as far as tool wear was concerned. The lowest surface roughness was achieved at the feed rate of equal to the cutting edge radius where as the lowest flank wear was observed at lowest depth of cut. Repeated machining trials have yet to be carried out in order to observe the tool life, sub-surface deformation and strain induced hardening which are also expecting to be amongst the critical issues in micro machining of NiTi. The machining performance using different cutting fluids and strategies have yet to be studied.

Keywords: nickel titanium, micro-machining, surface roughness, machinability

Procedia PDF Downloads 337

Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery as bearing is important in order to improve its stability of works. Acoustic emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that the success of vibration analysis does not take place below 100 rpm rotational speed. This because the energy generated below 100 rpm rotational speed is not detectable using conventional vibration. From this pint, this paper has presented a focused review of using acoustic emission techniques for monitoring bearings condition.

Keywords: condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis

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Authors: Fahim Ullah, Muhammad Usman

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Energy consumption and environmental sustainability are hard challenges of 21st century. Energy richness increases environmental pollution while energy poverty hinders economic growth. Considering these two aspects, present study calculates energy deficiency and examines the role of renewable energy to overcome rising energy deficiency and carbon emission for selected countries from 1990 to 2021. For empirical analysis, this study uses methods of moments panel quantile regression analysis and to check the robustness, study used panel quantile robust analysis. Graphical analysis indicated rising global energy deficiency since last three decades where energy consumption is higher than energy production. Empirical results showed that renewable energy is a significant factor for reducing energy deficiency. Secondly, the energy deficiency increases carbon emission level and again renewable energy decreases emissions level. This study recommends that global energy deficiency and rising carbon emissions can be controlled through structural change in the form of energy transition to replace non-renewable resources with renewable resources.

Keywords: energy deficiency, renewable energy, carbon emission, energy trade, PQL analysis

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Authors: Hwang Ho Kim, Do Gyun Kim, Jin Young Choi, Sang Chul Park

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Since tire production process is very complicated, company-wide management of it is very difficult, necessitating considerable amounts of capital and labors. Thus, productivity should be enhanced and maintained competitive by developing and applying effective production plans. Among major processes for tire manufacturing, consisting of mixing component preparation, building and curing, the mixing process is an essential and important step because the main component of tire, called compound, is formed at this step. Compound as a rubber synthesis with various characteristics plays its own role required for a tire as a finished product. Meanwhile, scheduling tire mixing process is similar to flexible job shop scheduling problem (FJSSP) because various kinds of compounds have their unique orders of operations, and a set of alternative machines can be used to process each operation. In addition, setup time required for different operations may differ due to alteration of additives. In other words, each operation of mixing processes requires different setup time depending on the previous one, and this kind of feature, called sequence dependent setup time (SDST), is a very important issue in traditional scheduling problems such as flexible job shop scheduling problems. However, despite of its importance, there exist few research works dealing with the tire mixing process. Thus, in this paper, we consider the scheduling problem for tire mixing process and suggest an efficient particle swarm optimization (PSO) algorithm to minimize the makespan for completing all the required jobs belonging to the process. Specifically, we design a particle encoding scheme for the considered scheduling problem, including a processing sequence for compounds and machine allocation information for each job operation, and a method for generating a tire mixing schedule from a given particle. At each iteration, the coordination and velocity of particles are updated, and the current solution is compared with new solution. This procedure is repeated until a stopping condition is satisfied. The performance of the proposed algorithm is validated through a numerical experiment by using some small-sized problem instances expressing the tire mixing process. Furthermore, we compare the solution of the proposed algorithm with it obtained by solving a mixed integer linear programming (MILP) model developed in previous research work. As for performance measure, we define an error rate which can evaluate the difference between two solutions. As a result, we show that PSO algorithm proposed in this paper outperforms MILP model with respect to the effectiveness and efficiency. As the direction for future work, we plan to consider scheduling problems in other processes such as building, curing. We can also extend our current work by considering other performance measures such as weighted makespan or processing times affected by aging or learning effects.

Keywords: compound, error rate, flexible job shop scheduling problem, makespan, particle encoding scheme, particle swarm optimization, sequence dependent setup time, tire mixing process

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Authors: Sajee A. Gamage, Champa J. Wijesinghe, Patricia Burtner, Ananda R. Wickremasinghe

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Background: Teachers working in the context of special education have an enormous responsibility of enhancing performance skills of children in their classroom settings. Fine Motor Skills (FMS) are essential functional skills for children to gain independence in Activities of Daily Living. Children with Down Syndrome (DS) are predisposed to specific challenges due to deficits in FMS. This study is aimed to determine the teachers’ experience on improving FMS of children with DS in the context of special education of Southern Province, Sri Lanka. Methodology: A cross-sectional study was conducted among all consenting eligible teachers (n=147) working in the context of special education in government schools of Southern Province of Sri Lanka. A self-administered questionnaire was developed based on literature and expert opinion to assess teachers’ experience regarding deficits of FMS, limitations of classroom activity performance and barriers to improve FMS of children with DS. Results: Approximately 93% of the teachers were females with a mean age ( ± SD) of 43.1 ( ± 10.1) years. Thirty percent of the teachers had training in special educationand 83% had children with DS in their classrooms. Major deficits of FMS reported were deficits in grasping (n=116; 79%), in-hand manipulation (n=103; 70%) and bilateral hand use (n=99; 67.3%). Paperwork (n=70; 47.6%), painting (n=58; 39.5%), scissor work (n=50; 34.0%), pencil use for writing (n=45; 30.6%) and use of tools in the classroom (n=41; 27.9%) were identified as major classroom performance limitations of children with DS. Parental factors (n=67; 45.6%), disease specific characteristics (n=58; 39.5%) and classroom factors (n=36; 24.5%), were identified as major barriers to improve FMS in the classroom setting. Lack of resources and standard tools, social stigma and late school admission were also identified as barriers to FMS training. Eighty nine percent of the teachers informed that training fine motor activities in a special education classroom was more successful than work with normal classroom setting. Conclusion: Major areas of FMS deficits were grasping, in-hand manipulation and bilateral hand use; classroom performance limitations included paperwork, painting and scissor work of children with DS. Teachers recommended regular practice of fine motor activities according to individual need. Further research is required to design a culturally specific FMS assessment tool and intervention methods to improve FMS of children with DS in Sri Lanka.

Keywords: classroom activities, Down syndrome, experience, fine motor skills, special education, teachers

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Authors: Mohsen Hajian Foroushani, Samane Maroufi, Rasoul Khayyam Nekouei, Veena Sahajwalla

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Lithium (Li) metal possesses outstanding characteristics, with the highest specific capacity (3860 mAh g-1) and the lowest electrochemical potential (-3.04 V vs. SHE) among available metal anodes. The collaborative impact of Li and sulfur, featuring a specific capacity of 1670 mAh g-1, positions Li–S batteries (LSBs) as highly promising contenders for the next generation of high-energy-density batteries. However, the comprehensive commercialization of LSBs relies on addressing various challenges inherent to these batteries. One of the most formidable hurdles is the widespread issue of Li dendrite nucleation and growth on the anode surface, stemming from the inherent instability of the solid electrolyte interphase (SEI) layer. In this study, we employed a Zn-based coating derived from waste materials, significantly enhancing the performance of the symmetrical cell across various current densities. The applied coating not only improved the cyclability of the cell by more than fourfold but also reduced the charge transfer resistance from over 300 to less than 10 before cycling. Examination through SEM micrographs of both samples revealed the successful suppression of Li dendrites by the applied coating.

Keywords: Li-S batteries, Li dendrite, sustainability, Li anode

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Authors: Varun Joshi, Manish K. Rathod

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The latent heat thermal energy storage system is a thrust area of research due to exuberant thermal energy storage potential. The thermal performance of PCM is significantly augmented by installation of the high thermal conductivity fins. The objective of the present study is to obtain optimum size and location of the fins to enhance diffusion heat transfer without altering overall melting time. Hence, the constructal theory is employed to eliminate, resize, and re-position the fins. A numerical code based on conjugate heat transfer coupled enthalpy porosity approached is developed to solve Navier-Stoke and energy equation.The numerical results show that the constructal fin design has enhanced the thermal performance along with the increase in the overall volume of PCM when compared to conventional. The overall volume of PCM is found to be increased by half of total of volume of fins. The elimination and repositioning the fins at high temperature gradient from low temperature gradient is found to be vital.

Keywords: constructal theory, enthalpy porosity approach, phase change materials, fins

Procedia PDF Downloads 174

Authors: Alireza Ladan Moghaddam, Hadi Rezghi Shirsavar, Panteha Pirayandeh

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This study aimed to investigate the Relationship between Teachers' Empowerment and Personality Traits (Case Study: Tehran Public Schools of region 5). To achieve this objective, a descriptive research in correlation type has been used. The statistical population of this research includes all teachers and administrators in Tehran Public Schools of region 5. In this study, a five factor model of personality and a 49-item questionnaire to measure empowerment of teachers have been used to assess personality traits of administrators and the teachers' empowerment, respectively. The research hypotheses test has been done using SPSS and LISREL software. The results show that in general there is no significant relationship between personality traits of administrators and teachers' empowerment, and among the 5 dimensions of personality, there is only significant relationship between the characteristic of administrators' agreeableness and teachers' empowerment. The results suggested a way to improve knowledge and skills of teachers a top priority administrator consider. In addition, the performance of teachers affected by the performance of the executive directors, so it is necessary to improve their yield towering notice.

Keywords: personality traits, five factor model of personality, teacher, empowerment

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Authors: Luke Andrighetto, Paul G. Stevenson, Luke C. Henderson, Jim Pearson, Xavier A. Conlan

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As pseudoephedrine, a common ingredient in cold and flu medications is closely monitored and restricted in Australia, alternative methods of accessing it are of interest. The impurities and by-products of every reaction step of pseudoephedrine/ephedrine and methamphetamine synthesis have been mapped in order to develop a chemical fingerprint based on synthetic route. Likewise, seized methamphetamine contains a combination of different cutting agents and starting materials. Therefore, in-silico optimised two-dimensional HPLC with DryLab® and OpenMS® software has been used to efficiently separate complex seizure samples. An excellent match between simulated and real separations was observed. Targeted separation of model compounds was completed with significantly reduced method development time. This study produced a two-dimensional separation regime that offers unprecedented separation power (separation space) while maintaining a rapid analysis time that is faster than those previously reported for gas chromatography, single dimension high performance liquid chromatography or capillary electrophoresis.

Keywords: chemical fingerprint, ephedrine, methamphetamine, two-dimensional HPLC

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

Procedia PDF Downloads 177

Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

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Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

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Authors: W. I. Mazyan, A. Ahmadi, M. Hoorfar

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Objectives/Scope: This paper proposes a mathematical model for calculating the solid-gas separation efficiency in cyclones. This model provides better agreement with experimental results compared to existing mathematical models. Methods: The separation ratio efficiency, ϵsp, is evaluated by calculating the outlet to inlet count ratio. Similar to mathematical derivations in the literature, the inlet and outlet particle count were evaluated based on Eulerian approach. The model also includes the external forces acting on the particle (i.e., centrifugal and drag forces). In addition, the proposed model evaluates the exact length that the particle travels inside the cyclone for the evaluation of number of turns inside the cyclone. The separation efficiency model derivation using Stoke’s law considers the effect of the inlet tangential velocity on the separation performance. In cyclones, the inlet velocity is a very important factor in determining the performance of the cyclone separation. Therefore, the proposed model provides accurate estimation of actual cyclone separation efficiency. Results/Observations/Conclusion: The separation ratio efficiency, ϵsp, is studied to evaluate the performance of the cyclone for particles ranging from 1 microns to 10 microns. The proposed model is compared with the results in the literature. It is shown that the proposed mathematical model indicates an error of 7% between its efficiency and the efficiency obtained from the experimental results for 1 micron particles. At the same time, the proposed model gives the user the flexibility to analyze the separation efficiency at different inlet velocities. Additive Information: The proposed model determines the separation efficiency accurately and could also be used to optimize the separation efficiency of cyclones at low cost through trial and error testing, through dimensional changes to enhance separation and through increasing the particle centrifugal forces. Ultimately, the proposed model provides a powerful tool to optimize and enhance existing cyclones at low cost.

Keywords: cyclone efficiency, solid-gas separation, mathematical model, models error comparison

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Authors: Bin Liu

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As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors.

Keywords: protein remote homology detection, protein fold recognition, top frequency profile, support vector machines

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Hemant Kumar

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Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position.

Keywords: aerosol, change detection, spatial analysis, trend analysis

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Authors: Ferry Kurniawan

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In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

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Authors: Zoran Visak, Joana Lopes, Vesna Najdanovic-Visak

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Within this study, the separation of nicotine from its aqueous solutions, using inorganic salt sodium chloride or ionic liquid (molten salt) ECOENG212® as salting-out media, was carried out. Thus, liquid-liquid equilibria of the ternary solutions (nicotine+water+NaCl) and (nicotine+water+ECOENG212®) were determined at ambient pressure, 0.1 MPa, at three temperatures. The related phase diagrams were constructed in two manners: by adding the determined cloud-points and by the chemical analysis of phases in equilibrium (tie-line data). The latter were used to calculate two important separation parameters - partition coefficients of nicotine and separation factors. The impacts of the initial compositions of the mother solutions and of temperature on the liquid-liquid phase separation and partition coefficients were analyzed and discussed. The results obtained clearly showed that both investigated salts are good salting-out media for the efficient and sustainable separation of nicotine from its solutions with water. However, when compared, sodium chloride exhibited much better separation performance than the ionic liquid.

Keywords: nicotine, liquid-liquid separation, inorganic salt, ionic liquid

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Authors: Nathakhun Wiroonsri

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There are various cluster validity measures used for evaluating clustering results. One of the main objectives of using these measures is to seek the optimal unknown number of clusters. Some measures work well for clusters with different densities, sizes and shapes. Yet, one of the weaknesses that those validity measures share is that they sometimes provide only one clear optimal number of clusters. That number is actually unknown and there might be more than one potential sub-optimal option that a user may wish to choose based on different applications. We develop two new cluster validity indices based on a correlation between an actual distance between a pair of data points and a centroid distance of clusters that the two points are located in. Our proposed indices constantly yield several peaks at different numbers of clusters which overcome the weakness previously stated. Furthermore, the introduced correlation can also be used for evaluating the quality of a selected clustering result. Several experiments in different scenarios, including the well-known iris data set and a real-world marketing application, have been conducted to compare the proposed validity indices with several well-known ones.

Keywords: clustering algorithm, cluster validity measure, correlation, data partitions, iris data set, marketing, pattern recognition

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Authors: A. A. Saki, Y. Karimi, H. J. Najafabadi, P. Zamani, Z. Mostafaie

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The restaurant residuals reasons such as competition between human and animal consumption of cereals, increasing environmental pollution and the high cost of production of livestock products is important. Therefore, in this restaurant residuals have a high nutritional value (protein and high energy) that it is possible can replace some of the poultry diets are especially Japanese quail. Today, the challenges of processing and consumption of these lesions occurring in modern industry would be confronting. Increasing costs, pressures, and problems associated with waste excretion, the need for re-evaluation and utilization of waste to livestock and poultry feed fortifies. This study aimed to investigate the effects of different levels of restaurant residuals on performance of 300 layer Japanese quails. This experiment included 5 treatments, 4 replicates, and 15 quails in each from 10 to 18 weeks age in a completely randomized design (CRD). The treatments consist of basal diet including corn and soybean meal (without residual restaurants), and treatments 2, 3, 4 and 5, includes a basal diet containing 5, 10, 15 and 20% of restaurant residuals, respectively. There were no significant effect of restaurant residuals levels on body weight (BW), feed conversion ratio (FCR), percentage of egg production (EP), egg mass (EM) between treatments (P > 0/05). However, feed intake (FI) of 5% restaurant residual was significantly higher than 20% treatment (P < 0/05). Egg weight (EW) was also higher by receiving 20% restaurant residuals compared with 10% in this respect (P < 0/05). Yolk weight (YW) of treatments containing 10 and 20% of the residual restaurant were significantly higher than control (P < 0/05). Eggs white weight (EWW) of 20 and 5% restaurants residual treatments were significantly increased compared by 10% (P < 0/05). Furthermore, EW, egg weight to shell surface area and egg surface area in 20% treatment were significantly higher than control and 10% treatment (P < 0/05). The overall results of this study have shown that restaurant residuals for laying quail diets in levels of 10 and 15 percent could be replaced with a part of the quail ration without any adverse effect.

Keywords: by-product, laying quail, performance, restaurant residuals

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Authors: Wisam K. Hussam, Ali Alfeeli, Gergory J. Sheard

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Solar panels that use photovoltaic (PV) cells are popular for converting solar radiation into electricity. One of the major problems impacting the performance of PV panels is the overheating caused by excessive solar radiation and high ambient temperatures, which degrades the efficiency of the PV panels remarkably. To overcome this issue, an aluminum heat sink was used to dissipate unwanted heat from PV cells. The dimensions of the heat sink were determined considering the optimal fin spacing that fulfils hot climatic conditions. In this study, the effects of cooling on the efficiency and power output of a PV panel were studied experimentally. Two PV modules were used: one without and one with a heat sink. The experiments ran for 11 hours from 6:00 a.m. to 5:30 p.m. where temperature readings in the rear and front of both PV modules were recorded at an interval of 15 minutes using sensors and an Arduino microprocessor. Results are recorded for both panels simultaneously for analysis, temperate comparison, and for power and efficiency calculations. A maximum increase in the solar to electrical conversion efficiency of 35% and almost 55% in the power output were achieved with the use of a heat sink, while temperatures at the front and back of the panel were reduced by 9% and 11%, respectively.

Keywords: photovoltaic cell, natural convection, heat sink, efficiency

Procedia PDF Downloads 147

Authors: Karim Sobh, Khaled El-Ayat, Fady Morcos, Amr El-Kadi

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Distributed applications deployed on LEO satellites and ground stations require substantial communication between different members in a constellation to overcome the earth coverage barriers imposed by GEOs. Applications running on LEO constellations suffer the earth line-of-sight blockage effect. They need adequate lab testing before launching to space. We propose a scalable cloud-based net-work simulation framework to simulate problems created by the earth line-of-sight blockage. The framework utilized cloud IaaS virtual machines to simulate LEO satellites and ground stations distributed software. A factorial ANOVA statistical analysis is conducted to measure simulator overhead on overall communication performance. The results showed a very low simulator communication overhead. Consequently, the simulation framework is proposed as a candidate for testing LEO constellations with distributed software in the lab before space launch.

Keywords: LEO, cloud computing, constellation, satellite, network simulation, netfilter

Procedia PDF Downloads 378

Authors: Klary Geisler, Dalit Lev-Arey, Yael Hacohen

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It is a well-known fact that exercise has favorable effects on people's physical health, as well as mental well-being. However, as for as excessive exercise, it may likely elevate negative consequences (e.g., physical injuries, negligence of everyday responsibilities such as work, family life). Lately, there is a growing interest in exercise addiction, sometimes referred to as exercise dependence, which is defined as a craving for physical activity that results in extreme work-out sessions and generates negative physiological and psychological symptoms (e.g., withdrawal symptoms, tolerance, social conflict). Exercise addiction is considered a behavioral addiction, yet it was not included in the latest editions of the diagnostic and statistical manual of mental disorders (DSM-IV), due to lack of significant research. Specifically, there is scarce research on the relationship between exercise addiction and personality dimensions. The purpose of the current research was to examine the relationship between primary exercise addiction symptoms and the big five dimensions, perfectionism (high performance expectations and self-critical performance evaluations) and subjective affect. participants were 152 trainees on a variety of aerobic sports activities (running, cycling, swimming) that were recruited through sports groups and trainers. 88% of participants trained for at least 5 hours per week, 24% of the participants trained above 10 hours per week. To test the predictive ability of the IVs a hierarchical linear regression with forced block entry was performed. It was found that Neuroticism significantly predicted exercise addiction symptoms (20% of the variance, p<0.001), while consciousness was negatively correlated with exercise addiction symptoms (14% of variance p<0.05); both had a unique contribution. Other dimensions of the big five (agreeableness, openness and extraversion) did not have any contribution to the dependent. Moreover, maladaptive perfectionism (self-critical performance evaluations) significantly predicted exercise addiction symptoms as well (10% of the variance P < 0.05). The overall regression model explained 54% of variance.

Keywords: big five, consciousness, excessive exercise, exercise addiction, neuroticism, perfectionism, personality

Procedia PDF Downloads 215

Authors: Narges Shayesteh Moghaddam

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Tungsten (W), a refractory metal known for its remarkably high melting temperature, offers tremendous potential for use in challenging environments prevalent in sectors such as space exploration, defense, and nuclear industries. Additive manufacturing, especially the Laser Powder-Bed Fusion (LPBF) technique, emerges as a beneficial method for fabricating tungsten parts. This technique enables the production of intricate components while simultaneously reducing production lead times and associated costs. However, the inherent brittleness of tungsten and its tendency to crack under high-temperature conditions pose significant challenges to the manufacturing process. Our research primarily focuses on the process of rolling tungsten parts in a layer-by-layer manner in LPBF and the subsequent changes in microstructure. Our objective is not only to identify the alterations in the microstructure but also to assess their implications on the physical properties and performance of the fabricated tungsten parts. To examine these aspects, we conducted an extensive series of experiments that included the fabrication of tungsten samples through LPBF and subsequent characterization using advanced materials analysis techniques. These investigations allowed us to scrutinize shifts in various microstructural features, including, but not limited to, grain size and grain boundaries occurring during the rolling process. The results of our study provide crucial insights into how specific factors, such as plastic deformation occurring during the rolling process, influence the microstructural characteristics of the fabricated parts. This information is vital as it provides a foundation for understanding how the parameters of the layer-by-layer rolling process affect the final tungsten parts. Our research significantly broadens the current understanding of microstructural evolution in tungsten parts produced via the layer-by-layer rolling process in LPBF. The insights obtained will play a pivotal role in refining and optimizing manufacturing parameters, thus improving the mechanical properties of tungsten parts and, therefore, enhancing their performance. Furthermore, these findings will contribute to the advancement of manufacturing techniques, facilitating the wider application of tungsten parts in various high-demand sectors. Through these advancements, this research represents a significant step towards harnessing the full potential of tungsten in high-temperature and high-stress applications.

Keywords: additive manufacturing, rolling, tungsten, refractory materials

Procedia PDF Downloads 84

Authors: N. Allouache, W. Elgahri, A. Gahfif, M. Belmedani

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Solar radiation is by far the largest and the most world’s abundant, clean and permanent energy source. The amount of solar radiation intercepted by the Earth is much higher than annual global energy use. The energy available from the sun is greater than about 5200 times the global world’s need in 2006. In recent years, many promising technologies have been developed to harness the sun's energy. These technologies help in environmental protection, economizing energy, and sustainable development, which are the major issues of the world in the 21st century. One of these important technologies is the solar cooling systems that make use of either absorption or adsorption technologies. The solar adsorption cooling systems are a good alternative since they operate with environmentally benign refrigerants that are natural, free from CFCs, and therefore they have a zero ozone depleting potential (ODP). A numerical analysis of thermal and solar performances of an adsorption solar refrigerating system using different adsorbent/adsorbate pairs, such as activated carbon AC35 and activated carbon BPL/Ammoniac; is undertaken in this study. The modeling of the adsorption cooling machine requires the resolution of the equation describing the energy and mass transfer in the tubular adsorber, that is the most important component of the machine. The Wilson and Dubinin- Astakhov models of the solid-adsorbat equilibrium are used to calculate the adsorbed quantity. The porous medium is contained in the annular space, and the adsorber is heated by solar energy. Effect of key parameters on the adsorbed quantity and on the thermal and solar performances are analysed and discussed. The performances of the system that depends on the incident global irradiance during a whole day depends on the weather conditions: the condenser temperature and the evaporator temperature. The AC35/methanol pair is the best pair comparing to the BPL/Ammoniac in terms of system performances.

Keywords: activated carbon-methanol pair, activated carbon-ammoniac pair, adsorption, performance coefficients, numerical analysis, solar cooling system

Procedia PDF Downloads 68

Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

Procedia PDF Downloads 323

Authors: Anand R. Nair, Markus Trenker

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Lifting body concepts were conceived as early as 1917 and patented by Roy Scroggs. It was an idea of using the fuselage as a lift producing body with no or small wings. Many of these designs were developed and even flight tested between 1920’s to 1970’s, but it was not pursued further for commercial flight as at lower airspeeds, such a configuration was incapable to produce sufficient lift for the entire aircraft. The concept presented in this contribution is combining the lifting body design along with a fixed wing to maximise the lift produced by the aircraft. Conventional aircraft fuselages are designed to be aerodynamically efficient, which is to minimise the drag; however, these fuselages produce very minimal or negligible lift. For the design of an unmanned fixed wing transport aircraft, many of the restrictions which are present for commercial aircraft in terms of fuselage design can be excluded, such as windows for the passengers/pilots, cabin-environment systems, emergency exits, and pressurization systems. This gives new flexibility to design fuselages which are unconventionally shaped to contribute to the lift of the aircraft. The two lifting body concepts presented in this contribution are targeting different applications: For a fast cargo delivery drone, the fuselage is based on a scaled airfoil shape with a cargo capacity of 500 kg for euro pallets. The aircraft has a span of 14 m and reaches 1500 km at a cruising speed of 90 m/s. The aircraft could also easily be adapted to accommodate pilot and passengers with modifications to the internal structures, but pressurization is not included as the service ceiling envisioned for this type of aircraft is limited to 10,000 ft. The next concept to be investigated is called a multi-purpose drone, which incorporates a different type of lifting body and is a much more versatile aircraft as it will have a VTOL capability. The aircraft will have a wingspan of approximately 6 m and flight speeds of 60 m/s within the same service ceiling as the fast cargo delivery drone. The multi-purpose drone can be easily adapted for various applications such as firefighting, agricultural purposes, surveillance, and even passenger transport. Lifting body designs are not a new concept, but their effectiveness in terms of cargo transportation has not been widely investigated. Due to their enhanced lift producing capability, lifting body designs enable the reduction of the wing area and the overall weight of the aircraft. This will, in turn, reduce the thrust requirement and ultimately the fuel consumption. The various designs proposed in this contribution will be based on the general aviation category of aircrafts and will be focussed on unmanned methods of operation. These unmanned fixed-wing transport drones will feature appropriate cargo loading/unloading concepts which can accommodate large size cargo for efficient time management and ease of operation. The various designs will be compared in performance to their conventional counterpart to understand their benefits/shortcomings in terms of design, performance, complexity, and ease of operation. The majority of the performance analysis will be carried out using industry relevant standards in computational fluid dynamics software packages.

Keywords: lifting body concept, computational fluid dynamics, unmanned fixed-wing aircraft, cargo drone

Procedia PDF Downloads 233

Authors: Dong-Hyun Ha, Young-Min Ko, Chang-Bin Ha, Hyoung-Kyu Song

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In Heterogeneous network (HetNet) can provide high quality of a service in a wireless communication system by composition of small cell networks. The composition of small cell networks improves cell coverage and capacity to the mobile users.Recently, various techniques using small cell networks have been researched in the wireless communication system. In this paper, the cooperative scheme obtaining high reliability is proposed in the small cell networks. The proposed scheme suggests a cooperative small cell system and the new signal transmission technique in the proposed system model. The new signal transmission technique applies a cyclic delay diversity (CDD) scheme based on the multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system to obtain improved performance. The improved performance of the proposed scheme is confirmed by the simulation results.

Keywords: adaptive transmission, cooperative communication, diversity gain, OFDM

Procedia PDF Downloads 491